1 

Evaluating Wealth Data in the Redesigned 2014 Survey of Income 

and Program Participation*

 

Jonathan S. Eggleston

Social, Economic, and Housing Statistics Division

U.S. Census Bureau

Michael Gideon

Social, Economic, and Housing Statistics Division

U.S. Census Bureau

Proceedings of the 2018 Federal Committee on Statistical Methodology (FCSM) Research Conference

ABSTRACT: The U.S. Census Bureau redesigned the 2014 panel of the Survey of Income and Program 

Participation (SIPP). As part of the redesign, several changes were made to the wealth module of the survey to fill 

gaps in question content and improve clarity of existing questions. To evaluate the effects of these changes, we 

compare SIPP wealth data with Survey of Consumer Finances (SCF) wealth data to investigate how the match 

between SIPP and SCF changed after the questionnaire redesign. We find that the match between SIPP and SCF has 

improved for many wealth estimates, although numerous discrepancies remain. We offer potential explanations for 

why some estimates have changed and why the difference between SIPP and SCF is large for certain estimates. 

 

*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; Gideon: 

michael.s.gideon@census.gov, (301) 763-2221. Address: Social, Economic, and Housing Statistics Division; U.S. 

Census Bureau; 4600 Silver Hill Road; Washington, DC 20233.
2 

1. Introduction

Accurate measurement of household wealth is important for studying economic behavior and well-being. 

Wealth data allows researchers and policy makers to explore how household wealth varies across social and 

economic characteristics and how various groups within the U.S. might endure hardships when faced with 

unexpected expenses or drops in income. At the same time, wealth inequality has received increasing attention from 

some researchers and policymakers.1

 

The Survey of Income and Program Participation (SIPP) is one of the primary sources of wealth data for

the U.S. population. SIPP has a large sample size, is a panel study, and includes a wide breadth of content on 

employment, health insurance coverage, and participation in government programs. Because of the decentralized 

nature of asset and debt holdings, household survey data provide the most comprehensive measure of wealth in the 

U.S. However, survey data are prone to measurement error, and questions on financial topics prove particularly 

challenging for many respondents.

The purpose of this paper is to support the careful use of SIPP data by researchers by examining how 

changes to the Survey of Income and Program Participation from the 2008 panel to the 2014 panel affected wealth 

data quality. In 2014, numerous changes were made to SIPP. The asset section underwent a major revision in which 

new assets were added and asset income and values were asked together rather than in separate sections. These 

changes may impact wealth data quality and estimates of wealth for various demographic groups. 

To address this question, we compare estimates of wealth using SIPP to estimates using the Survey of 

Consumer Finances (SCF). The SCF is considered the “gold standard” for wealth data from surveys because of its 

dual-frame sample design that oversamples high-wealth families and the detailed questions aimed at capturing 

complex asset holdings held by these families.2 We estimate differences between SIPP 2014 (wave 1) and SCF 2013 

and compare them to differences between SIPP 2008 (wave 7) and SCF 2010, as reported in Eggleston and Klee 

(2015). The SIPP 2014 and SCF 2013 data refer to wealth from calendar year 2013, while the SIPP 2008 and SCF 

2010 data refer to calendar year 2010.

This paper builds on previous research evaluating the quality of SIPP wealth data. These studies similarly 

focus on comparing estimates from SIPP to other surveys. Curtin, Juster, and Morgan (1989) and Wolff (1999) 

compared SIPP and SCF from the 1980s and early 1990s. They found the level and distribution of wealth to be 

comparable in general across these surveys upon excluding the wealthiest individuals. Czajka, Jacobson, and Cody 

(2003) find larger discrepancies in the 1996 Panel (calendar year 1998)—aggregate net worth estimated using SIPP 

is just under half of the magnitude estimated using the SCF, and median net worth estimate in SIPP data is 

approximately two-thirds of the analog in SCF data. Most of this discrepancy is due to lower estimates of the 

holdings of the wealthy.

Eggleston and Klee (2015) investigate how changes implemented based on recommendations from Czajka 

et al. (2003) affected the match between SIPP and SCF for the 2008 SIPP Panel (calendar year 2010). They find 

that the match between SCF and SIPP improved in some dimensions but not others. For example, the SIPP estimate 

of median net worth went from about 67 percent of the SCF estimate in Czajka et al. (2003) to about 84 percent in 

Eggleston and Klee (2015). However, the SIPP estimate of the 25th percentile of net worth went from about 42

percent of the net worth estimate to about 28 percent. 

In this paper, we compare the first wave of the 2014 SIPP Panel (calendar year 2013) to the 2013 SCF. 

Our methodology largely resembles Czajka et al. (2003) and Eggleston and Klee (2015). We compare the net worth 

of U.S. families as well as the components of net worth. One difference in our methodology is that we break down 

net worth by major asset categories, such as financial assets held outside retirement accounts. Next, we analyze the

relationship between components of net worth using correlation median statistics as described in (Falk 1998). We 

use this statistic rather than the standard Pearson’s correlation coefficient in order to reduce the effect of outliers. 

Finally, we discuss survey quality as reflected by the incidence of imputed values. 

We find that many of the differences between the 2014 SIPP and 2013 SCF estimates (calendar year 2013) 

are smaller than the differences between 2008 SIPP and 2010 SCF estimates (calendar year 2010). In other words, 

 

1 For example, Saez and Zucman’s (2013) paper on wealth inequality looks at how much wealth is held by the 

wealthiest 0.1 percent of households. In addition, Munoz et al. (2015) examine how wealth varies across numerous 

racial and ethnic group in the Boston Metropolitan Statistical Area. 

2 National Research Council (2009) is one among many sources that have applied this label in reference to SCF.
3 

there is less of a discrepancy between the SIPP and SCF in the 2014 Panel than there is in the 2008 Panel. At the 

same time, median net worth has increased in SIPP between 2010 and 2013 (Smith et al. 2017), but median net 

worth had decreased in SCF between 2010 and 2013 (Bricker et al. 2016). We conclude that (1.) the overall level of 

the SIPP variables could be more reliable in 2014 than in 2008, and (2.) the 2008 and 2014 estimates are not directly 

comparable over time. Because of these changes, measures of household wealth should not be directly compared 

with earlier panels, unless data users account for changes due to the redesign. 

Nevertheless, there are still potential quality problems with some individual asset categories, with large 

differences between SIPP and SCF estimates. For example, the estimate of the median value for trusts is $100,000 

in the 2014 SIPP (calendar year 2013), but is $253,216 in the 2013 SCF. However, for broader asset categories, 

such as assets in tax-preferred retirement accounts, the differences between SIPP and SCF are smaller. Moreover,

for the broadest category, household net worth, there is no statistically significant difference in the SIPP and SCF 

estimates in 2013 for the median or 75th percentile. Taking SCF as a benchmark, the SIPP data appears to have 

improved in a variety of dimensions, although there are still some large discrepancies for some individual assets.

2. Data

Our primary dataset of interest is the Survey of Income and Program Participation (SIPP). The SIPP is a

longitudinal survey from the U.S. Census Bureau which interviews between about 30,000-45,000 households over a 

four-to-five year period.3

 The survey collects information about the income, assets, labor market activity, and 

participation in government welfare programs of U.S. households. Information on a wide variety of assets and debts 

is collected and includes variables on savings accounts, checking accounts, retirement accounts, property values, and 

credit card debt. Wealth data have been collected in every panel since the survey began in 1984. However, in prior 

panels, there was a strong focus on income, so only questions about the ownership of income earning assets and 

income amounts were asked in every interview. Periodically, there was a topical module that asked questions about

asset values and debt as well as questions on non-income earning assets, such as vehicles. SIPP also oversamples 

low-income areas to improve its estimation of program participation.

In 2014, the SIPP underwent numerous revisions that could affect wealth measurement. Respondents in the 

2014 SIPP Panel are interviewed less frequently in order to reduce costs (U.S. Census Bureau 2016). In the 2014 

Panel, respondents are interviewed once a year, but in earlier panels, respondents were interviewed every four 

months. However, in contrast with earlier panels, wealth data are now collected during every wave. There were 

also many other changes made to the wealth content. New questions about student loans, education savings 

accounts, businesses owned as an investment, annuities, trusts, and the face-value of life insurance were added to the 

survey (Smith et al. 2017). The question text for several other questions was simplified. In particular, the question 

text for non-interest earning (regular) checking accounts was simplified, and questions on interest-earning and noninterest earning accounts were asked together, rather than at different times during the survey (as was the case in 

SIPP 2008). Additional examples were provided in the questions asking about other real estate and other assets. 

Finally, we developed a new methodology for assigning vehicle trade-in values from the National Automobile 

Dealers Association (NADA) using reported year, make, and model.

In Appendix A, we provide additional details about changes made to wealth questions in 2014 SIPP.

 The first wave of 2014 SIPP has a higher unit nonresponse rate than the first wave of the 2008 Panel (31.2 

percent vs 19.4 percent), which is expected because unit nonresponse rates have been increasing for surveys in 

general over time (de Leeuw and de Heer 2002). In addition, the 2014 SIPP has a smaller sample size than the 2008 

Panel for the first wave (29,685 vs 42,032), although the sample size in the 2008 Panel did drop to 33,827 by wave 

seven, which is the wave we use in this paper. Thus, because the 2014 SIPP has a smaller sample size and higher 

nonresponse rates, there exist the potential for the 2014 to have more unit nonresponse bias and higher sampling 

error, although higher unit nonresponse rates don’t necessarily lead to higher unit nonresponse bias (Groves and 

Peytcheva 2008).

2.1 Survey of Consumer Finances

To evaluate the changes in 2014 SIPP, we compare the SIPP wealth data to wealth data from the Survey of 

Consumer Finances (SCF). SCF is a triennial interview survey sponsored by the Federal Reserve Board of 

 

3 The sample size varies across panels.
4 

Governors in collaboration with the U.S. Department of the Treasury. Because wealth is more of a focus for SCF 

than in SIPP, the SCF has more detailed wealth questions than SIPP, including scarcely held assets and liabilities. 

Data are collected by the National Opinion Research Center (NORC) at the University of Chicago. In total, 6,026 

families were interviewed as part of the 2013 survey. 

2.2 Sampling Frames

In both SIPP and SCF, the sample frame and questionnaire content are specifically designed to construct 

nationally representative estimates of wealth holdings. The SCF sample design consists of two parts; a standard 

geographically-based random sample, and a sample of primarily high-wealth families based on data from the 

Internal Revenue Service (IRS).4

 

This is in contrast to the SIPP, which oversamples low-income areas based on data from other Census 

surveys and the decennial Census. Using sample weights corrects for oversampling of various populations, so if the 

weights are designed correctly, the SIPP and SCF comparisons should not be impacted by differences in the 

sampling methodologies.5

 However, because SCF uses IRS tax return data to sample some respondents, they have 

more information on some non-respondents than SIPP does. Because of this, the SCF weights are potentially able to 

better-correct for nonresponse bias than SIPP weights. In addition, oversampling may affect the precision of various 

estimates. For example, because the SCF oversamples high-wealth households, the SCF data may have a more 

precise estimate of the wealth of high-wealth households for a given sample size.

2.3 Unit of Analysis

One major difference between SIPP and SCF is the unit of analysis. The SCF’s unit of observation is a

Primary Economic Unit (PEU), which includes a household’s economically dominant individual or couple and their 

financial dependents. In SIPP, the main unit of observation is a household, which consists of everyone living 

together in a housing structure.6

 For many common household and family structures, such as a married couple with

children, the Census household is the same as the PEU. However, if a married couple has a relative live with them 

who has her own job and maintains her own finances, then she would be in the Census household but not in the 

PEU. 

We use demographic and family relationship variables collected in SIPP to generate PEUs.7

 This is 

necessary because SIPP does not collect data on economic dependence. We only include household members who 

are in the household head’s family, are an unmarried partner of the household head, or are a child under 25 of an 

unmarried partner of the household head.8

 We exclude siblings and other relatives in the household head’s family 

who are over 25. This procedure generates a comparable unit of analysis, although we likely exclude some 

household members who are economic dependents, such as a parent with a disability who lives with his or her child 

and relies on this person for financial support. 

2.4 Group Quarters

Another difference between SIPP and SCF is the sampling of group quarters, such as student dorms and 

convents. SIPP includes noninstitutional group quarters in its sampling frame, while SCF does not. Because of 

this, we exclude SIPP respondents living in group quarters. With the group quarters and primary economic unit 

restriction, SIPP 2014 wave 1 median net worth is $77,949, which is larger than the estimate of $74,894 with group 

quarters included.

9

 Our decision to exclude group quarters diverges from the methodology in Czajka et al. (2003) 

 

4 To sample high-wealth households, the SCF imputes the wealth of potential respondents based on income reported 

in tax returns. For more details on this methodology, see Bricker et al. (2015).

5

In addition, the weighing methodology did not change in the 2014 Panel, so changes in the difference between SCF 

and SIPP over time should not be impacted by changes in weighting.

6 This is the standard unit of analysis for household surveys conducted by the U.S. Census Bureau.

7 This procedure was first developed by Czajka et al. (2003) and used by Eggleston and Klee (2015).

8Czajka et al. (2003) and Eggleston and Klee (2015) have a condition which excludes subfamilies in which the 

subfamily head was over 25. Because 2014 SIPP no longer has subfamily indicators, we do not include this 

restriction. This difference should be minor, as our procedure still excludes many relatives in the household head’s 

family who are over 25. 

9

 P-value for the difference in medians is 0.07. 
5 

and Eggleston and Klee (2015), which included group quarters in their SIPP samples. In this paper, we remove 

group quarters for our estimates using SIPP 2008. As a result, the differences between SIPP 2008 and SCF 2010 

reported in this paper are slightly smaller than the estimates reported in Eggleston and Klee (2015).

2.5 Reference and Recall Period

Finally, the 2014 SIPP and SCF also differ in the reference and recall period of the wealth data. In wave 1 

of 2014 SIPP, interviews occurred between February and May of 2014, and respondents were asked to report asset

and debt values as of the last day of 2013. The SCF, on the other hand, interviewed respondents throughout 2013, 

and respondents were supposed to report the value of assets as of the day of the interview. Because SIPP had a 

longer recall period for assets questions, it is possible that recall error may affect the comparisons between SIPP and 

SCF. In addition, the 2008 SIPP also asked respondents to report the value of assets as of the interview, so the 

comparisons between the 2008 and 2014 SIPP Panels may also be affected by changes in recall error as well. 

3 Results

We start by benchmarking SIPP and SCF for net worth and broad categories of assets and debts, and across 

the distribution of their values. Our measure of net worth consists of (i.) financial assets inside tax-preferred 

retirement accounts; (ii.) financial assets outside tax-preferred retirement accounts; (iii.) miscellaneous financial 

assets; (iv.) unsecured debt; (v.) equity in real estate; and (vi.) equity in vehicles. Notably absent from our measures 

in both SIPP and SCF is the expected present value of defined benefit pensions, which might be an important source 

of wealth for older cohorts, but less important in recent years.

In order to provide more details about the changes in SIPP net worth estimates, we then compare ownership 

rates and the median value conditional on ownership for these broad asset and debt variables, as well as their subcomponents. Next, we examine correlation between certain asset value and debt variables to provide details on how 

portfolio compositions compare between SIPP and SCF. Finally, we compare imputation rates across SIPP and SCF 

to help reconcile some of the large differences in correlation rates when we do not observe differences in the levels 

and ownership rates. Appendix B provides details on how we construct standard errors in order to compare SIPP

estimates with SCF estimates.

Table 1: Overview of Net Worth Estimates

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

 2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Statistic SIPP 

Estimate

SCF Estimate Difference

Difference 

Standard 

Error

SIPP/SCF 

Ratio

Ratio 

Standard 

Error

SIPP/SCF 

Ratio

Ratio 

Standard 

Error

25th Percentile 3,706 8,743 5,037 469 ***42.4 4.0 ***28.3 2.3

Median 77,949 81,077 3,128 3,545 96.1 4.3 ***84.5 3.4

75th Percentile 313,095 314,243 1,148 10,376 99.6 3.3 ***86.9 4.0

Mean 372,011 525,963 153,952 25,245 ***70.7 4.6 ***75.8 6.1

Aggregate (Sum, 

in trillions) 46.25 64.45 18.19 3.13 ***71.8 4.7 ***76.1 6.1

Note: Table gives net worth estimates from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 2013 

SCF, which are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 

SIPP (Wave 7) and all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 25 or older. The SIPP and SCF 

estimates for medians are given in 2013 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.
6 

3.1 Benchmarking Net Worth in SIPP and SCF

Table 1 presents net worth estimates using SIPP 2014 and SCF 2013 (calendar year 2013), and compares 

them with estimates from 2008 SIPP and 2010 SCF (calendar year 2010). Overall, there are now smaller 

discrepancies between SIPP and SCF at the median, as well as the 25th and 75th percentiles. Median net worth for 

calendar year 2013 is $77,949 in SIPP and $81,077 in SCF, with a difference that is not statistically different from 

zero. The ratio of SIPP to SCF in 2013 is 96.1, which means the SIPP estimate is 96.1 percent of the SCF estimate. 

A ratio closer to 100 indicates a closer match, and the estimate of 96.1 is not statistically different from 100. This 

ratio for calendar year 2013 is higher than the ratio of 84.5 from calendar year 2010. For the 25th percentile, the 

SIPP estimate is 42.4 percent of the SCF estimate in 2013. This ratio from calendar year 2013 is higher than the

28.3 percent in calendar year 2010. For the 75th percentile, the SIPP estimate in calendar year 2013 is 99.6 percent 

of the SCF estimate, up from 86.9 percent in calendar year 2010. The mean is substantially lower in SIPP than in 

SCF, where the SIPP estimate is 70.7 percent of the SCF estimate. However, the ratio for 2010 was 75.8, which is 

not statistically different from the 2013 estimate. A similar pattern exists for aggregate wealth. 

3.2 Benchmarking specific assets and debts

We present the ownership rates and median value conditional on ownership for all the subcomponents as 

well as for the overall category. 

Table 2: Financial Assets in Tax-Preferred Retirement Accounts

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

 2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Statistic SIPP 

Estimate

SCF 

Estimate

Difference

Difference 

Standard 

Error

Difference Difference 

Standard Error

Ownership rates

Total 49.66 49.23 -0.43 0.64 0.77 0.71

IRA/Keogh 27.02 28.10 *1.08 0.57 -0.01 0.64

Employer-Sponsored Plans 38.18 34.52 ***-3.66 0.65 ***-3.87 0.59

Median value conditional on ownership

Total 60,000 58,228 -1,772 3,134 -563 2,986

IRA/Keogh 40,000 49,767 ***9,767 3,102 ***10,475 3,134

Employer-Sponsored Plans 50,000 37,027 ***-12,973 2,408 1,260 2,469

Note: Table gives retirement account estimates from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 

2013 SCF, which are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 

SIPP (Wave 7) and all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates for 

medians are given in 2013 dollars, and the ownership rates are 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.

Retirement Accounts

Table 2 presents the results for retirement accounts. Measures of assets held in any tax-preferred retirement 

accounts are similar in SIPP and SCF in calendar year 2013, as they had been in calendar year 2010. According to 

the SIPP estimates from calendar year 2013, 49.7 percent of households have any tax-preferred retirement accounts, 

which is similar to the SCF estimate of 49.2 percent of households. The conditional median value of assets held in 

these accounts is $60,000 in SIPP and $58,228 in SCF in 2013. There is not a statistically significant difference 

between the ownership rates or the conditional medians in SIPP and SCF, and these differences in calendar year 

2013 are comparable to those in calendar year 2010. 

Tax-preferred retirement accounts include both employer-sponsored plans (e.g., 401(k), 403(b), or Thrift 

plans), as well as Individual Retirement Accounts (IRAs) and Keogh plans. While the ownership rates and value of 

assets held in any tax-preferred retirement accounts are similar in SIPP and SCF, there are differences between 
7 

estimates for the types of plans. For employer-sponsored plans, 2014 SIPP has three percentage points higher 

ownership rates. This is similar to estimates in calendar year 2010. The median value of IRA/Keogh accounts is 

now $49,767 in SCF and $40,000 in SIPP, for a difference in median values of $9,767. In calendar year 2010, this 

difference is $10,475, which is not statistically different from the estimate in calendar year 2013. For employersponsored plans, the difference in median values for calendar year 2013 is -$12,973, in which the SIPP estimate is 

higher than the SCF estimate. This is in contrast with the difference of +$1,260 in calendar year 2010, in which the 

SCF estimate is not statistically different from the SIPP estimate. 

To summarize, while the SIPP and SCF estimates for all tax-advantaged retirement accounts are similar, 

there are differences in the estimates for the employer-sponsored plans compared with IRA/Keogh plans for 

calendar year 2013. For employer-sponsored plans, both the ownership rates and the median value conditional on 

ownership are higher in SIPP than in SCF. For IRA/Keogh, both estimates are lower in SIPP than in SCF. The redesigned SIPP survey instrument had only minor changes to the questions on tax-preferred retirement accounts. In 

light of this, we did not anticipate changes among these variables, and, indeed, these findings for calendar year 2013

are comparable to results from calendar year 2010. Nevertheless, the employer-sponsored plans median value 

conditional on ownership increased in SIPP relative to SCF. Because the question text for employer-sponsored 

plans changed very little in the 2014 Panel, this result is surprising. While the median value could be affected by 

overall changes made to the survey, such as changes due to context effects or data processing, it is unclear whether 

these effects would result in the large change in median values. 

Financial Assets Outside of Retirement Accounts

Table 3 presents results for financial assets outside of retirement accounts, such as bank accounts, directly 

owned bonds, and shares of stocks held outside of retirement accounts. For savings accounts, SCF classifies 

education savings accounts, such as a 529 plan, as a type of savings account. Because of this, for the 2014 SIPP 

panel, we include data on 529 plans in our savings account variable. Questions on education savings accounts were 

added in the 2014 Panel, so the SIPP estimates from calendar year 2010 do not include any data on educational 

savings accounts. 
8 

Table 3: Financial Assets Outside Tax-Preferred Retirement Accounts

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

 2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Statistic SIPP 

Estimate

SCF 

Estimate

Difference

Difference 

Standard 

Error

Difference

Difference 

Standard 

Error

Ownership rates

Total 85.95 92.41 ***6.46 0.39 ***17.74 0.48

 Bank Accounts 85.74 92.19 ***6.46 0.40 ***18.76 0.49

Checking Accounts 81.61 87.10 ***5.49 0.48 ***24.67 0.59

Savings Accounts 61.65 49.61 ***-12.04 0.75

Money market deposit accounts 12.36 14.21 ***1.85 0.46

Certificates of deposit (CDs) 7.62 7.75 0.13 0.39

Bonds (Other Interest Earning Assets) 8.82 11.02 ***2.20 0.43 ***1.48 0.42

Stocks and Mutual Funds 19.20 18.42 -0.78 0.50 ***2.18 0.52

Median value conditional on ownership

Total 5,400 5,373 -27 299 ***1,130 221

 Bank Accounts 4,200 4,559 359 277 ***1,492 175

Checking Accounts 1,500 1,991 ***491 1

Savings Accounts 2,500 3,952 ***1,452 249

Money market deposit accounts 15,358 29,860 ***14,502 1,896

Certificates of deposit (CDs) 24,000 16,125 **-7,875 3,185

Bonds (Other Interest Earning Assets) 3,000 1,493 ***-1,507 281 -265 183

Stocks and Mutual Funds 35,000 42,601 *7,601 4,411 **7,933 3,623

Note: Table gives estimates on bank accounts and other financial assets outside of retirement accounts from a sample of all SCF-like families in 2014 SIPP 

(Wave 1) and all primary economic units in 2013 SCF, which are for calendar year 2013. These estimates are compared to comparable estimates from a sample 

of all SCF-like families in 2008 SIPP (Wave 7) and all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates for 

medians are given in 2013 dollars, and the ownership rates are 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.

Ownership rates and median values of financial assets outside tax-preferred retirement accounts are 

typically smaller in SIPP than in SCF, although the differences between the estimates are smaller in calendar year 

2013 than they are in calendar year 2010. According to the SIPP estimates from calendar year 2013, 86.0 percent 

of households have financial assets outside of tax-preferred retirement accounts, which is smaller than the SCF 

estimate of 92.4 percent of households. This difference of 6.5 percentage points in calendar year 2013 declined from 

a 17.7 percentage point difference in calendar year 2010. The conditional median value of assets held in these 

accounts is $5,400 in SIPP and $5,373 in SCF for calendar year 2013. This difference in the medians declined from 

$1,130 in calendar year 2010 to -$27 in calendar year 2013. The 2013 difference is not statistically different from 

zero.

In addition to these changes in aggregated ownership rates and the conditional median, there were also 

changes in the specific components of financial assets outside tax-preferred accounts. The current SIPP ownership 

for savings accounts is 61.7, compared with 49.6 percent of SCF households. This 12.0 percentage point difference 

is the largest among these components of financial assets outside tax-preferred retirement accounts. 

One of the important changes is for bank accounts, specifically for checking accounts. In calendar year 

2013, the SIPP ownership rate for bank accounts is 85.7, compared with 92.2 percent of SCF households. This

difference of 6.5 percentage points in calendar year 2013 is in contrast with an 18.8 percentage point difference 

between SIPP and SCF in calendar year 2010. The changes in the ownership rate for checking accounts are even 

more striking. In calendar year 2013, the SIPP ownership rate is 5.5 percentage points lower than the SCF estimate, 

but in calendar year 2010, the difference was 24.7 percentage points. These large changes occurred following 

substantial changes to the checking account questions between the SIPP 2008 and 2014 panels, as described in 

Appendix A, suggesting that the redesigned SIPP survey led to improved estimation of checking account ownership. 
9 

Miscellaneous Financial Assets

Our measure of miscellaneous assets consists of data from less-commonly owned assets and data from a 

catch-all question which asks respondents to report about any remaining assets they have not yet reported to the 

interviewer. For SCF and both the 2008 and 2014 SIPP, miscellaneous assets includes business equity (positive) 

and other financial assets.10

 In 2014 SIPP, questions on annuities, trusts, were added to the survey. Annuities and 

trusts are included in the SCF. When an asset is not explicitly asked about in the survey, respondents are expected 

to include it in their response for “Other financial assets.”

Finally, the 2008 SIPP, 2014 SIPP, and SCF all have questions on the cash value of life insurance plans. 

However, in the 2008 Panel, many respondents conflated life insurance face value and cash value (Gottschalck and 

Moore, 2007), so cash value was excluded from the net worth calculations. In the 2014 Panel, the question text was 

revised in an attempt to eliminate the confusion between face and cash value. Because of this change, cash value is 

now included in net worth for 2014 SIPP.

 

Table 4: Miscellaneous Financial Assets

Statistic SIPP 

Estimate

SCF 

Estimate

Difference

Difference 

Standard 

Error

Difference Difference 

Standard Error

Ownership rates

Total 31.06 37.65 ***6.60 0.65 ***28.02 0.63

Annuities 3.66 3.87 0.21 0.26

Trusts 1.50 1.48 -0.02 0.17

Cash Life Insurance 18.47 19.18 0.71 0.56

Business Equity (Positive) 11.86 10.38 ***-1.48 0.42 ***2.99 0.43

Other financial assets 2.58 13.19 ***10.62 0.41 ***11.46 0.46

Median value conditional on ownership

Total 18,600 15,841 -2,759 1,934 ***-10,404 3,249

Annuities 48,750 72,461 **23,711 11,340

Trusts 100,000 253,216***153,216 49,924

Cash Life Insurance 12,000 7,704 ***-4,296 1,301

Business Equity (Positive) 15,000 100,000 ***85,000 6,341 ***73,166 4,045

Other financial assets 30,000 9,953 ***-20,047 5,841 ***-33,756 4,211

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

 2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Note: Table gives estimates of miscellaneous assets from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 2013 SCF, 

which are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all 

primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other 

relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates for medians are given in 2013 dollars, and the ownership 

rates are 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.

Table 4 presents the results comparing miscellaneous assets between SIPP and SCF. Miscellaneous 

financial assets ownership rates are lower overall in SIPP than in SCF. In calendar year 2013, the SIPP ownership

rate of miscellaneous assets is 31.1 percent of households, compared with 37.7 percent of SCF households. This

difference in ownership rates of 6.6 percentage points in calendar year 2013 is in contrast with a 28.0 percentage 

point difference between SIPP and SCF in calendar year 2010. The median value conditional on ownership is larger 

in SIPP than in SCF. The conditional median is $18,600 in 2014 SIPP and $15,841 in 2013 SCF. This difference of 

-$2,759 in calendar year 2013 is closer to zero than the than the difference of -$10,404 in calendar year 2010. In 

other words, while the SIPP estimate is still higher than the SCF estimate in 2013, this disparity was even larger in 

2010. This change is potentially driven by SIPP capturing more miscellaneous assets that have low values, such as 

cash life insurance, which drives down median values. 

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

a business. By construction, this SCF variable cannot be negative. Because of this, we only code a SIPP respondent 

as having a business if his or her business equity is positive. 
10

For the subcomponents, ownership rates for annuities, trusts, and cash life insurance are not statistically 

different between the SIPP and SCF in calendar year 2013, and the SIPP ownership rate for business equity 

(positive) is only 1.48 percentage points higher than the SCF estimate. However, the ownership rate in calendar 

year 2013 for other financial assets captured from a catch-all question is 2.6 percent of households in SIPP 

compared with 13.2 percent of households in SCF. These differences between SIPP and SCF in calendar year 2013 

are not statistically different from the differences in calendar year 2010, despite the fact that annuities and trusts are 

no longer included in the catch-all question for SIPP.11

 The question text about other financial assets was revised 

between SIPP 2008 and SIPP 2014 to include additional examples of other assets. However, even with this 

modification, the rates in SIPP are still much smaller than in SCF. 

The conditional median values of specific assets are typically smaller in SIPP than in SCF. In particular, the

median value of annuities is $48,750 in SIPP and $72,461 in SCF for calendar year 2013, and the median value of 

assets in trusts is $100,000 in SIPP and $253,216 in SCF. The median positive business equity in calendar year 2013 

is $15,000 in SIPP and $100,000 in SCF. However, the median value of both the cash value of life insurance and 

other financial assets is higher in SIPP than in SCF in calendar year 2013. Thus, while the overall match between 

SIPP and SCF for other assets has improved in the 2014 Panel, there are large discrepancies for the individual 

subcomponents.

Unsecured Debt

Table 5 presents estimates for unsecured debt. For the overall category, 50.6 percent of households in SIPP 

hold unsecured debt in calendar year 2013, compared with 53.2 percent of SCF households. In calendar year 2013, 

the conditional median is $8,000 in SIPP and $6,274 in SCF. Comparing the estimates from calendar year 2013 to 

the estimates from calendar year 2010, the difference in SIPP and SCF ownership rates declined from 7.5 to 2.7 

percentage points, and the difference in conditional median values increased in absolute value from -$314 to -

$1,726. 

 

Table 5: Unsecured Debt

Statistic SIPP 

Estimate

SCF 

Estimate

Difference

Difference 

Standard 

Error

Difference Difference 

Standard Error

Ownership rates

Total 50.55 53.22 ***2.68 0.75 ***7.52 0.79

Credit Cards 38.16 38.14 -0.01 0.69 0.54 0.72

Student Loans 17.29 19.92 ***2.63 0.51

Residual Debt 15.79 29.57 ***13.77 0.61

Median value conditional on ownership

Total 8,000 6,274 ***-1,726 359 -314 305

Credit Cards 3,500 2,289 ***-1,211 240 ***-1,474 184

Student Loans 19,000 16,642 *-2,358 1,349

Residual Debt 3,950 255 ***-3,695 516

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

 2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Note: Table gives unsecured debt estimates from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 2013 SCF, 

which are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) 

and all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 

siblings and other relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates for medians are given in 2013 

dollars, and the ownership rates are 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.

Unsecured debt consists of revolving credit card debt, student loans, and any other residual debt. While the 

rate in calendar year 2013 of having credit card debt is not statistically different between the surveys at just over 38 

11 Because annuities and trust are not included in the catch-all question for 2014 SIPP, the estimates for other assets 

in SIPP between the 2008 and 2014 Panels are not comparable over time.
11

percent of households, the rate of having student loan debt is slightly lower in SIPP than in SCF—17.3 percent 

compared with 19.9 percent of households. Ownership of residual debt is 15.8 percent in SIPP compared with 29.6 

percent in SCF in calendar year 2013. In 2014 SIPP, a direct question on student loans was added to the survey. A 

common assumption in the literature is that additional detailed questions produce more reliable measures of 

aggregate wealth (see Juster, Smith, and Stafford, 1999). Because of this, we think a likely reason that the rates of 

having debt increased in SIPP is because of the additional question explicitly asking about student loans. In 

addition, the median value of overall unsecured debt may have increased in SIPP if student loan balances have 

higher values than other types of unsecured debt. Given the addition of the student loan question, the specific 

components of unsecured debt are not directly comparable to the results from calendar year 2010. Nevertheless, for 

residual debt, the lower ownership rate and higher median value in SIPP relative to SCF suggests that SIPP is not 

capturing certain smaller debts.

Equity in Real Estate

Table 6 presents the results for real estate. In 2013, fewer households own real estate in SIPP than in SCF. 

In SIPP, 64.7 percent of households own real estate, compared with 67.2 percent of households in SCF. This

difference of 2.5 percentage points in calendar year 2013 is not significantly different from the difference in 

calendar year 2010. 

Statistic

SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

Difference

Difference 

Standard 

Error

Ownership rates

Total 64.73 67.20 ***2.46 0.34 ***2.45 0.27

Primary Residence 63.22 65.15 ***1.93 0.29 ***1.56 0.24

Primary Residence Debt 39.30 42.92 ***3.62 0.55 ***5.45 0.61

Rental Property and Other Real Estate 12.72 17.12 ***4.40 0.48 ***8.47 0.44

Rental Property and Other Real Estate Debt 6.22 5.28 ***-0.95 0.33 ***2.94 0.26

Median value conditional on ownership

Total equity 90,000 89,183 -817 2,830 -4,948 4,680

Primary Residence 175,000 169,209 -5,791 4,159 -213 4,579

Primary Residence Debt 120,000 114,664 **-5,336 2,412 ***-11,361 2,343

Rental Property and Other Real Estate 150,000 96,310 ***-53,690 9,129 ***-34,822 9,126

Rental Property and Other Real Estate Debt 110,000 89,581 **-20,419 9,574 ***-29,447 9,923

Note: Table gives real estate estimates from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 2013 SCF, which 

are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and 

all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates for medians are given in 

2013 dollars, and the ownership rates are 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.

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Table 6: Equity in Real Estate

The SIPP ownership rates for primary residences are close to the SCF estimates, although the SCF

estimates are higher by about 1.9 percentage points in calendar year 2013. For rental property, 12.7 percent of 

households in the SIPP data own rental property or other real estate for calendar year 2013, compared with 17.1 

percent of households in SCF. The ownership rate of rental property and other real estate increased in SIPP relative 

to SCF from calendar years 2010 to 2013. This difference of 4.4 percentage points in calendar year 2013 is smaller 

than the 8.5 percentage points in calendar year 2010. One explanation for the higher reported ownership of other real 

estate is the SIPP 2014 instrument included explicit examples of other real estate (such as timeshares) in the 

question text, while the question text in the SIPP 2008 instrument did not include any examples. 

Equity in real estate equals the value of a primary residence and rental property and other real estate, minus 

debt secured by the primary residence as well as debt for rental property and other real estate. For overall equity in 
12

real estate, there is no statistically significant difference in median values conditional on ownership. The median 

value for 2013 is $90,000 in SIPP and $89,183 in SCF. This difference of -$817 in calendar year 2013 is not 

statistically different from the -$4,948 difference in calendar year 2010. The estimates for primary residence values 

are similar in SIPP and SCF, and primary residence debt is slightly higher in SIPP. In contrast, rental property and 

other real estate values and debt are much larger in SIPP than in SCF. This pattern, along with lower ownership rates

in SIPP, suggest that SIPP might be missing ownership of other real estate and rental property that are toward the 

lower end of resale values. 

Equity in Vehicles

Statistic SIPP 

Estimate

SCF 

Estimate

Difference

Difference 

Standard 

Error

Difference

Difference 

Standard 

Error

Ownership rates

Vehicles 83.99 86.29 ***2.30 0.46 ***3.65 0.44

Vehicle Debt 32.33 31.20 *-1.13 0.64 0.10 0.54

Median value conditional on 

ownership

Total equity 6,861 11,050 ***4,189 236 ***5,836 248

Vehicles 11,831 15,886 ***4,055 213 ***5,799 313

Vehicle Debt 12,499 12,000 -499 624 ***-667 147

Note: Table gives estimates on vehicles from a sample of all SCF-like families in 2014 SIPP (Wave 1) and all primary economic units in 2013 

SCF, which are for calendar year 2013. These estimates are compared to comparable estimates from a sample of all SCF-like families in 

2008 SIPP (Wave 7) and all primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 25 or older. The 

SIPP and SCF estimates for medians are given in 2013 dollars, and the ownership rates are 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.

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Table 7: Equity in Vehicles

Table 7 presents results for vehicles. Roughly 84 percent of SIPP households own vehicles in calendar 

year 2013, compared with 86.3 percent of SCF households. This difference of 2.3 percentage points in calendar year 

2013 is slightly smaller than the 3.65 percentage point difference in calendar year 2010. Median equity conditional 

on owning vehicles is $6,861 in SIPP and $11,050 in SCF for calendar year 2013. This is mostly explained by the 

difference in vehicle values (difference in median of $4,055) rather than vehicle debt (difference in median of -$499, 

which is not significantly different from $0). 

Both surveys use data from the National Automobile Dealers Association (NADA) to assign vehicle values 

based on reported year, make, and model. Given the two surveys have similar methodology for creating vehicle 

values, it is surprising that median vehicle values are different between SIPP and SCF. However, SIPP uses average 

trade-in value while SCF uses retail value. Retail value is typically larger than trade-in value. By construction, the 

same reported data in SIPP and SCF would generate smaller median vehicle values in SIPP than in SCF.

In the 2008 Panel, SIPP estimates for vehicle values were also lower than SCF estimates. However, the 

match between SIPP and SCF estimates is closer now than it was for calendar year 2010. The difference in median 

values conditional of ownership is now $4,055, but was $5,799 previously. This could be due to changes in the 

methodology used to assign vehicle values, as described in Appendix A. In particular, the NADA database used to 

14

Pairwise comparison

SIPP SCF Difference

Difference 

Standard 

Error

Difference Difference 

Standard Error

Retire Assets Non-Retire Assets 0.615 0.618 0.003 0.036 0.011 0.026

Retire Assets Unsecured debt 0.000 -0.026 -0.026 0.028 -0.014 0.027

Retire Assets Equity in real 

estate 0.368 0.553 ***0.185 0.038 ***0.185 0.032

Retire Assets Equity in vehicles 0.360 0.303 -0.057 0.035 **0.071 0.029

Retire Assets Stocks & MF 0.453 0.590 0.136 0.090 ***0.348 0.099

Retire Assets Cash Life Insurance 0.139 0.456 ***0.317 0.094

Retire Assets Checking Accounts 0.362 0.378 0.015 0.038 **0.138 0.055

Non-Retire Assets Unsecured debt 0.000 0.005 0.005 0.017 *0.044 0.023

Non-Retire Assets Equity in real 

estate 0.549 0.584 0.035 0.030 ***0.114 0.034

Non-Retire Assets Equity in vehicles 0.500 0.451 *-0.049 0.025 ***0.088 0.030

Equity in real 

estate

Equity in vehicles 0.279 0.210 **-0.070 0.032 -0.022 0.021

Equity in real 

estate

Stocks & MF 0.474 0.542 0.068 0.095 ***0.431 0.077

Stocks & MF Cash Life Insurance

0.197 0.245 0.048 0.110

Stocks & MF Checking Accounts 0.270 0.387 *0.116 0.066 ***0.253 0.087

Primary Residence Primary Resid. Debt

0.596 0.579 -0.018 0.038 -0.017 0.038

Vehicles Vehicles Debt 0.461 0.468 0.007 0.054 0.041 0.036

Assets Debt 0.750 0.716 -0.034 0.025 ***-0.073 0.020

Table 8: Median Correlation Coefficients

Note: Table gives the correlation median, introduced by Falk (1998) and discussed in the paper, from a sample of all SCFlike families in 2014 SIPP (Wave 1) and all primary economic units in 2013 SCF, which are for calendar year 2013. These 

estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all 

primary economic units in 2010 SCF, both of which are measured for calendar year 2010. 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 siblings and other relatives of the household reference person who are age 

25 or older. Retirement Assets is short for "Financial Assets in Tax-Preferred Retirement Accounts." Similarly, "NonRetirement Assets" is short for "Financial Assets Outside Tax-Preferred Accounts." 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. 

2014 SIPP and 2013 SCF 

(Calendar Year 2013)

2008 SIPP and 2010 SCF 

(Calendar Year 2010)

Table 8 compares the median correlation coefficients among variables in SIPP and then in SCF. Each row 

examines the median correlation coefficient between two variables. We report the estimate using SIPP 2014, the 

estimate using SCF 2013, the difference between these estimates, and the standard error of this difference. We also 

report the difference using data from SIPP 2008 wave 7 compared with SCF 2010, along with the standard error of 

the estimated difference. 
15

For many pairwise comparisons in this table, the correlation is similar between SIPP and SCF. For 

example, the correlation with financial assets outside of tax-preferred retirement accounts (“Non-Retire Assets”) is 

large in both 2014 SIPP (0.606) and 2013 SCF (0.618), and the difference between them (0.012) is not statistically 

significant. The difference between SIPP 2008 and SCF 2010 is also small (0.011) and not statistically significant. 

In general, the comparison between broader categories (such as retirement and non-retirement assets, as well as 

assets and debt) is similar between SIPP and SCF, with differences that are small and not statistically significant.

For some other pairwise comparisons, the correlations in SIPP are smaller than in SCF. The correlation of 

retirement assets and equity in real estate is smaller in 2014 SIPP (0.368) than in 2013 SCF (0.553). The correlation 

between retirement assets and cash value of life insurance is 0.139 in 2014 SIPP, but 0.456 in 2013 SCF. 

Finally, we look at how the difference in pairwise comparisons has changed after the SIPP redesign. 

Overall, the differences between SIPP and SCF are similar in both calendar year 2010 and 2013, with a few 

exceptions. For retirement assets and checking accounts, the SIPP correlations are closer to SCF in the 2014 Panel 

than in the 2008 Panel, potentially due to improvement in the regular checking account question. For other 

variables, the largest change using SIPP 2014 was for equity in real estate and stocks and mutual funds. The 

difference in the correlation between the two categories was 0.431 for calendar year 2010, but there is no 

statistically significant difference between the SIPP and SCF estimates for calendar year 2013. 

In summary, the relationship between asset variables is similar between SIPP and SCF, at least for broad 

asset categories. For smaller asset grouping, the relationship is close between SIPP and SCF for many variables, 

although there are some notable exceptions, such as the correlation between cash life insurance and retirement 

accounts, and the correlation between real estate equity and retirement accounts. These results are in contrast to 

Czajka et al. (2003) and Eggleston and Klee (2015), which found that the Pearson’s correlation coefficient was 

much lower in SIPP than in SCF. However, given the Pearson’s correlation coefficient can be sensitive the outliers, 

these low correlations may be driven by the data of a few high-wealth individuals, rather than being reflective of

most respondents’ wealth data. 

3.4 Imputation rates

The reported amounts in the preceding sections use data from all respondents, which includes both reported 

amounts and imputed data. In this section, we use the share of imputed data as an indicator of data quality, 

comparing imputation rates between the 2008 and 2014 SIPP Panels and benchmarking these to rates from SCF 

2013. For both SIPP and SCF, we code responses into three categories: (1) reported values (or logically edited 

values); (2) values imputed from a range follow-up; and (3) values imputed without a range follow-up. 

Given the large number of asset questions in both surveys, we combine the asset imputation rates into 

meaningful categories (e.g., bank account, vehicles) as well as a net worth category which combines all asset and 

debt variables. To combine imputation rates from individual questions, we construct a weighted average of the 

response rates in which the weight is the median value times the ownership rate.13

 This procedure gives more 

weight to variables that constitute a larger proportion of net worth, which reflects the frequency of ownership and 

the value conditional on ownership.

14

 For example, in our net worth category, the imputation rate for primary 

residences is given a relatively high weight, given that home values constitute a large portion of many people’s 

wealth portfolios and homeownership rates are also relatively high. Using the data reported in Table 6, for 

example, the weight for primary residences in SIPP (before the weights are normalized to sum to one) would be the 

ownership rate of 63.22 times the median value of $175,000, which equals 11,063,500. On the other hand, bonds 

are given a lower weight because very few households directly own bonds. Using the data reported in Table 3, for 

example, the weight for bonds in SIPP would be the ownership rate of 8.82 times the median value of $3,000, which 

equals 26,460. More details about this methodology along with other metrics for comparing item nonresponse rates 

across surveys can be found in Eggleston (forthcoming).

 

13 In this section, we use the terms imputation rates and item nonresponse rates synonymously. However, some 

missing value are the results of a respondent exiting the interview before the asset section, rather than saying “Don’t 

Know” or “Refuse” to a question. 

14 The weights are normalized to sum to one.
16

Table 9: Imputation Rates for SIPP 2008 and 2014

Category Reported Imputed 

without 

range

Imputed 

within 

range

Reported Imputed 

without 

range

Imputed 

within 

range

P-value 

Difference

Bank accounts 60.2 22.0 17.8 52.7 33.4 13.9 <.001

(0.63) (0.60) (0.46) (0.49) (0.43) (0.34)

Bonds 47.5 28.1 24.3 57.5 21.6 20.9 0.014

(2.59) (1.81) (1.84) (2.26) (2.38) (1.67)

Stocks 47.5 27.7 24.8 37.7 39.6 22.6 <.001

(0.93) (0.83) (0.69) (0.78) (0.84) (0.73)

Financial Assets 53.4 25.1 21.5 44.0 36.0 20.0 <.001

(0.72) (0.62) (0.51) (0.71) (0.72) (0.55)

Business 54.7 22.2 23.1 23.8 76.2 <.001

(1.48) (1.36) (1.18) (1.01) (1.01)

Other Assets 52.3 31.0 16.7 30.2 65.2 4.6 <.001

(0.99) (1.10) (0.79) (1.31) (1.80) (0.80)

Retirement Assets 57.2 19.4 23.4 42.1 31.9 26.0 <.001

(0.53) (0.35) (0.37) (0.52) (0.51) (0.47)

Real Estate 83.8 15.1 1.1 68.5 30.7 0.8 <.001

(0.29) (0.29) (0.07) (0.42) (0.42) (0.06)

Vehicles 79.3 20.7 66.1 33.9 <.001

(0.28) (0.28) (0.47) (0.47)

Unsecured Debt 75.1 24.9 71.9 28.1 <.001

(0.45) (0.45) (0.56) (0.56)

Net Worth 77.3 17.3 5.3 64.4 31.8 3.8 <.001

(0.30) (0.26) (0.12) (0.39) (0.40) (0.08)

SIPP 2014 (Calendar Year 2013) SIPP 2008 (Calendar Year 2010)

Note: 2008 SIPP Panel (Wave 7), for calendar year 2010, and 2014 SIPP Panel (Wave 1), for calendar year 2013. These 

estimates are compared to comparable estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all 

primary economic units in 2010 SCF, both of which are measured for calendar year 2010. Table present allocation rates 

across aggregated wealth categories. The allocation rates are a weighted average of the allocation rates from the 

underlying variables, where the weights are the ownership rate times the median value conditional on ownership. 

Replicate weights used to construct standard errors. For the statistical test comparing the allocation rates between the 

2008 and 2014 Panels, a Z-test was used for categories without a range follow-up option in either panel, and a Chisquared test was used for all the other categories. 

Table 9 presents the results comparing 2008 and 2014 SIPP. When looking at any asset or debt variable, 

the weighted response rate is 64.4 percent in the 2008 Panel but 77.3 percent the 2014 Panel. These numbers mean 

than on average, SIPP asset value and debt questions had a weighted average response rate of 64.4 percent in the 

2008 panel, but such questions had a response rate of 77.3 percent the 2014 Panel, on average. Allocation rates vary 

across assets, with the weighted average rates being low for real estate (16.2 percent in the 2014 Panel), but higher 

for stocks (52.5 percent in the 2014 Panel). When comparing across panels, the 2014 Panel has lower imputation 

rates for every asset except for bonds. 
17

Table 10: Imputation Rates for SIPP 2014 and SCF 2013 (Calendar Year 2013)

Category

Reported Imputed 

without 

range

Imputed 

within 

range

Reported Imputed 

without 

range

Imputed 

within 

range

P-value 

Difference

Bank accounts 60.2 22.0 17.8 80.8 4.0 15.2 <.001

(0.63) (0.60) (0.46) (1.34) (0.63) (1.26)

Bonds 47.5 28.1 24.3 71.7 17.1 11.2 0.032

(2.59) (1.81) (1.84) (7.82) (6.87) (4.41)

Stocks 47.5 27.7 24.8 73.6 7.5 18.8 <.001

(0.93) (0.83) (0.69) (2.48) (1.57) (2.17)

Financial Assets 53.4 25.1 21.5 75.6 7.2 17.2 <.001

(0.72) (0.62) (0.51) (1.70) (1.18) (1.59)

Business 54.7 22.2 23.1 72.5 6.3 21.2 <.001

(1.48) (1.36) (1.18) (3.35) (1.40) (3.32)

Other Assets 52.3 31.0 16.7 73.7 8.9 17.4 <.001

(0.99) (1.10) (0.79) (3.89) (2.35) (3.39)

Retirement Assets 57.2 19.4 23.4 73.6 7.9 18.5 <.001

(0.53) (0.35) (0.37) (1.72) (0.80) (1.41)

Real Estate 83.8 15.1 1.1 88.8 1.4 9.7 <.001

(0.29) (0.29) (0.07) (0.67) (0.23) (0.72)

Vehicles 79.3 20.7 90.8 1.3 7.9 <.001

(0.28) (0.28) (0.73) (0.19) (0.74)

Unsecured Debt 75.1 24.9 85.5 3.3 11.2 0.129

(0.45) (0.45) (1.48) (0.58) (1.49)

Net Worth 77.3 17.3 5.3 85.0 3.4 11.7 <.001

(0.30) (0.26) (0.12) (0.72) (0.23) (0.73)

SIPP 2014 SCF 2013

Note: 2014 SIPP Panel (Wave 1) and 2013 SCF, which are for calendar year 2013. Table present allocation rates across 

aggregated wealth categories. The allocation rates are a weighted average of the allocation rates from the underlying 

variables, where the weights are the ownership rate times the median value conditional on ownership. Replicate weights 

used to construct standard errors in both survey, and imputation implicates used to construct the standard error for SCF. For 

the statistical test comparing the allocation rates between the 2008 and 2014 Panels, a Z-test was used for categories without 

a range follow-up option in either panel, and a Chi-squared test was used for all the other categories. 

Table 10 compares imputation rates in the SIPP 2014 Panel and 2013 SCF. This table shows that item 

nonresponse rates are lower in the SCF than in SIPP for all wealth categories. For bank accounts, SCF has a 

weighted response rate of 80.8 percent, but only 60.2 percent for SIPP. These numbers mean than on average, SIPP 

questions on bank account balances have a weighted average response rate of 60.2 percent, but similar questions in 

the SCF have a response rate of 80.8 percent, on average. In addition, SCF also has fewer individuals whose values 

are imputed without a range follow-up. The rate of imputed without a range follow-up for SCF is only 4 percent, 
18

but the rate is 22 percent in SIPP. This trend holds when looking at all asset and debt variables, in which the 

response rate for SCF is 85 percent but is 77.3 percent in SIPP 2014 

In summary, imputation rates are lower in SIPP 2014 than in 2008, but the 2014 imputation rates are still 

higher than in SCF. These differences in imputation rates are unlikely to be solely attributed to differences in the 

instrument or question text. For example, the allocation rates for real estate is different in the 2008 SIPP, 2014 

SIPP, and 2013 SCF, even though the question text for the value of primary residences is almost identical between 

all these surveys. 

For the 2008 and 2014 SIPP comparisons, one difference in the wealth data between the panels is that 

wealth data for calendar year 2010 is from the 7th wave of the 2008 Panel, but wealth data for calendar year 2013 is 

collected in the 1st wave of the 2014 Panel. Because of this, attrition or repeated interviewing may have some 

impact on the responses rates in the 2008 Panel. For example, respondents who skip items in the initial interview 

might be more likely to attrit from the SIPP panel, which would decrease imputation rates in later waves. On the 

other hand, respondents may learn the interview is quicker when they report not knowing answers (rather than 

spending time to find the answer), which would increase imputation rates in later waves. 

For the SIPP and SCF comparisons, one overall difference between the surveys is that wealth questions are 

asked near the end of the SIPP interview, while SCF has more wealth questions earlier on in the survey. Because of 

this, some respondents may drop out of the SIPP interview by the time they reach the section, or they may be

fatigued by the time they get to the wealth questions. Both of these factors would increase imputation rates in SIPP

relative to SCF.

4. Conclusion

In 2014, SIPP underwent numerous revisions that affected wealth measurement. In addition to the changes 

to the wealth content, respondents in the redesigned SIPP 2014 panel are interviewed less frequently, wealth 

questions are included in every wave rather than only in occasional Topical Modules, and the reference period is 

always the last day of the calendar year. Some improvements in SCF and SIPP differences by asset category, such as 

for checking accounts and student loans, are consistent with changes to the wealth content. However, there are other 

changes in the SCF and SIPP differences--such as for estimates of financial assets in tax-preferred retirement 

accounts, and the lower imputation rates across all wealth questions--that cannot be directly attributed to changes to 

the survey content.

Nevertheless, our results suggest the redesigned SIPP 2014 survey led to improved measures of household 

wealth. Differences between wealth estimates in SIPP 2014 wave 1 and SCF 2013 are smaller than differences in 

these estimates from SIPP 2008 wave 7 and SCF 2010. The results show increases in wealth in SIPP relative to 

SCF. At the same time, there were large differences between SIPP 2008 and SCF 2010 in the covariance structure of 

particular assets and debts, but the estimates for SIPP 2014 and SCF 2013 were more comparable. Finally, 

imputation rates for asset and debt questions were lower in SIPP 2014 than in SIPP 2008, although they are still

higher in SIPP than in SCF. Because of the changes in the SIPP survey, measures of household wealth should not be 

directly compared with earlier panels, unless data users account for changes due to the redesign.

While the quality of SIPP appears to have improved in a variety of dimensions, there are still concerns 

about data quality for individual asset categories. For example, there is evidence that the types of life insurance may 

still confuse respondents in 2014 SIPP. Many interviewers have left notes that respondents are realizing at the cash 

value question that they do not have whole life insurance, but instead have term insurance. It appears that many of 

these respondents are entering zero for the cash value in these situations, so it seems that they are at least not putting 

in the face value.15

 Moreover, 2014 SIPP has an imputation rate of 58.6 percent for cash value life insurance. 

Finally, the median correlation coefficient of this and other asset measures is quite a bit lower in SIPP than it is in 

SCF, again suggesting there might be issues with the cash life insurance data. Despite attempts to simplify the life 

 

15 In the final SIPP data, these respondents are recoded to have term life insurance.
19

insurance question, the reported confusion and high imputation rates suggest there are still problems with the quality 

of these data.16

While there are still discrepancies between SIPP and SCF for some individual assets, such as trusts and 

cash value of life insurance, SIPP and SCF estimates are similar for other assets, such as primary residences. 

Moreover, for broader asset categories, such as assets in tax-preferred retirement accounts, the differences between 

SIPP and SCF are smaller. For the broadest category, household net worth, there is no statistically significant 

difference in the SIPP and SCF estimates for the median or 75th percentile. Taking SCF as a benchmark, the SIPP 

data appears to have improved in a variety of dimensions, although there are still some large discrepancies for some 

individual assets.

 

16 In the 2018 SIPP Panel, the life insurance question is being revised in order to address these potential issues with 

data quality.
20

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University of Michigan Press.

Appendix A: Detailed descriptions of changes to SIPP 2014

As mentioned in Section 2, numerous changes were made to the SIPP wealth section in the 2014 Panel. 

New asset questions were added, and question text was modified for numerous assets. Besides question-specific 

changes, some overall changes to the wealth section include:

1. Rearranging questions in order to ask about the value and income of an asset consecutively,

2. Modifying the imputation procedure to change the variables on the hot deck, and to impute asset value 

and income variables jointly as well as asset value and debt variables jointly (For example, the balance 

of a savings account is now used to impute the interest income someone receives from a savings 

account),17

3. Adding range follow-up questions for regular checking accounts, savings bonds, and business value 

and debt,

4. Adding soft checks which ask the interviewer to confirm the value of an asset if the reported amount is 

unusually high, and

5. Removing a screening procedure described in Moore and Griffiths (2003) which took respondents offpath for less common assets, such as rental properties, if they did not own more common types of 

assets, such as savings accounts. 

 

17 A description of Census’s hot deck methodology can be found in U.S. Census Bureau (2016)
22

Variable

Question 

Substationally 

Revised

Details Potential Impact on Wealth 

Statistics

Financial Assets in Tax-Preferred 

Retirement Accounts

IRA/Keogh 

401(k)/Thrift

Financial Assets Outside Tax-Preferred 

Retirement Accounts

Checking Accounts X

Question text simplified; order of questions 

improved

Increase in ownership rates; 

Increase in net worth

Savings Accounts X

Separate reporting of account balance from 

other interest-earning accounts Unclear impact on net worth

Education Savings Accounts X New question Increase in net worth

Money market deposit accounts X

Separate reporting of account balance from 

other interest-earning accounts Unclear impact on net worth

Certificates of deposit (CDs) X

Separate reporting of account balance from 

other interest-earning accounts Unclear impact on net worth

Bonds (Other Interest Earning Assets) X

Separate reporting of account balance from 

other interest-earning accounts Unclear impact on net worth

Stocks and Mutual Funds X

Separate reporting of value of stocks and 

mutual funds Unclear impact on net worth

Miscellaneous financial assets

Annuities X New question Increase in net worth

Trusts X New question Increase in net worth

Cash Life Insurance X

Question on face value of life insurance added; 

cash value now included in net worth

Improved data on cash value; 

increase in net worth

Business Equity (Actively Managed 

Businesses)

Business Equity (Businesses as an 

Investment) X New question Increase in net worth

Mortgages Owned as an Asset X Question removed Minor impact on net worth

Royalties X Question removed Minor impact on net worth

Other financial assets X Question text changed; examples now given Increase in ownership rates; 

Increase in net worth

Unsecured debt

Credit Cards

Student Loans X Question added Decrease in net worth

Residual Debt X Questions combined and simplified Unclear impact on net worth

Equity in real estate

Primary Residence

Primary Residence Debt

Rental Property and Other Real 

Estate

X Example of other real estate now given Increase in ownership rates; 

Increase in net worth

Rental Property and Other Real 

Estate Debt X Example of other real estate now given Increase in ownership rates; Increase in net worth

Equity in vehicles

Vehicles X Data editing procedure revised Unclear impact on net worth

Vehicle Debt

Table 11: Overview of Changes in Wealth Questions and Data

Note: This table gives an overview of wealth questionnaire changes between SIPP 2008 Topical Modules and SIPP 2014 core questionnaires. For each 

variable we describe whether or not the text underwent a substantial revision, whether it was altogether added or removed, and whether this is expected to 

impact ownership rates and net worth. 
23

To summarize the question-specific changes in the 2014 Panel, Table 11 gives an overview of all the 

changes made to SIPP asset and debt questions. For each variable, we describe whether the question text underwent 

a substantial revision and whether the question was added to or removed from the 2014 Panel. This table shows that 

question text was changed for most wealth questions, although the question text for IRAs, 401(k)s, primary 

residences, and credit cards changed very little between the 2008 and 2014 Panels.18

 In the rest of this section, we 

discuss the changes for each asset in more detail.

• Checking Accounts: SIPP asks separately whether a respondent owns an interest-earning 

checking account, and whether they own a non-interest earning checking account. In the 2008 and 

prior panels, questions about interest-earning and non-interest checking accounts were asked in 

separate sections of the interview. For respondents who had a checking account but were unsure 

whether it earned interest, this gap between the two checking account questions could have cause some 

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

resulting in errors. In the 2014 Panel, on the other hand, the question about whether the respondent 

owns a non-interest earning checking accounts is asked right after the question on interest-earning 

checking. 

In addition, the question text for non-interest earning checking accounts was changed in the 2014 

Panel. In the 2008 and prior panels, the text read “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. To mitigate any confusion, In 2014, the text was changed to ask whether the respondent has 

“a non-interest (regular) checking account?” in order to simply the question text.

• Other Bank Accounts (Savings, Money Market, CDs): For other types of bank accounts, there 

were not as many revisions as there were for checking accounts. The biggest change in 2014 was 

allowing respondents to report the balance of their interest-earning checking, savings, CDs, and money 

market accounts separately. In the 2008 and prior panels, respondents had to add up all these value in 

one single question. For respondents with multiple types of accounts, this question would require 

respondents to sum values in order to construct their answers, which may result in errors. In addition, 

the ownership question for money market accounts was changed to ask whether the respondent had “a 

money market deposit account or fund?” In prior panels, the question only asked about money market 

deposit accounts.

• Bonds: SIPP asks about three types of bonds 1) U.S. Savings bonds 2) U.S. Government 

securities, and 3) municipal and corporate bonds. In the 2008 and prior panels, the value of U.S. 

Government securities and municipal and corporate bonds were combined into one question. In the 

2014 Panel, the value of U.S. Government securities was instead combined with U.S. savings bonds 

into one single question. In addition, the 2014 Panel has one ownership question for U.S. Government 

savings bonds and U.S. Government securities, while in previous panels, the ownership question was 

separated for these two types of bonds.

• Stocks and Mutual Funds: In the 2008 and prior panels, the respondent had to combine the value 

of stocks and mutual funds in one single question. For respondents with both stocks and mutual funds, 

this question would require respondents to sum values in order to construct their answers, which may 

result in errors. In the 2014 Panel, the respondent could report the values of stocks and mutual funds 

separately. 

• Cash Value of Life Insurance: In the 2004 and 2008 Panels, SIPP asked about the cash value of 

life insurance. However, there is evidence that many respondents were confused about the difference 

between cash value and face value, and reported the face value of their life insurance instead 

(Gottschalck and Moore 2007). Because of this confusion, data on the cash value of life insurance 

was not included in the calculation of net worth. In the 2014 Panel, SIPP asks about both the cash 

value and face value of life insurance, and the question text explains the difference between them. 

• Business Equity: In the 2014 Panel, there were numerous changes made to the business value and 

debt questions. The most notable change in 2014 is that SIPP now asks about business owned as an 

 

18 In the 2008 Panel, there was a separate question on Keogh retirement accounts. In the 2014 Panel, the question 

for Keogh accounts was combined with the IRA question. Because so few households own Keogh account (about 

1.7% of households in wave 4 of the 2008 Panel), we do not classify this change as a substantial revision.
24

investment but not actively managed. These questions ask about percent owned, value of the business 

and debt against business for up to three businesses.

• Added and Removed Asset Questions: New questions about annuities, trust, and education 

savings accounts were added to the 2014 Panel. In prior panels, these should have been included in a 

catch-all question for any remaining assets owned. Question of mortgages owned as an asset and 

royalties were removed in the 2014 Panel because of low ownership rates. Instead, these assets are 

now included in the examples of “other assets” for the catch-all question.

• Other Financial Assets: The catch-all question for any remaining assets was substantially revised 

in the 2014 Panel. In prior panels, the question text asked whether the respondent owned “any other 

financial investments.” In the 2014 Panel, the question asked whether the respondent owned “any 

other financial investments? Examples include coins, jewelry, artwork, mortgages paid to you, other 

loans owed to you, or royalties.” Thus, more examples were given to help the respondent think of 

additional assets, including physical assets the respondent may have not considered to be “financial 

investments.” Tourangeau et al. (2014) discuss how changing the examples provided in survey 

questions can have a large effect on how respondents interpret a question. 

• Student Loans and Other Debt: In the 2014 Panel, a new direct question was added on whether 

the respondent had any “educational loans or education-related expenses.” In prior panels, education 

loans were included in a catch-all question on other debt. In these prior panels, the question text for 

other debt gave student loans as an example of a type of other debt. However, given there were other 

types of debt listed as examples, respondents may have overheard the student loan example, potentially 

causing them to answer no even if they had student loans. Also in the 2014 Panel, a question on 

whether the respondent had any other loans from a credit union or bank was removed and combined 

with the other debt catch-all question. 

• Rental Property and Other Real Estate: SIPP has separate questions on rental property and 

other real estate. The 2014 Panel gives examples of other types of real estate when asking whether the 

respondent owns other real estate. These examples are “such as a vacation home, a deeded timeshare, 

or an undeveloped lot.” Given some respondents may be unsure about the definition of other real 

estate, the addition of examples to the survey question may help respondents answer yes to the 

question if, for example, they have a timeshare but don’t consider it to be a type of other real estate. 

• Vehicle Values: In SIPP, respondents are asked to report the year, make, and model of their 

vehicles. Using this information, values are assigned from a dataset created by the National 

Automobile Dealers Association (NADA), which is one of the major providers of vehicle price data. 

In 2014 SIPP, some adjustments were made in the vehicle value assignment methodology. 

Starting in 2014, SIPP assigns values using an electronic copy of the National Automobile Dealers 

Association(NADA) data that are updated for the reference month (December). In contrast, in SIPP 

2008 the NADA trade-in values were purchased at the beginning of the panel. In this older 

methodology, a constant rate of depreciation was applied to adjust vehicle values across waves. This 

change in methodology likely has the biggest effect on vehicle values, as almost every vehicle is 

affected by this change. For example, if the rate of depreciation was too high for the majority of cars, 

then this would cause vehicle values to be lower in the 2008 Panel than they would have been if up-todate NADA data had been used.

The methodology to assign values to new model year vehicles was also heavily revised between 

SIPP 2008 and SIPP 2014. These vehicles often do not have average trade-in values in the NADA data 

for December of the reference year. However, these vehicles often have data in the next year, when 

the data are collected and revised.19

 2014 SIPP uses this information to identify a trend in the prices, 

and then extrapolates from this trend to generate a value for December of the reference year. In 

previous panels, the procedure was different. As an example, consider wave 7 of the 2008 Panel. In 

this wave, interviews occurred near the end of 2010, and car manufacturers had begun to sell vehicles

with a model year of 2011. If data were available for the 2010 model year but not the 2011 model 

year, then this value for the 2010 year was used instead. If data for the 2010 model year were also 

unavailable, the data cleaning program calculated the ratio of manufacturer's suggested retail price 

(MSRP) divided by average trade-in values ratio of all vehicles with both the same make as the new 

 

19 For example, cars that are new in 2013 typically do not yet have NADA trade-in values by December 2013, but 

have them by December 2014. 

26

Statistics from surveys are subject to sampling and nonsampling error. For further information on the source of the 

data and accuracy of the estimates, including standard errors and confidence intervals, see < 

http://www.census.gov/programs-surveys/sipp/tech-documentation/source-accuracy-statements.html >. All 

comparative statements in this report have undergone statistical testing, and, unless otherwise noted, all comparisons 

are statistically significant at the 10 percent significance level.