Survey Estimates of Wealth - Mathematica Policy Research

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most relevant subpopulations, and presents recommendations regarding both the use and production of SIPP wealth data. The remainder of this chapter is organized as follows. Section A presents background information on ORES’s use of SIPP data and the reasons why ORES would want to continue using SIPP wealth data in many of its applications. Section B provides an overview of the SIPP, the SCF, the PSID, and the newer Health and Retirement Study (HRS), which represents the population born before 1948 and collects data on wealth along with a wide range of aging-related topics. Section C discusses factors that may account for differences between the SIPP and these other surveys. Section D reviews earlier evidence on the quality of SIPP wealth data, and Section E presents an overview of the rest of the report. A. VALUE OF SIPP DATA There are several reasons why ORES should want to maintain its reliance on SIPP data. First, the size of the sample—nearly 40,000 households at the start of the 1996 panel—supports analysis of a broad array of subpopulations. Second, the capture of monthly rather than just annual income and program participation and the survey’s focus on these data make SIPP uniquely well suited to modeling SSI and developing projections of the populations that are most dependent on social security as a major component of their retirement income. Third, the possibility—perhaps only remote—that the SIPP might replace the CPS as the source of official estimates of poverty in the United States, as a National Academy of Sciences panel recommended, holds out the possibility of changes to the design of SIPP and the data it collects, which would further enhance the value of these data for SSA’s modeling needs. Fourth, the SIPP collects social security numbers, which the SSA is able to match to its own administrative records, thereby producing exceedingly rich databases for policy analysis. These are significant strengths that no other survey can match. 2
At the same time, however, evidence suggesting a possible deterioration in the quality of SIPP asset data cannot be ignored. Table I.1, which is drawn from published sources, supplemented by tabulations from recent SIPP and PSID files, reports median net worth (in 1999 dollars) as measured at different points in time by the SIPP, the SCF, and the PSID. 1 Setting aside, for now, issues of comparability in what the survey estimates represent, we observe the following. SIPP estimates of median household wealth in the 1980s were fairly close to those obtained from the PSID while the SCF estimates ran 20 to 25 percent higher. Between 1988 and 1991, SIPP median wealth declined along with SCF median wealth, albeit more sharply. Through the rest of the 1990s, however, the SCF recorded a steady rise in median wealth while the SIPP reported no growth until the final year, by which point the SCF was close to doubling the SIPP median. The addition of 401(k) plans to the SIPP measure of wealth in 1997 narrowed the gap between the SIPP and the SCF and generated modest annual growth. Furthermore, with the upsurge in 2000, the median value of this enhanced SIPP measure of wealth grew by nearly 12 percent between 1997 and 2000, compared to 10 percent for the SCF between 1998 and 2001. But three-quarters of the growth in the SIPP median occurred in the final year, whereas the SCF trend suggests a more steady rise. Comparison with the PSID is more difficult because of its less frequent measurement of wealth and a design change in 1997 that expanded the survey’s population coverage but ratcheted down the estimate of median wealth. Nevertheless, the gap between the SIPP and the PSID—when both are compared without 401(k) and other pension accounts, which the PSID added in 1999—has grown to the same magnitude as the 1980s difference between the SIPP and the SCF. 1 All tables in this report appear in a separate section following the References. 3
Page 1: Contract No.: 0600-01-60121/0440-02
Page 4 and 5: CONTENTS (continued) Chapter Page I
Page 7: ACKNOWLEDGMENTS The authors would l
Page 10 and 11: Wealthy Families. Wealth is highly
Page 12 and 13: Growth in Aggregate Assets. The SIP
Page 14 and 15: SIPP estimate of aggregate or mean
Page 16 and 17: difference between SIPP and SCF est
Page 19: I. INTRODUCTION The Office of Resea
Page 23 and 24: sample included about 4,300 househo
Page 25 and 26: C. ACCOUNTING FOR DIFFERENCES IN SU
Page 27 and 28: either the 1984 SIPP or the PSID. T
Page 29 and 30: II. COMPARATIVE ESTIMATES OF WEALTH
Page 31 and 32: children) or a parent and child who
Page 33 and 34: identification of unmarried partner
Page 35 and 36: to make comparisons with earlier SI
Page 37 and 38: from the March 1997 CPS, and the pe
Page 39 and 40: weights, which is reasonably close
Page 41 and 42: Rates of home ownership are nearly
Page 43 and 44: the marginally greater PSID populat
Page 45 and 46: 3. The Distribution of Net Worth Ta
Page 47 and 48: families and a little over 84 perce
Page 49 and 50: which members of our expert panel r
Page 51 and 52: and 96 to 105 percent of the aggreg
Page 53 and 54: ut we imposed a floor of zero. This
Page 55 and 56: 3. Liabilities Table II.12 provides
Page 57 and 58: 2. Assets and liabilities not measu
Page 59 and 60: y only one percent, which provides
Page 61 and 62: underreporting of amounts. Conditio
Page 63 and 64: ownership rates across the three su
Page 65 and 66: more slowly than the SCF estimates
Page 67 and 68: What is immediately evident is an a
Page 69 and 70: donors to cases with missing values
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SIPP shows 81 percent growth in fin
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III. SUBPOPULATIONS Each of SSA’s
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differences by gender are smaller t
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somewhat more pronounced than in th
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Comparisons of SIPP and PSID median
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65.0 percent of the HRS aggregate,
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exceeds the SIPP median by nearly $
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wealth, and, furthermore, the Censu
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TANF and other forms of welfare or
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We can use the information containe
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B. COVERAGE AND CONTENT In developi
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We constructed an additional measur
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Table IV.6 summarizes the effects o
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generated under earlier jobs. The w
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Because of the growing importance o
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tax payment if the money is used fo
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From the marginal distribution of a
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suggests greater error in their wav
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statistics on item nonresponse. Whi
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2. Response Brackets One device for
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of the second bracket. The same is
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4. Motor Vehicles One area where th
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The deficiencies in the Census Bure
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A. REWEIGHTING THE SIPP DATABASE As
Page 120 and 121:
levels in the public use version of
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the cash value of life insurance, a
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To adjust for the topcoding in the
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the disparity between the surveys w
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Checking/Savings Accounts. Only 77
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V.8, the fully adjusted SIPP distri
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here are only able to reduce the SI
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assets that are concentrated among
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of this year, and we were able to i
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way to do so is to use a Pareto dis
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insurance as well as annuities and
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The PSID and the HRS have had consi
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imputed values and a change from th
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institutions and in IRA and Keogh a
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REFERENCES Curtin, Richard, F. Thom
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TABLES
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TABLE II.1 SIPP ESTIMATES OF THE NE
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TABLE II.3 ESTIMATES OF NET WORTH,
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TABLE II.5 PERCENTILES OF NET WORTH
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TABLE II.6 PERCENTILES OF ASSETS AN
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TABLE II.7 DISTRIBUTION OF NET WORT
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TABLE II.9 CLASSIFICATION OF ASSETS
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TABLE II.11 DECOMPOSITION OF DIFFER
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TABLE II.13 DECOMPOSITION OF DIFFER
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TABLE II.15 AGGREGATE ESTIMATES OF
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TABLE II.17 OWNERSHIP AND CONDITION
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TABLE II.19 CHANGE IN SELECTED PERC
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TABLE II.21 SELECTED PERCENTILE VAL
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TABLE II.23 SELECTED PERCENTILE VAL
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TABLE II.25 CONSTANT DOLLAR GROWTH
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TABLE III.2 DISTRIBUTION OF ASSETS
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TABLE III.4 OPERATIONAL DEFINITIONS
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TABLE III.6 ESTIMATES OF MEAN AND M
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TABLE III.8 ESTIMATES OF NET WORTH,
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TABLE III.10 OWNERSHIP AND CONDITIO
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Table IV.1 Continued Cumulative Rat
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Table IV.2 Continued Ratio of Ratio
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TABLE IV.4 SELECTED PERCENTILES AND
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TABLE IV.6 COMPARISON OF SIPP AND S
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TABLE IV.8 PERCENTAGE DISTRIBUTION
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TABLE IV.10 NET WORTH OF SIPP FAMIL
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TABLE IV.12 COMPARISON OF SIPP AND
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TABLE IV.14 DISTRIBUTION OF FAMILIE
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TABLE IV.16 WEIGHTED CORRELATION BE
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TABLE IV.18 MEAN VALUE OF FIRST THR
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TABLE V.2 MEANS OF INDEPENDENT VARI
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TABLE V.2 (continued) MEANS OF INDE
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TABLE V.4 LEVEL AND DISTRIBUTION OF
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Page 220 and 221:
Page 222 and 223:
TABLE V.10 LEVEL AND DISTRIBUTION O
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TABLE VI.1 SIPP 1996 PANEL FILES US
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FIGURE V.1 DISTRIBUTION OF TOTAL RE
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FIGURE V.3 DISTRIBUTION OF OWN HOUS
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FIGURE V.5 DISTRIBUTION OF CHECKING
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FIGURE V.7 DISTRIBUTION OF MOTOR VE
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FIGURE V.9 DISTRIBUTION OF LIFE INS
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FIGURE V.11 DISTRIBUTION OF OTHER N
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FIGURE V.13 DISTRIBUTION OF TOTAL N
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FIGURE V.15 DISTRIBUTION OF TOTAL A
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FIGURE V.17 DISTRIBUTION OF TOTAL L
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APPENDIX A SURVEY QUESTIONS BY ASSE
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Assets Explicitly Covered 1996 SIPP
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Assets Explicitly Covered 1996 SIPP
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113: STOCKS AND MUTUAL FUNDS Assets
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114: US SAVINGS BONDS Assets Explic
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115: OTHER FINANCIAL ASSETS 1996 SI
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116: IRA AND KEOGH ACCOUNTS 1996 SI
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117: 401(k) AND THRIFT ACCOUNTS 199
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118: OTHER, QUASI-LIQUID PENSION AS
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118: OTHER, QUASI-LIQUID PENSION AS
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Assets Explicitly Covered 121: HOME
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Assets Explicitly Covered 123: ALL
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123: ALL OTHER REAL ESTATE Key Vari
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124: MOTOR VEHICLES 1996 SIPP, Wave
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125: BUSINESS EQUITY 1996 SIPP, Wav
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130: OTHER NON-SIPP ASSETS 1996 SIP
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131: SIPP PENSION ASSETS 1996 SIPP,
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211: MARGIN AND BROKER ACCOUNTS Ass
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212: MORTGAGES ON OWN HOME 1996 SIP
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214: PERSONAL BUSINESS DEBT Assets
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215: VEHICLE LOANS 1996 SIPP, Wave
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221: CREDIT CARD OR STORE DEBT Asse
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222: LOANS FROM FINANCIAL INSTITUTI
Page 292 and 293:
223: OTHER UNSECURED LIABILITIES 19
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A. CODE FOR 1998 SCF *This program
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*[note, this is the value side of t
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IF EHOWNER3 = EPPPNUM THEN DO; MPR1
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* hmowner not in hhld in this month
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MPR100 = MPR110 + MPR120; /* MPR130
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IF ER15222 = 3 AND ER15223 = . THEN
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APPENDIX C EQUATION COEFFICIENTS FO
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APPENDIX TABLE C.2 COEFFICIENTS OF
Page 312 and 313:
Page 314 and 315:
APPENDIX TABLE C.5 (continued) COEF
Page 316 and 317:
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