Survey Estimates of Wealth - Mathematica Policy Research

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Many of the ways in which ORES uses SIPP wealth data must be questioned if the SIPP is shown to be seriously off the mark in its estimates of levels and trends in household wealth among the subpopulations of particular interest to SSA. ORES already uses other wealth data to supplement the SIPP. For example, ORES uses wealth data from the PSID to enhance its retirement income modeling. Nevertheless, SIPP data continue to play a significant role in this work. But unless the SIPP data can be adjusted in the short term to compensate for their most pertinent deficiencies and, very likely, unless the Census Bureau can be persuaded to make improvements in the longer term, ORES may have to consider more substantial substitution of asset data from other sources for those collected in the SIPP. We now describe these other sources. B. OTHER SURVEYS OF WEALTH The SCF, which is sponsored by the Federal Reserve Board, is the nation’s premier survey of wealth. The strengths of the SCF are these: (1) its principal focus is the measurement of wealth; it devotes several hundred questions to this topic, which its interviewers and respondents are well prepared to address, (2) it contains a high-income supplement, which gives the SCF a large sample of observations from the upper tail of the income distribution, where wealth is heavily concentrated, and (3) it employs very sophisticated imputation procedures to adjust for item nonresponse, which presents a serious problem in the measurement of asset holdings. The sample for the high-income supplement is drawn from tax records, which are used to develop strata based on predicted wealth and which provide data to support very detailed nonresponse adjustments. As a result, the SCF provides much better representation of very wealthy households than any other survey that measures wealth. The principal limitations of the SCF, relative to the SIPP, are its small sample size and its more limited ability to identify all of the subpopulations that are of interest to SSA. The 1998 4
sample included about 4,300 households, with nearly a third of these coming from the highincome subsample and, therefore, not having much policy relevance to SSA. While the data are rich in financial information, measures of participation in SSA programs and the non-economic characteristics that contribute to eligibility—such as disability—are weak. Nonresponse—both unit and item—are concerns as well, although these are offset, at least in part, by detailed weighting adjustments and rigorous and extensive imputation of missing data. But these add complexity to the weighting, which is compounded by issues in combining the area probability sample and the high-income list frame sample, which have very different sampling and response rates (Wolff 1999, Kennickell 2000b). Furthermore, to reduce the risk of disclosure the weights cannot reveal the sample frame from which a family was selected. The PSID was initiated in 1968 with a sample of about 5,000 families and has followed the members of this initial sample—including children—and all of the families that they have created or joined since that time. Until relatively recently, when interviews were shifted to every two years, panel members have been interviewed annually. A Latino supplement was added in 1990 to help compensate for the survey’s under-representation of part of the immigrant population. This supplement was later dropped due to insufficient funding, but a new and more broadly representative sample of immigrants was added in 1997. A wealth module was introduced in 1984 and repeated in 1989, 1994, 1999, and 2001. With fewer than a dozen questions, the wealth module has nevertheless yielded data that compare remarkably well, in a number of respects, to the data collected in the SCF. The PSID data have also been shown to provide a more complete accounting of wealth than the SIPP, which has substantially more questions. About 7,000 households responded to the wealth module in 1999. The PSID pioneered the use of “unfolding brackets” to collect at least some useful data when respondents refuse or are unable to answer questions about the dollar amounts of their asset 5
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 and 20: I. INTRODUCTION The Office of Resea
Page 21: At the same time, however, evidence
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
Page 71 and 72: 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
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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:
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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
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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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