Survey Estimates of Wealth - Mathematica Policy Research

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The SIPP distribution lags the PSID distribution by less than it lags the SCF distribution, but differences are still large. For any given percentile in the positive range the SIPP never exceeds 77 percent of the corresponding PSID percentile value. We have seen that the SIPP does a better job of capturing total liabilities than total assets. Table II.6 shows the percentile distributions of both assets and liabilities. At the bottom of the distribution, the reporting of assets grows more slowly in the SIPP than in the SCF. Reported SIPP assets are less than half of the reported SCF assets between the 3rd and 12th percentiles, but after that they are no less than 60 percent of the SCF assets through the 96th percentile. Between the 42nd and 83rd percentiles (roughly the third and fourth quintiles), SIPP assets are consistently 79 to 83 percent of the SCF assets, then fall back gradually to 65 percent by the 95th percentile, after which they drop at an increasing rate. Liabilities display a rather different profile. Except for the lowest nonzero amounts and the top few percentiles, the SIPP and SCF distributions of liabilities are essentially identical. 4. Differential Measurement of the Wealthy As we noted in discussing Table II.5, over much of the percentile range the SIPP net worth distribution appears to merely lag behind the SCF distribution rather than display a substantially different profile. We can see further evidence of this if we compare the distributions of SIPP and SCF net worth with respect to fixed categories of net worth. Table II.7 provides such a comparison. To enhance the resemblance between the two distributions, we have excluded from SCF net worth those assets and liabilities that are not measured in the SIPP (see the next section). Looking at the ratio of population estimates by category of net worth, we see the greater concentration of SIPP families at the very bottom but then nearly identical populations beginning with the lowest positive net worth class and continuing up to a million dollars. Referring to the cumulative percentage distributions in the final two columns, we see that 83 percent of SIPP 28
families and a little over 84 percent of SCF families fall into the range from one dollar to a million dollars. At the same time, the fraction of SCF families with net worth above one million dollars, or 3.8 percent, is two-and-a-half times the SIPP fraction, and the proportion with net worth above two million dollars, or 1.7 percent, is five times the SIPP fraction. The substantially larger number of very wealthy families estimated by the SCF versus the SIPP even after we have excluded assets and liabilities not measured in the SIPP can be attributed, in some combination, to three factors: (1) the SCF’s better representation of wealthy families in its sample, (2) the SCF’s more complete capture of the wealth of the wealthiest families, and (3) the SIPP’s use of topcoding as a disclosure limitation technique, which removes reported wealth from the wealthiest sample members. As we discussed in chapter I, the SCF devotes about one-third of its total sample to a list frame of high-income families, which are drawn from tax return data and stratified on the basis of predicted wealth. By using predicted wealth to post-stratify the sample, the SCF can make adjustments for differential nonresponse within this high-income group—and the area frame sample, as well. Both these features—the sample design and the post-stratification—give the SCF a considerable advantage over the SIPP and the PSID in measuring wealth at the top of the distribution. 21 As a result, the SCF has consistently obtained higher estimates of wealth at the top of the distribution and, therefore, higher total wealth than either the SIPP or the PSID. 21 Even if the estimates obtained from the SIPP and PSID were unbiased (implying that, over all possible samples, estimated mean wealth equaled true mean wealth), the extreme skew of the wealth distribution together with the expected small samples of very wealthy families makes it much more likely that individual samples drawn for these surveys will underestimate rather than overestimate the wealth of the wealthiest families. The occasional sample with a high estimate will overstate wealth by a large amount. 29
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 and 22: At the same time, however, evidence
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: 3. The Distribution of Net Worth Ta
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
Page 73 and 74: III. SUBPOPULATIONS Each of SSA’s
Page 75 and 76: differences by gender are smaller t
Page 77 and 78: somewhat more pronounced than in th
Page 79 and 80: Comparisons of SIPP and PSID median
Page 81 and 82: 65.0 percent of the HRS aggregate,
Page 83: exceeds the SIPP median by nearly $
Page 86 and 87: wealth, and, furthermore, the Censu
Page 88 and 89: TANF and other forms of welfare or
Page 90 and 91: We can use the information containe
Page 92 and 93: B. COVERAGE AND CONTENT In developi
Page 94 and 95: We constructed an additional measur
Page 96 and 97:
Table IV.6 summarizes the effects o
Page 98 and 99:
generated under earlier jobs. The w
Page 100 and 101:
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
Page 124 and 125:
To adjust for the topcoding in the
Page 126 and 127:
the disparity between the surveys w
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Checking/Savings Accounts. Only 77
Page 130 and 131:
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
Page 162 and 163:
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
Page 218 and 219:
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
Page 235 and 236:
FIGURE V.9 DISTRIBUTION OF LIFE INS
Page 237 and 238:
FIGURE V.11 DISTRIBUTION OF OTHER N
Page 239 and 240:
FIGURE V.13 DISTRIBUTION OF TOTAL N
Page 241 and 242:
FIGURE V.15 DISTRIBUTION OF TOTAL A
Page 243 and 244:
FIGURE V.17 DISTRIBUTION OF TOTAL L
Page 245:
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
Page 286 and 287:
215: VEHICLE LOANS 1996 SIPP, Wave
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221: CREDIT CARD OR STORE DEBT Asse
Page 290 and 291:
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
Page 305 and 306:
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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