1983 Survey of Consumer Finances Here: Introduction Next: Question Text, Variable Names, & Responses

DESIGN AND METHODS
The 1983 SCF sample consists of a randomly selected, nationally representative, area probability sample of all U.S. households (cross-section sample) and a supplemental sample of high-income households drawn from federal income tax files (high-income sample). The area probability sample used the 1970 SRC National Sample frame. The sample was designed to represent housing units in the coterminous United States (the 48 lower states) exclusive of those on military reservations, nursing and rest homes, college dorms, jails, hotels, missions, convents, monasteries, and other institutional quarters.[2] Housing units were selected by a multi-stage procedure that samples successively smaller geographic areas. Probability selection was enforced at all stages of sampling.

The first stage of the sample selection procedure was that used to draw the 1970 SRC National Sample. The 1970 frame was used because full data from the 1980 Decennial Census were not available at the time of the survey. The frame was adjusted, however, to represent 1980 population values. The 1970 SRC National sample was drawn by assigning all U.S. metropolitan areas (SMSAs) and non-SMSA counties to 74 relatively homogeneous strata, and selecting one SMSA or county (primary sampling unit or PSU) per stratum.[3] Twelve of these strata, representing New York City and the other 11 largest SMSAs, contained only one PSU, and thus, were selected with certainty.[4] From each of the remaining 62 strata, which contained between 2 and 200 PSUs, one primary area was selected with a probability proportionate to population. Thirty-two of these strata were SMSAs and 30 were strata made up of non-SMSA counties or county groups. The sample was stratified by region of the country, so that each of the four major geographic regions -- Northeast, North Central, South, and West -- received representation in proportion to their population. In order to reduce variances below those that would be obtained with ordinary stratification, PSUs in each region were selected using a controlled selection method. This method controlled the distribution of sample PSUs by state and degree of urbanization. It results in a more geographically balanced sample and increases the precision of sample estimates relative to a more conventional random design. The PSUs selected for the 1983 SCF were drawn from 37 states and the District of Columbia as shown in figure 1.

                              Response Rates

                               Total  Area Probability          High-income
                          Households            Sample               Sample
_______________________________________________________________________________

Original Sample units             --              6062                 5000
Sample Households               5855              5396                  459
Complete Interviews             4262              3824                  438
Refusals*                       1224              1209                   15
Non-interviews**                 369               362                    6
Response Rate			 .73		   .71			.95
Interview length (minutes)        75                74                   87
______________________________________________________________________________

*  Contact with household complete, and eligible respondent refused.
** Interview not obtained for reasons other than respondent refusal
   (for example, household members on vacation or respondent health
   problems preclude interview).

DATA CLEANING AND IMPUTATION
The actual responses given by respondents may contain missing or inconsistent information due to respondents' misunderstanding, lack of knowledge, or unwillingness to answer certain questions. These problems make analysis of the raw data difficult and, depending on the pattern of errors, may bias conclusions. A series of consistency checks and imputation procedures was developed at the Federal Reserve Board to clean the raw data and to estimate values for the missing data. The computer code to implement these data cleaning and imputation procedures is over 40,000 lines long. Specific information is given for each variable listed in this manual. The general procedures are described below.

Overview:
Three basic methods were used to impute missing data. The first method computed missing values by formulas based on respondent information that was closely related to the missing items. For example, missing earned income could be imputed from reported wage rates, hours worked, and work history. Asset income could be inferred using average rates of return if asset values were given. Similarly, asset values could be estimated from reported asset income. Length of unemployment coupled with the appropriate state benefit formula could be used to impute unemployment income; and work history and Social Security benefit formulas could be used to impute Social Security income. Where appropriate, random disturbances were added in making imputations. The detailed data were also useful for resolving inconsistencies in reported values.

The second method assigned missing values on the basis of random draws from conditional frequency distributions. This method was used primarily to impute missing values for variables with discrete values. It was also used to estimate dollar amounts in a few cases in which a very small number of missing values were present. A variant of this method involved using a conditional mean together with information reported by the respondent to estimate the value of a missing item. The amount borrowed for a first mortgage, for example, was sometimes estimated by multiplying the purchase price of the house by the average loan-to-price ratio in the year of purchase.

The third method estimated missing values by regression. Missing values were assigned the value predicted by the regression plus a random disturbance term, which was generally assumed to be a truncated log-normal variable with the same variance as the residual term of the regression. This method was used to estimate most missing dollar amounts. Income and asset regression imputations were done simultaneously, using an iterative technique, in order to preserve all second moments.

Much more was involved in the process of preparing the data set than imputation of missing values. In many instances respondents gave inconsistent answers to similar questions, which had to be resolved. There were other instances where assets and/or debts appeared to be reported twice. A more common type of problem involved the categories to which assets were assigned. For example, many elderly respondents appeared to confuse Social Security and SSI. The assignment of assets to money market accounts or SUPER NOW checking accounts was done inconsistently by respondents. Many of these kinds of problems and inconsistencies resulted in data being changed or moved.

The area probability and high-income samples were handled separately. Missing values for all observations in the high-income sample were imputed. In the area probability sample, however, 159 of the original 3824 observations were discarded because virtually all dollar amounts for income and assets were missing (this procedure is described in the next section). All missing values for the remaining 3665 observations were imputed.

Most of the imputations for the 1983 survey were made entirely on the basis of 1983 data. In order to preserve the appropriate inter-temporal correlations, however, some use was made of 1986 responses for those households responding to the 1986 SCF. Specifically, some 1983 values were reimputed where respondents gave "hard data" in 1986 for an item which was missing in 1983.

Wages and Income:
Dollar amounts for each missing income source were imputed separately, and total family income was obtained by summing income sources. Missing earned income was estimated using a wage index to deflate the current wages of the respondent and their spouse to 1982 levels. If the current wages were missing as well, they were estimated using conditional mean tables constructed from the March 1983 CPS. Missing values were assigned average log-wages for persons of the same sex, race, age, and three-digit occupation code plus a random error term. The random error term was chosen so that the correlation in wages between an individual's current and past jobs was the same as that observed for the part of the sample with complete information. Missing wage and business income of self-employed individuals were imputed separately using CPS data for self-employed individuals. For the high-income sample, a similar procedure was used to impute wage and business income. Missing values were assigned on the basis of occupation and age, with random error terms added to the conditional means. The conditional means were obtained from the high-income sample itself, however, not the CPS.

Unemployment compensation was imputed using state benefit rules and job information reported in the employment section. Some pension and Social Security income was estimated using information from the employment section, but most was imputed by regression. Welfare was imputed by regression using state benefit formulas. Interest and dividend income was estimated by multiplying dollar amounts of various types of assets held by the appropriate average yields for those assets in 1982. Average yields were obtained from the Federal Reserve Bulletin and other publications.

Assets:
Missing values for most financial assets were estimated either by capitalizing interest and dividend income or by regression if these income sources were not reported. The procedure used when both income and financial asset values were missing was an iterative one that imputes income and asset values simultaneously. It builds maximum likelihood estimates of the covariance matrix of the set of imputed variables conditioned on demographic and other variables under the assumption that they are jointly log-normal and that given such information observations are missing randomly. Imputed values are conditional expectations plus a random error.

Missing house values were imputed by inflating reported purchase prices by regional housing price indices. Some house values, and all other missing real estate, life insurance, net equity of businesses, and some land contracts were imputed by regression. Automobile values were imputed by matching the year and make of the vehicle to the National Association of Automobile Dealers "blue book" listing of used car values at the time of the survey.

Debts:
Respondents were asked to report dollar amounts outstanding for open-end and non- installment closed-end debts. Missing dollar amounts were estimated by regression. Asset values, income, and other demographic characteristics of the family were used as predictor variables. Random errors were added to the predicted amounts outstanding.

Respondents reported credit terms -- payment size, frequency, amount borrowed, due date, and interest rate -- for mortgages, land contracts, and other real estate loans. Some payments had to be adjusted because the reported amount included taxes and insurance. The amount outstanding on each loan could then be obtained by calculating the present value of remaining payments of principal and interest. Missing credit terms were estimated using appropriate average interest rates, maturities, and loan-to-price ratios published in the Federal Reserve Bulletin (the amount outstanding could actually be computed even if one term was not reported, because of a redundancy).

Similar information was collected for closed-end installment debt, although interest rates were not reported. Sufficient information was available if all other terms were reported to solve for the implied interest rate. The amount outstanding for each loan could then be computed in the same fashion as used for mortgages. When one of the terms was missing or the computed interest was implausible, an appropriate interest rate from the Federal Reserve Bulletin was assumed for area probability sample observations. For high-income sample observations, missing values were assigned on the basis of matches to other comparable loans in the high-income sample.

Summary Data on Imputation:
The impact of the imputation process used for the SCF is apparent from the non-response rates given for selected assets and liabilities in table 2. Non-response rates for ownership ("do you have a .. .?") and dollar amounts ("what is its value?") were computed for three subsamples -- the high-income sample, the 3665 edited area probability observations, and the 159 discarded area probability observations.

       NON-RESPONSE RATES FOR SELECTED ASSETS AND LIABILITIES, 1983 SCF  

                           EDITED          HIGH-INCOME        DISCARDED
                           CROSS-SECTION                      CROSS-SECTION

ASSETS                     OWNRSHP  $ AMT  OWNRSHP  $ AMT     OWNRSHP  $ AMT

Principal residence             .1    7.8       .0    1.4          .6   32.8  
Other real estate (gross)       .1    9.2       .0    3.0         2.5   35.3 
Public stock                   1.3   25.4      1.7    6.7        15.8   97.8  
Bonds and trusts                .7   24.7      2.4    6.2         8.5   82.9  
Checking accounts               .1    9.6       .5    4.7         5.0   71.7  
Savings accounts                .2   14.1       .7    4.8         8.2   89.6  
Money market accounts           .2   18.3       .3    9.3         5.0   93.1  
Certificates of deposit        1.3   25.6       .9    9.0        15.2   97.4  
IRA and Keogh accounts          .3   11.9       .3    2.1         4.4   79.6  
Savings bonds                   .1   17.4       .2    4.6         6.3   71.4  
Life insurance cash value      2.4   71.7       .5   33.5         9.4   91.8 
Business assets (net)           .1   37.2       .5   17.6         4.4   96.9  
Automobiles                     .0     --       .0     --          .1     -- 

DEBT 

Automobile debt                 .7    4.6       .0    1.7         2.2   36.4  
Consumer debt                   .2    5.6       .4    3.7          .6   59.1  
Principal residence debt        .3    9.6       .5    3.6         1.6   50.8  
Other real estate debt         1.0    8.2       .0    3.9         7.7   40.0  
 

          IMPUTATION PERCENTAGES AND IMPLIED MEAN IMPUTATIONS, 1983 SCF*

                             HIGH-INCOME                EDITED CROSS-SECTION

                             RESPONDENT   IMPUTATION    RESPONDENT   IMPUTATION
                             DATA         BY MEANS      DATA         BY MEANS
                             AS % OF      IMPLIED %     AS % OF      IMPLIED %
                             TOTAL        OF TOTAL      TOTAL        OF TOTAL
		  							   
Principal residence           99.2        100.3          95.6        102.9
Other real estate (gross)     98.8        101.1          90.1         97.5
Public stock                  95.6        100.2          88.4        104.7
Bonds and trusts              93.7        100.3          74.9         94.3
Checking accounts             96.5        101.1          92.6        101.1
Savings, CDs, money mkt.
  Savings accounts            96.7        101.1          86.3        99.9
  Money market accounts       95.7        100.9	         84.3        98.1
  Certificates of deposit     91.8         97.5          76.4        98.9
  IRA and Keogh accounts      98.5        101.2          91.3       102.6
  Savings bonds               99.0        100.7          89.9       106.9
Life insurance cash value     58.8         80.7          24.1        70.4
Business assets (net)         80.1        101.1          82.4       118.9

* Full sample weights are used for each subgroup

SAMPLE WEIGHTS
For a variety of reasons, final household survey data will rarely represent a random draw from the U.S. population. Even if the original survey design were random, different types of households will have different likelihoods of completing interviews. Very high and very low income households, for example, are thought to be less likely to participate in surveys. The 1983 SCF had an additional source of non-randomness because of the inclusion of the high-income supplement. These factors necessitated the construction of weighting variables to compensate for known or estimable sources of stratification in the final data set. Several different weights were constructed.

Inclusion of households in the final "cleaned" data set described in this manual results from a series of implicit stratified selection criteria. There are four major sources of implicit stratification. First, there are different response rates for different household types. This occurs because households cannot be reached (up to six attempts to contact participants were made) or because they refuse to participate when contacted. Second, the sample may not fully reflect U.S. population due to sampling error in the survey itself (in the sense that any random sample will not have exactly the same average characteristics as the population from which it is drawn). Third, as mentioned above, not all survey observations were usable for analysis because of significant missing information due to deliberate or inadvertent actions. Of the 3,824 area probability sample survey households included in the original SRC data tape, 159 observations were dropped in the cleaned file. Finally, the SCF sample is drawn from both the area probability and high-income sampling frames. If both samples are used, it is necessary to properly mix them to have a representative national sample.

There are a number of ways which can be used to compensate for these types of sample stratification. The method employed here is to construct sample weights, which can be used to adjust the final sample. Briefly, the weights were calculated as follows.

Area Probability sample weights:
The original SRC sampling frame called for sampling 6,062 housing units in the cross-section. If each housing unit had yielded an interview, each observation would represent 13,333 housing units. In fact as shown earlier in table 1, only about 89 percent of the housing units were occupied, and of these, only about 71 percent (3824) contained households who were willing to participate in the survey. Obviously, the exact characteristics of the non-responding households are unknown; however, the response and occupancy rate differed significantly across the survey's 75 PSUs. The "non-response" weight adjusts for this differential non-response. This weight is equal to the reciprocal of the household response rate for the PSU for which the household belongs. This weight should compensate for some location-related characteristics associated with non-response and occupancy.

The second type of stratification cited above can be adjusted for by taking into account how the final area probability sample (weighted for "non-response") compares with the population according to certain select characteristics. Obviously the sample could be adjusted for any one of a number of characteristics (age, income, size of household, region, etc.). SRC elected to compensate for regional sampling error by computing a "post-stratification" weight which adjusts the sample to have the same relative population as the 1980 census in the four major sample regions (Northeast, South, North Central, and West) further subdivided into urban and rural. We computed a similar weight, but post-stratified to the population and urban/rural definitions represented by the March 1983 CPS.

The third type of stratification cited above can be adjusted for in two different ways. The criteria for inclusion in the cleaned area probability sample was as follows. Any observation with any dollar values given for earnings or income was automatically included. Remaining observations were excluded only if there were virtually no dollar values given for housing value (or rent), other property values, business, and financial assets. Information on debts was not used in this section. Specific rules were employed to exclude observations whose ratio of missing data to relevant questions exceeded a certain level. In practice those observations that were dropped were missing values for virtually all dollar value questions they were asked.[16] None of the high-income sample observations were dropped. In fact, these observations were remarkably clean, with comparatively little missing information.

To compensate for these exclusions, a probit model was fit to model the probability that an observation in the complete 3824 area probability sample file would be included in the cleaned 3665-observation data set. Variables used in this model were selected as follows. Most asset questions consisted of two parts. An initial screen question which asked if the respondent had an asset, and a follow-up for those acknowledging ownership, asking how much they had. In virtually every instance of missing values, it was the second question which was not answered rather than the first. Thus, the probit model predicting inclusion in the cleaned sample used a number of the first type of questions, as well as standard demographic data available for all observations, as predictor variables.

The fitted probit equation results can be used in two ways. The inverse of the predicted probability that an observation would have been included in the cleaned sample (conditioned on the predictor variables used), can be used to weight observations in analysis. In practice, this procedure implies higher weights for observations holding a large number of different assets.

An alternative way of using the probit results attempts to account explicitly for correlation between inclusion in the sample and the error term of an estimated equation. A variable has been created which is the expectation of the "error" in the inclusion equation, conditioned on the fact that the observation indeed turned out to be included (the so-called Mills ratio). This variable can be included as a regressor in estimated equations and will account for "sample selection" bias under certain assumptions.

High-Income and full sample weights:
The weights for the combined sample containing both the area probability and high-income samples were more difficult to compute, and are more subject to question. Unfortunately full information on the high-income sampling procedure is not publicly available. Additional complications stem from the fact that the high-income observations were drawn from a 1980 sampling frame of taxpayers (but were contacted in 1983) and the fact that the reporting basis for tax files (individuals or married couples) is not always the same as the survey (families).

Initially, the Office of Tax Analysis used a statistical file set up by the IRS to construct relative weights for the high-income sample (the sample was divided into nine cells each with a separate weight). These weights are supposed to make the high-income sample representative of the unknown full IRS sampling frame. SRC constructed sample distributions for the overlap of the area probability and high-income samples (over $100,000 dollars in household income) to compute a "meshed" combined sample weight. The SRC weight retained the relative weights of the high-income sample and left the weight for area probability observations below $100,000 largely unchanged.[17]

Well after the initial SRC full sample weights were computed, information on the upper tail of the distribution of 1982 taxpayers became available. When this was compared to reported 1982 household income in the SCF sample, it appeared that the SRC meshed weight may have given too much weight to the high-income sample. As a consequence, another full-sample weight was constructed at the Federal Reserve Board which used a different approach to combine the two samples. It was decided to construct sampling weights for the high-income sample (and area probability observations with income above a certain level) using a post-stratification scheme based on control totals for an "extended" income measure constructed from the 1982 Tax Model File (TMF) of the IRS. The TMF is a stratified sample of 88,218 individual tax returns with a significant over- sampling of high incomes.[18]

This income measure, which was constructed for all survey households using reported 1982 income data, is roughly comparable to the IRS measure of adjusted gross income plus excluded realized capital gains. Despite the fairly detailed income questions in the SCF, it is clear that the survey measure of business income almost surely overstates the TMF measure. It appears likely that survey respondents often report something much closer to a cash-flow concept of income rather than income netted of expenses and depreciation. Unfortunately, there is not sufficient information in either the SCF or the TMF to make a precise compensating adjustment. A gross adjustment for the aggregate difference between the survey and TMF business income totals was made in constructing the survey measure of extended income. Reported business income in the SCF was deflated by about 40 percent so that IRS and adjusted survey aggregates of business income were the same. This adjustment was quite ad hoc, however, and the potential for distortion at the individual level remains, with weights for households with business income particularly suspect.

Because the reporting units in the survey and the TMF differ, the TMF data were adjusted in order to estimate income on a family basis. Most of the high-income returns in the TMF are either joint filings or those of single individuals. However, there are some married couples filing separately, particularly in the $50,000 to $100,000 income range. These observations were "aggregated" into households by assuming that separate filers were all married to people with the same income (weights for such observations were halved). The final weights are only slightly affected by variations in this adjustment.

Post-stratification cells were defined by the seven categories of extended income shown in table 4. For each of the top six income cells (above $80,000), weights were determined so that the weighted number of survey observations equaled the TMF totals. High-income observations were assigned the cell average. Area probability sample observations were assigned relative weights based on their cross-section weight, but scaled so that the mean for each of the six cells was the same as that of the high-income observations. The original weights of the area probability observations with income below $80,000 were adjusted so that the weighted number of SCF households equaled the population estimated from the March 1983 CPS. High-income sample observations with income below $80,000 were arbitrarily assigned the same weight as observations in the $80,000 to $90,000 group.

                              FRB Full Sample Weight

Household                Number of    Number of     TMF control   Average
extended income	         Area Prob.   High-income   totals of     weight
(dollars)                cases        cases         households    assigned
___________________________________________________________________________

under $80,000             3,579           49	    82,364,760      22,703
$80,000-89,999               22           11           356,324      10,798
$90,000-99,999               13           16           250,746       8,646
$100,000-124,999             23           40           362,022       5,746
$125,000-199,999             16           92           356,386       3,300
$200,000-499,999             11          148           182,424       1,147
$500,000 and over             1           82            45,338         546
___________________________________________________________________________

All cases                  3,665         438        83,918,000      20,453

                          Weighted and Unweighted Percentages

                  Unweighted                          Weighted

                  Area           High-     Full       Area           FRB
                  Probability    Income    Sample     Probability    Full

Age (head)
34 or less
 married            17.9          1.4       16.1        17.0         16.9
 unmarried male      6.0           .5        5.4         6.2          6.2
 unmarried female    7.4           .2        6.7         7.6          7.6
35 to 44
 married            13.6         12.6       13.5        13.6         13.6
 unmarried male      1.8           .4        1.7         1.9          1.8
 unmarried female    4.1           .2        3.7         4.1          4.1
45 to 54
 married            10.7         22.8       12.0        10.6         10.5
 unmarried male      1.6          2.3        1.7         1.7          1.7
 unmarried female    3.2           .0        2.9         3.2          3.2
55 to 64
 married             9.6         28.3       11.6         9.7          9.7
 unmarried male      1.4          2.5        1.5         1.5          1.5
 unmarried female    3.7           .5        3.3         3.7          3.7
65 or more  
 married             9.4         24.7       11.0         9.7          9.9
 unmarried male      1.8          1.8        1.8         1.8          1.8
 unmarried female    7.8           .9        7.1         7.7          7.7

Race
 Caucasian          82.8         98.6       84.5        82.3         82.2
 Nonwhite or 
   Hispanic         17.2          1.4       15.5        17.7         17.8

Family Income
 < $10,000  25.0      .0         22.2       24.1        24.0
 $10,000-$19,999    26.8           .0       23.9        26.7         26.8
 $20,000-$29,999    19.2           .2       17.3        19.2         19.3
 $30,000-$49,999    19.6           .9       17.5        19.8         19.7
 $50,000-$99,999     7.4          7.8        7.5         8.1          8.2
 >= $100,000         2.0         91.1       11.5         2.2          2.0
  
Family Net Worth
 < $5,000           25.5           .0       22.8        25.3         25.3
 $5,000-$24,999     18.3           .0       16.4        18.0         18.0
 $25,000-$50,000    16.3           .0       14.6        16.0         16.0
 $50,000-$99,999    17.6          1.4       15.8        17.3         17.2
 $100,000-$249,999  14.3          2.3       13.0        15.0         14.7
 $250,000-$999,999   7.0         26.0        9.0         7.3          7.1
 >= $1,000,000       1.0         70.2        8.4         1.2          1.7

Homeownership       64.1         95.4       67.4        63.5         63.4

Education
 of the Head
 0 to 8 grades      15.2           .5       13.6        14.4         14.5
 9 to 12 grades     46.1          5.0       41.7        45.0         44.9
 some college       16.9         13.0       16.5        17.6         17.7
 college graduate   21.7         81.5       28.1        22.9         22.9

Labor Force
 Participation
 single,
   not working      17.1          1.8       15.5        16.9         17.0
 single, working    21.7          8.4       20.3        22.4         22.4
 married,
   neither working   9.7          8.0        9.5         9.8          9.8
 married,
   one working      23.1         52.5       26.2        22.8         23.0
 married, both
   working          28.4         29.2       28.5        28.1         27.8

                   SAMPLE STATISTICS, 1983 SCF CROSS-SECTION ONLY

                SUM      STD ERR   % GROSS   OVERALL  PERCNT   MEAN     MEDIAN
                ($ B)    SUM       ASSETS    MEAN     OWNING   OWNERS   OWNERS*

GROSS ASSETS  $10,127.2  $692.8    100.0%   $120,678   96.0%  $125,693  $49,885

Principal
 residence      3,623.8    87.5     35.8%     43,182   63.5%    68,002   52,342
Other real
 estate (gross) 1,416.7   149.4     14.0%     16,882   18.8%    89,699   36,000
Public stock      642.8   127.8      6.3%      7,660   20.2%    37,860    4,696
Bonds and
 trusts           344.9    57.9      3.4%      4,110    7.3%    56,421   11,000
Checking
 accounts         119.2     6.9      1.2%      1,421   78.6%     1,808      500
Savings, cds,
 money mkt.       959.2    44.2      9.5%     11,430   74.0%    15,445    3,500
Life insurance
 cash value       262.1    16.0      2.6%      3,124   33.9%     9,205    3,428
Business
 assets (net)   2,127.3   567.5     21.0%     25,349   13.9%   181,861   40,538
Automobiles       372.3     6.7      3.7%      4,436   84.4%     5,255    4,100
Miscellaneous     258.8    22.5      2.6%      3,084   15.5%    19,845    5,575

DEBT            1,444.6    69.6     14.3%     17,215   69.8%    24,655   10,797

Automobile
 debt              92.2     3.6      0.9%      1,098   28.9%     3,797    3,052
Consumer debt     178.5    12.5      1.8%      2,127   54.9%     3,872    1,120
Principal
 residence debt   855.3    29.4      8.4%     10,192   37.3%    27,355   21,468
Other real
 estate debt      318.7    56.4      3.1%      3,797    8.2%    46,291   18,797

NET WORTH       8,682.5   666.4     85.7%    103,463     --       ----   34,537

INCOME (GROSS)  2,233.5    46.8       --      26,615     --       ----   19,625

* For gross assets, net worth, and income, this is the overall median

                     SAMPLE STATISTICS, FULL SAMPLE 1983 SCF

                SUM      STD ERR   % GROSS   OVERALL  PERCNT   MEAN     MEDIAN
                ($ B)    SUM       ASSETS    MEAN     OWNING   OWNERS   OWNERS*

GROSS ASSETS  $11,572.2  $504.6    100.0%   $137,899   96.0%  $143,638  $49,588

Principal
 residence      3,751.7    80.2     32.4%     44,706   63.4%    70,465   52,000
Other real
 estate (gross) 1,689.9   225.1     14.6%     20,137   18.8%   106,997   37,765
Public stock    1,033.8   180.2      8.9%     12,319   20.5%    60,161    5,000
Bonds and
 trusts           677.4   138.1      5.9%      8,072    7.6%   106,365   12,500
Checking
 accounts         119.4     5.7      1.0%      1,423   78.6%     1,811      500
Savings, cds,
 money mkt.     1,052.3    44.4      9.1%     12,539   74.0%    16,951    3,500
Life insurance
 cash value       285.3    18.9      2.5%      3,400   34.1%     9,978    3,373
Business
 assets (net)   2,276.3   292.0     19.7%     27,125   14.3%   190,025   45,768
Automobiles       373.7     6.5      3.2%      4,453   84.3%     5,282    4,085
Miscellaneous     312.5    25.4      2.7%      3,724   15.5%    24,073    5,506

DEBT            1,510.6    74.5     13.1%     18,001   69.6%    25,853   10,787

Automobile
 debt              90.6     3.5      0.8%      1,080   28.6%     3,772    3,029
Consumer debt     228.8    26.8      2.0%      2,726   54.9%     4,969    1,117
Principal
 residence debt   865.5    28.1      7.5%     10,314   37.0%    27,898   21,673
Other real
 estate debt      325.6    57.2      2.8%      3,880    8.2%    47,317   18,797

NET WORTH      10,061.6   479.3     86.9%    119,898     --      ----    34,466

INCOME (GROSS)  2,247.4    33.8      --       26,781     --      ----    19,523

* For gross assets, net worth, and income, this is the overall median

COMPARABILITY WITH OTHER SURVEYS
The 1983 Survey of Consumer Finances is the most recent survey in a series of surveys of household finances conducted by the Survey Research Center of the University of Michigan. Surveys of Consumer Finances were conducted annually from 1946 and 1970 but were then discontinued.[20] In 1977, the Survey Research Center conducted a comprehensive household finance survey again, under the sponsorship of the Federal Reserve Board and other federal bank regulatory agencies.[21] The same basic methods were used in all these surveys, although changes in sampling and interviewing procedures were introduced from time to time to improve survey results. The content of the surveys also changed over time because of shifts in interest in various aspects of consumer finances. The early surveys, which were sponsored by the Federal Reserve Board, were concerned with the effect of the postwar accumulation of liquid assets on consumer spending. Mortgage and consumer credit received greater attention in later surveys, and a major part of the 1977 survey was concerned with the effect of federal regulations on consumer credit use. Nevertheless, several areas of inquiry were followed through much of the 1946-77 period, and results from the earlier surveys are generally comparable to those from the area probability sample for the 1983 SCF.

The Federal Reserve Board also sponsored the Survey of Financial Characteristics of Consumers (SFCC) in 1963, with a followup reinterview in 1964.[22] Methodological work for this survey was conducted by the SRC, and interviewing was performed by the Bureau of the Census. Like the 1983 SCF, the 1963 SFCC collected a more detailed inventory of assets and liabilities than is customary in other consumer surveys. The 1963 survey also used Federal tax information to oversample high- income households. For the 1963 survey, a sample of housing units stratified by income reported in the 1960 Decennial Census was selected to represent households with incomes below $50,000. Households with incomes of $50,000 or more were selected from a sample of 1960 Federal income tax returns. Although this sample selection procedure is not exactly the same as that used for the 1983 survey, it produced a heavy over-sampling of households in the upper end of the income distribution, making the 1963 sample the only household survey sample that is comparable to the full sample from the 1983 SCF.

The Federal Reserve has also sponsored several recent surveys on the use of different means of payment for household purchases. The Survey of Currency and Transaction Account Usage, conducted by the SRC in the summer of 1984, solicited detailed information on the use of checking, savings, and money market accounts for about 2,000 households. Data were also collected on the use of currency, credit cards, money orders, and electronic banking services. In 1986, the survey was repeated for a smaller sample.[23]

Wealth information is also available from other sources. Data from the Internal Revenue Service for federal estate tax returns have been used to estimate total household wealth and its percentage distribution. Unfortunately, data from this source are available only in aggregate form, with very limited demographic breakdowns. Another source is the 1979 Income Survey Development Program of the Department of Health and Human Services, which provides information for a sample of households larger than that of most other surveys of wealth. The New York Stock Exchange has also periodically conducted surveys of household stockholders, doing a survey at a time comparable to the SCF in 1983, and more recently, in 1985. Wealth data was also collected for respondents to the Panel Study of Income Dynamics (PSID) in 1984.

The most comprehensive recent survey of household wealth was conducted in 1984 (and repeated in 1985) by the Bureau of the Census on participants in the Survey of Income and Program Participation (SIPP). This survey solicited information similar to the SCF for a very large sample of households. Its initial panel was a random cross-section of about 21,000 households selected by procedures similar to those used to select the area probability sample for the 1983 SCF. Net worth information was collected between September and December 1984.[24] Aggregate wealth estimates from the earlier Surveys of Consumer Finances and SIPP are generally comparable to those from the area probability sample of the 1983 SCF in their understatement of aggregate wealth relative estimates from independent sources. Using comparably defined categories, we estimate an aggregate net worth for the SCF area probability sample of $8,277 billion versus a $7,740 billion total for the SIPP sample. The difference derives primarily from a smaller estimate of small business assets in the SIPP. The full-sample SCF estimate of the same net wealth concept is $9,610 billion. Thus, it appears that the major difference between the two surveys arises from the inclusion of the high-income sample in the SCF.

The annual March Current Population Survey is perhaps the most comprehensive U.S. household economic survey, soliciting economic information from approximately 59,000 households (U.S. Bureau of the Census (1984)). The representativeness of the SCF is demonstrated by a comparison of the sample distribution of various demographic variables for the SCF and comparable March 1983 CPS survey in table 8. The CPS data are given for "primary families" defined comparably to families in the SCF. As can be seen, the SCF has a very similar distribution for most items.

                   A Comparison of the 1983 SCF and CPS

				       SCF		       CPS

				Number     Weighted	Number	   Weighted
				of cases   share	of cases   share

Age (head)
34 or less
 married                         661        16.9         9922       16.4
 unmarried male                  223         6.2         3435        5.8
 unmarried female                273         7.6         4105        7.2
35 to 44
 married                         555        13.6         7830       13.0
 unmarried male                   71         1.8         1388        2.4
 unmarried female                151         4.1         2165        3.7
45 to 54
 married                         492        10.5         6253       10.3
 unmarried male                   70         1.7          912        1.6
 unmarried female                118         3.2         1735        3.8
55 to 64
 married                         475         9.7         5967       10.3
 unmarried male                   62         1.5          897        1.5
 unmarried female                136         3.7         2130        3.8
65 or more
 married                         452         9.9         5532        9.5
 unmarried male                   74         1.8         1437        2.4
 unmarried female                290         7.7         5293        9.2

Race
 Caucasian                      3468        82.3        47515       82.4
 Nonwhite or Hispanic            635        17.7        11486       17.6

Family Income
 less than $10,000               912        24.0        15053       25.2
 $10,000 to $19,999              982        26.8        15580       26.0
 $20,000 to $29,999              711        19.3        12072       20.5
 $30,000 to $49,999              717        19.7        11533       19.8
 $50,000 to $99,999              309         8.2         4480        7.9
 $100,000 or more                472         2.0          283         .5
 
Homeownership                   2766        63.4        38320       64.9
 
Education of the Head
 0 to 8 grades                   560        14.5         9155       14.8
 9 to 12 grades                 1713        44.9        27269       46.4
 some college                    678        17.7        10355       17.6
 college graduate               1152        22.9        12222       21.3

Labor Force Participation
 single not working              635        17.0        11130       19.2
 single, working                 833        22.4        12367       21.3
 married, neither working        389         9.8         7088       12.0
 married, one working           1077        23.0        14023       23.4
 married, both working          1169        27.8        14393       24.0

Totals                          4103       100.0        59001      100.0

              Comparisons of Income Measured in SCF, CPS, and IRS Data

                         1982 INCOME   1982 INCOME   1982 INCOME   1982 INCOME
                         1983 SCF      CPS           1983 SCF      IRS DATA
                                                                   TAXABLE INC
                         ($ B)         ($ B)         ($ B)         ($ B)

Salaries and wages       1,393.7       1,443.5       1,385.7       1,564.6 
Business or farm income    291.3         110.5         290.4          53.7 
Taxable interest income     98.5          95.1          95.9         157.2 
Dividend income             ----          ----          46.7          54.2 
Net gains from stocks       ----          ----          50.4          24.3 
Rental or trust income      ----          ----          54.8          -2.1 
Dividends/trust/
rental total               102.9          47.3	        ----          ----
Welfare or public
 assistance                 23.2          17.4          ----          ----
Unemployment or
 workman's comp             20.6          32.8	        ----          ----
Alimony or child
 support                    35.6          21.4          ----          ----
Retirement income          194.6         204.3          94.0          59.9 

Category totals          2,160.4       1,972.3       2,017.9       1,911.8

PENSION PROVIDER SURVEY
The sample for the study of Employer Sponsored Pension Benefit Plans was derived in three interdependent stages. The overall research design was based on the use of the 1983 SCF to identify, in turn: which households were covered by employer sponsored pensions; which pension providers and plans covered these employees; and which benefit formulas and requirements governed these pension entitlements. All respondents or spouses with work experience were questioned about pension coverage on their current job, as well as vested pension entitlements from prior employers as part of the SCF. Households that reported pension coverage were asked to identify the provider of the pension -- in most cases, their employer. All the pension providers thus identified were pooled and duplicate references to the same provider were combined.

In many instances sufficient information was available from the name of the pension provider to uniquely identify them, and from files available at the Department of labor to identify their Employer Identification Number (EIN). In other instances, a telephone interview was conducted with the pension provider. The provider was asked for their EIN and for documentation of the pension plans that covered workers in the occupational classification and work location that corresponded to the reference SCF respondent(s) applying to that provider (the names of the SCF respondents were not disclosed to the pension providers who were told that SRC was doing a survey of pensions). Pension Providers were recontacted by mail and telephone as necessary to insure a high response rate.

The EIN and plan names and numbers were sufficient in most instances for SRC staff to match to official plan descriptions (SPDs) on file with the Department of Labor for most pension providers. In other instances the providers sent the SPDs or similar material to SRC. The details of the plans were then coded from the SPDs using a coding instrument developed by Mathematica Policy Research and SRC. Coders were hired by SRC specifically for the project, which required more skill than is generally necessary for coding (the final instrument has about 2,700 variables). Information was compiled separately for defined benefit and defined contribution plans. 1886 households (2262 individuals) were eligible for potential inclusion in the Pension Provider Survey. Of this total 1421 households (1641 individuals) were represented in the Pension Plan Survey for a 75 percent completion rate. These span 845 pension providers and 1011 pension plans. Data for the Pension Provider Survey is currently being distributed as part of the 1983 SCF package. A description of the survey is found in "Survey of Consumer Finances: Employer Sponsored Pension Benefits Plans," the Survey Research Center, 1986, by Richard T. Curtin.[25]

1986 SURVEY OF CONSUMER FINANCES
The 1986 Survey re-interviewed respondents to the 1983 SCF. If the respondent had been divorced or separated since the 1983 interview, both the 1983 respondent and their 1983 spouse were included in the 1986 sample. Other members who left the family to form new households were not included. A total of 2,822 interviews were conducted, by telephone, between June and September 1986. Interviews lasted an average of 27 minutes. The survey was conducted by the SRC under the direction of Richard T. Curtin.

The 1986 interview was primarily designed to update essential information in the 1983 SCF on the household balance sheet (needed to calculate savings) and employment data. However, additional lines of inquiry were also opened. Questions were asked about health and educational expenses, insurance coverage, more detailed data on family and family change (including marriage, divorce, death of a spouse, children outside the home, and ages of parents), charitable gifts of money and time, shared living arrangements, intra-family transfers and the financial details of divorce settlements. A limited amount of analysis has been performed on the data. See:

Robert B. Avery, Gregory E. Elliehausen, and Arthur B. Kennickell, "Changes in Consumer Installment Debt: Evidence from the 1983 and 1986 Surveys of Consumer Finances," Federal Reserve Bulletin, Vol. 73, No. 10 (October 1987), pp. 761-778.

Robert B. Avery and Arthur B. Kennickell, "Savings and Wealth: Evidence from the 1986 Survey of Consumer Finances," presented at the May 1988 NBER Conference on Research in Income and Wealth.

MANUAL INSTRUCTIONS
In the remainder of the manual, information is given on all the variables included in the final data set. A brief description is given for each variable along with information on imputation and a listing of the values that the variable takes on. The question number corresponding to the actual survey questionnaire (e.g. R9) is also given for all variables except recodes. Variables are listed by number, with the numeric code used as the basis of the variable's internal name in the survey's SAS data set. All variables listed here have a "B" prefix followed by a four digit number ranging from 3001 to 5749. The original uncleaned survey responses are contained in variables with a "V" prefix. These variables range from V1 to V2613. The codes for the "V" variables are described in the original survey codebook released by the SRC. We note, though, that the "B" variables contain all the same types of information as those contained in the "V" variables. Thus, for most analyses, it is possible to use the "B" variables without reference to the "V" variables.

The range of allowable values of the variables is also given. The symbol xxxx is used for continuous variables with a statement of the units used and the sample range. For discrete variables with a small number of allowable codes, all possible values and their meanings are listed. The number of sample cases (out of the 4103 "cleaned" observations) taking on each value of discrete variables is also given. If the listing is for several variables (such as the 1st, 2nd, and 3rd automobiles), then the case totals are given for the listed variables, in order, separated by slashes (e.g. 123/45/87 cases). Although useful in giving a flavor of the distribution of responses to questions, the case listings should not be used for statistical purposes, as they are unweighted distributions.

Most of the information collected for the 1983 SCF applies to the full family unit. Some information, however, such as employment, education, health, and pension income, was collected individually for the survey respondent and their spouse (if they had one). For married couples, the respondent could have been either the husband or the wife. For ease of use in analysis most of the person-specific variables in the cleaned data set have been arranged as "head" and "spouse," (where head is always the husband for married couples), instead of "respondent" and "spouse." It is easy to switch data back, if desired, by using the variable B3122 which indicates whether the respondent was the head or the spouse.

Several different codes are used in the data set, including:

• The code "1" is almost always used for the answer "yes" to a question.
  
  
• The code "3" is generally used for the answer "sometimes" or "maybe".
  
  
• The code "5" is almost always used for the answer "no" to a question.
  
  
• The code "-4" is used to denote a "small negative number".
  
  
• The code "-6" is used to denote the answer "none" which is sometimes differentiated from zero.
  
  
• The code "-7" is used to denote a special "other" response which does not fit into existing codes. SRC has cards indicating what the actual response is. "-7" is also sometimes used to denote answers like "forever" or "never" when used for continuous variables.
  
  
• The code "-8" is used to denote the answer "don't know" (DK). Most DKs have been imputed, but some still exist for selected variables where imputation is not appropriate (e.g. attitudinal variables). In a few instances "8" is used for DK.
  
  
• The code "-9" is used to denote "not answered" (NA). This indicates either that the interviewer inadvertently did not ask a question or that a respondent refused to answer. Most NAs have been imputed, but a few remain. In a few instances "9" is used for NA.
  
  
• The code "0" is generally used to denote cases where a variable is inappropriate for a particular observation because the question which underlies the variable was not asked. For example, questions on spouses would be inappropriate for single households. Note that sometimes a question is asked, but the answer given is none or zero (such as "my business is worth nothing"). These answers are generally coded as -6 not 0. There are some instances, particularly with recoded variables, where 0 does denote none or nothing.
  
  

ACKNOWLEDGEMENTS
Many people contributed to the 1983 Survey of Consumer Finances. Sampling, field work, editing, and coding were conducted by the Survey Research Center of the University of Michigan under the direction of Richard T. Curtin. Mr. Curtin also oversaw the cleaning and processing of the Pension Provider Survey data. Other SRC staff also made substantial contributions. Mary Grace Moore and Lisa Poole coordinated much of the questionnaire development and data processing and editing procedures. Steve G. Heeringa supervised the sample design and drawing of the SRC sample. Field work was supervised by Nancy Gebler. Coding and editing staff were supervised by Joan Scheffler. Much of the field work, and the development of the coding instrument for the Pension Provider Survey was done by Mathematica Policy Research. Miles Maxfield and Tim Carr supervised these activities.

Thomas A. Gustafson, of the Department of Health and Human Services co-authored the questionnaire, taking primary responsibility for the pension questions and overseeing the development of the Pension Provider Survey. Arthur Kennickell, of the Federal Reserve Board, developed the Federal Reserve Board weights, helped in writing this manual, and played a critical role in the later stages of data cleaning and imputation. He also co-authored the 1986 Survey of Consumer Finances.

Many individuals helped in the development of the survey instrument. Particularly noteworthy contributions were made by Glenn B. Canner and James T. Fergus (Federal Reserve Board); Janet Gordon, Melanie Quinn, Peter Struck (Office of the Comptroller of the Currency); Daniel J. Villegas (Federal Trade Commission); Walter Kolodrubetz (Department of Labor); and Nelson McClung (Office of Tax Analysis). Comments and helpful suggestions were also received from Emily S. Andrews, Stuart B. Avery, Marshall E. Blume, Thomas A. Durkin, Robert M. Fisher, Gary Gilbert, Arnold A. Heggestad, Malcolm Jensen, F. Thomas Juster, Robert W. Johnson, Myron Kwast, Barbara R. Lowrey, Charles A. Luckett, Olivia S. Mitchell, Dorothy S. Projector, Lawrence H. Summers, Cameron Whiteman, and John D. Wolken. Tom Petska, Fritz Scheuren, and Dan Skelly, of the Statistics of Income Division of the Internal Revenue Service, provided the high-income sample and weights. Research assistance for data cleaning and imputation at the Federal Reserve Board was provided by Aliki Antonatos, Oscar Barnhardt, Phoebe Roaf, Julie Rochlin and Julia Springer. Additional assistance was provided by Neil Briskman, William Carbaugh, M. Elizabeth Crowell, Charlotte Jackson, Scott Hedges, Pat Ma, Elaine Peterson, Missi Reinkemeyer, Bob Schmitt, Paul Hughes- Cromwick, and Sharon Ward.

ENDNOTES
1. The interview questionnaire for the household survey was prepared by Robert B. Avery and Gregory E. Elliehausen, of the Federal Reserve Board, and Thomas A. Gustafson, of the Department of Health and Human Services, with assistance from staffs of the sponsoring agencies. Field work and editing and coding of survey responses was performed under the direction of Richard T. Curtin of the SRC. Mr. Curtin and Timothy Carr and Miles Maxfield, of Mathematica Policy Research, administered the Pension Provider Survey. The Statistics of Income Division of the Internal Revenue Service, provided the high-income sample.

2. A household consists of all the persons who occupy a housing unit or dwelling. Persons missed by the survey will be disproportionately young, because they are college or the military, and old, because they are in nursing homes. The later omission, effecting an estimated 1.4 million people, is the most serious in terms of wealth measurement. However, the failure to include a large number of younger persons is likely to effect the long run representativeness of the SCF when used as a panel.

3. Non-SMSA counties with less than 2,000 population were linked with adjacent counties to form multi-county PSUs. The SCF sample used the 1970 SRC sampling frame which was selected from a national population of 2,700 PSUs, of which 12 were self-representing. In addition to SMSA status, the 62 nonself-representing strata were designed to take into account the location (Census Region), the population, size of the largest city, and percent manufacturing (urban) or agricultural (rural) employment of each area. In the South Region, the percent black population and a special domain distinction labeled "the Deep South" (South Carolina, Georgia, Alabama, Mississippi, and Louisiana) was also used. By design, the nonself- representing strata are of approximately equal size each totaling between 1.7 and 2.5 million population in 1970.

4. Because New York City is so large, it is treated as though it is made up of two PSUs. Thus, we treat the sample as having 75 PSUs, not 74.

5. The sampling of the second stage units (SSU's) was performed with probability proportionate to size as measured by the 1970 Census count of year-round housing units. In most areas, the largest or "central" city of each sample PSU was included with certainty. The second stage was dropped from the 1980 SRC National Sample design.

6. The units selected in the third stage are termed "chunks." In urbanized areas chunks are defined to be housing units within the land area given by a Census Block. However, Blocks with less than 16 year- round housing units were combined with adjacent Blocks to meet a minimum 16 unit size. In rural areas, chunks were defined to be compact parcels of land with clearly recognizable physical boundaries (roads, rivers, rail lines, etc.) selected with an expected count of 24 year-round housing units. Within SSU's the sample of chunks were selected with probability proportionate to their number of year-round housing units.

7. Once the third stage of selection was complete, SRC personnel performed a complete listing of all housing units within the physical boundaries of each chunk. For the 1983 SCF about three-fourths of the chunks has been previously listed for other surveys; thus only updating of the listings were necessary. These lists formed the basis of selection for the fourth stage of sampling.

8. Housing units were selected randomly within each chunk. The sampling rate was set inversely proportional to the number of year- round housing units within the chunk as determined by the listings.

9. These procedures followed fairly standard SRC methods. For a more detailed description of these methods, see Irene Hess, Sampling for Social Research Surveys: 1947-1980, Ann Arbor: Institute for Social Research, 1985.

10. Unfortunately, because of legal restrictions, knowledge of the exact sampling procedures is restricted to employees of the SOI. The sample drawn appears to roughly coincide with individuals having an "adjusted gross income," modified for full capital gains and other exclusions, of $100,000 or more in 1980.

11. Actually only the 12 self-representing and 31 of the 62 nonself- representing PSUs were used for the high-income sample list. The decision to exclude high-income households in the remaining nonself- representing PSUs was based on a joint consideration of survey costs and the relatively small expected size of the high-income sample. Because the SOI listings were by address, and the area probability PSUs were defined by county, some slight approximations were used in defining the SOI sample. The actual SOI sample was defined by the ZIP codes corresponding to the SRC sample counties, with the county location of the main post office in a ZIP code used when county and ZIP code boundaries did not correspond exactly.

12. The overall response rate of the high-income mailing (9 percent) may not be quite as bad as it appears. SOI typically has response rates of no more than 20 to 30 percent even for mailings extremely favorable to the respondent. The low 1983 SCF response rate was also caused by the failure to send a followup letter.

13. These procedures differ slightly from the procedures normally used for the selection of household respondent by SRC. Generally, only the economic dominance and age closest to 45 criteria are used.

14. Rates for income non-response were much higher. 1.8 percent of the high-income sample gave no income data, and an additional 4.6 percent gave only partial data. Comparable figures for the edited area probability sample were 5.5 percent and 7.1 percent. Only 2 percent of the discarded area probability sample respondents gave any income data, and these respondents gave only partial data.

15. Means were computed separately for the high-income and cross- section sample on an item-by-item basis and were based only on respondents who gave dollar values.

16. One household was discarded which did not meet these criteria because it reported more than a billion dollars in assets and appeared to be an insincere interview.

17. See Steven G. Heeringa and Richard T. Curtin, "Household Income and Wealth: Sample Design and Estimation for the 1983 Survey of Consumer Finances," Statistics of Income and Related Record Research 1986-1987, Internal Revenue Service 1987.

18. See Michael Strudler, General Descriptive Booklet for the 1982 Individual Tax Model File, Statistics of Income Division, Internal Revenue Service, 1983.

19. An estimate of the standard error due to sampling of the estimated aggregate of each asset and liability category is given in column 2. These figures were computed by calculating the sample variance of each item within each sampling unit (75 area probability PSUs and nine high-income categories). Assuming independence of sample draws across each of these cells, the variance of an asset or debt category total was then calculated as the sum of the variances of each item included in that category weighted by the cell populations. Because these estimates take the sampling weights as fixed they are likely to understate the true sampling variance of the weighted sums.

20. See, for example, George Katona, Louis Mandell, and Jay Schmeideskamp, 1970 Survey of Consumer Finances, Ann Arbor: Institute for Social Research: 1971.

21. See Thomas A. Durkin and Gregory E. Elliehausen, 1977 Consumer Credit Survey, Washington D.C. :, Board of Governors of the Federal Reserve System, 1978.

22. See Dorothy S. Projector and Gertrude S. Weiss, Survey of Financial Characteristics of Consumers, Washington D.C.: Board of Governors of the Federal Reserve System, 1966.

23. See Robert B. Avery, Gregory E. Elliehausen, Arthur B. Kennickell, and Paul A. Spindt, "The Use of Cash and Transaction Accounts by American Families," Federal Reserve Bulletin 72 (February 1986): pp. 87-108; and Robert B. Avery, Gregory E. Elliehausen, Arthur B. Kennickell, and Paul A. Spindt, "Changes in the Use of Transaction Accounts and Cash from 1984 to 1986," Federal Reserve Bulletin 73 (March 1987): pp. 179-196.

24. Detailed discussion of the survey findings can be found in "Household Wealth and Asset Ownership: 1984," Household Economic Studies Series P-70, No. 7 (July 1986), Bureau of the Census; and John M. McNeil and Enrique J. Lamas, "Year-Apart Estimates of Household Net Worth from the Survey of Income and Program Participation," NBER Conference on Research in Income and Wealth, Baltimore, 1987. Richard T. Curtin, F. Thomas Juster, and James N. Morgan, "Survey Estimates of Wealth: An Assessment of Quality," NBER Conference on Research in Income and Wealth, Baltimore, 1987, provide a detailed comparison of the PSID, SIPP, and 1983 SCF wealth data.

25. The 1986 SCF was co-sponsored by the Federal Reserve Board, the Department of Health and Human Services, The Office of the Comptroller of the Currency, and the General Accounting Office.


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