The Integrated Macroeconomic Accounts of the United States

Cagetti, Marco; Holmquist, Elizabeth Ball; Lynn, Lisa; McIntosh, Susan Hume; Wasshausen, David

doi:10.2139/ssrn.2197527

Cited by 2 publications

(6 citation statements)

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“…This model combines the original index with another index (WINDEX1) derived from a regression of wealth observed in the preceding survey on the income and other characteristics available in the SOI data used in the design of that sample. 22 The value of WINDEX1 for the next survey is obtained by projecting the model estimates on the values in the more current SOI data. An advantage of this approach is that it allows for a more complicated modeling allowing implicitly for some differences in rates of returns, patterns of holdings of debts or assets that do not yield returns and other factors, that may be correlated with the information available in the SOI data including the income variables used in the WINDEX0 model and others such as the age of the tax filer or the amount of wage income, property taxes or charitable deductions.…”

Section: Background On the Scfmentioning

confidence: 99%

“…In addition, WINDEX1 also embeds any 21 Obviously, the model would not be able to reflect gains-focused assets that are intended to be bequeathed. 22 The match of the SOI data with the information from respondents is purely for purposes of estimating this model and it was done in such a way that identifying information was not included in the data set used for the regression. The regression is estimated using logarithms for net worth and other dollar values and converted to levels thereafter.…”

Section: Background On the Scfmentioning

confidence: 99%

“…Without the list sample, 38 See [22] for a critical discussion of the FAOTUS. 39 Each survey contains information on income for the preceding calendar year.…”

Section: Scf Wealth and Its Comparison With Other Sourcesmentioning

confidence: 99%

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Lining up: Survey and administrative data estimates of wealth concentration

Kennickell¹

2017

SJI

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Abstract. The Survey of Consumer Finances (SCF) has a dual-frame sample design that supplements a standard area-probability frame with a sample of observations drawn from statistical records derived from tax returns. The tax-based frame is stratified on the basis of a "wealth index" constructed largely from observed income flows, with the intent of heavily oversampling wealthy households. Although the SCF is not specifically designed to estimate wealth concentration, the design arguably provides sufficient support to enable such analysis with a reasonable level of credibility. Similar estimates may also be made by using tax-based data directly, as in Saez and Zucman [1], by using a construct very close to a key part of the SCF wealth index. Such an approach has appeal as a way of tapping a much larger set of information to improve SCF estimates. Not surprisingly, there are differences in the two approaches, largely as a result of conceptual differences or complications in the survey implementation. This paper focuses on the top 1 percent of the wealth distribution, the group most intensively covered by the SCF list sample and it explores the stability of the relationship between the patterns of concentration in the survey data and parallel patterns in tax-based estimates and considers how those patterns differ across survey participants, the full sample and the entire survey frame. In addition, the paper makes as series of recommendation for further research on the technical support of the survey.

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Section: Background On the Scfmentioning

confidence: 99%

Section: Background On the Scfmentioning

confidence: 99%

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Lining up: Survey and administrative data estimates of wealth concentration

Kennickell¹

2017

SJI

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“…To mitigate the risks of misclassification in the use of either index, WINDEXM is computed as a weighted combination of the two, where the two indexes are standardized in levels to have the same median and interquartile range. 22 WINDEXM is then divided into seven strata corresponding to fixed percentile points of its distribution. 23 Five of the strata apply to only the top 5 percent of the WINDEXM distribution and three entirely to a group smaller than the 0.5 percent at the top of the WINDEXM distribution.…”

Section: =1mentioning

confidence: 99%

“…Beginning with the 2001 survey, the SCF list sample has employed multiple years of data to compute weighted averages of the values used in calculating the wealth indices. 25 In recent surveys, the weighted average has attributed 22 Originally, WINDEXM weighted WINDEX0 and WINDEX1 equally. To reflect the better ex post predictive power of WINDEX1, as discussed in the text, the weighing was adjusted starting in 2004 to give more weight to WINDEX1.…”

Section: =1mentioning

confidence: 99%

Lining Up: Survey and Administrative Data Estimates of Wealth Concentration

Kennickell

2017

FEDS

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The Survey of Consumer Finances (SCF) has a dual-frame sample design that supplements a standard area-probability frame with a sample of observations drawn from statistical records derived from tax returns. The tax-based frame is stratified on the basis of a "wealth index" constructed largely from observed income flows, with the intent of heavily oversampling wealthy households. Although the SCF is not specifically designed to estimate wealth concentration, the design arguably provides sufficient support to enable such analysis with a reasonable level of credibility. Similar estimates may also be made by using tax-based data directly, as in [1], by using a construct very close to a key part of the SCF wealth index. Such an approach has appeal as a way of tapping a much larger set of information to improve SCF estimates. Not surprisingly, there are differences in the two approaches, largely as a result of conceptual differences or complications in the survey implementation. This paper focuses on the top 1 percent of the wealth distribution, the group most intensively covered by the SCF list sample and it explores the stability of the relationship between the patterns of concentration in the survey data and parallel patterns in tax-based estimates and considers how those patterns differ across survey participants, the full sample and the entire survey frame. In addition, the paper makes as series of recommendation for further research on the technical support of the survey. JEL codes: C83, D31 The views expressed in this paper are the views of the author alone and they do not necessarily reflect the views of the Board of Governors of the Federal Reserve System or its staff. The author is grateful to all those whose work has made this paper possible, but particularly Fritz Scheuren, who as director of Statistics of Income at IRS first made it possible to use tax-return-derived data for the SCF sample design; Barry 1The Survey of Consumer Finances provides information on household wealth and its composition and relationship to other variables, on a common basis from 1989 to 2013. Because wealth is highly concentrated in the U.S., a survey that did not take special account of that situation would tend to produce very noisy (or even biased) estimates of values strongly influenced by the upper tail of the wealth distribution, such as means or concentration ratios. The SCF addresses this issue through the use of a list sample selected from statistical records derived from individual tax returns, as a supplement to a multistage area-probability sample. The list sample uses a proxy variable that supports oversampling of very wealthy households. That proxy and its relationship to net worth measured in the survey are the principal focus of this paper.The wealth proxy is a "wealth index" constructed from income flows and other variables available in the frame data for tax filers. The sample design assumes there is a mapping from the observed data to a wealth concept close enough to an actual measure of wealth approp...

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The Integrated Macroeconomic Accounts of the United States

Cited by 2 publications

References 2 publications

Lining up: Survey and administrative data estimates of wealth concentration

Lining up: Survey and administrative data estimates of wealth concentration

Lining Up: Survey and Administrative Data Estimates of Wealth Concentration

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