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Thursday 16th July, 09:00 - 10:30 Room: O-201

Measurement errors in the wealth surveys 3

Convenor Mr Junyi Zhu (Deutsche Bundesbank )
Coordinator 1John Sabelhaus (Federal Reserve Board)
Coordinator 2Brian Bucks (Consumer Financial Protection Bureau)

Session Details

Obtaining a comprehensive picture of households’ balance sheets and understanding their wealth accumulation process is of increasing interest to a large audience ranging from poli-cymakers and researchers to the general public. Consequently, more and more wealth surveys have been established worldwide. However, wealth data are susceptible to measurement errors specific to the nature of various asset and liability items. For example, households may not assess the value and amount of their assets constantly. And the valuation of less traded or distinctive assets is not straightforward. The knowledge required to answer some question can be demanding. Financial topics are always sensitive. Typically, questions on ownership of assets or liabilities are answered more accurately than questions on their value and in most cases the reporting quality of the debts outperforms that of the assets. Households from both ends of the wealth distribution are hard to identify and reach. The longitudinal data adds another layer of difficulty in distinguishing true changes from measurement errors. On the other hand, reporting error, the main measurement error, does not have a homogeneous pattern but can be classified.
We would like to invite survey practitioners to discuss how to detect and tackle measurement errors in wealth surveys. Researchers can analyze the missing pattern within the survey as a signal of potential errors. Matching to external surveys or administrative data and utilizing the panel dimension are other options to gauge the plausibility of answers. But then, there have been many prevention and reconciling measures. They include careful design and sequencing of questions, specialized interviewer training, software real-time checks, editing by reviewing the comments, dependent interviewing, etc. Innovative approaches are especially welcome. For example, using tax records, property lien data, online finance websites or other sources can fill the gap in building comprehensive profile of wealth accumulation.

Paper Details

1. Wealth, Pensions, Debt, and Savings: Considerations for a Panel Survey
Mr Brian Bucks (Consumer Financial Protection Burea)
Mrs Karen Pence (Federal Reserve Board)

This paper reviews the challenges of collecting accurate survey data on household wealth and analyzes rates of missing and edited data in the U.S. Survey of Consumer Finances to gauge which assets and debts households report accurately. In general, households appear to reliably report the ownership and values of most assets and debts, but they have difficulty reporting retirement accounts, whole life insurance, and small businesses. The paper evaluates the feasibility and prospects of strategies to improve the accuracy of survey data on wealth, particularly in panel surveys, including methodological refinements and administrative data linkages.

2. The official personalised pension information and the projection of future pension incomes
Dr Dina Frommert (DRV Bund)

The paper examines if the data given in the personal pension information letters – which is cheap and easy to collect in surveys – is a good estimate for future pension entitlements. Life course data of pensioners is used to calculate virtual pension forecasts which are then compared to the actual entitlements. The comparison provides insight into which personal and life course characteristics make a good fit of pension forecast and pension entitlement more likely.

3. Are Homeowners in Denial about their House Values? Comparing Owner Perceptions with Transaction-Based Indexes
Miss Alice Henriques (Federal Reserve Board of Governors)

During the boom-bust of the housing market, owner-reported house values from the Survey of Consumer Finances rose more and fell less than a national house price index (HPI). Homeowners, on average, report changes in house values similar to local HPIs between 2007-2009, but there is a significant heterogeneity in owners’ errors. This study shows owners with a lot of home equity in boom areas and those with little equity in non-boom markets make the largest mistakes. Further, owners who refinanced during the boom make large errors, while boom buyers unravel errors in valuation made following purchase.

4. Estimating the Performance of Alternative Multiple Imputation Methods on Longitudinal Wealth Data
Mr Christian Westermeier (DIW Berlin, FU Berlin)
Dr Markus M. Grabka (DIW Berlin, TU Berlin)

In a simulation study the authors compare six combinations of cross-sectional and longitudinal imputation strategies for German wealth panel data. The authors create simulation data sets by blanking out observed data points: they induce item non response by missing at random as well as two separate mechanisms assuming there is differential nonresponse at the top and the bottom of the distribution. The row-and-column method performs well considering the cross-sectional evaluation criteria. As for wealth mobility, two additional criteria show that a model based approach such as MICE might be the preferable choice.