Conference Programme 2015
Tuesday 14th July Wednesday 15th July Thursday 16th July Friday 17th July
Wednesday 15th July, 16:00 - 17:30 Room: O-201
Measurement errors in the wealth surveys 2
|Convenor||Mr Junyi Zhu (Deutsche Bundesbank )|
|Coordinator 1||John Sabelhaus (Federal Reserve Board)|
|Coordinator 2||Brian Bucks (Consumer Financial Protection Bureau)|
Session DetailsObtaining 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 Details1. Comparing Wealth – Data Quality of the HFCS
Ms Anita Tiefensee (Hertie School of Governance)
Dr Markus M. Grabka (DIW)
The Household Finance and Consumption Survey (HFCS) provides information about household wealth (assets and liabilities) from 15 Euro‐countries. The survey will be the central dataset in this topic in the future. However, several aspects point to potential methodological constraints regarding crosscountry comparability. Therefore the aim of our presentation is to get a better insight in the data quality of this important data source. We will first present a synopsis of cross‐country differences, which is the core of the presentation. In addition we give a first insight in the selectivity of item nonresponse in a cross‐national setting.
2. Measuring Income and Wealth at the Top Using Administrative and Survey Data
Dr Jesse Bricker (Federal Reserve Board)
Dr Alice Henriques (Federal Reserve Board)
Dr John Sabelhaus (Federal Reserve Board)
Measuring wealth inequality by modeling wealth with administrative income data is an alternative to measuring wealth in a household survey. The Survey of Consumer Finances (SCF) uses a combination of survey and administrative data to produce wealth estimates. The SCF sampling procedure, though, demonstrates the limitations of predicting wealth from income tax data. Further, wealth estimates form administrative data are sensitive to differences in the unit of analysis and various decisions about income and wealth concepts. Finally, the SCF administrative sampling data confirm that the very top of the income and wealth distributions are well-represented in the SCF.
3. The Wealth of Wealthholders
Professor Matthew Shapiro (University of Michigan)
Dr John Ameriks (The Vanguard Group, Inc)
Professor Andrew Caplin (NYU)
This paper introduces the Vanguard Research Initiative (VRI)—a new panel study of clients of the Vanguard Group combining survey and administrative data—that is designed to yield high-quality measurements of a large sample of older Americans who face meaningful financial tradeoffs approaching and during retirement. The VRI features an account-by-account approach to asset measurement.
4. Micro and macro data: A comparison of the Household Finance and Consumption Survey with Financial Accounts in Austria
Mr Peter Lindner (Oesterreichische Nationalbank)
Mr Michael Andreasch (Oesterreichische Nationalbank)
This paper compares the survey results on savings deposits and total financial assets from the Household Finance and Consumption Survey (HFCS) in Austria with detailed administrative records from the national accounts. Cross-checking confirms that the HFCS-based aggregate estimates differ from the financial accounts data. Additionally, the paper shows that the underlying patterns have been captured adequately by the survey. Moreover, a micro level simulation serves to demonstrate that the undercoverage of the upper deposit ranges has only a relatively minor effect, in particular in the case of robust statistical measures such as the median or percentile ratios.