The sampling approach generates the same entry and exit behavior as present in the population, with young individuals and immigrants entering the sample and deceased individuals and emigrants leaving the sample each quarter at the same rate as in the U.S. population. As a result, for each quarter starting in 1999, the panel constitutes a representative cross-section of the population of borrowers. Moreover, once the random sample of individuals is identified, we then include the credit records of all individuals living at the same address, so the panel can be used to analyze new loan originations, balances, and delinquencies at the household level. Consumer debt traditionally has been measured at the individual level, but most economic decision making is done at the household level. In fact, consumer income and assets are commonly measured and analyzed at the household level. For studying household economic behavior, and in particular for understanding life-cycle household finance, it would similarly be more useful to examine debt and credit aggregated at the household level. It may also be important to consider the distribution of debt within households. Beyond the ability to link individual consumer and household-level loans over time, the panel improves on typical loan-level datasets by linking multiple loans to the same consumer. Thus, at each point in time, one can see whether the individual holds multiple first mortgages or a combination of first and second mortgages while at the same time holding various types of non-mortgage debt.
In addition to providing a measure of individual forecast uncertainty, these density forecasts yield richer information about how expectations vary across people and over time than do single-value forecasts, or “point predictions.” The SCE is the outcome of a five-year collaborative research project involving economists, psychologists, and survey design experts. During this period, we conducted a large number of experimental surveys to assess the information elicited with existing survey questions and to develop new questions, including those for measuring forecast uncertainty. This preliminary work and the probabilistic format of the new survey questions represent an important innovation that builds on academic work over the past 20 years by economist Charles Manski, as well as some early pioneering survey work conducted by the Bank of Italy. Creating the CCP and SCE has yielded some important lessons. First, data innovation requires an ability to think outside the box, and it benefits from the collaboration of people with diverse skills and backgrounds – academic economists, but also representatives of other academic disciplines and individuals with business and public policy experience. Skills in identifying valuable but underutilized data, the creation of data linkages, the development of appropriate sampling procedures, and survey methodology and questionnaire design experience are all particularly valuable. In this respect, our experiences mirror those at the many new interdisciplinary data and social research centers and numerous “Big Data” university centers being created across the country.