ABSTRACT. This paper is part of the State Economic Forecast Session

ABSTRACT. This presentation is part of the State Economic Forecast Session

ABSTRACT. This presentation is part of the State Economic Forecast session

ABSTRACT. This presentation is part of the State Economic Forecast session

ABSTRACT. Measuring wealth inequality in the United States relies on survey data, as limited administrative data sources exist. Survey respondents provide detailed answers to their net wealth's disaggregated components, including a self-assessment of the value of their home, which is typically their largest single asset. In this paper, we investigate whether self-reported home values align with market transaction values; and; to the extent they diverge, why do they, and what are the implications for racial wealth inequality and other key statistics? To answer these questions, we exploit a unique dataset linking a large national dataset of home transactions to internal American Community Survey (ACS) microdata at the address-level. Comparing transactions that occur in close proximity to the survey date from 2008-2016, our initial results suggest that homeowners modestly overestimate (5%) their home's value on average, but with substantial variation by region and over the business cycle (as much as 15% in 2008). Importantly, we find misperception in home value varies systematically across demographic, income, and economic characteristics of the household, most notably by race. Our findings imply heterogeneity in misperception can significantly alter recent trends in racial wealth gaps, as the Black-White wealth gap may be understated by as much as 40% by official statistics in some years. For context, we compare these results with other macroeconomic measurements that rely on self-assessed values, like Personal Consumption Expenditures (PCE), which are generally less distorted than wealth measurements over the period we study.

ABSTRACT. The U.S. Bureau of Economic Analysis (BEA), in partnership with the National Science Foundation (NSF), is researching improved measures of Research and Development (R&D) performance at both the state and national levels. As part of a new satellite account, results are presented on R&D value added, compensation, and employment by R&D performing sector for the 2017-2021 time period. This work makes a number of methodological advancements, including building up estimates of R&D value added based on each detailed component. Results on R&D can be compared to state economies as a whole to show the importance of R&D both overall and for specific industries.

ABSTRACT. The Bureau of Economic Analysis (BEA) publishes detailed statistics on capital stocks by various types of capital and by industry, as well as other detailed economic accounts statistics on the generation and distribution of income by state. However, BEA does not publish statistics on capital stocks by state. Because capital is a significant factor or production, such statistics would be of significant interest to data users. In this presentation, we compare four methods to compute annual total capital stocks by state, each of which is associated with its own set of assumptions and source data. The presentation will include background, a description of each method, and a comparison of experimental capital stock statistics from the four methods.

ABSTRACT. Big data offers potentially enormous benefits for improving economic measurement, but it also presents challenges (e.g., lack of representativeness and instability), implying that its value is not always clear. We propose a framework for quantifying the usefulness of these data sources for specific applications, relative to existing official sources. We specifically weigh the potential benefits of additional granularity and timeliness, while examining the accuracy associated with any new or improved estimates, relative to comparable accuracy produced in existing official statistics. We apply the methodology to employment estimates using data from a payroll processor, considering both the improvement of existing state-level estimates, but also the production of new, timelier, county-level estimates. We find that incorporating payroll data can improve existing state-level estimates by 8% and yields new county-level estimates that fall within an acceptable accuracy standard. We demonstrate the practical importance of these experimental estimates by investigating a hypothetical application during the COVID-19 pandemic, a period in which more timely and granular information could have assisted in implementing effective policies. Relative to existing estimates, we find that the payroll data series could help identify areas of the country where employment was lagging. Moreover, we also demonstrate the value of a timelier series, even when accuracy of the timelier series is lower than official estimates. More broadly, this presentation demonstrates how to systematically use big data to expand the frontier of economic measurement.