Methods

To better track the overall growth and relative contributions of small business in the U.S. economy, the U.S. Bureau of Economic Analysis is developing new economic statistics by business size. The paper begins with a description of existing economic statistics for small businesses, including those from the U.S. Small Business Administration, U.S. Department of the Treasury, and Statistics Canada. We then present experimental estimates of 2012–2016 employment, wages, and wages per employee by enterprise size and industry, based on publicly available source data. We find wage and employment growth over the period was slowest for very small enterprises (those with less than 20 employees) and fastest for large enterprises (those with 500 or more employees), although this relationship differs across industries. Additionally, enterprises with 0–99 employees saw wages increase at a slower rate than medium and large enterprises (those employing 100 or more employees), lagging by 1.5 percent. A discussion of the measurement challenges related to developing a full suite of economic statistics for small businesses concludes the paper.

This paper explores potential ways to develop experimental estimates of the value of U.S. imports of illegal drugs. It builds on the initial exploration of this topic by the Bureau of Economic Analysis (BEA) in Soloveichik (2019), which presents experimental estimates of U.S. domestic consumption of illegal drugs and of import of illegal drugs into the United States. In this paper, I extend Soloveichik’s research by exploring the feasibility of developing estimates of imports of methamphetamines and marijuana using seizure data, and I evaluate the extent to which source data allow us to estimate heroin and cocaine imports by geography. International guidelines for national economic accounts (the System of National Accounts 2008, or SNA) and international economic accounts (the Balance of Payments and International Investment Position Manual, sixth edition) explicitly recommend that some illegal market activity should be included in measured output. Soloveichik suggests that illegal drugs comprise the largest share of imports of this activity for the United States and would have added $111 billion to U.S. GDP in 2017.

Property markets do not fully price the public’s value for historic homes to correct the intergenerational externality associated with historical preservation. While preservation for future generations often provides the primary motivation for Pigovian subsidies, historical preservation or restoration policies may also have significant contemporary amenity effects. This study exploits unique data on the use of rehabilitative tax credits (RTCs) in Virginia to estimate the extent to which historic property investment generates market externalities for nearby nonhistoric properties. Using a difference-in-differences approach, the results indicate that homes in close proximity to RTCs sell at a premium, with only modest liquidity effects. (JEL H23, R38)

We study the external impact of foreclosures, exploring how foreclosed properties affect the liquidity of nearby homes. Empirically, we find a foreclosure increases a nearby home's time‐on‐market by approximately 30% on average, which is primarily driven by a disamenity effect. There is evidence that this delay comes from surprises or information shocks to nearby sellers, as foreclosures that come on and/or leave the market after a nearby home's listing date have the largest adverse liquidity effects. However, when there is no surprise and a nearby foreclosure remains through the entire marketing period, sellers discount list prices more steeply, effectively counteracting these liquidity effects. The results suggest that information, pricing and expectations play key roles in how this externality is absorbed by the real estate market.

Suppose a vector autoregressive moving-average model is estimated for m observed variables of primary interest for an appli-cation and n–m observed secondary variables to aid in the application. An application indicates the variables of primary interest but usually only broadly suggests secondary variables that may or may not be useful. Often, one has many potential sec-ondary variables to choose from but is unsure which ones to include in or exclude from the application. The article proposes a method called weighted-covariance factor decomposition (WCFD), comparable to Stock and Watson’s method here called principle-components factor decomposition (PCFD), for reducing the secondary variables to fewer factors to obtain a parsi-monious estimated model that is more effective in an application. The WCFD method is illustrated in the article by forecasting quarterly observed U.S. real GDP at monthly intervals using monthly observed four coincident and eight leading indicators from the Conference Board (http://www.conference-board.org). The results show that root mean-squared errors of GDP fore-casts of PCFD-factor models are 0.9–11.3% higher than those of WCFD-factor models especially as estimation-forecasting periods pass from the pre-2007 Great Moderation through the 2007–2009 Great Recession to the 2009–2016 Slow Recovery.

This paper reviews the efforts of the Bureau of Economic Analysis (BEA) to measure international services categorized by mode of supply. BEA has adopted a survey form that uses an innovative approach to collect information on mode of supply by simply having companies report the percentage of its services supplied through one mode as opposed to all modes, with the idea that the other modes can be estimated as a residual or using other data sources. Of the few previous efforts by countries to measure trade by mode of supply, most are based on assumptions about industry practices or on surveys that simply asked for the predominant mode of supply rather than a more precise percentage supplied by mode. BEA also uses a pioneering method to measure services supplied through affiliates across service types by mapping its comprehensive industry-based foreign affiliate statistics to its product-based trade statistics. The estimates also include a breakdown of the mode where consumers obtain the service outside their home territory, such as services received when traveling abroad, that more closely corresponds with guidelines set out in the General Agreement on Trade in Services than most previous efforts.

Developing a national account-based measure of the distribution of income from the commonly used Census based concept of money income has been the subject of earlier research. We use publicly available survey and administrative data to construct a distribution of personal income after enhancing the top income distribution in the Current Population Survey (2007 and 2012). We show that inequality measures are fairly sensitive to the definition of income contemporaneously and across time. This work helps bridge the gap between micro data and macro statistics and informs about results from other studies, such as Piketty et al. (2018).

Special district governments are a type of local government that may embody characteristics of both governments and private businesses and are an important source of public services. Given the combined characteristics of special districts and their economic importance, a complete and consistent economic accounting for special districts that operate like businesses – special district enterprises – is important to the relevance and accuracy of the U.S. national accounts. In this paper, we present a preliminary set of economic accounts for special district enterprises using two different approaches for identifying special district enterprises: by unit and by function. Our results suggest that the identification of enterprises by function rather than by unit yields differences in key economic accounting aggregates. Our results also suggest that some functions currently treated as enterprise functions under the U.S. national accounts existing methodology do not fit the SNA criteria for treatment as quasi-corporations. We conclude that identification of enterprises by function may be preferable to identification by unit because the sample selection is more precise. Based on identification by function, measured value-added for enterprises in 2012 is $33.2 billion, of which $16.7 billion reflects compensation and $16.5 billion reflects operating surplus.