In this paper we investigate the role of input-output data source in the regional econometric input-output models. While there has been a great deal of experimentation focused on the accuracy of alternative methods for estimating regional input-output coefficients, little attention has been directed to the role of accuracy when the input-output system is nested within a broader accounting framework. The issues of accuracy were considered in two contexts, forecasting and impact analysis focusing on a model developed for the Chicago Region. We experimented with three input-output data sources: observed regional data, national input-output, and randomly generated input-output coefficients. The effects of different sources of input-output data on regional econometric input-output model revealed that there are significant differences in results obtained in impact analyses. However, the adjustment processes inherent in the econometric input-output system seem to mute the initial differences in input- output data when the model is used for forecasting. Since applications of these types of models involve both impact and forecasting exercises, there would still seem to be a strong motivation for basing the system on the most accurate set of input-output accounts.
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Building the Census Bureau Index of Economic Activity (IDEA)
March 2023
Working Paper Number:
CES-23-15
The Census Bureau Index of Economic Activity (IDEA) is constructed from 15 of the Census Bureau's primary monthly economic time series. The index is intended to provide a single time series reflecting, to the extent possible, the variation over time in the whole set of component series. The component series provide monthly measures of activity in retail and wholesale trade, manufacturing, construction, international trade, and business formations. Most of the input series are Principal Federal Economic Indicators. The index is constructed by applying the method of principal components analysis (PCA) to the time series of monthly growth rates of the seasonally adjusted component series, after standardizing the growth rates to series with mean zero and variance 1. Similar PCA approaches have been used for the construction of other economic indices, including the Chicago Fed National Activity Index issued by the Federal Reserve Bank of Chicago, and the Weekly Economic Index issued by the Federal Reserve Bank of New York. While the IDEA is constructed from time series of monthly data, it is calculated and published every business day, and so is updated whenever a new monthly value is released for any of its component series. Since release dates of data values for a given month vary across the component series, with slight variations in the monthly release date for any one component series, updates to the index are frequent. It is unavoidably the case that, at almost all updates, some of the component series lack observations for the current (most recent) data month. To address this situation, component series that are one month behind are predicted (nowcast) for the current index month, using a multivariate autoregressive time series model. This report discusses the input series to the index, the construction of the index by PCA, and the nowcasting procedure used. The report then examines some properties of the index and its relation to quarterly U.S. Gross Domestic Product and to some monthly non-Census Bureau economic indicators.
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The Spatial Extent of Agglomeration Economies: Evidence from Three U.S. Manufacturing Industries
January 2012
Working Paper Number:
CES-12-01
The spatial extent of localized agglomeration economies constitutes one of the central current questions in regional science. It is crucial for understanding firm location decisions and for assessing the influence of proximity in shaping spatial patterns of economic activity, yet clear-cut answers are difficult to come by. Theoretical work often fails to define or specify the spatial dimension of agglomeration phenomena. Existing empirical evidence is far from consistent. Most sources of data on economic performance do not supply micro-level information containing usable geographic locations. This paper provides evidence of the distances across which distinct sources of agglomeration economies generate benefits for plants belonging to three manufacturing industries in the United States. Confidential data from the Longitudinal Research Database of the United States Census Bureau are used to estimate cross-sectional production function systems at the establishment level for three contrasting industries in three different years. Along with relevant establishment, industry, and regional characteristics, the production functions include variables that indicate the local availability of potential labor and supply pools and knowledge spillovers. Information on individual plant locations at the county scale permits spatial differentiation of the agglomeration variables within geographic regions. Multiple distance decay profiles are investigated in order to explore how modifying the operationalization of proximity affects indicated patterns of agglomeration externalities and interfirm interactions. The results imply that industry characteristics are at least as important as the type of externality mechanism in determining the spatial pattern of agglomeration benefits. The research methods borrow from earlier work by the author that examines the relationships between regional industrial structure and manufacturing production.
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Measuring Plant Level Energy Efficiency and Technical Change in the U.S. Metal-Based Durable Manufacturing Sector Using Stochastic Frontier Analysis
January 2016
Working Paper Number:
CES-16-52
This study analyzes the electric and thermal energy efficiency for five different metal-based durable manufacturing industries in the United States from 1987-2012 at the 3 digit North American Industry Classification System (NAICS) level. Using confidential plant-level data on energy use and production from the quinquennial U.S. Economic Census, a stochastic frontier regression analysis (SFA) is applied in six repeated cross sections for each five year census. The SFA controls for energy prices and climate-driven energy demand (heating degree days - HDD - and cooling degree days - CDD) due to differences in plant level locations, as well as 6-digit NAICS industry effects. A Malmquist index is used to decompose aggregate plant technical change in energy use into indices of efficiency and frontier (best practice) change. Own energy price elasticities range from -.7 to -1.0, with electricity tending to have slightly higher elasticity than fuel. Mean efficiency estimates (100 percent equals best practice level) range from a low of 32 percent (thermal 334 - Computer and Electronic Products) to a high of 86 percent (electricity 332 - Fabricated Metal Products). Electric efficiency is consistently better than thermal efficiency for all NAICS. There is no clear pattern to the decomposition of aggregate technical Thermal change. In some years efficiency improvement dominates; in other years aggregate technical change is driven by improvement in best practice.
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Who are the people in my neighborhood? The 'contextual fallacy' of measuring individual context with census geographies
February 2018
Working Paper Number:
CES-18-11
Scholars deploy census-based measures of neighborhood context throughout the social sciences and epidemiology. Decades of research confirm that variation in how individuals are aggregated into geographic units to create variables that control for social, economic or political contexts can dramatically alter analyses. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude in the literature and in their own projects. By using confidential access to the complete 2010 U.S. Decennial Census, we are able to construct'for all persons in the US'individual-specific contexts, which we group according to the Census-assigned block, block group, and tract. We compare these individual-specific measures to the published statistics at each scale, and we then determine the magnitude of variation in context for an individual with respect to the published measures using a simple statistic, the standard deviation of individual context (SDIC). For three key measures (percent Black, percent Hispanic, and Entropy'a measure of ethno-racial diversity), we find that block-level Census statistics frequently do not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the contextual variables at all three scales. Finally, we show that within-unit variation is greater in some parts of the country than in others. We publish county-level estimates of the SDIC statistics that enable scholars to assess whether mis-specification in context variables is likely to alter analytic findings when measured at any of the three common Census units.
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CONSTRUCTION OF REGIONAL INPUT-OUTPUT TABLES FROM ESTABLISHMENT-LEVEL MICRODATA: ILLINOIS, 1982
August 1993
Working Paper Number:
CES-93-12
This paper presents a new method for use in the construction of hybrid regional input-output tables, based primarily on individual returns from the Census of Manufactures. Using this method, input- output tables can be completed at a fraction of the cost and time involved in the completion of a full survey table. Special attention is paid to secondary production, a problem often ignored by input-output analysts. A new method to handle secondary production is presented. The method reallocates the amount of secondary production and its associated inputs, on an establishment basis, based on the assumption that the input structure for any given commodity is determined not by the industry in which the commodity was produced, but by the commodity itself -- the commodity-based technology assumption. A biproportional adjustment technique is used to perform the reallocations.
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Business Applications as a Leading Economic Indicator?
May 2021
Working Paper Number:
CES-21-09R
How are applications to start new businesses related to aggregate economic activity? This paper explores the properties of three monthly business application series from the U.S. Census Bureau's Business Formation Statistics as economic indicators: all business applications, business applications that are relatively likely to turn into new employer businesses ('likely employers'), and the residual series -- business applications that have a relatively low rate of becoming employers ('likely non-employers'). Growth in applications for likely employers significantly leads total nonfarm employment growth and has a strong positive correlation with it. Furthermore, growth in applications for likely employers leads growth in most of the monthly Principal Federal Economic Indicators (PFEIs). Motivated by our findings, we estimate a dynamic factor model (DFM) to forecast nonfarm employment growth over a 12-month period using the PFEIs and the likely employers series. The latter improves the model's forecast, especially in the years following the turning points of the Great Recession and the COVID-19 pandemic. Overall, applications for likely employers are a strong leading indicator of monthly PFEIs and aggregate economic activity, whereas applications for likely non-employers provide early information about changes in increasingly prevalent self-employment activity in the U.S. economy.
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A Flexible Test for Agglomeration Economies in Two U.S. Manufacturing Industries
August 2004
Working Paper Number:
CES-04-14
This paper uses the inverse input demand function framework of Kim (1992) to test for economies of industry and urban size in two U.S. manufacturing sectors of differing technology intensity: farm and garden machinery (SIC 352) and measuring and controlling devices (SIC 382). The inverse input demand framework permits the estimation of the production function jointly with a set of cost shares without the imposition of prior economic restrictions. Tests using plant-level data suggest the presence of population scale (urbanization) economies in the moderate- to low-technology farm and garden machinery sector and industry scale (localization) economies in the higher technology measuring and controlling devices sector. The efficiency and generality of the inverse input demand approach are particularly appropriate for micro-level studies of agglomeration economies where prior assumptions regarding homogeneity and homotheticity are less appropriate.
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An Examination of the Informational Value of Self-Reported Innovation Questions
October 2022
Working Paper Number:
CES-22-46
Self-reported innovation measures provide an alternative means for examining the economic performance of firms or regions. While European researchers have been exploiting the data from the Community Innovation Survey for over two decades, uptake of US innovation data has been much slower. This paper uses a restricted innovation survey designed to differentiate incremental innovators from more far-ranging innovators and compares it to responses in the Annual Survey of Entrepreneurs (ASE) and the Business R&D and Innovation Survey (BRDIS) to examine the informational value of these positive innovation measures. The analysis begins by examining the association between the incremental innovation measure in the Rural Establishment Innovation Survey (REIS) and a measure of the inter-industry buying and selling complexity. A parallel analysis using BRDIS and ASE reveals such an association may vary among surveys, providing additional insight on the informational value of various innovation profiles available in self-reported innovation surveys.
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Getting Patents and Economic Data to Speak to Each Other: An 'Algorithmic Links with Probabilities' Approach for Joint Analyses of Patenting and Economic Activity
September 2012
Working Paper Number:
CES-12-16
International technological diffusion is a key determinant of cross-country differences in economic performance. While patents can be a useful proxy for innovation and technological change and diffusion, fully exploiting patent data for such economic analyses requires patents to be tied to measures of economic activity. In this paper, we describe and explore a new algorithmic approach to constructing concordances between the International Patent Classification (IPC) system that organizes patents by technical features and industry classification systems that organize economic data, such as the Standard International Trade Classification (SITC), the International Standard Industrial Classification (ISIC) and the Harmonized System (HS). This 'Algorithmic Links with Probabilities' (ALP) approach incorporates text analysis software and keyword extraction programs and applies them to a comprehensive patent dataset. We compare the results of several ALP concordances to existing technology concordances. Based on these comparisons, we select a preferred ALP approach and discuss advantages of this approach relative to conventional approaches. We conclude with a discussion on some of the possible applications of the concordance and provide a sample analysis that uses our preferred ALP concordance to analyze international patent flows based on trade patterns.
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A Note on the Locational Determinants of the Agricultural Supply Chain
July 2021
Working Paper Number:
CES-21-16
Over the past several decades, an increasing share of the agricultural supply chain is located beyond the farmgate, implying that some set of economic factors are influencing the location decisions of food and agricultural establishments. We explore the location decisions of several food and agricultural industries for employer and non-employer establishments by expanding on the empirical implications of Carpenter et al. (2021)'s demand threshold models. While Carpenter et al. (2021) focus on methods to estimate these industries' demand thresholds using restricted access data, we focus on expanding the interpretations of their empirical research and explore additional industries along the agricultural supply chain using their refined methods. Results highlight the influential role of the Land Grant University system for specific establishment types, the importance of diverse industries within local economies, and the changing rurality of the agricultural supply chain.
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