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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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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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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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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How Does State-Level Carbon Pricing in the United States Affect Industrial Competitiveness?
June 2020
Working Paper Number:
CES-20-21
Pricing carbon emissions from an individual jurisdiction may harm the competitiveness of local firms, causing the leakage of emissions and economic activity to other regions. Past research concentrates on national carbon prices, but the impacts of subnational carbon prices could be more severe due to the openness of regional economies. We specify a flexible model to capture competition between a plant in a state with electric sector carbon pricing and plants in other states or countries without such pricing. Treating energy prices as a proxy for carbon prices, we estimate model parameters using confidential plant-level Census data, 1982'2011. We simulate the effects on manufacturing output and employment of carbon prices covering the Regional Greenhouse Gas Initiative (RGGI) in the Northeast and Mid-Atlantic regions. A carbon price of $10 per metric ton on electricity output reduces employment in the regulated region by 2.7 percent, and raises employment in nearby states by 0.8 percent, although these estimates do not account for revenue recycling in the RGGI region that could mitigate these employment changes. The effects on output are broadly similar. National employment falls just 0.1 percent, suggesting that domestic plants in other states as opposed to foreign facilities are the principal winners from state or regional carbon pricing.
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Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes
August 2016
Working Paper Number:
carra-2016-06
While commercial data sources offer promise to statistical agencies for use in production of official statistics, challenges can arise as the data are not collected for statistical purposes. This paper evaluates the use of 2008-2010 property tax data from CoreLogic, Inc. (CoreLogic), aggregated from county and township governments from around the country, to improve 2010 American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research evaluates the potential to use CoreLogic to reduce respondent burden, to study survey response error and to improve adjustments for survey nonresponse. The research found that the coverage of the CoreLogic data varies between counties as does the correspondence between ACS and CoreLogic property taxes. This geographic variation implies that different approaches toward using CoreLogic are needed in different areas of the country. Further, large differences between CoreLogic and ACS property taxes in certain counties seem to be due to conceptual differences between what is collected in the two data sources. The research examines three counties, Clark County, NV, Philadelphia County, PA and St. Louis County, MO, and compares how estimates would change with different approaches using the CoreLogic data. Mean county property tax estimates are highly sensitive to whether ACS or CoreLogic data are used to construct estimates. Using CoreLogic data in imputation modeling for nonresponse adjustment of ACS estimates modestly improves the predictive power of imputation models, although estimates of county property taxes and property taxes by mortgage status are not very sensitive to the imputation method.
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Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry
April 2018
Working Paper Number:
CES-18-16
This paper addresses the relative effectiveness of market vs program based climate policies. We compute the carbon price resulting in an equivalent reduction in energy from programs that eliminate the efficiency gap. A reduced-form stochastic frontier energy demand analysis of plant level electricity and fuel data, from energy-intensive chemical sectors, jointly estimates the distribution of energy efficiency and underlying price elasticities. The analysis controls for plant level price endogeneity and heterogeneity to obtain a decomposition of efficiency into persistent (PE) and time-varying (TVE) components. Total inefficiency is relatively small and price elasticities are relatively high. If all plants performed at the 90th percentile of their efficiency distribution, the reduction in energy is between 4% and 13%. A modest carbon price of between $9.48/ton and $14.01/ton CO2 would achieve reductions in energy use equivalent to all manufacturing plants making improvements to close the efficiency gap.
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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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Spatial Organization of Firms: Internal and External Agglomeration Economies and Location Choices Through the Value Chain
September 2012
Working Paper Number:
CES-12-33
We explore the impact of geographically bounded intra-firm spillovers (internal agglomeration economies) and geographically bounded inter-firm spillovers (external agglomeration economies) on firms' location strategies. Using data from the Census Bureau's Longitudinal Business Database and the U.S. Cluster Mapping Project, we analyze organic expansions of biopharmaceutical firms (by both new establishments and employment increase in existing establishments) in the U.S. in 1993-2005. We consider all activities in the value chain and allow location choices to vary by R&D, manufacturing, and sales. Our findings suggest that (1) internal and external agglomeration economies have separate, positive impacts on location, with relevant differences by activity; (2) internal economies of agglomeration arise within an activity (e.g., among plants) and across activities (e.g., between manufacturing and sales); (3) the effects of internal economies across and within activities vary by activity and type of organic expansion; and (4) across-activity internal economies are asymmetric.
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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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