There is a widely held perception that improved supply chain practices and new technologies have led to declines in the inventory-sales ratio. Our empirical analyses of 87 inventory-sales ratios in 45 manufacturing, wholesale distribution, and retail trade industries casts doubt on assumptions of widespread declines in these ratios. We find that less than half of the ratios showed statistically significant declines during the 12 year period from January 1992 through December 2003. Information technology may indeed have improved inventory management, but this improvement is not reflected in inventory-sales ratio data for many U.S. industries. Our detailed case study of the pharmaceutical supply chain also offers additional insights by showing how relevant technological investments led to an extended period in which inventory-to-sales ratios increased.
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IT for Information-Based Partnerships: Empirical Analysis of Environmental Contingencies to Value Co-Creation
December 2009
Working Paper Number:
CES-09-42
We empirically examine IT value co-creation in supply chains, incorporating key contingencies of the competitive environment. Prior research suggests that IT used for strategic informationbased partnerships may benefit supply chains facing higher volatility, enabling tightly coupled integration and enhanced strategic response to changing consumer preferences. Analyzing a unique dataset comprising over 6,000 U.S. manufacturing plants, we obtain three principal results. First, value co-creation using either IT for strategic information-based partnerships (ITIP) or merely IT for transaction efficiency (ITT) is positive and significant. Second, the co-created value from ITIP is larger than that for (ITT), suggesting that information-based partnerships, while perhaps requiring a greater investment, yield a higher return. Third and most importantly, co-created value from using IT for information-based partnerships is positively moderated by demand volatility, i.e., value is greater in higher demand volatility environments. However, we find the opposite is true for using IT for efficient transactions. This is a new contribution to the literature and has important theoretical and practical implications.
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The Diffusion of Modern Manufacturing Practices: Evidence from Retail-Apparel Sectors
February 1997
Working Paper Number:
CES-97-11
As in many industries, firms in the apparel industry exhibit substantial heterogeneity in the adoption of "modern manufacturing" practices. Based on detailed business-unit level data, we show that this heterogeneity can be explained firm inputs. We show that the interaction between these explanatory factors means that complementarities between inputs may emerge over time rather than all at once as is often assumed in other studies of complementarities.
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E-Tailing and its Prospects: Great Expectations Reconsidered
July 2006
Working Paper Number:
CES-06-16
This paper attributes slower than predicted growth in e-commerce retailing to four factors: consumer resistance; the ability of traditional retailers to become multi-channel sellers; prudent official survey and classification practices; and perhaps the limited range of 'pure-play' business models (i.e., retail models that rely mainly on electronic sales). Based on responses to the Census Bureau's Monthly Retail Trade Survey (MRTS) in the five fourth quarter periods from 2001 to 2005, the paper finds that e-commerce has claimed a small but rapidly growing share of U.S. retailing markets; and that pure play companies are still important drivers of this process. However, it also finds that the capacity of pure-play companies to continue in this role may be nearing its limits, and that the rate of continued growth in e-commerce retailing may depend on the business decisions of large, multi-channel sellers. Qualified researchers can access MRTS-based quarterly e-commerce data for 2001-2005 at the Census Bureau's Regional Data Centers.
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Concentration Levels in the U.S. Advertising and Marketing Services Industry: Myth vs. Reality
August 2009
Working Paper Number:
CES-09-16
We analyze changes in concentration levels in the U.S. Advertising and Marketing Services industry using data from the U.S. Census Bureau's quinquennial Economic Census and the Service Annual Survey. Heretofore largely ignored, these data allow us to redress some of the measurement problems surrounding estimates found in the existing literature Firm level concentration as measured by the Herfindahl-Hirschman Index varies across the sectors comprising the industry, but all are within the range generally considered as indicative of a competitive industry. At the holding company level, the four largest organizations account for about a quarter of the industry's total revenue, a share lower by an order of magnitude than that frequently cited in the trade press.
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Measuring the Impact of COVID-19 on Businesses and People: Lessons from the Census Bureau's Experience
January 2021
Working Paper Number:
CES-21-02
We provide an overview of Census Bureau activities to enhance the consistency, timeliness, and relevance of our data products in response to the COVID-19 pandemic. We highlight new data products designed to provide timely and granular information on the pandemic's impact: the Small Business Pulse Survey, weekly Business Formation Statistics, the Household Pulse Survey, and Community Resilience Estimates. We describe pandemic-related content introduced to existing surveys such as the Annual Business Survey and the Current Population Survey. We discuss adaptations to ensure the continuity and consistency of existing data products such as principal economic indicators and the American Community Survey.
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Measuring the Electronic Economy: Current Status and Next Steps
June 2000
Working Paper Number:
CES-00-10
The recent growth of consumer retailing over the Internet draws attention to the electronic economy. However, businesses also conduct other business processes over computer networks, and many have been doing so for some time. Uses of computer networks attract attention because of assertions that they lead to new products and services, new delivery methods, streamlined or re-engineered business processes, new business structures, and enhanced business performance. These changes, in turn, potentially affect the performance of the entire economy, including economic growth, productivity, prices, employment, trade, and the structures of businesses, regions, and markets. Evaluating these assertions, and their effects on economic performance, requires solid statistical information about the electronic economy. This paper develops principles for identifying information critical to measuring the size and evaluating the potential effects of the electronic economy, relates that information to current data collection programs, and notes relevant measurement issues. Some of the required information about the electronic economy can be collected by adding questions to existing surveys, making the scope of existing surveys consistent, or developing new surveys. However, many key pieces of information pose significant challenges to economic measurement. While some of those challenges are specific to the electronic economy, others are long-standing ones. Interest in the electronic economy highlights the importance of continuing attempts to address these challenges. Improving and enhancing the statistical system to provide information about the electronic economy, therefore, would also substantially improve the baseline information available for evaluating the performance of the entire economy.
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Re-engineering Key National Economic Indicators
July 2019
Working Paper Number:
CES-19-22
Traditional methods of collecting data from businesses and households face increasing challenges. These include declining response rates to surveys, increasing costs to traditional modes of data collection, and the difficulty of keeping pace with rapid changes in the economy. The digitization of virtually all market transactions offers the potential for re-engineering key national economic indicators. The challenge for the statistical system is how to operate in this data-rich environment. This paper focuses on the opportunities for collecting item-level data at the source and constructing key indicators using measurement methods consistent with such a data infrastructure. Ubiquitous digitization of transactions allows price and quantity be collected or aggregated simultaneously at the source. This new architecture for economic statistics creates challenges arising from the rapid change in items sold. The paper explores some recently proposed techniques for estimating price and quantity indices in large scale item-level data. Although those methods display tremendous promise, substantially more research is necessary before they will be ready to serve as the basis for the official economic statistics. Finally, the paper addresses implications for building national statistics from transactions for data collection and for the capabilities and organization of the statistical agencies in the 21st century.
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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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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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An 'Algorithmic Links with Probabilities' Crosswalk for USPC and CPC Patent Classifications with an Application Towards Industrial Technology Composition
March 2016
Working Paper Number:
CES-16-15
Patents are a useful proxy for innovation, technological change, and diffusion. However, fully exploiting patent data for economic analyses requires patents be tied to measures of economic activity, which has proven to be difficult. Recently, Lybbert and Zolas (2014) have constructed an International Patent Classification (IPC) to industry classification crosswalk using an 'Algorithmic Links with Probabilities' approach. In this paper, we utilize a similar approach and apply it to new patent classification schemes, the U.S. Patent Classification (USPC) system and Cooperative Patent Classification (CPC) system. The resulting USPC-Industry and CPC-Industry concordances link both U.S. and global patents to multiple vintages of the North American Industrial Classification System (NAICS), International Standard Industrial Classification (ISIC), Harmonized System (HS) and Standard International Trade Classification (SITC). We then use the crosswalk to highlight changes to industrial technology composition over time. We find suggestive evidence of strong persistence in the association between technologies and industries over time.
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