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A General Inter-Industry Relatedness Index

December 2006

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  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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  Authors: Robert H Mcguckin, Sang V Nguyen, Stephen H Andrews

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  This study develops detailed information on the relationships among the activities of acquiring and acquired firms at and near the time of merger for a sample of 94 takeovers undertaken between 1977-1982. We focus on takeovers for two reasons. First, takeovers are an important and controversial phenomenon. Second, takeovers allow us to look at marginal changes, admittedly large ones, in the firm's boundaries. Thus, they provide a useful way of examining relationships among activities of the firm without having to go into great detail regarding the historical decisions that generated the firm's current structure. While the individual establishment is our basic data unit, in this study we aggregate the activities of the firm to the line of business (LOB) level. Each LOB of an acquired firm is classified as to its relationship horizontal, vertical (upstream or downstream), and conglomerate to the LOBs of the acquiring firm. Using these categorizations we aggregate the LOB-level information to the firm level to investigate the degree to which our sample of mergers is specialized to particular types of relationships. While we find a significant group of unspecialized takeovers, most appear to fit a specific category. We also look at the pattern of closed operations immediately following the takeover. Closings are generally concentrated in operations involving horizontal relationships. Finally, we consider the pattern of relationships between hostile and friendly takeovers and whether takeover premiums vary by type of merger. Merger premiums are not related to the type of relationship between the acquiring and acquired firm, but they are tied to whether the takeover is friendly or hostile.
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• Working Paper

  
  
  Clusters, Convergence, and Economic Performance
  

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  Authors: Mercedes Delgado, Michael Porter, Scott Stern

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• Working Paper

  
  
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• Working Paper

  
  
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  CES-01-13

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• Working Paper

  
  
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  Working Paper Number:

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• Working Paper

  
  
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  November 2006

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  CES-06-26

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• Working Paper

  
  
  Exploring New Ways to Classify Industries for Energy Analysis and Modeling
  

  November 2022

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  CES-22-49

  Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand. First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation. The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations. Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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• Working Paper

  
  
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  September 2012

  Authors: Travis J. Lybbert, Nikolas Zolas

  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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• Working Paper

  
  
  Hiring through Startup Acquisitions: Preference Mismatch and Employee Departures
  

  September 2018

  Authors: J. Daniel Kim

  Working Paper Number:

  CES-18-41

  This paper investigates the effectiveness of startup acquisitions as a hiring strategy. Unlike conventional hires who choose to join a new firm on their own volition, most acquired employees do not have a voice in the decision to be acquired, much less by whom to be acquired. The lack of worker agency may result in a preference mismatch between the acquired employees and the acquiring firm, leading to elevated rates of turnover. Using comprehensive employee-employer matched data from the US Census, I document that acquired workers are significantly more likely to leave compared to regular hires. By constructing a novel peer-based proxy for worker preferences, I show that acquired employees who prefer to work for startups ' rather than established firms ' are the most likely to leave after the acquisition, lending support to the preference mismatch theory. Moreover, these departures suggest a deeper strategic cost of competitive spawning: upon leaving, acquired workers are more likely to found their own companies, many of which appear to be competitive threats that impair the acquirer's long-run performance.
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