Using a unique nationally representative sample of U.S. establishements surveyed in 1993 and 1996, we examine the relationship between workplace innovations and establishment productivity and wages. We match plant level practices with plant level productivity and wage outcomes and estimate production functions and wage equation using both cross sectional and longitudinal data. We find a positive and significant relationship between the proportion of non-managers using computers and productivity of establishments. We find that firms that re-engineer their workplaces to incorporate more high performance practices experience higher productivity.
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Prices, Spatial Competition, and Heterogeneous Producers: An Empirical Test
August 2004
Working Paper Number:
CES-04-16
In markets where spatial competition is important, many models predict that average prices are lower in denser markets (i.e., those with more producers per unit area). Homogeneous-producer models attribute this effect solely to lower optimal markups. However, when producers instead differ in their production costs, a second mechanism also acts to lower equilibrium prices: competition-driven selection on costs. Consumers' greater substitution possibilities in denser markets make it more difficult for high-cost firms to profitably operate, truncating the equilibrium cost (and price) distributions from above. This selection process can be empirically distinguished from the homogenous-producer case because it implies that not only do average prices fall as density rises, but that upper-bound prices and price dispersion should also decline as well. I find empirical support for this process using a rich set of price data from U.S. ready-mixed concrete plants. Features of the industry offer an arguably exogenous source of producer density variation with which to identify these effects. I also show that the findings do not simply result from lower factor prices in dense markets, but rather because dense-market producers are low-cost because they are more efficient.
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Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance
February 2020
Working Paper Number:
CES-20-05
This paper furthers a research agenda for modeling populations along spatial networks and expands upon an empirical analysis to a full U.S. county (Gaboardi, 2019, Ch. 1,2). Specific foci are the necessity of, and methods for, validating and benchmarking spatial data when conducting social science research with aggregated and ambiguous population representations. In order to promote the validation of publicly-available data, access to highly-restricted census microdata was requested, and granted, in order to determine the levels of accuracy and error associated with a network-based population modeling framework. Primary findings reinforce the utility of a novel network allocation method'populated polygons to networks (pp2n) in terms of accuracy, computational complexity, and real runtime (Gaboardi, 2019, Ch. 2). Also, a pseudo-benchmark dataset's performance against the true census microdata shows promise in modeling populations along networks.
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Fighting Fire with Fire(fighting Foam): The Long Run Effects of PFAS Use at U.S. Military Installations
December 2024
Working Paper Number:
CES-24-72
Tens of millions of people in the U.S. may be exposed to drinking water contaminated with perand poly-fluoroalkyl chemicals (PFAS). We provide the first estimates of long-run economic costs from a major, early PFAS source: fire-fighting foam. We combine the timing of its adoption with variation in the presence of fire training areas at U.S. military installations in the 1970s to estimate exposure effects for millions of individuals using natality records and restricted administrative data. We document diminished birthweights, college attendance, and earnings, illustrating a pollution externality from military training and unregulated chemicals as a determinant of economic opportunity.
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The Privacy-Protected Gridded Environmental Impacts Frame
December 2024
Working Paper Number:
CES-24-74
This paper introduces the Gridded Environmental Impacts Frame (Gridded EIF), a novel privacy-protected dataset derived from the U.S. Census Bureau's confidential Environmental Impacts Frame (EIF) microdata infrastructure. The EIF combines comprehensive administrative records and survey data on the U.S. population with high-resolution geospatial information on environmental hazards. While access to the EIF is restricted due to the confidential nature of the underlying data, the Gridded EIF offers a broader research community the opportunity to glean insights from the data while preserving confidentiality. We describe the data and privacy protection process, and offer guidance on appropriate usage, presenting practical applications.
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Endogenous Growth and Entrepreneurial Activity in Cities
January 2003
Working Paper Number:
CES-03-02
Recent theories of economic growth have stressed the role of externalities in generating growth. Using data from the Census Bureau that tracks all employers in the whole U.S. private sector economy, we examine the impact of these externalities, as measured by entrepreneurial activity, on employment growth in Local Market Areas. We find that differences in levels of entrepreneurial activity, diversity among geographically proximate industries, and the extent of human capital are positively associated with variation in growth rates, but the manufacturing sector appears to be an exception.
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DO HOUSING PRICES REFLECT ENVIRONMENTAL HEALTH RISKS? EVIDENCE FROM MORE THAN 1600 TOXIC PLANT OPENINGS AND CLOSINGS
April 2013
Working Paper Number:
CES-13-14
A ubiquitous and largely unquestioned assumption in studies of housing markets is that there is perfect information about local amenities. This paper measures the housing market and health impacts of 1,600 openings and closings of industrial plants that emit toxic pollutants. We find that housing values within one mile decrease by 1.5 percent when plants open, and increase by 1.5 percent when plants close. This implies an aggregate loss in housing values per plant of about $1.5 million. While the housing value impacts are concentrated within ' mile, we find statistically significant infant health impacts up to one mile away.
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Trends in Regional Industrial Concentration in the United States
April 2009
Working Paper Number:
CES-09-06
In a seminal article, Benjamin Chinitz (1961) raises the question of the effects that industry size, structure, and economic diversification may have on firm performance and regional economies. His line of inquiry suggests a related but conceptually distinct issue: how does the extent to which a industry is regionally dominated'concentrated locally in a single or small number of firms'impact the local performance of that industry? This question has received little attention, principally because accurately measuring industrial concentration at the regional scale requires firm-level information. This paper makes use of confidential plant- and firm-level manufacturing data to explore patterns of industrial concentration in the United States at the regional scale. Regional analogues of concentration ratios and other measures commonly used in the aspatial industrial organization literature indicate the extent to which manufacturing activity is concentrated in a small number of firms. Both the manufacturing sector as a whole and major manufacturing industry sectors are examined in order to determine the extent of industrial concentration in the continental United States, to explore changes over time in geographic patterns of concentration, and to investigate associations between industrial concentration and employment growth at the regional scale. Implications for understanding regional growth and for devising regional economic development policy are discussed.
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Ready-to-Mix: Horizontal Mergers, Prices, and Productivity
January 2017
Working Paper Number:
CES-17-38
I estimate the price and productivity effects of horizontal mergers in the ready-mix concrete industry using plant and firm-level data from the US Census Bureau. Horizontal mergers involving plants in close proximity are associated with price increases and decreases in output, but also raise productivity at acquired plants. While there is a significant negative relationship between productivity and prices, the rate at which productivity reduces price is modest and the effects of increased market power are not offset. I then present several additional new results of policy interest. For example, mergers are only observed leading to price increases after the relaxation of antitrust standards in the mid-1980s; price increases following mergers are persistent but tend to become smaller over time; and, there is evidence That firms target plants charging below average prices for acquisition. Finally, I use a simple multinomial logit demand model to assess the effects of merger activity on total welfare. At acquired plants, the consumer and producer surplus effects approximately cancel out, but effects at acquiring plants and non-merging plants, where prices also rise, cause a substantial decrease in consumer surplus.
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Fatal Errors: The Mortality Value of Accurate Weather Forecasts
June 2023
Working Paper Number:
CES-23-30
We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
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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