Recent empirical studies have established the importance of intra and inter-industry heterogeneity in investment in innovation and other outcomes. This paper examines the role of industry and technology heterogeneity in the diffusion of advanced manufacturing technologies from a simple Markovian approach. Using the Maximum Entropy estimator, I estimate transition probabilities and corresponding half-lives, look for outliers in technology and industry diffusion patterns, and try to find explanations of their unusual behavior in idiosyncratic technology and industry characteristics. A consistent industry-level pattern that emerged is one that relates consumer demand and production processes. It seems that in industries where hand-made products are a sign of quality to the customer, technology spreads very slowly. On the other hand, in industries where demand for sophisticated, high-precision goods is high or in industries where demand-driven product specifications vary quite rapidly over relatively short periods of time, advanced technologies diffuse much more rapidly.
    
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        Technology Usage in U.S. Manufacturing Industries: New Evidence from the Survey of Manufacturing Technology
        
        October 1991
        
        
            
                Working Paper Number:
            CES-91-07
        
        
            
            Using a new dataset on technology usage in U.S. manufacturing plants, this paper describes how technology usage varies by plant and firm characteristics. The paper extends the previous literature in three important ways. First, it examines a wide range of relatively new technologies. Second, the paper uses a much larger and more representative set of firms and establishments than previous studies. Finally, the paper explores the role of firm R&D expenditures in the process of technology adoption. The main findings indicate that larger plants more readily use new technologies, plants owned by firms with high R&D-to-sales ratios adopt technologies more rapidly, and the relationship between plant age and technology usage is relatively weak.
        
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        An Option-Value Approach to Technology in U.S. Maufacturing: Evidence from Plant-Level Data
        
        July 2000
        
        
            
                Working Paper Number:
            CES-00-12
        
        
            
            Numerous empirical studies have examined the role of firm and industry heterogeneity in the decision to adopt new technologies using a Net Present Value framework. However, as suggested by the recently developed option-value theory, these studies may have overlooked the role of investment reversibility and uncertainty as important determinants of technology adoption. Using the option-value investment model as my underlying theoretical framework, I examine how these two factors affect the decision to adopt three advanced manufacturing technologies. My results support the option-value model's prediction that plants operating in industries facing higher investment reversibility and lower degrees of demand and technological uncertainty are more likely to adopt advanced manufacturing technologies.
        
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        The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing
        
        June 2020
        
        
            
                Working Paper Number:
            CES-20-18
        
        
            
            This paper estimates stochastic frontier energy demand functions with non-public, plant-level data from the U.S. Census Bureau to measure the energy efficiency gap and energy price elasticities in the food processing industry. The estimates are for electricity and fuel use in 4 food processing sectors, based on the disaggregation of this industry used by the National Energy Modeling System Industrial Demand Module. The estimated demand functions control for plant inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. Own price elasticities range from  0.6 to -0.9 with little evidence of fuel/electricity substitution. The magnitude of the efficiency estimates is sensitive to the assumptions but consistently reveal that few plants achieve 100% efficiency. Defining a 'practical level of energy efficiency' as the 95th percentile of the efficiency distributions and averaging across all the models result in a ~20% efficiency gap. However, most of the potential reductions in energy use from closing this efficiency gap are from plants that are 'low hanging fruit'; 13% of the 20% potential reduction in the efficiency gap can be obtained by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industry; ranging from a low of 0.4% to a high of 5.7%.
        
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        Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey
        
        April 2022
        
            Authors:
            
            
            
            
John Haltiwanger,
            
            
            
            
            
Lucia Foster,
            
            
            
            
            
Emin Dinlersoz,
            
            
            
            
            
Nikolas Zolas,
            
            
            
            
            
Daron Acemoglu,
            
            
            
            
            
Catherine Buffington,
            
            
            
            
            
Nathan Goldschlag,
            
            
            
            
            
Zachary Kroff,
            
            
            
            
            
David Beede,
            
            
            
            
            
Gary Anderson,
            
            
            
            
            
Eric Childress,
            
            
            
            
            
Pascual Restrepo
            
         
        
            
                Working Paper Number:
            CES-22-12R
        
        
            
            This paper describes the adoption of automation technologies by US firms across all economic sectors by leveraging a new module introduced in the 2019 Annual Business Survey, conducted by the US Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES). The module collects data from over 300,000 firms on the use of five advanced technologies: AI, robotics, dedicated equipment, specialized software, and cloud computing. The adoption of these technologies remains low (especially for AI and robotics), varies substantially across industries, and concentrates on large and young firms. However, because larger firms are much more likely to adopt them, 12-64% of US workers and 22-72% of manufacturing workers are exposed to these technologies. Firms report a variety of motivations for adoption, including automating tasks previously performed by labor. Consistent with the use of these technologies for automation, adopters have higher labor productivity and lower labor shares. In particular, the use of these technologies is associated with a 11.4% higher labor productivity, which accounts for 20'30% of the difference in labor productivity between large firms and the median firm in an industry. Adopters report that these technologies raised skill requirements and led to greater demand for skilled labor, but brought limited or ambiguous effects to their employment levels.
        
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        The Effect Of Technology Use On Productivity Growth
        
        April 1996
        
        
            
                Working Paper Number:
            CES-96-02
        
        
            
            This paper examines the relationship between the use of advanced technologies and productivity and productivity growth rates. We use data from the 1993 and 1988 Survey of Manufacturing Technology (SMT) to examine the use of advanced (computer based) technologies at two different points in time. We are also able to combine the survey data with the Longitudinal Research Database (LRD) to examine the relationships between plant performance, plant characteristics, and the use of advanced technologies. In addition, a subset of these plants were surveyed in both years, enabling us to directly associate changes in technology use with changes in plant productivity performance. The main findings of the study are as follows. First, diffusion is not the same across the surveyed technologies. Second, the adoption process is not smooth: plants added and dropped technologies over the six-year interval 1988-93. In fact, the average plant showed a gross change of roughly four technologies in achieving an average net increase of less than one new technology. In this regard, technology appears to be an experience good: plants experiment with particular technologies before deciding to add additional units or drop the technology entirely. We find that establishments that use advanced technologies exhibit higher productivity. This relationship is observed in both 1988 and 1993 even after accounting for other important factors associated with productivity: size, age, capital intensity, labor skill mix, and other controls for plant characteristics such as industry and region. In addition, the relationship between productivity and advanced technology use is observed both in the extent of technologies used and the intensity of their use. Finally, while there is some evidence that the use of advanced technologies is positively related to improved productivity performance, the data suggest that the dominant explanation for the observed cross-section relationship is that good performers are more likely to use advanced technologies than poorly performing operations.
        
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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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        Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
        
        December 2020
        
        
            
                Working Paper Number:
            CES-20-40
        
        
            
            We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
        
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        Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
        
        March 2024
        
        
            
                Working Paper Number:
            CES-24-16R
        
        
            
            Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.
        
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        Cogeneration Technology Adoption in the U.S.
        
        January 2016
        
        
            
                Working Paper Number:
            CES-16-30
        
        
            
            Well over half of all electricity generated in recent years in Denmark is through cogeneration. In U.S., however, this number is only roughly eight percent. While both the federal and state governments provided regulatory incentives for more cogeneration adoption, the capacity added in the past five years have been the lowest since late 1970s. My goal is to first understand what are and their relative importance of the factors that drive cogeneration technology adoption, with an emphasis on estimating the elasticity of adoption with respect to relative energy input prices and regulatory factors. Very preliminary results show that with a 1 cent increase in purchased electricity price from 6 cents (roughly current average) to 7 cents per kwh, the likelihood of cogeneration technology adoption goes up by about 0.7-1 percent. Then I will try to address the general equilibrium effect of cogeneration adoption in the electricity generation sector as a whole and potentially estimate some key parameters that the social planner would need to determine the optimal cogeneration investment amount. Partial equilibrium setting does not consider the decrease in investment in the utilities sector when facing competition from the distributed electricity generators, and therefore ignore the effects from the change in equilibrium price of electricity. The competitive market equilibrium setting does not consider the externality in the reduction of CO2 emissions, and leads to socially sub-optimal investment in cogeneration. If we were to achieve the national goal to increase cogeneration capacity half of the current capacity by 2020, the US Department of Energy (DOE) estimated an annual reduction of 150 million metric tons of CO2 annually ' equivalent to the emissions from over 25 million cars. This is about five times the annual carbon reduction from deregulation and consolidation in the US nuclear power industry (Davis, Wolfram 2012). Although the DOE estimates could be an overly optimistic estimate, it nonetheless suggests the large potential in the adoption of cogeneration technology.
        
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        Linking Investment Spikes and Productivity Growth: U.S. Food Manufacturing Industry
        
        October 2008
        
        
            
                Working Paper Number:
            CES-08-36
        
        
            
            We investigate the relationship between productivity growth and investment spikes using Census Bureau's plant-level data set for the U.S. food manufacturing industry. We find that productivity growth increases after investment spikes suggesting an efficiency gain or plants' learning effect. However, efficiency and the learning period associated with investment spikes differ among plants' productivity quartile ranks implying the differences in the plants' investment types such as expansionary, replacement or retooling. We find evidence of both convex and non-convex types of adjustment costs where lumpy plant-level investments suggest the possibility of non-convex adjustment costs and hazard estimation results suggest the possibility of convex adjustment costs. The downward sloping hazard can be due to the unobserved heterogeneity across plants such as plants' idiosyncratic obsolescence caused by different R&D capabilities and implies the existence of convex adjustment costs. Food plants frequently invest during their first few years of operation and high productivity plants postpone investing due to high fixed costs.
        
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