The Role of Technological and Industrial Heterogeneity In Technology Diffusion: a Markovian Approach
February 2003
Working Paper Number:
CES-03-07
Abstract
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.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
demand,
investment,
market,
production,
economist,
estimating,
manufacturing,
industrial,
sale,
technology,
technological,
manufacturer,
product,
sector,
factory,
innovation,
expenditure,
industry heterogeneity,
industries estimate
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Standard Industrial Classification,
National Science Foundation,
Center for Economic Studies,
New York Times,
Survey of Manufacturing Technology
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'The Role of Technological and Industrial Heterogeneity In Technology Diffusion: a Markovian Approach' are listed below in order of similarity.
-
Working PaperAn 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.View Full Paper PDF
-
Working PaperThe 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.View Full Paper PDF
-
Working PaperTechnology 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.View Full Paper PDF
-
Working PaperAdvanced 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.View Full Paper PDF
-
Working PaperData in Action: Data-Driven Decision Making in U.S. Manufacturing
January 2016
Working Paper Number:
CES-16-06
Manufacturing in America has become significantly more data-intensive. We investigate the adoption, performance effects and organizational complementarities of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for 2005 and 2010, we observe the extent to which manufacturing firms track and use data to guide decision making, as well as their investments in information technology (IT) and the use of other structured management practices. Examining a representative sample of over 18,000 plans, we find that adoption of DDD is earlier and more prevalent among larger, older plants belonging to multi-unit firms. Smaller single-establishment firms adopt later but have a higher correlation with performance than similar non-adopters. Using a fixed-effects estimator, we find the average value-added for later DDD adopters to be 3% greater than non-adopters, controlling for other inputs to production. This effect is distinct from that associated with IT and other structured management practices and is concentrated among single-unit firms. Performance improves after plants adopt DDD, but not before ' consistent with a causal relationship. However, DDD-related performance differentials decrease over time for early and late adopters, consistent with firm learning and development of organizational complementarities. Formal complementarity tests suggest that DDD and high levels of IT capital reinforce each other, as do DDD and skilled workers. For some industries, the benefits of DDD adoption appear to be greater for plants that delegate some decision making to frontline workers.View Full Paper PDF
-
Working PaperCogeneration 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.View Full Paper PDF
-
Working PaperThe 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%.View Full Paper PDF
-
Working PaperAn '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.View Full Paper PDF
-
Working PaperIssues and Challenges in Measuring Environmental Expenditures by U.S. Manufacturing: The Redevelopment of the PACE Survey
July 2007
Working Paper Number:
CES-07-20
The Pollution Abatement Costs and Expenditures (PACE) survey is the most comprehensive source of information on U.S. manufacturing's capital expenditures and operating costs associated with pollution abatement. In 2003, the U.S. Environmental Protection Agency began a significant initiative to redevelop the survey, guided by the advice of a multi-disciplinary workgroup consisting of economists, engineers, survey design experts, and experienced data users, in addition to incorporating feedback from key manufacturing industries. This paper describes some of these redevelopment efforts. Issues discussed include the approach to developing the new survey instrument, methods used to evaluate (and improve) its performance, innovations in sampling, and the special development and role of outside expertise. The completely redesigned PACE survey was first administered in early 2006.View Full Paper PDF
-
Working PaperThe Adoption and Diffusion of Organizational Innovation: Evidence for the U.S. Economy
June 2007
Working Paper Number:
CES-07-18
Using a unique longitudinal representative survey of both manufacturing and nonmanufacturing businesses in the United States during the 1990's, I examine the incidence and intensity of organizational innovation and the factors associated with investments in organizational innovation. Past profits tend to be positively associated with organizational innovation. Employers with a more external focus and broader networks to learn about best practices (as proxied by exports, benchmarking, and being part of a multi-establishment firm) are more likely to invest in organizational innovation. Investments in human capital, information technology, R&D, and physical capital appear to be complementary with investments in organizational innovation. In addition, nonunionized manufacturing plants are more likely to have invested more broadly and intensely in organizational innovation.View Full Paper PDF
