Grantee Research Project Results
1999 Progress Report: Environmental Policy and Endogenous Technical Change: A Theoretical & Empirical Analysis
EPA Grant Number: R826610Title: Environmental Policy and Endogenous Technical Change: A Theoretical & Empirical Analysis
Investigators: Opaluch, James J. , Grigalunas, Thomas A. , Jin, Di
Institution: University of Rhode Island
Current Institution: University of Rhode Island , Woods Hole Oceanographic Institution
EPA Project Officer: Chung, Serena
Project Period: October 1, 1998 through September 30, 2001
Project Period Covered by this Report: October 1, 1998 through September 30, 1999
Project Amount: $325,000
RFA: Decision-Making and Valuation for Environmental Policy (1998) RFA Text | Recipients Lists
Research Category: Environmental Justice
Objective:
This project will develop a deeper understanding of relationship between productivity change and environmental policy that considers environmental inputs into the production process. A case study of OCS oil will be used to measure productivity change, accounting for environmental inputs. The study will compare engineering estimates of the costs of complying with environmental regulations to ex post performance, that includes innovative firm responses, like process change. The study also will estimate potential cost savings of policies that increase flexibility and encourage innovation.Progress Summary:
The first year of the project has involved four efforts: a literature review, conceptual modeling, data collection and identifying a historical timelines. Each of these is discussed below.Literature Review. We have carried out a careful literature review of recent papers on endogenous technical change. We have identified several new and highly relevant papers that were published since our proposal. The advances embodied in these papers have been incorporated into our conceptual modeling, which is discussed in the next section.
Conceptual Modeling. We have developed a simplified model of technical progress as a random process, where the time to discovery depends upon investments in research and development activities. Also, the "size" of the discovery is a random variable, where size is taken as an indicator of the degree productivity change. A "large" discovery means that the cost of producing a given output is decreased substantially, or viewed from the dual perspective, the quantity produced increases substantially for a given set of inputs. Similarly, a "small" discovery will lead to a small reduction in the cost of producing a given level of output, or the quantity produced increases marginally with output held fixed.
This framework allows us a potential test for our proposed hypothesis of technical progress as a series of minor changes in production processes, versus the discovery of discrete new technologies. One would expect that if the primary source of technical progress is subtle changes in production processes and "learning by doing," that technical progress should be dominated by frequent, but marginal discoveries. In contrast, if the primary source of technical progress is major discoveries, then technical progress should appear as infrequent, but major jumps in productivity.
This should show up as we fit our model to the data. We propose to estimate a technical progress production function by estimating parameters of the two statistical distributions for technical change: a count data model (e.g., a poisson or negative binomial) of new discoveries, plus a continuous distribution (e.g., a gamma distribution) on the degree of technical progress embodied in a particular discovery. Given these two estimated distributions, we can provide a measure of the extent to which technical progress is driven primarily by a relatively small number of new discoveries, or large discrete jumps in technology, versus a multitude of refinements, or minor incremental steps forward.
Several unresolved issues still remain to be explored, including: (1) the empirical specification and estimation of the production function for technical change; and (2) the design of a test or a measure that gets at the issue of many small refinements, versus a few large discoveries. More fundamentally, we need to identify possible interactions between the two types of advances. For example, there could be a small number of large technical advances spread over a significant period of time. The new discovery in itself might not engender a huge leap forward, but might be subject to a series of refinements over the intervening period. However, refinements of an existing technology might have diminishing returns. In this case, each successive new discovery not only results in a direct improvement in technology, but also opens the door to a series of technical refinements, which would not have been possible without the new advance. For example, when the automobile was initially invented, it did not give rise to a major advance in capabilities beyond the horse. However, refinements in the automobile have progressed to the point where the horse is no longer a reasonable substitute for an automobile. At the same time, the refinements that made the automobile superior are not at all applicable to improving transportation technology based on the horse. So, it may be that the new discovery engenders a major improvement in productivity only because of refinements, and at the same time the refinements are possible only because of the new discovery. This may imply that it is difficult, and perhaps not at all meaningful, to distinguish between technical development in the form of new discoveries, versus refinements of technology.
Data Collection. We have identified and obtained a substantial amount of data. We have collected outputs and inputs for each well in the Gulf of Mexico over the period 1947 to 1995. This is a huge data set. We have determined that the most sensible unit of production for OCS oil is the field level. We have been developing SAS programs to aggregate the data to the field level. The objective of this effort is to develop a new database for our estimation needs and our later simulations. We also are considering whether analysis should more appropriately focus on production of oil and gas from given fields, or whether the proper unit of analysis is discovery of fields. We plan to pursue both of these avenues to identify the more conceptually appropriate and empirically promising.
Historic Timelines. The objective is to construct a time profile of environmental regulatory activities and on identifiable changes in technology. These timelines will provide important inputs into our empirical analysis. For example, a timeline on discrete, identifiable new technologies will allow us to relate technical progress to particular new discoveries.
We have developed a draft timeline of environmental regulations on OCS oil production by reviewing some of our past work?Federal Register, API reports, ICF reports, and online legal database. We have identified several studies of OCS production technologies, including environmental technologies, and are in the process of developing a timeline on new discoveries.
Future Activities:
In the next year, we will complete data collection and further develop the theoretical model.Journal Articles:
No journal articles submitted with this report: View all 8 publications for this projectSupplemental Keywords:
petroleum, productivity, innovative technology, economics, technical change, offshore oil., RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Economics, decision-making, Engineering, Economics & Decision Making, ecosystem valuation, technical innovation, decision making, economic benefits, endogenous technical change, offshore oil, cost benefit, economic incentives, empirical analysis, environmental policy, theoretical analysis, compliance costs, innovative pollution control, benefits assessmentRelevant Websites:
http://www.uri.edu/cels/enre/Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.