Catálogo de publicaciones - libros

The purpose of this paper is to provide a link between two of the seminal contributions of Edwin Mansfield. The first focuses on the determinants of firm growth and the second is concerned with university-based knowledge spillovers. By linking both firm-specific characteristics as well as access to knowledge spillovers from universities, the empirical evidence found in this paper suggests that knowledge spillovers as well as firm-specific characteristics influence firm growth.

The trail I want to follow leads from Stigler to Mansfield with few, if any, intermediate stops. That should sound like a pretty good genealogy.

This chapter traces the idea that technological change is endogenous to microeconomic roots considerably earlier than those emphasized in the “new” macroeconomic theories of economic growth. Building upon contributions by Richard Nelson, Jacob Schmookler, F.M. Scherer, and Yoram Barzel, it presents a lean model of how incentives for technological innovation arise endogenously from the interplay of changes in knowledge and demand. Paradoxes attributable to monopoly, parallel but independent technical initiatives, uncertainty, and the divergence between social and private benefits are resolved. A simulation analysis explores the implications of skew stochastic payoff distributions for the optimal number of R&D approaches.

This paper discusses the foundational work and ideas of Edwin Mansfield to the economics of technological change and innovation, and introduces some of the recent work in the field. I argue that much of the recent work on patenting, technology strategy and the economics of knowledge has roots to the early Mansfield contributions, and that he should be recognized as a pioneer for these recent developments.

Innovation by users is now understood to be an important part of innovative activity in the economy. In this paper we explore the implications of adding innovation by users to existing models of social welfare that currently assume innovation by manufacturers only. We find this addition removes several inefficiencies, and that social welfare is likely to be increased by the presence of user innovation. Implications for policies that can impact users’ freedom to innovate are discussed.

Although underinvestment phenomena are the rationale for government subsidization of research and development (R&D), the concept is poorly defined and its impact is seldom quantified. Conceptually, underinvestment in industrial R&D can take the form of either a wrong amount or a suboptimal composition of R&D investment. In both cases, R&D policy has not adequately modeled the relevant economic phenomena and thus is unable to characterize, explain, and measure the underinvestment. Four factors can cause systematic underinvestment in R&D-intensive industries: complexity, timing, existence of economies of scale and scope, and spillovers. The impacts of these factors vary in intensity over the typical technology life cycle, so government policy responses must be managed dynamically. In addition to understanding the causes of underinvestment in R&D, the magnitude of the deficiency relative to some “optimum” must be estimated to enable a ranking of technology areas with respect to expected net economic benefits from a government subsidy. Project selection criteria must therefore be based on quantitative and qualitative indicators that represent the nature and the magnitude of identified market failures. The major requirement for management of R&D policy therefore is a methodology that regularly assesses long-term expected benefits and risks from current and proposed R&D portfolios. To this end, a three-stage process is proposed to effectively carry out R&D policy analysis. The three stages are (1) identify and explain the causes of the underinvestment, (2) characterize and assess the investment trends and their impacts, and (3) estimate the magnitude of the underinvestment relative to a perceived optimum in terms of its cost to the economy. Only after all three stages of analysis have been completed can the underinvestment pattern be matched with the appropriate policy response.

Griliches (1958) [, 66: 419–131] and Mansfield (1977) [, 91: 221–240] pioneered the application of fundamental economic insight to the development of measurements of private and social rates of return to innovative investments. This paper illustrates field-based methods for measuring the social rates of return to innovative investments by the public sector. The case study described herein relates to the development of an improved standard reference material for the measurement of the wavelength of light in an optical fiber network.

This paper provides an overview of the survey-based literature on industrial Research and Development (R&D) laboratories, beginning with the work of Edwin Mansfield. Topics covered include R&D projects, new products, and new processes; the appropriability of intellectual property; the limits of the firm in R&D; and spillovers of knowledge from other firms and universities into the laboratories. I discuss the value of collecting information from industrial R&D managers, who participate in a wide range of R&D decisions and are the natural best source of information on these decisions. I also emphasize gaps in our knowledge concerning R&D from past studies, such as the private and social returns to R&D, the nature of firms’ R&D portfolios, and other topics. The paper closes with a discussion of the benefits from building a national database on R&D laboratories that could be shared among researchers and that could take this area of research to a new and higher level of achievement.

The paper evaluates several potential public policies to increase research and development (R&D) investments with the goal of introducing innovations to reduce harmful industrial emissions. The policies—new pre-innovation taxes, more stringent emissions regulations, promotion of cooperative R&D, promotion of outside financing from other companies and from the public, diplomacy to encourage emissions regulations that are more uniformly stringent worldwide, promoting dynamic competition with appropriate antitrust policy, and promoting the dissemination of fundamental knowledge about emissions and of licensable emissions-reducing technology—are evaluated by using primary data from US industry to estimate a model of the environmental R&D investments of industry.

This paper compares the contribution of R&D to innovation in terms of the various innovation output measures provided by the third Community Innovation Survey (CIS 3) for French manufacturing firms and in terms of accounting for interindustry innovation differences.