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Many policies or programs surrounding us today, from highway safety policies, innovation or science and technology policies to privatization policies or child wellbeing programs are implemented or reiterated without a sufficiently thorough and systematic analysis of their impact. Understandably, using comprehensive public policy analysis methodologies is not always a welcome or affordable option. However, in those instances when the use of such methods is sought, as well as in many other instances, two extremely powerful and yet not so often used methodological tools can be of tremendous value: time series based analyses and pragmatic eliminative induction enhanced quasiexperimental inquiry (QEI). The Quasi-experimental tradition underscores the importance of identifying and eliminating threats to validity from our analysis based on quasiexperimental settings. A very new and not so well known development in this tradition is the addition of context validation (elimination of context based threats to validity) to the four already traditional types of validations, respectively statistical conclusion validity, internal validity, external validity, and construct validity (Dunn 1997). This is done through pragmatic eliminative induction (PEI). PEI suggests a way to determine and test “an approximately complete set of rival hypotheses” (Dunn 1997) and not solely some rival hypotheses chosen most often because they are readily known or easily testable. Employing the methodology in our study is (1) scientifically more complete, and (2) likely to yield interesting insights that may not have been uncovered so far. In this paper, we offer a systematic description of the use of the pragmatic combination of these methods, their advantages and practical challenges to their application. We exemplify by briefly applying the combined methods to various policy analytical settings, such as assessing success of innovation or science and technology policies, forecasting or assessing success of implemented privatization policies, and assessing success of some Public Health programs. We also propose and test a small modification to the original (Dunn 1997) PEI technique for enhancing QEI based methodologies, likely to make the process more affordable and less time and resources intensive This study thus offers one of the first practical applications of the extremely new and thus seldom used PEI enhanced quasi-experimental method in scientific policy analysis. Combining PEI with the use of other policy impact analytical tools should empower policy analysts with a new powerful, quasi-complete and now relatively affordable tool in their methodological toolbox. [ABSTRACT FROM AUTHOR]

In the US and Europe alike, science and technology policy initiatives in the early 1980s have focused much on improving the capacity to apply their good science base in practice, expecting a resulting increase in technological advancement and implicitly improved market presence and enhanced economic growth. Yet, results vary in the US and Europe. Some advanced industrial economies do not conform to the expected relationship between science and technology, whereby strong performance in science shall lead to strong technological performance. The relationship between science and technology is very much interdependent or symbiotic. The strength and primary direction of the relationship at a given moment in time varies largely by field of science or technological innovation, as well as across long periods of time. In this exploratory study, I identify plausible explanations for the puzzling relationship between science and technology in certain economically advanced countries. I find that the science-technology link in a country may depend on the overall scientific and technological level of development in that country. The strength and interdependent nature of this link has a historical evolution that varies across fields of science and technology. The strength of the link between science and technology in a country is affected by scientific and technological specialization. Different technological fields have different scientific intensities, or degrees of building upon the science base Specialization of countries across scientific and technological fields varies, making it natural for the strength of the science-technology link to differ from one country to another. The high technological specialization of a country may impact its technological performance more than its immediately current scientific performance does. History, tradition and knowledge trasnfers may affect more returns on R&D expenditures than the actual value of R&D funds spent in either science or technology in a country. As there are plausible explanations for the puzzling behavior of the science-technology link in developed countries, I convert these explanations into a few suggested policy recommendations, with applicability to developed countries and to developing countries alike. [ABSTRACT FROM AUTHOR]