R&D spillovers from subsidized firms that fail: Tracing knowledge by following employees across firms

Public R&D subsidies aim to target particularly risky R&D and R&D with large externalities. One would expect many such projects to fail from a commercial point of view, but they may still produce knowledge with social value. Such knowledge is likely to be embodied in workers or teams of workers. I test for knowledge diffusion from subsidised technology firms transmitted through the labor market. The specific case analysed is a series of Norwegian IT-programs so far considered unsuccessful. It has been argued that know-how built up during the programs still ‘fertilize’ the IT-industry. I find limited support for this claim.     

Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study

This simulation study assesses the statistical performance of two mathematically equivalent parameterizations for multitrait–multimethod data with interchangeable raters—a multilevel confirmatory factor analysis (CFA) and a classical CFA parameterization. The sample sizes of targets and raters, the factorial structure of the trait factors, and rater missingness are varied. The classical CFA approach yields a high proportion of improper solutions under conditions with small sample sizes and indicator-specific trait factors. In general, trait factor related parameters are more sensitive to bias than other types of parameters. For multilevel CFAs, there is a drastic bias in fit statistics under conditions with unidimensional trait factors on the between level, where root mean square error of approximation (RMSEA) and χ2 distributions reveal a downward bias, whereas the between standardized root mean square residual is biased upwards. In contrast, RMSEA and χ2 for classical CFA models are severely upwardly biased in conditions with a high number of raters and a small number of targets.     

Grading Standards,Student Ability and Errors in College Admission

Grades are important for admission of students in most higher education programs. Analyzing admission and student performance data at a major Norwegian business school, we find that the grading practice of teachers at regional colleges sending students to the school is affected by the average performance of the students being graded. Teachers at colleges recruiting good students from upper secondary school tend to be strict in their grading practice, while teachers at colleges recruiting less good students tend to follow a lenient practice. This has implications for the interpretation of grades and hence for optimal admission procedures. We develop a methodology to assess the consequences of differential grading standards. Approximately 10% of the students in our data are admitted at the expense of more competent students. We demonstrate costs for the school admitting the wrong students and in particular for the rejected students.