Since the interpretation of results often depends crucially on the data, it is incumbent on the data user to invest at least as far as knowing how the data were made, but there is no mechanism for enforcing this investment and it does not appear to have been a focus of effort. Analysis has revealed some areas where findings are sensitive to modelling choices, including the representation of technological change in the model, the treatment of spillovers, and the R&D lag distribution. These are essentially empirical questions that are often difficult to resolve with the available data but must be settled, and can have substantial impacts on the findings. The issue of how to go about specifying the research-induced technical change in models is largely unresolved. Better progress has been made with lags and spillovers. The trend has been to find larger spillover impacts and longer research lags in studies that test for these aspects.