Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Missing data is an issue in many empirical applications, as it may entail efficiency losses as well as biased results. We address these problems within the literature that investigates the effect of foreign aid on welfare. Using multiple imputation techniques to account for the missing data, we find lower aid effectiveness. In addition, imputation allows for comparison of different welfare indicators within the same framework. We find that the respective indicator choice can matter for the ensuing results.