Multiple Imputation of Missing Data in Educational Production Functions

Educational production functions rely mostly on longitudinal data that almost always exhibit missing data. This paper contributes to a number of avenues in the literature on the economics of education and applied statistics by reviewing the theoretical foundation of missing data analysis with a special focus on the application of multiple imputation to educational longitudinal studies. Multiple imputation is one of the most prominent methods to surmount this problem. Not only does it account for all available information in the predictors, but it also takes into account the uncertainty generated by the missing data themselves. This paper applies a multiple imputation technique using a fully conditional specification method based on an iterative Markov chain Monte Carlo (MCMC) simulation using a Gibbs sampler algorithm. Previous attempts to use MCMC simulation were applied on relatively small datasets with small numbers of variables. Therefore, another contribution of this paper is its application and comparison of the imputation technique on a large longitudinal English educational study for three iteration specifications. The results of the simulation proved the convergence of the algorithm.