Panel Dataset Analysis with Fixed Effects and Lags

Both papers considered in this chapter (Mark et al., 2004; Aakvik and Holmas, 2006) focus on the impact of healthcare providers on patient outcome, measured as hospital mortality rates (and various morbidity rates) in the first paper and as community mortality rates in the second paper. The treatment variables — right-hand side variables of interest — are, respectively, the nurse to patient ratio in hospitals, and the general healthcare provider/practitioner to municipal population ratio in the second paper. Among the possible techniques to get causal estimates given the authors’ data, random assignments of treatments are not feasible (from several politico-economic perspectives), neither are regression discontinuity designs. Matching on the basis of medical professional/exposed population is also rather difficult for these datasets. Hence, these papers rely heavily on fixed effects in panel datasets, and lagged outcomes in panel datasets as control variables, and instrumental variables to handle the problem using the lagged outcome as a predictor variable. The Arellano and Bond (1991) method is the generalized method of moments estimator employed for this purpose in each of the papers reviewed in this chapter…