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Improving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming
The estimation of RxC ecological inference contingency tables from aggregate data defines one of the most salient and challenging problems in the field of quantitative social sciences. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, they prove to be quite competitive and more accurate than the current linear programming baseline algorithm. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. We use a unique dataset with almost 500 elections, where the real transfer matrices are known, to assess their accuracy. Interested readers can easily use these new algorithms with the aid of the R package lphom.