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This paper demonstrates the utility of the iterative proportional fitting procedure (IPF) in generating disaggregated spatial data from aggregated data and evaluates the performance of the procedure. Estimates of individual level data created by IPF using data of equal-interval categories are reliable, but the performance of the estimation can be improved by increasing sample size. The improvement usually is enough to offset the increase in error created by other factors. If the two variables defining the cross-classification have a significant interaction effect and the number of categories in each variable is larger than two, then IPF is preferred over an independent model.
David W. S. Wong (1992) studied this question.