ГЕНДЕРНОЕ ИЗМЕРЕНИЕ В ПРОГРАММАХ СОЦИАЛЬНО-ЭКОНОМИЧЕСКОГО РАЗВИТИЯ: эволюция методологических подходов и целевых критериев
The paper discusses sociolinguistic implementations of statistical analysis of the spoken subcorpus of the Russian National Corpus. Given the considerable size of the corpus (about 10 mln tokens), an analysis of co-variation of various linguistic parameters with one of the few sociolinguistic parameters available – the speaker’s gender – may give rich and interesting results. One specific example of co-variation is considered in detail: the mean length of the utterance (in tokens). Comparing this parameter in public communication shows statistically significant difference between the speech of men and women (men talk more), while the same difference is absent in private communication. Another important parameter is the gender of the addressee. Again, co-variation is quite different in public and private discourse. In private communication, the utterances are longer when addressing someone of the same sex, the difference between men and women is not statistically significant. In public communication, the utterances are longer when addressing a woman, whether the speaker herself is a man or woman. These conclusions are consistent with the results of sociolinguistic gender studies obtained elsewhere and by other methods. Linguistic difference between men and women are not absolute but depend on the communicative situation (public vs. private). Public discourse is a playground for linguistic competition in which men are the winning party. In private discourse, competition dissolves.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.