A Decomposition Analysis of Regional Poverty in Russia

poverty, Russia, regions, decomposition

The Normal Mixture Decomposition

This talk will present the program for univariate normal mixture maximum likelihood estimation developed by the author. It will demonstrate the use of -ml lf- estimation method, as well as a number of programming tricks, including global macros manipulation and dynamic definition of the program to be used by -ml-. The merits and limitations of Stata's -ml- optimizer will be discussed. The application to income distribution analysis with a real data set will also be shown.

A decomposition analysis of regional poverty in Russia

This paper applies a new decomposition technique to the study of variations in poverty across the regions of Russia. The procedure, which is based on the Shapley value in cooperative game theory, allows the deviation in regional poverty levels from the all- Russia average to be attributed to three proximate sources; mean income per capita, inequality, and local prices. Contrary to expectation, regional poverty variations turn out to be due more to differences in inequality across regions than to differences in real income per capita. However, when real income per capita is split into nominal income and price components, differences in nominal incomes emerge as more important than either inequality or price effects for the majority of regions

Simultaneous Raking of Survey Weights at Multiple Levels

"This paper discusses the problem of creating general purpose calibrated survey weights when the control totals data exist at different levels of aggregation, such as households and individuals. We present and compare three different methods. The first does the weighting in two stages, using only the household data, and then only the individual data. The second redefines targets at the individual level, if possible, and uses these targets to calibrate only the individual level weights. The third uses multipliers of household size to produce household level weights that simultaneously calibrate to the individual level totals. We discuss the advantages and disadvantages of these approaches, including control total data accessibility and available software from the perspective a survey statistician working outside of a national statistical organization. We conclude by outlining directions for further research." (author's abstract

Finding Respondents in the Forest: A Comparison of Logistic Regression and Random Forest Models for Response Propensity Weighting and Stratification

Survey response rates for modern surveys using many different modes are trending downward leaving the potential for nonresponse biases in estimates derived from using only the respondents. The reasons for nonresponse may be complex functions of known auxiliary variables or unknown latent variables not measured by practitioners. The degree to which the propensity to respond is associated with survey outcomes casts light on the overall potential for nonresponse biases for estimates of means and totals. The most common method for nonresponse adjustments to compensate for the potential bias in estimates has been logistic and probit regression models. However, for more complex nonresponse mechanisms that may be nonlinear or involve many interaction effects, these methods may fail to converge and thus fail to generate nonresponse adjustments for the sampling weights. In this paper we compare these traditional techniques to a relatively new data mining technique- random forests – under a simple and complex nonresponse propensity population model using both direct and propensity stratification nonresponse adjustments. Random forests appear to offer marginal improvements for the complex response model over logistic regression in direct propensity adjustment, but have some surprising results for propensity stratification across both response models

Dynamism and Inertia on the Russian Labour Market: A Model of Segmentation

This paper proposes an explanation of the puzzling coexistence of elements of inertia and dynamism on the Russian labour market using a segmentation model. Risk averse workers are differentiated according to their productivity. They face a trade-off between wages and access to social services provided by the firm. The most productive workers leave their initial firm, contract on the spot labour market, and concentrate in the best performing firms. The model provides a possible interpretation of wage arrears which can be viewed as an element of an implicit contract between firms and less productive workers. We test some of the predictions of the model using a panel dataset containing 13,410 firms, for 1993 - 1997.http://deepblue.lib.umich.edu/bitstream/2027.42/39632/3/wp246.pd

RUSS_STATA: Stata tutorial in Russian

Applied econometric analysis with Stata 6 (in Russian) is a 110-page introduction into econometric uses of regression with Stata 6 written in Russian. The initial purpose of this book was to serve as the lecture notes on the author's weekly seminars on Stata. The organization of the package and the main data handling commands are given. The basic econometric methods, techniques and tests are discussed, and their Stata counterparts are mentioned.instruction, Russian

Dynamism and Inertia on the Russian Labour Market: A Model of Segmentation

This paper proposes an explanation of the puzzling coexistence of elements of inertia and dynamism on the Russian labour market using a segmentation model. Risk averse workers are differentiated according to their productivity. They face a trade-off between wages and access to social services provided by the firm. The most productive workers leave their initial firm, contract on the spot labour market, and concentrate in the best performing firms. The model provides a possible interpretation of wage arrears which can be viewed as an element of an implicit contract between firms and less productive workers. We test some of the predictions of the model using a panel dataset containing 13,410 firms, for 1993 - 1997.transition, labour market, wage arrears, Russia