A survey of poverty research in Russia: Does it follow the scientific method?

This paper presents the first critical review of the literature on poverty published in Russia between 1992 and 2006. Using a dataset of about 250 publications in Russian scientific journals we assess whether the poverty research in Russia satisfies the general criteria of a scientific publication and if such studies could provide reliable guidance to the Russian government as it maps out its anti-poverty policies. Our findings indicate that only a small proportion of papers on poverty published in Russia in 1992–2006 follow the universally recognized principles of the scientific method. The utility of policy advice based on such research is questionable. We also suggest certain steps that could, in our view, improve the quality of poverty research in Russia.     

The role of villages in households’ poverty exit: evidence from a multilevel model for rural Vietnam

Vietnam experienced a dramatic drop in overall poverty during the 90s. However, the poverty reduction showed substantial variation across households, villages and regions. Using a multilevel model on panel data from the rural sample of the Vietnam Living Standard Measurement Survey we demonstrate the important role of villages in household poverty exit dynamics. We also show how an analysis of village-level random effects predictions can help targeting of policies to reduce poverty.     

The evolving role of agricultural technology indicators and economic growth in rural poverty: has the ideas machine broken down?

The objective of the study is to examine the impact of technical progress in agriculture on changes in rural poverty in Pakistan by using annual data from 1975–2011. Data is analyzed by the set of sophisticated econometric techniques i.e., cointegration theory, Granger causality test and variance decomposition, etc. The results reveal that agricultural technology indicators act as an important driver to alleviate rural poverty in Pakistan. Granger causality test indicate that causality runs from technological indicators to rural poverty but not vice versa. However, agricultural irrigated land and industry value added, both does not Granger cause rural poverty, which holds neutrality hypothesis between the variables. Variance decomposition analysis shows that among all the technological indicators, agricultural machinery in form of tractors have exerts the largest contribution to changes in rural poverty in Pakistan. The study concludes that agricultural technology indicators are closely associated with economic growth and rural poverty in Pakistan. Technology in Pakistan has a low pace but still old technology continuously contributed towards poverty reduction. The question whether idea machine is broken down or not? Still need further exploration.     

Global estimates of growth–inequality–poverty (GIP) triangle: evidence from World Bank’s classification countries

This study extended the concept of ‘growth–inequality–poverty (GIP) triangle’ by using the principle component approach which allows us to composite different poverty and inequality indicators into one single index that contains most of the useful information from the original dataset. Using the idea of GIP triangle, this study examines the long-run relationship among weighted poverty index (which comprises headcount ratio, poverty gap and squared poverty gap); weighted inequality index (i.e., Watts Index, Gini Index and MLD Index) and average monthly per capita income in the designated 138 countries according to World Bank’s classification over a period of 2005–2010. The data set mainly contain countries’ unit record household survey at least one which is conducted between the countries during the sample period. The regression model encompassing the impact of economic growth and income inequality on poverty reflects that income inequality increases poverty while economic growth decreases poverty. It indicates that the impact of inequality in increasing poverty is a somewhat greater than that of growth in average income in reducing overall poverty in a sample countries. The other regression model encompassing the impact of economic growth and poverty on income inequality showed that the poverty itself is also likely to be a barrier for poverty reduction; and inequality seems to predict lower future growth rates. The final regression model depicting the impact of poverty and income inequality on mean income of the household suggests that poverty itself reduces mean income of the household while income inequality increases economic growth. The results are interesting and simply suggest that whenever social institutions malfunction, the incidence of damage would usually be distributed unevenly over the society’s members.