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The purpose of this paper is to ground in serious empirical evidence the debate on whether the post-reform acceleration in growth has helped bring poverty down for all economic, social and religious groups and in all state or has left certain groups or states.

The paper uses unit-level data from the so-called thick rounds of expenditure surveys by National Sample Survey (NSS) in the years 1993-1994, 2004-2005, 2009-2010 and 2011-2012 and estimates the proportion of the population below the official Tendulkar line. Adequate care is taken to address the issue of sample size in reporting the estimates.

Whether we slice the data by social, religious or economic groups, by states or by rural and urban areas, poverty has significantly declined between 1993-1994 and 2011-2012 with a substantial acceleration during the faster-growth period from 2004-2005 to 2011-2012. Poverty rates among the disadvantaged social groups and minorities have declined faster so that the gap in poverty rates between them and the general population has declined. In 7 of the 16 states with large Muslim populations, the poverty rate for them is now below that for the Hindus.

Use of survey data has its limitations, especially when the sample sizes are small. The paper also does not assess the direct contribution of growth in relation to that through redistribution.

The paper presents implications for identification of the poor for the purpose of designing targeted interventions.

This is the first paper to offer up-to-date estimates of poverty by social, religious and economic groups, by states and by rural and urban areas.

The author proposes analyzing the dynamics of income positions using dynamic panel ordered probit models. The author disentangles, simultaneously, the roles of state dependence and heterogeneity (observed and non-observed) in explaining income position persistence, such as poverty persistence and affluence persistence. The author applies the approach to Chile exploiting longitudinal data from the P-CASEN 2006–2009. First, the author finds that income position mobility at the bottom and the top of the income distribution is much higher than expected, showing signs that income mobility in the case of Chile might be connected to economic insecurity. Second, the observable individual characteristics have a much stronger impact than true state dependence to explain individuals’ current income position in the income distribution extremes.

By using the World Bank's new poverty data that are based on the most recent International Comparison Program report, this research aims to revisit the response of poverty rate to increase in real gross domestic product (GDP) per capita.

The response is summarized in terms of elasticity of poverty with respect to real GDP per capita, which is the ratio of annual percentage fall in poverty rate to annual percentage increase in real GDP per capita. The main calculations are done for the entire group of less‐developed countries (LDCs), poverty‐dense South Asia region, and India, which probably has the highest poverty rate. The periods studied are 1981‐1990, 1990‐1999, and 1999‐2005. The calculations are done for four different poverty measures.

Five major points are noted. First, the elasticities generally show a declining tendency over the period, indicating that poverty‐reducing impact of income growth has been weakening. Second, the elasticities show huge differences across the poverty lines, and generally decline with higher poverty lines. Third, while global elasticities for $1.00 poverty line bear some resemblance to those reported or used by many scholars, elasticities for $2.00 and 2.50 poverty rates are dramatically lower, and reinforce the view that many influential estimates show the effect of income growth on poverty to be much higher than the data indicate. Fourth, elasticities for poverty‐dense South Asia are again seen to be much lower than those for the entire LDC group. Fifth, for India, where $2.00 and 2.50 poverty rates are higher than even in Sub‐Saharan Africa, the elasticities are extremely low and have been declining despite an acceleration in income growth. The overall implication seems to be that income growth has generally been less pro‐poor during the globalization era of the 1990s and the 2000s than during the 1980s. In particular, income growth in India seems to have had an extremely small impact on poverty, and that impact, notably for $1.00 and 1.25 poverty lines, has been declining.

First, although there is a vast literature on growth elasticities of poverty, this seems to be the first study that uses World Bank's new poverty data to judge the impact of income growth on poverty. Second, this is the only study that directly estimates and compares elasticities for the four poverty lines of $1.00, 1.25, 2.00, and 2.50, and shows large differences in the elasticities for different poverty lines. Third, this is probably the only work that compares elasticities for the 1980s, 1990s, and the 2000s. Fourth, although some indication of very low elasticities for South Asia and India does exist in a recent study, $2.50 elasticities reported in the present work for India, and even South Asia, should constitute an eye‐opener for scholars, policy‐makers, and international organizations in regard to the potential role of income growth in poverty reduction. Fifth, the observed decline in most elasticities during the 1990s and 2000s, as compared with the 1980s, despite higher income levels and growth rates, may shed light on the likely role of globalization in reducing poverty.

The authors propose a framework to estimate the probability of being poor in a dynamic setting based on a large information set that includes individual characteristics and macro-economic variables. The joint inclusion of personal characteristics along with contextual factors allows separation of idiosyncratic shocks from aggregate shocks affecting poverty. The authors combine data from different cross-sectional surveys and fit a dynamic logistic hierarchical model within a Bayesian framework using standard Markov chain Monte Carlo techniques. The authors’ approach is exemplified by estimating household poverty status in Kyrgyz Republic as a function of time, regions, country, regional level variables and household level socio-demographic characteristics.

The purpose of this paper is to yield more plausible rural poverty lines for China and then assess the determinants of rural poverty using these newly revised rural poverty lines.

In this paper it is argued that the Chinese official poverty line substantially misestimates the actual rural poverty situations. The poverty lines are derived with Ravallion's method with a rural household survey data of China's two provinces, Hubei, and Inner Mongolia. Poverty determinants using the derived as well as the other rural poverty lines are compared.

The results indicate that the poverty lines derived from a pan‐country level food bundle cannot fully reflect the regional poverty situation. Merely adjusting rural poverty lines at the country‐level consumer price index without fully considering changes in the structure of food consumption and food prices with respect to different regions may also lead to wrong poverty estimates. The comparisons between the model regression results using the newly derived poverty lines with the alternative rural poverty lines suggest that the current literature uses the World Bank or the Chinese official rural poverty lines to assess how the rural poverty determinants might yield implausible policy implications.

China needs to adjust the rural poverty lines with full consideration to the structure of food consumption and food prices with respect to different regions.

It is indicated that the Chinese official poverty line substantially misestimates the actual rural poverty situations in China and this in turn affects the associated policy implications.

We examine the relationship between the business cycle and poverty for the period from 1960 to 2008 using income data from the Current Population Survey and consumption data from the Consumer Expenditure Survey. This new evidence on the relationship between macroeconomic conditions and poverty is of particular interest, given recent changes in antipoverty policies that have placed greater emphasis on participation in the labor market and in-kind transfers. We look beyond official poverty, examining alternative income poverty and consumption poverty, which have conceptual and empirical advantages as measures of the well-being of the poor. We find that both income and consumption poverty are sensitive to macroeconomic conditions. A 1 percentage point increase in unemployment is associated with an increase in the after-tax income poverty rate of 0.9–1.1 percentage points in the long run, and an increase in the consumption poverty rate of 0.3–1.2 percentage points in the long run. The evidence on whether income is more responsive to the business cycle than consumption is mixed. Income poverty does appear to be more responsive using national level variation, but consumption poverty is often more responsive to unemployment when using regional variation. Low percentiles of both income and consumption are sensitive to macroeconomic conditions, and in most cases, low percentiles of income appear to be more responsive than low percentiles of consumption.

The impacts of median income and other variables on the Sen index of poverty in the United States are investigated using panel data with fixed time period and cross sectional effects. Estimates for the Sen index and its decomposed components – the headcount ratio, poverty gap ratio, and Gini coefficient among the poor reveal that median income among state/regions and across time systematically influences the Sen index and each of its components. However, the results reveal that labor market and demographic control variables have quite different effects on the distinct components of the Sen index.

The purpose of this paper is to examine the regional profile of poverty in Uttar Pradesh, one of the most populated and impoverished states of India. It also identifies the factors underlying the inter-regional differences of poverty in the state.

Regional estimates have been evaluated by dividing the state into four economically classified regions (Western, Central, Southern, and Eastern), using the unit-level records of two latest available Consumption Expenditure Surveys of NSSO representing the period 2009-2010 and 2011-2012. Poverty has been defined by the latest available Rangarajan Expert Groups’ poverty line and aggregated in terms of headcount ratio and share of below poverty line population. Furthermore, to investigate the correlates of poverty, a survey-based logistic regression has been estimated specifically for each region and for both rural and urban areas.

Estimates reveal that though overall poverty in the state has declined, inter-regional poverty trends witness rise in the level of impoverishment particularly in urban Southern Region (SR), rural Eastern Region (ER), and in both rural and urban areas of Central Region. Nevertheless, the inter-regional disparity in poverty has observed a decline; it can further be eliminated if such high poverty reduction in urban ER and rural SR is sustained along with a similar progress in their impoverished counterparts.

The study recommends that poverty alleviating policies in the state should focus more on reducing the household size, development of socially excluded sub-groups (Scheduled Castes and Other Backward Classes), delivery of basic facilities (education and health care), and enhancement of employment prospects for casual laborers, with special emphasis on identified impoverished regions.

This paper aims to test three hypotheses in city growth literature documenting the poverty reduction observed in Brazil and exploring a rich spatial dataset for 5,564 Brazilian cities observed between 1991 and 2010. The large sample and the author's improved econometric methods allows one to better understand and measure how important income growth is for poverty reduction, the patterns of agglomeration and population growth in all Brazilian cities.

The author identifies literature gaps and use a sizeable spatial dataset for 5,564 Brazilian cities observed in 1991, 2000 and 2010 applying instrumental variables methods. The bias-corrected accelerated bootstrap percentile interval supports the author's point estimates.

This manuscript finds that Brazilian data for cities does not support Gibrat's law, raising the scope for urban planning and associated policies. Second, economic growth on a sustainable basis is still a vital source of poverty reduction (The author estimates the poverty elasticity at four percentage points). Lastly, agglomeration effects positively affect the city's productivity, while negative externalities underlie the city's development patterns.

Data for cities in Brazil possess unique characteristics such as spatial autocorrelation and endogeneity. Applying proper methods to find more reliable answers to the above three questions is a desirable procedure that must be encouraged. As the author points out in the manuscript, dealing with endogenous regressors in regional economics is still a developing matter that regional scientists could more generally apply to many regional issues.

Micro-businesses contribute to economic development by improving individual welfare. Women are the primary drivers and owners of such businesses in urban Ethiopia. The purpose of this study is to investigate the poverty status and determinants among women-owned micro-businesses.

The basic study units were women who owned micro-businesses. A sample of 384 women-owned micro-business was chosen using a stratified and systematic random sampling technique. Thirty-six participants were purposely chosen for in-depth interviews and focus group discussions. Questionnaires, in-depth interviews and focus group discussions were used to collect data. The poverty head count, poverty gap and poverty severity indices were computed to estimate poverty status. The major determinants of women’s poverty were investigated using a logistic regression model.

The overall poverty incidence, gap and severity were estimated to be 24.27%, 3.85%, and 1.11% respectively, among the women who owned micro-businesses. Eight of the 14 poverty determinants, including age, dependents, savings, remittance and the number of days and hours women work in their businesses, were found to have a significant effect on women’s poverty. The results suggest that local governments, technical and vocational training institutions should work together to reduce the impact of poverty-aggravating factors on women and increase the contribution of women-owned micro-businesses to poverty reduction.

This study addressed the poverty status of women who run micro-businesses, which is a crucial issue in Ethiopia’s urban context. It adds new knowledge to the issue of gendered economic participation, poverty reduction and poverty determinants in the Ethiopian context.