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Inequality matters for poverty reduction. However, it is often unclear what policy could do to change the distribution of income as to date there is little quantitative evidence about the household characteristics that determine the level of income inequality and its changes over time. This paper sets out to identify these determinants by adapting a regression-based inequality decomposition technique and applying it to panel data from rural Pakistan. Land ownership is key to explaining the level of inequality, but not its changes. In contrast, higher education drives changes, but not the level of inequality. Household location affects both, reflecting growing differences in market access across regions. [ABSTRACT FROM AUTHOR]

In rural and low-income urban households, energy is 'women's business': women are responsible for providing energy, and use it for domestic chores and productive activities. However, the poor quality fuels many women use contribute to their time poverty, ill health, and level of drudgery. Despite these negative impacts, energy policy remains gender-blind. This can be attributed to the invisibility of women's needs to energy planners, stemming from a lack of appropriate gender-analysis tools to meet the particular data requirements of the energy sector. This article analyses why standard gender tools do not provide appropriate gender-disaggregated energy data, and describes a set of tools that have been developed for that purpose. The paper concludes with an evaluation of recent experiences testing the tools in Pakistan, India, and Nepal. [ABSTRACT FROM AUTHOR]

We deviate from standard literature on assessment of household poverty. Contrary to standard practice of using household income and consumption, we use Wealth Index, calculated through Principal Component Analysis (PCA), as measure of poverty. Data from Pakistan Demographic and Health Survey (PDHS) 2006–07, gathered from female respondents, suggests an interplay of several socio-demographic factors underlying the incidence of household poverty. The multivariate analysis shows that household size, dependency ratio, and sex and age of the head of household are significantly associated with poverty status. Higher education lowers the likelihood of poverty while the opposite is true for the dependency ratio. Most interestingly, male headed households are more likely to have poverty-lower wealth index-compared to households headed by female. We also provide evidence that the impact of household characteristics on poverty is sensitive to the area of residence, rural or urban, and different regions. We conclude that DHS data provides an assessment of household poverty which goes beyond economic factors and single dimension measures. [ABSTRACT FROM AUTHOR]

Landlessness and poverty are closely linked to each other. Being agricultural country, land is considered as a principal asset in Pakistan. This study aims to identify the poverty situation of landless peasants in Pakistan. Primary data are collected by simple random sampling method. Binary logistic regression model are used to estimate the relationship between landlessness and poverty. Result shows that high age of household head, education level, land ownership, operational land holding, and income earners in a household significantly reduces the chances of household's poverty. The value of Gini coefficient is found 0.63 that indicates that there is high income inequality. [ABSTRACT FROM AUTHOR]

Reporting the results of a novel survey of shocks, coping, outcomes, and safety nets in Pakistan, we find high incidence and cost of shocks borne by households, with health and other idiosyncratic shocks dominating in frequency, costliness, and adversity. Sample households lack effective coping options and use mostly self-insurance and informal credit. Many shocks result in food insecurity, informal debts, child and bonded labour, and recovery is slow. Private and public social safety nets exist but offer little effective protection. Public action is needed to better control public health hazards and provide non-exploitative credit and more effective safety nets. [ABSTRACT FROM AUTHOR]