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This paper studies the effects of mining intensity and presence on Peru's mining districts' welfare from 2004 to 2019. A pooled cross-section regression is used which is constructed from different sources and two sets of comparisons are made: the first compare districts with and without mining presence within mining provinces, and the second compares districts with and without mining presence without the constraint of being within mining provinces. The primary dependent variables included in the model are income inequality, labor income, and poverty rate. In mining districts, inequality has increased, but labor income has increased, and poverty has decreased compared to non-mining districts. However, once control for province-fixed effects and clustered by standard errors at the district level, the significance of inequality is lost, while the impacts on labor income and poverty remain. The transmission mechanisms are human capital, employment, and redistributive policies. Also the mining presence has had positive effects on labor income in other sectors such as construction and commerce; Finally, the labor incomes of unskilled workers increases but not the labor incomes of skilled workers, and it has negatively impacted informal employment. [ABSTRACT FROM AUTHOR]

Peru is an emerging nation with a nonuniform development where the growth is focused on some specific cities and districts, as a result there is serious economic inequalities across the country. Despite the poverty in Peru has declined in the last decades, there is still poor districts in risk to become extremely poor, even in its capital, Lima. In this context, it is relevant to study the incidence of extreme poverty at district levels. In this paper, we propose to estimate the quantiles of the incidence of extreme poverty of districts in Lima by using spatial quantile models based on the Kumaraswamy distribution and spatial random effects for areal data. Furthermore, in order to deal with spatial confounding random effects we used the Spatial Orthogonal Centroid "K"orrection approach. Bayesian inference for these hierarchical models is conveniently performed based on the Hamiltonian Monte Carlo method. Our modeling is flexible and able to describe the quantiles of incidence of extreme poverty in Lima. [ABSTRACT FROM AUTHOR]

Counting poverty measures have gained prominence in the analysis of multidimensional poverty in recent decades. Poverty orderings based on these measures typically depend on methodological choices regarding individual poverty functions, poverty cut-offs, and dimensional weights whose impact on poverty rankings is often not well understood. In this paper we propose new dominance conditions that allow the analyst to evaluate the robustness of poverty comparisons to those choices. These conditions provide an approach to evaluating the sensitivity of poverty orderings superior to the common approach of considering a restricted and arbitrary set of indices, cut-offs, and weights. The new criteria apply to a broad class of counting poverty measures widely used in empirical analysis of poverty in developed and developing countries including the multidimensional headcount and the adjusted headcount ratios. We illustrate these methods with an application to time-trends in poverty in Australia and cross-regional poverty in Peru. Our results highlight the potentially large sensitivity of poverty orderings based on counting measures and the importance of evaluating the robustness of results when performing poverty comparisons across time and regions. [ABSTRACT FROM AUTHOR]

Inadequate provision of water-related services in developing countries continues to undermine strategies for poverty alleviation. The root lies in the inability of policy makers to tackle resource development in a holistic and integrated manner. This requires a multi-faceted approach to combine physical estimates of water availability with the socio-economic drivers of poverty. It is with this in mind that the Water Poverty Index (WPI) was created. However, water resources are dynamic, and the linkages between water scarcity and poverty incorporate complex cause-effect relationships. Water poverty should thus be addressed in a more systemic way. This would allow a comprehensive understanding of the crosscutting nature of water issues and impacts. In this paper, a system approach has been adopted to develop a structured framework for a multi-dimensional evaluation of water poverty in basins. It is an attempt to assess the diverse, interacting components of catchment processes, societal pressures, and policy actions. An enhanced Water Poverty Index (eWPI) has been developed and is proposed in this study. To exemplify the utilisation of the index, and to test its applicability and validity, eWPI has been piloted in a Peruvian watershed as initial case study. Results highlight the likely utility of the tool to identify areas for improvement, and ultimately guide appropriate action towards better service delivery and sustainable management of water resources. [ABSTRACT FROM AUTHOR]

Despite of poverty and extreme poverty estimations having decreased during the period 2006–2016 in Peru, from 49.1 to 20.7% and from 16.1 to 3.8%, respectively, the poor population still represents a latent concern for policy makers. Essentially, the main reason lies on the multidimensional problems of quality of life. Then, a research question yet unaddressed rises: Is it possible to find very poor people who feel happy? If so, what controls this relationship? The first main contribution of this study is the application of modern measurement theory to measure happiness. The short Oxford Happiness Questionnaire jointly with an ad hoc Multidimensional Poverty Index-MPI (8 items) were applied to a random sample of 537 household heads who live in the five poorest districts in Peru. Item response theory analysis was conducted to measure happiness scores. Findings reveal happiness scores and the MPI are negatively associated in the very poor Peru. Friendship, religiosity, and some relevant non-material characteristics of a family are the most important covariates of the relationship between these variables. In fact, the second contribution of this study represents the inclusion of variables associated to the quantity and quality of friendship as relevant controllers of happiness and poverty. [ABSTRACT FROM AUTHOR]

Five billion people in the world do not have the kind of ledgers that provide the documented information that allows them to transfer, partition, and aggregate assets and talents in such a way that they can be scaled up, secure investment, guarantee credit, certify reputation, and capture abstract surplus value. The difficulty that most people have in making combinations is a major cause of global inequality and unnecessary poverty. [ABSTRACT FROM AUTHOR]

Is there a correspondence between subjective and objective wellbeing indicators in development programs? We investigate this question by evaluating the impact of Fair Trade affiliation on the subjective wellbeing of a sample of Peruvian (treatment and control) producers from two different Fair Trade projects in two socioeconomic environments, one relatively poorer (Juliaca) and the other relatively more affluent (Chulucanas). We find a direct and an indirect effect. The direct effect acts positively via affiliation years in the poorer project and via trade diversification in both projects. The indirect effect acts through the reduction of poverty and relative income. Consistently with the concave income-happiness assumption, the variables generating the indirect effect have a much weaker impact for producers living in the relatively better-off socioeconomic environment, net of the lower FT economic impact in this area. [ABSTRACT FROM AUTHOR]

The recent focus on ‘pro-poor growth’ led also to an intense debate on how exactly to define and to measure pro-poor growth. All suggested measures have in common that they are based on the anonymity axiom. Such a perspective may provide a very incomplete picture given that the common objective of most studies investigating the pro-poorness of growth is to test whether specific policy reforms where beneficial to the initially poor or not. I suggest a new concept of pro-poor growth which removes the anonymity axiom, and, using an illustration based on data from Indonesia and Peru, I check whether the assessment of pro-poor growth is different when an anonymous and a non anonymous approach to pro-poor growth is used. I also suggest an original decomposition of poverty changes over time which links both concepts. The results show that the choice of the approach has a drastic impact on the interpretation of the data. [ABSTRACT FROM AUTHOR]