The Experts below are selected from a list of 37362 Experts worldwide ranked by ideXlab platform
Yanhua Liu - One of the best experts on this subject based on the ideXlab platform.
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a geographic identification of multidimensional Poverty in rural china under the framework of sustainable livelihoods analysis
Applied Geography, 2016Co-Authors: Yanhua LiuAbstract:Abstract Developing methods of measuring multidimensional Poverty and improving the accuracy of Poverty identification have been hot topics in international Poverty research for decades. They are also key issues for improving the quality and effectiveness of rural Poverty reduction programs in China. So far, selection and integration of Poverty Indicators remains the main difficult for measurement of multidimensional Poverty. Guided by the sustainable livelihoods framework developed in the UK by the Department for International Development (DFID), an index system and an integration method for geographical identification of multidimensional Poverty were established, and they were further used to carry out a county-level identification of Poverty in rural China. Additionally, comparisons were made of the identification results with counties having single-dimension income Poverty in rural areas and poor counties designated by the Chinese central government. The results showed that a total of 655 counties, with 141 million rural residents, were identified as multidimensionally poor. They are concentrated and conjointly distributed geographically, and evil natural conditions are their common features. In comparison to the income poor and the designated poor counties, the multidimensionally poor counties were not only worse in single-dimensional and composite scores, but also having multiple disadvantages and deprivations. By identifying the disadvantage and deprived dimensions, the measurement of multidimensional Poverty should be very helpful for each county to work out and implement antiPoverty programs accordingly, and it would make contribution to improve the sustainability of Poverty reduction. Hopefully, this research may also shed light on multidimensional Poverty measurement for other developing countries.
Monica Pratesi - One of the best experts on this subject based on the ideXlab platform.
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small area estimation of Poverty Indicators
2013Co-Authors: Monica Pratesi, Caterina Giusti, Stefano MarchettiAbstract:The estimation of Poverty, inequality and life condition Indicators all over the European Union has become one topic of primary interest. A very common target is the core set of Indicators on Poverty and social exclusion agreed by the Laeken European Council in December \(2001\) and called Laeken Indicators. They include measures of the incidence of Poverty, such as the Head Count Ratio (also known as at-risk-of-Poverty-rate) and of the intensity of Poverty, as the Poverty Gap. Unfortunately, these Indicators cannot be directly estimated from EU-SILC survey data when the objective is to investigate Poverty at sub-regional level. As local sample sizes are small, the estimation must be done using the small area estimation approach. Limits and potentialities of the estimators of Laeken Indicators obtained under EBLUP and M-quantile small area estimation approaches are discussed here, as well as their application to EU-SILC Italian data. The case study is limited to the estimation of Poverty Indicators for the Tuscany region. However, additional results are available and downloadable from the web site of the SAMPLE project, funded under the 7FP (http://www.sample-project.eu).
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robust small area estimation and oversampling in the estimation of Poverty Indicators
Survey research methods, 2012Co-Authors: Caterina Giusti, Monica Pratesi, Stefano Marchetti, Nicola SalvatiAbstract:There has been rising interest in research on Poverty mapping over the last decade, with the European Union proposing a core of statistical Indicators on Poverty commonly known as Laeken Indicators. They include the incidence and the intensity of Poverty for a set of domains (e.g. young people, unemployed people). The EU-SILC (European Union - Statistics on Income and Living Conditions) survey represents the most important source of information to estimate these Poverty Indicators at national or regional level (NUTS 1-2 level). However, local policy makers also require statistics on Poverty and living conditions at lower geographical/domain levels, but estimating Poverty Indicators directly from EU-SILC for these domains often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that direct estimates become reliable and ii. resort to small area estimation techniques. In this paper we compare these two alternatives: with the availability of an oversampling of the EU-SILC survey for the province of Pisa, obtained as a side result of the SAMPLE project (Small Area Methods for Poverty and Living Conditions, http://www.sample-project.eu/), we can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Results show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, considering the oversample estimates as a benchmark, we show how direct estimates computed without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates.
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non parametric bootstrap mean squared error estimation for m quantile estimators of small area averages quantiles and Poverty Indicators
Computational Statistics & Data Analysis, 2012Co-Authors: Stefano Marchetti, Nikos Tzavidis, Monica PratesiAbstract:Small area estimation is conventionally concerned with the estimation of small area averages and totals. More recently emphasis has been also placed on the estimation of Poverty Indicators and of key quantiles of the small area distribution function using robust models, for example, the M-quantile small area model. In parallel to point estimation, Mean Squared Error (MSE) estimation is an equally crucial and challenging task. However, while analytic MSE estimation for small area averages is possible, analytic MSE estimation for quantiles and Poverty Indicators is difficult. Moreover, one of the main criticisms of the analytic MSE estimator for M-quantile estimates of small area averages is that it can be unstable when the area-specific sample sizes are small. A non-parametric bootstrap framework for MSE estimation for small area averages, quantiles and Poverty Indicators estimated with the M-quantile small area model is proposed. Emphasis is placed on second order properties of MSE estimators with results suggesting that the bootstrap MSE estimator is more stable than corresponding analytic MSE estimators. The proposed bootstrap is evaluated in a series of simulation studies under different parametric assumptions for the model error terms and different scenarios for the area-specific sample and population sizes. Finally, results from the application of the proposed MSE estimator to real income data from the European Survey of Income and Living Conditions (EU-SILC) in Italy are presented and information on the availability of R functions that can be used for implementing the proposed estimation procedures in practice is provided.
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small area methods in the estimation of Poverty Indicators the case of tuscany
Politica economica, 2009Co-Authors: Caterina Giusti, Monica Pratesi, Nicola SalvatiAbstract:In order to implement policies against Poverty and inequality, policy makers all over Europe should have at their disposal information referring to appropriate domains, since the impossibility to access to goods and services varies with age, gender and zone of residence. However, the major Italian and European surveys collecting information on Poverty and living conditions can be used to produce accurate Indicators only at the regional level. To compute these Indicators at the Provincial and Municipality levels there is the need to resort to small area methodologies. The aim of this paper is to produce and compare monetary Indicators of Poverty for the Provinces of Tuscany using parametric and nonparametric small area estimation methods. The target is the estimation of the "at-risk-of-Poverty rate" (Head Count Ratio) and of the mean and the quantiles of the household income distribution using census data and data from the EUSILC survey. These measures can be considered as a starting point for more in depth analyses, such as the estimation of the income cumulative distribution function.
Alexander S. Antonarakis - One of the best experts on this subject based on the ideXlab platform.
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Financial crises and the attainment of the SDGs: an adjusted multidimensional Poverty approach
Sustainability Science, 2019Co-Authors: Andreas Antoniades, Indra Widiarto, Alexander S. AntonarakisAbstract:This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating Poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 Indicators that span across key Poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these Indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on Poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating Poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing Poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other Poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty Indicators, warn of a negative domino effect on a number of SDGs related to Poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community.
Sondès Kahouli - One of the best experts on this subject based on the ideXlab platform.
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On the power of Indicators: how the choice of fuel Poverty indicator affects the identification of the target population
American Economic Journal: Applied Economics, 2018Co-Authors: Florian Fizaine, Sondès KahouliAbstract:In light of the creation of the EU Energy Poverty Observatory (EPOV) in January 2018 and the increase in debates on how fuel Poverty is measured, we propose a critical analysis of fuel Poverty Indicators and demonstrate that choosing a given fuel Poverty indicator and, in particular, its threshold level, is central to the identification of the fuel-poor population. First, we conducted an inter-indicator analysis to show how profiles of fuel-poor households vary depending on the indicator selected. More specifically, after identifying groups of affected households using a set of objective and subjective Indicators, we designed a multidimensional approach based on a combination of a multiple correspondence analysis and a hierarchical and partitioning clustering analysis to study their characteristics. Using this framework, we highlight the difficulty of identifying a "typical profile" of fuel-poor households due to the significant variability in their characteristics, and we show that the composition of the population depends on the choice of the indicator. Second, we applied an intra-indicator analysis using two objective expenditure-based Indicators with thresholds. In particular, we conducted a sensitivity analysis based on a logit model including variables describing household and dwelling characteristics. We show that the profiles of fuel-poor households as well as the drivers of fuel Poverty vary considerably with the chosen threshold level. Given these findings, we stress the need to review how we currently rely on conventional fuel Poverty Indicators to identify target groups and give some recommendations.
Stefano Marchetti - One of the best experts on this subject based on the ideXlab platform.
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small area estimation of Poverty Indicators
2013Co-Authors: Monica Pratesi, Caterina Giusti, Stefano MarchettiAbstract:The estimation of Poverty, inequality and life condition Indicators all over the European Union has become one topic of primary interest. A very common target is the core set of Indicators on Poverty and social exclusion agreed by the Laeken European Council in December \(2001\) and called Laeken Indicators. They include measures of the incidence of Poverty, such as the Head Count Ratio (also known as at-risk-of-Poverty-rate) and of the intensity of Poverty, as the Poverty Gap. Unfortunately, these Indicators cannot be directly estimated from EU-SILC survey data when the objective is to investigate Poverty at sub-regional level. As local sample sizes are small, the estimation must be done using the small area estimation approach. Limits and potentialities of the estimators of Laeken Indicators obtained under EBLUP and M-quantile small area estimation approaches are discussed here, as well as their application to EU-SILC Italian data. The case study is limited to the estimation of Poverty Indicators for the Tuscany region. However, additional results are available and downloadable from the web site of the SAMPLE project, funded under the 7FP (http://www.sample-project.eu).
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robust small area estimation and oversampling in the estimation of Poverty Indicators
Survey research methods, 2012Co-Authors: Caterina Giusti, Monica Pratesi, Stefano Marchetti, Nicola SalvatiAbstract:There has been rising interest in research on Poverty mapping over the last decade, with the European Union proposing a core of statistical Indicators on Poverty commonly known as Laeken Indicators. They include the incidence and the intensity of Poverty for a set of domains (e.g. young people, unemployed people). The EU-SILC (European Union - Statistics on Income and Living Conditions) survey represents the most important source of information to estimate these Poverty Indicators at national or regional level (NUTS 1-2 level). However, local policy makers also require statistics on Poverty and living conditions at lower geographical/domain levels, but estimating Poverty Indicators directly from EU-SILC for these domains often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that direct estimates become reliable and ii. resort to small area estimation techniques. In this paper we compare these two alternatives: with the availability of an oversampling of the EU-SILC survey for the province of Pisa, obtained as a side result of the SAMPLE project (Small Area Methods for Poverty and Living Conditions, http://www.sample-project.eu/), we can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Results show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, considering the oversample estimates as a benchmark, we show how direct estimates computed without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates.
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non parametric bootstrap mean squared error estimation for m quantile estimators of small area averages quantiles and Poverty Indicators
Computational Statistics & Data Analysis, 2012Co-Authors: Stefano Marchetti, Nikos Tzavidis, Monica PratesiAbstract:Small area estimation is conventionally concerned with the estimation of small area averages and totals. More recently emphasis has been also placed on the estimation of Poverty Indicators and of key quantiles of the small area distribution function using robust models, for example, the M-quantile small area model. In parallel to point estimation, Mean Squared Error (MSE) estimation is an equally crucial and challenging task. However, while analytic MSE estimation for small area averages is possible, analytic MSE estimation for quantiles and Poverty Indicators is difficult. Moreover, one of the main criticisms of the analytic MSE estimator for M-quantile estimates of small area averages is that it can be unstable when the area-specific sample sizes are small. A non-parametric bootstrap framework for MSE estimation for small area averages, quantiles and Poverty Indicators estimated with the M-quantile small area model is proposed. Emphasis is placed on second order properties of MSE estimators with results suggesting that the bootstrap MSE estimator is more stable than corresponding analytic MSE estimators. The proposed bootstrap is evaluated in a series of simulation studies under different parametric assumptions for the model error terms and different scenarios for the area-specific sample and population sizes. Finally, results from the application of the proposed MSE estimator to real income data from the European Survey of Income and Living Conditions (EU-SILC) in Italy are presented and information on the availability of R functions that can be used for implementing the proposed estimation procedures in practice is provided.
