The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
Jana Turcinkova - One of the best experts on this subject based on the ideXlab platform.
-
Material deprivation of households in the Czech Republic in comparison with selected EU countries based on EU-SILC Income Statistics
PsycEXTRA Dataset, 2020Co-Authors: Jana Turcinkova, Jana Stavkova, Nada BirciakovaAbstract:The paper deals with the assessment of Income situation of households in the Czech Republic. The primary source for the analysis were the data of the survey EU-SILC European Union – Statistics on Income and Living Conditions. The basic variable for the analysis is the level of the household Income in 2005–2008. In addition to the decile classification, characteristics such as the average Income per one household member, poverty threshold, poverty depth coefficient, Lorenz curve and Gini coefficient. were calculated in order to evaluate the Income situation. The results show an increase of the average household Income. The Lorenz curve followed by the Gini coefficient demonstrate the uniformity of distribution of Income values. The results show a decreasing Income differentiation. The poverty threshold was defined on the level of 60% of the median value and with this given threshold, the households were assessed, whether they belong to the ones at the risk of poverty. The results reveal a decreasing number of households at the risk of poverty. The poverty depth coefficient has a stronger explanatory power and shows how far below the poverty threshold the households are, or what is an Income deficit of these households. Each category of households at the risk of poverty varies with the depth of poverty. The analysis also provides the results of how the households’ Income situation or poverty is perceived by the households themselves.
-
material deprivation in selected eu countries according to eu silc Income Statistics
Journal of Cryptology, 2012Co-Authors: Jana Stavkova, Nada Birciakova, Jana TurcinkovaAbstract:The article focuses on issues of households at risk of poverty in a relative conception. Income poverty represents a situation when the threshold of 0.6 of median Income is not achieved. The analysis of a broader definition of poverty bases on identification and assessment of material deprivation factors, which include: Financial stress, Housing conditions, Availability of consumer durables and Basic needs. Data sources are based EU-SILC dataset. The presented analysis focuses on selected EU countries, namely Czech Republic, Finland, France, Spain and United Kingdom. The result is the identification of problem areas that cause deprivation symptoms.
Jana Stavkova - One of the best experts on this subject based on the ideXlab platform.
-
Material deprivation of households in the Czech Republic in comparison with selected EU countries based on EU-SILC Income Statistics
PsycEXTRA Dataset, 2020Co-Authors: Jana Turcinkova, Jana Stavkova, Nada BirciakovaAbstract:The paper deals with the assessment of Income situation of households in the Czech Republic. The primary source for the analysis were the data of the survey EU-SILC European Union – Statistics on Income and Living Conditions. The basic variable for the analysis is the level of the household Income in 2005–2008. In addition to the decile classification, characteristics such as the average Income per one household member, poverty threshold, poverty depth coefficient, Lorenz curve and Gini coefficient. were calculated in order to evaluate the Income situation. The results show an increase of the average household Income. The Lorenz curve followed by the Gini coefficient demonstrate the uniformity of distribution of Income values. The results show a decreasing Income differentiation. The poverty threshold was defined on the level of 60% of the median value and with this given threshold, the households were assessed, whether they belong to the ones at the risk of poverty. The results reveal a decreasing number of households at the risk of poverty. The poverty depth coefficient has a stronger explanatory power and shows how far below the poverty threshold the households are, or what is an Income deficit of these households. Each category of households at the risk of poverty varies with the depth of poverty. The analysis also provides the results of how the households’ Income situation or poverty is perceived by the households themselves.
-
material deprivation in selected eu countries according to eu silc Income Statistics
Journal of Cryptology, 2012Co-Authors: Jana Stavkova, Nada Birciakova, Jana TurcinkovaAbstract:The article focuses on issues of households at risk of poverty in a relative conception. Income poverty represents a situation when the threshold of 0.6 of median Income is not achieved. The analysis of a broader definition of poverty bases on identification and assessment of material deprivation factors, which include: Financial stress, Housing conditions, Availability of consumer durables and Basic needs. Data sources are based EU-SILC dataset. The presented analysis focuses on selected EU countries, namely Czech Republic, Finland, France, Spain and United Kingdom. The result is the identification of problem areas that cause deprivation symptoms.
Nada Birciakova - One of the best experts on this subject based on the ideXlab platform.
-
Material deprivation of households in the Czech Republic in comparison with selected EU countries based on EU-SILC Income Statistics
PsycEXTRA Dataset, 2020Co-Authors: Jana Turcinkova, Jana Stavkova, Nada BirciakovaAbstract:The paper deals with the assessment of Income situation of households in the Czech Republic. The primary source for the analysis were the data of the survey EU-SILC European Union – Statistics on Income and Living Conditions. The basic variable for the analysis is the level of the household Income in 2005–2008. In addition to the decile classification, characteristics such as the average Income per one household member, poverty threshold, poverty depth coefficient, Lorenz curve and Gini coefficient. were calculated in order to evaluate the Income situation. The results show an increase of the average household Income. The Lorenz curve followed by the Gini coefficient demonstrate the uniformity of distribution of Income values. The results show a decreasing Income differentiation. The poverty threshold was defined on the level of 60% of the median value and with this given threshold, the households were assessed, whether they belong to the ones at the risk of poverty. The results reveal a decreasing number of households at the risk of poverty. The poverty depth coefficient has a stronger explanatory power and shows how far below the poverty threshold the households are, or what is an Income deficit of these households. Each category of households at the risk of poverty varies with the depth of poverty. The analysis also provides the results of how the households’ Income situation or poverty is perceived by the households themselves.
-
material deprivation in selected eu countries according to eu silc Income Statistics
Journal of Cryptology, 2012Co-Authors: Jana Stavkova, Nada Birciakova, Jana TurcinkovaAbstract:The article focuses on issues of households at risk of poverty in a relative conception. Income poverty represents a situation when the threshold of 0.6 of median Income is not achieved. The analysis of a broader definition of poverty bases on identification and assessment of material deprivation factors, which include: Financial stress, Housing conditions, Availability of consumer durables and Basic needs. Data sources are based EU-SILC dataset. The presented analysis focuses on selected EU countries, namely Czech Republic, Finland, France, Spain and United Kingdom. The result is the identification of problem areas that cause deprivation symptoms.
Paul A Jargowsky - One of the best experts on this subject based on the ideXlab platform.
-
mcib stata module to estimate Income distribution and inequality Statistics from grouped data
Statistical Software Components, 2019Co-Authors: Paul A JargowskyAbstract:mcib estimates Income distribution and inequality Statistics from grouped data. The program implements a method detailed in Jargowsky and Wheeler (2018), "Estimating Income Statistics from Grouped Data: Mean-Constrained Integration over Brackets" (Sociological Methodology 48(1): 337-374).
-
estimating Income Statistics from grouped data mean constrained integration over brackets
Sociological Methodology, 2018Co-Authors: Paul A Jargowsky, Christopher A WheelerAbstract:Researchers studying Income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified Income brackets. The distribution of households within the brackets is unknown, and highest Incomes are often included in an open-ended top bracket, such as “$200,000 and above.” Common approaches to this estimation problem include calculating midpoint estimators with an assumed Pareto distribution in the top bracket and fitting a flexible multiple-parameter distribution to the data. The authors describe a new method, mean-constrained integration over brackets (MCIB), that is far more accurate than those methods using only the bracket counts and the overall mean of the data. On the basis of an analysis of 297 metropolitan areas, MCIB produces estimates of the standard deviation, Gini coefficient, and Theil index that are correlated at 0.997, 0.998, and 0.991, respectively, with the par...
Christopher A Wheeler - One of the best experts on this subject based on the ideXlab platform.
-
estimating Income Statistics from grouped data mean constrained integration over brackets
Sociological Methodology, 2018Co-Authors: Paul A Jargowsky, Christopher A WheelerAbstract:Researchers studying Income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified Income brackets. The distribution of households within the brackets is unknown, and highest Incomes are often included in an open-ended top bracket, such as “$200,000 and above.” Common approaches to this estimation problem include calculating midpoint estimators with an assumed Pareto distribution in the top bracket and fitting a flexible multiple-parameter distribution to the data. The authors describe a new method, mean-constrained integration over brackets (MCIB), that is far more accurate than those methods using only the bracket counts and the overall mean of the data. On the basis of an analysis of 297 metropolitan areas, MCIB produces estimates of the standard deviation, Gini coefficient, and Theil index that are correlated at 0.997, 0.998, and 0.991, respectively, with the par...
