LEADER 05444oam 22011294 450 001 9910788232703321 005 20230721045625.0 010 $a1-4623-9111-7 010 $a1-4527-8641-0 010 $a1-4518-7041-8 010 $a1-282-84134-3 010 $a9786612841347 035 $a(CKB)3170000000055083 035 $a(EBL)1607966 035 $a(SSID)ssj0000944161 035 $a(PQKBManifestationID)11503328 035 $a(PQKBTitleCode)TC0000944161 035 $a(PQKBWorkID)10983260 035 $a(PQKB)10048888 035 $a(OCoLC)761981611 035 $a(MiAaPQ)EBC1607966 035 $a(IMF)WPIEE2008183 035 $a(EXLCZ)993170000000055083 100 $a20020129d2008 uf 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aKernel Density Estimation Based on Grouped Data : $eThe Case of Poverty Assessment /$fCamelia Minoiu, Sanjay Reddy 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2008. 215 $a1 online resource (36 p.) 225 1 $aIMF Working Papers 225 0$aIMF working paper ;$vWP/08/183 300 $aDescription based upon print version of record. 311 $a1-4519-1494-6 320 $aIncludes bibliographical references. 327 $aContents; I. Motivation; II. The Data Structure and the Bias of the Estimator; III. The Bandwidth and Kernels Considered; IV. Monte Carlo Study; A. Theoretical Distributions; B. Summary Statistics, Density Estimates and Diagrams; C. Poverty Estimates; V. Country Studies; VI. Global Poverty; VII. Conclusions; References; Appendix; Appendix Figures; 1. Distributions used in Monte Carlo analysis; 2. Bias of KDE-based density (log-normal distribution); Appendix Tables; 1. Summary statistics from KDE-based sample; 3. Bias of estimated density (multimodal distribution) 327 $a4. Bias of estimated density (Dagum distribution)2. Bias of poverty measures (Low and High Poverty Lines); 5. Bias in the poverty headcount ratio versus location of poverty line; 3. Bias of poverty measures (Triweight kernel, Poverty line: 0.25 x median); 4. Bias of poverty measures (Hybrid bandwidth, Poverty line: 0.5 x median); 5. Bias of poverty measures (Epanechnikov kernel, Silverman bandwidth); 6. Bias of poverty measures (Gaussian kernel, Poverty line: Capability); 6. Survey-based and grouped data KDE-based density estimates; 7. Global poverty rates (% poor) 327 $a8. Global poverty counts (millions) 330 3 $aWe analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data. 410 0$aIMF Working Papers; Working Paper ;$vNo. 2008/183 606 $aPoverty$xMeasurement 606 $aIncome distribution$xEconometric models 606 $aKernel functions 606 $aEconometrics$2imf 606 $aMacroeconomics$2imf 606 $aDemography$2imf 606 $aPoverty and Homelessness$2imf 606 $aWelfare, Well-Being, and Poverty: General$2imf 606 $aPersonal Income, Wealth, and Their Distributions$2imf 606 $aAggregate Factor Income Distribution$2imf 606 $aDemographic Economics: General$2imf 606 $aEstimation$2imf 606 $aPoverty & precarity$2imf 606 $aPopulation & demography$2imf 606 $aEconometrics & economic statistics$2imf 606 $aPoverty$2imf 606 $aPersonal income$2imf 606 $aIncome distribution$2imf 606 $aPopulation and demographics$2imf 606 $aEstimation techniques$2imf 606 $aIncome$2imf 606 $aPopulation$2imf 606 $aEconometric models$2imf 607 $aNicaragua$2imf 615 0$aPoverty$xMeasurement. 615 0$aIncome distribution$xEconometric models. 615 0$aKernel functions. 615 7$aEconometrics 615 7$aMacroeconomics 615 7$aDemography 615 7$aPoverty and Homelessness 615 7$aWelfare, Well-Being, and Poverty: General 615 7$aPersonal Income, Wealth, and Their Distributions 615 7$aAggregate Factor Income Distribution 615 7$aDemographic Economics: General 615 7$aEstimation 615 7$aPoverty & precarity 615 7$aPopulation & demography 615 7$aEconometrics & economic statistics 615 7$aPoverty 615 7$aPersonal income 615 7$aIncome distribution 615 7$aPopulation and demographics 615 7$aEstimation techniques 615 7$aIncome 615 7$aPopulation 615 7$aEconometric models 676 $a339.46 700 $aMinoiu$b Camelia$0874355 701 $aReddy$b Sanjay$0602369 801 0$bDcWaIMF 906 $aBOOK 912 $a9910788232703321 996 $aKernel Density Estimation Based on Grouped Data$93704156 997 $aUNINA