Download presentation
Presentation is loading. Please wait.
Published byMitchell Stone Modified over 9 years ago
1
Poverty Estimation in Small Areas Agne Bikauskaite European Conference on Quality in Official Statistics (Q2014) Vienna, 3-5 June 2014
2
Data: The population size N = 3000 The sample size n = 300 Number of mutually exclusive strata H = 7 The income of individuals (y h1,..., y hN ) The auxiliary information (x 1i,..., x ji,..., x Ji ) 1000 simple random samples
3
Income distribution
4
Data: The population size N = 3000 The sample size n = 300 Number of mutually exclusive strata H = 7 The income of individuals (y h1,..., y hN ) The auxiliary information (x 1i,..., x ji,..., x Ji ) 1000 simple random samples
5
Strata size Number of strataPopulation sizeStrata size 1 49650 2 33333 3 17718 4 11912 5 929 6 79479 7 98999 Total 3000300
6
Stratified Sampling The sample design probability when element i belongs to stratum h is The sampling weight for selected person i from the h stratum is
7
Estimated parameters The Average Income The Poverty Line The Headcount Index The Poverty Gap Index
8
The Average Income The average income in strata h is The average income estimate is
9
The Poverty Line The Poverty Line is defined as 60 per cent of the median equivalent disposable income The Poverty Line estimate is
10
The Headcount Index The headcount index is defined as the number of persons below the poverty line divided by the population number The Headcount index estimate is
11
The Poverty Gap The poverty gap G is defined as an amount of difference between poverty line and income value y of i th person living in poverty or social exclusion The poverty gap estimate
12
The Poverty Gap Index The poverty gap index is a proportion of the poverty gap and the poverty line The poverty gap index estimate is
13
Population
14
What is Small Area?
15
Sampling in Small Areas
16
Direct and Indirect Estimates Direct Estimates: –Not using auxiliary information –Using auxiliary information from the same area Indirect Estimates: –Using auxiliary information from adjacent areas
17
Simulated Estimation Methods The Horvitz-Thompson (HT) The Generalised Regression (GREG) The Synthetic (S)
18
The Absolute Relative Bias The Absolute Relative Bias (ARB) assessed the accuracy of the estimates
19
The Horvitz-Thompson estimator The sum estimate is
20
The ARB of the average income estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised Regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.064475440.098310539-0.08398375 1 -0.3211974-0.31518106-0.34310121 2 -0.02643092-0.014056-0.06902109 3 0.4655713930.5517990550.403882282 4 -0.81562095-0.88208503-0.65375062 5 0.4857153320.5108412720.492216146 6 -0.1417938-0.13401672-0.14913289 7 0.0792527930.0909450550.188597999
21
The ARB of the headcount index estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population 0.363963290.1476656640.152247869 1 -3.51959494-3.7958481-3.8266288 2 1.4684931511.1920298881.003491015 3 4.6447619054.7571445435.058255185 4 2.8597826092.6010869572.877924901 5 -2.80634921-2.90370419-1.68042706 6 -0.63675717-0.78252971-0.8622043 7 1.3440970791.0683577861.298860988
22
ARB of the poverty gap index estimate Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.1594528-0.35543944-0.41065525 1 -1.37126072-1.59705543-1.57592157 2 -0.9619282-1.23793553-1.64985282 3 -0.49766038-0.6453229-0.69178013 4 -1.21749012-1.2989069-1.45047831 5 -0.73358855-0.956284860.02299011 6 0.7029895530.4776107190.357964671 7 0.196259620.023796320.278143276
23
The GREG estimator The sum estimate
24
The ARB of the average income estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised Regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.064475440.098310539-0.08398375 1 -0.3211974-0.31518106-0.34310121 2 -0.02643092-0.014056-0.06902109 3 0.4655713930.5517990550.403882282 4 -0.81562095-0.88208503-0.65375062 5 0.4857153320.5108412720.492216146 6 -0.1417938-0.13401672-0.14913289 7 0.0792527930.0909450550.188597999
25
The ARB of the headcount index estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population 0.363963290.1476656640.152247869 1 -3.51959494-3.7958481-3.8266288 2 1.4684931511.1920298881.003491015 3 4.6447619054.7571445435.058255185 4 2.8597826092.6010869572.877924901 5 -2.80634921-2.90370419-1.68042706 6 -0.63675717-0.78252971-0.8622043 7 1.3440970791.0683577861.298860988
26
ARB of the poverty gap index estimate Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.1594528-0.35543944-0.41065525 1 -1.37126072-1.59705543-1.57592157 2 -0.9619282-1.23793553-1.64985282 3 -0.49766038-0.6453229-0.69178013 4 -1.21749012-1.2989069-1.45047831 5 -0.73358855-0.956284860.02299011 6 0.7029895530.4776107190.357964671 7 0.196259620.023796320.278143276
27
The Synethetic estimator The sum estimate is
28
The ARB of the average income estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised Regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.064475440.098310539-0.08398375 1 -0.3211974-0.31518106-0.34310121 2 -0.02643092-0.014056-0.06902109 3 0.4655713930.5517990550.403882282 4 -0.81562095-0.88208503-0.65375062 5 0.4857153320.5108412720.492216146 6 -0.1417938-0.13401672-0.14913289 7 0.0792527930.0909450550.188597999
29
The ARB of the headcount index estimates Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population 0.363963290.1476656640.152247869 1 -3.51959494-3.7958481-3.8266288 2 1.4684931511.1920298881.003491015 3 4.6447619054.7571445435.058255185 4 2.8597826092.6010869572.877924901 5 -2.80634921-2.90370419-1.68042706 6 -0.63675717-0.78252971-0.8622043 7 1.3440970791.0683577861.298860988
30
ARB of the poverty gap index estimate Stratum Horvitz-Thompson estimate’s ARB (%) Generalised regression estimate’s ARB (%) Synthetic estimate’s ARB (%) Population -0.1594528-0.35543944-0.41065525 1 -1.37126072-1.59705543-1.57592157 2 -0.9619282-1.23793553-1.64985282 3 -0.49766038-0.6453229-0.69178013 4 -1.21749012-1.2989069-1.45047831 5 -0.73358855-0.956284860.02299011 6 0.7029895530.4776107190.357964671 7 0.196259620.023796320.278143276
31
The mean estimate’s variance
32
The Jack-Knife method The Jack-Knife method’s idea is to divide stratified sample into mutually exclusive subgroups. The modified sampling weights
33
The Jack-Knife variance estimator Then the Jack-Knife variance estimator of estimated parameter is
34
Conclusions: Poverty parameters estimation Different estimation methods for large and for small areas The Synthetic method for poverty estimation in small areas If auxiliary information from adjacent areas is not available then the most appropriate estimation method is Horvitz-Thompson
35
Conclusions: Variances estimation of the estimated parameters Large ARBs The best results of estimation are given by the Horvitz-Thompson method Applying Jack-Knife method precision of the estimates increases when the group size is extremely small
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.