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DASP: USER MANUAL - Université Laval

1. es es After clicking SUBMIT the following appears Figure 49 Quantile curves Quantile Curves 30000 Q p 20000 10000 o T 87 Q 3 Steps To open the relevant dialog box type db cnpe Choose variables and parameters as in Figure 50 Drawing non parametric regression curves E DASP Non parametric regression gt cnpe command Local linear approach k After clicking SUBMIT the following appears 88 Figure 51 Non parametric regression curves Non parametric regression Linear Locally Estimation Approach Bandwidth 3699 26 E Y X 10000 15000 20000 J 5000 4 0 12000 24000 36000 48000 60000 X values Q 4 Steps Choose variables and parameters as in 89 Figure 52 Drawing derivatives of non parametric regression curves E DASP Non parametric regression gt cnpe command After clicking SUBMIT the following appears Figure 53 Derivatives of non parametric regression curves Non parametric derivative regression Linear Locally Estimation Approach Bandwidth 3699 26 dE Y X dX T T T T 12000 24000 36000 48000 X values 90 1 60000 21 11 function Plotting the joint density and joint distribution What does the joint distribution of gross and net incomes look like in Canada Using the c
2. ccccccccceeeeeseeceeeeeceeeeeaaeeeeeeeesaaeeesaaeeeeeeeseeeesaeesiaeeeeaes 71 Figure 32 Drawing the difference between FGT curves with confidence interval 72 Figure 33 Difference between FGT curves with confidence interval O ee 72 Figure 34 Difference between FGT curves with confidence interval Q 1 eee 73 Figure 35 Testing for poverty COMINANCE cceeeeeeeceteeeeeeeeeeeeeeeeeeceaaeeeeaeeeeeeeeseaeeesaaeseeeeeseeeseeaeeseeeseaes 74 Figure 36 Decomposing FGT indices by group cccccceceeeeeseeeeeeeceaeeeeeaeeseeeeeseaeeesaaesteneeseeeesaaesseneseaes 75 Figure 37 Lorenz and concentration CUIVES 0000 ee eeececeeene cette nett eeeeae eee eeeeaeeeeeeaaeeeseeaaeeeeeeaaeeeeeeeaeeeeseaeeeeseaaes 78 Figure 38 Lorenz CUIVES ideove oa aa natu eee dei teeth tees idea ae heed E kk Snead ae thea nn Shape 78 Figure 39 Drawing concentration CUIVES 22 ee 2eeeeee ee eee 0000 ee nene KKK KK KKK KKK PEK K KE aaraa EAEAN 79 Figure 40 Lorenz and concentration CUIVES ese 2eeeeeee20 ee eee 00 eee nene KKK KK KKK RKK REKREA RP n 80 Figure 41 Drawing Lorenz CUIVES 2 eeee eee eee eee a KKK PEK K KE REK K aa RER E KR EPER R E 81 Figure 423 LOPEZ CUVE Seuran ea akon t k k dees k ketu de svekade ive A hates otek arth eee 81 Figure 43 Estimating Gini and concentration indices cccccececeeceececeeeeeeeeeeeeecaeeesaaeseeeeeseaeeeseaeeseaeeeeaes 82 Figure 44
3. Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed A level for the parameter o can be chosen for each of the two distributions 18 2DASP and pro poor curves Pro poor curves can be drawn using either the primal or the dual approach The former uses income levels The latter is based on percentiles 18 2 1 Primal pro poor curves 37 The change in the distribution from state 1 to state 2 is s order absolutely pro poor with standard cons if A z s P z cons a s RE z a s 1 lt 0 VzeE 0 2 The change in the distribution from state 1 to state 2 is s order relatively pro poor if A z 8 on s D R z a s 1 lt 0 Vz e 0 2 4 The module cpropoorp can be used to draw these primal pro poor curves and their associated confidence interval by taking into account sampling design The module can draw pro poor curves and their two sided lower bounded or upper bounded confidence intervals list or save the coordinates of the differences between the curves as well as those of the confidence intervals save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical opt
4. 2 Differences 3 o 20000 40000 80000 80000 400000 Foverty line z Null Horizontal Line FGT_Urban FGT_Rural Q 6 68 Figure 29 Differences between FGT curves EE Difference Between FGT Curves alpha 1 Burkina 1994 Differences 40000 80000 80000 400000 Foverty line z Null Horizontal Line FGT_Urban FGT_Rural 0 20000 21 5Estimating FGT curves and differences between FGT curves with confidence intervals Is the poverty increase between 1994 and 1998 in Burkina Faso statistically significant 1 Using the file bkf94I dta draw the FGT curve and its confidence interval for the variable of interest exppc with a parameter a 0 b poverty line between 0 and 100 000 Franc CFA c size variable set to size 2 Using simultaneously the files bkf94I dta and bkf98I dta draw the difference between FGT curves and associated confidence intervals with a The variable of interest exppc for 1994 and exppcz for 1998 b parameter a 0 c poverty line between 0 and 100 000 Franc CFA d size variable set to size 3 Redo 2 with parametera 1 Answers Q 1 Steps Type 69 use C data bkf94l dta clear To open the relevant dialog box type db cfgts Choose variables and parameters as in Figure 30 Drawing FGT curves with confidence interval E DASP FGT Curve with Confidence Interval gt cfgts command Aft
5. 6 The average benefit at the level of those eligible to a service from sector s and for those observations that belong to a group g is defined as gt wBil i eg ABE M weii eg i l 7 The average benefit for those that use the service s and belong to a group g is defined as n gt wBil i eg ABF 8 n gt wif IG eg i l 8 The proportion of benefits from the service from sector s that accrues to observations that belong to agroup g is defined as n where B gt w Bili eg i l These statistics can be restricted to specific socio demographic groups e g rural urban by replacing IG eg byl iec The bian ado module allows the computation of these different statistics Some characteristics of the module o Possibility of selecting between one and six sectors o Possibility of using frequency data approach when information about the level of total public expenditures is not available o Generation of benefit variables by the type of public services ex primary secondary and tertiary education levels and by sector o Generation of unit cost variables for each sector 4 o Possibility of computing statistics according to groups of observations o Generation of statistics according to social demographic groups such as quartiles quintiles or deciles Generally public expenditures on a given service can vary from one geographical or administrative area to another When the information about
6. 65 Figure 25 Graph of FGT curves by zone 5 Graph Graph ln x FGT Curves alpha 0 Burkina 1994 6 8 FGT z alpha 0 4 0 20000 80000 100000 66 Choose the option DIFFERENCE and select WITH THE FIRST CURVE Indicate that the group variable is zone Select the Results panel and choose the option LIST in the COORDINATES quadrant In the GRAPH quadrant select the directory in which to save the graph in gph format and to export the graph in wmf format Figure 26 Differences of FGT curves JS DASP FGT Curves gt cfgt command Main Resuts Y Awis X Axis Title Caption Legend Overall Variable s of interest Type of curvefs epe o J Type Normalised JV Difference With thefistcuve Size variable Be E Group variable Parameters Parameter alpha l Minimum Maximum Poverty line z O fiooooo Cancel Submit Figure 27 Listing coordinates E DASP FGT Curves gt cfgt command Main Results y axis X Axis Title Caption Legend Overall Coordinates Iv List Save Browse Graph IV Display Save JCAStata_graphs grapht gph Browse Export CAStata araphskarapht m Browse oa Cancel Submit 67 After clicking SUBMIT the following appears Figure 28 Differences between FGT curves t Graph Graph Difference Between FGT Curves alpha 0 Burkina 1994
7. CADATA DKI94 dta Browse Variable of interest Variable of interest Size variable Size variable Poverty line Poverty line Absolute 10000 Absolute 10000 C Relative 503 Z of the Mean z C Relative od of the Mean z I Condition s fi T Condition s fi s Parameters and Options Parameter alpha fo Type Normalised Cancel Submit 1 DASP considers two distributions to be statistically dependent for statistical inference purposes if the same data set is used the same loaded data or data with the same path and filename for the two distributions 2 Ifthe option DATA IN FILE is chosen the keyboard must be used to type the name of the required variables 9 Basic Notation The following table presents the basic notation used in DASP s user manual Symbol Indication y variable of interest i observation number yi value of the variable of interest for observation i hw sampling weight hw sampling weight for observation i hs size variable hs size of observation i for example the size of household i W hw hs hg group variable hg group of observation i wi swi sw if hg k and 0 otherwise 11 n sample size For example the mean of y is estimated by DASP as n 10 DASP and poverty indices 10 1FGT and EDE FGT poverty indices ifgt The non normalised Foster Greer Thorbecke or FGT index
8. Estimating concentration indiCeS ccccceceeeeeeeeeeeeeeeeeaeeeeeeeaeeeeeeaaeeeeeeaaeeeseeaaeeeseeaaeeeeneaeeeeseaaes 83 Figure 45 Estimating differences in Gini and concentration INdiCES ee 84 Figure 46 Drawing densitieS ceeeecceceeseeeeeeeeeeeeeeeeeeeeeeesaneeeeesaeeeeessaneeseesaneeeeeaaeecesneeaeeeeneaeeeeneeeeesennnaes 85 Figure 47 Density CUVOS orrainn Ea E E EEIE at KOKA KA E ob gad A E E a O 86 Figure 48 Drawing quantile CUIVES cece eeeeee ee eeee eter eeeeae eee aaaeeeeeeaaeeeeeeaaeeeseeaaeeeeesaaeeeseeaaeeeesenaeeeesenaes 87 Figure 49 Quantile curves 2 cease eeeneeeaaeeeceaeeeeaaesaaneeeaaeeeeaaesaaeesaeeesaaaeseeaeeseaeessaeeseaeseeneeeseas 87 Figure 50 Drawing non parametric regression CUIVES 0 eeeeeceeeeseeeeeeeneeeeeeeaaeeeeeeeaaeeeeeeaaeeeeeeaaeeeeeeaeeeenenaes 88 Figure 51 Non parametric regression CUIVES 2 2 teens ee eeeeeeeeeeaeeeeeceaaeeeseeaaeeeeeeaaeeeeeeaaeeeeeeaaeeeeeaaes 89 Figure 52 Drawing derivatives of non parametric regression CUTVBS 2 2 2 eeeeeeeeeee ee eeenn nn 90 Figure 53 Derivatives of non parametric regression CUIVES ccecccceeeeeeeeeeeeeeeeeceaeeeeeaeeeeeeeseaeeetiaeeeeneeteaes 90 Figure 54 Figure 55 Figure 56 Figure 57 Figure 58 Figure 59 Figure 60 Figure 61 Plotting joint density funCti0N ee e 24 2e 22200400 004eee eee een K KRKA PAE K ARK R AA Kn 91 Plotting joint di
9. Size variable size 7 Group variable 7 Parameters Parameter alpha jo Minimum Maximum Poverty line z 0 fi 00000 oa ancel Submit To change the subtitle select the Title panel and write the subtitle Figure 22 Editing FGT curves E DASP FGT Curves gt cfgt command Main Results Y Axis X Axis Title Caption Legend Overall Title Subtitle E Burkina Size Defaut Justify Defaut Size beat x Justify beat Color Defaut z Alignment Defaut Color Defaut z Alignment Defaut Position Defaut Margin E Position Defaut x Margin B Orientation Defaut x Line gap E Orientation Defaut x Line gap JO T Inside plot region T Inside plot region T Span width of graph T Span width of graph T Box T Box Fill color Defaut z Fill color Defaut z Line color Defaut z Line color Defaut s Margin a Margin s I Ignore text size J Ignore text size After clicking SUBMIT the following graph appears 63 Figure 23 Graph of FGT curves 5 Graph Graph IE x FGT Curves alpha 0 Burkina 1994 FGT z alpha 0 80000 100000 64 Q 4 Choose variables and parameters as in the following window Figure 24 FGT curves by zone E DASP FGT Curves gt cfgt command o 00 After clicking SUBMIT the following graph appears
10. indpoy exppc pliterate hsizelsize index alphal1 betal1 gannal1 pl1l400 pl2 0 9 H D Poverty index Bourguignon and Chakravarty 2003 Household size size 61 21 4Estimating FGT curves How sensitive to the choice of a poverty line is the rural urban difference in poverty 1 Open bkf941 dta 2 Open the FGT curves dialog box 3 Draw FGT curves for variables of interest exppc and expeg with a parameter a 0 b poverty line between 0 and 100 000 Franc CFA c size variable set to size d subtitle of the figure set to Burkina 1994 4 Draw FGT curves for urban and rural residents with a variable of interest set to expcap b parameter a 0 c poverty line between 0 and 100 000 Franc CFA d size variable set to size 5 Draw the difference between these two curves and a save the graph in gph format to be plotted in Stata and in wmf format to be inserted in a Word document b List the coordinates of the graph 6 Redo the last graph witha 1 Answers Q 1 Open the file with use C data bkf94I dta clear 0 2 Open the dialog box by typing db difgt 0 3 Choose variables and parameters as follows 62 Figure 21 Drawing FGT curves E DASP FGT Curves gt cfgt command Main Resuts Y Axis Axis Title Caption Legend Overall Variable s of interest exppc expeq Type of curvels T Type Normalised T Difference No x
11. type the following command to label variables and labels do C do_files lab_bkf94 do Typing the command describe we obtain obs 8 625 vars 9 31 Oct 2006 13 48 size 285 087 99 6 of memory free storage display value variable name type format label variable label weight float 9 0g Sampling weight size byte 8 0g Household size strata byte 8 0g Stratum in which a household lives psu byte 8 0g Primary sampling unit gse byte 29 0g gse Socio economic group of the household head sex byte 8 0g sex Sex of household head zone byte 8 0g zone Residential area exp double 10 0g Total household expenditures expeg double 10 0g Total household expenditures per adult eguivalent exppc float 9 0g Total household expenditures per capita Typing label list we find zone 1 Rural 2 Urban sex 1 Male 2 Female gse 49 wage earner public sector wage earner private sector Artisan or trader Other type of earner Crop farmer Food farmer Inactive NOOR WD 0 2 You can set the sampling design with a dialog box as indicated in Section 20 3 or simply by typing svyset psu pweight weight strata strata vce linearized Typing svydes we obtain Survey Describing stage 1 sampling units pueight weight UCE linearized Strata 1 strata SU 1 psu FPC 1 lt zero gt 0bs per Unit Stratun nits bs nin nean nak 1 2 838 19 20 0 20 2 3 73 1 19 8 a 3 98 1959 19 20 0 20 4 5 1093 19 19 9 20
12. 0 016980 Farners food 0 162894 0 680885 0 110912 0 796800 0 008643 0 016403 0 005823 0 019015 Inactive 0 144916 0 075856 0 010993 0 078973 0 014994 0 004839 0 001332 0 008520 POPULATION 0 139197 1 000000 0 139197 1 000000 0 006553 0 000000 0 006553 0 000000 75 0 2 Using the RESULTS panel change the number of decimals and unselect the option DISPLAY STANDARD ERRORS After clicking SUBMIT the following information is obtained dfgtg exppc hgrouplgse hsizelsize alphal1 pline 41099 dstd 0 typelnor decl4 FGT Index Deconposition by Groups Group FGT Index Popu lat ion Absolute Relat ive Share Contribution Contribution Hage earning public sector Hage earning private sector Artisan or trading Others activities Farners crop Farners food Inactive POPULATION 76 21 8Estimating Lorenz and concentration curves How much do taxes and transfers affect inequality in Canada By using the can6 dta file 1 Draw the Lorenz curves for gross income X and net income N How can you see the redistribution of income 2 Draw Lorenz curves for gross income X and concentration curves for each of the three transfers B1 B2 and B3 and the tax T What can you say about the progressivity of these elements of the tax and transfer system What is the extent of inequality among Burkina Faso rural and urban households in 1994 By using the bkf94I dta file 3 Draw Lorenz curves for ru
13. 10 2800 Interested users are encouraged to consider the exercises that appear in Section 21 14 42 20 Appendices 20 1 Appendix A illustrative household surveys 20 1 1 The 1994 Burkina Faso survey of household expenditures bkf94I dta This is a nationally representative survey with sample selection using two stage stratified random sampling Seven strata were formed Five of these strata were rural and two were urban Primary sampling units were sampled from a list drawn from the 1985 census The last sampling units were households List of variables strata Stratum in which a household lives psu Primary sampling unit weight Sampling weight size Household size exp Total household expenditures expeq Total household expenditures per adult equivalent expcp Total household expenditures per capita gse Socio economic group of the household head 1 wage earner public sector 2 wage earner private sector 3 Artisan or trader 4 Other type of earner 5 Crop farmer 6 Subsistence farmer 7 Inactive sex Sex of household head 1 Male 2 Female zone Residential area 1 Rural 2 Urban 43 20 1 2 The 1998 Burkina Faso survey of household expenditures bkf98I dta This survey is similar to the 1994 one although ten strata were used instead of seven for 1994 To express 1998 data in 1994 prices two alternative procedures have been used First 1998 expenditure data were multiplied by the ratio of the 1994 official poverty line to th
14. 800 Using this information the following variables are generated cap drop varl gen varl size weight 3800 qui sum varl qui gen pri pub exp 0 03 0 352 r sum qui gen sec pub exp 0 03 0 212 r sum qui gen uni pub exp 0 03 0 160 r sum cap drop varl Total public expenditures on primary sector pri pub exp Total public expenditures on secondary sector sec sec exp Total public expenditures on university sector uni pub exp Estimate the average benefits per guintile and generate the benefit variables Answer Set variables and options as follows 104 Figure 61 Benefit Incidence Analysis unit cost approach Ee DASP Benefit incidence analysis gt bian command Main Results Label the public service Education Variable s of interest Options Standard living expe X Approach Unit cost benet Number of sectors 2 v Labels Frequency Eligible HH members Area indicator Regional pub expenditures Sector 1 Primary fra prim z Jel prim pri pub exp Sector 2 Secondary firq_sec Jel sec sec pub exp Cancel Submit E DASP Benefit incidence analysis gt bian command Main Results Result options Number of Decimals 34 Social groups Quintiles C Group variable x Displayed results JV Share and rate of participation IV Average benefits IV Proportion of benefits Can
15. Each of these three sub samples contains the following variables strata The stratum psu The primary sampling unit weight Sampling weight inc Income hhsz Household size 1 Using the files mex 92 2ml dta and mex 98 2ml dta test for first order relative pro poorness of growth when e The primal approach is used e The range of poverty lines is 0 3000 2 Repeat with the dual approach 3 By using the files mex 98 2ml dta and mex 04 2ml dta test for absolute second order pro poorness with the dual approach 4 Using mex 98 2ml dta and mex 04 2ml dta estimate the pro poor indices of module ipropoor e Parameter alpha set to 1 e Poverty line equal to 600 Answer Q 1 Steps To open the relevant dialog box type db cpropoorp 97 Choose variables and parameters as in select the upper bounded option for the confidence interval Figure 57 Testing the pro poor growth primal approach EE DASP Pro poor curves primal approach gt cpropoorp command After clicking SUBMIT the following graph appears Relative propoor curve LO Order s 1 Dif P 2 m2 m1 z a s 1 P 1 z a s 1 AN A P Ne T T T T 1 0 600 1200 1800 2400 3000 Poverty line z Difference Upper bound of 95 confidence interval Null horizontal line 98 0 2 Steps To open the relevant dialog box type db cpropoord Choose variables and
16. command db clorenz in the command window Figure 10 Lorenz and concentration curves E DASP Lorenz amp Concentration Curves gt clorenz command i Io xj Main Results Y Axis X Axis Title Caption Legend Overall T Wariable s of interest Type of curve s T Type Normalised by default T Ranking Variable v T Difference No z Size variable Range of percentiles p Group variable PE Minimum Maximum fo 0 fi 0 Cancel Submit Interested users are encouraged to consider the exercises that appear in Section 21 8 15 5 Lorenz concentration curves with confidence intervals clorenzs The clorenzs module draws a Lorenz concentration curve and its confidence interval by taking sampling design into account The module can draw a Lorenz concentration curve and two sided lower bounded or upper bounded confidence intervals condition the estimation on a population subgroup draw Lorenz concentration curves and generalized Lorenz concentration curves 31 list or save the coordinates of the curves and their confidence interval save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs 15 6 Differences between Lorenz concentration
17. design has been well set type the command svydes The following will be displayed Survey Describing stage 1 sanpling units pueight weight V E Linear Strata 1 strata SU 1 psu FPC 1 lt zero gt ized 0bs per Unit Stratun nits bs nin nean nax j B o E 48 21 Examples and exercises 21 1 Estimation of FGT poverty indices How poor was Burkina Faso in 1994 1 Open the bkf94 dta file and label variables and values using the information of Section 20 1 1 Type the describe command and then label list to list labels 2 Use the information of Section 20 1 1 to set the sampling design and then save the file 3 Estimate the headcount index using variables of interest exocc and expeg a You should set SIZE to household size in order to estimate poverty over the population of individuals b Use the so called 1994 official poverty line of 41099 Francs CFA per year 4 Estimate the headcount index using the same procedure as above except that the poverty line is now set to 60 of the median 5 Using the official poverty line how does the headcount index for male and female headed households compare 6 Can you draw a 99 confidence interval around the previous comparison Also set the number of decimals to 4 Answer Q 1 If bkf94 dta is saved in the directory c data type the following command to open it use C data bkf94 dta clear If lab_bkf94 do is saved in the directory c do_files
18. hsizelsize hgrouplsex plinel 41099 Poverty Index FGT Index Household size size Sanpling weight weight Group variable sex Paraneter alpha 0 00 Group Estinate STD LB UB P Line l Hale 0 42176 0 016633 0 419484 0 484867 41099 00 gt Fenale 0 281850 0 028206 0 226411 0 337290 41099 00 OPULATION 0 444565 0 016124 0 412873 0 476256 41099 00 Q 6 Using the panel CONFIDENCE INTERVAL set the confidence level to 99 and set the number of decimals to 4 in the RESULTS panel 53 ifgt exppc alphal0 hsizelsize hgrouplsex dec 4 level 99 pline 41099 Poverty Index FGT Index Household size size Sanpling weight weight Group variable sex Paraneter alpha 0 00 Group Est inate ST LB UB P Line 1 Hale 0 4522 0 0166 0 4091 0 4962 41099 00 2 Fenale 0 2819 0 0282 0 2089 0 3548 41099 00 POPULATION 0 4446 0 0161 0 4028 0 4863 41099 00 54 21 2Estimating differences between FGT indices Has poverty Burkina Faso decreased between 1994 and 1998 1 Open the dialog box for the difference between FGT indices Estimate the difference between headcount indices when a Distribution 1 is year 1998 and distribution 2 is year 1994 b The variable of interest is exppe for 1994 and exppcz for 1998 c You should set size to household size in order to estimate poverty over the population of individuals d Use 41099 Francs CFA per year as the poverty line for both distributions 3 Estimate the difference betwe
19. index 7 ve p X Z C G C 4 4 ZT Xi Z2 7 Xi 2 o h and Cz ace Z1 i Z2 8 imdpov estimates the above multidimensional poverty indices as well as their standard errors where The user can select among the seven multidimensional poverty indices The number of dimensions can be selected 1 to 6 If applicable the user can choose parameter values relevant to a chosen index 14 A group variable can be used to estimate the selected index at the level of a categorical group Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 3 decimals this can be also changed Users are encouraged to consider the exercises that appear in Section 21 3 11 Poverty marginal impacts and elasticities 11 1 FGT Elasticity with respect to within between group components of inequality efgtg This module estimates the FGT marginal impact and elasticity with respect to within between group components of inequality A group variable must be provided This module is mostly based on Araar and Duclos 2007 Araar Abdelkrim and Jean Yves Duclos 2007 Poverty and inequality components a micro framework Working Paper 07 35 CIRPEE Department of Economics Universit Laval To open the dialog box of this module type the command db efgtg E DASP FGT Povert
20. parameters as in with the lower bounded option for the confidence interval Figure 58 Testing the pro poor growth dual approach A DASP Pro poor curves dual A E Se ne gt cpropoord command DE Data in file C Documents and Settingst raa aE Datainfle C Documents and Settingst raa m After clicking SUBMIT the following graph appears 99 Absolute propoor curves Order s 1 Dif Q_2 p Q_1 p mu 2 mu 1 T T T T 0 184 368 552 736 92 Percentiles p Difference Lower bound of 95 confidence interval Null horizontal line Q 2 Steps To open the relevant dialog box type db cpropoord Choose variables and parameters as in with the lower bounded option for the confidence interval Figure 59 Testing the pro poor growth dual approach B 100 E DASP Pro poor curves dual approach gt cpropoord command Data in file C ADocuments and Settingst raa n After clicking SUBMIT the following graph appears Absolute propoor curves Order s 2 Dif GL 2 p GL_1 p GL_2 p 0 184 368 552 736 92 Percentiles p Difference Lower bound of 95 confidence interval Null horizontal line Q 4 Steps To open the relevant dialog box type db ipropoor 101 Choose variables and parameters as
21. sector s There are S sectors fs be the number of members of observation i that effectively use the public service provided I by sector s g be the socio economic group of eligible members of observation i typically classified by income percentiles c be a subgroup indicator for observation e g 1 for a rural resident and 2 for an urban resident Eligible members can thus be grouped into population exclusive subgroups ES be total public expenditures on sector s in area r There are R areas the area here refers to the geographical division which one can have reliable information on total public expenditures on the studied public service R be total public expenditures on sector s r 1 Here are some of the statistics that can be computed 1 The share of ag in sector s is defined as follows 3 wif ieg SH Va wif i l G Note that gt SH g l 2 The rate of participation of a group g in sector s is defined as follows n Xowf li eg s isl CR gt we l ieg i l This rate cannot exceed 100 since f lt e Vi 3 The unit cost of a benefit in sector s for observation j which refers to the household members that live in area r uc E Ny S 2 vifi j where n is the number of sampled households in area r 40 4 The benefit of observation i from the use of public sector s is B f UC 5 The benefit of observation i from the use of the S public sectors is S B B s l
22. 5 66 12 13 19 5 20 6 4 7m 1 19 0 20 7 97 193 19 20 0 20 4 8625 1 19 8 20 Q 3 Type bd ifgt to open the dialog box for the FGT poverty index and choose variables and parameters as indicated in the following window Click on SUBMIT 50 Figure 13 Estimating FGT indices E DASP FGT and EDE FGT Index gt ifgt command The following results should then be displayed ifgt exppc expeg alphall hsizelsize plinel41099 Poverty Index FGT Index Household size size Sanpling eight weight Paraneter alpha 0 00 Est inate ST LB UB 0 46565 0 016124 0 412873 0 476256 0 255400 0 013326 0 229208 0 281592 0 4 Select RELATIVE for the poverty line and set the other parameters as above 51 P Line 41099 00 41099 00 Figure 14 Estimating FGT indices with relative poverty lines E DASP FGT and EDE FGT Index gt ifgt command After clicking on SUBMIT the following results should be displayed ifgt exppc alphal0 hsizelsize opl nedian prop 60 Poverty Index FGT Index Household size size Sanpling weight weight Parameter alpha 0 00 Variable 0 5 Set the group variable to sex 52 P Line 27046 71 Figure 15 FGT indices differentiated by gender E DASP FGT and EDE FGT Index gt ifgt command Clicking on SUBMIT the following should appear ifgt exppc alpha 0
23. 6 dta file 1 Draw the density for gross income X and net income N The range for the x axis should be 0 60 000 2 Draw the quantile curves for gross income X and net income N The range of percentiles should be 0 0 8 3 Draw the expected tax benefit according to gross income X The range for the x axis should be 0 60 000 Use a local linear estimation approach 4 Estimate marginal rates for taxes and benefits according to gross income X The range for the x axis should be 0 60 000 Use a local linear estimation approach Type use C data can6 dta clear To open the relevant dialog box type db cdensity Choose variables and parameters as in Figure 46 Drawing densities E DASP Density Curves gt cdensity command iol x Main Resuts Y Axis X Axis Title Caption Legend Overall Variable s of interest Parameters xN Minimum Maximum Range 0 60000 Size variable JV Override optimal bandwidth Group variable Bandwidth of 1 0 Cancel Submit After clicking SUBMIT the following appears 85 Figure 47 Density curves Density Curves 00002 00003 00004 00005 00001 0 Q 2 Steps To open the relevant dialog box type db c_quantile Choose variables and parameters as in 86 Figure 48 Drawing guantile curves E DASP Ouantile amp Normalised Curves gt c guantile command
24. A98I dta hsizel size file C XDATAMbkf941 dta hsize2 size plinel 41099 pline2 41099 Poverty Index FGT Index Paraneter alpha 0 00 Est inate 1 LB UB P Line 0 45267 0 01092 0 431199 0 474156 41099 00 0 466 0 016124 0 412873 0 476256 41099 00 0 008113 0 01947 0 030062 0 046288 Distribution 1 Distribution 2 Difference 56 Q 3 Restrict the estimation to rural residents as follows o Select the option Condition s o Write ZONE in the field next to CONDITION 1 and type 1 in the next field Figure 17 Estimating differences in FGT indices E DASP Difference Between FGT Indices gt difgt command Data in File CADATA DKISAI dta Datain File _ C DATA DKIS4I dta a of the z K ce m After clicking on SUBMIT we should see Poverty Index FGT Index Paraneter alpha 0 00 Est inate STD LB UB P Line Distribution 1 0 510344 0 011601 0 487539 0 533149 41099 00 Distribution 2 0 51049 0 019975 0 471236 0 549758 41099 00 Difference 0 000153 0 023100 0 045427 0 045121 0 4 Poverty Index FGT Index Paraneter alpha 0 00 Est inate ST LB UB P Line Distribution 1 0 16673 0 016297 0 132538 0 196608 41099 00 Distribution 2 0 103684 0 013419 0 077309 0 130059 41099 00 Difference 0 060889 0 021114 0 019513 0 102265 57 One can see that the change in poverty was significant only for urban residents Q 5 Restrict the
25. AKA Kane 12 10 1 FGT and EDE FGT poverty indices ifgf 2e eeeeeeeee eee eee 0000 nenene eee anne te nn n 12 10 2 Difference between FGT indices difgt e e4 2eee22eee2eee20 eee eeee nenene nat en nene nn n 13 10 3 DASP and multidimensional poverty indices IMAPOV 2 e eeeeee eee eee eee eee 13 11 Poverty marginal impacts and elasticities 2 0 0 2 cece ee eee ee ee eene eset eae ee ee eeaaeeeeeeaaeeeeeeaeeeeeeaeeeeneea 15 1 1 FGT Elasticity with respect to within between group components of inequality efgtg 15 1 2 FGT Elasticity with respect to within between income components of inequality efgtc 16 12 DASP and inequality indices ccccccceeeeesceeeeeee eee eee ee ee seas eecaaeeeeaeeeseaeeesaaeseeaeeseeeeesaeeesaeeseneeees 18 12 1 Gini and concentration indices igini ec ceeeeeeeeeeceee cece eeeeaeeeeeeeceaeeesaaeeeeneeseaeeesaeeesaaeeeaeees 18 12 2 Difference between Gini concentration indices digini c cceeeeceeeeeeeeeeeeeeeeeeeeaeeeeaeeeeeeeeaes 18 12 3 Generalised entropy index ientropy ccceceeeeececeeeeceeeeeeaeeeeaeeseeeeeceaeeeseaaeseeeeeseaeeeseaeeseneeeeaes 19 12 4 Difference between generalized entropy indices diengtropy 4 eee 19 12 5 Quantile share ratio indices of inequality ININEG cceeeeeeeeeceeeeeeeeeesaeeeeneeseeeeeesaeeseaeeeeaes 20 12 6 Difference between Quantile Share indice
26. ES DASP Pro poor indices gt difgt command Main Confidence Interval Results Distribution 1 Distribution 2 Data in file C DATA Mexico mex_98_2mi d Browse Data in file CADATA Mexico mex_04_2ml d Browse Variable of interest finc Variable of interest finc Size variable hhsz Size variable hhsz T Condition s fi z I Condition s fi z m Parameters and options Parameter alpha fi Poverty line eoo Type Normalised hat 0 After clicking SUBMIT the following results appear Poverty line 600 00 Paraneter alpha 1 00 Pru puur indices Esl inale Grouth ratelg 0 582359 Chen amp Ravallion 2003 index 0 712285 Kakuani amp Pernia 2000 index 1 325436 PEGF index 0 771879 PEGR g 0 18920 21 14 education in Peru 1994 1 Using the peredu94I dta file estimate participation and coverage rates of two types of public spending on education when The standard of living is exppc The number of household members that benefit from education is fr prim for Cancel Submit Sm LB UB 0 125512 0 336361 0 828357 1 009337 1 265979 2 6949 0 1074 1 11562 1 53524 0 137331 0 502716 1 041042 0 04935 0 092783 0 286257 Benefit incidence analysis of public spending on the primary sector and fr_sec for the secondary one 102 The number of eligible household members is el prim for the primary sector and el sec for the secondary
27. PA k ne aaa dou ora a PA k ee ese 35 17 4 DASP and joint density functionS 22 24 cece erect 00000 eee een nen n ate h ea n KKK na nan 36 17 5 DASP and joint distribution FUNCTIONS 4 2 eee ceee cece eee eee eee nenene nene aneta nen nt 36 18 DASP and pro poor growth 2 ee eee eee eeee eee eee nene n nea P KKK K KK RKK EPO R KA KRKA RAKA PARA nenn n n 36 18 1 DASP and pro poor indiCeS 5 3 245 sdiubxuu dee te a iaiia datini ainiai aiaiai aiiai ka iiaii aaa 37 18 2 DASP and pro poor CUIVES ssssssessiissirssrtsttsttnstt tssk untk un tt Ansk RKK KK PAK RAKA PAR KKK KKK AKA nt t 37 18 2 1 Primal Droz DOOM CUIVES yaaa klene a oni nd kou o koda k Roo V EA RA sate da kAU ODER Kaunda ak ae odA ka dan deda hudbu 37 18 2 2 Dual pro poor CUIVES rocinas Eaa dvi kn aE NE a nauk Kadov hk nd e vd aaa Ka RANEES 38 19 DASP and Benefit Incidence AnalySiS 4 2 2 eeee 00000 0eee 0000 eee nenene ne onen nt n atak 39 19 1 Benefit incidence analySiS 2 2 2 e2 eee eee eee 000000 eee ena K KK RKK KK A Kent 39 20 Appendice S ahei n a ab d dp ddd dph dne nds ki blade n nk v ban k ds v ea Sten 43 20 1 Appendix A illustrative household SUrVeYS 2 2 eee tenets cae eee ee eee eee n nana nana tnn 43 20 1 1 The 1994 Burkina Faso survey of household expenditures bkf941 dta 43 20 1 2 The 1998 Burkina Faso survey of househol
28. Results Variable s of interest M Decomposition approach 1 pecon95 pecon97 pecon99 pecon 1 Approach Censored incomes Parameters Parameter alpha 2 Size variable hs he i Poverty line z j W Bias correction Approach Analytic x Survey settings Cancel Submit The user can select more than one variable of interest simultaneously where each variable represents the income standard of livings for one period The user can select one of the two presented approaches above Bias correction can be done using either an analytical asymptotic or bootstrap approach Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed Main references e Jalan Jyotsna and Martin Ravallion 1998 Transient Poverty in Postreform Rural China Journal of Comparative Economics 26 2 pp 338 57 e Jean Yves Duclos amp Abdelkrim Araar amp John Giles 2006 Chronic and Transient Poverty Measurement and Estimation with Evidence from China W P 0611 CIRPEE 14 4 Inequality decomposition by income sources diginis The diginis module decomposes the Gini index or the absolute Gini index by income sources The three available approaches are e Rao s approach 1969 e Lerman and Yitzhaki s
29. T curve and its confidence interval by taking into account sampling design The module can draw an FGT curve and two sided lower bounded or upper bounded confidence intervals around that curve condition the estimation on a population subgroup draw a FGT curve that is not normalized by the poverty lines list or save the coordinates of the curve and of its confidence interval save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs Interested users are encouraged to consider the exercises that appear in Section 21 5 15 3 Difference between FGT CURVES with confidence interval cfgts2d The cfgts2d module draws differences between FGT curves and their associated confidence interval by taking into account sampling design The module can draw differences between FGT curves and two sided lower bounded or upper bounded confidence intervals around these differences normalize or not the FGT curves by the poverty lines list or save the coordinates of the differences between the curves as well as the confidence intervals save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex
30. USA 800 STATA PC http uun stata con 979 696 4600 statalstata con 979 696 4601 fax i x Bingle user Stata for Hindous perpetual license Yored E Serial nunber 1990520454 weight 4 Licensed to Araar Abdelkrin size Universit Laval strata x psu ae Command 1 zone ifgt exppc pline 41099 hsize size alpha 2 exppc C data An alternative is to use dialog boxes For this the command db should be typed and followed by the name of the relevant DASP module Example db ifgt 7 How can help be accessed for a given DASP module Type the command help followed by the name of the relevant DASP module Example help ifgt Figure 4 Accessing help on DASP iewer 2 help ifgt x Back Refresh Search Help Contents What s New News Command help ifgt A DASP Distributive Analysis Stata Package world Bank PEP and CIRPEE help for ifgt Dialog box ifgt FGT Poverty Indices ifgt varl t HSize var ame HGroup var are PLine ea OPL string PROP Crea PERC rea ALpha rea TYPE string INDex string LEVEL rea CONF string where varlist is a list of variables Version 9 2 and higher Description Poverty FGT and EDE FGT indices Users should set their surveys sampling design before using this module and to save their data files If the sampling design is not set simple random sampling SRS will be automatically assigned by default with ifgt the follow
31. USER MANUAL DASP version 1 4 DASP Distributive Analysis Stata Package By Abdelkrim Araar Jean Yves Duclos Universit Laval PEP CIRPEE and World Bank December 2007 Table of contents Pable of Contents sitar A be ae ooo en 2 ist Of Figure Siionista akva nite tere Poeta R d ur o een bal pob ora a tae Core AAR a thd 4 1 IMTOGUCTIONS fs kenti tob ven antd e abe ods e kaz nooo E E Poet oku dates E cated 6 2 DASP and Stata Versions i304 raai een ed alae tie i nite el neh Re EAE 6 3 Installing and updating the DASP package 2 2 22 eee eee scenes caeeeeeaeeseeeesecaeeesaeeeeeeeeeneess 6 3 1 installing DASP MOduIeS an cus aa ial ia aad aie th id tte 7 3 2 Adding the DASP submenu to STATA S main MENU eu eee eee eee eee nenene 7 4 DASP and datatiles ii nisi eel A E a ead cA oon te hea 8 5 Main variables for distributive ANALYSIS ccecceeceeeeeeeeeceeeeeceaeeeeaaeeeeeeeceaeeecaaeeesaaeseeneeseaeeesaeeeeeeseenees 8 6 How can DASP commands be invoked 2 2 2 e eee eee eee nenene nee KARA P ent 9 7 How can help be accessed for a given DASP Module ee e eee ee eee nenene een 9 8 Applications and files in DASP erratis neni casi dedi tie a eon atl annie einai 10 9 Basic Notation sisi sc secest othr eee a a a esc l Otten wade pal k o e n d orvaz e a eba dvd ete 11 10 DASP and poverty INdiCES 2 eee eeeee 00000 eee een ana ee KKK AAA PAKA PEKA AKA
32. an6 dia file 4 Estimate the joint density function for gross income X and net income N o X range 0 60000 o N range 0 60000 5 Estimate the joint distribution function for gross income X and net income N o X range 0 60000 o N range 0 60000 Q 1 Steps Type use C data can6 dta clear To open the relevant dialog box type db sjdensity Choose variables and parameters as in Figure 54 Plotting joint density function E DASP Joint Density Surfaces gt sjdensity command Main Results M Variable s of interest Dim 1 variable K Dim 2 variable JN hed Size variable Group variable Group number In xi Parameters Minimum Maximum of partitions Range Dim 1 0 60000 jso Range Dim 2 fo e0000 W Override optimal bandwidths Bandwidth of kernel Dim 1 fi 0 Bandwidth of kernel Dim 2 fi 0 Cancel Submit After clicking SUBMIT the following graph is plotted interactively with Gnu Plot 4 2 91 Joint Density Function f x y 3e 009 2 be 009 2e 009 1 5e 009 12 009 ZZ 5e 010 LE ZZ 0 220000 30000 40000 Dimension 1 10000 20000 30000 lt lt a 40000 50000 Dimension 2 50000 6000060000 Q 2 Steps To open the relevant dialog box type db sjdistrub Choose variables and parameters as in 92 Figure 55 Plotting joint distr
33. approach 1985 e Araar s approach 2006 26 Reference s e Lerman R I and S Yitzhaki Income Inequality Effects by Income Source A New Approach and Applications to the United States Review of Economics and Statistics 67 1985 151 56 e Araar Abdelkrim 2006 On the Decomposition of the Gini Coefficient an Exact Approach with an Illustration Using Cameroonian Data W P 02 06 CIRPEE University 14 5 Gini index decomposition by population subgroups diginig The diginig module decomposes the Gini index or the absolute Gini index by population subgroups Let there be G population subgroups We wish to determine the contribution of every one of those subgroups to total population inequality The Gini index can be decomposed as follows G 12991 ER Within Overlap U Between where 9 the population share of group g 9 the income share of group g I the between group ineguality when each individual has the average income of its group R The residue implied by group income overla P y group P 14 6 Generalized entropy indices of inequality decomposition by population subgroups dentropyg The Generalised Entropy indices of inequality can be decomposed as follows A 0 x K m A A ie gt oft k 0 10 k 1 where k is the proportion of the population found in subgroup k u k is the mean income of group k I k 0 is ineguality within group k 1 0 is population inequalit
34. ats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps_ typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs 32 16 Dominance 16 1 Poverty dominance dompov Distribution 1 dominates distribution 2 at order s over the range 2 2 if only if P Ga lt P G a V Ge z 2 for a s 1 This involves comparing stochastic dominance curves at order s or FGT curves with 5 1 This application estimates the points at which there is a reversal of the ranking of the curves Said differently it provides the crossing points of the dominance curves that is the values of C and P q for which P a P a when sign P 1 a P 1 a sign P G 1n a P a for a small 7 The crossing points can also be referred to as critical poverty lines The dompov module can be used to check for poverty dominance and to compute critical values This module is mostly based on Araar 2006 Araar Abdelkrim 2006 Poverty Inequality and Stochastic Dominance Theory and Practice Illustration with Burkina Faso Surveys Working Paper 06 34 CIRPEE Department of Economics Universit Laval Interested users are encouraged to consider the exercises that appear in Section 21 6 16 2 Ineguality dominance domineg Distribution 1 ineguality dominates distribution 2 at the seco
35. be used to estimate inequality at the level of a categorical group If a group variable is selected only the first variable of interest is then used Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 12 4 Difference between generalized entropy indices diengtropy This module estimates differences between the generalized entropy indices of two distributions For each of the two distributions 19 One variable of interest should be selected Conditions can be specified to focus on specific population subgroups Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 12 5 Quantile share ratio indices of inequality inineq The quantile ratio is estimated as AP R py pa LPL Q p2 where Q p denotes a p quantile and pjand p gt are percentiles The share ratio is estimated as GL p2 GL p1 SR pl p2 p3 p4 GL p4 GL p3 where GL p is the Generalised Lorenz curve and py p2 P3 and p4 are percentiles The user can select more than one variable of interest simultaneously For example one can estimate inequality sim
36. bers pliterate literate size 20 1 6 The 1995 Colombia DHS survey columbial dta This sample is a part of the Data from the Demographic and Health Surveys Colombia_1995 witch contains the following information for children aged 0 59 months List of variables hid Household id haz height for age waz weight for age whz weight for height sprob survival probability wght sampling weight Asset asset index 20 1 7 The 1996 Dominican Republic DHS survey Dominican_republic1996l dta This sample is a part of the Data from the Demographic and Health Surveys Republic Dominican_1996 witch contains the following information for children aged 0 59 months List of variables hid Household id haz height for age waz weight for age 45 whz sprob wght Asset weight for height survival probability sampling weight asset index 20 2Appendix B labelling variables and values The following do file can be used to set labels for the variables in bkf94 dta For more details on the use of label command type help label in the command window delim To drop all label values label drop _all To assign labels label var strata Stratum in which a household lives label var psu Primary sampling unit label var weight Sampling weight label var size Household size label var totexp Total household expenditures label var exppc Total household expend
37. ble with respect to the second without having to specify the functional form linking them Regressions with the cnpe module can be performed with one of the following two approaches 17 3 1 Nadaraya Watson approach A Gaussian kernel regression of y on x is given by DLiwi Kj E y x y1x ko From this the derivative of y x with respect to x is given by a 4 x Pola 17 3 2 Local linear approach The local linear approach is based on a local OLS estimation of the following functional form K x y HK WOK Aa x v or alternatively of K 2y aK x BK x x y Estimates are then given by dy E E y x as 2 9 Interested users are encouraged to consider the exercises that appear in Section 21 10 35 17 4 DASP and joint density functions The module sjdensity can be used to draw a joint density surface The Gaussian kernel estimator of the joint density function f x y is defined as 2 2 A n E V v ip o l 2nhhy Z w T y i l With this module The two variables of interest dimensions should be selected specific population subgroup can be selected surfaces showing the joint density function are plotted interactively with the GnuPlot tool coordinates can be listed c coordinates can be saved in Stata or GnuPlot ASCII format Interested users are encouraged to consider the exercises that appear in Section 21 1177 17 5DASP and joint distribution function
38. cceeececeeeeeeeeeeeeeeceeeeeeaeeeeeeeeseaeeesaeeteneeteaes 11 Figure 7 Decomposition of the FGT index by Qroups cccccceesceeeeeeeeeeeeeeeeeeeeeeeceaeeesaaeseeeeeseaeeeseaeeseeeeeees 23 Figure 8 Decomposition of poverty into transient and chronic Component cccecceeeeeeeeseeeeeeeeeees 26 PIQUED SF ENO RR S Eo n O Prat E T terete us esc agudestt n cutest vee 29 Figure 10 Lorenz and concentration CUIVES es4 22eeeee2200eee 000 eeee nn KKK KK POKR KKK KKK nn 31 Figure 11 Survey data SettingS 0 20 422 20 eeee 000000000 e neon K KKK KK PRK KKK K AKA A AK KARR AKA A ant th 47 Figure 12 Setting sampling weights 22 222 eee eee eee ee een nenene KaK KK KKK AKE PARK ARA K AAA t 48 Figure 13 Estimating FGT indices 2 2 eee eee eee eee eee 000K KKK KKK KRKA KA PA EKK A RK KRKA KK th 51 Figure 14 Estimating FGT indices with relative poverty liN S ccccccsseceeeeeeeeeeeesaeeeeeeeseeeeesaeeseneeeeees 52 Figure 15 FGT indices differentiated by gender 2 2 ee eeeee eee eee eee nenene ena ent n teen nn 53 Figure 16 Estimating differences between FGT indices ccsccceecceceeeeeeeeeeeeeeeeceaeeeeaaeeeeeeeseaeessaeesseneeeaes 56 Figure 17 Estimating differences in FGT iNdiCES 22 2 2000 eee eee eee nenene ena n tn en nn 57 Figure 18 FGT differences across years by gender and ZONE 2 22 eeeee
39. cel Submit After clicking on Submit the following appears 105 Average Benefits by Quintile Groups at the level of eligible nenbers Groups Sector 1 Sector 2 Quintile 1 248 662 128 548 Quintile 2 257 483 179 816 Quintile 3 250 395 186 119 Quintile 4 225 52 192 340 Quintile 5 157 982 143 327 All 227 961 166 095 Groups Sector 1 Sector 2 Quintile 1 312 084 260 540 Quintile 2 312 084 260 540 Quintile 3 312 084 280 540 Quintile 4 312 084 280 540 Quintile 5 312 084 260 540 All 312 084 260 540 Groups Sector 1 Sector 2 Quintile 1 0 136 0 168 Quintile 2 0 141 0 081 Quintile 3 0 137 0 084 Quintile 4 0 123 0 087 Quintile 5 0 087 0 06 All 0 624 0 376 106
40. centration Curves gt clorenz command Main Results Y Axis X Axis Tite Caption Legend Overall Variables of interest Type of curvels Jexpeq v T Type Normalised by default z T Ranking Variable s T Difference No z Size variable size Range of percentiles p Group variable zone Minimum Maximum foo fi 0 Cancel Submit Figure 42 Lorenz curves 4 Graph Graph Lorenz Curves 81 21 9Estimating Gini and concentration curves By how much do taxes and transfers affect inequality in Canada Using the can6 dta file 1 Estimate the Gini indices for gross income X and net income N 2 Estimate the concentration indices for variables T and N when the ranking variable is gross income X By how much has inequality changed in Burkina Faso between 1994 and 1998 Using the bkf94I dta file 3 Estimate the difference in Burkina Faso s Gini index between 1998 and 1994 a with variable of interest expegz for 1998 and expeg for 1994 b with size variable set to size 0 1 Steps Type use C data can6 adta clear To open the relevant dialog box type db igini Choose variables and parameters as in Figure 43 Estimating Gini and concentration indices ES DASP Gini amp Concentration Indices gt igini command i E oj x Main Confidence Interval Results Varable s of interest xN z Ranking Variable v Size
41. curves with confidence interval clorenzs2d The clorenz2d module draws differences between Lorenz concentration curves and their associated confidence intervals by taking sampling design into account The module can draw differences between Lorenz concentration curves and associated two sided lower bounded or upper bounded confidence intervals list or save the coordinates of the differences and their confidence intervals save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs 15 7 Poverty curves cpoverty The cpoverty module draws the poverty gap or the cumulative poverty gap curves o The poverty gap at a percentile p is G p z z 0 p o The cumulative poverty gap at a percentile p noted by CPG p z is given by w c yal vi 500 CPG p z Wi TMs The module can thus draw more than one poverty gap or cumulative poverty gap curves simultaneously whenever more than one variable of interest is selected draw poverty gap or cumulative poverty gap curves for different population subgroups whenever a group variable is selected draw differences between poverty gap or cumulative poverty gap curves list or save the coordinates of the curves save the graphs in different form
42. d expenditures bkf98 dta 44 20 1 3 Canadian Survey of Consumer Finance a sub sample of 1000 observations can6 dta 44 20 1 4 Peru LSMS survey 1994 A sample of 3623 household observations PEREDES94I dta 44 20 1 5 Peru LSMS survey 1994 A sample of 3623 household observations PERU A I dta 45 20 1 6 The 1995 Colombia DHS survey columbial dta e e 4 eee eeeeeeeeeeeeeeeeeee nenene 45 20 1 7 The 1996 Dominican Republic DHS survey Dominican republic19961 dta 45 20 2 Appendix B labelling variables and VAlu6S eee eeeee nenene nenene 46 20 3 Appendix C setting the sampling design e 4e eee eeeeeee eee eee eee een nenene n tn 47 21 Examples and 6xercis S zz ad e dan lee ain ia la kanoe zd lin andes 49 21 1 Estimation of FGT poverty indices 4 244 eee2e4ee 42 eee een een nan en aaa nana nen n ene 49 21 2 Estimating differences between FGT iNdiCeS 2 22 eeeeeeeee eee eee eee nenene een 55 21 3 Estimating multidimensional poverty indices eee eee eee eee eee 59 214 Estimating FOT CUNVBS zs 6 226 kz zoe d o eae vada Aiea ha aea ae abies Areas 62 21 5 Estimating FGT curves and differences between FGT curves with confidence intervals 69 21 6 Testing poverty dominance and estimating Critical VAIUCS 0 00 eee cee een 73 21 7 D composingiFGiT Indice
43. documents Many graphical options are available to change the appearance of the graphs Interested users are encouraged to consider the exercises that appear in Section 21 5 15 4Lorenz and concentration CURVES clorenz Lorenz and concentration curves are useful distributive tools that can inter alia be used to show the level of inequality test for inequality dominance between two distributions test for welfare dominance between two distributions test for progressivity PoON gt The clorenz module draws Lorenz and concentration curves simultaneously The module can draw more than one Lorenz or concentration curve simultaneously whenever more than one variable of interest is selected 30 draw more than one generalized or absolute Lorenz or concentration curve simultaneously whenever more than one variable of interest is selected draw more than one deficit share curve draw Lorenz and concentration curves for different population subgroups whenever a group variable is selected draw differences between Lorenz and concentration curves list or save the coordinates of the curves save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs To open the dialog box of the module clorenz type the
44. e 1998 official poverty line z_1994 z_1998 Second 1998 expenditure data were multiplied by the ratio of the 1994 consumer price index to the 1998 consumer price index ipc_1994 ipc_ 1998 List of new variables expcpz Total household expenditures per capita deflated by z 1994 z 1998 expcpi Total expenditures per capita deflated by ipc_1994 ipc_1998 20 1 3 Canadian Survey of Consumer Finance a sub sample of 1000 observations can6 dta List of variables X Yearly gross income per adult equivalent T Income taxes per adult equivalent B1 Transfer 1 per adult equivalent B2 Transfer 2 per adult equivalent B3 Transfer 3 per adult equivalent Sum of transfers B1 B2 and B3 N Yearly net income per adult equivalent X minus T plus B 20 1 4 Peru LSMS survey 1994 A sample of 3623 household observations PEREDE94I dta List of variables exppc Total expenditures per capita constant June 1994 soles per year weight Sampling weight size Household size 44 npubprim Number of household members in public primary school npubsec Number of household members in public secondary school npubuniv Number of household members in public post secondary school 20 1 5 Peru LSMS survey 1994 A sample of 3623 household observations PERU A I dta List of variables hhid Household Id exppc Total expenditures per capita constant June 1994 soles per year size Household size literate Number of literate household mem
45. e 95 confidence interval is above zero 58 21 3Estimating multidimensional poverty indices How much is bi dimensional poverty total expenditures and literacy in Peru in 1994 Using the peru94I dta file 1 Estimate the Chakravarty et al 1998 index with parameter alpha 1 and Var of interest Pov line aj Dimension 1 exppc 400 1 Dimension 2 pliterate 0 90 1 2 Estimate the Bourguignon and Chakravarty 2003 index with parameters alpha beta gamma 1 and Var of interest Pov line Dimension 1 exppc 400 Dimension 2 literate 0 90 0 1 Steps Type use C data peru94l dta clear To open the relevant dialog box type db imdpov Choose variables and parameters as in Figure 19 Estimating multidimensional poverty indices A 59 DASP Multidimensional poverty indices gt imdpov command feo d fo ee fs After clicking SUBMIT the following results appear indpoy exppc pliterate hsizelsize index 1 alpha O a1 1 pli 400 a2 1 pl2to 9 H D Poverty index Chakravarty et al 1998 Household size size Est inate opulat ion 0 418 a 2 Steps Choose variables and parameters as in 60 Figure 20 Estimating multidimensional poverty indices B DASP Multidimensional poverty indices gt imdpov command NN fo fiese N EN After clicking SUBMIT the following results appear
46. e between FGT CURVES with confidence interval cfgtS2d 30 15 4 Lorenz and concentration CURVES Clorenz ccccceeeeeeeeeceeeee eee eeeeaeeeeaeeeeeaeeesaaeeeeaaeeeeeeeeaas 30 15 5 Lorenz concentration curves with confidence intervals ClorenzS eee 31 15 6 Differences between Lorenz concentration curves with confidence interval clorenzs2d 32 15 7 Poverty CURVES CDOVEMTY eranmi leeuccebeand eaa ae aaa n raaa a aa ea a a lebharabedeel eivetieeun seta aiai 32 16 DOMINANCE E A EE EEEE TE AA beetecel abe teetbeevegi teat et acebeee 33 16 1 Poverty dominance dompov eeta ess rie T eee a een naten a e Er a ESERE E REEE EN EE RRE 33 16 2 Inequality dominance domine0 4 2 4224 eee eee ee eee eee eee ENE E EERE EEDAN EENES 33 16 3 DASP and bi dimensional poverty dominance doMbdpov eee 33 17 Distributiv 10018 2 135246zh zbo sdk zoe cent A A A k ctlaren cen ied cael Geared steele 34 121 Quante curves C QUANTIIC iists osre erruten ies ninaa taaa aa aaar raaa a aa O SAE OE EAEE EEAS 34 17 2 Density curves cdensity hea nE EEEE eee eee een eee EEN KKK EA RAKA eA E FERRA EAEE 34 17 3 Non parametric regression curves CNPe e ee 2eee ee eee eee ee eee nenene anne anna nt n nn 35 17 3 1 Nadaraya WatSOn approach eeececeeeeeseceeeenceeeeeeaeeeeeeaaeeeeeeaaeeeeeeeaaeeeeeeaaeeeeeeaeeeeeenaeeeeneaas 35 17 3 2 Locallinear approach ein n aaa cda and koda
47. eeee eee eee eee ent 58 Figure 19 Estimating multidimensional poverty indices A 2 eeeeee eee eee nenene 59 Figure 20 Estimating multidimensional poverty indices B c cceeeseeeeeeenneeeeeeaeeeeeeaaeeeeeeeaeeeeeenaeeeeeeaees 61 Figure 21 Drawing FOT CUrVeS noniis aiai de dd v dee tans eee eal Vanek v ea adaki yas n st descr Ki aa Kia a 63 Fig re 22 Editing FOT CUVE Sairis iraniani Bete hit in sees ddd l da nK dae ented Saula V ne Sound fra enone 63 Figure 23 Graph of FGT Curves 8 c sceveccecveseccsueeyeccevee Jednu ajov eee seat iaaa aiai edida vee Govan aie K Taada Kiina 64 Figure 24 FGT Curves DY ZOMG min esanaia aaniu vee Sadi aa Vee evnad Sadu Ana Hound nab macu A iaaa Kaina 65 Figure 25 Graph of FGT curves by ZONE 2 eee eee eee eee ena ea K KRKA KA KARA K KARR Ana h th 66 Figure 26 Differences of FGT CUrVES 22 22 2220 eee ee ee een nen KK RKK AK LKAA RKK PRK AKA ant e 67 Figure 27 Listing coordinates seiteacivron iiad edn tear otne i konev Ka taa AEAEE EOE n KEE VLNA E deku ne 67 Figure 28 Differences between FGT CUTVBS u eee ee eee eee ena nan K KKK R KE KRKA KRKA R KA th tn 68 Figure 29 Differences between FGT CUTVBS u 2 2 e ee eee eee een een KKK RKK KK PAA AKA teen 69 Figure 30 Drawing FGT curves with confidence iNterval e e eee eee eee eee eee een nenene nene 70 Figure 31 FGT curves with confidence interval
48. en headcount indices when a Distribution 1 is rural residents in year 1998 and distribution 2 is rural residents in year 1994 b The variable of interest is exppe for 1994 and exppcz for 1998 c You should set size to household size in order to estimate poverty over the population of individuals d Use 41099 Francs CFA per year as the poverty line for both distributions Redo the last exercise for urban residents 4 5 Redo the last exercise only for members of male headed households 6 Test if the estimated difference in the last exercise is significantly different from zero Thus test Hy AP z 41099 a 0 0 against H AP z 41099 a 0 0 Set the significance level to 5 and assume that the test statistics follows a normal distribution Answers Q 1 Open the dialog box by typing db difgt Q 2 For distribution 1 choose the option DATA IN FILE instead of DATA IN MEMORY and click on BROWSE to specify the location of the file bkf98I dta Follow the same procedure for distribution 2 to specify the location of bkf94I dta Choose variables and parameters as follows 55 Figure 16 Estimating differences between FGT indices E DASP Difference Between FGT Indices gt difgt command Data in File oy C DATASbkf98l dta Data in File CADATADKE94I dta je R em semi After clicking on SUBMIT the following should be displayed difgt exppcz exppc alphalO filed C DATANDK
49. er clicking SUBMIT the following appears 70 Figure 31 FGT curves with confidence interval FGT curve alpha 0 Burkina Faso T T T T l 0 20000 40000 60000 80000 100000 Poverty line z Confidence interval 95 Estimate 0 2 Steps To open the relevant dialog box type db cfgtsd2 Choose variables and parameters as in 71 Fi gure 32 Drawing the difference between FGT curves with confidence interval EE DASP Curve of difference between FGT Indices gt cfgts2d command Data in file Uy JCADATANDKSSI dta je E D o fow Figure 33 Difference between FGT curves with confidence interval a 0 Difference between FGT curves alpha 0 T T T T 1 20000 40000 60000 80000 100000 Poverty line z SE Estimated difference Confidence interval 95 72 Figure 34 Difference between FGT curves with confidence interval a 1 Difference between FGT curves alpha 1 T T T T l 0 20000 40000 60000 80000 100000 Poverty line z Confidence interval 95 Estimated difference 21 6 Testing poverty dominance and estimating critical values Has the poverty increase in Burkina Faso between 1994 and 1998 been statistically significant 1 Using simultaneously files bkf94 dta and bkf98l dta check for second order poverty dominance and estimate
50. estimation to male headed urban residents as follows o Set the number of Condition s to 2 o Set sex in the field next to Condition 2 and type 1 in the next field Figure 18 FGT differences across years by gender and zone EE DASP Difference Between FGT Indices gt difgt command Main Confidence Interval Results Distribution 1 Distribution 2 Data in File CADATANDKSAI cta Browse Data in File k J CADATAbkf941 dta Browse Variable of interest Jexppcz Variable of interest Jexppe Size variable size Size variable size Poverty line Poverty line Absolute ja 099 Absolute a 099 C Relative 503 ot the Mean s C Relative 54 of the Mean z JV Condition s 2 I Condition s 2 Condition 1 fzone I yi l2 Condition 1k zone ZP Jano Condition 2 sex gt I zihi AND 7 Condition 2 sex a vif m Parameters and Options Parameter alpha fo Type Normalised a 20 Cancel Submit After clicking on SUBMIT the following should be displayed Poverty Index FGT Index Paraneter alpha 0 00 Est inate STD LB UB P Line Distribution 1 0 172384 0 047701 0 134590 0 207179 41099 00 Distribution 2 0 115997 0 013945 0 078598 0 13345 41099 00 Difference 0 066388 0 022534 0 022222 0 110553 0 6 We have that Lower Bound 0 0222 Upper Bound 0 1105 The null hypothesis is rejected since the lower bound of th
51. et 4 PLUS 2 5 PERSONAL Personal ado files 3 1 installing DASP modules a Unzip the file dasp zip in the directory c b Make sure that you have c dasp dasp pkg or c dasp stata toc c Inthe Stata command windows type the syntax d net from c dasp Figure 1 Ouput of net describe dasp ersion Version 1 4 ate December 2007 Stata Version Required 9 2 and higher ASP is conceived and programmed by r Abdelkrim Araar aabd ecn ulaval ca Co author r Jean Yves Duclos jyvesfecn ulaval ca efore using modules of this package users have to pdate the executable Stata file to Stata 9 2 or higher ttp wiy stata com support updates stata9 html pdate the ado files ttp wi stata com support updates statad ada e two follwing sub packages must be installed to run DASP PACKAGES you could net describe dasp pl Distributive Analysis Stata Package PART I dasp p Distributive Analysis Stata Package PART II e Type the syntax net install dasp p1 pkg force replace net install dasp p2 pkg force replace 3 2 Adding the DASP submenu to STATA s main menu With STATA 9 sub menus can be added to the menu item User Figure 2 DASP submenu atistics User Window Help B x Data gt E Graphics J Statistics gt a J Dase gt inequalky gt Copyright 1984 2007 EE B gt StataCorp l Poverty gt 4905 Lakeway Drive College Station Texas 77845 USA Pro poor 800 STATA PC http w
52. f a group variable is selected only the first variable of interest is then used Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed Interested users are encouraged to consider the exercises that appear in Section 21 9 12 2 Difference between Gini concentration indices digini This module estimates differences between the Gini concentration indices of two distributions For each of the two distributions 18 One variable of interest should be selected To estimate a concentration index a ranking variable must be selected Conditions can be specified to focus on specific population subgroups Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 12 3 Generalised entropy index ientropy The generalized entropy index is estimated as A 1 a sy 2 1 if 050 1 i H i 0 zwie E if 0 0 Wi 1 1 i l M e 2 if 6 1 Wi 1 u The user can select more than one variable of interest simultaneously For example one can estimate inequality simultaneously for per capita consumption and for per capita income A group variable can
53. ibution function E DASP Joint Distribution Surfaces gt sjdistrub command After clicking SUBMIT the following graph is plotted interactively with Gnu Plot 4 2 Joint Distribution Function Fy SA 0 9 SSSA 0 8 SSA 0 7 S 05 S 0 4 Ni 0 3 0 2 nk 0 1 sauce A LIER EOIN A i RSS n 00 SS lt s SSSSSs gt RSS ZSSS 93 21 12 Testing the bi dimensional poverty dominance Using the columbia95I dta distribution 1 and the dominican republic95I dta distribution 2 files A Draw the difference between the bi dimensional multiplicative FGT surfaces and the confidence interval of that difference when Var of interest Range alpha j Dimension 1 haz height for age 3 0 6 0 0 Dimension 2 sprob_ survival probability 0 7 1 0 0 2 Test for bi dimensional poverty using the information above Answer 0 1 Steps To open the relevant dialog box type db dombdpov Choose variables and parameters as in Figure 56 Testing for bi dimensional poverty dominance ES DASP Difference Between Multiplicative FGT indices gt dombipov command Main Confidence interval Results Distribution 1 Data in file v JI ADATASBD 2 coir31 fl dta Browse Dimension_1 D1 haz Dimension_2 D2 sprob Distribution 2 Data in file v J CADATAMBD2 drr21 fl dta Browse Dime
54. idence used can be changed Surfaces showing the difference the lower bound and the upper bound of the confidence surfaces are plotted interactively with the GnuPlot tool Coordinates can be listed Coordinates can be saved in Stata or GnuPlot ASCII format Interested users are encouraged to consider the exercises that appear in Section 21 12 17 Distributive tools 17 1 Quantile curves c_quantile The quantile at a percentile p of a continuous population is given by O p F p where p F y is the cumulative distribution function at y For a discrete distribution let m observations of living standards be ordered such that y lt y Zk y lt Yia L e lt Yy If F y lt p lt F 9 we define Q p y The normalised quantile is defined as Q p Q p u Interested users are encouraged to consider the exercises that appear in Section 21 10 17 2 Density curves cdensity The Gaussian kernel estimator of a density function f x is defined by 34 K ZMK and K x 1 2 O Wa x X f exp 0 5 4 and A x where h is a bandwidth that acts as a smoothing parameter Interested users are encouraged to consider the exercises that appear in Section 21 10 17 3Non parametric regression curves cnpe Non parametric regression is useful to show the link between two variables without specifying beforehand a functional form It can also be used to estimate the local derivative of the first varia
55. in Section 21 7 14 2 Decomposition of the variation in FGT indices into growth and redistribution components dfgtgr Datt and Ravallion 1992 decompose the change in the FGT index between two periods t1 and t2 into growth and redistribution components as follows P P Pp n P nl P n p n R ref 1 B m m variation Cl C2 P P Pau n P t x Pa n 2 Pa n R ref 2 l variation Cl C2 where variation difference in poverty between t1 and t2 C1 growth component C2 redistribution component R residual Ref period of reference 23 Plu z the FGT index of the first period P t 2 the FGT index of the second period Pu n the FGT index of the first period when all incomes y of the first period are multiplied by Ta Ju Pu nt the FGT index of the second period when all incomes y of the second period are multiplied by u Ip The Shapley value decomposes the variation in the FGT Index between two periods t1 and t2 into growth and redistribution components as follows P2 P C Cy Variation 1 1 Cy putt x i Putt sn put x Put nt 14 3 Decomposition of the FGT by transient and chronic poverty components dtcpov This type of decomposition decomposes total poverty observed over some time periods into transient and chronic components The Jalan and Ravallion 1998 approach Let y be the inco
56. index is as follows 2 wif yj a yj DER a n wi i 1 The normalised DER that the module estimates is defined as follows DER DER a e 0 1 2u a Where i i l 2X wj Wi ZZ wjyj twiyi o j l 1 j l a y u Yi N N ZW ZW i l i The Gaussian kernel estimator is used to estimate the density function The user can select more than one variable of interest simultaneously For example one can estimate inequality for instance by using simultaneously per capita consumption and per capita income A group variable can be used to estimate polarization at the level of a categorical group If a group variable is selected only the first variable of interest is then used Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 21 Main reference DUCLOS J Y J ESTEBAN AND D RAY 2004 Polarization Concepts Measurement Estimation Econometrica 72 1737 1772 13 2 Difference between DER polarization indices dipolar This module estimates differences in DER indices of two distributions For each of the two distributions One variable of interest should be selected Conditions can be specified to focus on specific population subgroups Standard errors and confidence intervals with a confidence le
57. ing poverty indices and their standard errors will be estimated Per dd mar Zl A 8 Applications and files in DASP Two main types of applications are provided in DASP For the first one the estimation procedures require only one data file In such cases the data file in memory is the one that is used or loaded it is from that file that the relevant variables must be specified by the user to perform the required estimation Figure 5 Estimating FGT poverty with one distribution E DASP FGT and EDE FGT Index gt ifgt command Main Confidence Interval Results ji v Index FGT Index Ba Type Normalised k M Parameter s m of interest Index options s Size variable 7 Parameter alpha jo Group variable x Poverty line Absolute fi 0000 C Relative 504 of the Mean z F group variable is used poverty line is relative to Survey settings The population k aod Bal Cancel Submit 10 For the second type of applications two distributions are needed For each of these two distributions the user can specify the currently loaded data file the one in memory or one saved on disk Figure 6 Estimating FGT poverty with two distributions ES DASP Difference in FGT Indices gt difgt command iol x Main Confidence Interval Results Distribution 1 Distribution 2 Jata in Memory Data in File gt
58. ing the poverty line to a proportion of the mean 3 Setting the poverty line to a proportion of a quantile Q p One variable of interest should be selected Conditions can be specified to focus on specific population subgroups Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed A level for the parameter a can be chosen for each of the two distributions Interested users are encouraged to consider the exercises that appear in Section 21 2 10 3DASP and multidimensional poverty indices imdpov The general form of an additive multidimensional poverty index is X wP X Z P X Z where p X Z is individual s poverty function with vector of attributes X gery and vector of poverty lines Z ast determining Fs contribution to total poverty P X Z 1 Chakravarty et al 1998 index J a Z X p n a sz Z j l J 2 Extended Watts index 13 J Z p X Z X a m ja min z 3X j 3 Multiplicative extended FGT index bz P J j l 4 Tsui 2002 index b J Zj J gt i j j l min z 5 Intersection headcount index J PX Z TTI z gt j l 6 Union headcount index J p X Z 1 1 z lt x j l 7 Bourquignon and Chakravarty bi dimensional 2003
59. ions are available to change the appearance of the graphs Interested users are encouraged to consider the exercises that appear in Section 21 13 18 2 2 Dual pro poor curves Let Q p quantile at percentile p GL p Generalised Lorenz curve at percentile p u average living standards The change in the distribution from state 1 to state 2 is first order absolutely pro poor with standard cons 0 if A z s Q p 0 p gt 0 Y p e 0 p F z or equivalently if _Q p Q p 5 gt 0V pe 0 p F z A z S The change in the distribution from state 1 to state 2 is first order relatively pro poor if Q p W A z s gt 0 V 0 p F S ay pe 0 p F z 38 The change in the distribution from state 1 to state 2 is second order absolutely pro poor if A z s GL p GL p gt 0 V p e 0 p F z or equivalently if GL p GL p 697 ne gt 0 Y pe 0 p F z The change in the distribution from state 1 to state 2 is first order relatively pro poor if _CGL p wm A CG E gt 0 Y pe 0 p F z The module cpropoord can be used to draw these dual pro poor curves and their associated confidence interval by taking into account sampling design The module can draw pro poor curves and their two sided lower bounded or upper bounded confidence intervals list or save the coordinates of the differences between the curves as well as those of the confidence inter
60. is estimated as n i Ewyk P z n wi i where z is the poverty line and x max x 0 The usual normalised FGT index is estimated as P z a P z a z The EDE FGT index is estimated as EDE P z a P for a gt 0 There exist three ways of fixing the poverty line 1 Setting a deterministic poverty line 2 Setting the poverty line to a proportion of the mean 3 Setting the poverty line to a proportion of a quantile O p The user can choose the value of parameter a The user can select more than one variable of interest simultaneously For example one can estimate poverty by using simultaneously per capita consumption and per capita income A group variable can be used to estimate poverty at the level of a categorical group If a group variable is selected only the first variable of interest is then used Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed Interested users are encouraged to consider the exercises that appear in Section 21 1 12 10 2 Difference between FGT indices difgt This module estimates differences between the FGT indices of two distributions For each of the two distributions There exist three ways of fixing the poverty line 1 Setting a deterministic poverty line 2 Sett
61. itures per capita label var expeq Total household expenditures per adult equivalent label var gse Socio economic group of the household head To define the label values that will be assigned to the categorical variable gse label def ine lvgs I O U BUN F To label assign the label trader arner val wage earner public sector wage earner private sector Artisan or Other type of Crop farmer Subsistence farmer Inactive values lvgse to the variable gse gse lvgse label label var def sex lvsex L Mal e 2 Female r label val Sex of household head sex lvsex label var zone Residential area 46 label def lvzone 1 Rural 2 Urban i label val zone lvzone 20 3 Appendix C setting the sampling design To set the sampling design for the data file bkf94 dta open the dialog box for the command svyset by typing the syntax db svysetin the command window In the Main panel set STRATA and SAMPLING UNITS as follows Figure 11 Survey data settings ES svyset Survey data settings More Weights SE Poststratitication 1 i esu ovate oo Ho AH In the Weights panel set SAMPLING WEIGHT VARIABLE as follows 47 Figure 12 Setting sampling weights S svyset Survey data settings Click on OK and save the data file To check if the sampling
62. le of interest exppc b with size variable set to size c atthe official poverty line of 41099 Francs CFA d and using the group variable gse Socio economic groups 2 Do the above exercise without standard errors and with the number of decimals set to 4 Answers 74 Type use C data bkf94l dta clear To open the relevant dialog box type db d gtg Choose variables and parameters as in Figure 36 Decomposing FGT indices by groups E DASP Decomposiotion of the FGT Index by Groups gt dfgtg command Main Results Index option s Variable of interest Jexppe v Type Normalised v Size variable size Group variable gse v Parameters Parameter alpha fi Poverty line z ja 094 Survey settings Cancel Submit After clicking SUBMIT the following information is provided dfgtg exppc hgrouplgse hsizelsize alphal1 pline 41099 tupelnor FGT Index Deconposit ion by Groups Group FGT Index Popu lat ion Absolute Relat ive Share Contribution Contribution 0 001308 uage earning public sector 0 004237 0 042971 0 000182 0 002574 0 003790 0 00011 Hage earning private sector 0 122176 0 126598 0 000690 0 010678 0 002164 0 000291 Artisan or trading 0 027741 0 062640 0 001738 0 004653 0 004288 0 000325 Others activities 0 163853 0 006650 0 000425 0 025805 0 001308 0 000170 Farners crop 0 137525 0 10402 0 114358 0 011808 0 014896 0 002459
63. me of household i in period t and u be the average income over the T periods for household i Total poverty is defined as follows T N P gt pa W Z yj 4 TP a z 2 t li 1 x TY Wi i l The chronic poverty component is then defined as 24 N a wi z ui CPC a z N 2 wi i l The transient poverty component is finally defined as TPC a z TP a z CPC a z Duclos Araar and Giles 2006 approach Let y be the income of household i in period t and u be the average income over the T periods for household i Let a z be the equally distributed equivalent EDE poverty gap such as TN ie L LWEN Tr a z TP a z _ t li l i T gt Wi i The transient poverty component is defined as follows N gt w 9 a z TPC a z F x X wi i l T l a where 0 a z 1 z and y a z gt a yb r i t The chronic poverty component is defined as follows CPC a z I a z TPC a z Note that the number of periods available for this type of exercise is generally small Because of this a bias correction is typically useful using either an analytical asymptotic or bootstrap approach 25 To open the dialog box for module dtcpov type db d cpov in the command window Figure 8 Decomposition of poverty into transient and chronic components E DASP Decomposition of the total poverty into transient and chronic poverty gt dtcpov command At x Main
64. nd order if and only if L p lt L p V pe 0 1 The module domineg can be used to check for such inequality dominance It is based mainly on Araar 2006 Araar Abdelkrim 2006 Poverty Inequality and Stochastic Dominance Theory and Practice Illustration with Burkina Faso Surveys Working Paper 06 34 CIRPEE Department of Economics Universit Laval Intersections between curves can be estimated with this module It can also used to check for tax and transfer progressivity by comparing Lorenz and concentration curves 16 3 DASP and bi dimensional poverty dominance dombdpov Let two dimensions of well being be denoted by k 1 2 The intersection bi dimensional FGT index for distribution D is estimated as 33 n 2 Em rt d Pp Z A i l k 1 n wi i l where Z zi z and A aa are vectors of poverty lines and parameters G respectively and x max x 0 Distribution 1 dominates distribution 2 at orders 51 5 over the range 0 Z if and only if R Z A s 1 lt P Z A s l V Ze 0 z x 0 z andfora 5 1 a 5 1 The DASP dombdpov module can be used to check for such dominance For each of the two distributions The two variables of interest dimensions should be selected Conditions can be specified to focus on specific population subgroups Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of conf
65. nsion_1 D1 haz Dimension_2 D2 sprob Size variable Size variable I Conditions fi z I Condition s fi z Parameters Minimum Maximum Hof partitions Paremeter alpha Range Dim tfs Ja 20 jo Range Dim 2fos fi 20 jo Cancel Submit After clicking SUBMIT the following graph is plotted interactively with Gnu Plot 4 2 94 Bi dimensional poverty dominance Difference Lower bounded 0 5 Upper bounded m _ 0 78 Dimensioni 14 0 gg0 860 840 82 0 8 0 9 2 5 4 0 980 960 9499 3 Dimension 2 0 2 To make a simple test of multidimensional dominance one should check if the lower bounded confidence interval surface is always above zero for all combinations of relevant poverty lines or conversely o For this click on the panel Confidence interval and select the option lower bounded o Click again on the button Submit After clicking SUBMIT the following graph is plotted interactively with Gnu Plot 4 2 95 Bi dimensional poverty dominance Lower bounded 0 05 7 07 0 05 jj 0 17 IN 0 15 7 0 2 0 25 0 37 03 ZA 1X 0 X 1X Dimension 1 2 5 Y 0 98 1 4 5 X G8 09 0 920 940 96 n 0 82 0 840 86 0 78 Dimension 2 21 13 Testing for pro poorness of growth in Mexico 96 The three sub samples used in these exercises are sub samples of 2000 observations drawn randomly from the three ENIGH Mexican household surveys for 1992 1998 and 2004
66. one Social groups are quintiles Answer Type db bian in the windows command and set variables and options as follows Figure 60 Benefit incidence analysis E DASP Benefit incidence analysis gt bian command Main Results Label the public service Education Variable s of interest M Options Standard living fex 5 as Approach z Number of sectors 2 Labels Freguency Eligible HH members Sector 1 Primary J ra prim Jel_prim v Sector 2 Secondary fra sec kA el sec ho Cancel Submit After clicking on Submit the following appears 103 Benefit Incidence Analysis Education Share by uintile Groups Groups Prinary Secondary Quintile 1 0 218 0 155 Quintile 2 0 226 0 216 Quintile 3 0 220 0 224 Quint ile 4 0 197 0 231 Quintile 5 0 139 0 173 All 1 000 1 000 Groups Prinary Secondary Quintile 1 0 797 0 48 Quintile 2 0 825 0 641 Quintile 3 0 802 0 663 Quintile 4 0 723 0 687 Quintile 5 0 506 0 511 All 0 730 0 592 2 To estimate total public expenditures on education by sector at the national level the following macro information was used Pre primary and primary public education expenditure as of all levels 1995 35 2 Secondary public education expenditure as of all levels 1995 21 2 Tertiary public education expenditure as of all levels 1995 16 Public education expenditure as of GNP 1995 3 GDP per capita about 3
67. public expenditures is available at the level of areas this information can be used with the bian module to estimate unit cost more accurately Example 1 Observation i HH Eligible HH Freguency Area indicator Total level of regional size members public expenditures 1 7 3 2 1 14000 2 4 2 2 1 14000 3 5 5 3 1 14000 4 6 3 2 2 12000 5 4 2 1 2 12000 In this example the first observation contains information on household 1 e This household contains 7 individuals e Three individuals in this household are eligible to the public service e Only 2 among the 3 eligible individuals benefit from the public service e This household lives in area 1 In this area the government spends a total of 14000 to provide the public service for the 7 users of this area 2 2 3 The unit cost in area 1 equals 14000 7 2000 The unit cost in area 2 equals 12000 3 4000 By default the area indicator is set to 1 for all households When this default is used the variable Regional public expenditures the fifth column that appears in the dialog box should be set to total public expenditures at the national level This would occur when the information on public expenditures is only available at the national level Example 2 Observation i HH Eligible Frequency Area indicator Regional public size members expenditures 1 7 3 2 1 28000 2 4 2 2 1 28000 3 5 5 3 1 28000 4 6 3 2 1 28000 5 4 2 1 1 28000 The unit cost benefit at the national level equals 28000
68. ral and urban households a with variable of interest exppc b with size variable set to size c and using the group variable zone as residential area 0 1 a Type use C data can6 dta clear To open the relevant dialog box type db clorenz Choose variables and parameters as in 77 Figure 37 Lorenz and concentration curves JE DASP Lorenz amp Concentration Curves gt clorenz command Main Results Y Axis X Axis Title Caption Legend Overall Variables of interest Type of curvels xN v T Type Normalised by default T Ranking Variable J s T Difference No Size variable Range of percentiles p Group variable Minimum Maximum po ho Cancel Submit After clicking SUBMIT the following appears Figure 38 Lorenz curves 3 Graph Graph Lorenz Curves L p 6 78 Q 2 Steps Choose variables and parameters as in Figure 39 Drawing concentration curves E DASP Lorenz amp Concentration Curves gt clorenz command M T B1B2B3 E After clicking on SUBMIT the following appears 79 Figure 40 Lorenz and concentration curves 5 Graph Graph 6 LP amp C p line_45 Cp Bl Cip B2 Cpr 63 Q 3 Steps Type use C data bkf94l dta clear Choose variables and parameters as in 80 Figure 41 Drawing Lorenz curves E DASP Lorenz amp Con
69. s Estimate standard errors and confidence intervals by taking full account of survey design Support distributive analysis on more than one data base Perform the most popular poverty and decomposition procedures Check for the ethical robustness of distributive comparisons Unify syntax and parameter use across various estimation procedures for distributive analysis For each DASP module three types of files are provided ado This file contains the program of the module hlp This file contains help material for the given module dlg This file allows the user to perform the estimation using the module s dialog box The dlg files in particular makes the DASP package very user friendly and easy to learn When these dialog boxes are used the associated program syntax is also generated and showed in the review window The user can save the contents of this window in a do file to be subsequently used in another session 2 DASP and Stata versions DASP requires o STATA version 9 2 or higher o ado files must be updated To update the executable file from 9 0 to 9 2 and the ado files see http www stata com support updates 3 Installing and updating the DASP package In general the ado files are saved in the following main directories Priority Directory Sources 1 UPDATES Official updates of STATA ado files 2 BASE ado files that come with the installed STATA software 3 SITE ado files downloaded from the n
70. s The module sjdistrub can be used to draw joint distribution surfaces The joint distribution function F x y is defined as Sw Ix SAY lt y F x y With this module The two variables of interest dimensions should be selected specific population subgroups can be selected surfaces showing the joint distribution function are plotted interactively with the GnuPlot tool coordinates can be listed coordinates can be saved in Stata or GnuPlot ASCII format Interested users are encouraged to consider the exercises that appear in Section 21 11 18 DASP and pro poor growth 36 18 1DASP and pro poor indices The module ipropoor estimates simultaneously the three following pro poor indices 1 The Chen and Ravallion pro poor index 2003 _W z Wx z F z Index where Wp z is the Watts index for distribution D 1 2 and Fi z is the headcount for index for the first distribution both with poverty lines z 2 The Kakwani and Pernia pro poor index 2000 R z a P za Index A F z F x Ly 4h 3 The Kakwani Khandker and Son pro poor index 2003 P z a Py za Index g L LL lt L P z P 2z 44 4h a where the average growth is g 42 44 u and where a second index is given by Index 2 Index l g One variable of interest should be selected for each distribution Conditions can be specified to focus on specific population subgroups
71. s GiNiNe ceccceceeeeeeeeeeeeneeeceeeeesaeeeeneeeeeeeeeees 20 13 DASP and polarization indices ccccceesceeeeeeeceeeeeeeeee seas eeceaeeeeeeeseeeeecaaeeseaaeseeeeeseaeeesaeeeeaeesseneeeas 21 13 1 The DER index ipolar cece eee eee eee eee eee ena nn K KKK KA PAK PAKA KRKA AKA AK ena t 21 13 2 Difference between DER polarization indices dipolar e ee eeeeeeeeeeee eee eee een 22 14 DASP and decompositionS 2 ee 244ee2 0200000000000 de ee eee nade K K RAKA P RKK ARK AKA AKA tank 22 14 1 FGT Poverty decomposition by population Subgroups AfQtQ cceccsseceeeeceeeeeeeeeeeeeeeees 22 14 2 Decomposition of the variation in FGT indices into growth and redistribution components dfgtgr 23 14 3 Decomposition of the FGT by transient and chronic poverty components dtcpov 24 14 4 Inequality decomposition by income sources digiNiS 2 eee eee 26 14 5 Gini index decomposition by population subgroups diginig 4 ee 2e eee 27 14 6 Generalized entropy indices of inequality decomposition by population subgroups dentropyg 27 15 DASP and CuUIVeS whi uz seine v ieee te en Mid ee ST da eee ani Wee eed Geet eae 28 151 FG1 GURVESCT9D imari nei eRe a evict al AKVA dan Ea eee dea ode v ear aN 28 15 2 FGT CURVE with confidence interval C gTS e 2 eee eee eee eee eee een nenene nen 30 15 3 Differenc
72. s ses atit et ee dae cain aiid Arita ed 74 21 8 Estimating Lorenz and concentration CUIVES eee eee een nee 77 21 9 Estimating Gini and concentration CUIVES 0 ccccccececeeeeeceneeeeeeeseeeeeseaeeeeaaeseeneeseaeeeteaeeseaeeseaees 82 21 10 Using basic distributive tools e 4 24e eee2220e 424 400020 eeeeee ena anne ARK ARK nA n n 85 21 11 Plotting the joint density and joint distribution FUNCTION 2 eee eeeeeeeeeeeeeeeeeeeeeennn 91 21 12 Testing the bi dimensional poverty COMINANCE 2 22402 eeeeeeeee eee eee nn 94 21 13 Testing for pro poorness of growth in MEXICO eeeeeeeeee eee eeeeee eee nenene nen 96 21 14 Benefit incidence analysis of public spending on education in Peru 1994 n 102 Listo Figure 1 f Figures Ouput of net describe dASp es 2 eee2e2e tenteen tett nett PARK K RKK LEPE K KK KKK AKA KEE EKK K R KRK K 7 Figure 2 DASP SUDMENU s a a line ko e beogb taka bance crip Rattan a odv d Poea bedra aa aan 8 Figure 3 Using DASP with a command WINdOW 2 2 2222 eee eee eee nene eee anne nee RKK KKK ent 9 Figure 4 Accessing help on DASP 2 eee eee eee eee nee nen KK KARA PARK K R KAR A K KEE KKK K neunten nnt 10 Figure 5 Estimating FGT poverty with one distribution 4 2 44 2 lt eee 2220000000000 eeee een een ante en nn 10 Figure 6 Estimating FGT poverty with two distributions ccc
73. sent for instance income per capita expenditures per adult equivalent calorie intake normalized height for age scores for children or household wealth SIZE VARIABLE This refers to the ethical or physical size of the observation For the computation of many statistics we will indeed wish to take into account how many relevant individuals or statistical units are found in a given observation GROUP VARIABLE This should be used in combination with GROUP NUMBER It is often useful to focus one s analysis on some population subgroup We might for example wish to estimate poverty within a country s rural area or within female headed families One way to do this is to force DASP to focus on a population subgroup defined as those for whom some GROUP VARIABLE say area of residence equals a given GROUP NUMBER say 2 for rural area SAMPLING WEIGHT Sampling weights are the inverse of the sampling probability This variable should be set upon the initialization of the dataset 6 How can DASP commands be invoked STATA commands can be entered directly into a command window Figure 3 Using DASP with a command window 5 Intercooled Stata 9 2 Results In xf fm File Edit Prefs Data Graphics Statistics User Window Help 18 x S M M HE S 0H 0 8 Review Ax use C DATASbKIS4I dta clear fof f i f f 9 2 Copyright 1984 2006 Stat ist ics Data Analysis StataCorp 4905 Lakeuay Drive College Station Texas 77845
74. stribution fuNCLION e e e4eeee eee 0000000 0eee eee een K RAKA A PRK A KKK AKA 93 Testing for bi dimensional poverty dominance ee 442 220 ee eeeeeeee eee eee ee eee 94 Testing the pro poor growth primal aApproach 2 2 2442 eeeeeeeee eee eee eee nenene nn 98 Testing the pro poor growth dual approach A 2 eee eee eee eee eee eee nene n 99 Testing the pro poor growth dual approach B eee 100 Benefit incidence analysis 2ee 4 2 0022222000000 ee ea ne ee aan KA KKK RAKA PARK RAKA RAP h th 103 Benefit Incidence Analysis unit cost approach 2 2200 eeee eee eee eee anne 105 1 Introduction The STATA software has become a very popular tool to transform and process data It comes with a large number of basic data management modules that are highly efficient for transformation of large datasets The flexibility of STATA also enables programmers to provide specialized ado routines to add to the power of the software This is indeed how DASP interacts with STATA DASP which stands for Distributive Analysis STATA Package is mainly designed to assist researchers and policy analysts interested in conducting distributive analysis with STATA In particular DASP is built to Estimate the most popular statistics indices curves used for the analysis of poverty inequality social welfare and equity Estimate the differences in such statistic
75. te T income components After clicking on SUBMIT the following should be displayed efgtc sourcel source tot income hsize hhsize alpha 0 pline 14897 prc l Poverty and Inequality Indices Marginal Impacts Elasticities of poverty with respect to the within between inequality in income components sourcel 1 source2 sources sourced 1 sources In case one is interested in changing income component only among individuals that are effectively active in some economic sectors schemes n k t and X inthe paper of reference the user should select the approach Truncated income component 17 12 DASP and ineguality indices 12 1 Gini and concentration indices igini The Gini index is estimated as pase u where 2 2 V V n D S y and V Y wp and Y12227 Yn 12Yn gt i l v h i The concentration index for the variable T when the ranking variable is Y is estimated as ICT r HT where i is the average of variable T nooo 21 V V r z Vi Co ti al m n and where V Y w and V122 Yn 12Yn h i The user can select more than one variable of interest simultaneously For example one can estimate inequality for instance by using simultaneously per capita consumption and per capita income To estimate a concentration index the user must select a ranking variable A group variable can be used to estimate inequality at the level of a categorical group I
76. the values of the poverty line at which the two FGT curves cross a The variable of interest is exppc for 1994 and exppcz for 1998 b The poverty line should vary between 0 and 100 000 Franc CFA c The size variable should be set to size Answers Q 1 Steps To open the relevant dialog box type db dompov Choose variables and parameters as in 73 Figure 35 Testing for poverty dominance ES DASP Poverty Dominance gt dompov command Main Results Distribution 1 Distribution 2 Data in File hd C 5DATASbkf941 dta Browse Data in File Ez J CADATA bk98 dta Browse Variable of interest Jexppe Variable of interest Jexppck Size variable size Size variable size T Conditions fi z I Condition s fi z Second order 7 Dominance order 00 Cancel Submit After clicking SUBMIT the following results appear Hunber of Critical Hin range of Haw range of Case intersect ion pov line poy lines pov line 1 24262 871 P A 2 46775 02 a B Hotes _case A Before this intersection distribution 2 doninates distribution 1 _case B Before this intersection distribution 1 doninates distribution 2 case No dominance before this intersect ion 21 7Decomposing FGT indices What is the contribution of different types of earners to total poverty in Burkina Faso 1 Open bkf94l dta and decompose the average poverty gap a with variab
77. ultaneously for per capita consumption and for per capita income A group variable can be used to estimate inequality at the level of a categorical group If a group variable is selected only the first variable of interest is then used Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 12 6 Difference between Quantile Share indices dinineg This module estimates differences between the Quantile Share indices of two distributions For each of the two distributions One variable of interest should be selected Conditions can be specified to focus on specific population subgroups 20 Standard errors and confidence intervals with a confidence level of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 13 DASP and polarization indices 13 1 The DER index ipolar The Duclos Esteban and Ray 2004 DER polarization index is estimated as Denote the Duclos Esteban and Ray DER index of polarisation for the group k by DER k q It can be expressed as follows DER a J f x y y x dydx where f x denotes the density function for group k The discrete formula that is used to estimate this
78. vals save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs Interested users are encouraged to consider the exercises that appear in Section 21 13 19 DASP and Benefit Incidence Analysis 19 1 Benefit incidence analysis The main objective of using a benefit incidence approach is to analyse the distribution of benefits from the use of public services according to the distribution of living standards Two main sources of information are used The first informs on the access of household members to public services This information can be found in the usual household surveys The second deals with the amount of total public expenditures on each public service This information is usually available at the national level and sometimes in a more disaggregated format such as at the regional level The benefit incidence approach combines the use of these two sources of information to analyse the distribution of public benefits and its progressivity Formally let W be the sampling weight of observation 1 39 yi be the living standard of members belonging to observation i i e per capita income es be the number of eligible members of observation i e members that need the public i service provided by
79. variable Group variable Survey settings eo l Cancel Submit After clicking SUBMIT the following results are obtained 82 Variable Estinate ST 1 GINI X 0 508456 0 016234 2 GINI H 0 332355 0 112758 Q 2 Steps Choose variables and parameters as in Figure 44 Estimating concentration indices LB UB 0 476599 0 540313 0 307318 0 357391 E DASP Gini amp Concentration Indices gt igini command Variable Estinate STD 1 OHC T 0 595339 0 022931 2 COMC_H 0 306060 0 013268 Q 3 Steps To open the relevant dialog box type db digini Choose variables and parameters as in 83 LB UB 0 551340 0 641338 0 260014 0 332087 Figure 45 Estimating differences in Gini and concentration indices E DASP Difference Between Gini Concentration Indices gt digini command Data in File w C ADATASbkf98l dta Data in File C ADATASbkf94l dta After clicking SUBMIT the following information is obtained digini expegz expeg filel C data bkf98I dta hsizellsize file2 C data bkf94I dta hsize2 size Estinate STD LB UB Distr ibut ion_1 GINT 0 446563 0 012816 0 419371 0 469745 Distr ibut ion_2 GINT 0 450055 0 008618 0 433116 0 466994 Difference 0 005492 0 015444 0 035762 0 024778 84 21 10 Using basic distributive tools What does the distribution of gross and net incomes look like in Canada Using the can
80. vel of 95 are provided Both the type of confidence intervals provided and the level of confidence used can be changed The results are displayed with 6 decimals this can be changed 14 DASP and decompositions 14 1 FGT Poverty decomposition by population subgroups dfgtg The dgfgt module decomposes the FGT poverty index by population subgroups This decomposition takes the form G A A P za Z g PG a g 871 where G is the number of population subgroups The results show The estimated FGT index of subgroup g P z a32 The estimated population share of subgroup g g The estimated absolute contribution of subgroup g to total poverty Plz a The estimated relative contribution of subgroup g to total poverty 8 P z3a 8 Pa An asymptotic standard error is provided for each of these statistics To open the dialog box for module dfgtg type db dfgtg in the command window 2 Figure 7 Decomposition of the FGT index by groups E DASP Decomposiotion of the FGT Index by Groups gt dfgtg command iol xi Main Results Index option s Variable of interest ji P Type Not Normalised v Size variable v Group variable M Parameters Parameter alpha jo Poverty line 2 fi 0000 Survey settings Cancel Submit Note that the user can save results in Excel format Interested users are encouraged to consider the exercises that appear
81. ww stata com Poverty elasticities gt 979 696 4600 statalistata com Q79 A494 dan1 ifaw gt FGT Decomposition by Groups Single user Sta Dominance gt FGT Growth and redistribution Serial n Curves FGT Decomposition into transient and chronic components Licens Distributive Tools gt Decomposition Generalised Entropy Decomposition by Groups Benefit Analysis Y Gini Decomposition by Groups Gini Decomposition by Income Sources l m option or set mem running c ado personal profile do To add the DASP sub menus the file profile do which is provided with the DASP package must be copied into the PERSONAL directory If the file profile do already exists add the contents of the DASP provided profile do file into that existing file and save it To check if the file profile do already exists type the command findfile profile do 4 DASP and data files DASP makes it possible to use simultaneously more than one data file The user should however initialize each data file before using it with DASP This initialization is done by 1 Labeling variables and values for categorical variables 2 Initializing the sampling design with the command svyset 3 Saving the initialized data file Users are recommended to consult appendices A B and C 5 Main variables for distributive analysis VARIABLE OF INTEREST This is the variable that usually captures living standards It can repre
82. y elasticities with respect to population group inequalities gt efgtg command q C x Main Results Parameters Variable of interest Jincome z 3 Parameter alpha jo Size variable hhsize kl Poverty line 2 fi 4897 Group variable fone 7 Percentage of change 100 Survey settings Cancel Submit After clicking on SUBMIT the following should be displayed 15 efgtg income hgroup zone hsize hhsize alpha 0 pline 14897 prc l dec 3 Poverty and Ineguality Indices FGT Marginal Impact Elasticities By Groups Population Marginal Marginal Elasticity Share Impact on Ineq Impact on Pov South south South east o g South west North central North east North west o Y 11 2 FGT Elasticity with respect to within between income components of inequality efgtc This module estimates the FGT marginal impact and elasticity with respect to the within between income components of inequality A list of income components must be provided This module is mostly based on Araar and Duclos 2007 Araar Abdelkrim and Jean Yves Duclos 2007 Poverty and inequality components a micro framework Working Paper 07 35 CIRPEE Department of Economics Universit Laval To open the dialog box of this module type the command db efgtc 16 E DASP FGT Poverty elasticities with respect to income sources inequalities gt efgtc command on tais one iat z
83. y if each individual in subgroup k is given the mean income of subgroup k u k 27 15 DASP and curves 15 1 FGT CURVES cfgt FGT curves are useful distributive tools that can inter alia be used to 1 Show how the level of poverty varies with different poverty lines 2 Test for poverty dominance between two distributions 3 Test pro poor growth conditions FGT curves are also called primal dominance curves The cfgt module draws such curves easily The module can draw more than one FGT curve simultaneously whenever more than one variable of interest is selected draw FGT curves for different population subgroups whenever a group variable is selected draw FGT curves that are not normalized by the poverty lines draw differences between FGT curves list or save the coordinates of the curves save the graphs in different formats o gph STATA format o wmf typically recommended to insert graphs in Word documents o eps typically recommended to insert graphs in Tex Latex documents Many graphical options are available to change the appearance of the graphs To open the dialog box of the module cfgt type the command db dfgt in the command window 28 Figure 9 FGT curves S DASP FGT Curves gt cfgt command o es Interested users are encouraged to consider the exercises that appear in Section 21 4 29 15 2FGT CURVE with confidence interval cfgts The cfgts module draws an FG

DASP: USER MANUAL - Université Laval

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DASP: USER MANUAL - Université Laval