Targeting Analysis Protocol March 18, 2009
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- Arline Lynch
- 5 years ago
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1 Targeting Analysis Prtcl March 18, 2009 Analysis 0: Test f balance Examine all 10 f the fllwing variables. Run regressins fr each f the 10 variables: X = COMMUNITY + HYBRID + KECAGROUPFE + epc Key cefficients are a jint test acrss regressins f all 10 X s f COMMUNITY and HYBRID, separately and tgether. Cluster at village level. We will run specificatins with n kecagrup effects, kecagrup randm effects, and kecagrup fixed effects. The 10 X s we examine are frm a cmbinatin f PODES 2008 and SUSETI. SUSETI: Per capita expenditure frm SUSETI Educatin level f HH head PMT scre calculated frm assets in SUSETI data % f husehlds that are agricultural Educatin level f RT head Lg number f husehlds in 0052T frm SurveyMeter listing frm PODES08: Distance t kecamatan in km (902_2_a) Lg size f village in hectares (ln f 1001) Religius buildings (except surau/langgar) per husehld ((703_a + 703_c + 703_d + 703_e + 704f) / 401c) Primary schls per husehld ((601_2_b + 601_3_b ) / 401c) Analysis 0.1: Wh knws what? Analysis f SUSETI data CR sectin vs. matched cnsumptin data. 1. Basic regressins. RANK ijv = α jv + βpercapconsumption iv + eps RANK ijv = α jv + βrankconsumption iv + eps Clustered at village level. PERCAPCONSUMPTION iv is per capita cnsumptin f hh i by ranker j. RANKCONSUMPTION iv is the rank f per capita cnsumptin f hh i within village v. 2. D they have residual infrmatin beynd PMT?: RANK ijv = α jv + βpercapconsumption iv + PMTSCORE + eps RANK ijv = α jv + βpercapconsumption iv + ALLASSETVARIABLES + eps RANK ijv = α jv + βrankconsumption iv + PMTRANK + eps RANK ijv = α jv + βrankconsumption iv + ALLASSETVARIABLES + eps PMTSCORE is ur predicted lg per capita cnsumptin frm the PMT regressin we are using in the actual prject t target peple ALLASSETVARIABLES are all the variables that g int the PMT regressin with the same functinal frms we use in the PMT regressin Key questin is: is there still psitive infrmatin frm PERCAPCONSUMPTION / RANKCONSUMPTION nce yu put in all this bservable infrmatin i.e., des the cmmunity knw mre than the PMT enumeratrs culd find ut. 3. Hw des the cmmunity assess pverty? We will examine several X variables, demarcated belw.
2 First, estimate the predictive pwer f the X s n cnsumptin: PERCAPCONS = α v + X iv + eps. Predict PERCAPCONSHAT. RANKCONS = α v + X iv + eps. Predict RANKCONSHAT. Tw questin: Hw d the X s affect cmmunity rankings? RANK ijv = α jv + βpercapconsumption iv + X iv + eps RANK ijv = α jv + βrankconsumption iv + X iv + eps Hw d the X s affect cmmunity rankings, cnditinal n the X s additinal predictive pwer fr cnsumptin? RANK ijv = α jv + βpercapconsumption iv + X iv + PERCAPCONSHAT + eps RANK ijv = α jv + βrankconsumption iv + X iv + RANKCONSHAT + eps This secnd regressin estimates hw X affects individuals ranks, cntrlling fr the actual additinal predictive pwer f X n cnsumptin. Examine these with each family f indicatrs separately (separate clumn), then all tgether. We will als estimate these regressins separately fr men & wmen respndents (based n ur randmizatin) t get hw the SWF differs by gender. X variables t examine: we want t fcus n these 5 majr variables, althugh we will als examine the mre detailed versins belw Aggregated variables: Shcks (any f the shcks in sectin GE) Cnspicuusness General Cnnectedness (NOTE: this will be refined later based n Arun s wrk, but fr nw we will use the definitin belw) Elite Cnnectedness (NOTE: this will be refined later based n Arun s wrk, but fr nw we will use the definitin belw) Really, really pr (dummy fr being in the bttm 16 th percentile f ur SUSETI sample in terms f per capita expenditure) Mre detailed versins f variables: Equivalence scale: What equivalence scale des the cmmunity use? Run the regressin with lg expenditure, and add variables fr lg HH size and percent kids f agegrup 0 4, 4 9, t estimate a revealed equivalence scale (as in Olken 2005). Shcks (all f sectin GE) Dummy fr any f the fllwing shcks ccurring: Death in HH in last year f primary earner Illness in HH in last year f primary earner Unemplyment in last year Harvest failure in last year Widw Infrmatin questins. Fr all f these, we run the cnsumptin regressin first t get the impact n lg cnsumptin per asset. The hypthesis is that rati f the RANK cefficient t the cnsumptin cefficient is larger fr visible assets than fr invisible assets.
3 Cnspicuusness: The rati f the number f visible assets yu have t yur ttal husehld per capita cnsumptin, where visible assets is defined as: Number f the fllwing assets/characteristics yu have: Car Mtrbike Mtrbat Parabla Rf material tp quality (ptin 1 & 2) Wall material tembk (ptin 1) Being a Haji Scial cnnectins. In the detail regressins we will examine the fllwing variables: Hw many HH in RT yu are related t (CR01) Frmal leader HH (as judged by 2 respndents in the village ther than the respndent ding the ranking) Family member f frmal leader HH (as judged by 2 respndents in the village ther than the respndent ding the ranking) Infrmal leader HH (as judged by 2 respndents in the village ther than the respndent ding the ranking) Family member f infrmal leader HH (as judged by 2 respndents in the village ther than the respndent ding the ranking) Number f grups HH participates in Within the RT Outside the RT D yu reprt being clse t the pwer abve Ketua RT (village head etc)? D yu reprt being clse t the Ketua RT (village head etc)? Gegraphically islated peple (and in particular, peple wh live in clusters f pr peple Ethnic r religius minrity (dummy fr being either an ethnic r a religius minrity in the RT, where majrity we get frm Ketua RT) Fr the aggregate variables, we will define Cnnectedness as: Numeratr: Number f husehlds with whm yu are family (including extended family) Denminatr: Number f husehlds in the RT Elite cnnectedness as: Numeratr: Number f elite husehlds with whm yu are family (including extended family, where elite is defined as any husehld
4 that tw r mre peple in SUSETI list as a frmal r infrmal leader) Denminatr: Number f elite husehlds in the RT 3.1 Mre sphisticated scial infrmatin analysis this is expected t be a separate paper with Arun. We want t answer the questin: hw des infrmatin get transmitted ver a village scial netwrk. Fr every husehld in the village, we will cnstruct the fllwing weighed N*N netwrk matrices Gegraphic cnnectin (GIS distance frm ne husehld t anther husehld) Family relatinship (family link defined by degrees remved, i.e., 1 is brther/sister r parent/sn, 2 is uncle/aunt/nephew/neice/grandparent/granddaughter, 3 is cusin, 4 is ther) Participatin in scial grup (ne netwrk fr each scial grup, 1 in same scial grup, 0 therwise) Ptentially culd als examine: Kids in same schl Lans Transfers We then will have R f these N*N weighted matrices. But sme f these (all but gegraphy) will have hles. We will use Arun s prcedure t fill the hles in prbabilistically. We will then calculate the weights n each f these R matrices as fllws: Suppse a vectr f weights X. Then yu can calculate an N*N matrix f least cst paths, where the cst f using a link n matrix r is X r. Using these least cst paths, yu can calculate the average least cst weights fr the village, called D(X) Yu then slve fr the vectr f weights X that maximizes γ in the regressin f RANK ijv = α jv + βrankpercapconsumption iv + γrankpercapconsumption iv D(X) v + D(X) v + eps We will then use this D(X) matrix vectr belw in calculating interactins fr evaluating whether the treatment wrked better r wrse. 4. Wh gets ranked mre accurately? Interactins with variables abut persn being ranked r village in which ranking is taking place: RANK ijv = α jv + βpercapconsumption iv + γpercapconsumption iv X iv + X iv + eps RANK ijv = α jv + βrankconsumption iv + γrankconsumption iv X iv + X iv + eps Individual level variables: Shcks Cnspicuusness General Cnnectedness Elite Cnnectedness Really, really pr Village level variables: Urban rural.
5 Inequality (defined as Gini cefficient). One hypthesis is that cmmunity is better at finer distinctins whereas PMT is better at large discrepancies. Village level average cnnectedness defined abve 5. Wh ranks better? Interactins with variables abut persn ranking: RANK ijv = α jv + βpercapconsumption iv + γpercapconsumption iv X jv + eps RANK ijv = α jv + βrankconsumption iv + γrankconsumption iv X jv + eps X variables are: Years f educatin f respndent Gender f respndent Per capita cnsumptin Yur cnnectedness RT head dummy Are yu ther elite (Did at least 2 HH in village put yur HH n list f frmal r infrmal village leaders, ther than RT) Interactins abut ranker rankee relatinship RANK ijv = α jv + βpercapconsumption iv + γpercapconsumption iv X ijv + X ijv + eps RANK ijv = α jv + βrankconsumption iv + γrankconsumption iv X ijv + X ijv + eps Family members Attend same type f scial grups (by default we will match n rganizatin cdes, if pssiblewe will als match n name f grup) Have kids in the same schl class Nte fr interpretatin f analysis: Suppsed village has higher inequality. That leads t lwer beta n PERCAPITACONSUMPTION mechanically since ranks are always 0 1, thugh it has n mechanical effect n rank cnsumptin. Nte that the Ketua RT is included in this analysis every time, s he is verweighted in these regressins. We will d a rbustness versin f this analysis where we drp him.
6 Analysis 1: Mistargeting We have tw families f indicatrs. Fr the plicy analysis, we fcus n the inclusin/exclusin dummies; fr the academic ecnmic paper, we fcus n the average marginal utility gain frm the transfer. First, define POOR the bttm 32% f SUSETI husehlds in terms f per capita cnsumptin, and define VERYPOOR as the bttm 16% f SUSETI husehlds in terms f per capita cnsumptin. In making this calculatin, we will weight all husehlds by the inverse f their sampling prbabilities Nn ketua RT: sampling prbability is 8/NR where N is number f husehlds in RT and R is the number f RT in the village, s weight is NR/8 Ketua RT: sampling prbability is 1/R, s weight is R (nte that this is the nly place in the entire analysis where we will use sampling weights) INCLUSION/EXCLUSION DUMMIES (individual level regressin) MISTARGETDUMMY. This is ur *main utcme* fr plicy purpses. 1 if POOR and did nt receive 1 if NOTPOOR and did receive 0 therwise MISTARGETRICHDUMMY (inclusin errr) 1 if NOTPOOR and did receive 0 if NOTPOOR and did nt receive Missing if POOR MISTARGETPOORDUMMY (exclusin errr) 1 if POOR and did nt receive 0 if POOR and did receive Missing if NOTPOOR MISTARGETVERYPOORDUMMY (exclusin errr) 1 if VERYPOOR and did nt receive 0 if VERYPOOR and did receive Missing if nt VERYPOOR AVERAGE MARGINAL UTILITY GAIN FROM TRANFER: (village level regressin) Fr each village, calculate the average marginal utility f thse n the list minus the average marginal utility f everyne in the ppulatin. I.e., define B t be the set f beneficiaries and N the number f husehlds in the sample, then this measure fr Nv ( cvj ) u ( cvj ) u' ' j Bv j= village v is equal t 1 Bv Nv Fr ur main metric, we ll use quadratic utility, s that u (c vj ) is just equal t 1 * percapita cnsumptin; we will reprt rbustness tables fr CRRA utility and r = 1,2,3,4,5. This metric with quadratic utility (s just per capita cnsumptin) is the main metric we ll use fr the academic paper. Other analyses t d: Nte that mistarget dummy is defined using the actual beneficiary lists we used t distribute the mney. There are tw imprtant cunterfactuals that are als imprtant theretically:
7 PMT with crss village reallcatin. Keep ttal number f beneficiaries the same, but take the nes wh have the lwest PMT scres glbally in the entire universe, nt just per village. This allws PMT t take advantage f its cardinal infrmatin t d better crss village cmparisns. Hybrid with crss village reallcatin. Keep ttal number f beneficiaries the same, but take the nes wh have the lwest PMT scres glbally in the entire universe, nt just per village. This allws PMT t take advantage f its cardinal infrmatin t d better crss village cmparisns. The key regressins will be individual linear prbability mdels (fr MISTARGET) r village OLS mdels (fr AVERAGE MARGINAL UTILITY OF BENEFICIARIES). We will run three versins: n kecagrup effects, kecagrup randm effects, and kecagrup fixed effects. We will cluster n village fr the linear prbability mdels, but run a rbustness check with clustering at kecagrup t take int accunt the fact that sme facilitatrs may be better r wrse than thers. Key regressins will be: A. Analysis f actual distributins: Y = COMMUNITY + HYBRID + KECAGROUPFE + EPS B. Cmparing cmmunity subtreatments. Analyze results f meeting and restrict t cmmunity and hybrid nly. In hybrid, we re using the rank list frm the meeting here, NOT the final pstverificatin results. (rbustness check: d n subsample f cmmunity nly t see if cefficients n elite, 10prest, and daymeeting remain stable) Y = ELITE + 10POOREST + DAYMEETING + HYBRID + KECAGROUPFE + EPS C. Cmparing interactins f cmmunity subtreatments. Analyze results f meeting and restrict t cmmunity and hybrid nly. In hybrid, we re using the rank list frm the meeting here, NOT the final pst verificatin results. (rbustness check: d n subsample f cmmunity nly t see if cefficients n elite, 10prest, and daymeeting remain stable) Nte that we nly run the 3 f the tw way interactins with elite. We expect that ELITE*HYBRID will be negative i.e., the wrst mistargeting is elite full cmmunity, since there is n check and balance n the elite. We expect ELITE * 10POOREST will be negative, since the 10POOREST will help the elites fcus n the very pr part f the distributin. We expect ELITE * DAYMEETING t be negative, since we think wmen will be better than men at targeting. Y = ELITE + 10POOREST + HYBRID + DAYMEETING + ELITE * HYBRID + ELITE * 10POOREST + ELITE * DAYMEETING+ KECAGROUPFE + EPS We repeat the regressins abve fr all f the dependent variables listed abve.
8 Analysis 2: Rank crrelatins. This analysis lets us lk at tw measures which treatment des better vis à vis the gvernment s bjective functin (per capita expenditure), and which des better vis à vis the ex ante pinins f peple in the cmmunity. By using rank crrelatins we put bth n the same fting. Define a dependent variable TREATRANK as the rank frm the experiment. Fr PMT, this is the rank f PMT scres Fr Cmmunity, this is rank frm the meeting Fr Hybrid, we will d this tw ways: fr analysis f verall impact, this is the rank frm the meeting fr thse wh were nt reverified, and rank f PMT scres fr thse wh were reverified. Fr analysis f cmmunity subtreatments, this is just the rank frm the meeting. We cnstruct the treatment rank nly amng the 9 husehlds in ur survey, s it is cmparable t the right hand side ranks. This is a percentile variable frm 0 1. Fr each f the husehlds in ur data, we cmpute their within village rank based n percapita cnsumptin (the gvernment s metric), called CONSRANK, as a percentile variable frm 0 1. Fr each f the husehlds in ur data (excluding the ketua RT), we als cmpute the average rank f that husehld frm the CR sectin. We then turn this int a percentile scre as well, and define this as COMMRANK. That is, the persn wh has the lwest average rank is 0, and the persn wh has the highest average rank is 1. Fr each village, we cmpute tw rank crrelatins f the treatment with cnsumptin rank and cmmunity rank: RANKCORRTREATCONSUMPTION and RANKCORRTREATCOMMUNITY. We then run the same regressins as abve n these tw dependent variables. This answers the questin: within villages, which treatment des best n the gvernment s SWF and n the cmmunity s SWF. Run same regressins as befre: D. Analysis f actual distributins: RANKCORRTREATCONSUMPTION = COMMUNITY + HYBRID + KECAGROUPFE + EPS RANKCORRTREATCOMMUNITY = COMMUNITY + HYBRID + KECAGROUPFE + EPS E. Cmparing cmmunity subtreatments. Analyze results f meeting and restrict t cmmunity and hybrid nly. In hybrid, we re using the rank list frm the meeting here, NOT the final pstverificatin results. (rbustness check: d n subsample f cmmunity nly t see if cefficients n elite, 10prest, and daymeeting remain stable) F. RANKCORRTREATCONSUMPTION = ELITE + 10POOREST + DAYMEETING + HYBRID + KECAGROUPFE + EPS RANKCORRTREATCOMMUNITY = ELITE + 10POOREST + DAYMEETING + HYBRID + KECAGROUPFE + EPS Cmparing interactins f elite with ther cmmunity subtreatments. Analyze results f meeting and restrict t cmmunity and hybrid nly. In hybrid, we re using the rank list frm the
9 meeting here, NOT the final pst verificatin results. (rbustness check: d n subsample f cmmunity nly t see if cefficients n elite, 10prest, and daymeeting remain stable) RANKCORRTREATCONSUMPTION = ELITE + 10POOREST + HYBRID + DAYMEETING + ELITE * HYBRID + ELITE * 10POOREST + ELITE * DAYMEETING+ KECAGROUPFE + EPS RANKCORRTREATCOMMUNITY = ELITE + 10POOREST + HYBRID + DAYMEETING + ELITE * HYBRID + ELITE * 10POOREST + ELITE * DAYMEETING+ KECAGROUPFE + EPS
10 Analysis 3: Interactins: Elites, gegraphy, and ther characteristics. Tw types f interactins: Which methd wrks better in WHICH TYPE OF LOCATION Which methd wrks better fr WHICH TYPE OF HOUSEHOLDS Cmmunity level interactins Key interactins fr theretical ecnmic analysis: Urban rural. Inequality (defined as Gini cefficient). One hypthesis is that cmmunity is better at finer distinctins whereas PMT is better at large discrepancies. Cnnectedness in aggregate. Other imprtant variables fr analysis: P2KP experience Implementatin variables, such as facilitatr quality. Oligarchy measure. Is ketua RT family with any ther village fficial? BLT ver r under predicting i.e., is this an area where they managed t add a lt f BLT beneficiaries Husehld level interactins. Shcks Cnspicuusness General Cnnectedness Elite Cnnectedness Really, really pr Regressins On list? D these characteristics make yu mre likely t get n the list cnditinal n yur cnsumptin level? Main effects f all characteristics as ne clumn, and then interactins with treatments as secnd clumn ONLIST (are yu n the list) TREATRANK (what is yur rank in the final list). Regressins: Analysis f actual distributins: ONLIST iv = PERCAPCONSUMPTION iv + X iv + COMMUNITY v + HYBRID v + COMMUNITY v * X iv + HYBRID v * X iv + KECAGROUPFE + EPS Cmparing cmmunity subtreatments. Analyze results f meeting and restrict t cmmunity and hybrid nly. In hybrid, we re using the rank list frm the meeting here, NOT the final pst verificatin results. (rbustness check: d n subsample f cmmunity nly t see if cefficients n elite, 10prest, and daymeeting remain stable). Fcus n Hybrid and Elite subtreatments nly (nt 10prest and daymeeting) t limit number f interactins. ONLIST iv = PERCAPCONSUMPTION iv + X iv + HYBRID v + ELITE v + 10POOREST v + DAYMEETING v + ELITE v * X iv + HYBRID v * X iv + KECAGROUPFE + EPS
11 Triple interactin f Elite & Hybrid ONLIST iv = PERCAPCONSUMPTION iv + X iv + HYBRID v + ELITE v + 10POOREST v + DAYMEETING v + HYBRID v * ELITE v + ELITE v * X iv + HYBRID v * X iv + ELITE v * HYBRID v * X iv + KECAGROUPFE + EPS D these characteristics make the rank mre accurate? MISTARGET. Repeat all regressins abve. TREATRANK regressins interactins. Tw versins: ne based n CONSRANK (as written) but als d it based n the rank in the CR sectin. Main treatment effects: TREATRANK iv = X iv + CONSRANK iv * KECAGROUPFE + CONSRANK iv * COMMUNITY v + CONSRANK iv * HYBRID v + CONSRANK iv * COMMUNITY v * X iv + CONSRANK iv * HYBRID v * X iv + COMMUNITY + HYBRID + KECAGROUPFE + EPS All cmmunity subtreatments, using the ranklist frm the meeting: TREATRANK iv = X iv + CONSRANK iv * KECAGROUPFE + CONSRANK iv * ELITE v + CONSRANK iv * HYBRID v + + CONSRANK iv * 10POOREST v + + CONSRANK iv * DAYMEETING v + CONSRANK iv * ELITE v * X iv + CONSRANK iv * HYBRID v * X iv + + CONSRANK iv * 10POOREST v * X iv + CONSRANK iv * DAYMEETING v * X iv + HYBRID + ELITE + DAYMEETING + 10POOREST KECAGROUPFE + EPS Triple interactin f hybrid and elite: TREATRANK iv = X iv + CONSRANK iv * KECAGROUPFE + CONSRANK iv * ELITE v + CONSRANK iv * ELITE v * HYBRID + CONSRANK iv * HYBRID v + CONSRANK iv * ELITE v * X iv + CONSRANK iv * HYBRID v * X iv + CONSRANK iv * ELITE v * HYBRID * X iv + HYBRID + ELITE + HYBRID * ELITE + KECAGROUPFE + EPS Althugh there are many interactins we will lk at, ur key hyptheses are: 10POOREST imprves targeting fr very pr ELITE makes elite HH and their relatives mre likely t get n list HYBRID makes elite HH and their relatives less likely t get n list ELITE * HYBRID makes elite HH and their relatives less likely t get n list than in elite alne
12 Analysis 4: Buy in variables. Key independent variables: Fund disbursement Did yu experience any prblems distributing the mney (questin Q7) What methd was used (huse t huse r in meeting if they are cncerned abut prtests they will very likely chse dr t dr) What percent f peple were yu able t give ut the mney fr. (nte that this wuld be 0 if the entire village refused t participate) Cmplaints Number f cmplaints received in cmplaint bx Number f negative cmplaints received in cmplain bx Number f cmplaints received by RT Cmplaints/satisfactin Satisfactin ( puas rdered prbit) In pinin f RT In peple s pinin frm HH survey Apprpriateness ( sudah tepat rdered prbit) In pinin f RT In peple s pinin frm HH survey Better than BLT questin (rdered prbit) In pinin f RT In peple s pinin frm HH survey Number f husehlds that shuld be added t list (dummy fr nt saying it s k ) In pinin f RT In peple s pinin frm HH survey Number f husehlds that shuld be added t list In pinin f RT In peple s pinin frm HH survey Number f husehlds that shuld be subtracted frm list In pinin f RT In peple s pinin frm HH survey (nt a main variable) Culd be interesting t lk at whm they wanted t add/subtract frm list. (nt a main variable) What percent f peple did yu have t give the mney ut t smene else (e.g., Ketua RT r neighbr). Ptentially captures elite capture f the mney (e.g., if in elite treatment the Ketua RT ends up taking all the mney in fund disbursement this wuld be suspicius). (nt a main variable but culd be interesting: If we end up finding a lt f neighbrs wh receive the mney, was it elites)? (anther versin f elite capture) ther interesting things t lk at: are RT/peple mre likely t say that we shuld add n relatives and hw pr are they (based n the huse characteristics frm the full listing) and des this vary by treatment this wuld be capturing smething
13 like whether cmmunity treatment makes peple mre public spirited in their targeting views) Regressins: OUTCOME = COMMUNITY + HYBRID + KECAGROUPFE + EPS OUTCOME = ELITE + 10POOREST + DAYMEETING + HYBRID + KECAGROUPFE + EPS (nte that fr anything where we want t cmpare RT respnse t HH respnse, we need t run RT respnse fr whle sample, RT respse fr subsample f villages where we d HH survey, and HH survey)
14 Analysis 5: Interpretatin f cmmunity results (e.g., attendance, time spent, etc) Dependent variables Attendance Ttal (frm frm 2) Share f attendance that is wmen (frm frm 2) Did pr peple attend (subjective; frm frm 9b). Did pr peple attend (frm matching frm 1a/1b with SUSETI) Share f attendance that is tkh (frm frm 1a/1b). **NOTE that this is the ne that is hard given the data issue with the attendance frm** What ccurs at meeting Time spent n ranking (as measure f hw difficult r easy it was) Level f dminatin by few peple (subjective, frm frm 9b) Level f wmen s participatin. What percentage f the peple wh were listed as mst talkative were wmen. Independent variables: Day vs. evening Raining at start f meeting vs. nt (cntrlling fr level f rain during meeting) Tkh vs. nt (but these cefficients need t be interpreted with care) 10Prest vs. nt (fr what ccurs at meeting, nt fr attendance) Other X variables that we use as interactins abve. Nte: all meeting regressins are village level regressin with facilitatr FE and meeting rder number FE (nt main analysis) Nte that we can als match attendance data with SUSETI t lk at the fllwing variables, but given the 1a/1b issue: Lk at the fllwing variables: Number f attendance Elite vs. nn elite attendance Pr vs. rich attendance Gender f attendance Newcmers vs. peple wh have been there fr a while Gegraphic dispersin peple frm clse t meeting vs. further
15 Analysis 6: Plitics f preference aggregatin and hw d ur treatments change this. This mre structural analysis will be determined later. Whse preferences get aggregated int cmmunity weights? Can we back ut a vting weight fr each individual r type f individual in the cmmunity preference functin, and then think abut wh has higher weights. Hw d ur treatments change the scial welfare weights f different peple? Are there varius theries that we can shed light n? Or sme kind f structural mdel f plitics t estimate? Attendence at meetings? Maybe can instrument with distance t meeting lcatin, cntrlling fr distance t alternate meeting lcatin? RT vs. cmmunity preferences. 2 COMMUNITY VARIABLES: RANKOFPAKRT AND RANKOFEVERYONEELSE. Hypthesis is that the RT s preferences shuld be clser t rank results in elite treatment f curse.
16 Analysis 7: Further PMT analysis PMT frm BPS vs. PMT based n ur survey. Fr whm d their really differ mre? PMT fr rank (within village) vs. the ttal benefit f PMT (which includes the benefit f ding better acrss villages).
17 Analysis 8: Details matter Using the randm rder f ranking peple wh were ranked first vs. ranked last which are mre accurate?
18 Analysis 9: This recrds additinal ntes that we may want t think abut, but these issues will nt necessarily be dealt with in the main analysis. D we want t use the HH subjective assessments f their wn pverty as anther metric t lk at? Which methd picks the husehlds that rate themselves as prest? We want t cmbine this with the SWF analysis we run this as the LHS variable n the SWF analysis, and if we get similar ceffiecients t what the cmmunity says this blsters the idea that we are picking up aspects f the cmmunity s scial welfare functin. Use the full rank cmbined with the infrmatin frm the listing. This culd be a way f gaining pwer, althugh we culd nly lk at bservable characteristics. D we want t lk at crruptin scres, i.e., we predict that hybrid des better than cmmunity in crrupt villages? We have this cl scial netwrk data. We culd d a very nice descriptive paper n hw the scial netwrk smthes shcks by lking at wh gets a shck and wh they brrw and receive transfers frm in the scial netwrk. Remittances are als interesting this regard. Think mre systematically abut inequality in village. We knw that PMT shuld d better at grss differences because f the errr term in the regressin. Abhijit thught maybe we can benchmark what the rank crrelatin shuld be fr PMT based just n the errr structure and use that t sak up residual variatin. Elite analysis. Discuss they knw better vs. elite capture. Culd simulate results n elite villages using CR ranking frm elite husehlds in thse villages and see whether the elite treatment is wrse. Can we d mre t cme up with a general verall views n elites. Key here is t have analysis f elite infrmatin be cmparable t analysis f the final prgram. Maybe d mre n what d elite knw. Discuss: where are we n the pverty mapping. This needs t be equal in sme sense between the cmmunity and the hybrid. Anything mre here?
19 Analysis 10. Plicy analysis questins (nt fr main paper but fr plicy reprt) Different versins f PMT. Quta vs. using actual pverty line. BPS PMT. Etc. Cst effectiveness f hybrid vs full pmt. Can als cmpare t pure gegraphic targeting using ur qutas and randm targeting. Cmparisn t BLT Negative list analysis Hw gd was negative list frm hybrid. Hw des it cmpare t the negative list variables yu can cllect frm utside the huse
20 Appendix: PMT regressin
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