Phillippe G Leite, HDNSP-SSN team Anna Fruttero, LCSHS
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1 Phllppe G Lete, HDNSP-SSN team Anna Fruttero, LCSHS Knowledge and Learnng forum PSIA March 5 th,2010
2 Introducton Can we generate a meanngful analyss of the lkely effect of a program before ts mplementaton? To answer ths queston: An analyss of Ex-Ante model s needed but Heavly dependent on assumptons and on the model t s based on; Requres valdaton: Ex-Ante smulaton s a component of a complex Ex-Post evaluaton scheme. 2
3 Introducton Man Objectve of presentaton: Descrbe the Ex Ante toolkt model for schoolng behavor developed by HDNSP on the bass of the Bourgugnon, Ferrera and Lete (2003) model Approach: Descrbe the model Valdate the model Show the how s to of the BFL model 3
4 Ex-Ante Model Generate the lkely mpact of a (C or U) cash transfer program How useful? Can be used to desgn or re-desgn exstent polces; Expand set of relevant questons What s the best transfer amount of a program? What are the dstrbutonal effects of dfferent transfer levels for a new program? Should programs promote beneft dfferentaton? Should benefts be dentcal for all partcpants? Do condtonaltes matter? What are the ncome and substtuton effects of a transfer? Should a transfer be made explctly uncondtonal, or should condtonalty be enforced? To what extent do results vary accordng to gender, ncome level or geographcal context (e.g. rural, urban) How much would a program cost regardng dfferent desgn consderatons? 4
5 BFL model Why BFL nstead of Attanaso, Meghr and Santago (2005) or Todd and Wolpn (2005)? Smplcty: dynamc Ex-Ante models as AMS and TW are data ntensve; rely on panel data; model end-up beng calbrate on a especal sub-sample (convergence) BFL man hypothess Untary model of household; Adults are unaffected by chldren s choce; Sblngs nteracton are gnored; Household composton s exogenous 5
6 Ex-Ante model: BFL Chld occupatonal choce (0) Not gong to school; (1) Gong to school and pad work; (2) Gong to school and non-pad work U (0) Z 0 (Y y 0 ) 0 v 0 U (1) Z 1 (Y y 1 ) 1 v 1 U (2) Z 2 (Y y 2 ) 2 v 2 6
7 Ex-Ante model: BFL Chld s contrbuton to ncome n each state 0, 1 and 2 y 0 w ; y1 Mw ; y2 Chld (household) chooses the alternatve that yelds the hghest utlty U (0) Z 0 Y 0 w 0 v 0 U (1) Z 1 Y 1 w 1 v 1 U (2) Z 2 Y 2 w 2 v 2 where 0 0 ; 1 1 M; 2 2 D Dw 7
8 8 Ex-Ante model: BFL Smulated utlty If elgble for the program accordng to the (U)CCT program targetng crtera If not elgble for the program accordng to the (U)CCT program targetng Smplfed by j j j j j v w T Y Z j U * j j j j j v w T Y Z j U ˆ ˆ ˆ * j j j j v w Y Z j U ˆ ˆ ˆ * j j T j U j U ˆ *
9 But Parameters alfa are unknown Parameters M and D are unknown Utlty functons are unknown Labor market wage, w, assumng that the ndvdual does not study s unknown for chldren that do not work or f the chld work part-tme We need to make use of statstcal models to estmate the values of the unknown parameters and we need to draw the unobserved error components.
10 Valdaton Table 1: Chldren's Occupatonal Choces n Mexco: Actual and Counterfactual Enrollment Rate for Target Populaton Observed Ex-Post ITT 1 Ex-Ante ITT B o y s G r l s 8-11 years-old 93.8% 1.8% 0.0% 0.7% 0.4% years-old 57.5% 5.8% 5.9% 2.1% 0.8% 8-11 years-old 93.9% -0.3% -0.2% 1.0% 0.4% years-old 47.9% 9.5% 6.6% 2.2% 0.8% Source: Baselne Survey 1997 and Rounds 1-4;Authors' calculaton. Note: 1: Results from Skoufas and Parker (2001) - table 6. ** Sgnfcant at 5% level; * Sgnfcant at 10% level. Standard Devaton computed by bootstrap method. ** ** ** ** ** Table 2: Impact on Poverty Indeces for Mexco: Observed and Smulated FGT(0) FGT(1) FGT(2) % sd % sd % sd Treatment Comuntes - Baselne 60.9% 1.1% 30.8% 1.1% 21.5% 1.1% Change Ex Post -16.5% 1.6% -24.3% 1.5% -29.2% 1.4% Smulated Change Ex Ante -13.1% -26.6% -33.1% Source: Baselne Survey 1997 and Rounds 1-4;Authors' calculaton;skoufas and D Maro (2006) table 4. Note: Prces 11/99; Pesos Poverty Lne = Mean of Nov 98 Consumpton per Capta. Obtaned from Skoufas and D Maro (2006)
11 The BFL model Data requrement: Representatve household survey for the populaton of nterest A representatve cross-sectonal household survey for the populaton of nterest. A mult-topc household survey to better measure the dfferent aspects of the ndvdual behavoral and ther correlatons 11
12 Key varables
13 Key varables ocup_struct = 0 f out of scool eter workng or not 1 f studyng wle workng 2 f only studyng **occupatonal structure gen ocup_struc=0 f enrol==0 /*out of school */ replace ocup_struc=1 f (ecoact==1) & enrol==1 /* studyng and workng */ replace ocup_struc=2 f (ecoact==0) & enrol==1 /* only at school */ *household ncome wthout chld contrbuton *Y- gen hhncome0=(hhncome-wage_) f wage_~=. replace hhncome0=hhncome f wage_==.
14 Key varables eancome g = medan wage of all workng cldren lvng n regon g f te cld dd not work medan wage of all workng cldren, but cld, lvng n regon g f te cld worked To generate ths varable we must frst check how low we can go. In the example data we dentfy for each household a three level of geographcal dsaggregaton as follows: 1. Geographcal Zone 2. Cluster 3. Urban/Rural cluster 4. Household
15 Estmaton of w Earnngs equaton needed to mpute wages for all chldren We make use of a mncer-type regresson Ordnary least squares Log w m Ind( S 1) X u where M exp( m) 15
16 Estmaton of w Use Regress n STATA *OLS model: Frst regresson x: regress l_wage ws age pr prc leawage [pw=_weght], cluster(cluster) (sum of wgt s e+06) Lnear regresson Number of obs = 1232 F( 5, 38) = Prob > F = R-squared = Root MSE = (Std. Err. adjusted for 39 clusters n cluster) Robust l_wage Coef. Std. Err. t P> t [95% Conf. Interval] ws age pr prc leawage _cons
17 Estmaton of w Set the predcted ncome assumng that a chld works full tme as: wˆ exp( X ˆ) Estmate the counterfactual of the full wage, whch nclude the error term (dyosncratc component), for each chld that does not have an observed wage or works part-tme as: wage f wage 0 and ocup_struc = 0 exp X δ + u f wage = 0 and ocup_struc = 0 w = exp (log wage m) f wage 0 and ocup_struc = 1 exp X δ + u f wage = 0 and ocup_struc = 1 exp X δ + u f wage = 0 and ocup_struc = 2 17
18 Behavoral model MLOGIT only estmate dfferences of parameters a system of equatons and unknown parameters must be estmated ˆ ) ( ˆ ). ( ˆ ˆ b D b M a a D and M ; Because: ( j - 0 ) and ( j - 0 )
19 Behavoral model Therefore the soluton of ths system s ˆ D and ˆ ˆ ˆ 1 ˆ ˆ b a a a M b a To test the relevance of the dentfyng assumptons check f 0, 1 and 2 are postve and f the value of D s n the nterval (0, 1).
20 Behavoral model Estmate the Mlogt model Save coeffcents Solve the system to estmate the values of alfa s and D. symmetrc alfa0_[1,1] c1 r matrx lst alfa1_ symmetrc alfa1_[1,1] r1 r matrx lst alfa2_ symmetrc alfa2_[1,1] c1 r matrx lst D symmetrc D[1,1] r1 r matrx lst M symmetrc M[1,1] M r
21 Behavoral model Draw random errors based on Bourgugnon, Fourner and Gurgand (2001) draw ocup_struc Generate the actual utltes *lnear combnaton for each occupatonal choce category predct xb0, outcome(0) xb predct xb1, outcome(1) xb predct xb2, outcome(2) xb gen ut0=xb0+u0 gen ut1=xb1+u1 gen ut2=xb2+u2
22 Behavoral model Smulate the CCT program Scenaro 1 CCT transfer for boys and grls wthout dfferentatng amounts. **Transfer amount per chld $10 gen T1=(10) **New utlty functon after smulaton gen ut0_s1=ut0+ alfa0*(0) f ((hhncome0+wsm)/hhsze)<=lp replace ut0_s1=ut0 f ut0_s1==. gen ut1_s1=ut1+ alfa1*(t1) f ((hhncome0+wsm*m)/hhsze)<=lp replace ut1_s1=ut1+ alfa1*(0 ) f ut1_s1==. gen ut2_s1=ut2+ alfa2*(t1) f (hhncome0/hhsze)<=lp replace ut2_s1=ut2+ alfa2*(0 ) f ut2_s1==. **NEW SITUATION gen ocup_struc_s1=. replace ocup_struc_s1=0 f ut0_s1== max(ut0_s1,ut1_s1,ut2_s1 ) replace ocup_struc_s1=1 f ut1_s1== max(ut0_s1,ut1_s1,ut2_s1 ) replace ocup_struc_s1=2 f ut2_s1== max(ut0_s1,ut1_s1,ut2_s1 )
23 Behavoral model Smulate the UCT program Scenaro 2 UCT transfer for boys and grls wthout dfferentatng amounts. The only dfference for creatng such smulaton s that n the frst lne of commands we must add alfa0 tmes the transfer amount for the utlty 0. **Transfer amount per chld $10 gen T2=(10) **New utlty functon after smulaton gen ut0_s2=ut0+ alfa0*(t2) f ((hhncome0+wsm)/hhsze)<=lp replace ut0_s2=ut0 f ut0_s2==. gen ut1_s2=ut1+ alfa1*(t2) f ((hhncome0+wsm*m)/hhsze)<=lp replace ut1_s2=ut1+ alfa1*(0 ) f ut1_s2==. gen ut2_s2=ut2+ alfa2*(t2) f (hhncome0/hhsze)<=lp replace ut2_s2=ut2+ alfa2*(0 ) f ut2_s2==. **NEW SITUATION gen ocup_struc_s2=. replace ocup_struc_s2=0 f ut0_s2== max(ut0_s2,ut1_s2,ut2_s2 ) replace ocup_struc_s2=1 f ut1_s2== max(ut0_s2,ut1_s2,ut2_s2 ) replace ocup_struc_s2=2 f ut2_s2== max(ut0_s2,ut1_s2,ut2_s2 )
24 Ex-Ante estmator Average Treatment Effect (ATE) and Average Intent to Treat effect (AIT) Assumes good mplementaton of program Ex-Ante model s statc,.e., no tme or trend effects. 24
25 Ex-Ante outcomes tab ocup_struc ocup_struc_s1 [w=round(_weght)],row col (frequency weghts assumed) Key frequency row percentage column percentage ocup_struc_s1 ocup_struc Total 0 3,921,718 69, ,659 4,319, ,799 37, , ,085,938 21,085, Total 3,921, ,767 21,451,495 26,345,
26 Ex-Ante outcomes tab ocup_struc ocup_struc_s1 [w=round(_w)] f ((hhncome/hhsze)<=lp ),row col (frequency weghts assumed) Key frequency row percentage column percentage ocup_struc_s1 ocup_struc Total 0 1,186,154 65, ,451 1,546, ,265 26, , ,285,338 5,285, Total 1,186, ,103 5,605,841 7,105,
27 Ex-Ante outcomes. tab ecoact1 [w=round(_w)] f ecoact==1 (frequency weghts assumed) ecoact1 Freq. Percent Cum , ,082, Total 3,253, tab ecoact1 [w=round(_w)] f ecoact==1 &((hhncome/hhsze)<=lp ) (frequency weghts assumed) ecoact1 Freq. Percent Cum , , Total 1,022,
28 Ex-Ante outcomes -> sex = 0 (frequency weghts assumed) Key frequency row percentage enrol1 enrol 0 1 Total 0 2,027, ,164 2,229, ,604,016 10,604, Total 2,027,520 10,806,180 12,833, > sex = 1 Key frequency row percentage enrol1 enrol 0 1 Total 0 1,894, ,463 2,089, ,422,619 11,422, Total 1,894,198 11,618,082 13,512,
29 Ex-Ante outcomes tot_bu~1 tot_ch~ tab enrol f treat1==1 [w=round(_weght)] (frequency weghts assumed) enrol Freq. Percent Cum , ,545, Total 5,943,
30 Ex-Ante outcomes on poverty Step 1: New wage of a chld f orgnal occupaton s out of school ether workng or not : o chld new wage: M tmes the orgnal wage, wage_, f the chld moves to the category workng whle studyng and wage wage_ s observed o chld new wage: M tmes the smulated wage, wsm, f the chld moves to the category workng whle studyng and wage wage_ s not observed o chld new wage: zero f the chld moves to the category workng whle studyng Step 2: New household ncome
31 Ex-Ante outcomes on poverty FGT(0) FGT(1) FGT(2) Gn Cost ($ n mllon) # of benefcares Impact on enrolment among the poor Actual Scenaro ,943, ,289 Scenaro ,752,919 85,793 Scenaro ,939, ,829
32 Concluson Ex-Ante BFL model analyss ndcates so far that t can be very useful as well as powerful n predctng program mpacts. Useful for smulatng the desgn or re-desgn of a transfer program. Increasng demand from polcymakers MUST be complemented wth Ex-Post to really estmate mpact of the program 32
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