/*****************************************************************************

This do file performs the main regression analysis presented in Tables 3-5

Updated on 6/17/2024

*****************************************************************************/

*-------------------------------------------------------*
*	Table 3. ITT Effects on Child Nutrition				*
*-------------------------------------------------------*

use "${processed}/child_target_analysis.dta", clear
drop if comunidad_elegible==0
drop if tratamiento_hogar==. // 1 without treatment assignment

	* Generate age-sex dummies
	tab child_age_cat child_sex
	tab child_age_cat, gen(child_age)
	gen child_age1_m = (child_age1==1 & child_sex==1)
	gen child_age1_f = (child_age1==1 & child_sex==0)
	gen child_age2_m = (child_age2==1 & child_sex==1)
	gen child_age2_f = (child_age2==1 & child_sex==0)
	gen child_age3_m = (child_age3==1 & child_sex==1)
	gen child_age3_f = (child_age3==1 & child_sex==0)
	gen child_age4_m = (child_age4==1 & child_sex==1)
	gen child_age4_f = (child_age4==1 & child_sex==0)
	gen child_age5_m = (child_age5==1 & child_sex==1)
	gen child_age5_f = (child_age5==1 & child_sex==0)
	gen child_age6_m = (child_age6==1 & child_sex==1)
	gen child_age6_f = (child_age6==1 & child_sex==0)
	gen child_age7_m = (child_age7==1 & child_sex==1)
	gen child_age7_f = (child_age7==1 & child_sex==0)
	gen child_age8_m = (child_age8==1 & child_sex==1)
	gen child_age8_f = (child_age8==1 & child_sex==0)
	gen child_age9_m = (child_age9==1 & child_sex==1)
	gen child_age9_f = (child_age9==1 & child_sex==0)
	gen child_age10_m = (child_age10==1 & child_sex==1)
	gen child_age10_f = (child_age10==1 & child_sex==0)
	gen child_age11_m = (child_age11==1 & child_sex==1)
	gen child_age11_f = (child_age11==1 & child_sex==0)
	gen child_age12_m = (child_age12==1 & child_sex==1)
	gen child_age12_f = (child_age12==1 & child_sex==0)

	lab var child_age1_m "Child age: <30 months, male"
	lab var child_age2_m "Child age: 30-31 months, male"
	lab var child_age3_m "Child age: 32 months, male"
	lab var child_age4_m "Child age: 33 months, male"
	lab var child_age5_m "Child age: 34 months, male"
	lab var child_age6_m "Child age: 35 months, male"
	lab var child_age7_m "Child age: 36 months, male"
	lab var child_age8_m "Child age: 37 months, male"
	lab var child_age9_m "Child age: 38 months, male"
	lab var child_age10_m "Child age: 39 months, male"
	lab var child_age11_m "Child age: 40-41 months, male"
	lab var child_age12_m "Child age: >41 months, male"

	lab var child_age1_f "Child age: <30 months, female"
	lab var child_age2_f "Child age: 30-31 months, female"
	lab var child_age3_f "Child age: 32 months, female"
	lab var child_age4_f "Child age: 33 months, female"
	lab var child_age5_f "Child age: 34 months, female"
	lab var child_age6_f "Child age: 35 months, female"
	lab var child_age7_f "Child age: 36 months, female"
	lab var child_age8_f "Child age: 37 months, female"
	lab var child_age9_f "Child age: 38 months, female"
	lab var child_age10_f "Child age: 39 months, female"
	lab var child_age11_f "Child age: 40-41 months, female"
	lab var child_age12_f "Child age: >41 months, female"

	lab var wealth_bl "Wealth"

	* Run regressions and output results to Excel files for each outcome
	global outcomes _zlen hfa_2sd hfa_3sd _zwei wfa_2sd _zwfl wfh_2sd wfh_2sdplus _zbmi bmi_2sd bmi_3sd adj_haemoglobin anemia_d practice_index practice_index_bf practice_index_sup practice_index_food

	foreach var of global outcomes {
** model 1) base model with no controls (unadjusted difference in means)
	reg `var' T1 T2, cluster(communityid)
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) replace excel addstat("Significance test T1=T2:",r(p)) 

** model 2) + endline child age-sex dummies, leaving out youngest group for each sex, and child order
	reg `var' T1 T2 child_age2_m child_age2_f child_age3_m child_age3_f child_age4_m child_age4_f child_age5_m child_age5_f child_age6_m child_age6_f child_age7_m child_age7_f child_age8_f child_age9_m child_age9_f child_age10_m child_age10_f child_age11_m child_age11_f child_age12_m child_age12_f child_order, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95)  append excel addstat("Significance test T1=T2:",r(p)) 

** model 3) + primary caregiver's education in years 
*			+ primary caregiver's age in years
*			+ primary caregiver's baseline decision-making power 
*			+ primary caregiver's baseline  grit score 
*			+ primary caregiver's baseline Rosenberg (self-esteem) score
*			+ baseline household wealth 
*			+ baseline household demographic composition (proportion of household members by age/sex categories)
		
	reg `var' T1 T2 child_age2_m child_age2_f child_age3_m child_age3_f child_age4_m child_age4_f child_age5_m child_age5_f child_age6_m child_age6_f child_age7_m child_age7_f child_age8_f child_age9_m child_age9_f child_age10_m child_age10_f child_age11_m child_age11_f child_age12_m child_age12_f child_order cg_age cg_educ decisiones_bl decisiones_bl_imputed grit_bl grit_bl_imputed rosenberg_bl rosenberg_bl_imputed wealth_bl hh_imputed prop_males0_5_bl prop_males6_18_bl prop_males19_49_bl prop_males50_bl prop_females0_5_bl prop_females6_18_bl prop_females19_49_bl prop_females50_bl, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95)  append excel addstat("Significance test T1=T2:",r(p)) 
}

*--------------------------------------------*
*	Table 4. ITT Effects on IYCF Practices	 *
*--------------------------------------------*

	* Run regressions and output results to Excel files for each outcome
	global outcomes practice_index practice_index_bf practice_index_sup practice_index_food 

	foreach var of global outcomes {
** model 1) base model with no controls (unadjusted difference in means)

	reg `var' T1 T2, cluster(communityid)
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) replace excel addstat("Significance test T1=T2:",r(p)) 

** model 2) + endline child age-sex dummies, leaving out youngest group for each sex, and child order

	reg `var' T1 T2 child_age2_m child_age2_f child_age3_m child_age3_f child_age4_m child_age4_f child_age5_m child_age5_f child_age6_m child_age6_f child_age7_m child_age7_f child_age8_f child_age9_m child_age9_f child_age10_m child_age10_f child_age11_m child_age11_f child_age12_m child_age12_f child_order, cluster(communityid) 
	test T1=T2	
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) append excel addstat("Significance test T1=T2:",r(p)) 

** model 3) + primary caregiver's education in years 
*			+ primary caregiver's age in years
*			+ primary caregiver's baseline decision-making power 
*			+ primary caregiver's baseline  grit score 
*			+ primary caregiver's baseline Rosenberg (self-esteem) score
*			+ baseline household wealth 
*			+ baseline household demographic composition (proportion of household members by age/sex categories)

	reg `var' T1 T2 child_age2_m child_age2_f child_age3_m child_age3_f child_age4_m child_age4_f child_age5_m child_age5_f child_age6_m child_age6_f child_age7_m child_age7_f child_age8_f child_age9_m child_age9_f child_age10_m child_age10_f child_age11_m child_age11_f child_age12_m child_age12_f child_order cg_age cg_educ decisiones_bl decisiones_bl_imputed grit_bl grit_bl_imputed rosenberg_bl rosenberg_bl_imputed wealth_bl hh_imputed prop_males0_5_bl prop_males6_18_bl prop_males19_49_bl prop_males50_bl prop_females0_5_bl prop_females6_18_bl prop_females19_49_bl prop_females50_bl, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) append excel addstat("Significance test T1=T2:",r(p))


}

*-----------------------------------------------------------*
*			Table 5. ITT Effects on Caregiver Knowledge		*
*-----------------------------------------------------------*

use "${processed}/caregiver_target_analysis.dta", clear
drop if comunidad_elegible==0
drop if tratamiento_hogar==. // 1 without treatment assignment

	* Run regressions and output results to Excel files for each outcome
	global outcomes knowledge_indexV1 knowledge_indexV1_bf knowledge_indexV1_food knowledge_indexV1_health

	foreach var of global outcomes {
	** model 1) base model with no controls (unadjusted difference in means)
	reg `var' T1 T2, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) replace excel addstat("Significance test T1=T2:",r(p))

** model 2) + primary caregiver's education in years + primary caregiver's age in years
	reg `var' T1 T2 cg_age cg_educ, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) append excel addstat("Significance test T1=T2:",r(p))

** model 3) + primary caregiver's baseline decision-making power 
*			+ primary caregiver's baseline grit score 
*			+ primary caregiver's baseline Rosenberg (self-esteem) score
*			+ baseline household wealth
*			+ baseline household demographic composition (proportion of household members by age/sex categories)
*			+ baseline knowledge 
		
	reg `var' T1 T2 cg_age cg_educ decisiones_bl decisiones_bl_imputed grit_bl grit_bl_imputed rosenberg_bl rosenberg_bl_imputed wealth_bl hh_imputed prop_males0_5_bl prop_males6_18_bl prop_males19_49_bl prop_males50_bl prop_females0_5_bl prop_females6_18_bl prop_females19_49_bl prop_females50_bl conocimiento_bl conocimiento_bl_imputed, cluster(communityid) 
	test T1=T2
	outreg2 using "$tables/`var'.xls", label dec(3) coef ci level(95) append excel addstat("Significance test T1=T2:",r(p)) 
}