March 15, 2013 15:03
diff --git a/gistfile1.txt b/gistfile1.txt
 ***************************************************************************************************
 /*  Do file to check consistency of figures/statistics presented in Dupas & Robinson (2013) against 
 	what appears in the data */
 ***************************************************************************************************

 clear 
 set mem 500m

 * set directory
 gl DRIVE	"/Users/katiacovarrubias/Documents/Academic/IHEID/Courses/Advanced Econometrics/Exam/Dupas_data/"

 * log file
 capture log close
 log using "$DRIVE/dupas_consistencycheck.log",replace

 * load data and run variable creation from their do file
 use "$DRIVE/dataset_savingsAEJ.dta",clear

 
 *********************************************************
 *Variable creation from dupas do file
  * Active if had more than one bank transaction in first 6 months
  gen active=(first6_num_trans_savings>1) if first6_num_trans_savings!=.
  * Treatment interacted with works as boda-boda dummy
  gen treatment_bg_boda=treatment*bg_boda
  * Active bank account interacted with works as boda-boda dummy
  gen active_bg_boda=active*bg_boda
  * Works as boda-boda and sampled at wave 2
  gen bg_boda_wave2=bg_boda & wave2
  * Works as male vendor and sampled at wave 2
  gen bg_malevendor_wave2=bg_malevendor  & wave2
  * Works as male vendor and sampled at wave 3
  gen bg_malevendor_wave3=bg_malevendor  & wave3
  * Treatment interacted with works as male vendor
  gen treatment_bg_malevendor=treatment*bg_malevendor
  * Active bank account interacted with works as male vendor
  gen active_bg_malevendor=active*bg_malevendor
  * literacy var
  gen literate_swahili=1 if bg_kis_read==1 & bg_kis_write==1
  replace literate_swahili=0 if bg_kis_read==0 | bg_kis_write==0

 
 *****************************************************************************
 * PAGE 166
 	sum investment
 		* 353 Ksh
 	sum investment if investment!=0 
 		* 365 Ksh
 	sum investment if !(wave1==. & wave2==. & wave3==.)
 		* 353 Ksh
 	sum investment if !(wave1==. & wave2==. & wave3==.) & investment !=0
 		* 365 Ksh


 	tab bg_loan_bank if !(wave1==. & wave2==. & wave3==.)
 		* 2.8 percent
 	tab bg_loan_bank if !(wave1==. & wave2==. & wave3==.) & not_trac==0
 		* 3.26 percent if only consider individuals that were able to be tracked down.

 *****************************************************************************
 * page 167
 	tab per_hard_save if !(wave1==. & wave2==. & wave3==.)
 		* 85.62% (125 obs)
 	tab per_hard_save 
 		* 85.71% (126)
 	
 *****************************************************************************
 * PAGE 168
 * explore number of observations in male bicycle taxi drivers	 (92 OR 96?)

 *****************************************************************************
 * PAGE 169
 	g illiterate=literate==0
 	table bg_gender if inlog==1 , c(mean illiterate)
 		* 7.5% males; 34.1% females
 		
 	sum exp_total if inlog==1
 	display 50/r(mean)
 		* 28.97%

 *****************************************************************************	
 * PAGE 170
 	g logrefuse=inlog==0
 	table  not_traced_acco, c(mean inlog mean logref) row
 		* 28.9% overall

 	count if per_hard_save!=. & per_invest_choice2!=. & per_somewhat_patient!=. & per_time_consistent!=. & per_hyperbolic!=. & per_pat_now_impat_later!=. & per_maximpat!=. 
 		* 143 obs
 		
 *****************************************************************************
 * PAGE 173
 	tab num_dep_sav if (treatment==1 & not_traced_account_opening==0) ,m
 		* 10 missing obs (6.4%), 72 zeroes (46%)
 		
 *****************************************************************************	
 * PAGE 174
 	* median deposits for both genders
 	sum first6_dep_savings if treatment==1 &  wave1!=. & wave2!=. & wave3!=. , det
 		display r(p50) /*0*/
 			
 	*75th and 90th percentile of total deposits- males	
 	sum first6_dep_savings if treatment==1 & bg_gender==1  & wave1!=. & wave2!=. & wave3!=. , det
 		display r(p75) /*300*/
 		display r(p90) /*1200*/
 			
 	*75th and 90th percentile of total deposits- females	
 	sum first6_dep_savings  if treatment==1 & bg_gender==0 & wave1!=. & wave2!=. & wave3!=. , det
 		display r(p75) /*500*/
 		display r(p90) /*5000*/

 	* Mean deposits across gender
 	table bg_gender if treatment==1 & wave1!=. & wave2!=. & wave3!=. , c(mean first6_dep_savings)


 *****************************************************************************	
 * PAGE 176
 	tab active if treatment==1
 	tab active if treatment==1 & filled_log  !=. & filled_log  !=0 

 *****************************************************************************
 * PAGE 187
 	* check median of average deposit size
 		* From D&R do file: Average deposit size is total of all deposits ever made/total number of deposits ever made
 			gen size_deposit=total_dep_savings/num_dep_savings
 			sum size_deposit, detail
 			sum size_deposit if active==1, detail
 				display r(p50)
 				* median= 300 Ksh
 		* for active women:	
 			sum size_deposit if active==1 & bg_gender==0, detail
 				display r(p50)
 				* median=588.8889 
 		* this median should be equivalent to 1.6 days of mean expenditures for women in the sample
 			sum exp_total if active==1 & bg_gender==0, detail
 				display 588.8889/r(mean)	/*2.41 times average daily expenditure for active women*/
 				display 300/r(mean)			/*1.23 times average daily expenditure for active women*/
 			sum exp_total if bg_gender==0, detail
 				display 588.8889/r(mean)	/*3.25 times average daily expenditure for women*/
 				display 300/r(mean)			/*1.65 times average daily expenditure for women*/

 *****************************************************************************
 * EXTRA: Check trimming
 	count if filled_log!=0 & filled_log!=.
 	
 	list investment* bg_boda bg_malevendor bg_femalevendor if filled_log!=0 & filled_log!=. & (investment!=investment_t5), separator(0)
 	count if filled_log!=0 & filled_log!=. & (investment!=investment_t5)
 	 	* 36 obs (14%)
 	list revenues* bg_boda bg_malevendor bg_femalevendor if filled_log!=0 & filled_log!=. & (revenues!=revenues_t5), separator(0)
 	count if filled_log!=0 & filled_log!=. & (revenues!=revenues_t5)
 		* 46 obs (18.4%)
 		
 		* it seems that trimming was not on the overall sample, but for each individual's records



 *****************************************************************************
 log close
	***************************************************************************************************
	/* Do file to check consistency of figures/statistics presented in Dupas & Robinson (2013) against
	what appears in the data */
	***************************************************************************************************

	clear
	set mem 500m

	* set directory
	gl DRIVE "/Users/katiacovarrubias/Documents/Academic/IHEID/Courses/Advanced Econometrics/Exam/Dupas_data/"

	* log file
	capture log close
	log using "$DRIVE/dupas_consistencycheck.log",replace

	* load data and run variable creation from their do file
	use "$DRIVE/dataset_savingsAEJ.dta",clear


	*********************************************************
	*Variable creation from dupas do file
	* Active if had more than one bank transaction in first 6 months
	gen active=(first6_num_trans_savings>1) if first6_num_trans_savings!=.
	* Treatment interacted with works as boda-boda dummy
	gen treatment_bg_boda=treatment*bg_boda
	* Active bank account interacted with works as boda-boda dummy
	gen active_bg_boda=active*bg_boda
	* Works as boda-boda and sampled at wave 2
	gen bg_boda_wave2=bg_boda & wave2
	* Works as male vendor and sampled at wave 2
	gen bg_malevendor_wave2=bg_malevendor & wave2
	* Works as male vendor and sampled at wave 3
	gen bg_malevendor_wave3=bg_malevendor & wave3
	* Treatment interacted with works as male vendor
	gen treatment_bg_malevendor=treatment*bg_malevendor
	* Active bank account interacted with works as male vendor
	gen active_bg_malevendor=active*bg_malevendor
	* literacy var
	gen literate_swahili=1 if bg_kis_read==1 & bg_kis_write==1
	replace literate_swahili=0 if bg_kis_read==0 \| bg_kis_write==0


	*****************************************************************************
	* PAGE 166
	sum investment
	* 353 Ksh
	sum investment if investment!=0
	* 365 Ksh
	sum investment if !(wave1==. & wave2==. & wave3==.)
	* 353 Ksh
	sum investment if !(wave1==. & wave2==. & wave3==.) & investment !=0
	* 365 Ksh


	tab bg_loan_bank if !(wave1==. & wave2==. & wave3==.)
	* 2.8 percent
	tab bg_loan_bank if !(wave1==. & wave2==. & wave3==.) & not_trac==0
	* 3.26 percent if only consider individuals that were able to be tracked down.

	*****************************************************************************
	* page 167
	tab per_hard_save if !(wave1==. & wave2==. & wave3==.)
	* 85.62% (125 obs)
	tab per_hard_save
	* 85.71% (126)

	*****************************************************************************
	* PAGE 168
	* explore number of observations in male bicycle taxi drivers (92 OR 96?)

	*****************************************************************************
	* PAGE 169
	g illiterate=literate==0
	table bg_gender if inlog==1 , c(mean illiterate)
	* 7.5% males; 34.1% females

	sum exp_total if inlog==1
	display 50/r(mean)
	* 28.97%

	*****************************************************************************
	* PAGE 170
	g logrefuse=inlog==0
	table not_traced_acco, c(mean inlog mean logref) row
	* 28.9% overall

	count if per_hard_save!=. & per_invest_choice2!=. & per_somewhat_patient!=. & per_time_consistent!=. & per_hyperbolic!=. & per_pat_now_impat_later!=. & per_maximpat!=.
	* 143 obs

	*****************************************************************************
	* PAGE 173
	tab num_dep_sav if (treatment==1 & not_traced_account_opening==0) ,m
	* 10 missing obs (6.4%), 72 zeroes (46%)

	*****************************************************************************
	* PAGE 174
	* median deposits for both genders
	sum first6_dep_savings if treatment==1 & wave1!=. & wave2!=. & wave3!=. , det
	display r(p50) /0/

	*75th and 90th percentile of total deposits- males
	sum first6_dep_savings if treatment==1 & bg_gender==1 & wave1!=. & wave2!=. & wave3!=. , det
	display r(p75) /300/
	display r(p90) /1200/

	*75th and 90th percentile of total deposits- females
	sum first6_dep_savings if treatment==1 & bg_gender==0 & wave1!=. & wave2!=. & wave3!=. , det
	display r(p75) /500/
	display r(p90) /5000/

	* Mean deposits across gender
	table bg_gender if treatment==1 & wave1!=. & wave2!=. & wave3!=. , c(mean first6_dep_savings)


	*****************************************************************************
	* PAGE 176
	tab active if treatment==1
	tab active if treatment==1 & filled_log !=. & filled_log !=0

	*****************************************************************************
	* PAGE 187
	* check median of average deposit size
	* From D&R do file: Average deposit size is total of all deposits ever made/total number of deposits ever made
	gen size_deposit=total_dep_savings/num_dep_savings
	sum size_deposit, detail
	sum size_deposit if active==1, detail
	display r(p50)
	* median= 300 Ksh
	* for active women:
	sum size_deposit if active==1 & bg_gender==0, detail
	display r(p50)
	* median=588.8889
	* this median should be equivalent to 1.6 days of mean expenditures for women in the sample
	sum exp_total if active==1 & bg_gender==0, detail
	display 588.8889/r(mean) /2.41 times average daily expenditure for active women/
	display 300/r(mean) /1.23 times average daily expenditure for active women/
	sum exp_total if bg_gender==0, detail
	display 588.8889/r(mean) /3.25 times average daily expenditure for women/
	display 300/r(mean) /1.65 times average daily expenditure for women/

	*****************************************************************************
	* EXTRA: Check trimming
	count if filled_log!=0 & filled_log!=.

	list investment* bg_boda bg_malevendor bg_femalevendor if filled_log!=0 & filled_log!=. & (investment!=investment_t5), separator(0)
	count if filled_log!=0 & filled_log!=. & (investment!=investment_t5)
	* 36 obs (14%)
	list revenues* bg_boda bg_malevendor bg_femalevendor if filled_log!=0 & filled_log!=. & (revenues!=revenues_t5), separator(0)
	count if filled_log!=0 & filled_log!=. & (revenues!=revenues_t5)
	* 46 obs (18.4%)

	* it seems that trimming was not on the overall sample, but for each individual's records



	*****************************************************************************
	log close