*******************************************************************
*******************************************************************
* This is the do file for
*
* Pearson, M. and Schipper, B.C. (2011). Menstrual cycle and competitive 
* bidding, The University of California, Davis.
*
*******************************************************************
*******************************************************************
*
*******************************************************************
* Notice to users
*******************************************************************
* Thanks for your interest. 
* By using the data or STATA do file the user agrees to submit 
* for publication his work using the data only after Pearson and 
* Schipper (2011) have been published in peer reviewed journals. 
* Moreover, the user agrees to cite Pearson and Schipper (2011) 
* in any work by using this data or this STATA do file. 
*******************************************************************
* To receive the datasets please send an email to
* bcschipper@ucdavis.edu
* We are happy if the data are used again. 
*******************************************************************


clear
clear matrix

set more off
set mem 50m


*******************************************
*******************************************
*COMBINE DATA
*******************************************
*******************************************

* mensdr study data set (2007 data set)

use "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_quest_20110626.dta"
sort session subject
save "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_quest_20110626.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_dr_20110626.dta" 
sort session subject period
save "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_dr_20110626.dta", replace 
clear

use "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_auction_20110626.dta"
sort session subject period
save "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_auction_20110626.dta", replace

merge session subject using "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_quest_20110626.dta", uniqusing
sort session subject period
drop _merge

merge session subject period using "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_dr_20110626.dta", uniqusing
sort session subject period
drop _merge

gen new = 0
* generates variable indicating 2007 data set

save "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_data_20110626.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_data_20110626.dta",
sort session subject period
save "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_data_20110626.dta", replace
clear

* hormone study data set (2010 data set)

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_quest_20100824.dta"
sort session subject
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_quest_20100824.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_saliva_20100824.dta"
*egen sdtestf = sd(testf)
*label variable sdtestf "Testosterone"
*egen sdestf = sd(estf)
*label variable sdestf "Estradiol"
*egen sdprogf = sd(progf)
*label variable sdprogf "Progesterone"
*egen sdcortf = sd(cortf)
*label variable sdcortf "Cortisol" 

*egen sdtests = sd(tests)
*egen sdests = sd(ests)
*egen sdprogs = sd(progs)
*egen sdcorts = sd(progs)

*gen testavg = (testf + tests)/2
*gen estavg = (estf + ests)/2
*gen progavg = (progf + progs)/2
*gen cortavg = (cortf + corts)/2

*egen sdtestavg = sd(testavg)
*egen sdestavg = sd(estavg)
*egen sdprogavg = sd(progavg)
*egen sdcortavg = sd(cortavg)

sort session subject
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_saliva_20100824.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_risk_20100824.dta"
sort session subject
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_risk_20100824.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_dr_20110728.dta"
sort session subject
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_dr_20110728.dta", replace
clear 

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_auction_20100824.dta"
sort session subject period
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_auction_20100824.dta", replace


merge session subject using "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_quest_20100824.dta", uniqusing
sort session subject period
drop _merge

merge session subject using "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_saliva_20100824.dta", uniqusing
sort session subject period
drop _merge

merge session subject using "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_risk_20100824.dta", uniqusing
sort session subject period
drop _merge

merge session subject using "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_dr_20110728.dta", uniqusing
sort session subject period
drop _merge

gen new = 1
* indicates 2010 data set

save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_data_20110728.dta", replace
clear

use "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_data_20110728.dta"
sort session subject period

append using "C:\Users\bcschipper\burkhard\research\mensdr\stats\mensdr_data_20110626.dta"


*./.

******************************************
******************************************
* Generate labels and covariates *********
******************************************
******************************************


********************************
* GENERATE ARBITRARY ID VARIABLE
********************************

gen test = substr(session, 3, 7)
gen test2 = regexr(test,"_16","1")
gen test3 = regexr(test2,"_17","2")
gen test4 = regexr(test3,"_15","3")
destring test4, gen(test5)
egen id=concat(test5 subject)
destring id, replace
drop test test2 test3 test4 test5
label variable id "Arbitrary ID number"



**********************
* Demographics
**********************

label variable siblings "Num. of Siblings"
label variable age "Age"
label variable height "Height"
label variable weight "Weight"

* One subject states a weight of 60 pounds. Probably mixed it up with 60kg. 
* We replace the weight with 132 pounds. 
replace weight = 132 if weight == 60

gen bmi = (weight * 703)/(height^2) if new == 1
label variable bmi "BMI"

gen dbmi = bmi - 21.75 if new == 1
label variable dbmi "Difference Standard BMI"

gen obese = 0 if new == 1
replace obese = 1 if bmi > 30
label variable obese "Obese"

gen age2 = age^2
label variable age2 "Age Squared"

gen ysiblings = youngersib
label variable ysiblings "Num. of Younger Siblings"
gen osiblings = oldersib 
label variable osiblings "Num. of Older Siblings"
gen totalsiblings = ysiblings + osiblings
gen consistentsib = totalsiblings - siblings

egen meanage = mean(age)
egen meansib = mean(siblings)
gen cage = age - meanage
gen csib = siblings - meansib

gen female = regexm(gender,"Female")
gen female1 = regexm(gender,"female")
replace female = 1 if female == 1 | female1 == 1
drop female1
label variable female "Female"

gen gender1 = "Female" if female == 1
replace gender1 = "Male" if female == 0
*(This is for consistent labels in graphs.)


* Ethnicities
*************

gen white = regexm(ethnic,"White")
label variable white "White"

gen black = regexm(ethnic,"African")
label variable black "Black"

gen asian = regexm(ethnic,"Asian")
label variable asian "Asian"

gen hispanic = regexm(ethnic,"Hispanic")
label variable hispanic "Hispanic"

gen other = regexm(ethnic,"Other")
label variable other "Other"

* Generate variable comparable to 'Other Ethnicities' in CKO 2005

gen otherethn = 0
replace otherethn = 1 if hispanic==1 | other==1 | black==1
label variable otherethn "Other"


* Academic variables
********************

gen science = regexm(major,"Chemistry")
label variable science "Chemistry"
* category is "Chemistry/Physics/Biology"

gen othersci = regexm(major,"Natural Science")
label variable othersci "Natural Science"

gen humanities = regexm(major,"Humanities")
label variable humanities "Humanities"

gen econ = regexm(major,"Economics")
label variable econ "Economics"

gen socsci = regexm(major,"Social Science")
label variable socsci "Social Science"

gen eng = regexm(major,"Engineering")
label variable eng "Engineering"

gen math = regexm(major,"Mathematics")
label variable math "Mathematics"

gen english = regexm(major,"English")
label variable english "English"
replace humanities = 1 if english == 1

* Generate variable comparable to majors in CKO 2005
gen sciothersci=0
replace sciothersci=1 if  science==1 |  othersci==1 | eng==1
label variable sciothersci "Science \& Engineering"
replace humanities = humanities + english

* Generate more aggregate variables of majors
gen msci = 0
replace msci = 1 if sciothersci == 1 | math == 1
label variable msci "Science, Eng., Math."

gen sochum = 0 
replace sochum = 1 if socsci == 1 | humanities == 1 | english == 1
label variable sochum "Social Sci., Humanities"


* Further academic variables
label variable gpa "GPA"
label variable quarters "Quarters Studied"

label variable sat "SAT"
label variable satyear "SAT Year"
gen sat2005 = 1 if satyear < 2005 & new == 1
label variable sat2005 "SAT before 2005"



* Sexual orientation, dating and relationship status
****************************************************

gen hetero = regexm(sex_orient, "Heterosexual") if new == 1
label variable hetero "heterosexual"
gen homo = regexm(sex_orient, "Homosexual") if new == 1
label variable homo "homosexual"
gen bi = regexm(sex_orient, "Bisexual") if new == 1
label variable bi "bisexual"

gen homobi = 0 if new == 1
replace homobi = 1 if homo == 1 | bi == 1
label variable homobi "Homo/Bisexual"

gen bgfriend = regexm(relationship, "Boyfriend/Girlfriend") if new == 1
label variable bgfriend "Boy/Girlfriend"
gen marry = regexm(relationship, "Married") if new == 1
label variable marry "Married"

gen relation = 0  if new == 1
replace relation = 1 if bgfriend == 1 | marry == 1
label variable relation "In relationship"

gen date1 = regexm(date, "1 person") if new == 1
gen date2 = regexm(date, "2 persons") if new == 1
gen date3 = regexm(date, "3 persons") if new == 1
gen date4 = regexm(date, "4 persons") if new == 1
gen date5 = regexm(date, "5 persons") if new == 1
gen date6 = regexm(date, "6 persons") if new == 1
gen date7 = regexm(date, "7 persons") if new == 1
gen date8 = regexm(date, "8 persons") if new == 1
gen date9 = regexm(date, "9 persons") if new == 1
gen date10 = regexm(date, "10 persons") if new == 1
gen date11 = regexm(date, "11 persons") if new == 1
gen date12 = regexm(date, "12 persons") if new == 1
gen dating = 0 if new == 1
replace dating = date1 + 2*date2 + 3*date3 + 4*date4 + 5*date5 + 6*date6 + 7*date7 + 8*date8 + 9*date9 + 10*date10 + 11*date11 + 12*date12
drop date1 date2 date3 date4 date5 date6 date7 date8 date9 date10 date11 date12
label variable dating "No. of Person's Dating"

gen kids = regexm(num_kids, "1 child") if new == 1
* Remark: No one in the sample has more than one child
label variable kids "Child"

gen preg = regexm(pregnant, "Yes") if new == 1
label variable preg "Pregnant"

gen roommate = regexm(living, "roommate") if new == 1
label variable roommate "Living with roommate"
gen partner = regexm(living, "partner") if new == 1
label variable partner "Living with boyfriend/girlfriend/partner/spouse"
gen parents = regexm(living, "parents") if new == 1
label variable parents "Living with parents"
gen livalone = regexm(living, "alone") if new == 1  
label variable livalone "Living alone"


* Dietary preferences, smoking, live style
******************************************

gen smokeO = regexm(smoke, "Occasionally") if new == 1
gen smokeD = regexm(smoke, "Daily") if new == 1
gen smoking = 0 if new == 1
replace smoking = 1 if smokeO == 1
replace smoking = 2 if smokeD == 1
label variable smoking "Frequency of Smoking"

gen supplement = regexm(supplements, "Yes") if new == 1
label variable supplement "Taking Supplements"

gen vegetarian = regexm(veg, "Yes") if new == 1
label variable vegetarian "Vegetarian"

gen soyN = regexm(soy, "Not very often") if new == 1
label variable soyN "Occational soybean food"
gen soyW = regexm(soy, "several times a week") if new == 1
label variable soyW "Weekly soybean food"
gen soyD = regexm(soy, "daily") if new == 1
label variable soyD "Daily soybean food"
gen soybean = 0 if new == 1
replace soybean = soyN + 2*soyW + 3*soyD
label variable soybean "Amount of Soybean Intake"


* Exercising behavior
***********************

gen workout1 = regexm(exercise, "once") if new == 1
gen workout2 = regexm(exercise, "twice") if new == 1
gen workout3 = regexm(exercise, "three") if new == 1
gen workout4 = regexm(exercise, "Four") if new == 1
gen workout = 0 if new == 1
replace workout = workout1 + 2*workout2 + 3*workout3 + 4*workout4
drop workout1 workout2 workout3 workout4
label variable workout "Workout Frequency"


* Psychological variables
*************************

* Personality
replace personality = "optimistic" if personality == "Optimistic"
gen optimistic = regexm(personality, "optimistic")
label variable optimistic "Optimistic Personality"

replace personality = "pessimistic" if personality == "Pessimistic"
gen pessimistic = regexm(personality, "pessimistic")
label variable optimistic "Pessimistic Personality"

replace personality = "neither" if personality == "Neither"
gen neither = regexm(personality, "neither")
label variable neither "Neither Optimistic/Pessimist"


* Emotions
gen anger = regexm(emotions, "anger")
gen anger1 = regexm(emotions, "Anger")
replace anger = 1 if anger1 == 1
drop anger1
label variable anger "Anger"

gen anxiety = regexm(emotions, "anxiety")
gen anxiety1 = regexm(emotions, "Anxiety")
replace anxiety = 1 if anxiety1 == 1
drop anxiety1
label variable anxiety "Anxiety"

gen confusion = regexm(emotions, "confusion")
gen confusion1 = regexm(emotions, "Confusion")
replace confusion = 1 if confusion1 == 1
drop confusion1
label variable confusion "Confusion"

gen contentment = regexm(emotions, "contentment")
gen contentment1 = regexm(emotions, "Contentment")
replace contentment = 1 if contentment1 == 1
drop contentment1 
label variable contentment "Contentment"
 
gen fatigue = regexm(emotions, "fatigue")
gen fatigue1 = regexm(emotions, "Fatigue")
replace fatigue = 1 if fatigue1 == 1
drop fatigue1
label variable fatigue "Fatique"
 
gen happiness = regexm(emotions, "happiness")
gen happiness1 = regexm(emotions, "Happiness")
replace happiness = 1 if happiness1 == 1
drop happiness1
label variable happiness "Happiness"

gen irritation = regexm(emotions, "irritation")
gen irritation1 = regexm(emotions, "Irritation")
replace irritation = 1 if irritation1 == 1
drop irritation1
label variable irritation "Irritation"

gen moodswings = regexm(emotions, "mood swings")
gen moodswings1 = regexm(emotions, "Mood swings")
replace moodswings = 1 if moodswings1 == 1
drop moodswings1
label variable moodswings "Mood Swings"

gen withdrawal = regexm(emotions, "withdrawl")
gen withdrawal1 = regexm(emotions, "Withdrawl")
replace withdrawal = 1 if withdrawal1 == 1
drop withdrawal1
label variable withdrawal "Withdrawal"




***************************
* Auction related variables
***************************

label variable v "Value"
label variable b "Bid"
label variable win "Won round"
label variable b_adj "Bid + U(0,1) to break ties"
label variable bh "The Winning Bid"
label variable disttype "1=high 2=low distribution"
drop v21 v22 v23 v24 v25 v26

gen v2 = v^2
label variable v2 "Value Squared"

gen v3 = v^3
label variable v3 "Value Cubed"

gen period2 = period^2
label variable period2 "Period Squared"

gen period3 = period^2
label variable period3 "Period Cubed"

gen dom = 0
replace dom = 1 if b > v
* weakly dominated bid

* PERIOD DUMMIES
tab period, gen(pdum)

* MOMENTS OF INDIVIDUAL EMPIRICAL VALUE DISTRIBUTIONS
egen meanv = mean(v) if period > 0, by(id)
egen sdv = sd(v) if period > 0, by(id)
egen skewv = skew(v) if period > 0, by(id)

* GENERATE DOLLAR PROFITS 
gen dollarprofit = totalprofit/13
label variable dollarprofit "Total Profit in US Dollars"

* Markup
gen markup = b - v
egen meanmarkup = mean(markup), by(id)

* High value
gen hv = 0
replace hv = 1 if v > 50

* Low value
gen lv = 1 - hv

* Early bidding periods
gen fperiod = 0
replace fperiod = 1 if period > 0 & period < 16

* Later bidding periods
gen lperiod = 0
replace lperiod = 1 if fperiod == 0 & period > 0


gen datum = substr(session, 1, 6)
destring(datum), replace
label variable datum "Session Date"

* generate session dummies
tab datum, gen(sessiondum)



*************************
* 2D4D related variables 
*************************

* mensdr sample (2007 data set)

* GENERATE DIGIRATIO AVERAGE

replace fourd_mp = fourd_mp/11.811 
label variable fourd_mp "Fourth Digit Length in mm, Matthew"
replace twod_mp = twod_mp/11.811 
label variable twod_mp "Second Digit Length in mm, Matthew"

replace fourd_bcs = fourd_bcs/11.811 
label variable fourd_bcs "Fourth Digit Length in mm, Burkhard"
replace twod_bcs = twod_bcs/11.811 
label variable twod_bcs "Second Digit Length in mm, Burkhard"

gen dr_mp = twod_mp/fourd_mp
label variable dr_mp "Matthew's DR measurement"
gen dr_bcs = twod_bcs/fourd_bcs
label variable dr_bcs "Burkhard's DR Measurement"
gen avdr = (dr_bcs + dr_mp)/2
label variable avdr "Average digit ratio"
gen avdrfem = avdr*female
label variable avdrfem "Av. Digitratio*Female"

gen twod = (twod_mp + twod_bcs)/2
gen fourd = (fourd_mp + fourd_bcs)/2
label variable twod "Second Digit, mm"
label variable fourd "Fourth Digit, mm"

gen dr = twod/fourd 
label variable dr "Digit Ratio"

gen drfem = dr*female
la var drfem "Digit Ratio*Female"

* only 2010 data set

gen broken = regexm(brokenfinger, "Yes") if new == 1
label variable brokenf "Broken Finger" 

gen broken2d4d = regexm(_4_digit, "Yes") if new == 1
label variable broken2d4d "Broken 2D:4D"

gen unclear2 = 0
replace unclear2 = 1 if unclear2_bcs == 1 | unclear2_mp == 1

gen unclear4 = 0
replace unclear4 = 1 if unclear4_bcs == 1 | unclear4_mp == 1

gen uncleardr = 0
replace uncleardr = 1 if unclear2 == 1 | unclear4 == 1
label variable uncleardr "DR difficult to measure"



gen drfperiod = dr*fperiod
label variable drfperiod "Digit Ratio and Early Bids"
gen drlperiod = dr*lperiod
label variable drlperiod "Digit Ratio and Late Bids"
gen cagedr = cage*dr
gen csibdr = csib*dr
gen drhv = dr*hv
label var drhv "Digit Ratio and High Values"
gen drlv = dr*lv
label var drlv "Digit Ratio and Low Values"
gen femaledr = dr*female
label variable femaledr "Digit Ratio and Female"
gen maledr = dr*(1 - female)
label variable maledr "Digit Ratio and Male"
gen mathdr = math*dr
gen sciotherscidr = sciothersci*dr
gen econdr = econ*dr
gen socscidr = socsci*dr
gen humanitiesdr = humanities*dr
gen asiandr = asian*dr
gen otherethndr = otherethn*dr





******************************
* Menstrual cycle & fertility
******************************

* Contraceptives
gen pill = regexm(contraceptive,"Yes")
*(2009 data)
gen pill1 = regexm(on_pill,"Yes")
*(2007 data)
replace pill = 1 if pill == 1 | pill1 == 1
drop pill1
label variable pill "Contraceptives"

* PMS
replace symptoms_pms = "none" if symptoms_pms == "None"
gen none = regexm(symptoms_pms,"none")
label variable none "No PMS"

replace symptoms_pms = "mild" if symptoms_pms == "Mild"
gen mild = regexm(symptoms_pms,"mild")
label variable mild "Mild PMS"

replace symptoms_pms = "severe" if symptoms_pms == "Severe"
gen severe = regexm(symptoms_pms,"severe")
label variable severe "Severe PMS"

replace usual_pms = "none" if usual_pms == "None"
gen usual_none = regexm(usual_pms, "none") if new == 1
label variable none "Usually no PMS"

replace usual_pms = "mild" if usual_pms == "Mild"
gen usual_mild = regexm(usual_pms, "mild") if new == 1
label variable mild "Usually mild PMS"

replace usual_pms = "severe" if usual_pms == "Severe"
gen usual_severe = regexm(usual_pms, "none") if new == 1
label variable severe "Usually severe PMS"

* there is only one participant with severe PMS: combine with mild
gen pms = 0
replace pms = 1 if severe == 1 | mild == 1
label variable pms "Currently PMS"


*Generate standardized phase dummies of the menstrual cycle
rename men_awaydays mens
*label variable mens "Days from 1st day of last period"
la var mens "Day of Menstrual Cycle"

gen menst=0
gen folic=0
gen peri=0
gen lut=0
gen premens=0

replace menst=1 if mens<6 & female==1
replace folic=1 if 5<mens & mens<13
replace peri=1 if 12<mens & mens<16
replace lut=1 if 15<mens & mens<25
replace premens=1 if 24<mens & female==1

label variable menst "Menstrual Phase"
label variable folic "Follicular Phase"
label variable peri "Peri-Ovulatory Phase"
label variable lut "Luteal Phase"
label variable premens "Pre-Menstrual Phase"


*GENERATE "Individual" Individualized PHASE DUMMIES
gen menst_adj = 0
label variable menst_adj "Uni. Adj. Menstrual Phase"

gen folic_adj =0
label variable folic_adj "Uni. Adj. Follicular Phase"

gen peri_adj =0
label variable peri_adj "Uni. Adj. Peri-ovular Phase"

gen lut_adj =0
label variable lut_adj "Uni. Adj. Luteal Phase"

gen premens_adj =0
label variable premens_adj "Uni. Adj. Premenstrual Phase"


replace duration = "37" if duration=="more than 35" | duration==">35"
replace duration = "24" if duration=="less than 25" | duration=="< 25"
destring duration, replace
label variable duration "Length Menstrual Cycle"

gen mens_adj = 0
label variable mens_adj "Mens/Duration"
replace mens_adj = mens/duration if female==1

gen mensttest = 5.5/28
gen folictest = 13.5/28
gen peritest = 16.5/28
gen luttest = 23.5/28

replace menst_adj = 1 if mens_adj<=mensttest & female==1
replace folic_adj =1 if mens_adj>mensttest & mens_adj<=folictest
replace peri_adj =1 if mens_adj>folictest & mens_adj<=peritest
replace lut_adj =1 if mens_adj>peritest & mens_adj<=luttest
replace premens_adj = 1 if mens_adj>luttest

drop mensttest folictest peritest luttest


*GENERATE folicular phase adjusted menstrual phases
gen premensfoltest = 0
gen lutfoltest = 0
gen perifoltest = 0

replace premensfoltest = duration - 5
replace lutfoltest = duration - 13
replace perifoltest = duration - 16

gen menst_fol = 0
label variable menst_fol "Fol. Adj.  Menstrual Phase"

gen folic_fol = 0
label variable folic_fol "Fol. Adj. Follicular Phase"

gen peri_fol = 0
label variable peri_fol "Fol. Adj. Peri-ovular Phase"

gen lut_fol = 0
label variable lut_fol "Fol. Adj. Luteal Phase"

gen premens_fol = 0
label variable premens_fol "Fol. Adj. Premenstrual Phase"

replace premens_fol = 1 if mens > premensfoltest & female==1
replace lut_fol = 1 if mens > lutfoltest & premens_fol == 0 & female==1
replace peri_fol = 1 if mens > perifoltest & lut_fol == 0 & premens_fol == 0 & female==1 
replace menst_fol = 1 if menst ==1 & female==1 
replace folic_fol = 1 if menst_fol==0 & premens_fol==0 & lut_fol==0 &  peri_fol==0 & female==1 

drop premensfoltest lutfoltest perifoltest



* Women on the pill or who are pregnant don't have a natural menstrual cycle. Generate phase dummies for naturally
* cycling women
gen nmenst = 0
replace nmenst = menst if pill == 0
label variable nmenst "Menstrual Phase"

gen nfolic = 0 
replace nfolic = folic if pill == 0
label variable nfolic "Follicular Phase"

gen nperi = 0 
replace nperi = peri if pill == 0
label variable nperi "Peri-ovular Phase"

gen nlut = 0
replace nlut = lut if pill == 0
label variable nlut "Luteal Phase"

gen npremens = 0
replace npremens = premens if pill == 0
label variable npremens "Premenstrual Phase"



gen nmenst_adj = 0
replace nmenst_adj = menst_adj if pill == 0
label variable nmenst_adj "Uni. Adj. Menstrual Phase"

gen nfolic_adj = 0
replace nfolic_adj = folic_adj if pill == 0
label variable nfolic_adj "Uni. Adj. Follicular Phase"

gen nperi_adj = 0
replace nperi_adj = peri_adj if pill == 0
label variable nperi_adj "Uni. Adj. Peri-ovular Phase"

gen nlut_adj = 0
replace nlut_adj = lut_adj if pill == 0
label variable nlut_adj "Uni. Adj. Luteal Phase"

gen npremens_adj = 0
replace npremens_adj = premens_adj if pill == 0
label variable npremens_adj "Uni. Adj. Premenstrual Phase"



gen nmenst_fol = 0
replace nmenst_fol = menst_fol if pill == 0
label variable nmenst_fol "Fol. Adj. Menstrual Phase"

gen nfolic_fol = 0 
replace nfolic_fol = folic_fol if pill == 0
label variable nfolic_fol "Fol. Adj. Follicular Phase"

gen nperi_fol = 0
replace nperi_fol = peri_fol if pill == 0
label variable nperi_fol "Fol. Adj. Peri-ovular Phase"

gen nlut_fol = 0
replace nlut_fol = lut_fol if pill == 0
label variable nlut_fol "Fol. Adj. Luteal Phase"

gen npremens_fol = 0
replace npremens_fol = premens_fol if pill == 0
label variable npremens_fol "Fol. Adj. Premenstrual Phase"


gen nduration = 0
replace nduration = duration if pill == 0 & female == 1
label variable nduration "Length Menstrual Cycle"



* High progesterone phases of the menstrual cycle (second half)
gen shalf = 0 
replace shalf = 1 if nperi == 1 | nlut == 1 | npremens == 1

gen shalf_adj = 0
replace shalf_adj = 1 if nperi_adj == 1 | nlut_adj == 1 | npremens_adj == 1

gen shalf_fol = 0 
replace shalf_fol = 1 if nperi_fol == 1 | nlut_fol == 1 | npremens_fol == 1


* Generate another type high progesterone phase dummy of the menstrual cycle
gen hprog = 0
replace hprog = 1 if lut==1 | premens==1

gen hprog_adj = 0 
replace hprog_adj = 1 if lut_adj == 1 | premens_adj == 1

gen hprog_fol = 0
replace hprog_fol = 1 if lut == 1 | premens_fol == 1


* Generate "high" estradiol phase dummy of the menstrual cycle

gen hest = 0
replace hest = 1 if folic == 1 | peri == 1 | lut == 1


* Alternative definition of follicular phase

gen follic = 0
replace follic = 1 if lut == 0 & premens == 0 & female == 1
label variable follic "Follicular Phase"




* Regularity of menstrual cycle 
gen irregular = regexm(men_changes, "irregular") if new == 1
label variable irregular "Irregular menses"
gen regular = regexm(men_changes, "same number") if new == 1
label variable regular "Regular menses"

* Two females with natural cycles have duration missing (IDs 113013, 120516)
gen durationmissing = 0
replace durationmissing = 1 if duration == . & female == 1 & pill == 0


* One female with natural cycle reports mens = 160 (ID 31138)
gen longmens = 0
replace longmens = 1 if mens ~= . & mens > 100 & female == 1


* Calendar keeping
gen calendar = regexm(men_calendar, "Yes") if new == 1
label variable calendar "Menstrual Calendar"
gen ncalendar = regexm(men_calendar, "No") if new == 1
label variable ncalendar "No Menstrual Calendar"


* To do: Consider alternative asking for menstrual cycle info



* generate specific contraceptives variables 
**********************************************
gen tmp1 = regexm(bcontrol_type, "DepoProvera")
gen tmp2 = regexm(pill_name, "DepoProvera")
gen tmp3 = regexm(bcontrol_type, "Seasonale")
gen tmp4 = regexm(pill_name, "Seasonale")
gen tmp5 = regexm(bcontrol_type, "Seasonellw")
gen tmp6 = regexm(pill_name, "Seasonellw")
gen threemonthpill = 0 
replace threemonthpill = 1 if tmp1 == 1 | tmp2 == 1 | tmp3 == 1 | tmp4 == 1 | tmp5 == 1 | tmp6 == 1
drop tmp1 tmp2 tmp3 tmp4 tmp5 tmp6

* 24 days pills
gen yaz1 = regexm(bcontrol_type, "Yaz")
gen yaz2 = regexm(pill_name, "yaz")
gen yaz = 0
replace yaz = 1 if yaz1 == 1 | yaz2 == 2
drop yaz1 yaz2

* reported name of pill 

gen rep01 = regexm(bcontrol_type, "DepoProvera")
gen rep02 = regexm(pill_name, "DepoProvera")
gen rep03 = regexm(bcontrol_type, "Seasonale")
gen rep04 = regexm(pill_name, "Seasonale")
gen rep05 = regexm(bcontrol_type, "Seasonellw")
gen rep06 = regexm(pill_name, "Seasonellw")
gen rep07 = regexm(bcontrol_type, "Yaz")
gen rep08 = regexm(pill_name, "yaz")
gen rep09 = regexm(bcontrol_type, "Microgestin") 
gen rep10 = regexm(bcontrol_type, "Nuvaring")
gen rep11 = regexm(bcontrol_type, "Junel")
gen rep12 = regexm(bcontrol_type, "sprintec")
gen rep13 = regexm(bcontrol_type, "YAZ")
gen rep14 = regexm(bcontrol_type, "Desogen")
gen rep15 = regexm(pill_name, "Ortho Tri cyclen Lo")
gen rep16 = regexm(pill_name, "Yasmin")
gen rep17 = regexm(pill_name, "Avian?")
gen rep18 = regexm(pill_name, "Ortho-Tricylcen Lo")
gen rep19 = regexm(pill_name, "ortho lo")
gen rep20 = regexm(pill_name, "Ortho-tri-cyclen")
gen rep21 = regexm(pill_name, "yazmine")
gen rep22 = regexm(pill_name, "Yasmin")
gen rep23 = regexm(pill_name, "Orthotricyclen")

gen reppill = 0 
replace reppill = 1 if rep01 + rep02 + rep03 + rep04 + rep05 + rep06 + rep07 + rep08 + rep09 + rep10 + rep11 + rep12 + rep13 + rep14 + rep15 + rep16 + rep17 + rep18 + rep19 + rep20 + rep21 + rep22 + rep23 > 0
drop rep01 rep02 rep03 rep04 rep05 rep06 rep07 rep08 rep09 rep10 rep11 rep12 rep13 rep14 rep15 rep16 rep17 rep18 rep19 rep20 rep21 rep22 rep23
label variable reppill "Name of Pill reported"




* we assume that women on 3-month contraceptives are not in the pill break since we have no way of measuring it 
* and because of the long intake/effect it is more likely that they are not in a break

gen pillbreak1 = 1 if yaz == 1 & mens > 24 & mens < 29 & female == 1
gen pillbreak2 = 1 if pill == 1 & mens > 21 & mens < 29 & female == 1 
 
gen pillbreak = 0 
replace pillbreak = 1 if pillbreak1 == 1 | pillbreak2 == 1
drop pillbreak1 pillbreak2
label variable pillbreak "Contraception Break"
* doesn't make much sense in our samples given that only a small number of women are on pillbreak

gen pilluse = 0
replace pilluse = 1 if pill == 1 & pillbreak == 0
label variable pilluse "Contraception Use"



*GENERATE Probability of Conception (use data from Wilcox et al., 2001)
*****************************************************************************

* Average probabilities ("all women" in Wilcox et al., 2001, Table 1, p. 213)
gen all_prob = 0.000
replace all_prob = 0.000 if mens==0 | mens==1 | mens==2
replace all_prob = 0.001 if mens==3
replace all_prob = 0.002 if mens==4
replace all_prob = 0.004 if mens==5
replace all_prob = 0.009 if mens==6
replace all_prob = 0.017 if mens==7
replace all_prob = 0.029 if mens==8
replace all_prob = 0.044 if mens==9
replace all_prob = 0.061 if mens==10 
replace all_prob = 0.075 if mens==11
replace all_prob = 0.084 if mens==12
replace all_prob = 0.086 if mens==13
replace all_prob = 0.081 if mens==14
replace all_prob = 0.072 if mens==15
replace all_prob = 0.061 if mens==16
replace all_prob = 0.050 if mens==17
replace all_prob = 0.040 if mens==18
replace all_prob = 0.032 if mens==19
replace all_prob = 0.025 if mens==20
replace all_prob = 0.020 if mens==21
replace all_prob = 0.016 if mens==22
replace all_prob = 0.013 if mens==23 | mens==38
replace all_prob = 0.011 if mens==24 | mens==36
replace all_prob = 0.009 if mens==25 | mens==33 | mens==34
replace all_prob = 0.008 if mens==26 | mens==31 | mens==32
replace all_prob = 0.007 if mens==27 | mens==28 | mens==29 | mens==30
replace all_prob = 0.010 if mens==35
replace all_prob = 0.012 if mens==37
replace all_prob = 0.014 if mens==39
replace all_prob = 0.015 if mens==40 | mens==41 | mens==42 | mens==43 | mens==44 | mens==45 | mens==46 | mens==47 | mens==48 | mens==49 | mens==50

* women with regular cycles
gen r_prob = 0.000
replace r_prob = 0.001 if mens==3
replace r_prob = 0.002 if mens==4
replace r_prob = 0.004 if mens==5
replace r_prob = 0.009 if mens==6
replace r_prob = 0.018 if mens==7
replace r_prob = 0.032 if mens==8
replace r_prob = 0.050 if mens==9
replace r_prob = 0.069 if mens==10
replace r_prob = 0.085 if mens==11
replace r_prob = 0.094 if mens==12
replace r_prob = 0.093 if mens==13
replace r_prob = 0.085 if mens==14
replace r_prob = 0.073 if mens==15
replace r_prob = 0.059 if mens==16
replace r_prob = 0.047 if mens==17
replace r_prob = 0.036 if mens==18
replace r_prob = 0.028 if mens==19
replace r_prob = 0.021 if mens==20
replace r_prob = 0.016 if mens==21
replace r_prob = 0.013 if mens==22
replace r_prob = 0.010 if mens==23 | mens==36 | mens==37
replace r_prob = 0.008 if mens==24 | mens==34
replace r_prob = 0.007 if mens==25 | mens==32 | mens==33
replace r_prob = 0.006 if mens==26 | mens==30 | mens==31
replace r_prob = 0.005 if mens==27 | mens==28 | mens==29
replace r_prob = 0.009 if mens==35
replace r_prob = 0.011 if mens==38
replace r_prob = 0.012 if mens==39
replace r_prob = 0.013 if mens==40 | mens==41 | mens==42 | mens==43 | mens==44 | mens==45 | mens==46 | mens==47 | mens==48 | mens==49 | mens==50

* women with irregular cycles
gen ir_prob = 0.000
replace ir_prob = 0.001 if mens==4 | mens==5
replace ir_prob = 0.002 if mens==6
replace ir_prob = 0.004 if mens==7
replace ir_prob = 0.007 if mens==8
replace ir_prob = 0.011 if mens==9
replace ir_prob = 0.018 if mens==10
replace ir_prob = 0.026 if mens==11
replace ir_prob = 0.035 if mens==12
replace ir_prob = 0.045 if mens==13
replace ir_prob = 0.055 if mens==14
replace ir_prob = 0.061 if mens==15
replace ir_prob = 0.065 if mens==16 | mens==17
replace ir_prob = 0.062 if mens==18
replace ir_prob = 0.057 if mens==19
replace ir_prob = 0.051 if mens==20
replace ir_prob = 0.044 if mens==21
replace ir_prob = 0.038 if mens==22
replace ir_prob = 0.032 if mens==23
replace ir_prob = 0.027 if mens==24
replace ir_prob = 0.023 if mens==25 
replace ir_prob = 0.020 if mens==26
replace ir_prob = 0.017 if mens==27
replace ir_prob = 0.015 if mens==28
replace ir_prob = 0.013 if mens==29 | mens==36 | mens==37
replace ir_prob = 0.012 if mens==30 | mens==35
replace ir_prob = 0.011 if mens==31 | mens==32 | mens==33 | mens==34
replace ir_prob = 0.014 if mens==38 | mens==39 | mens==40 | mens==41 | mens==42 | mens==43 | mens==44 | mens==45 | mens==46 | mens==47 | mens==48 | mens==49 | mens==50


* OVERALL PROBABILITY OF CONCEPTION DEPENDING ON THE REGULARITY OF THE CYCLES (as in mens paper) 
gen conception = 0
label variable conception "Prob. of Conception"  
replace conception = r_prob
replace conception = ir_prob if mens > duration | mens > 40 | duration > 40

gen conceptionpill = conception*pill

* conception prob for nat. cycling women only
gen nconception = 0 
replace nconception = conception if pill == 0 & preg == 0



***********************
* Hormones Data
***********************

* Label hormone variables

label variable testf "Ex ante Testosterone"
label variable estf "Ex ante Estradiol"
label variable progf "Ex ante Progesterone"
label variable cortf "Ex ante Cortisol"

label variable tests "Ex post Testosterone"
label variable ests "Ex post Estradiol"
label variable progs "Ex post Progesterone"
label variable corts "Ex post Cortisol"

label variable testavg "Avg. Testosterone"
label variable estavg "Avg. Estradiol"
label variable progavg "Avg. Progesterone"
label variable cortavg "Avg. Cortisol" 

label variable sdtestf "SD EA Testosterone"
label variable sdestf "SD EA Estradiol"
label variable sdprogf "SD EA Progesterone"
label variable sdcortf "SD EA Cortisol"

label variable sdtests "SD EP Testosterone"
label variable sdests "SD EP Estradiol"
label variable sdprogs "SD EP Progesterone"
label variable sdcorts "SD EP Cortisol"

label variable sdtestavg "SD Testosterone"
label variable sdestavg "SD Estradiol"
label variable sdprogavg "SD Progesterone"
label variable sdcortavg "SD Cortisol"

* Interaction terms

gen femtestf = female*testf
gen femestf = female*estf
gen femprogf = female*progf
gen femcortf = female*cortf
gen malecortf = (1 - female)*cortf

gen menstprogf = menst*progf
gen folicprogf = folic*progf
gen periprogf = peri*progf
gen lutprogf = lut*progf
gen premensprogf = premens*progf

gen progmensprogf = hprog*progf
gen progmensprogf_adj = hprog_adj*progf
gen progmensprogf_fol = hprog_fol*progf


* Normalized hormones

gen ntestf = testf/sdtestf
label variable ntestf "Testosterone"
gen nestf = estf/sdestf
label variable nestf "Estradiol"
gen nprogf = progf/sdprogf
label variable nprogf "Progesterone"
gen ncortf = cortf/sdcortf
label variable ncortf "Cortisol" 

gen ntestavg = testavg/sdtestavg
label variable ntestavg "Testosterone"
gen nestavg = estavg/sdestavg
label variable nestavg "Estradiol"
gen nprogavg = progavg/sdprogavg
label variable nprogavg "Progesterone"
gen ncortavg = cortavg/sdcortavg
label variable ncortavg "Cortisol"


* Assay quality variables 
* See original assay results from CNPRC for brief explanation of what is LDD.
* Essentially LDD refers to score at the lower limit of the assay.

label variable limittestf "LDD Ex Ante Testosterone"
label variable limitestf "LDD Ex Ante Estradiol"
label variable limitprogf "LDD Ex Ante Progesterone"
label variable limitcortf "LDD Ex Ante Cortisol"

label variable limittests "LDD Ex Post Testosterone"
label variable limitests "LDD Ex Post Estradiol"
label variable limitprogs "LDD Ex Post Progesterone"
label variable limitcorts "LDD Ex Post Cortisol"


* Interaction terms

gen limtestf = limittestf*ntestf if new == 1
label variable limtestf "LDD x Testost."

gen limestf = limitestf*nestf if new == 1 
label variable limestf "LDD x Estrad."

gen limprogf = limitprogf*nprogf if new == 1
label variable limprogf "LDD x Progest."

gen limcortf = limitcortf*ncortf if new == 1
label variable limcortf "LDD x Cort."

gen follicprogf = follic*nprogf if new == 1
label variable follicprogf "Foll. Ph. x Progest." 

gen dbmiprogf = dbmi*nprogf if new == 1
label variable dbmiprogf "Diff. Standard BMI x Progest."

gen obeseprogf = obese*nprogf if new == 1
label variable obeseprogf "Obese x Progest."

gen pillprogf = pill*nprogf if new == 1
label variable pillprogf "Contracept. x Progest."

gen smokeprogf = smoking*nprogf if new == 1
label variable smokeprogf "Smoking x Progest."

gen soyprogf = soybean*nprogf if new == 1
label variable soyprogf "Soybean x Progest."

gen workoutprogf = workout*nprogf if new == 1
label variable workoutprogf "Workout x Progest."

gen homoprogf = homobi*nprogf if new == 1
label variable homoprogf "Homosex. x Progest."


* Lunch before saliva collection 

gen skipped = regexm(lunch, "skipped") if new == 1
label variable skipped "Skipped lunch"

gen lunch11 = regexm(lunch, "11.00 am") if new == 1
gen lunch12 = regexm(lunch, "12.00 pm") if new == 1
gen lunch13 = regexm(lunch, "1.00 pm") if new == 1
gen lunch14 = regexm(lunch, "2.00 pm") if new == 1
gen lunch15 = regexm(lunch, "3.00 pm") if new == 1

gen luncht = lunch11 + 2*lunch12 + 3*lunch13 + 4*lunch14 + 5*lunch15 if new == 1
* measures the contamination of saliva with food
label variable luncht "Lunch today"


* Last food intake 

gen food30 = regexm(time_last_ate, "30 min") if new == 1
gen food60 = regexm(time_last_ate, "1") if new == 1
gen food120 = regexm(time_last_ate, "2") if new == 1
gen food180 = regexm(time_last_ate, "3 hours") if new == 1
gen food240b = regexm(time_last_ate, "4") if new == 1
gen food300 = regexm(time_last_ate, "ago") if new == 1
gen food240 = 0 if new == 1
replace food240 = food240b if food300 == 0 & new == 1

gen foodtime = 30*food30 + 60*food60 + 120*food120 + 180*food180 + 240*food240 + 300*food300 if new == 1
* measures the minutes ago when last eaten
label variable foodtime "Time last eaten"

gen drink30 = regexm(drinks_when, "30 min") if new == 1
gen drink60 = regexm(drinks_when, "1") if new == 1
gen drink90 = regexm(drinks_when, "1.5") if new == 1
gen drink120 = regexm(drinks_when, "2 hours") if new == 1
* 3 subjects stated drink60 AND drink90. In this case we set drink90 to zero since
* the lowest matters for the quality of the assays. 
replace drink90 = 0 if drink60 == 1

gen drinktime = 30*drink30 + 60*drink60 + 90*drink90 + 120*drink120 if new == 1
* measures the minutes ago when last eaten
label variable drinktime "Time last drunken"


* Mouth injuries
gen mouth = regexm(mouth_injuries, "Yes") if new == 1
label variable mouth "Mouth injuries"


* Teeth brushing 
gen brush1 = regexm(brushperday, "Once") if new == 1
gen brush2 = regexm(brushperday, "Twice") if new == 1
gen brush3 = regexm(brushperday, "Three") if new == 1
gen brush4 = regexm(brushperday, "More") if new == 1
gen brush = brush1 + 2*brush2 + 3*brush3 + 4*brush4 if new == 1
label variable brush "No. teethbrushing"

gen blast30 = regexm(brush_last, "30") if new == 1
gen blast60 = regexm(brush_last, "1 hour") if new == 1
gen blast120 = regexm(brush_last, "2 hours") if new == 1
gen blast180 = regexm(brush_last, "3 hours") if new == 1
gen blast240 = regexm(brush_last, "More") if new == 1
* Some subjects brushed several times. We look for the most recent time since
* this is what matters for the assays. 
replace blast60 = 0 if blast30 ==1
replace blast120 = 0 if blast60 == 1 | blast30 == 1
replace blast180 = 0 if blast120 == 1 | blast60 == 1 | blast30 == 1
replace blast240 = 0 if blast180 == 1 | blast120 == 1 | blast60 == 1 | blast30 == 1

gen blast = 30*blast30 + 60*blast60 + 120*blast120 + 180*blast180 + 240*blast240 if new == 1
label variable blast "Min. Last Teethbrushing"




***************************
* Risk variables
***************************

gen rg1 = regexm(g1, "A")
replace rg1 = . if new == 0

gen rg2 = regexm(g2, "A")
replace rg2 = . if new == 0

gen rg3 = regexm(g3, "A")
replace rg3 = . if new == 0

gen rg4 = regexm(g4, "A")
replace rg4 = . if new == 0

gen rg5 = regexm(g5, "A")
replace rg5 = . if new == 0

gen rg6 = regexm(g6, "A")
replace rg6 = . if new == 0

gen rg7 = regexm(g7, "A")
replace rg7 = . if new == 0

gen rg8 = regexm(g8, "A")
replace rg8 = . if new == 0

gen rg9 = regexm(g9, "A")
replace rg9 = . if new == 0

gen rg10 = regexm(g10, "A")
replace rg10 = . if new == 0

gen rgconsistent = 1
replace rgconsistent = 0 if (rg2 > rg1 | rg3 > rg2 | rg4 > rg3 | rg5 > rg4 | rg6 > rg5 | rg7 > rg6 | rg8 > rg9 | rg10 > rg9)
replace rgconsistent = . if new == 0
* regconsistent is zero if choice in the gain domain are inconsistent

gen riskgain = rg1 + rg2 + rg3 + rg4 + rg5 + rg6 + rg7 + rg8 + rg9 + rg10
label variable riskgain "Risk Aversion Gains"

gen rgxincon = riskgain*(1 - rgconsistent)
label variable rgxincon "Inconsist. x R.A. Gains"
* interaction variable to be used jointly with riskgain to measure the effect of 
* risk aversion on the gain domain if subject is not inconsistent


gen rl1 = regexm(l1, "A")
replace rl1 = . if new == 0

gen rl2 = regexm(l2, "A")
replace rl2 = . if new == 0

gen rl3 = regexm(l3, "A")
replace rl3 = . if new == 0

gen rl4 = regexm(l4, "A")
replace rl4 = . if new == 0

gen rl5 = regexm(l5, "A")
replace rl5 = . if new == 0

gen rl6 = regexm(l6, "A")
replace rl6 = . if new == 0

gen rl7 = regexm(l7, "A")
replace rl7 = . if new == 0

gen rl8 = regexm(l8, "A")
replace rl8 = . if new == 0

gen rl9 = regexm(l9, "A")
replace rl9 = . if new == 0

gen rl10 = regexm(l10, "A")
replace rl10 = . if new == 0

gen rlconsistent = 1
replace rlconsistent = 0 if (rl2 < rl1 | rl3 < rl2 | rl4 < rl3 | rl5 < rl4 | rl6 < rl5 | rl7 < rl6 | rl8 < rl9 | rl10 < rl9)
replace rlconsistent = . if new == 0
* rlconsistent is zero if choices in the loss domain are inconsistent

gen riskloss = rl1 + rl2 + rl3 + rl4 + rl5 + rl6 + rl7 + rl8 + rl9 + rl10
label variable riskloss "Risk Aversion Losses"

gen rlxincon = riskloss*(1 - rlconsistent)
label variable rlxincon "Inconsist. x R.A. Losses"
* interaction variable to be used jointly with riskloss to measure the effect of 
* risk aversion on the loss domain if subject is not inconsistent







*************************
* Other
*************************

* Treatment of male sample
**************************

replace mens = 0 if mens==.
replace mens_adj = 0 if mens_adj==.
replace mens = 0 if female==0
replace mens_adj = 0 if female==0
replace duration = 0 if female==0

*gen std_days = mens_adj*28
*replace std_days = std_days-28
*replace std_days = sqrt(std_days^2)
*gen std_days2 = std_days^2
*gen std_days3 = std_days^3
*gen std_days4 = std_days^4
*gen std_days5 = std_days^5
*gen std_days6 = std_days^6
*gen std_days7 = std_days^7

*la var std_days "Adjusted Days of Menstrual Cycle"

*gen mens2 = mens^2
*gen mens3 = mens^3
*gen mens4 = mens^4
*gen mens5 = mens^5
*gen mens6 = mens^6
*gen mens7 = mens^7



foreach v in v v2 v3 period age siblings asian otherethn math sciothersci econ socsci humanities pill female {
replace `v' = 0 if `v' == .
}

****



* Save data set
*********************
save "C:\Users\bcschipper\burkhard\research\hormone\stats\hormone_data_20110728.dta", replace


* Log use
**********

log using "C:\Users\bcschipper\burkhard\research\mensdr\stats\mens_log_20110929.smcl", replace

 
***********************************************
***********************************************
* Analysis of menstrual cycle: Redoing CKO 2005
***********************************************
***********************************************


* SUMMARY STATISTICS
*******************************************

sum age siblings female asian otherethn math sciothersci econ socsci humanities if period == 30

sum menst folic peri lut premens menst_adj folic_adj peri_adj lut_adj premens_adj duration pill if period == 30 & female == 1

sum nmenst nfolic nperi nlut npremens nduration pill pilluse pillbreak if period == 30 & female == 1

sum nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj nduration if period == 30 & female == 1 & durationmissing == 0

sum nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol nduration if period == 30 & female == 1 & durationmissing == 0

pwcorr b profit v age siblings female asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens duration pill none mild severe

pwcorr menst folic peri lut premens menst_adj folic_adj peri_adj lut_adj premens_adj menst_fol folic_fol peri_fol lut_fol premens_fol



* REGRESSIONS ON BIDS
*******************************************

* Run CKO 2005 type regressions on bids with standardized mens
reg b v v2 v3 period new female age siblings asian otherethn if period > 0, robust cluster(session)
reg b v v2 v3 period new female age siblings asian otherethn math sciothersci econ socsci humanities if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn menst folic peri lut premens if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period > 0, robust cluster(session)
test peri=menst
test peri=folic
test peri=lut
test peri=premens
test lut=menst
test lut=folic
test lut=premens
test folic=menst
test folic=premens
test premens=menst
*bs: reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill none mild severe if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids with standardized mens and period dummies
*(First five are specifications 1 to 5 in the paper)
reg b v v2 v3 pdum* new female age siblings asian otherethn if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn menst folic peri lut premens if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period > 0, robust cluster(session)
test peri=menst
test peri=folic
test peri=lut
test peri=premens
test lut=menst
test lut=folic
test lut=premens
test folic=menst
test folic=premens
test premens=menst
*bs: reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill none mild severe if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids with uniformly adj. mens 
reg b v v2 v3 period new age siblings asian otherethn menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period > 0, robust cluster(session)
test peri_adj=menst_adj
test peri_adj=folic_adj
test peri_adj=lut_adj
test peri_adj=premens_adj
test lut_adj=menst_adj
test lut_adj=folic_adj
test lut_adj=premens_adj
test folic_adj=menst_adj
test folic_adj=premens_adj
test premens_adj=menst_adj
*bs: reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill none mild severe if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids with uniformly adj. mens and period dummies 
*(First three are specifications 6 to 8 in the paper) (???? check again)
reg b v v2 v3 pdum* new age siblings asian otherethn menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period > 0, robust cluster(session)
test peri_adj=menst_adj
test peri_adj=folic_adj
test peri_adj=lut_adj
test peri_adj=premens_adj
test lut_adj=menst_adj
test lut_adj=folic_adj
test lut_adj=premens_adj
test folic_adj=menst_adj
test folic_adj=premens_adj
test premens_adj=menst_adj
*bs: reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill none mild severe if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids using follicular-phase adjusted phases
reg b v v2 v3 period new age siblings asian otherethn menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period > 0, robust cluster(session)
test peri_fol=menst_fol
test peri_fol=folic_fol
test peri_fol=lut_fol
test peri_fol=premens_fol
test lut_fol=menst_fol
test lut_fol=folic_fol
test lut_fol=premens_fol
test folic_fol=menst_fol
test folic_fol=premens_fol
test premens_fol=menst_fol
*bs: reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period > 0, robust cluster(session)
reg b v v2 v3 period new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill none mild severe if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids using follicular-phase adjusted phases and period dummies
reg b v v2 v3 pdum* new age siblings asian otherethn menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period > 0, robust cluster(session)
test peri_fol=menst_fol
test peri_fol=folic_fol
test peri_fol=lut_fol
test peri_fol=premens_fol
test lut_fol=menst_fol
test lut_fol=folic_fol
test lut_fol=premens_fol
test folic_fol=menst_fol
test folic_fol=premens_fol
test premens_fol=menst_fol
*bs: reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill none mild severe if period > 0, robust cluster(session)


*************************************
* REGRESSIONS ON PROFITS
*************************************

* Run CKO 2005 type regressions on profits with standardized mens
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst folic peri lut premens if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period==30, robust cluster(session)
test peri=menst
test peri=folic
test peri=lut
test peri=premens
test lut=menst
test lut=folic
test lut=premens
test folic=menst
test folic=premens
test premens=menst
*bs: reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill none mild severe if period==30, robust cluster(session)


* Run CKO 2005 type regressions on profits with uniformly adjusted phases 
*(First three are apecifications 9 to 11 in the paper) (check ????)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst_adj folic_adj peri_adj lut_adj premens_adj if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period==30, robust cluster(session)
test peri_adj=menst_adj
test peri_adj=folic_adj
test peri_adj=lut_adj
test peri_adj=premens_adj
test lut_adj=menst_adj
test lut_adj=folic_adj
test lut_adj=premens_adj
test folic_adj=menst_adj
test folic_adj=premens_adj
test premens_adj=menst_adj
*bs: reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill none mild severe if period==30, robust cluster(session)

* Run CKO 2005 type regressions on profits with follicular-phase adjusted phases
*(First three are specifications ???? to ???? in the paper) (check ????)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst_fol folic_fol peri_fol lut_fol premens_fol if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period==30, robust cluster(session)
test peri_fol=menst_fol
test peri_fol=folic_fol
test peri_fol=lut_fol
test peri_fol=premens_fol
test lut_fol=menst_fol
test lut_fol=folic_fol
test lut_fol=premens_fol
test folic_fol=menst_fol
test folic_fol=premens_fol
test premens_fol=menst_fol
*bs: reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill none mild severe if period==30, robust cluster(session)


**********************************************
* REGRESSIONS USING PROBABILITY OF CONCEPTION
**********************************************


* Run regressions on bids with prob of conception with period dummies
reg b v v2 v3 pdum* new female age siblings asian otherethn conception if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities conception if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period > 0, robust cluster(session)
*bs: reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities nconception pill if period > 0, robust cluster(session)

* Run regressions on profits with prob of conception
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn conception if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities conception if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period==30, robust cluster(session)
*bs: reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities nconception pill if period==30, robust cluster(session)


**************************************************
**************************************************
* TO BE GENERATED IN LaTeX for 2009 working paper
**************************************************
**************************************************

corrtex profit age siblings female white asian otherethn math sciothersci econ socsci humanities, ///
file("C:\Users\bcschipper\burkhard\research\mensdr\demog.tex") replace digits(2) title(Pairwise Correlations ///
of Demographics and Education Background) key(demog)

corrtex white asian otherethn menst folic peri lut premens menst_adj folic_adj peri_adj lut_adj premens_adj ///
duration pill none mild severe, file("C:\Users\bcschipper\burkhard\research\mensdr\menscorr.tex") replace digits(2) title(Pairwise Correlations ///
of Menstrual Cycle Phases) key(menscorr)

gen junk = uniform()

eststo sumstat: qui reg junk menst_adj folic_adj peri_adj lut_adj premens_adj if period > 29
estadd summ
estout sumstat using "C:\Users\bcschipper\burkhard\research\mensdr\sumstat.tex", replace notype  ///
cells("mean(fmt(%9.2g)) sd(par fmt(%9.2f)) min(fmt(%9.2g)) max(fmt(%9.2g))") ///
style(tex) drop(_cons) mlabels(, none) collabel(Mean "Std.\ Dev."  Min Max) label ///
prehead("\begin{tabular}{l*{4}{d}}\toprule") ///
posthead(\midrule) postfoot("\end{tabular}") notype



* Create CKO 2005 type regressions with standardized mens and period dummies (specifications 1 to 5) in 2009 working paper

eststo t01: qui reg b v v2 v3 pdum* new female age siblings asian otherethn if period > 0, robust cluster(session)
eststo t02: qui reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities if period > 0, robust cluster(session)
eststo t03: qui reg b v v2 v3 pdum* new age siblings asian otherethn menst folic peri lut premens if period > 0, robust cluster(session)
eststo t04: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens if period > 0, robust cluster(session)
eststo t05: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period > 0, robust cluster(session)


estout t01 t02 t03 t04 t05 using "C:\Users\bcschipper\burkhard\research\mensdr\bid.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(female age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill) ///
mlabels((1) (2) (3) (4) (5), prefix(\multicolumn{1}{c}{) suffix(})) order(female age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") notype


* Create CKO 2005 type regressions on PROFITS with standardized mens (specifications 6 to 10) in 2009 working paper paper

eststo t06: qui reg dollarprofit meanv sdv skewv new female age siblings asian otherethn if period==30, robust cluster(session)
eststo t07: qui reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities if period==30, robust cluster(session)
eststo t08: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst folic peri lut premens if period==30, robust cluster(session)
eststo t09: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens if period==30, robust cluster(session)
eststo t10: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill if period==30, robust cluster(session)

estout t06 t07 t08 t09 t10 using "C:\Users\bcschipper\burkhard\research\mensdr\profit.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(female age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill) ///
mlabels((6) (7) (8) (9) (10), prefix(\multicolumn{1}{c}{) suffix(})) order(female age siblings asian otherethn math sciothersci econ socsci humanities menst folic peri lut premens pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") notype


* The next three regressions are specifications 11 to 13 in the paper grouped into one table in 2009 working paper

eststo t11: qui reg b v v2 v3 pdum* new age siblings asian otherethn menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
eststo t12: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj if period > 0, robust cluster(session)
eststo t13: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period > 0, robust cluster(session)

estout t11 t12 t13 using "C:\Users\bcschipper\burkhard\research\mensdr\\bid_adj.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill) ///
mlabels((11) (12) (13), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") notype



* The next three regressions are apecifications 14 to 16 in the paper to be grouped into one table. Supress meanv sdv skewv if possible. 2009 working paper

eststo t14: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst_adj folic_adj peri_adj lut_adj premens_adj if period==30, robust cluster(session)
eststo t15: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj if period==30, robust cluster(session)
eststo t16: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill if period==30, robust cluster(session)

estout t14 t15 t16 using "C:\Users\bcschipper\burkhard\research\mensdr\profit_adj.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill) ///
mlabels((14) (15) (16), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities menst_adj folic_adj peri_adj lut_adj premens_adj pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") 


* Regressions on bids using follicular-phase adjusted phases 
* The next three regressions are specifications 17 to 19 in the paper grouped into one table. 2009 working paper
eststo t17: qui reg b v v2 v3 pdum* new age siblings asian otherethn menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
eststo t18: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol if period > 0, robust cluster(session)
eststo t19: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period > 0, robust cluster(session)

estout t17 t18 t19 using "C:\Users\bcschipper\burkhard\research\mensdr\bid_fol.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill) ///
mlabels((17) (18) (19), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") notype


* Regressions on dollarprofit using follicular-phase adjusted phases
* The next three regressions are apecifications 20 to 22 in the paper to be grouped into one table. Supress meanv sdv skewv if possible. 2009 working paper
eststo t20: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn menst_fol folic_fol peri_fol lut_fol premens_fol if period==30, robust cluster(session)
eststo t21: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol if period==30, robust cluster(session)
eststo t22: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill if period==30, robust cluster(session)

estout t20 t21 t22 using "C:\Users\bcschipper\burkhard\research\mensdr\profit_fol.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill) ///
mlabels((20) (21) (22), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities menst_fol folic_fol peri_fol lut_fol premens_fol pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") 


* Regressions on bids using conception probabilities 2009 working paper
eststo t23: qui reg b v v2 v3 pdum* new female age siblings asian otherethn conception if period > 0, robust cluster(session)
eststo t24: qui reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities conception if period > 0, robust cluster(session)
eststo t25: qui reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period > 0, robust cluster(session)

estout t23 t24 t25 using "C:\Users\bcschipper\burkhard\research\mensdr\bid_conception.tex", replace ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(female age siblings asian otherethn math sciothersci econ socsci humanities conception pill) ///
mlabels((23) (24) (25), prefix(\multicolumn{1}{c}{) suffix(})) order(female age siblings asian otherethn math sciothersci econ socsci humanities conception pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") 


* Regressions on profits using conception probabilities 2009 working paper
eststo t26: qui reg dollarprofit meanv sdv skewv new female age siblings asian otherethn conception if period==30, robust cluster(session)
eststo t27: qui reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities conception if period==30, robust cluster(session)
eststo t28: qui reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities conception pill if period==30, robust cluster(session)

estout t26 t27 t28 using "C:\Users\bcschipper\burkhard\research\mensdr\profit_conception.tex", replace ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(female age siblings asian otherethn math sciothersci econ socsci humanities conception pill) ///
mlabels((26) (27) (28), prefix(\multicolumn{1}{c}{) suffix(})) order(female age siblings asian otherethn math sciothersci econ socsci humanities conception pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{4}{l}{Standard errors (Clustered at the session level) in Parentheses} \\" ///
"\multicolumn{4}{l}{Significance levels: *10\%; ** 5\%; *** 1\%} \\" "\end{tabular}") 


* Figures differences in bids and profits

* standardized phases

parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill menst folic peri lut premens if period>0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst"
replace days = 9.5 if parm== "folic"
replace days = 15 if parm== "peri"
replace days = 20 if parm== "lut"
replace days = 25.75 if parm== "premens"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_bid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill menst folic peri lut premens if period == 30, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst"
replace days = 9.5 if parm== "folic"
replace days = 15 if parm== "peri"
replace days = 20 if parm== "lut"
replace days = 25.75 if parm== "premens"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_profit.png", replace
restore

* Uniformly adjusted phases
parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill menst_adj folic_adj peri_adj lut_adj premens_adj if period>0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst_adj"
replace days = 9.5 if parm== "folic_adj"
replace days = 15 if parm== "peri_adj"
replace days = 20 if parm== "lut_adj"
replace days = 25.75 if parm== "premens_adj"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_unibid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill menst_adj folic_adj peri_adj lut_adj premens_adj if period == 30, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst_adj"
replace days = 9.5 if parm== "folic_adj"
replace days = 15 if parm== "peri_adj"
replace days = 20 if parm== "lut_adj"
replace days = 25.75 if parm== "premens_adj"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_uniprofit.png", replace
restore

* Follicular adjusted phases
parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill menst_fol folic_fol peri_fol lut_fol premens_fol if period>0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm== "new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst_fol"
replace days = 9.5 if parm== "folic_fol"
replace days = 15 if parm== "peri_fol"
replace days = 20 if parm== "lut_fol"
replace days = 25.75 if parm== "premens_fol"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_folbid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill menst_fol folic_fol peri_fol lut_fol premens_fol if period == 30, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "menst_fol"
replace days = 9.5 if parm== "folic_fol"
replace days = 15 if parm== "peri_fol"
replace days = 20 if parm== "lut_fol"
replace days = 25.75 if parm== "premens_fol"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_folprofit.png", replace
restore



***************************************************************************
***************************************************************************
* 2011 Revision: Separating naturally cycling women from women on the pill
* making also use of the new 2010 data
* exclude the one pregnant lady
***************************************************************************
***************************************************************************

drop if preg == 1

* Regression on bids
*************************************
* Run CKO 2005 type regressions on bids with standardized natural mens
reg b v v2 v3 pdum* new female age siblings asian otherethn if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new female age siblings asian otherethn math sciothersci econ socsci humanities if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn nmenst nfolic nperi nlut npremens pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids with uniformly adj. nat. mens 
reg b v v2 v3 pdum* new age siblings asian otherethn nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0, robust cluster(session)

* Run CKO 2005 type regressions on bids using follicular-phase adjusted nat. phases
reg b v v2 v3 pdum* new age siblings asian otherethn nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0, robust cluster(session)


* REGRESSIONS ON PROFITS
*************************************
* Run CKO 2005 type regressions on profits with standardized nat. mens
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new female age siblings asian otherethn math sciothersci econ socsci humanities if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn nmenst nfolic nperi nlut npremens pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period==30, robust cluster(session)

* Run CKO 2005 type regressions on profits with uniformly adjusted nat. phases 
reg dollarprofit meanv sdv skewv new age siblings asian otherethn nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period==30, robust cluster(session)

* Run CKO 2005 type regressions on profits with follicular-phase nat. adjusted phases
reg dollarprofit meanv sdv skewv new age siblings asian otherethn nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period==30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period==30, robust cluster(session)


*********************************
* Latex files 2011 working paper
*********************************

*Table 3
**************
eststo bid01: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities female if period > 0, robust cluster(session)
eststo bid02: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period > 0, robust cluster(session)

eststo profit01: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities female if period == 30, robust cluster(session)
eststo profit02: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period == 30, robust cluster(session)

estout bid01 bid02 profit01 profit02 using "C:\Users\bcschipper\burkhard\research\mensdr\baseline.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities female nmenst nfolic nperi nlut npremens pill) ///
mlabels((bids1) (bids2) (profits1) (profits2), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities female nmenst nfolic nperi nlut npremens pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{5}{l}{Standard errors (clustered at the session level) in parentheses. Significance levels: *10\%; ** 5\%; *** 1\%.} \\" ///
"\multicolumn{5}{l}{We omit to report coefficients for the cubic polynomial in value and period indicators (bids1 and bids2),} \\" ///
"\multicolumn{5}{l}{the mean, standard deviation, and skewness of the empirical distribution of values (profits1 and profits2)} \\" ///
"\multicolumn{5}{l}{as well as the dummy for the 2010 sample.}\\" "\end{tabular}") notype


* Table 4
*************
eststo ub01: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
eststo fb01: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)

eststo up01: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)
eststo fp01: qui reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)

estout ub01 up01 fb01 fp01 using "C:\Users\bcschipper\burkhard\research\mensdr\robust.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill) ///
mlabels((bids3) (profits3) (bids4) (profits4), prefix(\multicolumn{1}{c}{) suffix(})) order(age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill) ///
style(tex)  collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") ///
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{5}{l}{Standard errors (clustered at the session level) in parentheses. Significance levels: *10\%; ** 5\%; *** 1\%.} \\" ///
"\multicolumn{5}{l}{We omit to report coefficients for the cubic polynomial in value and period indicators (bids3 and bids4),} \\" ///
"\multicolumn{5}{l}{the mean, standard deviation, and skewness of the empirical distribution of values (profits3 and profits4)} \\" ///
"\multicolumn{5}{l}{as well as the dummy for the 2010 sample.}\\" "\end{tabular}") notype


* Figures differences in bids and profits with naturally cycling women
* standardized phases

parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst nfolic nperi nlut npremens if period>0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst"
replace days = 9.5 if parm== "nfolic"
replace days = 15 if parm== "nperi"
replace days = 20 if parm== "nlut"
replace days = 25.75 if parm== "npremens"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nbid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst nfolic nperi nlut npremens if period == 30, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst"
replace days = 9.5 if parm== "nfolic"
replace days = 15 if parm== "nperi"
replace days = 20 if parm== "nlut"
replace days = 25.75 if parm== "npremens"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nprofit.png", replace
restore

* Uniformly adjusted phases
parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj if period>0 & durationmissing == 0 & longmens == 0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst_adj"
replace days = 9.5 if parm== "nfolic_adj"
replace days = 15 if parm== "nperi_adj"
replace days = 20 if parm== "nlut_adj"
replace days = 25.75 if parm== "npremens_adj"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nunibid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst_adj"
replace days = 9.5 if parm== "nfolic_adj"
replace days = 15 if parm== "nperi_adj"
replace days = 20 if parm== "nlut_adj"
replace days = 25.75 if parm== "npremens_adj"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nuniprofit.png", replace
restore

* Follicular adjusted phases
parmby "reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol if period>0 & durationmissing == 0 & longmens == 0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="v" | parm=="v2" | parm=="v3" | parm=="pdum*" | parm== "new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst_fol"
replace days = 9.5 if parm== "nfolic_fol"
replace days = 15 if parm== "nperi_fol"
replace days = 20 if parm== "nlut_fol"
replace days = 25.75 if parm== "npremens_fol"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nfolbid.png", replace
restore

parmby "reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities pill nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)", saving(junk, replace)
preserve
use junk, clear
drop if parm=="meanv" | parm=="sdv" | parm=="skewv" | parm=="new" | parm=="age" | parm=="siblings" | parm=="asian" | parm=="otherethn" | parm=="math" | parm=="sciothersci" | parm=="econ" | parm=="socsci" | parm=="pill" | parm=="_cons" | parm=="humanities"
gen days = .
la var days "Menstrual Phase"
replace days = 2.75 if parm== "nmenst_fol"
replace days = 9.5 if parm== "nfolic_fol"
replace days = 15 if parm== "nperi_fol"
replace days = 20 if parm== "nlut_fol"
replace days = 25.75 if parm== "npremens_fol"
twoway (scatter estimate days) (rcap min95 max95 days, sort), legend(on order(1 "Female/Male Difference" 2 "95% Confidence Interval")) xtitle(Menstrual Phase ,margin(small)) xscale(range(0 28))  xlabel(2.75 "Menstrual " 9.5 "Folicular" 15 "Peri-Ovulatory" 20 "Luteal" 25.75 "Pre-Menstrual", labsize(small)) xline(5.5 13.5 16.5 23.5, lwidth(thin) lcolor(black))
graph export "C:\Users\bcschipper\burkhard\research\mensdr\phases_nfolprofit.png", replace
restore


* Further observations
************************

* Understanding the auction: Excluding dominated bids
*******************************************************
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities female if period > 0 & markup <= 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period > 0 & markup <= 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & markup <= 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0 & markup <= 0 & durationmissing == 0 & longmens == 0, robust cluster(session)


* Learning: Earlier vs. later auction rounds 
***********************************************

* interaction of menstrual phases with first 15 auction periods
gen nmenst15 = nmenst*fperiod
gen nfolic15 = nfolic*fperiod
gen nperi15 = nperi*fperiod
gen nlut15 = nlut*fperiod
gen npremens15 = npremens*fperiod

gen nmenst15_adj = nmenst_adj*fperiod
gen nfolic15_adj = nfolic_adj*fperiod
gen nperi15_adj = nperi_adj*fperiod
gen nlut15_adj = nlut_adj*fperiod
gen npremens15_adj = npremens_adj*fperiod

label variable nmenst15_adj "Menst. Ph. x Periods 15-30"
label variable nfolic15_adj "Foll. Ph. x Periods 15-30"
label variable nperi15_adj "P.-ovul. Ph. x Periods 15-30"
label variable nlut15_adj "Lut. Ph. x Periods 15-30"
label variable npremens15_adj "Premens. Ph. x Periods 15-30"

gen nmenst15_fol = nmenst_fol*fperiod
gen nfolic15_fol = nfolic_fol*fperiod
gen nperi15_fol = nperi_fol*fperiod
gen nlut15_fol = nlut_fol*fperiod
gen npremens15_fol = npremens_fol*fperio

gen pill15 = pill*fperiod
label variable pill15 "Contracept. x Periods 15-30"

gen female15 = female*fperiod

* interaction of menstrual phases with last 15 auction periods
gen nmenst30 = nmenst*lperiod
gen nfolic30 = nfolic*lperiod
gen nperi30 = nperi*lperiod
gen nlut30 = nlut*lperiod
gen npremens30 = npremens*lperiod

gen nmenst30_adj = nmenst_adj*lperiod
gen nfolic30_adj = nfolic_adj*lperiod
gen nperi30_adj = nperi_adj*lperiod
gen nlut30_adj = nlut_adj*lperiod
gen npremens30_adj = npremens_adj*lperiod

gen nmenst30_fol = nmenst_fol*lperiod
gen nfolic30_fol = nfolic_fol*lperiod
gen nperi30_fol = nperi_fol*lperiod
gen nlut30_fol = nlut_fol*lperiod
gen npremens30_fol = npremens_fol*lperio

gen pill30 = pill*lperiod

* effect on late bids
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities female15 female if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst15 nfolic15 nperi15 nlut15 npremens15 pill15 nmenst nfolic nperi nlut npremens pill if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst15_adj nfolic15_adj nperi15_adj nlut15_adj npremens15_adj pill15 nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst15_fol nfolic15_fol nperi15_fol nlut15_fol npremens15_fol pill15 nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)


* Control for current PMS
******************************
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill pms if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill pms if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill pms if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)

reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill pms if period == 30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill pms if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill pms if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)


* Robustness to inclusion of dr
*********************************************************
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill dr if period > 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill dr if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill dr if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)

reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill dr if period == 30, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill dr if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill dr if period == 30 & durationmissing == 0 & longmens == 0, robust cluster(session)


* Robustness to inclusion of risk variables
*******************************************************
reg riskgain age siblings asian otherethn math sciothersci econ socsci humanities female if period == 30 & rgconsistent == 1, robust cluster(session)
reg riskgain age siblings asian otherethn math sciothersci econ socsci humanities female pill if period == 30 & rgconsistent == 1, robust cluster(session)
reg riskgain age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period == 30 & rgconsistent == 1, robust cluster(session)
reg riskgain age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period == 30 & rgconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg riskgain age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period == 30 & rgconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)

reg riskloss age siblings asian otherethn math sciothersci econ socsci humanities female if period == 30 & rlconsistent == 1, robust cluster(session)
reg riskloss age siblings asian otherethn math sciothersci econ socsci humanities female pill if period == 30 & rlconsistent == 1, robust cluster(session)
reg riskloss age siblings asian otherethn math sciothersci econ socsci humanities nmenst nfolic nperi nlut npremens pill if period == 30 & rlconsistent == 1, robust cluster(session)
reg riskloss age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period == 30 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg riskloss age siblings asian otherethn math sciothersci econ socsci humanities nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period == 30 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)

reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst nfolic nperi nlut npremens pill if period > 0 & rgconsistent == 1 & rlconsistent == 1, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & rgconsistent == 1 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period > 0 & rgconsistent == 1 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)

reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst nfolic nperi nlut npremens pill if period == 30 & rgconsistent == 1 & rlconsistent == 1, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period == 30 & rgconsistent == 1 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)
reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities riskgain riskloss rgxincon rlxincon nmenst_fol nfolic_fol nperi_fol nlut_fol npremens_fol pill if period == 30 & rgconsistent == 1 & rlconsistent == 1 & durationmissing == 0 & longmens == 0, robust cluster(session)


* In Latex (Tabel 5)
*********************

eststo bid05: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & markup <= 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
eststo bid06: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst15_adj nfolic15_adj nperi15_adj nlut15_adj npremens15_adj pill15 nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
eststo bid07: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill pms if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)
eststo bid08: qui reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill dr if period > 0 & durationmissing == 0 & longmens == 0, robust cluster(session)

estout bid05 bid06 bid07 bid08 using "C:\Users\bcschipper\burkhard\research\mensdr\further.tex", replace  ///
cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(N r2, fmt(%9.0g %9.4f) labels("\textit{Number of Observations}" "R\textsuperscript{2}") layout("\multicolumn{1}{c}{\quad@}")) label ///
starlevels(\text{*} 0.10 \text{**} 0.05 \text{***} 0.01) keep(nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill nmenst15_adj nfolic15_adj nperi15_adj nlut15_adj npremens15_adj pill15 pms dr) ///
mlabels((bids5) (bids6) (bids7) (bids8), prefix(\multicolumn{1}{c}{) suffix(})) order(nmenst_adj nfolic_adj nperi_adj nlut_adj npremens_adj pill nmenst15_adj nfolic15_adj nperi15_adj nlut15_adj npremens15_adj pill15 pms dr) ///
style(tex) collabels(,none) prehead("\begin{tabular}{l*{@span}{d}}\toprule") /// 
posthead(\midrule) prefoot(\bottomrule) postfoot("\addlinespace[.2cm]\multicolumn{5}{l}{Standard errors (clustered at the session level) in parentheses. Significance levels: *10\%; ** 5\%; *** 1\%.} \\" ///
"\multicolumn{5}{l}{We suppress from the report coefficients for age, num. of siblings, asian, other, mathematics, science \& engin.,} \\" /// 
"\multicolumn{5}{l}{economics, social science, humanities, the dummy for the 2010 sample as well as the cubic polynomial in value} \\" /// 
"\multicolumn{5}{l}{and period indicators.}" "\end{tabular}") notype


log close


./.
**************************************************
**************************************************
* Random Assignment and count of false positives
***** run separately **************
**************************************************
**************************************************

clear all
set more off
cap log close
set mem 50m
set seed 101011
cd "C:\Users\bcschipper\burkhard\research\hormone\stats\"
tempfile temp


log using mens_robust_2.txt, text replace
use hormone_data_20110728.dta, clear

*--------+---------+---------+---------+---------+---------+---------+---------+
local R = 1000 // CHANGE THIS LINE TO SET THE NUMBER OF REPLICATIONS
*--------+---------+---------+---------+---------+---------+---------+---------+

* Regression on bids
*************************************
* Run CKO 2005 type regressions on bids with standardized natural mens

preserve
keep id gender
duplicates drop

replace gender = lower(gender)
gen female = (gender=="female")
qui sum if female == 1
local N = r(N)

*frequencies like for uniformaly adjusted phases
local m_cut = int(.17*`N')
local f_cut = int(.33*`N')
local po_cut = int(.40*`N')
local l_cut = int(.64*`N')
local pm_cut = int(.81*`N')
local pi_cut = int(`N')

gen m_fals = 0
gen f_fals = 0
gen po_fals = 0
gen l_fals = 0
gen pm_fals = 0
gen pi_fals = 0

gen double random = runiform()
gsort -female random

foreach v in m f po l pm pi {
replace `v'_fals = 1 in 1/``v'_cut'	
}

drop random

save `temp', replace
restore

merge m:1 id using `temp', nogen	
	
preserve
reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities m_fals f_fals po_fals l_fals pm_fals pi_fals if period > 0, robust cluster(session)
parmest, norestore
keep if parm == "m_fals" | parm == "f_fals" | parm == "po_fals" | parm == "l_fals" | parm== "pm_fals" | parm == "pi_fals"
keep estimate stderr t p parm
gen n = 1
reshape wide estimate stderr t p, i(n) j(parm) string
save _fals_robust_1, replace
restore

drop m_fals f_fals po_fals l_fals pm_fals pi_fals



forval n=2/`R' {
	preserve
	keep id gender
	duplicates drop
	gen _fals = 0
	replace gender = lower(gender)
	gen female = (gender=="female")
	qui sum if female == 1
	local N = r(N)

	local m_cut = int(.17*`N')
	local f_cut = int(.33*`N')
	local po_cut = int(.40*`N')
	local l_cut = int(.64*`N')
	local pm_cut = int(.81*`N')
	local pi_cut = int(`N')

	gen m_fals = 0
	gen f_fals = 0
	gen po_fals = 0
	gen l_fals = 0
	gen pm_fals = 0
	gen pi_fals = 0

	gen double random = runiform()
	gsort -female random
	
	foreach v in m f po l pm pi {
		replace `v'_fals = 1 in 1/``v'_cut'	
	}
	drop random
	save `temp', replace
	restore
	
	merge m:1 id using `temp', nogen	
	
	preserve
		reg b v v2 v3 pdum* new age siblings asian otherethn math sciothersci econ socsci humanities m_fals f_fals po_fals l_fals pm_fals pi_fals if period > 0, robust cluster(session)
		parmest, norestore
		keep if parm == "m_fals" | parm == "f_fals" | parm == "po_fals" | parm == "l_fals" | parm== "pm_fals" | parm == "pi_fals"
		keep estimate stderr t p parm
		gen n = `n'
		reshape wide estimate stderr t p, i(n) j(parm) string
		append using _fals_robust_1
		save _fals_robust_1, replace
	restore
	drop m_fals f_fals po_fals l_fals pm_fals pi_fals
}



* REGRESSIONS ON PROFITS
	*************************************
use hormone_data_20110728.dta, clear

preserve
keep id gender
duplicates drop
gen _fals = 0
replace gender = lower(gender)
gen female = (gender=="female")
qui sum if female == 1
local N = r(N)

local m_cut = int(.17*`N')
local f_cut = int(.33*`N')
local po_cut = int(.40*`N')
local l_cut = int(.64*`N')
local pm_cut = int(.81*`N')
local pi_cut = int(`N')

gen m_fals = 0
gen f_fals = 0
gen po_fals = 0
gen l_fals = 0
gen pm_fals = 0
gen pi_fals = 0

gen double random = runiform()
gsort -female random

foreach v in m f po l pm pi {
replace `v'_fals = 1 in 1/``v'_cut'	
}

drop random
save `temp', replace
restore

merge m:1 id using `temp', nogen	

preserve
	reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities m_fals f_fals po_fals l_fals pm_fals pi_fals if period==30, robust cluster(session)
	parmest, norestore	
	keep if parm == "m_fals" | parm == "f_fals" | parm == "po_fals" | parm == "l_fals" | parm== "pm_fals" | parm == "pi_fals"
	keep estimate stderr t p parm
	gen n = 1
	reshape wide estimate stderr t p, i(n) j(parm) string
	save _fals_robust_2, replace
restore
drop m_fals f_fals po_fals l_fals pm_fals pi_fals



	forval n=2/`R' {
		preserve
		keep id gender
		duplicates drop
		gen _fals = 0
		replace gender = lower(gender)
		gen female = (gender=="female")
		qui sum if female == 1
		local N = r(N)

		local m_cut = int(.17*`N')
		local f_cut = int(.33*`N')
		local po_cut = int(.40*`N')
		local l_cut = int(.64*`N')
		local pm_cut = int(.81*`N')
		local pi_cut = int(`N')

		gen m_fals = 0
		gen f_fals = 0
		gen po_fals = 0
		gen l_fals = 0
		gen pm_fals = 0
		gen pi_fals = 0

		gen double random = runiform()
		gsort -female random

		foreach v in m f po l pm pi {
			replace `v'_fals = 1 in 1/``v'_cut'	
		}

		drop random
		save `temp', replace
		restore
		
		merge m:1 id using `temp', nogen	

		preserve
			reg dollarprofit meanv sdv skewv new age siblings asian otherethn math sciothersci econ socsci humanities m_fals f_fals po_fals l_fals pm_fals pi_fals if period==30, robust cluster(session)
			parmest, norestore
			keep if parm == "m_fals" | parm == "f_fals" | parm == "po_fals" | parm == "l_fals" | parm== "pm_fals" | parm == "pi_fals"
			keep estimate stderr t p parm
			gen n = `n'
			reshape wide estimate stderr t p, i(n) j(parm) string
			append using _fals_robust_2
			save _fals_robust_2, replace
		restore
		drop m_fals f_fals po_fals l_fals pm_fals pi_fals
	}
	
*--------+---------+---------+---------+---------+---------+---------+---------+
* How many of the replications have a significant coefficient?
*--------+---------+---------+---------+---------+---------+---------+---------+
use _fals_robust_1.dta, clear
*count if tm_fals > 1.96 & tpm_fals > 1.96 & tpi_fals > 1.96 & tf_fals <= 1.96 & tpo_fals <= 1.96 & tl_fals <= 1.96
count if tm_fals > 1.96 & tpo_fals <= 1.96


use _fals_robust_2.dta, clear
*count if tm_fals < -1.96 & tpm_fals < -1.96 & tpi_fals >= -1.96 & tf_fals >= -1.96 & tpo_fals >= -1.96 & tl_fals >= -1.96
count if tm_fals < -1.96 & tpo_fals >= -1.96