-----------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/log/mturkAnalysis.txt
  log type:  text
 opened on:  29 Mar 2019, 14:48:56

. 
. 
. foreach ado in estout spmap mixlogit {
  2.      ssc install `ado', replace
  3. }
checking estout consistency and verifying not already installed...
all files already exist and are up to date.
checking spmap consistency and verifying not already installed...
all files already exist and are up to date.
checking mixlogit consistency and verifying not already installed...
all files already exist and are up to date.

. 
. *-------------------------------------------------------------------------------
. *--- (1) Append and Generate Variables
. *-------------------------------------------------------------------------------
. insheet using "$DAT/mturk_ANONYMYSED_JITTERED.csv", clear names comma case
(50 vars, 51,254 obs)

. saveold "$DAT/mturk_ANONYMYSED_JITTERED.dta", replace version(11)
(saving in Stata 12 format, which Stata 11 can read)
file C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/data/MTurk/mturk_ANONYMYSED_JIT
> TERED.dta saved

. 
. *DOB and BWT replace missings as separate indicator
. replace dob        ="missing" if dob        ==""
(25,718 real changes made)

. replace birthweight="missing" if birthweight==""
(25,536 real changes made)

. replace birthweight_p=5 if birthweight_p==.
(25,536 real changes made)

. replace dob_position =5 if dob_position ==.
(25,718 real changes made)

. 
. tab gender     , gen(_gend)

     gender |      Freq.     Percent        Cum.
------------+-----------------------------------
        Boy |     25,485       49.72       49.72
       Girl |     25,769       50.28      100.00
------------+-----------------------------------
      Total |     51,254      100.00

. tab cost       , gen(_cost)

       cost |      Freq.     Percent        Cum.
------------+-----------------------------------
     $1,000 |      5,233       10.21       10.21
    $10,000 |      5,217       10.18       20.39
     $2,000 |      5,148       10.04       30.43
       $250 |      5,139       10.03       40.46
     $3,000 |      5,050        9.85       50.31
     $4,000 |      5,058        9.87       60.18
     $5,000 |      5,117        9.98       70.16
     $6,000 |      5,124       10.00       80.16
     $7,500 |      5,141       10.03       90.19
       $750 |      5,027        9.81      100.00
------------+-----------------------------------
      Total |     51,254      100.00

. tab birthweight, gen(_bwt)

       birthweight |      Freq.     Percent        Cum.
-------------------+-----------------------------------
5 pounds 13 ounces |      2,361        4.61        4.61
 5 pounds 8 ounces |      2,296        4.48        9.09
6 pounds 13 ounces |      2,385        4.65       13.74
 6 pounds 3 ounces |      2,304        4.50       18.23
 6 pounds 8 ounces |      2,238        4.37       22.60
7 pounds 13 ounces |      2,326        4.54       27.14
 7 pounds 3 ounces |      2,323        4.53       31.67
 7 pounds 8 ounces |      2,326        4.54       36.21
8 pounds 13 ounces |      2,433        4.75       40.96
 8 pounds 3 ounces |      2,373        4.63       45.59
 8 pounds 8 ounces |      2,353        4.59       50.18
           missing |     25,536       49.82      100.00
-------------------+-----------------------------------
             Total |     51,254      100.00

. tab sob        , gen(_sob)

        sob |      Freq.     Percent        Cum.
------------+-----------------------------------
       Fall |     12,885       25.14       25.14
     Spring |     12,661       24.70       49.84
     Summer |     12,787       24.95       74.79
     Winter |     12,921       25.21      100.00
------------+-----------------------------------
      Total |     51,254      100.00

. tab dob        , gen(_dob)

        dob |      Freq.     Percent        Cum.
------------+-----------------------------------
    Weekday |     12,697       24.77       24.77
    Weekend |     12,839       25.05       49.82
    missing |     25,718       50.18      100.00
------------+-----------------------------------
      Total |     51,254      100.00

. 
. drop _gend1 _cost5 _bwt2 _sob4 _dob1

. rename _cost1 _costx

. rename _cost4 _cost1

. rename _cost3 _cost4

. rename _costx _cost3

. rename _cost2 _cost11

. rename _cost10 _cost2

. rename _cost11 _cost10

. rename _bwt1 _bwt2

. rename _bwt4 _bwtx

. rename _bwt5 _bwt4

. rename _bwt3 _bwt5

. rename _bwtx _bwt3

. rename _bwt6 _bwtx

. rename _bwt7 _bwt6

. rename _bwt8 _bwt7

. rename _bwtx _bwt8

. rename _bwt9 _bwtx

. rename _bwt10 _bwt9

. rename _bwt11 _bwt10

. rename _bwtx _bwt11

. rename _sob1 _sob4

. gen goodSeason=_sob2==1|_sob3==1

. gen     costNumerical = subinstr(cost,"$","",1)

. replace costNumerical = subinstr(costNumerical,",","",1)
(41,088 real changes made)

. destring costNumerical, replace
costNumerical: all characters numeric; replaced as int

. replace costNumerical = costNumerical/1000
variable costNumerical was int now float
(51,254 real changes made)

. gen spring = _sob2

. gen summer = _sob3

. gen all = 1

. lab var age "Age"

. lab var age2 "Age Squared"

. lab var someCollege "Some College +"

. lab var hispanic "Hispanic"

. lab var teacher "Teacher"

. lab var parent  "Parent"

. lab var _bwt2  "5lbs, 13oz"

. lab var _bwt3  "6lbs, 3oz"

. lab var _bwt4  "6lbs, 8oz"

. lab var _bwt5  "6lbs, 13oz"

. lab var _bwt6  "7lbs, 3oz"

. lab var _bwt7  "7lbs, 8oz"

. lab var _bwt8  "7lbs, 13oz"

. lab var _bwt9  "8lbs, 3oz"

. lab var _bwt10 "8lbs, 8oz"

. lab var _bwt11 "8lbs, 13oz"

. lab var _dob2  "Weekend Day"

. lab var _gend2 "Girl"

. 
. local oFEs i.round i.option i.surveyV

. 
. bys ID: gen N=_n

. 
. bys RespState: gen statePop = _N

. count
  51,254

. gen surveyProportion = statePop/r(N)

. gen censusProportion = .
(51,254 missing values generated)

. tokenize `sprop'

. local total = 0

. foreach state of local snames {
  2.     dis "State: `state', pop: `1'"
  3.     qui replace censusProportion = `1' if RespState=="`state'"
  4.     local total = `total'+`1'
  5.     macro shift
  6. }
State: Alabama, pop: 151
State: Alaska, pop: 23
State: Arizona, pop: 212
State: Arkansas, pop: 93
State: California, pop: 1218
State: Colorado, pop: 170
State: Connecticut, pop: 112
State: Delaware, pop: 29
State: District of Columbia, pop: 21
State: Florida, pop: 631
State: Georgia, pop: 318
State: Idaho, pop: 51
State: Illinois, pop: 400
State: Indiana, pop: 206
State: Iowa, pop: 97
State: Kansas, pop: 91
State: Kentucky, pop: 138
State: Louisiana, pop: 145
State: Maine, pop: 41
State: Maryland, pop: 187
State: Massachusetts, pop: 211
State: Michigan, pop: 309
State: Minnesota, pop: 171
State: Mississippi, pop: 93
State: Missouri, pop: 189
State: Montana, pop: 32
State: Nebraska, pop: 59
State: Nevada, pop: 90
State: New Hampshire, pop: 41
State: New Jersey, pop: 279
State: New Mexico, pop: 65
State: New York, pop: 616
State: North Carolina, pop: 312
State: North Dakota, pop: 24
State: Ohio, pop: 361
State: Oklahoma, pop: 122
State: Oregon, pop: 125
State: Pennsylvania, pop: 398
State: Rhode Island, pop: 33
State: South Carolina, pop: 152
State: South Dakota, pop: 27
State: Tennessee, pop: 205
State: Texas, pop: 855
State: Utah, pop: 93
State: Virginia, pop: 261
State: Washington, pop: 223
State: West Virginia, pop: 57
State: Wisconsin, pop: 180
State: Hawaii, pop: 45
State: Vermont, pop: 19
State: Wyoming, pop: 18

. dis `total'
9999

. replace censusProportion = censusProportion/10000
(51,254 real changes made)

. gen weight = surveyProportion/censusProportion

. replace weight=1/weight
(51,254 real changes made)

. 
. *-------------------------------------------------------------------------------
. *-- (2) Summary Statistics [TABLE A18 AND FIGURE A7]
. *-------------------------------------------------------------------------------
. preserve

. keep if N==1
(47,593 observations deleted)

. 
. lab var sex       "Female"

. lab var birthyr   "Year of Birth"

. lab var age       "Age"

. lab var educY     "Years of Education"

. lab var nchild    "Number of Children"

. lab var pregnant1 "Currently Pregnant"

. lab var married   "Married"

. lab var black     "Black"

. lab var white     "White"

. lab var otherRac  "Other Race (Asian/Native American)"

. lab var hispanic  "Hispanic"

. lab var employed  "Employed"

. lab var unemploy  "Unemployed"

. lab var highEduc  "Some College +"

. lab var parent    "Parent"

. lab var teacher   "Education, Training, and Library occupation"

. lab var ftotinc   "Total Family Income (1000s)"

. lab var mturkSal  "Hourly earnings on MTurk"

. #delimit ;
delimiter now ;
. estpost sum sex age black white otherRace hispanic married highEduc educY
> employed ftotinc teacher parent nchild mturkSal;

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         sex |      3661       3661   .5348266   .2488551   .4988538          0          1       1958 
         age |      3661       3661   36.74051   132.1884   11.49732         18         87     134507 
       black |      3661       3661   .0830374    .076163   .2759765          0          1        304 
       white |      3661       3661   .8426659   .1326163   .3641652          0          1       3085 
   otherRace |      3661       3661   .0742966   .0687954   .2622889          0          1        272 
    hispanic |      3661       3661   .0576345   .0543276   .2330829          0          1        211 
     married |      3661       3661   .4676318   .2490203   .4990194          0          1       1712 
    highEduc |      3661       3661   .8852772   .1015892   .3187306          0          1       3241 
       educY |      3661       3661   14.70527   3.089888   1.757808          8         17      53836 
    employed |      3661       3661   .7375034    .193645   .4400512          0          1       2700 
     ftotinc |      3661       3661   59.90303   1552.593   39.40296          5        175     219305 
     teacher |      3661       3661   .1098061   .0977754   .3126906          0          1        402 
      parent |      3661       3661   .5015023    .250066    .500066          0          1       1836 
      nchild |      3661       3661   1.023491   1.606552   1.267498          0          6       3747 
    mturkSal |      3661       3661    4.41068   8.033823     2.8344        1.5       11.5    16147.5 

. estout using "$OUT/MTurkSum.tex", replace label style(tex)
> cells("count(fmt(%7.0gc) label(N)) mean(fmt(2) label(Mean))
> sd(fmt(2) label(Std.\ Dev.)) min(fmt(2) label(Min)) max(fmt(2) label(Max))");
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/MTu
> rkSum.tex)

. #delimit cr
delimiter now cr
. restore

. 
. 
. gen statename=RespState

. count
  51,254

. bys statename: gen stateProportion = _N/r(N)

. preserve

. collapse stateProportion, by(statename)

. rename statename NAME

. merge 1:1 NAME using "$GEO/US_db"

    Result                           # of obs.
    -----------------------------------------
    not matched                             1
        from master                         0  (_merge==1)
        from using                          1  (_merge==2)

    matched                                51  (_merge==3)
    -----------------------------------------

. format stateProportion %5.3f

. #delimit ;
delimiter now ;
. spmap stateProportion if NAME!="Alaska"&NAME!="Hawaii"&NAME!="Puerto Rico"
> using "$GEO/US_coord_mercator",
> point(data($DAT/mturk_ANONYMYSED_JITTERED) xcoord(long3) ycoord(lat3)
>       select(drop if (latitude<24.39|latitude>49.38)|(longitude<-124.84|longitude>-66.9))
>       size(*0.5) fcolor(red))
> id(_ID) osize(thin) legtitle("Proportion of Respondents") legstyle(2) fcolor(Greens)
> legend(symy(*1.2) symx(*1.2) size(*1.4) rowgap(1));

. graph export "$OUT/surveyCoverage.eps", as(eps) replace;
(file C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/surveyCoverage.e
> ps written in EPS format)

. #delimit cr
delimiter now cr
. restore

. 
. preserve

. insheet using "$GEO/population2015.csv", delim(";") names clear
(3 vars, 51 obs)

. replace state=subinstr(state,".","",1)
(51 real changes made)

. rename state NAME

. merge 1:1 NAME using "$GEO/US_db"

    Result                           # of obs.
    -----------------------------------------
    not matched                             1
        from master                         0  (_merge==1)
        from using                          1  (_merge==2)

    matched                                51  (_merge==3)
    -----------------------------------------

. format proportion %5.3f

. #delimit ;
delimiter now ;
. spmap proportion if NAME!="Alaska"&NAME!="Hawaii"&NAME!="Puerto Rico"
> using "$GEO/US_coord_mercator", id(_ID) osize(thin)
> legtitle("Proportion of Respondents (Census)") legstyle(2) fcolor(Greens)
> legend(symy(*1.2) symx(*1.2) size(*1.4) rowgap(1));

. graph export "$OUT/usaCoverage.eps", as(eps) replace;
(file C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/usaCoverage.eps 
> written in EPS format)

. #delimit cr
delimiter now cr
. restore

. 
. 
. replace age    = childBYear-RespYOB if parent==1
(25,228 real changes made, 56 to missing)

. gen osample = RespSex=="Female"&married==1&parent==1&age>=20&age<=45

. 
. *-------------------------------------------------------------------------------
. *--- (3A) Comparison with NVSS [TABLE A19: PANEL A]
. *-------------------------------------------------------------------------------
. preserve

. gen ageBirth=age
(56 missing values generated)

. gen race=11 if white==1
(8,064 missing values generated)

. gen marst=married

. gen cbirthmonth     = 1  if RespKidBMonth=="January"
(49,602 missing values generated)

. replace cbirthmonth = 2  if RespKidBMonth=="February"
(2,170 real changes made)

. replace cbirthmonth = 3  if RespKidBMonth=="March"
(2,618 real changes made)

. replace cbirthmonth = 4  if RespKidBMonth=="April"
(2,030 real changes made)

. replace cbirthmonth = 5  if RespKidBMonth=="May"
(2,562 real changes made)

. replace cbirthmonth = 6  if RespKidBMonth=="June"
(2,310 real changes made)

. replace cbirthmonth = 7  if RespKidBMonth=="July"
(2,184 real changes made)

. replace cbirthmonth = 8  if RespKidBMonth=="August"
(2,394 real changes made)

. replace cbirthmonth = 9  if RespKidBMonth=="September"
(2,198 real changes made)

. replace cbirthmonth = 10 if RespKidBMonth=="October"
(1,750 real changes made)

. replace cbirthmonth = 11 if RespKidBMonth=="November"
(2,072 real changes made)

. replace cbirthmonth = 12 if RespKidBMonth=="December"
(1,932 real changes made)

. keep  if ageBirth>=25&ageBirth<=45&sex==1
(35,042 observations deleted)

. keep if nchild!=0
(7,224 observations deleted)

. keep if N==1
(8,346 observations deleted)

. gen Q1 = cbirthmonth >= 1 & cbirthmonth <=3

. gen Q2 = cbirthmonth >= 4 & cbirthmonth <=6

. gen Q3 = cbirthmonth >= 7 & cbirthmonth <=9

. gen Q4 = cbirthmonth >=10 & cbirthmonth <=12

. gen     sexchild = 1 if RespKidGender=="Girl"
(346 missing values generated)

. replace sexchild = 0 if RespKidGender=="Boy"
(346 real changes made)

. #delimit ;
delimiter now ;
. collapse (sum) N (mean) nchild sexchild Q1 Q2 Q3 Q4 ageBirth hispanic
>   black white otherRace highEduc married (sd) sd_nchild=nchild sd_sexchild=sexchild
>   sd_Q1=Q1 sd_Q2=Q2 sd_Q3=Q3 sd_Q4=Q4 sd_ageBirth=ageBirth sd_black=black
>   sd_hispanic=hispanic sd_white=white sd_otherRace=otherRace sd_highEduc=highEduc
>   sd_married=married;

. #delimit cr
delimiter now cr
. 
. expand 13
(12 observations created)

. gen mean  = .
(13 missing values generated)

. gen stdev = .
(13 missing values generated)

. gen var   = ""
(13 missing values generated)

. local i = 1

. #delimit ;
delimiter now ;
. local comvars nchild sexchild Q1 Q2 Q3 Q4 ageBirth black white
>               otherRace hispanic highEduc married;

. #delimit cr
delimiter now cr
. foreach var of varlist `comvars' {
  2.     replace mean  = `var' in `i'
  3.     replace stdev = sd_`var' in `i'
  4.     replace var = "`var'" in `i'
  5.     local ++i
  6. }
(1 real change made)
(1 real change made)
variable var was str1 now str6
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str6 now str8
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str8 now str9
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. gen data = "MTurk"

. keep mean stdev var data N

. tempfile MTurkSum

. save `MTurkSum'
file C:\Users\cq224\AppData\Local\Temp\ST_4774_000005.tmp saved

. restore

. preserve

. insheet using "$NVS/natl2013.csv", comma names clear
(8 vars, 3,940,764 obs)

. gen oneChild = lbo_rec==1

. sum oneChild

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    oneChild |  3,940,764    .3933537    .4884942          0          1

. 
. gen N_NV = 1

. gen nchild = lbo_rec

. replace nchild = 6 if nchild>6&nchild<20
(59,565 real changes made)

. replace nchild = . if nchild>=20
(0 real changes made)

. gen sexchild = sex=="F"

. gen Q1 = dob_mm >= 1 & dob_mm <=3

. gen Q2 = dob_mm >= 4 & dob_mm <=6

. gen Q3 = dob_mm >= 7 & dob_mm <=9

. gen Q4 = dob_mm >=10 & dob_mm <=12

. gen ageBirth = mager

. gen highEduc = meduc>=4 if meduc!=9&meduc!=.
(418,167 missing values generated)

. gen hispanic = umhisp!=0

. gen black    = mracerec==2

. gen white    = mracerec==1

. gen otherRace= mracerec==3|mracerec==4

. gen married  = mar==1

. #delimit ;
delimiter now ;
. collapse (sum) N (mean) nchild sexchild Q1 Q2 Q3 Q4 ageBirth highEduc hispanic
>   black white otherRace married (sd) sd_nchild=nchild sd_sexchild=sexchild sd_Q1=Q1
>   sd_Q2=Q2 sd_Q3=Q3 sd_Q4=Q4 sd_ageBirth=ageBirth sd_hispanic=hispanic
>   sd_black=black sd_white=white sd_otherRace=otherRace sd_highEduc=highEduc
>   sd_married=married;

. #delimit cr
delimiter now cr
. expand 13
(12 observations created)

. gen meanNV  = .
(13 missing values generated)

. gen stdevNV = .
(13 missing values generated)

. gen var   = ""
(13 missing values generated)

. local i = 1

. foreach var of varlist `comvars' {
  2.     replace meanNV  = `var' in `i'
  3.     replace stdevNV = sd_`var' in `i'
  4.     replace var = "`var'" in `i'
  5.     local ++i
  6. }
(1 real change made)
(1 real change made)
variable var was str1 now str6
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str6 now str8
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str8 now str9
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. keep meanNV stdevNV var N_NV

. tempfile NVSSSum

. save `NVSSSum'
file C:\Users\cq224\AppData\Local\Temp\ST_4774_000007.tmp saved

. merge 1:1 var using `MTurkSum'

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                                13  (_merge==3)
    -----------------------------------------

. local i = 1

. #delimit ;
delimiter now ;
. local vnames `""Number of Children" "Age at First Birth" "Female Child"
>                "Some College +" "Born January-March" "Born April-June"
>                "Born July-September" "Born October-December"
>                "Black" "White" "Other Race" "Hispanic" "Married" "';

. local variables nchild ageBirth sexchild highEduc Q1 Q2 Q3 Q4
>                 black white otherRace hispanic married;

. #delimit cr
delimiter now cr
. 
. tokenize `variables'

. file open bstats using "$OUT/NVSScomp.txt", write replace

. foreach var of local vnames {
  2.     foreach stat in N mean stdev N_NV meanNV stdevNV {
  3.         qui sum `stat' if var=="`1'"
  4.         local val`stat'=r(mean)
  5.     }
  6.     qui ttesti `valN' `valmean' `valstdev' `valN_NV' `valmeanNV' `valstdevNV'
  7.     foreach val in mu_1 sd_1 mu_2 sd_2 t {
  8.         local `val'=string(r(`val'), "%5.3f")
  9.         *local `val'=round(r(`val')*1000)/1000
.         *if ``val''<1&``val''>0 local `val' = "0``val''"
.     }
 10.     local dif = string((`mu_1'-`mu_2'),"%5.3f")
 11.     *if `dif'<1&`dif'>0 local dif = "0`dif'"
.     file write bstats "`var'&`mu_1'&(`sd_1')&`mu_2'&(`sd_2')&`dif'&`t'\\ " _n
 12.     macro shift
 13. }

. file close bstats

. restore

. 
. 
. *-------------------------------------------------------------------------------
. *--- (3B) Comparison with ACS [TABLE A19: PANEL B]
. *-------------------------------------------------------------------------------
. preserve

. keep if N==1
(47,593 observations deleted)

. keep if RespSex=="Female" &age>=20&age<=45
(1,982 observations deleted)

. gen Q1=RespMOB=="January"|RespMOB=="February"|RespMOB=="March"     if RespMOB!=""
(888 missing values generated)

. gen Q2=RespMOB=="April"  |RespMOB=="May"     |RespMOB=="June"      if RespMOB!=""
(888 missing values generated)

. gen Q3=RespMOB=="July"   |RespMOB=="August"  |RespMOB=="September" if RespMOB!=""
(888 missing values generated)

. gen Q4=RespMOB=="October"|RespMOB=="November"|RespMOB=="December"  if RespMOB!=""
(888 missing values generated)

. #delimit ;
delimiter now ;
. collapse (sum) N (mean) ftotinc highEduc someCollege married employed hispanic
> black white otherRace age educY teacher Q1 Q2 Q3 Q4 parent
>   (sd) sd_ftotinc=ftotinc
>   sd_highEduc=highEduc sd_someCollege=someCollege sd_married=married
>   sd_employed=employed sd_hispanic=hispanic sd_black=black sd_white=white
>   sd_otherRace=otherRace sd_age=age sd_educY=educY sd_teacher=teacher
>   sd_Q1=Q1 sd_Q2=Q2 sd_Q3=Q3 sd_Q4=Q4 sd_parent=parent;

. #delimit cr
delimiter now cr
. expand 17
(16 observations created)

. gen mean  = .
(17 missing values generated)

. gen stdev = .
(17 missing values generated)

. gen var   = ""
(17 missing values generated)

. local i = 1

. #delimit ;
delimiter now ;
. local comvars ftotinc highEduc someCollege married employed hispanic black white
>               otherRace age educY teacher Q1 Q2 Q3 Q4 parent;

. #delimit cr
delimiter now cr
. foreach var of varlist `comvars' {
  2.     replace mean  = `var' in `i'
  3.     replace stdev = sd_`var' in `i'
  4.     replace var = "`var'" in `i'
  5.     local ++i
  6. }
(1 real change made)
(1 real change made)
variable var was str1 now str7
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str7 now str8
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str8 now str11
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. gen data = "MTurk"

. keep mean stdev var data N

. tempfile MTurkSum2

. save `MTurkSum2'
file C:\Users\cq224\AppData\Local\Temp\ST_4774_000009.tmp saved

. restore

. 
. preserve

. insheet using "$ACS/ACS2014_20-45yrs.csv", clear names comma case
(13 vars, 346,353 obs)

. gen oneChild=nchild==1

. 
. gen parent = nchild!=0

. keep if race<7
(24,353 observations deleted)

. drop if occ2010 == 9920
(38,379 observations deleted)

. gen N_ACS = 1

. replace ftotinc = ftotinc/1000
variable ftotinc was long now double
(280,051 real changes made)

. gen educY     = 0  if educ==0
(282,167 missing values generated)

. replace educY = 4  if educ==1
(497 real changes made)

. replace educY = 8  if educ==2
(2,709 real changes made)

. replace educY = 9  if educ==3
(1,915 real changes made)

. replace educY = 10 if educ==4
(2,303 real changes made)

. replace educY = 11 if educ==5
(3,162 real changes made)

. replace educY = 12 if educ==6
(68,420 real changes made)

. replace educY = 13 if educ==7
(45,946 real changes made)

. replace educY = 14 if educ==8
(32,152 real changes made)

. replace educY = 16 if educ==10
(77,675 real changes made)

. replace educY = 17 if educ==11
(47,388 real changes made)

. gen teacher = twoLevelOcc=="Education, Training, and Library Occupations"

. gen hispanic=hispan!=0

. gen age=motherAge

. gen white = race==1

. gen black = race==2

. gen someCollege = educ>=7

. gen otherRace = race!=1&race!=2

. gen employed  = empstat==1

. gen Q1 = birthQuarter==1

. gen Q2 = birthQuarter==2

. gen Q3 = birthQuarter==3

. gen Q4 = birthQuarter==4

. 
. *rename hispan hispanic
. #delimit ;
delimiter now ;
. collapse (sum) N_ACS (mean) ftotinc highEduc someCollege married employed
>   hispanic black white otherRace age educY teacher Q1 Q2 Q3 Q4 parent (sd)
>   sd_ftotinc=ftotinc sd_highEduc=highEduc sd_someCollege=someCollege
>   sd_married=married sd_employed=employed sd_hispanic=hispanic sd_black=black
>   sd_white=white sd_otherRace=otherRace sd_age=age sd_educY=educY
>   sd_teacher=teacher sd_Q1=Q1 sd_Q2=Q2 sd_Q3=Q3 sd_Q4=Q4 sd_parent=parent;

. #delimit cr
delimiter now cr
. expand 17
(16 observations created)

. gen meanACS  = .
(17 missing values generated)

. gen stdevACS = .
(17 missing values generated)

. gen var      = ""
(17 missing values generated)

. local i = 1

. foreach var of varlist `comvars' {
  2.     replace mean  = `var' in `i'
  3.     replace stdev = sd_`var' in `i'
  4.     replace var = "`var'" in `i'
  5.     local ++i
  6. }
(1 real change made)
(1 real change made)
variable var was str1 now str7
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str7 now str8
(1 real change made)
(1 real change made)
(1 real change made)
variable var was str8 now str11
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

. keep meanACS stdevACS var N_ACS

. tempfile ACSSum

. save `ACSSum'
file C:\Users\cq224\AppData\Local\Temp\ST_4774_00000b.tmp saved

. merge 1:1 var using `MTurkSum2'

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                                17  (_merge==3)
    -----------------------------------------

. keep if _merge==3
(0 observations deleted)

. local i = 1

. #delimit ;
delimiter now ;
. local vnames `" "Family Income (1000s)" "Age" "Some College +"
>                 "Respondent Born January-March" "Respondent Born April-June"
>                 "Respondent Born July-September" "Respondent Born October-December"
>                 "Parent" "Employed" "Education, Training, Library Occ." "Black" "White"
>                 "Other Race" "Hispanic" "Married"  "';

. local variables ftotinc age highEduc Q1 Q2 Q3 Q4 parent employed teacher black white
>                 otherRace hispanic married;

. #delimit cr
delimiter now cr
. tokenize `variables'

. file open mstats using "$OUT/ACScomp.txt", write replace

. foreach var of local vnames {
  2.     dis "`var'"
  3.     foreach stat in N mean stdev N_ACS meanACS stdevACS {
  4.         qui sum `stat' if var=="`1'"
  5.         local val`stat'=r(mean)
  6.     }
  7.     qui ttesti `valN' `valmean' `valstdev' `valN_ACS' `valmeanACS' `valstdevACS'
  8.     foreach val in mu_1 sd_1 mu_2 sd_2 t {
  9.         local `val'=string(r(`val'), "%5.3f")
 10.         *local `val'=round(r(`val')*1000)/1000
.         *if ``val''<1&``val''>0 local `val' = "0``val''"
.     }
 11.     local dif = round((`mu_1'-`mu_2')*1000)/1000
 12.     if `dif'<1&`dif'>0 local dif = "0`dif'"
 13.     if `dif'>-1&`dif'<0  {
 14.         local adif = abs(`dif')
 15.         local dif = "-0`adif'"
 16.     }
 17.     file write mstats "`var'&`mu_1'&(`sd_1')&`mu_2'&(`sd_2')&`dif'&`t'\\ " _n
 18.     macro shift
 19. }
Family Income (1000s)
Age
Some College +
Respondent Born January-March
Respondent Born April-June
Respondent Born July-September
Respondent Born October-December
Parent
Employed
Education, Training, Library Occ.
Black
White
Other Race
Hispanic
Married

. file close mstats

. restore

. 
. *-------------------------------------------------------------------------------
. *-- (4) Main analysis  [TABLE 4, TABLE A20]
. *-------------------------------------------------------------------------------
. ** GENERATE GREEDY LEAMER CRITERION LEVEL
. reg chosen `oFEs' _sob* _cost* _gend* _bwt* _dob*
note: _dob3 omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =    51,254
-------------+----------------------------------   F(33, 51220)    =    392.38
       Model |  2585.65516        33  78.3531868   Prob > F        =    0.0000
    Residual |  10227.8448    51,220  .199684593   R-squared       =    0.2018
-------------+----------------------------------   Adj R-squared   =    0.2013
       Total |     12813.5    51,253  .250004878   Root MSE        =    .44686

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       round |
          2  |  -.0056082   .0073887    -0.76   0.448    -.0200901    .0088737
          3  |  -.0014787   .0073862    -0.20   0.841    -.0159558    .0129984
          4  |   .0040831   .0073864     0.55   0.580    -.0103943    .0185605
          5  |  -.0003154   .0073871    -0.04   0.966    -.0147942    .0141635
          6  |   .0047713   .0073873     0.65   0.518    -.0097079    .0192504
          7  |  -.0023113   .0073871    -0.31   0.754      -.01679    .0121675
             |
    2.option |  -.0004174   .0039483    -0.11   0.916    -.0081561    .0073213
             |
  surveyVers |
       2018  |  -.0020009   .0039489    -0.51   0.612    -.0097408     .005739
             |
       _sob4 |   .0283555   .0055649     5.10   0.000     .0174482    .0392628
       _sob2 |   .0423369   .0055898     7.57   0.000     .0313809    .0532929
       _sob3 |   .0203251   .0055761     3.65   0.000     .0093959    .0312544
      _cost3 |   .1638803   .0088174    18.59   0.000     .1465981    .1811624
     _cost10 |  -.3881118   .0088235   -43.99   0.000    -.4054059   -.3708176
      _cost4 |   .0841262   .0088526     9.50   0.000     .0667751    .1014773
      _cost1 |   .3048679   .0088563    34.42   0.000     .2875094    .3222263
      _cost6 |   -.071239   .0088912    -8.01   0.000    -.0886658   -.0538122
      _cost7 |  -.1357375   .0088662   -15.31   0.000    -.1531153   -.1183597
      _cost8 |  -.2083292   .0088629   -23.51   0.000    -.2257006   -.1909577
      _cost9 |  -.2945065   .0088553   -33.26   0.000     -.311863     -.27715
      _cost2 |   .2432455   .0089053    27.31   0.000      .225791       .2607
      _gend2 |  -.0056681   .0039489    -1.44   0.151    -.0134081    .0020718
       _bwt2 |   .0135717   .0131011     1.04   0.300    -.0121066      .03925
       _bwt5 |   .1255824   .0130694     9.61   0.000     .0999663    .1511985
       _bwt3 |   .1186942   .0131805     9.01   0.000     .0928604    .1445281
       _bwt4 |   .1412556   .0132763    10.64   0.000     .1152339    .1672773
       _bwt8 |   .1486728   .0131502    11.31   0.000     .1228982    .1744474
       _bwt6 |   .1797332   .0131526    13.67   0.000      .153954    .2055124
       _bwt7 |   .1829084   .0131495    13.91   0.000     .1571351    .2086816
      _bwt11 |   .1454404    .013004    11.18   0.000     .1199524    .1709284
       _bwt9 |    .166137   .0130855    12.70   0.000     .1404892    .1917848
      _bwt10 |   .1461791   .0131131    11.15   0.000     .1204772    .1718809
      _bwt12 |   .1186909   .0101368    11.71   0.000     .0988226    .1385592
       _dob2 |   .0065407   .0055938     1.17   0.242    -.0044232    .0175045
       _dob3 |          0  (omitted)
       _cons |   .3891165   .0130035    29.92   0.000     .3636296    .4146034
------------------------------------------------------------------------------

. local tvL  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))

. local pvL  = ttail(e(N),sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)))*2

. dis `pvL'
.00099478

. 
. local nvar1 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9 _bwt10 _bwt11

. order `nvar1'

. local nvar2 _dob2

. cap drop nkids

. gen     nkids = 1 if RespNumKids=="1"
(41,958 missing values generated)

. replace nkids = 2 if RespNumKids!="0"&nkids==.
(16,408 real changes made)

. 
. local ctrl `oFEs' _gend* _bwt* _dob*

. local se cluster(ID)

. foreach spec in main wt {
  2.     if `"`spec'"'=="main" {
  3.         local wnote
  4.         #delimit ;
delimiter now ;
.         local ttitle "Birth Characteristics and Willingness to Pay
>                       for Season of Birth";
  5.         #delimit cr
delimiter now cr
.         local wt
  6.     }
  7.     else if `"`spec'"'=="wt" {
  8.         local wt [pw=weight]
  9.         #delimit ;
delimiter now ;
.         local ttitle "Birth Characteristics and WTP for Season of Birth
>                       (reweighted sample)";
 10.         local wnote "Observations are re-weighted based on Census Bureau
>         state population cells.  Each respondent is weighted such that the
>         frequency of observations by state in MTurk data is identical to
>         that in Census Bureau data.  In practice this is calculated as
>         $ Pwt=(Pr(Census)_s/Pr(MTurk)_s)$ for each state $ s$.";
 11.         #delimit cr
delimiter now cr
.     }    
 12.     eststo: logit chosen spring summer _sob4 costNumerical `ctrl' s2018 `wt', `se'
 13.     local numR = e(N_clust)
 14.     margins, dydx(spring summer costNumerical  _gend2 `nvar1' `nvar2' _sob4) post
 15.     est store n1
 16.     estadd scalar numR  = `numR'
 17.     estadd scalar wtpSp = -1000*_b[spring]/_b[costNumerical]
 18.     estadd scalar wtpSu = -1000*_b[summer]/_b[costNumerical]
 19.     nlcom ratio:_b[spring]/_b[costNumerical], post
 20.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 21.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 22.     estadd local conf95sp "[`ub';`lb']": n1
 23.     est restore n1
 24.     nlcom ratio:_b[summer]/_b[costNumerical], post
 25.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 26.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 27.     estadd local conf95su "[`ub';`lb']": n1
 28. 
.     **NVSS main sample
.     local c osample==1
 29.     eststo: logit chosen spring summer _sob4 costNumerical `ctrl' s2018 `wt' if `c', `se'
 30.     local numR = e(N_clust)
 31.     margins, dydx(spring summer costNumerical  _gend2 `nvar1' `nvar2' _sob4) post
 32.     est store nmain1
 33.     estadd scalar numR  = `numR'
 34.     estadd scalar wtpSp = -1000*_b[spring]/_b[costNumerical]
 35.     estadd scalar wtpSu = -1000*_b[summer]/_b[costNumerical]
 36.     nlcom ratio:_b[spring]/_b[costNumerical], post
 37.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 38.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 39.     estadd local conf95sp "[`ub';`lb']": nmain1
 40.     est restore nmain1
 41.     nlcom ratio:_b[summer]/_b[costNumerical], post
 42.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 43.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 44.     estadd local conf95su "[`ub';`lb']": nmain1
 45. 
.     **1 child sample
.     local c osample==1&nkids==1
 46.     eststo: logit chosen spring summer _sob4 costNumerical `ctrl' s2018 `wt' if `c', `se'
 47.     local numR = e(N_clust)
 48.     margins, dydx(spring summer costNumerical  _gend2 `nvar1' `nvar2' _sob4) post
 49.     est store nkids1
 50.     estadd scalar numR  = `numR'
 51.     estadd scalar wtpSp = -1000*_b[spring]/_b[costNumerical]
 52.     estadd scalar wtpSu = -1000*_b[summer]/_b[costNumerical]
 53.     nlcom ratio:_b[spring]/_b[costNumerical], post
 54.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 55.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 56.     estadd local conf95sp "[`ub';`lb']": nkids1
 57.     est restore nkids1
 58.     nlcom ratio:_b[summer]/_b[costNumerical], post
 59.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 60.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 61.     estadd local conf95su "[`ub';`lb']": nkids1
 62. 
.     **2 child sample
.     local c osample==1&nkids==2
 63.     eststo: logit chosen spring summer _sob4 costNumerical `ctrl' s2018 `wt' if `c', `se'
 64.     local numR = e(N_clust)
 65.     margins, dydx(spring summer costNumerical  _gend2 `nvar1' `nvar2' _sob4) post
 66.     est store nkids2
 67.     estadd scalar numR  = `numR'
 68.     estadd scalar wtpSp = -1000*_b[spring]/_b[costNumerical]
 69.     estadd scalar wtpSu = -1000*_b[summer]/_b[costNumerical]
 70.     nlcom ratio:_b[spring]/_b[costNumerical], post
 71.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 72.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 73.     estadd local conf95sp "[`ub';`lb']": nkids2
 74.     est restore nkids2
 75.     nlcom ratio:_b[summer]/_b[costNumerical], post
 76.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 77.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 78.     estadd local conf95su "[`ub';`lb']": nkids2
 79. 
.     **Intended childless sample
.     local c planning==0&parent!=1&age>=20&age<=45
 80.     eststo: logit chosen spring summer _sob4 costNumerical `ctrl' s2018 `wt' if `c', `se'
 81.     local numR = e(N_clust)
 82.     margins, dydx(spring summer costNumerical  _gend2 `nvar1' `nvar2' _sob4) post
 83.     est store nplan1
 84.     estadd scalar numR  = `numR'
 85.     estadd scalar wtpSp = -1000*_b[spring]/_b[costNumerical]
 86.     estadd scalar wtpSu = -1000*_b[summer]/_b[costNumerical]
 87.     nlcom ratio:_b[spring]/_b[costNumerical], post
 88.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 89.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 90.     estadd local conf95sp "[`ub';`lb']": nplan1
 91.     est restore nplan1
 92.     nlcom ratio:_b[summer]/_b[costNumerical], post
 93.     local lb = string(-1000*(_b[ratio]-`tvL'*_se[ratio]), "%5.1f")
 94.     local ub = string(-1000*(_b[ratio]+`tvL'*_se[ratio]), "%5.1f")
 95.     estadd local conf95su "[`ub';`lb']": nplan1
 96. 
.     lab var _dob2 "Weekend Day"
 97.     lab var _sob4 "Fall"
 98.     lab var spring "Spring"
 99.     lab var summer "Summer"
100.     lab var costNumerical "Cost (in 1000s)"
101.     lab var goodSeason "Quarter 2 or Quarter 3"
102.     #delimit ;
delimiter now ;
.     esttab n1 nmain1 nkids1 nkids2 nplan1  using "$OUT/WTP-seasons-`spec'.tex", replace
>     cells(b(star fmt(%-9.3f)) se(fmt(%-9.3f) par([ ]) )) stats
>     (wtpSp conf95sp N numR, fmt(%5.1f %5.1f %9.0gc %9.0gc)
>         label("WTP for Spring (USD)" "95\% CI" Observations "Number of Respondents"))
>     starlevel ("$ ^{\ddagger} $" `pvL') collabels(,none)
>     mgroups("Full Sample" "Married Mothers 20-45" "Intended Childless", pattern(1 1 0 0 1)
>         prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span}))
>     mlabels("All" "All" "1 child" "$\geq$ 2 children" "20-45") booktabs
>     label title(`ttitle'\label{conjointWTP`spec'})
>     keep(spring summer _sob4 costNumerical _gend2 `nvar1' `nvar2') style(tex)
>     postfoot("\bottomrule           "
>              "\multicolumn{6}{p{17.0cm}}{\begin{footnotesize} Each estimation sample  "
>              "consists of US-based respondents to a Mechanical Turk survey with waves "
>              "completed in September 2016 and May 2018.  Any subsets of this sample   "
>              "are listed in column headings. Average marginal effects from a logit    "
>              "regression are displayed. All columns include option order fixed effects"
>              "and round fixed effects, plus an indicator for the survey wave (2016 or "
>              "2018). The sample in each column is indicated in column headings.       "
>              "Standard errors are clustered by respondent. `wnote' Willingness to pay "
>              "and its 95\% confidence interval is estimated based on the ratio of     "
>              "costs to the probability of choosing a spring birth. The 95\% confidence"
>              "interval is calculated using the delta method for the (non-linear)      "
>              "ratio, with confidence levels based on Leamer values.                   "
>              "$^{\ddagger}$ Significance based on Leamer criterion at 5\%."
>              "\end{footnotesize}}\end{tabular}\end{table}");
103.     #delimit cr
delimiter now cr
.     estimates clear
104. }

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -35526.566  
Iteration 1:   log pseudolikelihood = -30126.563  
Iteration 2:   log pseudolikelihood = -30106.883  
Iteration 3:   log pseudolikelihood = -30106.866  
Iteration 4:   log pseudolikelihood = -30106.866  

Logistic regression                             Number of obs     =     51,254
                                                Wald chi2(25)     =    6117.79
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -30106.866               Pseudo R2         =     0.1526

                                  (Std. Err. adjusted for 3,661 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |    .208887   .0288594     7.24   0.000     .1523235    .2654504
       summer |   .1000876   .0288861     3.46   0.001     .0434719    .1567032
        _sob4 |   .1420079   .0286794     4.95   0.000     .0857973    .1982184
costNumerical |  -.3368872   .0044818   -75.17   0.000    -.3456713    -.328103
              |
        round |
           2  |  -.0274619   .0184421    -1.49   0.136    -.0636078    .0086839
           3  |  -.0064928   .0185026    -0.35   0.726    -.0427573    .0297717
           4  |   .0204819   .0185152     1.11   0.269    -.0158072    .0567711
           5  |   .0021279   .0185286     0.11   0.909    -.0341876    .0384434
           6  |   .0216686    .018214     1.19   0.234    -.0140302    .0573674
           7  |  -.0102472   .0184467    -0.56   0.579     -.046402    .0259077
              |
     2.option |  -.0005554   .0264881    -0.02   0.983    -.0524711    .0513604
              |
   surveyVers |
        2018  |  -.0136718   .0097407    -1.40   0.160    -.0327632    .0054195
              |
       _gend2 |   -.027862   .0246914    -1.13   0.259    -.0762563    .0205324
        _bwt2 |   .0702545   .0695375     1.01   0.312    -.0660366    .2065456
        _bwt3 |   .5896758    .070582     8.35   0.000     .4513378    .7280139
        _bwt4 |   .7156934   .0700158    10.22   0.000     .5784649    .8529218
        _bwt5 |   .6227591   .0710837     8.76   0.000     .4834375    .7620806
        _bwt6 |   .9023364   .0734155    12.29   0.000     .7584446    1.046228
        _bwt7 |   .9142923   .0742509    12.31   0.000     .7687631    1.059821
        _bwt8 |   .7492552    .071599    10.46   0.000     .6089238    .8895866
        _bwt9 |   .8309994   .0751403    11.06   0.000     .6837271    .9782716
       _bwt10 |   .7314842   .0728162    10.05   0.000     .5887671    .8742014
       _bwt11 |   .7287104   .0722982    10.08   0.000     .5870085    .8704122
       _bwt12 |   .5938023   .0536125    11.08   0.000     .4887238    .6988807
        _dob2 |   .0325187   .0278102     1.17   0.242    -.0219882    .0870257
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .5864062   .0614459     9.54   0.000     .4659745    .7068379
-------------------------------------------------------------------------------
(est1 stored)

Average marginal effects                        Number of obs     =     51,254
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0419629   .0057879     7.25   0.000     .0306188     .053307
       summer |   .0201064   .0057976     3.47   0.001     .0087432    .0314696
        _sob4 |   .0285277   .0057528     4.96   0.000     .0172524     .039803
costNumerical |  -.0676766   .0005988  -113.02   0.000    -.0688502    -.066503
       _gend2 |  -.0055971   .0049598    -1.13   0.259    -.0153182    .0041239
        _bwt2 |   .0141133   .0139699     1.01   0.312    -.0132672    .0414938
        _bwt3 |   .1184588   .0141492     8.37   0.000      .090727    .1461907
        _bwt4 |   .1437743   .0140303    10.25   0.000     .1162753    .1712732
        _bwt5 |   .1251049   .0142424     8.78   0.000     .0971902    .1530195
        _bwt6 |   .1812686   .0146843    12.34   0.000     .1524879    .2100493
        _bwt7 |   .1836704   .0148722    12.35   0.000     .1545214    .2128194
        _bwt8 |   .1505164   .0143516    10.49   0.000     .1223878    .1786451
        _bwt9 |   .1669379   .0150645    11.08   0.000      .137412    .1964637
       _bwt10 |   .1469465   .0145997    10.07   0.000     .1183316    .1755613
       _bwt11 |   .1463892    .014495    10.10   0.000     .1179795    .1747989
        _dob2 |   .0065326   .0055849     1.17   0.242    -.0044136    .0174789
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  3661

added scalar:
              e(wtpSp) =  620.05022

added scalar:
              e(wtpSu) =  297.09521

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.6200502   .0859352    -7.22   0.000    -.7884801   -.4516203
------------------------------------------------------------------------------
(results n1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.2970952   .0856752    -3.47   0.001    -.4650156   -.1291749
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -7142.1885  
Iteration 1:   log pseudolikelihood = -5938.1535  
Iteration 2:   log pseudolikelihood = -5931.5137  
Iteration 3:   log pseudolikelihood = -5931.5032  
Iteration 4:   log pseudolikelihood = -5931.5032  

Logistic regression                             Number of obs     =     10,304
                                                Wald chi2(25)     =    1504.02
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5931.5032               Pseudo R2         =     0.1695

                                    (Std. Err. adjusted for 736 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .3147463    .065312     4.82   0.000     .1867371    .4427555
       summer |   .0587601   .0654404     0.90   0.369    -.0695008     .187021
        _sob4 |   .1956954   .0660326     2.96   0.003     .0662738    .3251169
costNumerical |  -.3587411   .0098492   -36.42   0.000    -.3780453    -.339437
              |
        round |
           2  |  -.0033472   .0430614    -0.08   0.938    -.0877459    .0810516
           3  |   .0345715   .0447664     0.77   0.440     -.053169    .1223119
           4  |   .0444096   .0421208     1.05   0.292    -.0381456    .1269648
           5  |   .0518208   .0452826     1.14   0.252    -.0369314     .140573
           6  |   -.009688   .0437701    -0.22   0.825    -.0954758    .0760999
           7  |   .0146665   .0450052     0.33   0.745    -.0735421    .1028751
              |
     2.option |  -.0310468   .0598926    -0.52   0.604    -.1484342    .0863405
              |
   surveyVers |
        2018  |  -.0035763   .0226922    -0.16   0.875    -.0480522    .0408996
              |
       _gend2 |   .0581253   .0560389     1.04   0.300    -.0517089    .1679595
        _bwt2 |   .0638673   .1641577     0.39   0.697     -.257876    .3856105
        _bwt3 |   .6693657    .160969     4.16   0.000     .3538723    .9848591
        _bwt4 |   .8037618   .1680717     4.78   0.000     .4743473    1.133176
        _bwt5 |     .83376   .1686201     4.94   0.000     .5032708    1.164249
        _bwt6 |   1.017845   .1590444     6.40   0.000     .7061238    1.329566
        _bwt7 |   .8473909   .1730483     4.90   0.000     .5082224    1.186559
        _bwt8 |   .6751925   .1614842     4.18   0.000     .3586892    .9916958
        _bwt9 |   1.188772   .1763391     6.74   0.000     .8431538     1.53439
       _bwt10 |    .918303   .1697356     5.41   0.000     .5856274    1.250979
       _bwt11 |   .7496522   .1703822     4.40   0.000     .4157093    1.083595
       _bwt12 |   .6785344   .1235987     5.49   0.000     .4362854    .9207833
        _dob2 |   .0573782   .0623845     0.92   0.358    -.0648931    .1796495
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .5040193    .143505     3.51   0.000     .2227547    .7852838
-------------------------------------------------------------------------------
(est2 stored)

Average marginal effects                        Number of obs     =     10,304
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0616347   .0127361     4.84   0.000     .0366723     .086597
       summer |   .0115066   .0128083     0.90   0.369    -.0135973    .0366105
        _sob4 |   .0383217   .0128811     2.98   0.003     .0130752    .0635683
costNumerical |  -.0702499   .0012349   -56.89   0.000    -.0726703   -.0678294
       _gend2 |   .0113823   .0109731     1.04   0.300    -.0101245    .0328891
        _bwt2 |   .0125067   .0321515     0.39   0.697    -.0505091    .0755225
        _bwt3 |   .1310774    .031486     4.16   0.000     .0693659    .1927889
        _bwt4 |   .1573953    .032856     4.79   0.000     .0929988    .2217918
        _bwt5 |   .1632697   .0328817     4.97   0.000     .0988228    .2277165
        _bwt6 |   .1993178    .031086     6.41   0.000     .1383904    .2602451
        _bwt7 |   .1659389   .0338657     4.90   0.000     .0995634    .2323144
        _bwt8 |   .1322184   .0315683     4.19   0.000     .0703457    .1940911
        _bwt9 |   .2327893    .034419     6.76   0.000     .1653293    .3002493
       _bwt10 |   .1798251   .0331836     5.42   0.000     .1147865    .2448638
       _bwt11 |   .1467994   .0333413     4.40   0.000     .0814516    .2121471
        _dob2 |    .011236   .0122081     0.92   0.357    -.0126915    .0351634
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  736

added scalar:
              e(wtpSp) =  877.36324

added scalar:
              e(wtpSu) =  163.79537

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.8773632    .182602    -4.80   0.000    -1.235257   -.5194698
------------------------------------------------------------------------------
(results nmain1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.1637954   .1822354    -0.90   0.369    -.5209703    .1933795
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -2105.7811  
Iteration 1:   log pseudolikelihood = -1829.1728  
Iteration 2:   log pseudolikelihood = -1828.6564  
Iteration 3:   log pseudolikelihood = -1828.6562  

Logistic regression                             Number of obs     =      3,038
                                                Wald chi2(25)     =     302.35
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1828.6562               Pseudo R2         =     0.1316

                                    (Std. Err. adjusted for 217 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0978403   .1137456     0.86   0.390     -.125097    .3207777
       summer |  -.0236375   .1128983    -0.21   0.834    -.2449141    .1976391
        _sob4 |   .0747186   .1196007     0.62   0.532    -.1596945    .3091317
costNumerical |  -.3050208   .0190959   -15.97   0.000    -.3424481   -.2675936
              |
        round |
           2  |   -.070557   .0665375    -1.06   0.289    -.2009682    .0598542
           3  |   .0257758   .0684028     0.38   0.706    -.1082913    .1598428
           4  |   .0711596   .0667591     1.07   0.286    -.0596859    .2020051
           5  |  -.0322613    .068267    -0.47   0.637    -.1660622    .1015396
           6  |  -.0514951   .0634166    -0.81   0.417    -.1757892    .0727991
           7  |  -.0727879   .0664316    -1.10   0.273    -.2029915    .0574157
              |
     2.option |  -.0850976   .1170114    -0.73   0.467    -.3144358    .1442406
              |
   surveyVers |
        2018  |   -.007417   .0354724    -0.21   0.834    -.0769417    .0621077
              |
       _gend2 |  -.0739933   .1111938    -0.67   0.506    -.2919291    .1439425
        _bwt2 |   .0672958   .3401424     0.20   0.843    -.5993711    .7339628
        _bwt3 |      .3716   .3383229     1.10   0.272    -.2915006    1.034701
        _bwt4 |   .4050432   .2871309     1.41   0.158     -.157723    .9678094
        _bwt5 |   .6464526    .315435     2.05   0.040     .0282113    1.264694
        _bwt6 |   .7630011   .2961837     2.58   0.010     .1824916    1.343511
        _bwt7 |   .6892169   .3252616     2.12   0.034     .0517157    1.326718
        _bwt8 |    .335417   .3278059     1.02   0.306    -.3070708    .9779048
        _bwt9 |   1.171023   .3539935     3.31   0.001     .4772088    1.864838
       _bwt10 |   .5439144   .3176152     1.71   0.087    -.0785999    1.166429
       _bwt11 |   .2970647   .3397823     0.87   0.382    -.3688964    .9630258
       _bwt12 |    .435373   .2411669     1.81   0.071    -.0373053    .9080514
        _dob2 |   .0741142   .1059135     0.70   0.484    -.1334726    .2817009
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .7613241    .262436     2.90   0.004     .2469591    1.275689
-------------------------------------------------------------------------------
(est3 stored)

Average marginal effects                        Number of obs     =      3,038
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0202805    .023581     0.86   0.390    -.0259374    .0664985
       summer |  -.0048996   .0233993    -0.21   0.834    -.0507613    .0409621
        _sob4 |   .0154878   .0247559     0.63   0.532    -.0330329    .0640086
costNumerical |  -.0632253   .0027916   -22.65   0.000    -.0686969   -.0577538
       _gend2 |  -.0153375   .0230234    -0.67   0.505    -.0604626    .0297876
        _bwt2 |   .0139492   .0705084     0.20   0.843    -.1242447    .1521431
        _bwt3 |    .077026   .0701147     1.10   0.272    -.0603962    .2144483
        _bwt4 |   .0839582   .0594993     1.41   0.158    -.0326583    .2005746
        _bwt5 |    .133998    .065011     2.06   0.039     .0065788    .2614172
        _bwt6 |   .1581564   .0611295     2.59   0.010     .0383447    .2779681
        _bwt7 |   .1428623   .0675055     2.12   0.034      .010554    .2751705
        _bwt8 |   .0695259   .0678723     1.02   0.306    -.0635014    .2025532
        _bwt9 |   .2427321   .0730503     3.32   0.001      .099556    .3859081
       _bwt10 |   .1127437   .0658453     1.71   0.087    -.0163108    .2417981
       _bwt11 |   .0615762   .0704323     0.87   0.382    -.0764686    .1996209
        _dob2 |   .0153625   .0219358     0.70   0.484    -.0276308    .0583558
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  217

added scalar:
              e(wtpSp) =  320.76611

added scalar:
              e(wtpSu) =  -77.49468

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.3207661   .3739128    -0.86   0.391    -1.053622    .4120895
------------------------------------------------------------------------------
(results nkids1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   .0774947   .3700494     0.21   0.834    -.6477889    .8027782
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -5036.4074  
Iteration 1:   log pseudolikelihood = -4091.0196  
Iteration 2:   log pseudolikelihood = -4082.7665  
Iteration 3:   log pseudolikelihood = -4082.7417  
Iteration 4:   log pseudolikelihood = -4082.7417  

Logistic regression                             Number of obs     =      7,266
                                                Wald chi2(25)     =    1413.28
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -4082.7417               Pseudo R2         =     0.1894

                                    (Std. Err. adjusted for 519 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |    .423254   .0801387     5.28   0.000     .2661851    .5803229
       summer |   .0964035   .0807713     1.19   0.233    -.0619052    .2547123
        _sob4 |   .2484654   .0798431     3.11   0.002     .0919758    .4049549
costNumerical |  -.3853622   .0110681   -34.82   0.000    -.4070553   -.3636691
              |
        round |
           2  |   .0433086   .0560075     0.77   0.439     -.066464    .1530813
           3  |   .0414068   .0585352     0.71   0.479    -.0733201    .1561338
           4  |    .037548   .0544511     0.69   0.490    -.0691742    .1442702
           5  |   .1036072   .0594272     1.74   0.081    -.0128681    .2200824
           6  |   .0225173   .0582754     0.39   0.699    -.0917005     .136735
           7  |   .0729622   .0592974     1.23   0.219    -.0432587     .189183
              |
     2.option |   -.007002   .0690655    -0.10   0.919    -.1423679    .1283639
              |
   surveyVers |
        2018  |   .0013673   .0289049     0.05   0.962    -.0552853    .0580199
              |
       _gend2 |   .1202112   .0643593     1.87   0.062    -.0059308    .2463531
        _bwt2 |   .0528592   .1900121     0.28   0.781    -.3195577    .4252761
        _bwt3 |    .784582   .1827652     4.29   0.000     .4263689    1.142795
        _bwt4 |    .977726   .2088555     4.68   0.000     .5683767    1.387075
        _bwt5 |    .910522    .200589     4.54   0.000     .5173747    1.303669
        _bwt6 |   1.105744   .1898081     5.83   0.000     .7337269    1.477761
        _bwt7 |    .919073   .2067386     4.45   0.000     .5138728    1.324273
        _bwt8 |    .808151    .188225     4.29   0.000     .4392368    1.177065
        _bwt9 |   1.185352    .203375     5.83   0.000     .7867439    1.583959
       _bwt10 |   1.054271   .2030473     5.19   0.000     .6563052    1.452236
       _bwt11 |   .9110411    .199494     4.57   0.000       .52004    1.302042
       _bwt12 |   .7811043   .1460495     5.35   0.000     .4948526    1.067356
        _dob2 |   .0550725   .0774999     0.71   0.477    -.0968246    .2069696
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |     .39133   .1752544     2.23   0.026     .0478377    .7348223
-------------------------------------------------------------------------------
(est4 stored)

Average marginal effects                        Number of obs     =      7,266
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |    .080416   .0151181     5.32   0.000      .050785     .110047
       summer |   .0183162   .0153251     1.20   0.232    -.0117205    .0483528
        _sob4 |   .0472071   .0151067     3.12   0.002     .0175985    .0768157
costNumerical |  -.0732167   .0012963   -56.48   0.000    -.0757575    -.070676
       _gend2 |   .0228395   .0122209     1.87   0.062     -.001113    .0467919
        _bwt2 |    .010043   .0361097     0.28   0.781    -.0607308    .0808168
        _bwt3 |   .1490664   .0346948     4.30   0.000     .0810659    .2170669
        _bwt4 |   .1857627   .0396139     4.69   0.000     .1081209    .2634045
        _bwt5 |   .1729943   .0380152     4.55   0.000     .0984859    .2475027
        _bwt6 |   .2100854   .0361033     5.82   0.000     .1393242    .2808466
        _bwt7 |    .174619   .0392181     4.45   0.000     .0977529     .251485
        _bwt8 |   .1535444   .0357393     4.30   0.000     .0834966    .2235921
        _bwt9 |   .2252105    .038513     5.85   0.000     .1497263    .3006947
       _bwt10 |   .2003057   .0385075     5.20   0.000     .1248324    .2757791
       _bwt11 |   .1730929    .037899     4.57   0.000     .0988123    .2473735
        _dob2 |   .0104635   .0147149     0.71   0.477    -.0183772    .0393041
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  519

added scalar:
              e(wtpSp) =  1098.3279

added scalar:
              e(wtpSu) =  250.16341

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -1.098328   .2083508    -5.27   0.000    -1.506688   -.6899678
------------------------------------------------------------------------------
(results nkids2 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.2501634   .2090732    -1.20   0.231    -.6599394    .1596126
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -7666.2078  
Iteration 1:   log pseudolikelihood =  -6425.181  
Iteration 2:   log pseudolikelihood =  -6419.408  
Iteration 3:   log pseudolikelihood = -6419.4008  
Iteration 4:   log pseudolikelihood = -6419.4008  

Logistic regression                             Number of obs     =     11,060
                                                Wald chi2(25)     =    1434.14
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -6419.4008               Pseudo R2         =     0.1626

                                    (Std. Err. adjusted for 790 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .1608198   .0639552     2.51   0.012       .03547    .2861696
       summer |   .0486478   .0626444     0.78   0.437     -.074133    .1714286
        _sob4 |   .0689813   .0621968     1.11   0.267    -.0529222    .1908848
costNumerical |  -.3535838   .0098817   -35.78   0.000    -.3729516    -.334216
              |
        round |
           2  |  -.0332733   .0413744    -0.80   0.421    -.1143656    .0478191
           3  |  -.0482961   .0413938    -1.17   0.243    -.1294266    .0328343
           4  |  -.0287127   .0406199    -0.71   0.480    -.1083262    .0509009
           5  |  -.0353801   .0428956    -0.82   0.409    -.1194538    .0486937
           6  |   .0382476    .040262     0.95   0.342    -.0406645    .1171597
           7  |  -.0691618   .0406288    -1.70   0.089    -.1487928    .0104691
              |
     2.option |   .0414968   .0573244     0.72   0.469    -.0708569    .1538505
              |
   surveyVers |
        2018  |  -.0180726   .0225821    -0.80   0.424    -.0623328    .0261875
              |
       _gend2 |   .0390039   .0540798     0.72   0.471    -.0669905    .1449983
        _bwt2 |   .1474415   .1540539     0.96   0.339    -.1544987    .4493816
        _bwt3 |   .6114494      .1441     4.24   0.000     .3290186    .8938802
        _bwt4 |   .6893263   .1494661     4.61   0.000     .3963781    .9822745
        _bwt5 |   .5794471   .1486564     3.90   0.000     .2880859    .8708082
        _bwt6 |   .7749725   .1549283     5.00   0.000     .4713186    1.078626
        _bwt7 |   .8154977   .1552939     5.25   0.000     .5111273    1.119868
        _bwt8 |   .7003528   .1470417     4.76   0.000     .4121563    .9885493
        _bwt9 |   .5890588   .1551376     3.80   0.000     .2849948    .8931229
       _bwt10 |   .7448731    .156871     4.75   0.000     .4374115    1.052335
       _bwt11 |   .8161601   .1536124     5.31   0.000     .5150853    1.117235
       _bwt12 |   .5303632   .1143386     4.64   0.000     .3062637    .7544628
        _dob2 |     .08137   .0616082     1.32   0.187    -.0393798    .2021198
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .6960208   .1341873     5.19   0.000     .4330186    .9590231
-------------------------------------------------------------------------------
(est5 stored)

Average marginal effects                        Number of obs     =     11,060
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0318153    .012672     2.51   0.012     .0069786     .056652
       summer |   .0096241   .0123934     0.78   0.437    -.0146665    .0339147
        _sob4 |   .0136467   .0123141     1.11   0.268    -.0104885    .0377819
costNumerical |  -.0699502   .0012558   -55.70   0.000    -.0724114    -.067489
       _gend2 |   .0077162   .0106976     0.72   0.471    -.0132507    .0286832
        _bwt2 |   .0291687    .030486     0.96   0.339    -.0305828    .0889201
        _bwt3 |   .1209643   .0284407     4.25   0.000     .0652216     .176707
        _bwt4 |   .1363709   .0294897     4.62   0.000     .0785721    .1941696
        _bwt5 |   .1146332   .0293597     3.90   0.000     .0570893    .1721772
        _bwt6 |   .1533144   .0305782     5.01   0.000     .0933823    .2132465
        _bwt7 |   .1613316   .0306753     5.26   0.000     .1012091    .2214541
        _bwt8 |   .1385523   .0291341     4.76   0.000     .0814505     .195654
        _bwt9 |   .1165347   .0306972     3.80   0.000     .0563693    .1767002
       _bwt10 |   .1473598   .0310114     4.75   0.000     .0865785    .2081411
       _bwt11 |   .1614627   .0303877     5.31   0.000     .1019039    .2210214
        _dob2 |   .0160976   .0121874     1.32   0.187    -.0077892    .0399844
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  790

added scalar:
              e(wtpSp) =  454.82805

added scalar:
              e(wtpSu) =  137.585

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.4548281   .1823905    -2.49   0.013    -.8123069   -.0973492
------------------------------------------------------------------------------
(results nplan1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   -.137585   .1772603    -0.78   0.438    -.4850088    .2098388
------------------------------------------------------------------------------
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/WTP
> -seasons-main.tex)

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -35523.013  
Iteration 1:   log pseudolikelihood = -30214.323  
Iteration 2:   log pseudolikelihood = -30196.202  
Iteration 3:   log pseudolikelihood = -30196.188  
Iteration 4:   log pseudolikelihood = -30196.188  

Logistic regression                             Number of obs     =     51,254
                                                Wald chi2(25)     =    5402.85
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -30196.188               Pseudo R2         =     0.1500

                                  (Std. Err. adjusted for 3,661 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .1892106   .0300261     6.30   0.000     .1303605    .2480606
       summer |   .0740009   .0309539     2.39   0.017     .0133325    .1346694
        _sob4 |   .1157409   .0310291     3.73   0.000      .054925    .1765567
costNumerical |   -.333506   .0046962   -71.02   0.000    -.3427103   -.3243016
              |
        round |
           2  |  -.0363748   .0189822    -1.92   0.055    -.0735792    .0008296
           3  |  -.0190642   .0198191    -0.96   0.336     -.057909    .0197805
           4  |    .013307   .0196636     0.68   0.499    -.0252329    .0518469
           5  |  -.0048715   .0201554    -0.24   0.809    -.0443755    .0346324
           6  |   .0080845   .0195323     0.41   0.679    -.0301982    .0463672
           7  |  -.0186749   .0196024    -0.95   0.341    -.0570948    .0197451
              |
     2.option |   .0254192   .0283098     0.90   0.369     -.030067    .0809055
              |
   surveyVers |
        2018  |  -.0194483   .0101474    -1.92   0.055    -.0393368    .0004402
              |
       _gend2 |  -.0275857   .0267465    -1.03   0.302    -.0800078    .0248365
        _bwt2 |   .0629429   .0740582     0.85   0.395    -.0822084    .2080943
        _bwt3 |   .5421682   .0752605     7.20   0.000     .3946602    .6896761
        _bwt4 |   .6919836   .0748117     9.25   0.000     .5453553    .8386118
        _bwt5 |   .6154511   .0773972     7.95   0.000     .4637554    .7671468
        _bwt6 |   .8789437   .0840441    10.46   0.000     .7142203    1.043667
        _bwt7 |   .8689662   .0814053    10.67   0.000     .7094147    1.028518
        _bwt8 |   .7349513   .0749927     9.80   0.000     .5879684    .8819342
        _bwt9 |   .7974521   .0828368     9.63   0.000     .6350949    .9598093
       _bwt10 |   .7114836     .08117     8.77   0.000     .5523932    .8705739
       _bwt11 |   .7027418   .0804169     8.74   0.000     .5451276     .860356
       _bwt12 |   .5774859   .0588338     9.82   0.000     .4621738     .692798
        _dob2 |   .0212589   .0286845     0.74   0.459    -.0349616    .0774794
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .6076875   .0673149     9.03   0.000     .4757527    .7396223
-------------------------------------------------------------------------------
(est1 stored)

Average marginal effects                        Number of obs     =     51,254
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0381586   .0060439     6.31   0.000     .0263127    .0500045
       summer |    .014924   .0062355     2.39   0.017     .0027026    .0271453
        _sob4 |   .0233418    .006247     3.74   0.000     .0110978    .0355858
costNumerical |  -.0672591   .0006324  -106.36   0.000    -.0684985   -.0660196
       _gend2 |  -.0055633   .0053936    -1.03   0.302    -.0161345    .0050079
        _bwt2 |   .0126939   .0149358     0.85   0.395    -.0165797    .0419674
        _bwt3 |   .1093405   .0151247     7.23   0.000     .0796966    .1389845
        _bwt4 |   .1395542   .0150191     9.29   0.000     .1101173    .1689912
        _bwt5 |   .1241197   .0155461     7.98   0.000       .09365    .1545895
        _bwt6 |    .177259   .0168272    10.53   0.000     .1442782    .2102398
        _bwt7 |   .1752468   .0163453    10.72   0.000     .1432107    .2072829
        _bwt8 |   .1482197   .0150667     9.84   0.000     .1186895    .1777498
        _bwt9 |   .1608244    .016651     9.66   0.000      .128189    .1934597
       _bwt10 |   .1434869   .0163305     8.79   0.000     .1114797     .175494
       _bwt11 |   .1417239   .0161537     8.77   0.000     .1100633    .1733845
        _dob2 |   .0042873   .0057837     0.74   0.459    -.0070484    .0156231
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  3661

added scalar:
              e(wtpSp) =  567.33795

added scalar:
              e(wtpSu) =  221.88793

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   -.567338   .0901949    -6.29   0.000    -.7441166   -.3905593
------------------------------------------------------------------------------
(results n1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.2218879   .0926607    -2.39   0.017    -.4034996   -.0402762
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -7035.0114  
Iteration 1:   log pseudolikelihood = -5864.6298  
Iteration 2:   log pseudolikelihood = -5858.4191  
Iteration 3:   log pseudolikelihood = -5858.4099  
Iteration 4:   log pseudolikelihood = -5858.4099  

Logistic regression                             Number of obs     =     10,304
                                                Wald chi2(25)     =    1350.30
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -5858.4099               Pseudo R2         =     0.1672

                                    (Std. Err. adjusted for 736 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .3029894   .0670146     4.52   0.000     .1716432    .4343356
       summer |   .0121777    .067074     0.18   0.856     -.119285    .1436404
        _sob4 |    .189088   .0677011     2.79   0.005     .0563962    .3217798
costNumerical |  -.3546774   .0102679   -34.54   0.000    -.3748021   -.3345528
              |
        round |
           2  |  -.0147891   .0444482    -0.33   0.739    -.1019059    .0723277
           3  |   .0368976   .0456277     0.81   0.419    -.0525309    .1263262
           4  |    .047397   .0427577     1.11   0.268    -.0364065    .1312006
           5  |   .0507254   .0457155     1.11   0.267    -.0388753    .1403261
           6  |  -.0115821   .0449968    -0.26   0.797    -.0997743    .0766101
           7  |   .0165861    .046401     0.36   0.721    -.0743582    .1075305
              |
     2.option |  -.0215859   .0617072    -0.35   0.726    -.1425297     .099358
              |
   surveyVers |
        2018  |  -.0053174   .0232249    -0.23   0.819    -.0508375    .0402026
              |
       _gend2 |   .0575767   .0578584     1.00   0.320    -.0558236     .170977
        _bwt2 |    .124099   .1688609     0.73   0.462    -.2068622    .4550602
        _bwt3 |    .677918   .1618161     4.19   0.000     .3607643    .9950717
        _bwt4 |   .8055023   .1746907     4.61   0.000     .4631148     1.14789
        _bwt5 |    .836428   .1747128     4.79   0.000     .4939971    1.178859
        _bwt6 |   1.073081   .1608595     6.67   0.000     .7578027     1.38836
        _bwt7 |   .9080447   .1737682     5.23   0.000     .5674652    1.248624
        _bwt8 |   .6770875   .1629412     4.16   0.000     .3577285    .9964465
        _bwt9 |   1.187056   .1782989     6.66   0.000     .8375963    1.536515
       _bwt10 |   .9468711   .1775365     5.33   0.000      .598906    1.294836
       _bwt11 |   .7673375   .1818233     4.22   0.000     .4109704    1.123705
       _bwt12 |   .7109605   .1264365     5.62   0.000     .4631496    .9587714
        _dob2 |   .0303593   .0636941     0.48   0.634    -.0944788    .1551974
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .4768341   .1456305     3.27   0.001     .1914036    .7622647
-------------------------------------------------------------------------------
(est2 stored)

Average marginal effects                        Number of obs     =     10,304
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0595375   .0131189     4.54   0.000     .0338249    .0852501
       summer |   .0023929   .0131792     0.18   0.856    -.0234379    .0282237
        _sob4 |   .0371558   .0132676     2.80   0.005     .0111519    .0631598
costNumerical |  -.0696942   .0013003   -53.60   0.000    -.0722427   -.0671457
       _gend2 |   .0113138   .0113674     1.00   0.320    -.0109658    .0335935
        _bwt2 |   .0243855   .0331889     0.73   0.462    -.0406636    .0894345
        _bwt3 |   .1332111   .0317504     4.20   0.000     .0709815    .1954407
        _bwt4 |   .1582814   .0342563     4.62   0.000     .0911403    .2254226
        _bwt5 |   .1643583   .0342263     4.80   0.000      .097276    .2314406
        _bwt6 |   .2108608   .0314361     6.71   0.000     .1492471    .2724745
        _bwt7 |    .178431   .0341013     5.23   0.000     .1115937    .2452684
        _bwt8 |   .1330479   .0319397     4.17   0.000     .0704473    .1956484
        _bwt9 |   .2332568   .0347771     6.71   0.000      .165095    .3014186
       _bwt10 |   .1860605   .0348204     5.34   0.000     .1178136    .2543073
       _bwt11 |    .150782   .0357346     4.22   0.000     .0807435    .2208206
        _dob2 |   .0059656   .0125132     0.48   0.634    -.0185598     .030491
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  736

added scalar:
              e(wtpSp) =  854.26757

added scalar:
              e(wtpSu) =  34.334592

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.8542676   .1896371    -4.50   0.000    -1.225949   -.4825857
------------------------------------------------------------------------------
(results nmain1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.0343346   .1890797    -0.18   0.856    -.4049239    .3362548
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood =  -2119.386  
Iteration 1:   log pseudolikelihood = -1842.9113  
Iteration 2:   log pseudolikelihood = -1842.3959  
Iteration 3:   log pseudolikelihood = -1842.3957  

Logistic regression                             Number of obs     =      3,038
                                                Wald chi2(25)     =     274.51
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1842.3957               Pseudo R2         =     0.1307

                                    (Std. Err. adjusted for 217 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0914774   .1191658     0.77   0.443    -.1420833    .3250382
       summer |  -.0566427   .1155261    -0.49   0.624    -.2830696    .1697842
        _sob4 |   .0595937   .1231335     0.48   0.628    -.1817435     .300931
costNumerical |  -.3019283   .0199531   -15.13   0.000    -.3410356    -.262821
              |
        round |
           2  |  -.0545474   .0688764    -0.79   0.428    -.1895426    .0804478
           3  |   .0200613   .0721414     0.28   0.781    -.1213332    .1614559
           4  |   .0691311   .0695302     0.99   0.320    -.0671456    .2054078
           5  |  -.0249884   .0699066    -0.36   0.721    -.1620029    .1120261
           6  |  -.0450992   .0639945    -0.70   0.481    -.1705261    .0803276
           7  |   -.083905   .0660028    -1.27   0.204    -.2132681    .0454581
              |
     2.option |  -.0891435   .1195964    -0.75   0.456    -.3235481    .1452612
              |
   surveyVers |
        2018  |  -.0104158   .0350479    -0.30   0.766    -.0791085    .0582769
              |
       _gend2 |   -.061731   .1137319    -0.54   0.587    -.2846414    .1611795
        _bwt2 |   .1954112   .3596457     0.54   0.587    -.5094814    .9003038
        _bwt3 |   .3614553   .3308421     1.09   0.275    -.2869833    1.009894
        _bwt4 |   .5754932   .3033092     1.90   0.058    -.0189819    1.169968
        _bwt5 |   .7830325   .3405141     2.30   0.021      .115637    1.450428
        _bwt6 |   .7939664   .3117634     2.55   0.011     .1829213    1.405011
        _bwt7 |   .7959474   .3313634     2.40   0.016      .146487    1.445408
        _bwt8 |   .4432668   .3341722     1.33   0.185    -.2116987    1.098232
        _bwt9 |    1.23901   .3457178     3.58   0.000     .5614157    1.916605
       _bwt10 |   .6648909   .3432782     1.94   0.053     -.007922    1.337704
       _bwt11 |   .3840852   .3605471     1.07   0.287    -.3225742    1.090745
       _bwt12 |   .5266401   .2499735     2.11   0.035      .036701    1.016579
        _dob2 |   .0650816   .1079205     0.60   0.546    -.1464387     .276602
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .6672496   .2743922     2.43   0.015     .1294508    1.205048
-------------------------------------------------------------------------------
(est3 stored)

Average marginal effects                        Number of obs     =      3,038
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0189874   .0247537     0.77   0.443     -.029529    .0675038
       summer |   -.011757   .0239535    -0.49   0.624    -.0587049     .035191
        _sob4 |   .0123695   .0255425     0.48   0.628    -.0376928    .0624318
costNumerical |  -.0626693   .0029192   -21.47   0.000    -.0683908   -.0569479
       _gend2 |  -.0128131   .0235782    -0.54   0.587    -.0590255    .0333993
        _bwt2 |   .0405603   .0746367     0.54   0.587    -.1057251    .1868456
        _bwt3 |    .075025   .0686658     1.09   0.275    -.0595576    .2096075
        _bwt4 |   .1194515   .0629522     1.90   0.058    -.0039326    .2428355
        _bwt5 |   .1625291   .0702047     2.32   0.021     .0249305    .3001277
        _bwt6 |   .1647986   .0643067     2.56   0.010     .0387598    .2908373
        _bwt7 |   .1652097   .0688128     2.40   0.016     .0303392    .3000803
        _bwt8 |   .0920061   .0692175     1.33   0.184    -.0436577    .2276699
        _bwt9 |   .2571735   .0710588     3.62   0.000     .1179008    .3964461
       _bwt10 |   .1380072   .0711676     1.94   0.052    -.0014787     .277493
       _bwt11 |   .0797221   .0748726     1.06   0.287    -.0670254    .2264696
        _dob2 |   .0135086   .0223961     0.60   0.546     -.030387    .0574042
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  217

added scalar:
              e(wtpSp) =  302.97733

added scalar:
              e(wtpSu) =  -187.60313

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.3029773    .396405    -0.76   0.445    -1.079917    .4739622
------------------------------------------------------------------------------
(results nkids1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   .1876031   .3815073     0.49   0.623    -.5601375    .9353437
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -4915.6255  
Iteration 1:   log pseudolikelihood = -4004.2699  
Iteration 2:   log pseudolikelihood = -3996.6046  
Iteration 3:   log pseudolikelihood = -3996.5828  
Iteration 4:   log pseudolikelihood = -3996.5828  

Logistic regression                             Number of obs     =      7,266
                                                Wald chi2(25)     =    1328.12
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3996.5828               Pseudo R2         =     0.1870

                                    (Std. Err. adjusted for 519 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |    .410785   .0813001     5.05   0.000     .2514397    .5701304
       summer |   .0464061   .0828586     0.56   0.575    -.1159937    .2088059
        _sob4 |   .2468975   .0816662     3.02   0.003     .0868347    .4069603
costNumerical |  -.3815758   .0114571   -33.30   0.000    -.4040313   -.3591203
              |
        round |
           2  |   .0171243   .0581152     0.29   0.768    -.0967794    .1310279
           3  |   .0465047   .0590694     0.79   0.431    -.0692692    .1622786
           4  |   .0412939   .0546369     0.76   0.450    -.0657924    .1483802
           5  |   .0968587   .0598327     1.62   0.105    -.0204113    .2141287
           6  |   .0161463   .0603835     0.27   0.789    -.1022031    .1344957
           7  |   .0812144   .0616205     1.32   0.188    -.0395596    .2019884
              |
     2.option |   .0091752   .0712417     0.13   0.898    -.1304559    .1488063
              |
   surveyVers |
        2018  |   .0008852   .0300547     0.03   0.977     -.058021    .0597913
              |
       _gend2 |   .1169205   .0666496     1.75   0.079    -.0137103    .2475513
        _bwt2 |   .0981414   .1941746     0.51   0.613    -.2824337    .4787166
        _bwt3 |   .8017329    .185464     4.32   0.000     .4382302    1.165236
        _bwt4 |   .9171748   .2171795     4.22   0.000     .4915107    1.342839
        _bwt5 |   .8659493     .20406     4.24   0.000     .4659991      1.2659
        _bwt6 |   1.182887   .1870509     6.32   0.000     .8162736      1.5495
        _bwt7 |   .9650042   .2068996     4.66   0.000     .5594886     1.37052
        _bwt8 |   .7781928    .189178     4.11   0.000     .4074108    1.148975
        _bwt9 |    1.16019   .2088775     5.55   0.000     .7507973    1.569582
       _bwt10 |   1.053249   .2097135     5.02   0.000     .6422186     1.46428
       _bwt11 |   .9076583   .2137174     4.25   0.000     .4887798    1.326537
       _bwt12 |   .7997605   .1487333     5.38   0.000     .5082486    1.091273
        _dob2 |   .0160649   .0791078     0.20   0.839    -.1389836    .1711135
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .3841522   .1756761     2.19   0.029     .0398335    .7284709
-------------------------------------------------------------------------------
(est4 stored)

Average marginal effects                        Number of obs     =      7,266
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0783363   .0153941     5.09   0.000     .0481644    .1085081
       summer |   .0088496   .0157886     0.56   0.575    -.0220955    .0397947
        _sob4 |   .0470831    .015526     3.03   0.002     .0166527    .0775134
costNumerical |  -.0727661   .0013581   -53.58   0.000     -.075428   -.0701042
       _gend2 |   .0222966   .0126951     1.76   0.079    -.0025852    .0471785
        _bwt2 |   .0187155   .0370441     0.51   0.613    -.0538897    .0913206
        _bwt3 |   .1528896   .0353323     4.33   0.000     .0836396    .2221396
        _bwt4 |   .1749042   .0412985     4.24   0.000     .0939607    .2558478
        _bwt5 |   .1651356   .0388756     4.25   0.000     .0889407    .2413305
        _bwt6 |   .2255752   .0355906     6.34   0.000      .155819    .2953314
        _bwt7 |   .1840253    .039368     4.67   0.000     .1068653    .2611852
        _bwt8 |   .1484005   .0360251     4.12   0.000     .0777927    .2190083
        _bwt9 |   .2212469   .0395648     5.59   0.000     .1437013    .2987925
       _bwt10 |   .2008535   .0399517     5.03   0.000     .1225497    .2791573
       _bwt11 |   .1730894   .0407992     4.24   0.000     .0931245    .2530544
        _dob2 |   .0030636   .0150831     0.20   0.839    -.0264987    .0326258
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  519

added scalar:
              e(wtpSp) =  1076.549

added scalar:
              e(wtpSu) =  121.61702

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -1.076549    .213218    -5.05   0.000    -1.494449   -.6586493
------------------------------------------------------------------------------
(results nkids2 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   -.121617   .2167514    -0.56   0.575    -.5464419    .3032078
------------------------------------------------------------------------------

note: _dob3 omitted because of collinearity
note: s2018 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -7840.6775  
Iteration 1:   log pseudolikelihood = -6593.1509  
Iteration 2:   log pseudolikelihood = -6587.7967  
Iteration 3:   log pseudolikelihood = -6587.7907  
Iteration 4:   log pseudolikelihood = -6587.7907  

Logistic regression                             Number of obs     =     11,060
                                                Wald chi2(25)     =    1296.63
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -6587.7907               Pseudo R2         =     0.1598

                                    (Std. Err. adjusted for 790 clusters in ID)
-------------------------------------------------------------------------------
              |               Robust
       chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .1423914   .0670886     2.12   0.034     .0109001    .2738827
       summer |  -.0082966   .0728977    -0.11   0.909    -.1511734    .1345802
        _sob4 |   .0303668     .07255     0.42   0.676    -.1118285    .1725622
costNumerical |  -.3491495   .0103631   -33.69   0.000    -.3694609   -.3288382
              |
        round |
           2  |  -.0449598   .0441439    -1.02   0.308    -.1314803    .0415608
           3  |   -.083632   .0506642    -1.65   0.099     -.182932    .0156679
           4  |  -.0510206   .0481297    -1.06   0.289     -.145353    .0433118
           5  |  -.0643743   .0545637    -1.18   0.238    -.1713172    .0425687
           6  |   .0089553   .0484054     0.19   0.853    -.0859175    .1038281
           7  |   -.097309   .0467065    -2.08   0.037     -.188852   -.0057659
              |
     2.option |    .085687   .0621874     1.38   0.168    -.0361981    .2075722
              |
   surveyVers |
        2018  |  -.0248071   .0228432    -1.09   0.277    -.0695789    .0199648
              |
       _gend2 |   .0701308   .0602195     1.16   0.244    -.0478973    .1881589
        _bwt2 |   .1709953   .1606129     1.06   0.287    -.1438003    .4857908
        _bwt3 |   .5442168   .1682866     3.23   0.001     .2143811    .8740526
        _bwt4 |    .643349    .177264     3.63   0.000      .295918      .99078
        _bwt5 |   .5876092   .1868338     3.15   0.002     .2214216    .9537968
        _bwt6 |   .7280425   .2137368     3.41   0.001      .309126    1.146959
        _bwt7 |   .7513444   .1830113     4.11   0.000     .3926488     1.11004
        _bwt8 |   .6880649   .1629184     4.22   0.000     .3687506    1.007379
        _bwt9 |   .6072903   .1738726     3.49   0.000     .2665062    .9480744
       _bwt10 |   .7113734    .181362     3.92   0.000     .3559103    1.066836
       _bwt11 |   .7380503   .2084041     3.54   0.000     .3295858    1.146515
       _bwt12 |   .5104545   .1433121     3.56   0.000     .2295681     .791341
        _dob2 |   .0813444   .0658797     1.23   0.217    -.0477776    .2104663
        _dob3 |          0  (omitted)
        s2018 |          0  (omitted)
        _cons |   .7160707   .1687136     4.24   0.000     .3853981    1.046743
-------------------------------------------------------------------------------
(est5 stored)

Average marginal effects                        Number of obs     =     11,060
Model VCE    : Robust

Expression   : Pr(chosen), predict()
dy/dx w.r.t. : spring summer _sob4 costNumerical _gend2 _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9
               _bwt10 _bwt11 _dob2

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
       spring |   .0282901   .0133346     2.12   0.034     .0021548    .0544254
       summer |  -.0016484   .0144853    -0.11   0.909    -.0300389    .0267422
        _sob4 |   .0060332   .0144126     0.42   0.676     -.022215    .0342815
costNumerical |  -.0693686   .0013228   -52.44   0.000    -.0719612   -.0667759
       _gend2 |   .0139335   .0119709     1.16   0.244     -.009529     .037396
        _bwt2 |   .0339731   .0318867     1.07   0.287    -.0285236    .0964698
        _bwt3 |   .1081243   .0332096     3.26   0.001     .0430346     .173214
        _bwt4 |   .1278197   .0349268     3.66   0.000     .0593645     .196275
        _bwt5 |   .1167454   .0368688     3.17   0.002     .0444838     .189007
        _bwt6 |   .1446465   .0421259     3.43   0.001     .0620813    .2272118
        _bwt7 |   .1492761   .0361247     4.13   0.000      .078473    .2200792
        _bwt8 |   .1367038   .0322114     4.24   0.000     .0735706     .199837
        _bwt9 |   .1206556   .0343599     3.51   0.000     .0533114    .1879999
       _bwt10 |   .1413347   .0358528     3.94   0.000     .0710646    .2116049
       _bwt11 |   .1466349    .041139     3.56   0.000      .066004    .2272657
        _dob2 |   .0161614   .0130821     1.24   0.217     -.009479    .0418018
-------------------------------------------------------------------------------

added scalar:
               e(numR) =  790

added scalar:
              e(wtpSp) =  407.82365

added scalar:
              e(wtpSu) =  -23.762286

       ratio:  _b[spring]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.4078236    .193237    -2.11   0.035    -.7865612   -.0290861
------------------------------------------------------------------------------
(results nplan1 are active now)

       ratio:  _b[summer]/_b[costNumerical]

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   .0237623   .2088337     0.11   0.909    -.3855442    .4330687
------------------------------------------------------------------------------
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/WTP
> -seasons-wt.tex)

. 
. *-------------------------------------------------------------------------------
. *-- (5) Bootstrap WTP predictors for NVSS analysis [TABLE A22]
. *-------------------------------------------------------------------------------
. cap gen price = costNumerical

. cap gen group = 1000*ID+round

. #delimit ;
delimiter now ;
. cap gen highEd  = RespEduc!="Eighth Grade or Less" &
>                   RespEduc!="High School Degree/GED" &
>                   RespEduc!="Some High School";

. #delimit cr
delimiter now cr
. local bwts _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9 _bwt10 _bwt11

. 
. 
. gen educYrs     = 8 if RespEduc=="Eighth Grade or Less"
(51,198 missing values generated)

. replace educYrs = 10 if RespEduc=="Eighth Grade or Less"
(56 real changes made)

. replace educYrs = 12 if RespEduc=="High School Degree/GED"
(5,614 real changes made)

. replace educYrs = 13 if RespEduc=="Some College"
(12,474 real changes made)

. replace educYrs = 14 if RespEduc=="2-year College Degree"
(5,964 real changes made)

. replace educYrs = 16 if RespEduc=="4-year College Degree"
(19,782 real changes made)

. replace educYrs = 17 if RespEduc=="Master's Degree"
(5,712 real changes made)

. replace educYrs = 17 if RespEduc=="Doctoral Degree"
(504 real changes made)

. replace educYrs = 17 if RespEduc=="Professional Degree (JD,MD,MBA)"
(938 real changes made)

. local wt

. 
. mixlogit chosen price `wt' if osample==1, id(ID) group(group) rand(_sob* _gend* `bwts')

Iteration 0:   log likelihood = -2138.7829  (not concave)
Iteration 1:   log likelihood = -2133.6636  (not concave)
Iteration 2:   log likelihood = -2045.6782  
Iteration 3:   log likelihood = -1990.4481  
Iteration 4:   log likelihood = -1986.4455  
Iteration 5:   log likelihood = -1985.8003  
Iteration 6:   log likelihood = -1985.7961  
Iteration 7:   log likelihood = -1985.7961  

Mixed logit model                               Number of obs     =     10,304
                                                LR chi2(14)       =     309.58
Log likelihood = -1985.7961                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Mean         |
       price |  -.8072696   .0334579   -24.13   0.000     -.872846   -.7416933
       _sob4 |   .3271387   .1134369     2.88   0.004     .1048066    .5494709
       _sob2 |   .5531192   .1055142     5.24   0.000     .3463153    .7599232
       _sob3 |   .1950815   .1084904     1.80   0.072    -.0175558    .4077187
      _gend2 |   .0540292   .1219621     0.44   0.658    -.1850122    .2930705
       _bwt2 |  -.3357276   .3338887    -1.01   0.315    -.9901375    .3186823
       _bwt3 |   1.362306    .276042     4.94   0.000     .8212737    1.903338
       _bwt4 |   1.689961   .2904019     5.82   0.000     1.120784    2.259138
       _bwt5 |   1.691391   .2852733     5.93   0.000     1.132266    2.250517
       _bwt6 |   2.004991   .2788064     7.19   0.000      1.45854    2.551442
       _bwt7 |   1.992769   .2940942     6.78   0.000     1.416355    2.569183
       _bwt8 |   1.731419   .2808625     6.16   0.000     1.180938    2.281899
       _bwt9 |   2.688316   .3287714     8.18   0.000     2.043936    3.332696
      _bwt10 |   1.849668   .2907995     6.36   0.000     1.279712    2.419625
      _bwt11 |   1.702179   .2783751     6.11   0.000     1.156574    2.247784
-------------+----------------------------------------------------------------
SD           |
       _sob4 |   .8430026   .1956922     4.31   0.000      .459453    1.226552
       _sob2 |  -.1622341   .2570636    -0.63   0.528    -.6660695    .3416012
       _sob3 |   .5906154   .2077281     2.84   0.004     .1834758     .997755
      _gend2 |   2.495633   .1829783    13.64   0.000     2.137003    2.854264
       _bwt2 |   2.216021   .3920662     5.65   0.000     1.447586    2.984457
       _bwt3 |   .7381145   .4953393     1.49   0.136    -.2327328    1.708962
       _bwt4 |   .5606133   .4036875     1.39   0.165    -.2305996    1.351826
       _bwt5 |     1.0538   .4620364     2.28   0.023     .1482251    1.959375
       _bwt6 |   .0021481   .4067414     0.01   0.996    -.7950505    .7993466
       _bwt7 |   .9541006    .378322     2.52   0.012      .212603    1.695598
       _bwt8 |  -.1928381   .4555451    -0.42   0.672     -1.08569    .7000138
       _bwt9 |     1.7412   .3705974     4.70   0.000     1.014842    2.467557
      _bwt10 |  -1.340644   .4433652    -3.02   0.002    -2.209624   -.4716638
      _bwt11 |   .8055233    .430751     1.87   0.061    -.0387332     1.64978
------------------------------------------------------------------------------
The sign of the estimated standard deviations is irrelevant: interpret them as
being positive

. local price = _b[price]

. tempfile betas

. set seed 1704

. mixlbeta _sob2 if osample==1, saving(`betas') replace
(note: file C:\Users\cq224\AppData\Local\Temp\ST_4774_00000c.tmp not found)
file C:\Users\cq224\AppData\Local\Temp\ST_4774_00000c.tmp saved

. rename _sob2 __sob2

. merge m:1 ID using `betas'
(note: variable ID was int, now double to accommodate using data's values)

    Result                           # of obs.
    -----------------------------------------
    not matched                        40,950
        from master                    40,950  (_merge==1)
        from using                          0  (_merge==2)

    matched                            10,304  (_merge==3)
    -----------------------------------------

. drop _merge

. rename _sob2 betaSOB

. rename __sob2 _sob2

. replace betaSOB=-1000*betaSOB/`price'
(10,304 real changes made)

. gen educYrsSq = educYrs^2
(210 missing values generated)

. gen female = _gend2

. cap gen ageSq=age*age

. 
. local educ educYrs educYrsSq

. local age  i.age

. local educ highEd

. local age  age ageSq

. 
. reg betaSOB `age' `educ' white black hispanic `wt' if osample==1

      Source |       SS           df       MS      Number of obs   =    10,304
-------------+----------------------------------   F(6, 10297)     =     20.72
       Model |  281067.201         6  46844.5334   Prob > F        =    0.0000
    Residual |  23280082.6    10,297   2260.8607   R-squared       =    0.0119
-------------+----------------------------------   Adj R-squared   =    0.0114
       Total |  23561149.8    10,303   2286.8242   Root MSE        =    47.549

------------------------------------------------------------------------------
     betaSOB |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |  -.1755137   .9456321    -0.19   0.853    -2.029137    1.678109
       ageSq |   -.007785   .0165906    -0.47   0.639    -.0403058    .0247358
      highEd |   9.527237   1.601569     5.95   0.000      6.38785    12.66662
       white |   15.77576    2.42536     6.50   0.000     11.02158    20.52994
       black |   21.48294   3.200542     6.71   0.000     15.20926    27.75663
    hispanic |  -5.414554   2.500513    -2.17   0.030    -10.31605    -.513062
       _cons |   670.9262    13.5487    49.52   0.000     644.3681    697.4843
------------------------------------------------------------------------------

. estimates store WTPfull

. 
. gen WTPests1  = .
(51,254 missing values generated)

. gen WTPests2  = .
(51,254 missing values generated)

. preserve

. insheet using "$NVS/nvss2005_2013_BSAMP.csv", comma names clear case
(18 vars, 4,182,531 obs)

. save "$NVS/nvss2005_2013_BSAMP.dta", replace
file C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/data/NVSS/nvss2005_2013_BSAMP.d
> ta saved

. gen ageSq = age*age

. local controls female birthweight weekend

. gen birthweight = bweightLin
(10,700 missing values generated)

. 
. 
. estimates restore WTPfull
(results WTPfull are active now)

. predict WTPhatFull, xb

. 
. sum WTPhatFull

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  WTPhatFull |  4,182,531    680.4202    7.173666   642.7173   695.3121

. local mean1=r(mean)

. local mean2=r(mean)

. 
. reg quarter2 WTPhatFull if smoker!=.&gest!=.

      Source |       SS           df       MS      Number of obs   = 4,182,531
-------------+----------------------------------   F(1, 4182529)   =    408.00
       Model |  76.0532826         1  76.0532826   Prob > F        =    0.0000
    Residual |  779643.389 4,182,529  .186404778   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  779719.443 4,182,530  .186422917   Root MSE        =    .43175

------------------------------------------------------------------------------
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  WTPhatFull |   .0005944   .0000294    20.20   0.000     .0005367    .0006521
       _cons |   -.156605   .0200248    -7.82   0.000     -.195853    -.117357
------------------------------------------------------------------------------

. local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))

. local wtpF `=_b[WTPhat]'

. local N1 = e(N)

. 
. reg quarter2 WTPhatFull `controls' if smoker!=.&gest!=.

      Source |       SS           df       MS      Number of obs   = 4,171,831
-------------+----------------------------------   F(4, 4171826)   =    113.65
       Model |  84.7420868         4  21.1855217   Prob > F        =    0.0000
    Residual |  777638.991 4,171,826  .186402547   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  777723.733 4,171,830  .186422681   Root MSE        =    .43174

------------------------------------------------------------------------------
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  WTPhatFull |   .0005783   .0000296    19.55   0.000     .0005204    .0006363
      female |  -.0001746    .000425    -0.41   0.681    -.0010076    .0006584
 birthweight |   2.45e-06   3.95e-07     6.21   0.000     1.68e-06    3.23e-06
     weekend |  -.0012277    .000512    -2.40   0.016    -.0022312   -.0002243
       _cons |  -.1534205   .0200629    -7.65   0.000     -.192743    -.114098
------------------------------------------------------------------------------

. local wtpC `=_b[WTPhat]'

. local N2 = e(N)

. 
. local beta1 = 1000*`wtpF'

. local beta2 = 1000*`wtpC'

. 
. restore

. replace WTPests1 = `wtpF'    in 1
(1 real change made)

. replace WTPests2 = `wtpC'    in 1
(1 real change made)

. drop betaSOB

. 
. 
. *-- Bootstrap N=100 --------------------------------------------------------
. set seed 1307

. 
. local j = 2

. local N = 100

. foreach num of numlist 1(1)`N' {
  2.     dis "BOOTSTRAP REPLICATION `num'"
  3.     preserve
  4. 
.     use "$NVS/nvss2005_2013_BSAMP.dta", clear 
  5.     gen ageSq = age*age
  6.     gen birthweight = bweightLin
  7.     bsample
  8.     local controls smoker WIC underweight overweight obese noART hispanic
  9.     local controls female birthweight weekend
 10. 
.     estimates restore WTPfull
 11.     predict WTPhatFull, xb
 12.     
.     qui reg quarter2 WTPhatFull if smoker!=.&gest!=.
 13.     local wtpF `=_b[WTPhatFull]'
 14. 
.     qui reg quarter2 WTPhatFull `controls' if smoker!=.&gest!=.
 15.     local wtpC `=_b[WTPhatFull]'
 16. 
.     restore
 17.     replace WTPests1  = `wtpF'    in `j'
 18.     replace WTPests2  = `wtpC'    in `j'
 19.     
.     local ++j
 20. }
BOOTSTRAP REPLICATION 1
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 2
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 3
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 4
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 5
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 6
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 7
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 8
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 9
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 10
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 11
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 12
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 13
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 14
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 15
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 16
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 17
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 18
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 19
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 20
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 21
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 22
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 23
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 24
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 25
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 26
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 27
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 28
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 29
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 30
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 31
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 32
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 33
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 34
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 35
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 36
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 37
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 38
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 39
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 40
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 41
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 42
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 43
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 44
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 45
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 46
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 47
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 48
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 49
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 50
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 51
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 52
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 53
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 54
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 55
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 56
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 57
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 58
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 59
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 60
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 61
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 62
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 63
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 64
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 65
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 66
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 67
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 68
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 69
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 70
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 71
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 72
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 73
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 74
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 75
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 76
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 77
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 78
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 79
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 80
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 81
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 82
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 83
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 84
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 85
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 86
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 87
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 88
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 89
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 90
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 91
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 92
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 93
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 94
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 95
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 96
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 97
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 98
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 99
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)
BOOTSTRAP REPLICATION 100
(10,700 missing values generated)
(results WTPfull are active now)
(1 real change made)
(1 real change made)

. foreach num of numlist 1(1)2 {
  2.     sum WTPests`num'
  3.     local se`num' = 1000*r(sd)
  4. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    WTPests1 |        101    .0005948    .0000282    .000512   .0006788

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    WTPests2 |        101    .0005785    .0000284   .0004969   .0006641

. 
. local betas

. local stdes

. file open table8 using "$OUT/WTPspringBirth.tex", replace write

. file write table8 "WTP (1000 USD)"

. 
. foreach num of numlist 1(1)2 {
  2.     local se = `se`num''
  3.     local t = `beta`num''/`se'
  4.     local beta `=string(`beta`num'', "%5.3f")'
  5.     file write table8 "&" `"`macval(beta)'"'
  6.     if abs(`t')>`tL1' file write table8 "$^{\ddagger}$"
  7. }

. file write table8 "\\" _n 

. foreach num of numlist 1(1)2 {
  2.     local se `=string(`se`num'', "%5.3f")'
  3.     file write table8 "& [" `"`macval(se)'"' "]"
  4. }

. file write table8 "\\ \\" _n

. file write table8 "Mean WTP (NVSS data) "

. foreach num of numlist 1(1)2 {
  2.     local mean `=string(`mean`num'', "%7.2f")'
  3.     file write table8 "& \\$" `"`macval(mean)'"'
  4. }

. file write table8 "\\" _n "Observations "

. foreach num of numlist 1(1)2 {
  2.     local NN `=string(`N`num'', "%12.0gc")'
  3.     file write table8 "&" `"`macval(NN)'"' 
  4. }

. file write table8 "\\" _n

. file close table8

. 
. *-------------------------------------------------------------------------------
. *-- (6) Heterogeneity using mixed logit [TABLE A21]
. *-------------------------------------------------------------------------------
. gen bweightLin = 2500 if birthweight=="5 pounds 8 ounces"
(48,958 missing values generated)

. replace bweightLin = 2637 if birthweight=="5 pounds 13 ounces"
(2,361 real changes made)

. replace bweightLin = 2807 if birthweight=="6 pounds 3 ounces"
(2,304 real changes made)

. replace bweightLin = 2948 if birthweight=="6 pounds 8 ounces"
(2,238 real changes made)

. replace bweightLin = 3090 if birthweight=="6 pounds 13 ounces"
(2,385 real changes made)

. replace bweightLin = 3260 if birthweight=="7 pounds 3 ounces"
(2,323 real changes made)

. replace bweightLin = 3402 if birthweight=="7 pounds 8 ounces"
(2,326 real changes made)

. replace bweightLin = 3544 if birthweight=="7 pounds 13 ounces"
(2,326 real changes made)

. replace bweightLin = 3714 if birthweight=="8 pounds 3 ounces"
(2,373 real changes made)

. replace bweightLin = 3856 if birthweight=="8 pounds 8 ounces"
(2,353 real changes made)

. replace bweightLin = 4000 if birthweight=="8 pounds 13 ounces"
(2,433 real changes made)

. 
. qui reg chosen `oFEs' _sob* _cost* _gend* _bwt* _dob*

. local tvL  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))

. local pvL  = ttail(e(N),sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)))*2

. dis `pvL'
.00099478

. 
. cap gen all=1 

. local conds osample==1 all==1

. 
. local j = 1

. foreach name in married all {
  2.     if `j'==1 local cond osample==1
  3.     if `j'==2 local cond all==1
  4.     
.     cap gen price = costNumerical
  5.     cap gen group = 1000*ID+round
  6.     cap tab round, gen(_rr)
  7.     cap tab option, gen(_oo)
  8.     local bwts _bwt2 _bwt3 _bwt4 _bwt5 _bwt6 _bwt7 _bwt8 _bwt9 _bwt10 _bwt11
  9.     
.     mixlogit chosen price if `cond', id(ID) group(group) rand(_sob* _gend* `bwts' _dob2)
 10.     estimates store ml`name'
 11.     levelsof ID if e(sample)==1
 12.     local numR = r(r)
 13.     estadd scalar numR = `numR'
 14.     estadd scalar pcb = 100*normal(_b[Mean:_sob2]/abs(_b[SD:_sob2]))
 15.     local price = _b[price]
 16.     
.     estadd scalar wtp = -1000*(_b[_sob2]/_b[price])
 17.     nlcom ratio:_b[_sob2]/_b[price], post
 18.     local lb = string(-1000*(_b[ratio]-1.96*_se[ratio]), "%5.1f")
 19.     local ub = string(-1000*(_b[ratio]+1.96*_se[ratio]), "%5.1f")
 20.     estadd local conf95 "[`ub';`lb']": ml`name'
 21. 
.     mixlogit chosen price if `cond', id(ID) group(group) rand(_sob* _gend* bweightLin)
 22.     estimates store mlbw`name'
 23.     levelsof ID if e(sample)==1
 24.     local numR = r(r)
 25.     estadd scalar numR = `numR'
 26.     estadd scalar pcb = 100*normal(_b[Mean:_sob2]/abs(_b[SD:_sob2]))
 27. 
.     estadd scalar wtp = -1000*(_b[_sob2]/_b[price])
 28.     nlcom ratio:_b[_sob2]/_b[price], post
 29.     local lb = string(-1000*(_b[ratio]-1.96*_se[ratio]), "%5.1f")
 30.     local ub = string(-1000*(_b[ratio]+1.96*_se[ratio]), "%5.1f")
 31.     estadd local conf95 "[`ub';`lb']": mlbw`name'
 32.     local ++j
 33. }

Iteration 0:   log likelihood = -2138.8199  (not concave)
Iteration 1:   log likelihood = -2133.9428  (not concave)
Iteration 2:   log likelihood = -2047.9186  
Iteration 3:   log likelihood =  -1990.347  
Iteration 4:   log likelihood = -1987.3319  
Iteration 5:   log likelihood = -1985.7122  
Iteration 6:   log likelihood =   -1985.69  
Iteration 7:   log likelihood =   -1985.69  

Mixed logit model                               Number of obs     =     10,304
                                                LR chi2(15)       =     309.58
Log likelihood =   -1985.69                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Mean         |
       price |  -.8081068   .0335562   -24.08   0.000    -.8738758   -.7423378
       _sob4 |   .3271623   .1136353     2.88   0.004     .1044413    .5498833
       _sob2 |   .5540779   .1055994     5.25   0.000     .3471068     .761049
       _sob3 |   .1951125   .1085422     1.80   0.072    -.0176262    .4078512
      _gend2 |   .0541549   .1220975     0.44   0.657    -.1851517    .2934616
       _bwt2 |  -.3366351   .3341917    -1.01   0.314    -.9916388    .3183685
       _bwt3 |   1.363626   .2762632     4.94   0.000     .8221599    1.905092
       _bwt4 |   1.691693   .2906602     5.82   0.000      1.12201    2.261377
       _bwt5 |   1.693127   .2855026     5.93   0.000     1.133552    2.252702
       _bwt6 |   2.006934     .27905     7.19   0.000     1.460005    2.553862
       _bwt7 |   1.994968   .2943581     6.78   0.000     1.418037    2.571899
       _bwt8 |   1.733196   .2810964     6.17   0.000     1.182257    2.284135
       _bwt9 |   2.691098   .3291368     8.18   0.000     2.046002    3.336194
      _bwt10 |   1.851811   .2911245     6.36   0.000     1.281217    2.422404
      _bwt11 |   1.703866   .2786117     6.12   0.000     1.157797    2.249935
       _dob2 |  -.0445613   .0977134    -0.46   0.648    -.2360759    .1469534
-------------+----------------------------------------------------------------
SD           |
       _sob4 |   .8487358   .1962763     4.32   0.000     .4640413     1.23343
       _sob2 |  -.1582751   .2591424    -0.61   0.541     -.666185    .3496347
       _sob3 |   .5892637   .2091471     2.82   0.005     .1793429    .9991844
      _gend2 |   2.498974   .1830458    13.65   0.000     2.140211    2.857738
       _bwt2 |   2.219199   .3921719     5.66   0.000     1.450556    2.987842
       _bwt3 |   .7396806   .4960349     1.49   0.136    -.2325299    1.711891
       _bwt4 |   .5621759   .4038606     1.39   0.164    -.2293764    1.353728
       _bwt5 |   1.054805   .4622647     2.28   0.022     .1487832    1.960828
       _bwt6 |   .0033105   .4071707     0.01   0.994    -.7947295    .8013505
       _bwt7 |   .9561806   .3784027     2.53   0.012      .214525    1.697836
       _bwt8 |  -.1913423   .4556231    -0.42   0.675    -1.084347    .7016626
       _bwt9 |   1.744915   .3706972     4.71   0.000     1.018362    2.471468
      _bwt10 |  -1.344427   .4434153    -3.03   0.002    -2.213505   -.4753493
      _bwt11 |   .8074068   .4307137     1.87   0.061    -.0367766     1.65159
       _dob2 |  -.0213062   .2740107    -0.08   0.938    -.5583572    .5157448
------------------------------------------------------------------------------
The sign of the estimated standard deviations is irrelevant: interpret them as
being positive
1 10 20 21 23 38 56 59 61 62 64 67 68 71 74 86 88 91 92 98 102 103 109 116 117 119 122 129 133 137 138 141 
> 142 145 163 164 165 167 169 176 188 193 200 204 208 211 212 215 223 233 236 237 241 243 251 254 255 256 2
> 58 260 267 272 275 281 284 287 290 291 292 296 304 310 319 320 327 334 336 339 347 348 354 356 362 364 36
> 6 367 384 392 395 402 405 408 409 415 416 418 420 426 432 437 439 440 442 444 480 522 523 530 533 540 548
>  558 568 574 579 584 592 619 623 624 626 631 642 644 646 655 659 671 673 674 679 681 683 694 696 706 708 
> 712 720 726 730 733 741 745 746 747 750 755 765 767 775 787 794 798 807 814 815 816 821 823 827 832 836 8
> 55 856 859 861 862 866 869 870 874 877 883 890 891 899 901 912 917 918 930 931 939 940 946 947 952 955 96
> 4 966 968 970 983 988 991 993 994 996 1002 1004 1013 1024 1028 1029 1048 1054 1057 1062 1063 1064 1067 10
> 76 1085 1093 1106 1108 1109 1112 1115 1124 1128 1129 1136 1148 1149 1152 1153 1156 1164 1169 1174 1176 11
> 84 1185 1193 1206 1208 1209 1213 1214 1216 1226 1232 1235 1239 1249 1250 1254 1264 1272 1289 1293 1295 13
> 00 1301 1303 1305 1307 1310 1311 1313 1327 1335 1337 1338 1340 1345 1351 1356 1357 1365 1366 1370 1377 13
> 78 1381 1384 1387 1388 1389 1393 1399 1400 1401 1407 1415 1416 1419 1421 1422 1429 1433 1435 1436 1441 14
> 43 1460 1462 1464 1465 1473 1478 1487 1491 1493 1496 1498 1502 1509 1516 1517 1524 1528 1529 1538 1544 15
> 45 1553 1555 1557 1564 1565 1571 1575 1579 1580 1587 1599 1615 1617 1629 1640 1644 1645 1649 1651 1657 16
> 60 1665 1667 1672 1676 1683 1690 1699 1701 1704 1712 1713 1716 1717 1727 1728 1732 1736 1738 1749 1754 17
> 55 1756 1759 1765 1775 1778 1781 1784 1786 1792 1797 1804 1808 1813 1815 1822 1828 1832 1835 1838 1839 18
> 41 1843 1847 1851 1859 1862 1875 1876 1889 1891 1901 1910 1912 1915 1925 1934 1935 1940 1942 1943 1949 19
> 52 1953 1958 1963 1966 1971 1972 1973 1977 1978 1983 1990 1992 1993 1996 1997 2008 2011 2017 2018 2025 20
> 31 2034 2040 2048 2058 2072 2093 2095 2102 2115 2119 2123 2125 2142 2146 2147 2159 2173 2185 2191 2194 22
> 02 2209 2224 2228 2230 2234 2235 2236 2240 2246 2255 2256 2262 2276 2289 2291 2297 2301 2310 2313 2314 23
> 24 2340 2342 2354 2361 2364 2371 2372 2375 2393 2394 2395 2396 2407 2411 2421 2422 2425 2426 2429 2460 24
> 61 2466 2473 2482 2484 2487 2490 2496 2497 2502 2503 2506 2516 2517 2519 2521 2541 2552 2553 2557 2562 25
> 74 2575 2581 2585 2586 2616 2625 2633 2635 2636 2638 2653 2663 2668 2674 2679 2690 2692 2714 2723 2724 27
> 26 2728 2732 2734 2735 2752 2753 2755 2770 2776 2777 2779 2801 2803 2815 2826 2829 2833 2856 2857 2859 28
> 63 2866 2868 2873 2874 2878 2880 2886 2888 2903 2904 2921 2929 2933 2935 2946 2950 2956 2970 2973 2976 29
> 81 2984 2986 2998 3007 3009 3013 3032 3037 3050 3066 3071 3072 3073 3081 3099 3130 3131 3137 3142 3143 31
> 45 3146 3150 3152 3153 3155 3160 3163 3164 3166 3169 3176 3177 3181 3195 3208 3210 3216 3224 3233 3244 32
> 46 3251 3287 3290 3299 3307 3312 3315 3318 3319 3320 3342 3343 3344 3348 3350 3351 3353 3364 3370 3378 33
> 88 3390 3410 3414 3422 3425 3426 3432 3433 3434 3435 3439 3450 3456 3458 3460 3462 3464 3475 3495 3501 35
> 05 3508 3511 3512 3516 3519 3520 3523 3536 3539 3553 3559 3560 3568 3572 3576 3582 3586 3595 3599 3604 36
> 09 3624 3638 3640 3641 3644 3648 3651 3652 3659 3662 3672 3674 3681 3686 3687 3697 3699 3702 3707 3711 37
> 12 3715 3727 3729 3735 3741 3745 3751 3754 3756 3765 3794 3796 3803 3814 3854 3862 3863 3864 3870 3880 38
> 86 3889 3892 3893 3894 3895 3901 3902 3905 3906 3907 3913 3929 3932 3949 3950 3958 3988 3992 3993

added scalar:
               e(numR) =  736

added scalar:
                e(pcb) =  99.9768

added scalar:
                e(wtp) =  685.64939

       ratio:  _b[_sob2]/_b[price]

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.6856494   .1286099    -5.33   0.000    -.9377202   -.4335786
------------------------------------------------------------------------------

Iteration 0:   log likelihood = -2581.7285  (not concave)
Iteration 1:   log likelihood =  -1759.945  (not concave)
Iteration 2:   log likelihood = -1364.2038  (not concave)
Iteration 3:   log likelihood = -1101.3501  
Iteration 4:   log likelihood = -1073.4599  
Iteration 5:   log likelihood = -1071.8475  
Iteration 6:   log likelihood = -1071.8402  
Iteration 7:   log likelihood = -1071.8402  

Mixed logit model                               Number of obs     =      4,914
                                                LR chi2(5)        =     140.25
Log likelihood = -1071.8402                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Mean         |
       price |  -.6454735   .0371204   -17.39   0.000    -.7182281   -.5727189
       _sob4 |    .160288   .1467163     1.09   0.275    -.1272708    .4478467
       _sob2 |   .4955823   .1422886     3.48   0.000     .2167017    .7744628
       _sob3 |   .0682021   .1450405     0.47   0.638    -.2160722    .3524763
      _gend2 |    .290982   .1545867     1.88   0.060    -.0120024    .5939664
  bweightLin |   .0010828   .0001627     6.66   0.000      .000764    .0014017
-------------+----------------------------------------------------------------
SD           |
       _sob4 |  -.6573416   .3067233    -2.14   0.032    -1.258508    -.056175
       _sob2 |  -.0008167   .3393666    -0.00   0.998     -.665963    .6643295
       _sob3 |   -.558106    .300846    -1.86   0.064    -1.147753    .0315414
      _gend2 |   2.125525   .2260608     9.40   0.000     1.682454    2.568596
  bweightLin |  -.0017882   .0002264    -7.90   0.000    -.0022319   -.0013446
------------------------------------------------------------------------------
The sign of the estimated standard deviations is irrelevant: interpret them as
being positive
21 61 67 68 74 88 102 117 119 133 141 142 145 164 188 204 208 211 215 223 237 241 251 267 272 275 281 284 2
> 87 290 292 296 304 310 320 327 334 339 347 348 354 356 362 364 366 384 395 402 409 415 416 437 440 442 44
> 4 533 540 558 579 584 619 623 624 626 671 679 708 720 726 730 741 745 746 747 750 798 807 815 816 821 823
>  827 832 836 859 861 862 866 877 912 917 930 931 952 964 966 970 983 988 1002 1013 1054 1062 1063 1076 10
> 93 1112 1129 1149 1156 1174 1184 1185 1214 1226 1272 1289 1293 1300 1303 1305 1307 1310 1335 1337 1338 13
> 40 1356 1357 1366 1378 1384 1389 1401 1407 1419 1422 1435 1441 1464 1465 1473 1491 1493 1496 1509 1516 15
> 17 1544 1545 1555 1557 1564 1565 1579 1587 1599 1615 1644 1645 1649 1657 1660 1676 1699 1704 1713 1727 17
> 28 1732 1738 1749 1759 1778 1781 1786 1808 1813 1822 1839 1859 1889 1891 1912 1925 1934 1942 1952 1953 19
> 72 1973 1983 1990 1992 1993 1997 2008 2017 2040 2048 2058 2095 2119 2123 2125 2142 2147 2173 2185 2191 21
> 94 2224 2234 2235 2240 2246 2255 2256 2262 2276 2289 2291 2324 2340 2342 2354 2361 2372 2375 2393 2407 24
> 22 2425 2429 2466 2473 2490 2502 2503 2517 2541 2553 2557 2616 2625 2635 2663 2668 2692 2732 2734 2735 27
> 52 2770 2776 2829 2856 2857 2859 2863 2880 2903 2921 2933 2935 2946 2950 2970 2981 3013 3032 3072 3081 31
> 31 3146 3152 3153 3160 3164 3177 3195 3233 3246 3287 3299 3312 3315 3342 3343 3351 3364 3370 3378 3390 34
> 10 3422 3426 3432 3433 3439 3458 3460 3462 3495 3508 3511 3512 3516 3519 3536 3553 3559 3560 3572 3586 36
> 09 3624 3638 3640 3641 3648 3652 3681 3687 3697 3699 3702 3707 3711 3712 3715 3727 3729 3754 3765 3796 38
> 03 3814 3862 3863 3870 3886 3889 3892 3893 3894 3901 3906 3950 3988 3992

added scalar:
               e(numR) =  351

added scalar:
                e(pcb) =  100

added scalar:
                e(wtp) =  767.78096

       ratio:  _b[_sob2]/_b[price]

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |   -.767781   .2184121    -3.52   0.000    -1.195861   -.3397011
------------------------------------------------------------------------------

Iteration 0:   log likelihood = -11383.701  (not concave)
Iteration 1:   log likelihood = -11374.061  (not concave)
Iteration 2:   log likelihood = -11358.913  (not concave)
Iteration 3:   log likelihood = -11177.265  
Iteration 4:   log likelihood = -10810.711  
Iteration 5:   log likelihood = -10765.462  (not concave)
Iteration 6:   log likelihood = -10764.173  
Iteration 7:   log likelihood = -10760.061  
Iteration 8:   log likelihood =  -10760.03  
Iteration 9:   log likelihood =  -10760.03  

Mixed logit model                               Number of obs     =     51,254
                                                LR chi2(15)       =    1265.12
Log likelihood =  -10760.03                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Mean         |
       price |  -.6550564   .0113456   -57.74   0.000    -.6772934   -.6328194
       _sob4 |   .3050118   .0430718     7.08   0.000     .2205927     .389431
       _sob2 |   .4234934   .0427593     9.90   0.000     .3396867    .5073001
       _sob3 |   .2140585   .0421374     5.08   0.000     .1314708    .2966462
      _gend2 |  -.0509567   .0463613    -1.10   0.272    -.1418232    .0399098
       _bwt2 |  -.1066781   .1300723    -0.82   0.412     -.361615    .1482589
       _bwt3 |   1.106547   .1114798     9.93   0.000     .8880503    1.325043
       _bwt4 |    1.40673   .1082533    12.99   0.000     1.194558    1.618903
       _bwt5 |   1.144639   .1070535    10.69   0.000     .9348175     1.35446
       _bwt6 |     1.6937   .1075149    15.75   0.000     1.482974    1.904425
       _bwt7 |   1.749916   .1104799    15.84   0.000     1.533379    1.966453
       _bwt8 |   1.569493   .1071754    14.64   0.000     1.359433    1.779553
       _bwt9 |   1.794247   .1119789    16.02   0.000     1.574772    2.013721
      _bwt10 |   1.509874   .1082648    13.95   0.000     1.297679    1.722069
      _bwt11 |   1.465816   .1074913    13.64   0.000     1.255137    1.676495
       _dob2 |    .039912   .0398426     1.00   0.316    -.0381781    .1180022
-------------+----------------------------------------------------------------
SD           |
       _sob4 |  -.3912489   .1321591    -2.96   0.003     -.650276   -.1322217
       _sob2 |   -.135375   .1529574    -0.89   0.376    -.4351659    .1644159
       _sob3 |   .0656298   .1473259     0.45   0.656    -.2231237    .3543832
      _gend2 |   2.054639   .0660909    31.09   0.000     1.925104    2.184175
       _bwt2 |   2.205234   .1686174    13.08   0.000      1.87475    2.535718
       _bwt3 |   1.211146   .1709275     7.09   0.000     .8761343    1.546158
       _bwt4 |  -.4888271     .21857    -2.24   0.025    -.9172164   -.0604379
       _bwt5 |  -.7295177   .1793338    -4.07   0.000    -1.081005   -.3780299
       _bwt6 |   .0162373   .2489909     0.07   0.948    -.4717759    .5042505
       _bwt7 |  -.7280757   .2054428    -3.54   0.000    -1.130736   -.3254152
       _bwt8 |  -.3536173   .2543867    -1.39   0.165    -.8522061    .1449715
       _bwt9 |   1.037795   .1960557     5.29   0.000     .6535329    1.422057
      _bwt10 |   .6859268   .2409112     2.85   0.004     .2137495    1.158104
      _bwt11 |   .7594221   .1983281     3.83   0.000     .3707063    1.148138
       _dob2 |   .0675865   .1063656     0.64   0.525    -.1408863    .2760593
------------------------------------------------------------------------------
The sign of the estimated standard deviations is irrelevant: interpret them as
being positive
1 2 3 4 5 6 8 9 10 12 13 14 15 16 17 18 20 21 22 23 24 25 26 27 28 29 30 31 32 33 35 36 37 38 39 40 41 42 4
> 4 46 47 48 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 81 8
> 2 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 102 103 104 105 106 107 108 109 110 111 112 113 114 115
>  116 117 118 119 120 121 122 123 124 125 127 128 129 130 133 134 135 136 137 138 139 140 141 142 143 144 
> 145 146 147 149 151 152 153 154 155 157 159 160 161 162 163 164 165 166 167 169 170 171 172 173 174 175 1
> 76 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 199 200 201 202 20
> 3 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229
>  230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 247 248 249 250 251 253 254 255 256 258 
> 259 260 261 262 263 264 265 267 268 269 270 271 272 273 274 275 276 278 279 280 281 282 283 284 286 287 2
> 88 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 314 31
> 5 316 317 318 319 320 321 322 323 324 325 326 327 328 329 331 332 333 334 335 336 338 339 340 341 342 343
>  344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 370 
> 371 373 374 375 376 377 378 379 380 381 382 383 384 385 387 388 389 390 391 392 393 394 395 396 397 398 3
> 99 400 401 402 403 404 405 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 424 425 426 427 42
> 8 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454
>  455 456 457 458 459 460 461 462 463 464 465 466 468 469 470 471 472 473 474 475 476 477 478 479 480 481 
> 482 483 484 485 486 487 488 491 492 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 5
> 12 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 53
> 8 540 541 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 564 565 567 568
>  569 570 572 573 574 575 576 578 579 580 581 582 583 584 585 586 587 588 589 591 592 593 594 595 596 597 
> 598 599 600 602 603 604 605 606 608 609 610 611 613 614 615 616 618 619 620 621 622 623 624 625 626 627 6
> 30 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 649 650 651 652 653 655 656 657 65
> 8 659 661 662 663 664 666 667 668 669 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687
>  688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 
> 714 715 716 717 718 719 720 721 722 723 724 725 726 727 729 730 731 733 734 735 736 737 738 739 741 742 7
> 43 744 745 746 747 748 749 750 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 769 771 77
> 2 773 775 776 777 778 779 780 781 783 784 785 786 787 788 789 790 792 793 794 795 796 797 798 799 800 801
>  802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 821 822 823 825 826 827 828 829 
> 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 849 851 852 853 854 855 856 857 8
> 58 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 877 878 880 881 882 883 884 885 88
> 6 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 911 912 914
>  915 916 917 918 919 920 921 922 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 
> 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 958 959 960 962 963 964 965 966 967 968 969 9
> 70 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 99
> 7 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1011 1012 1013 1014 1015 1016 1017 1018 1019 
> 1020 1021 1022 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 
> 1042 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1060 1061 1062 1063 1064 1065 
> 1066 1067 1068 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 
> 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 
> 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 
> 1132 1133 1134 1136 1137 1138 1139 1140 1141 1142 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 
> 1155 1156 1157 1159 1160 1161 1162 1163 1164 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 
> 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1191 1192 1193 1194 1195 1196 1197 1198 1199 
> 1200 1201 1202 1204 1205 1206 1207 1208 1209 1210 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 
> 1223 1224 1225 1226 1227 1228 1229 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1245 
> 1246 1247 1248 1249 1250 1251 1253 1254 1255 1256 1259 1261 1262 1263 1264 1265 1266 1270 1271 1272 1273 
> 1274 1275 1276 1277 1278 1280 1281 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1295 1296 1297 
> 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1318 1319 
> 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 
> 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1362 
> 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 
> 1385 1386 1387 1388 1389 1391 1392 1393 1394 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 
> 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1427 1428 1429 
> 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1450 1451 
> 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1469 1471 1472 1473 1474 
> 1475 1477 1478 1479 1480 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1498 
> 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 
> 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1535 1536 1537 1538 1539 1540 1541 
> 1542 1543 1544 1545 1546 1547 1548 1549 1550 1552 1553 1554 1555 1557 1558 1559 1560 1561 1562 1563 1564 
> 1565 1567 1568 1570 1571 1572 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1587 1588 1589 
> 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 
> 1611 1612 1614 1615 1616 1617 1618 1619 1620 1622 1623 1624 1626 1627 1628 1629 1630 1631 1632 1633 1634 
> 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1654 1655 1656 
> 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1678 
> 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 
> 1700 1701 1702 1703 1704 1705 1706 1707 1709 1710 1711 1712 1713 1714 1715 1716 1717 1719 1720 1722 1723 
> 1724 1725 1726 1727 1728 1729 1730 1731 1732 1734 1735 1736 1738 1739 1740 1741 1742 1743 1744 1745 1746 
> 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 
> 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 
> 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 
> 1811 1812 1813 1814 1815 1817 1818 1819 1820 1822 1823 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 
> 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 
> 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1872 1873 1874 1875 1876 1877 1878 
> 1879 1880 1881 1882 1883 1884 1885 1886 1888 1889 1890 1891 1895 1896 1897 1898 1899 1900 1901 1902 1903 
> 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1920 1921 1922 1923 1924 1925 
> 1926 1927 1928 1929 1930 1931 1933 1934 1935 1936 1937 1938 1940 1942 1943 1945 1946 1947 1948 1949 1950 
> 1951 1952 1953 1954 1955 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1971 1972 1973 
> 1974 1975 1976 1977 1978 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 
> 1996 1997 1998 2000 2001 2003 2004 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 
> 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 
> 2041 2042 2043 2044 2046 2048 2049 2050 2051 2053 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 
> 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 
> 2092 2093 2094 2095 2097 2098 2099 2100 2101 2102 2103 2105 2106 2107 2108 2109 2110 2111 2113 2114 2115 
> 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2132 2133 2134 2135 2136 2137 
> 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2161 
> 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 
> 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 
> 2204 2205 2206 2207 2208 2209 2210 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 
> 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2239 2240 2241 2242 2243 2244 2245 2246 2247 
> 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2268 2269 
> 2270 2271 2273 2274 2275 2276 2279 2280 2281 2282 2284 2285 2286 2287 2289 2290 2291 2292 2293 2294 2295 
> 2296 2297 2298 2299 2300 2301 2302 2303 2304 2306 2307 2308 2309 2310 2312 2313 2314 2315 2316 2317 2318 
> 2319 2320 2321 2322 2323 2324 2325 2326 2327 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 
> 2341 2342 2343 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2359 2360 2361 2363 2364 
> 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 
> 2386 2387 2389 2390 2391 2392 2393 2394 2395 2396 2397 2399 2400 2401 2402 2405 2406 2407 2408 2409 2410 
> 2411 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2424 2425 2426 2427 2428 2429 2431 2432 2433 2434 
> 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 
> 2457 2458 2460 2461 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 
> 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 
> 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 
> 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2538 2539 2540 2541 2542 2543 2545 
> 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2559 2560 2561 2562 2563 2565 2567 2568 2569 
> 2570 2571 2572 2574 2575 2576 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2592 2593 2594 
> 2595 2596 2597 2598 2599 2601 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 
> 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2633 2634 2635 2636 2637 2638 2639 2640 2641 
> 2642 2643 2644 2645 2646 2647 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 
> 2664 2666 2668 2669 2670 2671 2674 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 
> 2690 2691 2692 2693 2694 2695 2696 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 
> 2713 2714 2716 2717 2718 2719 2720 2721 2722 2723 2724 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 
> 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2752 2753 2754 2755 2756 2757 
> 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 
> 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2797 2798 2799 2800 2801 2802 
> 2803 2806 2807 2808 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 
> 2827 2828 2829 2831 2832 2833 2834 2835 2836 2837 2838 2841 2842 2843 2844 2845 2847 2848 2849 2850 2851 
> 2854 2855 2856 2857 2858 2859 2860 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 
> 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 
> 2897 2898 2899 2900 2901 2903 2904 2905 2906 2907 2908 2909 2911 2912 2913 2915 2916 2917 2918 2919 2920 
> 2921 2922 2924 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 
> 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2955 2956 2957 2958 2960 2961 2962 2963 2964 2965 2966 
> 2968 2969 2970 2971 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 
> 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3005 3006 3007 3008 3009 3010 3011 
> 3012 3013 3014 3015 3016 3017 3018 3020 3021 3022 3023 3024 3025 3026 3027 3028 3030 3031 3032 3033 3035 
> 3036 3037 3039 3040 3041 3042 3043 3045 3046 3047 3048 3050 3051 3052 3053 3054 3055 3059 3060 3061 3062 
> 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3078 3079 3081 3082 3083 3084 3086 
> 3088 3090 3092 3093 3094 3095 3096 3097 3098 3099 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 
> 3112 3113 3114 3116 3118 3119 3120 3121 3122 3123 3124 3125 3127 3128 3129 3130 3131 3132 3133 3134 3135 
> 3137 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3159 
> 3160 3161 3163 3164 3165 3166 3167 3168 3169 3170 3171 3173 3175 3176 3177 3178 3180 3181 3182 3183 3184 
> 3185 3186 3187 3188 3189 3190 3191 3192 3193 3195 3196 3197 3198 3200 3201 3202 3203 3204 3205 3206 3207 
> 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 
> 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 
> 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3265 3266 3267 3268 3269 3270 3271 
> 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3290 3291 3292 3293 3294 3295 
> 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3315 3316 3317 
> 3318 3319 3320 3323 3324 3325 3327 3328 3330 3332 3333 3334 3335 3336 3337 3339 3341 3342 3343 3344 3345 
> 3346 3347 3348 3349 3350 3351 3352 3353 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 
> 3369 3370 3371 3372 3373 3374 3375 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 
> 3391 3392 3393 3394 3395 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 
> 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3425 3426 3428 3429 3430 3431 3432 3433 3434 3435 3436 
> 3437 3438 3439 3440 3441 3443 3444 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 
> 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3477 3478 3479 3480 3481 
> 3482 3483 3484 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 
> 3504 3505 3506 3507 3508 3509 3510 3511 3512 3514 3516 3517 3518 3519 3520 3522 3523 3524 3525 3526 3527 
> 3528 3530 3531 3532 3533 3534 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3550 3551 
> 3552 3553 3554 3555 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3568 3569 3570 3571 3572 3573 3574 
> 3575 3576 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3592 3593 3594 3595 3596 3597 
> 3598 3599 3600 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 
> 3620 3621 3623 3624 3625 3626 3627 3628 3629 3631 3633 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 
> 3645 3646 3647 3648 3649 3651 3652 3653 3654 3655 3656 3658 3659 3660 3661 3662 3663 3664 3666 3667 3668 
> 3669 3670 3672 3673 3674 3675 3676 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 
> 3692 3694 3695 3696 3697 3698 3699 3701 3702 3704 3705 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 
> 3717 3719 3720 3721 3722 3723 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 
> 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 
> 3762 3763 3764 3765 3766 3769 3770 3771 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 
> 3786 3787 3788 3789 3791 3792 3793 3794 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 
> 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 
> 3830 3831 3832 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3850 3851 3852 
> 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 
> 3875 3876 3877 3878 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 
> 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3913 3914 3916 3917 3918 3919 3920 
> 3921 3922 3923 3924 3925 3926 3927 3929 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 
> 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 
> 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3985 3986 
> 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999

added scalar:
               e(numR) =  3661

added scalar:
                e(pcb) =  99.912089

added scalar:
                e(wtp) =  646.49914

       ratio:  _b[_sob2]/_b[price]

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.6464991   .0644546   -10.03   0.000    -.7728278   -.5201705
------------------------------------------------------------------------------

Iteration 0:   log likelihood =  -13740.02  (not concave)
Iteration 1:   log likelihood = -9000.6943  (not concave)
Iteration 2:   log likelihood = -8106.6832  (not concave)
Iteration 3:   log likelihood = -6442.2062  (not concave)
Iteration 4:   log likelihood = -6026.2208  
Iteration 5:   log likelihood = -5933.4373  
Iteration 6:   log likelihood = -5917.5801  
Iteration 7:   log likelihood = -5917.4201  
Iteration 8:   log likelihood = -5917.4166  
Iteration 9:   log likelihood = -5917.4166  

Mixed logit model                               Number of obs     =     25,718
                                                LR chi2(5)        =     739.85
Log likelihood = -5917.4166                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Mean         |
       price |  -.5549975   .0131577   -42.18   0.000    -.5807862   -.5292089
       _sob4 |   .1868965   .0575759     3.25   0.001     .0740497    .2997433
       _sob2 |   .3185461   .0582692     5.47   0.000     .2043406    .4327516
       _sob3 |   .1223419   .0581864     2.10   0.036     .0082987    .2363851
      _gend2 |   .0463504   .0608771     0.76   0.446    -.0729665    .1656674
  bweightLin |   .0009195   .0000647    14.21   0.000     .0007927    .0010462
-------------+----------------------------------------------------------------
SD           |
       _sob4 |  -.2231845   .2317528    -0.96   0.336    -.6774116    .2310426
       _sob2 |  -.2659079   .1687656    -1.58   0.115    -.5966824    .0648667
       _sob3 |   -.244568   .2084857    -1.17   0.241    -.6531925    .1640564
      _gend2 |   1.847298    .084581    21.84   0.000     1.681523    2.013074
  bweightLin |   .0018008   .0000925    19.47   0.000     .0016195    .0019821
------------------------------------------------------------------------------
The sign of the estimated standard deviations is irrelevant: interpret them as
being positive
4 5 12 14 15 17 18 21 25 26 27 28 32 36 39 40 44 46 48 54 55 57 61 65 66 67 68 72 73 74 75 76 78 79 81 82 8
> 4 85 88 89 90 93 94 95 96 97 102 104 108 110 114 117 118 119 120 123 125 127 133 135 139 141 142 143 145 
> 147 149 151 160 162 164 170 171 172 173 174 175 177 178 179 186 187 188 189 191 192 195 204 207 208 211 2
> 13 215 216 217 220 222 223 226 227 228 230 235 237 238 239 240 241 244 247 248 251 259 262 264 267 269 27
> 1 272 273 275 276 280 281 282 284 287 288 289 290 292 293 294 296 298 299 300 301 304 306 307 308 310 312
>  314 315 316 317 320 321 322 323 325 327 328 329 331 334 339 340 341 342 343 344 345 347 348 349 353 354 
> 356 357 359 361 362 363 364 365 366 368 374 376 378 379 382 383 384 385 387 388 390 391 395 396 398 399 4
> 00 401 402 409 410 411 412 415 416 417 419 421 425 427 429 430 435 436 437 440 441 442 443 444 445 447 44
> 8 449 450 452 455 457 459 461 462 466 468 469 470 471 472 473 474 485 487 492 494 495 497 498 502 503 504
>  505 507 510 512 513 514 516 518 525 527 528 529 533 534 535 540 543 544 545 549 552 553 554 555 556 558 
> 559 560 561 562 564 565 567 570 572 575 576 579 583 584 586 587 588 589 595 597 598 599 600 602 603 606 6
> 13 614 615 618 619 620 621 623 624 625 626 627 632 633 634 635 636 637 639 640 641 645 647 649 652 653 65
> 6 657 658 663 666 668 671 672 678 679 684 685 687 689 691 693 695 697 698 699 700 702 703 705 707 708 710
>  711 713 715 716 718 720 721 723 725 726 727 729 730 731 734 735 736 741 742 743 745 746 747 748 750 752 
> 753 754 760 762 764 776 777 778 781 786 789 792 795 796 797 798 801 803 805 806 807 808 811 815 816 817 8
> 18 819 821 823 825 826 827 828 831 832 834 836 841 842 843 844 845 847 851 852 858 859 860 861 862 863 86
> 4 865 866 868 871 877 878 880 882 885 889 893 894 896 898 900 902 904 907 908 911 912 915 917 919 922 924
>  930 931 932 933 934 935 944 945 949 950 951 952 954 959 963 964 966 969 970 973 974 975 977 980 981 982 
> 983 984 988 989 992 995 997 998 999 1002 1005 1008 1013 1014 1019 1021 1022 1030 1032 1035 1040 1041 1045
>  1054 1058 1062 1063 1066 1071 1074 1076 1077 1079 1081 1082 1083 1086 1090 1092 1093 1096 1098 1101 1102
>  1105 1111 1112 1113 1116 1117 1118 1119 1120 1121 1122 1123 1129 1130 1131 1132 1134 1137 1138 1141 1145
>  1146 1147 1149 1154 1155 1156 1157 1162 1163 1167 1168 1170 1172 1173 1174 1175 1178 1179 1180 1182 1183
>  1184 1185 1186 1188 1189 1194 1195 1196 1197 1199 1204 1207 1210 1212 1214 1215 1218 1219 1224 1226 1227
>  1228 1229 1231 1234 1236 1240 1241 1242 1245 1246 1248 1251 1253 1255 1259 1262 1263 1266 1272 1273 1276
>  1281 1285 1286 1288 1289 1290 1291 1293 1296 1299 1300 1303 1305 1307 1310 1315 1319 1326 1328 1330 1332
>  1334 1335 1336 1337 1338 1340 1341 1342 1347 1348 1350 1354 1355 1356 1357 1364 1366 1369 1372 1373 1375
>  1378 1379 1383 1384 1386 1389 1391 1392 1401 1402 1403 1404 1405 1407 1408 1410 1412 1418 1419 1420 1422
>  1423 1425 1427 1431 1435 1437 1438 1441 1444 1448 1450 1452 1454 1455 1456 1457 1464 1465 1466 1473 1474
>  1477 1479 1480 1483 1484 1485 1488 1489 1490 1491 1492 1493 1496 1499 1500 1501 1504 1505 1506 1508 1509
>  1510 1515 1516 1517 1518 1519 1520 1521 1523 1525 1530 1532 1533 1539 1540 1541 1543 1544 1545 1546 1548
>  1550 1555 1557 1558 1559 1560 1562 1564 1565 1572 1579 1581 1582 1585 1587 1589 1590 1591 1592 1593 1594
>  1598 1599 1600 1601 1603 1609 1610 1615 1616 1618 1619 1620 1626 1631 1632 1634 1636 1642 1644 1645 1649
>  1652 1654 1655 1656 1657 1660 1661 1668 1671 1673 1675 1676 1679 1685 1686 1687 1688 1689 1692 1697 1698
>  1699 1702 1703 1704 1713 1722 1723 1725 1727 1728 1731 1732 1738 1740 1741 1744 1747 1749 1750 1751 1752
>  1753 1759 1760 1761 1762 1763 1766 1774 1778 1779 1780 1781 1783 1785 1786 1787 1788 1794 1796 1799 1801
>  1803 1805 1807 1808 1811 1812 1813 1814 1817 1818 1819 1820 1822 1823 1827 1831 1837 1839 1842 1844 1849
>  1855 1857 1859 1861 1868 1873 1878 1879 1883 1885 1886 1888 1889 1891 1899 1900 1902 1903 1906 1907 1908
>  1909 1911 1912 1913 1914 1916 1920 1921 1922 1924 1925 1926 1928 1934 1942 1945 1946 1947 1948 1952 1953
>  1957 1962 1964 1972 1973 1974 1980 1983 1985 1986 1988 1990 1991 1992 1993 1994 1997 2000 2006 2007 2008
>  2009 2015 2017 2027 2029 2030 2033 2035 2040 2041 2042 2043 2046 2048 2049 2058 2060 2061 2062 2063 2066
>  2073 2074 2077 2078 2081 2082 2084 2090 2092 2095 2099 2100 2103 2107 2110 2113 2114 2116 2117 2119 2120
>  2121 2122 2123 2124 2125 2126 2127 2129 2130 2137 2139 2141 2142 2144 2147 2148 2151 2153 2155 2157 2158
>  2161 2164 2165 2167 2168 2172 2173 2175 2176 2177 2180 2182 2184 2185 2186 2187 2191 2194 2195 2196 2198
>  2199 2200 2201 2203 2204 2205 2208 2212 2213 2214 2216 2217 2219 2220 2221 2223 2224 2226 2227 2234 2235
>  2237 2240 2241 2243 2244 2245 2246 2247 2250 2251 2255 2256 2257 2258 2262 2265 2266 2268 2270 2271 2274
>  2275 2276 2279 2284 2285 2289 2290 2291 2293 2295 2296 2299 2300 2302 2304 2307 2308 2312 2315 2317 2321
>  2322 2324 2325 2327 2329 2330 2331 2332 2335 2336 2337 2338 2339 2340 2341 2342 2343 2346 2349 2352 2354
>  2355 2356 2359 2361 2365 2367 2370 2372 2373 2374 2375 2376 2377 2379 2380 2381 2382 2384 2385 2386 2387
>  2391 2392 2393 2399 2400 2401 2402 2405 2406 2407 2416 2417 2419 2422 2424 2425 2429 2431 2432 2433 2434
>  2436 2440 2441 2442 2448 2449 2450 2455 2456 2457 2458 2465 2466 2468 2469 2470 2471 2472 2473 2474 2475
>  2480 2486 2488 2490 2492 2493 2494 2498 2502 2503 2504 2505 2507 2508 2510 2511 2515 2517 2518 2522 2523
>  2526 2528 2529 2530 2535 2538 2539 2541 2545 2549 2550 2553 2554 2557 2560 2561 2563 2565 2567 2569 2570
>  2571 2576 2582 2583 2584 2589 2593 2596 2605 2606 2607 2608 2611 2616 2619 2620 2622 2623 2624 2625 2626
>  2627 2628 2630 2631 2634 2635 2640 2641 2647 2649 2650 2651 2654 2655 2656 2657 2658 2661 2662 2663 2664
>  2666 2668 2669 2671 2676 2678 2691 2692 2699 2700 2701 2702 2703 2704 2706 2707 2708 2711 2718 2720 2722
>  2729 2730 2732 2734 2735 2737 2738 2739 2740 2742 2743 2744 2748 2750 2752 2754 2756 2759 2760 2762 2764
>  2765 2768 2770 2771 2772 2773 2776 2780 2782 2783 2785 2786 2787 2790 2791 2792 2793 2798 2799 2802 2806
>  2807 2808 2812 2825 2828 2829 2831 2832 2834 2835 2843 2844 2845 2847 2849 2850 2854 2856 2857 2858 2859
>  2860 2863 2867 2869 2870 2871 2872 2876 2877 2880 2882 2885 2890 2893 2894 2897 2898 2899 2900 2901 2903
>  2905 2908 2909 2912 2913 2915 2916 2917 2918 2921 2922 2924 2926 2928 2930 2931 2933 2934 2935 2936 2937
>  2938 2939 2942 2943 2946 2947 2950 2951 2952 2953 2955 2958 2961 2962 2966 2969 2970 2974 2977 2978 2981
>  2983 2987 2988 2989 2993 2994 2995 2999 3001 3005 3006 3008 3010 3012 3013 3016 3024 3030 3031 3032 3040
>  3046 3048 3052 3053 3059 3061 3062 3068 3069 3072 3076 3079 3081 3083 3086 3090 3094 3097 3098 3101 3102
>  3103 3105 3106 3107 3109 3110 3111 3118 3121 3122 3123 3124 3127 3131 3134 3140 3141 3146 3149 3151 3152
>  3153 3154 3156 3157 3159 3160 3164 3167 3170 3171 3173 3177 3180 3183 3184 3186 3187 3188 3189 3191 3195
>  3198 3201 3203 3204 3206 3212 3214 3215 3217 3218 3219 3222 3227 3228 3229 3230 3233 3237 3238 3242 3243
>  3245 3246 3247 3248 3253 3254 3256 3260 3265 3266 3267 3270 3275 3279 3284 3285 3287 3295 3296 3298 3299
>  3301 3304 3305 3306 3308 3309 3312 3315 3316 3317 3323 3325 3330 3335 3336 3339 3342 3343 3345 3347 3349
>  3351 3356 3359 3360 3364 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3377 3378 3380 3383 3387 3390
>  3397 3402 3403 3404 3405 3406 3410 3416 3417 3418 3419 3422 3426 3428 3429 3432 3433 3438 3439 3440 3441
>  3447 3448 3449 3452 3453 3457 3458 3460 3461 3462 3463 3468 3470 3471 3473 3477 3478 3479 3484 3486 3487
>  3488 3489 3491 3495 3497 3498 3500 3503 3504 3506 3508 3510 3511 3512 3514 3516 3519 3522 3526 3532 3533
>  3534 3536 3550 3552 3553 3554 3557 3559 3560 3564 3565 3570 3572 3579 3583 3586 3587 3596 3600 3603 3605
>  3606 3608 3609 3610 3612 3614 3616 3620 3621 3624 3628 3629 3631 3638 3640 3641 3642 3646 3647 3648 3649
>  3652 3656 3661 3664 3667 3668 3670 3673 3675 3676 3678 3679 3681 3682 3684 3685 3687 3695 3697 3698 3699
>  3702 3705 3707 3710 3711 3712 3713 3714 3715 3721 3722 3723 3725 3727 3729 3730 3732 3734 3738 3739 3740
>  3742 3744 3748 3749 3754 3755 3759 3761 3762 3764 3765 3766 3770 3771 3773 3776 3777 3779 3781 3782 3783
>  3784 3785 3787 3788 3796 3798 3799 3800 3802 3803 3805 3806 3809 3810 3811 3812 3814 3815 3817 3819 3820
>  3821 3823 3824 3827 3828 3829 3831 3832 3834 3835 3836 3837 3839 3841 3843 3844 3845 3846 3847 3851 3855
>  3856 3857 3859 3861 3862 3863 3866 3868 3870 3871 3872 3873 3877 3878 3881 3882 3883 3886 3887 3888 3889
>  3890 3892 3893 3894 3898 3900 3901 3906 3908 3910 3911 3914 3919 3920 3921 3923 3927 3931 3933 3934 3936
>  3938 3939 3940 3943 3944 3945 3947 3948 3950 3952 3953 3954 3957 3959 3962 3963 3966 3967 3968 3973 3978
>  3981 3986 3988 3991 3992 3996

added scalar:
               e(numR) =  1837

added scalar:
                e(pcb) =  88.453306

added scalar:
                e(wtp) =  573.95947

       ratio:  _b[_sob2]/_b[price]

------------------------------------------------------------------------------
      chosen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       ratio |  -.5739595   .1043475    -5.50   0.000    -.7784768   -.3694422
------------------------------------------------------------------------------

. 
. lab var _sob2 "Spring"

. lab var _sob3 "Summer"

. lab var _sob4 "Fall"

. lab var price "Cost (in 1000s)"

. lab var bweightLin "Birthweight (in grams)"

. 
. #delimit ;
delimiter now ;
. esttab mlall mlbwall mlmarried mlbwmarried using "$OUT/WTP-mixedlogit-both.tex", replace
> cells(b(star fmt(%-9.3f)) se(fmt(%-9.3f) par([ ]) )) stats
> (wtp conf95 pcb N numR, fmt(%5.1f %5.1f %5.1f %9.0gc %9.0gc)
>     label("WTP for Spring Birth" "95\% CI"
>           "\% Positively Impacted by Spring Birth" Observations
>           "Number of Respondents")) starlevel ("$ ^{\ddagger} $" `pvL')
> mgroups("All" "Married Mothers, 20-45", pattern(1 0 1 0)
>         prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span}))
> collabels(,none) style(tex) booktabs label
> title("Allowing for Preference Heterogeneity with Mixed Logit"\label{WTPmix})
> postfoot("\bottomrule           "
>          "\multicolumn{5}{p{17.1cm}}{\begin{footnotesize} Panel A displays   "
>          "mean coefficients from the mixed logit, and panel B displays the   "
>          "estimated standard deviation of each coefficient.  All coefficients"
>          " with the exception of Cost are allowed to vary randomly throughout"
>          " the sample.  The WTP is calculated as the ratio of the coefficient"
>          " on spring birth tso that on costs, and confidence intervals are    "
>          "calculated by the delta method. The \% of respondents who value    "
>          "a spring birth positively based on individual coefficients is      "
>          "displayed at the foot of the table.  Standard errors are clustered "
>          "by respondent."
>          "\end{footnotesize}}\end{tabular}\end{table}");
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/MTurk/WTP
> -mixedlogit-both.tex)

. #delimit cr
delimiter now cr
. estimates clear

. 
. 
. *-------------------------------------------------------------------------------
. *-- (7) Close
. *-------------------------------------------------------------------------------
. log close
      name:  <unnamed>
       log:  C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/log/mturkAnalysis.txt
  log type:  text
 closed on:  29 Mar 2019, 15:29:50
-----------------------------------------------------------------------------------------------------------
