* NOTE: The first line of any Stata do file should contain a 'log using' command
* to open/start a text-format Stata log file where all the results of the
* Stata commands in this do file are recorded.
*
log using 452note08_exercises_f13.log, replace
version
set more off

* To have Stata execute (run) this do file, enter the following Stata command
* on the Stata command line:
*
*  do 452note08_exercises_f13.txt
*
* Do file: 452note08_exercises_f13.txt 
* Log file: 452note08_exercises_f13.log
*
* Input dataset: wage1_econ452_note08.dta
* Output dataset: wage1_econ452_note08_Stata12.dta (re-saved)
*
* Read input dataset 'wage1_econ452_note08.dta' into memory
use wage1_econ452_note08, clear
describe, short
describe
summarize

* Check gender indicators 'f' and 'm'
tab1 f m, missing
tab2 f m, missing

* Check industry dummy variables
*
tab1 industry, missing
tab2 industry ind1, missing col
tab2 industry ind2, missing col
tab2 industry ind3, missing col
tab2 industry ind4, missing col
tab2 industry ind5, missing col
tab2 industry ind6, missing col
tab2 industry ind7, missing col

* Generate Female Interactions with All Other Regressors in Model 5.5
*
generate fed = f*ed
generate fexp = f*exp
generate fedsq = f*edsq
generate fexpsq = f*expsq
generate fedexp = f*edexp

generate find1 = f*ind1
generate find2 = f*ind2
generate find3 = f*ind3
generate find4 = f*ind4
generate find5 = f*ind5
generate find6 = f*ind6
generate find7 = f*ind7

summarize fed fexp fedsq fexpsq fedexp find1 find2 find3 find4 find5 find6 find7

*
* Note 8 -- Model 5.5: Gender base group = males
*                      Industry base group = industry 1 (ind1)
* 
* Below we use '#delimit' to enable us to put a single Stata command on more than one line.
*
#delimit ;
regress lnw ed exp edsq expsq edexp ind2 ind3 ind4 ind5 ind6 ind7 f fed fexp fedsq fexpsq fedexp
 find2 find3 find4 find5 find6 find7 ;
#delimit cr

* Model 5.5: Compute the female coefficient estimates for Model 5.5
lincom _b[_cons] + _b[f]
lincom _b[ed] + _b[fed]
lincom _b[exp] + _b[fexp]
lincom _b[edsq] + _b[fedsq]
lincom _b[expsq] + _b[fexpsq]
lincom _b[edexp] + _b[fedexp]

lincom _b[ind2] + _b[find2]
lincom _b[ind3] + _b[find3]
lincom _b[ind4] + _b[find4]
lincom _b[ind5] + _b[find5]
lincom _b[ind6] + _b[find6]
lincom _b[ind7] + _b[find7]

* Model 5.5: Display the male coefficient estimates
lincom _b[_cons]
lincom _b[ed] 
lincom _b[exp] 
lincom _b[edsq]
lincom _b[expsq]
lincom _b[edexp]

lincom _b[ind2]
lincom _b[ind3]
lincom _b[ind4]
lincom _b[ind5]
lincom _b[ind6]
lincom _b[ind7]

* Model 5.5: Display the female-male coefficient differences
lincom _b[f]
lincom _b[fed] 
lincom _b[fexp] 
lincom _b[fedsq]
lincom _b[fexpsq]
lincom _b[fedexp]

lincom _b[find2]
lincom _b[find3]
lincom _b[find4]
lincom _b[find5]
lincom _b[find6]
lincom _b[find7]

*
* Model 5.5: Tests on marginal effect of ed for Males
* Test 1m: ME of ed for males is zero for all values of ed & exp
test ed edsq edexp
return list

* Test 2m: ME of ed for males is constant
test edsq edexp
return list

* Test 3m: ME of ed for males is unrelated to ed
test edsq 
test edsq = 0
return list
lincom _b[edsq]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Test 4m: ME of ed for males is unrelated to exp
test edexp 
test edexp = 0
return list
lincom _b[edexp]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Tests on marginal effect of ed for Females
* Test 1f: ME of ed for females is zero for all values of ed & exp
test ed + fed = 0, notest 
test edsq + fedsq	= 0, accumulate notest
test edexp + fedexp = 0, accumulate
return list

* Test 2f: ME of ed for females is constant
test edsq + fedsq	= 0, notest
test edexp + fedexp = 0, accumulate
return list

* Test 3f: ME of ed for females is unrelated to ed
test edsq + fedsq = 0 
return list
lincom _b[edsq] + _b[fedsq]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Test 4f: ME of ed for females is unrelated to exp
test edexp + fedexp = 0 
return list
lincom _b[edexp] + _b[fedexp]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Tests on Female-Male differences in marginal effect of ed
* Test 5: Female-Male difference in ME of ed is zero for all values of ed & exp
test fed fedsq fedexp
return list

* Test 6: Female-Male difference in ME of ed is a constant 
*         Female-Male difference in ME of ed is unrelated to ed & exp
test fedsq fedexp
return list

* Test 7: Female-Male difference in ME of ed is unrelated to ed
test fedsq
test fedsq = 0
return list
lincom _b[fedsq]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Test 8: Female-Male difference in ME of ed is unrelated to exp
test fedexp
test fedexp = 0
return list
lincom _b[fedexp]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Tests of Industry Effects
*
* Test 1-Industry: Test of industry effects for Males
test ind2 ind3 ind4 ind5 ind6 ind7
return list

* Test 2-Industry: Test of industry effects for Females
test ind2 + find2 = 0, notest
test ind3 + find3 = 0, notest accumulate
test ind4 + find4 = 0, notest accumulate
test ind5 + find5 = 0, notest accumulate
test ind6 + find6 = 0, notest accumulate
test ind7 + find7 = 0, accumulate
return list

* Test 3-Industry: Test of  Female-Male differences in industry effects
test find2 find3 find4 find5 find6 find7 
return list

*
* Model 5.5: Tests for Female-Male Differences in Conditional Mean lnw
*
* Test 1: Female-Male Difference in Conditional Mean lnw Equals Zero
test f fed fexp fedsq fexpsq fedexp find2 find3 find4 find5 find6 find7
return list

* Test 2: Female-Male Difference in Conditional Mean lnw Equals a Constant
test fed fexp fedsq fexpsq fedexp find2 find3 find4 find5 find6 find7
return list

* Test 3: Female-Male Difference in Conditional Mean lnw Unrelated to ed
test fed fedsq fedexp
return list

* Test 4: Female-Male Difference in Conditional Mean lnw Unrelated to exp
test fexp fexpsq fedexp
return list

* Test 5: Female-Male Difference in Conditional Mean lnw Unrelated to Industry
test find2 find3 find4 find5 find6 find7 
return list

*
* Model 5.5: Evaluating the Marginal Effect of ed at Median Values of ed & exp
*
summarize ed, detail
return list
scalar ed10p = r(p10)
scalar ed25p = r(p25)
scalar ed50p = r(p50)
scalar ed75p = r(p75)
scalar ed90p = r(p90)
scalar list ed10p ed25p ed50p ed75p ed90p

summarize exp, detail
return list
scalar exp50p = r(p50)
scalar list ed50p exp50p

*
* Model 5.5: Evaluate Marginal Effect of ed for Males 
*            at Selected Percentile Values of ed & Median Value of exp
*
* Male ME of ed at ed = ed10p = 10th percentile value of ed
lincom _b[ed] + 2*_b[edsq]*ed10p + _b[edexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Male ME of ed at ed = ed25p = 25th percentile value of ed
lincom _b[ed] + 2*_b[edsq]*ed25p + _b[edexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Male ME of ed at ed = ed50p = 50th percentile value of ed
lincom _b[ed] + 2*_b[edsq]*ed50p + _b[edexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Male ME of ed at ed = ed75p = 75th percentile value of ed
lincom _b[ed] + 2*_b[edsq]*ed75p + _b[edexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Male ME of ed at ed = ed90p = 90th percentile value of ed
lincom _b[ed] + 2*_b[edsq]*ed90p + _b[edexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluate Marginal Effect of ed for Females 
*            at Selected Percentile Values of ed & Median Value of exp
* Female ME of ed at ed = ed10p = 10th percentile value of ed
lincom (_b[ed] + _b[fed]) + 2*(_b[edsq] + _b[fedsq])*ed10p + (_b[edexp] + _b[fedexp])*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Female ME of ed at ed = ed25p = 25th percentile value of ed
lincom (_b[ed] + _b[fed]) + 2*(_b[edsq] + _b[fedsq])*ed25p + (_b[edexp] + _b[fedexp])*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Female ME of ed at ed = ed50p = 50th percentile value of ed
lincom (_b[ed] + _b[fed]) + 2*(_b[edsq] + _b[fedsq])*ed50p + (_b[edexp] + _b[fedexp])*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Female ME of ed at ed = ed75p = 75th percentile value of ed
lincom (_b[ed] + _b[fed]) + 2*(_b[edsq] + _b[fedsq])*ed75p + (_b[edexp] + _b[fedexp])*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Female ME of ed at ed = ed90p = 90th percentile value of ed
lincom (_b[ed] + _b[fed]) + 2*(_b[edsq] + _b[fedsq])*ed90p + (_b[edexp] + _b[fedexp])*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluate Female-Male Difference in Marginal Effect of ed 
*            at Selected Percentile Values of ed & Median Value of exp
* F-M diff in ME of ed at ed = ed10p = 10th percentile value of ed
lincom _b[fed] + 2*_b[fedsq]*ed10p + _b[fedexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M diff in ME of ed at ed = ed25p = 25th percentile value of ed
lincom _b[fed] + 2*_b[fedsq]*ed25p + _b[fedexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M diff in ME of ed at ed = ed50p = 50th percentile value of ed
lincom _b[fed] + 2*_b[fedsq]*ed50p + _b[fedexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M diff in ME of ed at ed = ed75p = 75th percentile value of ed
lincom _b[fed] + 2*_b[fedsq]*ed75p + _b[fedexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M diff in ME of ed at ed = ed90p = 90th percentile value of ed
lincom _b[fed] + 2*_b[fedsq]*ed90p + _b[fedexp]*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluate Marginal Effects of Industry for Males 
*            for Selected Pairs of Industries involving Industry 7
* Industry 7 = professional services industries

* Industry 7-Industry 1 difference in conditional mean Y for Males 
lincom _b[ind7]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 2 difference in conditional mean Y for Males 
lincom _b[ind7] - _b[ind2]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 3 difference in conditional mean Y for Males 
lincom _b[ind7] - _b[ind3]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 4 difference in conditional mean Y for Males 
lincom _b[ind7] - _b[ind4]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 5 difference in conditional mean Y for Males 
lincom _b[ind7] - _b[ind5]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 6 difference in conditional mean Y for Males 
lincom _b[ind7] - _b[ind6]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluate Marginal Effects of Industry for Females 
*            for Selected Pairs of Industries involving Industry 7
* Industry 7 = professional services industries

* Industry 7-Industry 1 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 2 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7] - (_b[ind2] + _b[find2]) 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 3 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7] - (_b[ind3] + _b[find3])
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 4 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7] - (_b[ind4] + _b[find4])
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 5 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7] - (_b[ind5] + _b[find5])
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Industry 7-Industry 6 difference in conditional mean Y for Females 
lincom _b[ind7] + _b[find7] - (_b[ind6] + _b[find6])
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluate Female-Male Differences in Marginal Effects of Industry 
*            for Selected Pairs of Industries involving Industry 7
* Industry 7 = professional services industries

* F-M difference in Industry 7-Industry 1 difference in conditional mean Y  
lincom _b[find7]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M difference in Industry 7-Industry 2 difference in conditional mean Y  
lincom _b[find7] - _b[find2] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M difference in Industry 7-Industry 3 difference in conditional mean Y  
lincom _b[find7] - _b[find3]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M difference in Industry 7-Industry 4 difference in conditional mean Y  
lincom _b[find7] - _b[find4]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M difference in Industry 7-Industry 5 difference in conditional mean Y  
lincom _b[find7] - _b[find5]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* F-M difference in Industry 7-Industry 6 difference in conditional mean Y  
lincom _b[find7] - _b[find6]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Evaluating Conditional Effect of Industry
*            at Median Values of ed & exp
*

* Model 5.5: Conditional Effect of Industry for Females 
*
* Model 5.5: Conditional Mean lnw for Females in Industry 1
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 2
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind2] + _b[find2]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 3
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind3] + _b[find3]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 4
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind4] + _b[find4]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 5
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind5] + _b[find5]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 6
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind6] + _b[find6]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Females in Industry 7
*            at Sample Median Values of ed & exp
#delimit ;
lincom _b[_cons] + _b[f] + (_b[ed] + _b[fed])*ed50p + (_b[exp] + _b[fexp])*exp50p
 + (_b[edsq] + _b[fedsq])*ed50p*ed50p + (_b[expsq] + _b[fexpsq])*exp50p*exp50p
 + (_b[edexp] + _b[fedexp])*ed50p*exp50p + (_b[ind7] + _b[find7]) ;
#delimit cr
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Model 5.5: Conditional Effect of Industry for Males 
*
* Model 5.5: Conditional Mean lnw for Males in Industry 1
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 2
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind2]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 3
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind3]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 4
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind4]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 5
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind5]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 6
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind6]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Conditional Mean lnw for Males in Industry 7
*            at Sample Median Values of ed & exp
lincom _b[_cons] + _b[ed]*ed50p + _b[exp]*exp50p + _b[edsq]*ed50p*ed50p + _b[expsq]*exp50p*exp50p + _b[edexp]*ed50p*exp50p + _b[ind7]
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Model 5.5: Female-Male Differences in Conditional Effect of Industry
*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 1
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 2
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find2] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 3
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find3] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 4
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find4] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 5
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find5] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 6
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find6] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

*
* Model 5.5: Female-Male Difference in Conditional Mean lnw for Industry 7
*            at Sample Median Values of ed & exp
lincom _b[f] + _b[fed]*ed50p + _b[fexp]*exp50p + _b[fedsq]*ed50p*ed50p + _b[fexpsq]*exp50p*exp50p + _b[fedexp]*ed50p*exp50p + _b[find7] 
return list
display r(estimate)/r(se)
display 2*ttail(r(df), abs(r(estimate)/r(se)))

* Re-save dataset in memory
describe, short
summarize
save, replace
describe, short
describe
summarize

log close