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miningschools_main.do
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/* Management, Productivity, and Technology Choices: Evidence from U.S. Mining Schools
- RESULTS IN THE MAIN TEXT -
Michael Rubens (KU Leuven)
===================================================================================================*/
set more off
*use miningschools_data, clear
/* ------------------------------------------------------------------------------
A. Production function (table 2)
-------------------------------------------------------------------------------- */
* locomotive dummies
foreach var of varlist locel locst locair {
gen d`var' = `var'>0
replace d`var' = . if `var'==.
}
* take logarithms of variables
gen y = log(q)
gen l = log(emp )
gen inp = qinp
gen m = log(inp)
gen lpowd = log(powd+1)
gen ke = dlocel
gen ks = dlocst
gen ka = dlocair
gen x = minsch
drop if yr<1900 // locomotives unobserved prior to 1900
* calculate variable inputs' cost shares
gen bl_cs = (w*emp*ndays) / (p*q) // factor revenue share of labor
gen bm_cs = qinp /q // factor revenue share of intermediate inputs
sum bl_cs bm_cs
* Table 1(a): output elasticities of variable inputs
******************************************************
egen avbl_cs = mean(bl_cs)
egen avbm_cs = mean(bm_cs)
egen medbl_cs = median(bl_cs)
egen medbm_cs = median(bm_cs)
bys yr: egen medbl_csyr = median(bl_cs)
bys yr: egen medbm_csyr = median(bm_cs)
label var medbl_cs "Labor"
label var medbm_cs "Materials"
twoway connect medbl_csyr medbm_csyr yr, lwidth(thick thick) msize(normalsize normalsize) mfcolor(white white) mcolor(black black) msymbol(square diamond) lcolor(black black) lpattern(solid longdash) graphregion(color(white)) ytitle("Median revenue share")
// graph export .\Paper\fig\fig_revshare.png, replace
drop bl_cs bm_cs
gen bl_cs = medbl_cs
gen bm_cs = medbm_cs
* bootstrapping standard errors around production function coefficients
save ./main/temp/data_temp, replace
// do ./main/miningschools_prodfun_bs // uncomment to bootstrap standard errors
use ./main/temp/data_temp, clear
estpost su bl_cs bm_cs // store estimates for table 2(a)
est store A_4a //
* Table 1(b): output elasticities of fixed inputs
******************************************************
preserve
keep if y~=. & l~=. & m~=. & ke~=. & ks~=. & ka~=. & x~=. & col~=.
di _N
restore
* ols - table 2(b) column (I)
gen ltfp_cs = y - medbl_cs *l - medbm_cs *m
reg ltfp_cs x ke ka ks yr col
local N_pf1 = e(N)
foreach var of varlist x ke ks ka col yr {
gen b`var'_cs_1 = _b[`var']
gen se`var'_cs_1 = _se[`var']
}
gen bc_cs_1 = _b[_cons]
gen bt_cs_1 = byr_cs_1
* fe - table 2(b) column (II)
xtreg ltfp_cs x ke ks ka col yr , fe r
local N_pf2 = e(N)
foreach var of varlist x ke ks ka yr col{
gen b`var'_cs_2 = _b[`var']
gen se`var'_cs_2 = _se[`var']
}
gen bc_cs_2 = _b[_cons]
gen bt_cs_2 = byr_cs_2
xtset mineid yr
* ar(1) model - table 2(b) column (III)
gmm (ltfp_cs - {rho}*L.ltfp_cs - ({bke})*(ke-{rho}*L.ke) - {bks}*(ks- {rho}*L.ks) - {bka}*(ka - {rho}*L.ka) - {bx}*(x- {rho}*L.x) - {bc}*(col- {rho}*L.col) ///
- {bt}*(yr - {rho}*L.yr)- {c}*(1-{rho}) ) , inst(L.l L.m ke ka ks x col yr )
local N_pf3 = e(N)
mat coef_cs = e(b)
mat cov_cs = e(V)
gen bke_cs_3 = coef_cs[1,2]
gen bks_cs_3 = coef_cs[1,3]
gen bka_cs_3 = coef_cs[1,4]
gen bx_cs_3 = coef_cs[1,5]
gen bcol_cs_3 = coef_cs[1,6]
gen bt_cs_3 = coef_cs[1,7]
gen bc_cs_3 = coef_cs[1,8]
gen seke_cs_3 = sqrt(cov_cs[2,2])
gen seks_cs_3 = sqrt(cov_cs[3,3])
gen seka_cs_3 = sqrt(cov_cs[4,4])
gen sex_cs_3 = sqrt(cov_cs[5,5])
gen secol_cs_3 = sqrt(cov_cs[6,6])
gen set_cs_3 = sqrt(cov_cs[7,7])
gen sec_cs_3 = sqrt(cov_cs[8,8])
save ./main/temp/data_temp, replace
* r-squared of each regression
forvalues n = 1/2 {
preserve
gen yhat = y - bke_cs_`n'*ke -bks_cs_`n'*ks -bka_cs_`n'*ka -bx_cs_`n'*x -bt_cs_`n'*yr -bc_cs_`n' -bl_cs*l - bm_cs*m - bcol_cs_`n'*col
reg y yhat
gen r2 = e(r2)
sum r2
local r2_pf`n' = round(r2,0.001)
restore
}
preserve
local n = 3
gen yhat = y - bke_cs_`n'*ke -bks_cs_`n'*ks -bka_cs_`n'*ka -bx_cs_`n'*x -bt_cs_`n'*yr -bc_cs_`n' -bl_cs*l - bm_cs*m - bcol_cs_`n'*col
xtset mineid yr
drop if yhat==. | L.yhat==.
reg y yhat
gen r2 = e(r2)
sum r2
local r2_pf`n' = round(r2,0.001)
restore
* locomotive coefficients
gen bke = bke_cs_3
gen bka = bka_cs_3
gen bks = bks_cs_3
gen bx = bx_cs_3
gen bt = bt_cs_3
gen bcol = bcol_cs_3
forvalues m = 1/3 {
rename (bx_cs_`m' bke_cs_`m' bka_cs_`m' bks_cs_`m' bcol_cs_`m') (bx_cs bke_cs bka_cs bks_cs bcol_cs)
estpost su bx_cs bcol_cs bke_cs bka_cs bks_cs // store point estimates for table 2b
est store A_4b_`m'
drop bx_cs bke_cs bka_cs bks_cs bcol_cs
}
forvalues m = 1/3 {
rename (sex_cs_`m' seke_cs_`m' seka_cs_`m' seks_cs_`m' secol_cs_`m') (bx_cs bke_cs bka_cs bks_cs bcol_cs)
// estpost su bx_cs bcol_cs bke_cs bka_cs bks_cs // store SE estimates for table 2b
// est store A_4b_`m'_se
drop bx_cs bke_cs bka_cs bks_cs bcol_cs
}
foreach var in "bx_cs" "bke_cs" "bka_cs" "bks_cs" "bcol_cs" {
gen `var' = .
}
label var bx_cs "1(Mining col. grad.)"
label var bcol_cs "1(Other grad.)"
label var bke_cs "1(Elec. loc.)"
label var bka_cs "1(Air loc.)"
label var bks_cs "1(Steam loc.)"
label var bl_cs "Labor"
label var bm_cs "Materials"
* write table 2(a)
// esttab A_4a A4a_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab4.tex", replace ///
// mtitle("" "" ) prehead( & \multicolumn{2}{c}{ (I)}& \multicolumn{2}{c}{ (II)} & \multicolumn{2}{c}{ (III)} \\\textit{(a) Variable inputs} & Estimate & S.E. & &&& \\ \hline) posthead( \\) ///
// cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f prefoot( &&&\\ )
* write table 2(b)
// esttab A_4b_1 A_4b_1_se A_4b_2 A_4b_2_se A_4b_3 A_4b_3_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab4.tex", append ///
// mtitle("" "" "" "" "" "") prehead( \hline & \multicolumn{6}{c}{ log(TFP)} \\\textit{(b) Fixed inputs} & Estimate & S.E. & Estimate & S.E.& Estimate & S.E. \\ \hline) posthead( \\) ///
// cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f prefoot( \\ Model & \multicolumn{2}{c}{ OLS}& \multicolumn{2}{c}{FE} & \multicolumn{2}{c}{ AR(1)} ///
// \\Observations & \multicolumn{2}{c}{`N_pf1'}& \multicolumn{2}{c}{`N_pf2'} & \multicolumn{2}{c}{`N_pf3'}\\ R-squared & \multicolumn{2}{c}{`r2_pf1'}& \multicolumn{2}{c}{`r2_pf2'} & \multicolumn{2}{c}{`r2_pf3'} \\)
/* ------------------------------------------------------------------------------
B. Interaction effects (table 3a)
-------------------------------------------------------------------------------- */
* Table 3(a): Interaction effect
*********************************
gen xke = x*ke
gmm (ltfp_cs - {rho}*L.ltfp_cs - ({bke})*(ke-{rho}*L.ke) - {bks}*(ks- {rho}*L.ks) - {bka}*(ka - {rho}*L.ka) - {bx}*(x- {rho}*L.x) ///
- {bxke}*(xke- {rho}*L.xke) - {bt}*(yr - {rho}*L.yr)- {bcol}*(col - {rho}*L.col)- {c}*(1-{rho}) ) , ///
inst(ke ka ks x yr xke L.l L.m col )
local N_inter = e(N)
mat inter_coef = e(b)
mat inter_cov = e(V)
mat list inter_coef
gen bkxe = inter_coef[1,6]
gen sekxe = sqrt(inter_cov[6,6])
estpost su bkxe
est store D_kxe // store estimates for table 3a
drop bkxe
rename sekxe bkxe
estpost su bkxe
est store D_kxe_se // store SE estimates for table 3a
/* ------------------------------------------------------------------------------
C. Technology choices (table 1)
-------------------------------------------------------------------------------- */
gen const = 1
drop ltfp*
gen ltfp = lq - bl_cs*l - bm_cs*m - bke *ke-bks *ks -bka *ka -bx *x -bcol*col -bt *yr -bc_cs_3
gen tfp=exp(ltfp)
xtset mineid yr
label var ke "Electrical"
label var ks "Steam"
label var ka "Air"
* Table 1(a): Extensive margin
*******************************
gen lw = log(w)
gen lp = log(p)
* technology choice regressions
foreach var of varlist ke ks ka {
xtreg `var' x col i.yr ltfp l m lw lp , fe r // wages and prices only vary by year so absorbed by year dummies. xtreg gives wrong SEs but needed to get the within R^2
local r2_`var' = round(e(r2_w) ,0.001)
areg `var' x col i.yr ltfp l m lw lp, absorb(mineid) cluster (man_id) // areg to get correct clustered standard errors
predict `var'hat1 if x ==1
predict `var'hat0 if x ==0 // predicted usage probabilities
local N_`var' = e(N)
gen cx`var' = _b[x]
gen co`var' = _b[col]
gen cx`var'_se = _se[x]
gen co`var'_se = _se[col]
}
estpost su cxke coke // save electrical locomotive estimates for table 1(a) column (I)
est store C_ke
foreach var of varlist ka ks {
replace cxke = cx`var'
replace coke = co`var'
estpost su cxke coke // same for air and steam locomotives, table 1(a) columns (II)-(III)
est store C_`var'
}
replace cxke = cxke_se
replace coke = coke_se
estpost su cxke coke
est store C_ke_se // save SEs for electrical locomotives, table 1(a) column (I)
foreach var of varlist ka ks {
replace cxke = cx`var'_se
replace coke = co`var'_se
estpost su cxke coke
est store C_`var'_se // save SEs for other locomotive types, table 1(a) column (II)-(III)
}
* average usage of each type
sum ke ka ks
foreach var of varlist ke ka ks {
egen av`var' = mean(`var')
local av`var' = round(av`var',0.001)
}
egen avprobel1 = mean(kehat1)
egen avprobel0 = mean(kehat0)
/*
* Write table 1a:
label var cxke "1(Mining col. grad.) "
label var coke "1(Other grad.) "
esttab C_ke C_ke_se C_ka C_ka_se C_ks C_ks_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab1.tex", replace ///
mtitle("" "" "" "" "" "") prehead( & \multicolumn{2}{c}{ (I)} & \multicolumn{2}{c}{ (II)} & \multicolumn{2}{c}{ (III)} \\ & \multicolumn{2}{c}{1(Elec. loc.)}& \multicolumn{2}{c}{1(Air. loc.)} & \multicolumn{2}{c}{1(Steam loc.)} \\ \textit{(a) Extensive margin}& Estimate & S.E. & Estimate & S.E. & Estimate & S.E.\\ \hline) ///
posthead( \\) cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f ///
prefoot( &&&\\ Average usage & \multicolumn{2}{c}{`avke'} & \multicolumn{2}{c}{`avka'} & \multicolumn{2}{c}{`avks'} \\ Observations & \multicolumn{2}{c}{ `N_ke'} & \multicolumn{2}{c}{`N_ka'} & \multicolumn{2}{c}{`N_ks'} ///
\\ Within R-squared & \multicolumn{2}{c}{ `r2_ke'}&\multicolumn{2}{c}{`r2_ka'}&\multicolumn{2}{c}{`r2_ks'}\\ &&&\\ )
*/
* Table 1(b): Intensive margin
*******************************
foreach var of varlist locel locair locst { // take logarithms of locomotive counts
gen l`var' = log(`var')
}
rename (llocel llocair llocst)(lke lka lks)
drop cxk* cok*
foreach var of varlist lke lks lka {
xtset mineid yr
xtreg `var' x col i.yr ltfp l m, fe r
local r2_`var' = round(e(r2_w) ,0.001)
areg `var' x col i.yr ltfp l m, absorb(mineid) cluster (man_id) // regress log number of locomotives on mining college graduates and other covaraites
local N_`var' = e(N)
predict `var'hat, xb // predicted usage probability
gen cx`var' = _b[x]
gen co`var' = _b[col]
gen cx`var'_se = _se[x]
gen co`var'_se = _se[col]
}
estpost su cxlke colke
est store C_lke
foreach var of varlist lka lks {
replace cxlke = cx`var'
replace colke = co`var'
estpost su cxlke colke
est store C_`var'
}
replace cxlke = cxlke_se
replace colke = colke_se
estpost su cxlke colke
est store C_lke_se
foreach var of varlist lka lks {
replace cxlke = cx`var'_se
replace colke = co`var'_se
estpost su cxlke colke
est store C_`var'_se
}
label var cxlke "1(Mining col. grad.) "
label var colke "1(Other grad.) "
* Write table 1
/*
esttab C_lke C_lke_se C_lka C_lka_se C_lks C_lks_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab1.tex", append ///
mtitle("" "" "" "" "" "") prehead( \hline & \multicolumn{2}{c}{log(Elec. loc.)}& \multicolumn{2}{c}{log(Air. loc.)} & \multicolumn{2}{c}{log(Steam loc.)} \\ \textit{(b) Intensive margin}& Estimate & S.E. & Estimate & S.E. & Estimate & S.E.\\ \hline) ///
posthead( \\) cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f ///
prefoot( &&&\\ Observations & \multicolumn{2}{c}{`N_lke'} & \multicolumn{2}{c}{`N_lka'} & \multicolumn{2}{c}{`N_lks'} \\ Within R-squared & \multicolumn{2}{c}{`r2_lke'}&\multicolumn{2}{c}{`r2_lka'}&\multicolumn{2}{c}{`r2_lks'}\\ &&&\\ )
*/
/* ---------------------------------
D. Quantify returns to managers
-----------------------------------*/
gen return = (exp(bke)*avprobel1 + (1-avprobel1))/(exp(bke)*avprobel0 + (1-avprobel0))
sum return // factor 1.0298 = increase of 3.0%
/* -------------------------------------------------------------------------------------
E. Event study (Figure 5)
-------------------------------------------------------------------------------- */
* Locomotive adoption regressions with various lags and leads
foreach var of varlist ke ka ks {
reg D.`var' F3D.x F2D.x F1D.x D.x L1D.x L2D.x L3D.x i.yr , cluster(man_id)
forvalues n = 1(1)3 {
local p = `n'+3
local k = 4-`n'
gen th_`var'_`k' = _b[F`n'D.x]
gen se_`var'_`k' = _se[F`n'D.x]
gen th_`var'_`p' = _b[L`n'D.x]
gen se_`var'_`p' = _se[L`n'D.x]
}
}
forvalues n = 1(1)6 {
foreach var in "ke" "ks" "ka" {
gen ci_`var'_lo_`n' = th_`var'_`n'-se_`var'_`n'*1.96
gen ci_`var'_hi_`n' = th_`var'_`n'+se_`var'_`n'*1.96
}
}
foreach var of varlist l {
reg D.`var' F3D.x F2D.x F1D.x D.x L1D.x L2D.x L3D.x , cluster(man_id)
forvalues n = 1(1)3 {
local p = `n'+3
local k = 4-`n'
gen th_`var'_`k' = _b[F`n'D.x]
gen se_`var'_`k' = _se[F`n'D.x]
gen th_`var'_`p' = _b[L`n'D.x]
gen se_`var'_`p' = _se[L`n'D.x]
}
}
forvalues n = 1(1)6 {
foreach var in "l" {
gen ci_`var'_lo_`n' = th_`var'_`n'-se_`var'_`n'*1.96
gen ci_`var'_hi_`n' = th_`var'_`n'+se_`var'_`n'*1.96
}
}
* Plot the event study
preserve
keep if _n==1
keep th* se_* cons
reshape long th_ke_ th_ks_ th_ka_ th_l_ se_ke_ se_ks_ se_ka_ se_l_, i(cons) j(b)
gen t = b-4
replace t = t+1 if t>=0
foreach var of varlist th*{
replace `var'=0 if t==0
}
foreach var in "ke" "ks" "ka" "l"{
gen ci_`var'_lo_ = th_`var'_-se_`var'_*1.96
gen ci_`var'_hi_ = th_`var'_+se_`var'_*1.96
}
twoway line th_ke_ t , lwidth(thick) msize(large) msymbol(diamond) lcolor(black) xlabel(-3(1)3)|| line ci_ke_lo_ ci_ke_hi t , mcolor( red red) lwidth(thick thick) lcolor( red red) lpattern( shortdash shortdash) ///
xtitle("Time since mining engineer hire") ytitle("Locomotive adoption difference") legend(order(1 "Diff. in elec. adoption rate" 2 "90% CI")) graphregion(color(white))
* graph export "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\fig\figure6a.png", replace
twoway line th_ks_ t , lwidth(thick) msize(large) msymbol(square) lcolor(black) lpattern(solid) mcolor(maroon) xlabel(-3(1)3)|| line ci_ks_lo_ ci_ks_hi t , mcolor( red red) lwidth(thick thick) lcolor( red red) lpattern( shortdash shortdash) ///
xtitle("Time since mining engineer hire") ytitle("Locomotive adoption difference") legend(order(1 "Diff. in steam adoption rate" 2 "90% CI")) graphregion(color(white))
* graph export "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\fig\figure6b.png", replace
twoway line th_ka_ t , lwidth(thick) msize(large) lcolor(black) lpattern(solid) xlabel(-3(1)3) || line ci_ka_lo_ ci_ka_hi t , mcolor( red red) lwidth(thick thick) lcolor( red red) lpattern( shortdash shortdash) ///
xtitle("Time since mining engineer hire") ytitle("Locomotive adoption difference") legend(order(1 "Diff. in air adoption rate" 2 "90% CI")) graphregion(color(white))
* graph export "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\fig\figure6c.png", replace
twoway line th_l_ t , lwidth(thick) msize(large) lcolor(black) lpattern(solid) xlabel(-3(1)3) || line ci_l_lo_ ci_l_hi t , mcolor( red red) lwidth(thick thick) lcolor( red red) lpattern( shortdash shortdash) ///
xtitle("Time since mining engineer hire") ytitle("Labor adoption difference") legend(order(1 "Diff. in air adoption rate" 2 "90% CI")) graphregion(color(white))
*graph export "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\fig\figure6d.png", replace
restore
/* -----------------------------------------------------------------------------
F. Information spillovers (Table 3(b) and 3(c)
-------------------------------------------------------------------------------- */
foreach var of varlist ke ks ka {
bys firmid countyid: egen n`var'fc = sum(`var') // number of locomotives at firms in same county
bys firmid: egen n`var'f = sum(`var') // number of locomotives at firm
gen n`var'fot = n`var'f-n`var'fc // number of locomotive at firm in other counties
gen d`var'fot = n`var'fot>0 // firm has locomotives in other counties
sum d`var'fot
}
gen xdkefot = x*dkefot
foreach var of varlist ke ks ka {
xtreg `var' x col i.yr l m ltfp if d`var'fot ==1, fe r // locomotive choice regression if no locomotives in other counties
local N_`var'ot1 = e(N)
local r2_`var'ot1 = round(e(r2_w),0.001)
areg `var' x col i.yr l m ltfp if d`var'fot ==1, absorb(mineid)
gen bx1`var' = _b[x]
gen sex1`var' = _se[x]
xtreg `var' x col i.yr l m ltfp if d`var'fot ==0, fe r // locomotive choice regression if already locomotives in other counties
local N_`var'ot0 = e(N)
local r2_`var'ot0 = round(e(r2_w),0.001)
areg `var' x col i.yr l m ltfp if d`var'fot ==0, absorb(mineid)
gen bx0`var' = _b[x]
gen sex0`var' = _se[x]
}
// store the estimates
foreach var of varlist ke ka ks {
gen bx0 = bx0`var'
gen bx1 = bx1`var'
estpost su bx0
est store E_`var'
estpost su bx1
est store F_`var'
drop bx0 bx1
rename ( sex0`var' sex1`var') ( bx0 bx1)
estpost su bx0
est store E_`var'_se
estpost su bx1
est store F_`var'_se
drop bx0 bx1
}
gen bx0 = .
gen bx1 = .
label var bx0 "1(Mining col. grad.) "
label var bx1 "1(Mining col. grad.) "
label var bkxe "1(M.C. grad)*1(Elec. loc.)"
*Write table 3
/*
esttab D_kxe D_kxe_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab3.tex", replace ///
mtitle("" "" "" "" "" "") prehead( & \multicolumn{2}{c}{(I)}& \multicolumn{2}{c}{(II)} & \multicolumn{2}{c}{(III)} \\ \textit{(a) Different returns} & Estimate & S.E. & &&&\\ \hline) ///
posthead( \\) cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f ///
prefoot( &&&\\ Observations & \multicolumn{2}{c}{`N_inter'} && \\ &&&\\ )
*/
/*
esttab E_ke E_ke_se E_ka E_ka_se E_ks E_ks_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab3.tex", append ///
mtitle("" "" "" "" "" "") prehead( \hline & \multicolumn{2}{c}{1(Elec. loc.)}& \multicolumn{2}{c}{1(Air. loc.)} & \multicolumn{2}{c}{1(Steam loc.)} \\ \textit{(b) Loc. not yet used } & Estimate & S.E. & Estimate & S.E. & Estimate & S.E.\\ \hline) ///
posthead( \\) cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f ///
prefoot( &&&\\ Observations & \multicolumn{2}{c}{`N_keot0'} & \multicolumn{2}{c}{`N_kaot0'} & \multicolumn{2}{c}{`N_ksot0'} \\ Within R-squared & \multicolumn{2}{c}{`r2_keot0'}&\multicolumn{2}{c}{`r2_kaot0'}&\multicolumn{2}{c}{`r2_ksot0'}\\ &&&\\ )
*/
/*
esttab F_ke F_ke_se F_ka F_ka_se F_ks F_ks_se using "C:\Users\MichaelRubens\Dropbox\Managerial education\Paper\tab\tab3.tex", append ///
mtitle("" "" "" "" "" "") prehead( \hline & \multicolumn{2}{c}{1(Elec. loc.)}& \multicolumn{2}{c}{1(Air. loc.)} & \multicolumn{2}{c}{1(Steam loc.)} \\ \textit{(c) Loc. already used } & Estimate & S.E. & Estimate & S.E. & Estimate & S.E.\\ \hline) ///
posthead( \\) cells(mean(fmt(3))) label booktabs nonum noobs collabels(none) gaps f ///
prefoot( &&&\\ Observations & \multicolumn{2}{c}{`N_keot1'} & \multicolumn{2}{c}{`N_kaot1'} & \multicolumn{2}{c}{`N_ksot1'} \\ Within R-squared & \multicolumn{2}{c}{`r2_keot1'}&\multicolumn{2}{c}{`r2_kaot1'}&\multicolumn{2}{c}{`r2_ksot1'}\\ &&&\\ )
*/