{smcl}
{com}{sf}{ul off}{txt}
{com}. 
. **** REPLICATION OF TABLE 9 ****
. 
. clear all
{res}{txt}
{com}. set mem 2g
{txt}{bf:set memory} ignored.
{p 4 4 2}
Memory no longer
needs to be set in modern Statas;
memory adjustments are performed on the fly
automatically.
{p_end}

{com}. set more off
{txt}
{com}. 
. /*      
>         OUTLINE
>         1. REGRESSIONS
>         2. TABLE FOR PAPER
> */
. 
. ********************************
. ***** 1. REGRESSIONS *****
. ********************************
. 
. // Import survey data
. 
. use $data\EEREV-D-21-00004_Data_Interreg.dta, clear
{txt}
{com}. 
. keep if dep_inc==1
{txt}(223,410 observations deleted)

{com}. 
. // Clustering
. 
. egen cluster=group(birthyr alsace)
{txt}
{com}. 
. // Transmission
. 
. drop born_1908_1913
{txt}
{com}. g born_1908_1913=1*(birthyr>=1908 & birthyr<=1913 & sexe==1)
{txt}
{com}. g born_1908_1913_fem=1*(birthyr>=1908 & birthyr<=1913 & sexe==2)
{txt}
{com}. 
. reghdfe confiance11 alsace /// 
>         c.born_1908_1913_fem#c.alsace c.born_1908_1913#c.alsace c.p_g1_1908_1913#c.alsace c.p_g2_1908_1913#c.alsace if birthyr>1907, ///
>         cluster(i.birthyr#i.alsace) a(i.birthyr#i.sexe i.nationalite_pop i.religion i.anciennete i.annee) //
{res}{txt}(dropped 1 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}HDFE Linear regression{col 51}Number of obs{col 67}= {res}     1,935
{txt}Absorbing 5 HDFE groups{col 51}F({res}   5{txt},{res}    132{txt}){col 67}= {res}     54.20
{txt}Statistics robust to heteroskedasticity{col 51}Prob > F{col 67}= {res}    0.0000
{txt}{col 51}R-squared{col 67}= {res}    0.0883
{txt}{col 51}Adj R-squared{col 67}= {res}    0.0139
{txt}{col 51}Within R-sq.{col 67}= {res}    0.0048
{txt}{col 1}Number of clusters ({res}birthyr#alsace{txt}) {col 30}= {res}       133{txt}{col 51}Root MSE{col 67}= {res}    0.4954

{txt}{ralign 95:(Std. err. adjusted for {res:133} clusters in birthyr#alsace)}
{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                  confiance11{col 31}{c |} Coefficient{col 43}  std. err.{col 55}      t{col 63}   P>|t|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}alsace {c |}{col 31}{res}{space 2} .0641719{col 43}{space 2} .0347286{col 54}{space 1}    1.85{col 63}{space 3}0.067{col 71}{space 4}-.0045248{col 84}{space 3} .1328686
{txt}{space 29} {c |}
c.born_1908_1913_fem#c.alsace {c |}{col 31}{res}{space 2} .1198645{col 43}{space 2} .1874597{col 54}{space 1}    0.64{col 63}{space 3}0.524{col 71}{space 4}-.2509492{col 84}{space 3} .4906783
{txt}{space 29} {c |}
{space 4}c.born_1908_1913#c.alsace {c |}{col 31}{res}{space 2}-.5356877{col 43}{space 2} .0529794{col 54}{space 1}  -10.11{col 63}{space 3}0.000{col 71}{space 4}-.6404862{col 84}{space 3}-.4308893
{txt}{space 29} {c |}
{space 4}c.p_g1_1908_1913#c.alsace {c |}{col 31}{res}{space 2}-.5998269{col 43}{space 2} .1451812{col 54}{space 1}   -4.13{col 63}{space 3}0.000{col 71}{space 4}-.8870096{col 84}{space 3}-.3126442
{txt}{space 29} {c |}
{space 4}c.p_g2_1908_1913#c.alsace {c |}{col 31}{res}{space 2}-.2182065{col 43}{space 2} .1571701{col 54}{space 1}   -1.39{col 63}{space 3}0.167{col 71}{space 4}-.5291044{col 84}{space 3} .0926915
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2} .5576815{col 43}{space 2} .0210679{col 54}{space 1}   26.47{col 63}{space 3}0.000{col 71}{space 4} .5160071{col 84}{space 3} .5993559
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}      133{col 31}{space 1}        0{col 43}{result}{space 1}      133{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        6{col 31}{space 1}        1{col 43}{result}{space 1}        5{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}{txt}
{com}. estimates store transmission
{txt}
{com}. qui sum confiance11 if e(sample)
{txt}
{com}. scalar mean_y=r(mean)
{txt}
{com}. estadd scalar mean_y

{txt}added scalar:
             e(mean_y) =  {res}.53488372
{txt}
{com}. 
. // Months at the front
. 
. ivreghdfe confiance11 alsace c.born_1908_1913#c.alsace c.born_1911_1913#c.alsace ///
>         if birthyr>=1908 & birthyr<=1927 & sexe==1, cluster(cluster) a(i.birthyr#i.sexe i.nationalite_pop i.religion i.anciennete i.annee) //
{res}{txt}(dropped 3 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    34{txt}{col 55}Number of obs = {res}     167
{txt}{col 55}F(  3,    33) = {res}   24.97
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 33.50821264{txt}{col 55}Centered R2   = {res}  0.0170
{txt}Total (uncentered) SS   = {res} 33.50821264{txt}{col 55}Uncentered R2 = {res}  0.0170
{txt}Residual SS             = {res} 32.93717531{txt}{col 55}Root MSE      = {res}   .4868

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              confiance11{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2} .0968452{col 39}{space 2} .0616511{col 50}{space 1}    1.57{col 59}{space 3}0.126{col 67}{space 4}-.0285848{col 80}{space 3} .2222752
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.5378717{col 39}{space 2} .0787409{col 50}{space 1}   -6.83{col 59}{space 3}0.000{col 67}{space 4}-.6980712{col 80}{space 3}-.3776723
{txt}{space 25} {c |}
c.born_1911_1913#c.alsace {c |}{col 27}{res}{space 2}-.0242658{col 39}{space 2} .1618731{col 50}{space 1}   -0.15{col 59}{space 3}0.882{col 67}{space 4}-.3535992{col 80}{space 3} .3050675
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{col 23}c.born_1911_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       18{col 31}{space 1}        0{col 43}{result}{space 1}       18{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        4{col 31}{space 1}        1{col 43}{result}{space 1}        3{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}{txt}
{com}. estimates store months
{txt}
{com}. qui sum confiance11 if e(sample)
{txt}
{com}. scalar mean_y=r(mean)
{txt}
{com}. estadd scalar mean_y

{txt}added scalar:
             e(mean_y) =  {res}.60479042
{txt}
{com}. 
. // Casualties (spouses)
. 
. ivreghdfe confiance11 alsace c.p_s_1908_1913#c.alsace ///
>         if birthyr>=1908 & birthyr<=1927 & sexe==2, cluster(cluster) a(i.birthyr#i.sexe i.nationalite_pop i.religion i.anciennete i.annee) //
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    35{txt}{col 55}Number of obs = {res}     166
{txt}{col 55}F(  2,    34) = {res}    0.18
{txt}{col 55}Prob > F      = {res}  0.8393
{txt}Total (centered) SS     = {res} 32.54094445{txt}{col 55}Centered R2   = {res}  0.0012
{txt}Total (uncentered) SS   = {res} 32.54094445{txt}{col 55}Uncentered R2 = {res}  0.0012
{txt}Residual SS             = {res} 32.50351403{txt}{col 55}Root MSE      = {res}   .4871

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             confiance11{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}alsace {c |}{col 26}{res}{space 2}  .038251{col 38}{space 2} .0787416{col 49}{space 1}    0.49{col 58}{space 3}0.630{col 66}{space 4}-.1217711{col 79}{space 3} .1982732
{txt}{space 24} {c |}
c.p_s_1908_1913#c.alsace {c |}{col 26}{res}{space 2} .0117478{col 38}{space 2} .3061708{col 49}{space 1}    0.04{col 58}{space 3}0.970{col 66}{space 4}-.6104661{col 79}{space 3} .6339617
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.p_s_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        4{col 31}{space 1}        1{col 43}{result}{space 1}        3{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}        2{col 31}{space 1}        1{col 43}{result}{space 1}        1{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}{txt}
{com}. estimates store spouse
{txt}
{com}. qui sum confiance11 if e(sample)
{txt}
{com}. scalar mean_y=r(mean)
{txt}
{com}. estadd scalar mean_y

{txt}added scalar:
             e(mean_y) =  {res}.6686747
{txt}
{com}. 
. // Only first gen, other aspects
. 
. foreach var in dipl_sup lrev business association memberFN{c -(} // 
{txt}  2{com}. 
.         ivreghdfe `var' alsace c.born_1908_1913#c.alsace ///
>                 if birthyr>=1908 & birthyr<=1927 & sexe==1, cluster(cluster) a(i.birthyr#i.sexe i.nationalite_pop i.religion i.anciennete i.annee) //
{txt}  3{com}.         estimates store `var'
{txt}  4{com}.         qui sum `var' if e(sample)
{txt}  5{com}.         scalar mean_y=r(mean)
{txt}  6{com}.         estadd scalar mean_y
{txt}  7{com}. 
. {c )-}
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 6 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    40{txt}{col 55}Number of obs = {res}    1086
{txt}{col 55}F(  2,    39) = {res}    4.00
{txt}{col 55}Prob > F      = {res}  0.0264
{txt}Total (centered) SS     = {res} 99.50466218{txt}{col 55}Centered R2   = {res}  0.0082
{txt}Total (uncentered) SS   = {res} 99.50466218{txt}{col 55}Uncentered R2 = {res}  0.0082
{txt}Residual SS             = {res} 98.69225392{txt}{col 55}Root MSE      = {res}   .3086

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                 dipl_sup{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2} .0651207{col 39}{space 2} .0246813{col 50}{space 1}    2.64{col 59}{space 3}0.012{col 67}{space 4} .0151982{col 80}{space 3} .1150433
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0141604{col 39}{space 2} .0460809{col 50}{space 1}   -0.31{col 59}{space 3}0.760{col 67}{space 4}-.1073678{col 80}{space 3}  .079047
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        7{col 31}{space 1}        1{col 43}{result}{space 1}        6{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        4{col 31}{space 1}        1{col 43}{result}{space 1}        3{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}       19{col 31}{space 1}        2{col 43}{result}{space 1}       17{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}
{txt}added scalar:
             e(mean_y) =  {res}.11049724
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    40{txt}{col 55}Number of obs = {res}     816
{txt}{col 55}F(  2,    39) = {res}    0.53
{txt}{col 55}Prob > F      = {res}  0.5953
{txt}Total (centered) SS     = {res} 213.6067153{txt}{col 55}Centered R2   = {res}  0.0008
{txt}Total (uncentered) SS   = {res} 213.6067153{txt}{col 55}Uncentered R2 = {res}  0.0008
{txt}Residual SS             = {res} 213.4315492{txt}{col 55}Root MSE      = {res}   .5261

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                     lrev{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2} .0318966{col 39}{space 2}  .036298{col 50}{space 1}    0.88{col 59}{space 3}0.385{col 67}{space 4} -.041523{col 80}{space 3} .1053161
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0751785{col 39}{space 2} .1008361{col 50}{space 1}   -0.75{col 59}{space 3}0.460{col 67}{space 4}-.2791387{col 80}{space 3} .1287817
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        7{col 31}{space 1}        1{col 43}{result}{space 1}        6{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        4{col 31}{space 1}        1{col 43}{result}{space 1}        3{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}       14{col 31}{space 1}        2{col 43}{result}{space 1}       12{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}
{txt}added scalar:
             e(mean_y) =  {res}8.8466059
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    40{txt}{col 55}Number of obs = {res}     981
{txt}{col 55}F(  2,    39) = {res}    0.89
{txt}{col 55}Prob > F      = {res}  0.4181
{txt}Total (centered) SS     = {res} 115.1377631{txt}{col 55}Centered R2   = {res}  0.0005
{txt}Total (uncentered) SS   = {res} 115.1377631{txt}{col 55}Uncentered R2 = {res}  0.0005
{txt}Residual SS             = {res} 115.0808664{txt}{col 55}Root MSE      = {res}   .3514

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                 business{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2}-.0039843{col 39}{space 2} .0247593{col 50}{space 1}   -0.16{col 59}{space 3}0.873{col 67}{space 4}-.0540647{col 80}{space 3} .0460962
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0630825{col 39}{space 2} .0492945{col 50}{space 1}    1.28{col 59}{space 3}0.208{col 67}{space 4} -.036625{col 80}{space 3} .1627899
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        7{col 31}{space 1}        1{col 43}{result}{space 1}        6{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        4{col 31}{space 1}        1{col 43}{result}{space 1}        3{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}       18{col 31}{space 1}        2{col 43}{result}{space 1}       16{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}
{txt}added scalar:
             e(mean_y) =  {res}.14475025
{txt}(dropped 1 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    37{txt}{col 55}Number of obs = {res}     525
{txt}{col 55}F(  2,    36) = {res}    3.77
{txt}{col 55}Prob > F      = {res}  0.0325
{txt}Total (centered) SS     = {res}   9.1808022{txt}{col 55}Centered R2   = {res}  0.0096
{txt}Total (uncentered) SS   = {res}   9.1808022{txt}{col 55}Uncentered R2 = {res}  0.0096
{txt}Residual SS             = {res} 9.092691908{txt}{col 55}Root MSE      = {res}   .1364

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}              association{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2}-.0316587{col 39}{space 2}  .013946{col 50}{space 1}   -2.27{col 59}{space 3}0.029{col 67}{space 4}-.0599425{col 80}{space 3}-.0033748
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0618422{col 39}{space 2}  .030536{col 50}{space 1}    2.03{col 59}{space 3}0.050{col 67}{space 4}-.0000876{col 80}{space 3}  .123772
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        6{col 31}{space 1}        1{col 43}{result}{space 1}        5{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        4{col 31}{space 1}        2{col 43}{result}{space 1}        2{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}        7{col 31}{space 1}        2{col 43}{result}{space 1}        5{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}
{txt}added scalar:
             e(mean_y) =  {res}.01904762
{txt}(dropped 1 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 7 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on cluster

Number of clusters (cluster) = {col 33}{res}    37{txt}{col 55}Number of obs = {res}     525
{txt}{col 55}F(  2,    36) = {res}    0.83
{txt}{col 55}Prob > F      = {res}  0.4453
{txt}Total (centered) SS     = {res} .9507598855{txt}{col 55}Centered R2   = {res}  0.0002
{txt}Total (uncentered) SS   = {res} .9507598855{txt}{col 55}Uncentered R2 = {res}  0.0002
{txt}Residual SS             = {res} .9505918302{txt}{col 55}Root MSE      = {res}  .04409

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                 memberFN{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      t{col 59}   P>|t|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}alsace {c |}{col 27}{res}{space 2} .0012749{col 39}{space 2} .0022231{col 50}{space 1}    0.57{col 59}{space 3}0.570{col 67}{space 4}-.0032337{col 80}{space 3} .0057836
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0036791{col 39}{space 2}   .00286{col 50}{space 1}   -1.29{col 59}{space 3}0.207{col 67}{space 4}-.0094795{col 80}{space 3} .0021213
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace c.born_1908_1913#c.alsace
{help ivreg2##partial:Partialled-out}:{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 17}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}     Absorbed FE{col 18}{c |} Categories{col 31} - Redundant{col 43}  = Num. Coefs{col 58}{c |}
{res}{col 1}{text}{hline 17}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}    birthyr#sexe{col 18}{c |}{space 1}       20{col 31}{space 1}        0{col 43}{result}{space 1}       20{col 57}{text} {col 58}{c |}
{res}{col 1}{text} nationalite_pop{col 18}{c |}{space 1}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{col 57}{text} {col 58}{c |}
{res}{col 1}{text}        religion{col 18}{c |}{space 1}        6{col 31}{space 1}        1{col 43}{result}{space 1}        5{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}      anciennete{col 18}{c |}{space 1}        4{col 31}{space 1}        2{col 43}{result}{space 1}        2{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}           annee{col 18}{c |}{space 1}        7{col 31}{space 1}        2{col 43}{result}{space 1}        5{col 57}{text}?{col 58}{c |}
{res}{col 1}{text}{hline 17}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
? = number of redundant parameters may be higher
{res}
{txt}added scalar:
             e(mean_y) =  {res}.00190476
{txt}
{com}. 
. ******************************
. ***** 2. TABLE FOR PAPER *****
. ******************************
. 
. // Fixed effects
. 
. estfe . association memberFN dipl_sup lrev business transmission months spouse, labels(nationalite_pop "Individual controls" annee "Survey year FE" birthyr#sexe "YoB $ \times $ gender FE") // proximite_enc "Political proximity FE")
{res}{txt}
{com}. return list

{txt}macros:
        r(indicate_fe) : "{res} "Individual controls=0.nationalite_pop"  "Survey year FE=0.annee"  "YoB $ \times $ gender FE=0.birthyr#0.sexe"  "religion=0.r{txt}.."

{com}. 
. // Labels
. 
. la var alsace                           "Alsace"
{txt}
{com}. la var born_1908_1913           "[0.3em] Born 1908-1913" 
{txt}
{com}. la var born_1911_1913           "[0.3em] Born 1911-1913" 
{txt}
{com}. la var born_1908_1913_fem       "[0.3em] B. 1908-13 (Woman)" 
{txt}
{com}. la var p_g1_1908_1913           "[0.3em] B. 1908-13 (Father)" 
{txt}
{com}. la var p_g2_1908_1913           "[0.3em] B. 1908-13 (G.-father)" 
{txt}
{com}. la var p_s_1908_1913            "[0.3em] B. 1908-13 (Husband)" 
{txt}
{com}. 
. // Table
. 
. estout transmission months spouse dipl_sup lrev business association memberFN ///
>         using "$results\\$program.tex", replace style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(alsace c.born_1908_1913#c.alsace c.born_1908_1913_fem#c.alsace c.born_1911_1913#c.alsace c.p_g1_1908_1913#c.alsace c.p_g2_1908_1913#c.alsace c.p_s_1908_1913#c.alsace) ///
>         order(alsace c.born_1908_1913#c.alsace c.born_1908_1913_fem#c.alsace c.p_g1_1908_1913#c.alsace c.p_g2_1908_1913#c.alsace c.born_1911_1913#c.alsace c.p_s_1908_1913#c.alsace) ///
>         varlabels(c.p_s_1908_1913#c.alsace "[0.3em] B 08-13 (Husband) $ \times $ Als." c.born_1908_1913_fem#c.alsace "[0.3em] B. 08-13 (Woman) $ \times $ Als." c.p_g1_1908_1913#c.alsace "[0.3em] B. 08-13 (Father) $ \times $ Als." c.p_g2_1908_1913#c.alsace "[0.3em] B. 08-13 (G.-father) $ \times $ Als.") ///
>         title(Party proximity and trust in institutions: Channels of persistence) ///
>         prehead("\begin{c -(}table{c )-}[t!]" ///
>         "\begin{c -(}center{c )-}" ///
>         "\begin{c -(}threeparttable{c )-}" ///
>         "\begin{c -(}scriptsize{c )-}" ///
>         "\caption{c -(}@title{c )-}\label{c -(}$program{c )-}" ///
>         "\begin{c -(}tabular*{c )-}{c -(}\columnwidth{c )-}{c -(}@{c -(}\extracolsep{c -(}\stretch{c -(}1{c )-}{c )-}{c )-}l*{c -(}@M{c )-}{c -(}c{c )-}@{c -(}{c )-}{c )-}" ///
>         "\hline" "\hline" ///
>         "&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(1){c )-}               &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(2){c )-}                &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(3){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(4){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(5){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(6){c )-}                &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(7){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(8){c )-}       \\" /// 
>         " &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Transmission{c )-} & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Service \& casualties{c )-} &\multicolumn{c -(}3{c )-}{c -(}c{c )-}{c -(}Human capital \& income{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Organizational skills{c )-} \\" /// 
>         "\cmidrule(lr){c -(}2-2{c )-} \cmidrule(lr){c -(}3-4{c )-} \cmidrule(lr){c -(}5-7{c )-} \cmidrule(lr){c -(}8-9{c )-}" /// 
>         "\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}                      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Higher{c )-}            &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Log{c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Owns{c )-}               &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Party{c )-}              &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Member{c )-}                      \\" ///
>         "&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}education{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}income{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}business{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}member{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\& Rad right{c )-}       \\" /// 
>         "\hline \\") ///
>         posthead("") ///
>         prefoot("") ///
>         postfoot("")
{res}
\begin{c -(}table{c )-}[t!]
\begin{c -(}center{c )-}
\begin{c -(}threeparttable{c )-}
\begin{c -(}scriptsize{c )-}
\caption{c -(}Party proximity and trust in institutions: Channels of persistence{c )-}\label{c -(}EEREV-D-21-00004_Table9{c )-}
\begin{c -(}tabular*{c )-}{c -(}\columnwidth{c )-}{c -(}@{c -(}\extracolsep{c -(}\stretch{c -(}1{c )-}{c )-}{c )-}l*{c -(}8{c )-}{c -(}c{c )-}@{c -(}{c )-}{c )-}
\hline
\hline
&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(1){c )-}               &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(2){c )-}                &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(3){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(4){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(5){c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(6){c )-}                &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(7){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(8){c )-}       \\
 &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Transmission{c )-} & \multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Service \& casualties{c )-} &\multicolumn{c -(}3{c )-}{c -(}c{c )-}{c -(}Human capital \& income{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Organizational skills{c )-} \\
\cmidrule(lr){c -(}2-2{c )-} \cmidrule(lr){c -(}3-4{c )-} \cmidrule(lr){c -(}5-7{c )-} \cmidrule(lr){c -(}8-9{c )-}
\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}                      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Trust{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Higher{c )-}            &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Log{c )-}        &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Owns{c )-}               &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Party{c )-}              &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Member{c )-}                      \\
&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}politicians{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}education{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}income{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}business{c )-}       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}member{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\& Rad right{c )-}       \\
\hline \\

Alsace              &       0.064*  &       0.097   &       0.038   &       0.065** &       0.032   &     --0.004   &     --0.032** &       0.001   \\
                    &     (0.035)   &     (0.062)   &     (0.079)   &     (0.025)   &     (0.036)   &     (0.025)   &     (0.014)   &     (0.002)   \\
[0.3em] Born 1908-1913 $\times$ Alsace&     --0.536***&     --0.538***&               &     --0.014   &     --0.075   &       0.063   &       0.062*  &     --0.004   \\
                    &     (0.053)   &     (0.079)   &               &     (0.046)   &     (0.101)   &     (0.049)   &     (0.031)   &     (0.003)   \\
[0.3em] B. 08-13 (Woman) $ \times $ Als.&       0.120   &               &               &               &               &               &               &               \\
                    &     (0.187)   &               &               &               &               &               &               &               \\
[0.3em] B. 08-13 (Father) $ \times $ Als.&     --0.600***&               &               &               &               &               &               &               \\
                    &     (0.145)   &               &               &               &               &               &               &               \\
[0.3em] B. 08-13 (G.-father) $ \times $ Als.&     --0.218   &               &               &               &               &               &               &               \\
                    &     (0.157)   &               &               &               &               &               &               &               \\
[0.3em] Born 1911-1913 $\times$ Alsace&               &     --0.024   &               &               &               &               &               &               \\
                    &               &     (0.162)   &               &               &               &               &               &               \\
[0.3em] B 08-13 (Husband) $ \times $ Als.&               &               &       0.012   &               &               &               &               &               \\
                    &               &               &     (0.306)   &               &               &               &               &               \\


{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table9.tex"'})

{com}. 
. estout transmission months spouse dipl_sup lrev business association memberFN /// 
>         using "$results\\$program.tex", append style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///
>         stats(mean_y N N_clust, lab("Mean dep. variable" "Observations" "Clusters") fmt(3 %9.0fc %9.0fc)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         drop(*) /// 
>         indicate( ///   
>         "Individual controls = 0.nationalite_pop" ///   
>         "Survey year FE = 0.annee" ///  
>         "Year-of-birth FE = 0.birthyr#0.sexe" ///
>         , labels(Yes )) ///
>         prehead("") /// 
>         posthead("\\") ///
>         prefoot("\hline") ///
>         postfoot("\hline" "\hline" ///
>         "\end{c -(}tabular*{c )-}" /// 
>         "\end{c -(}scriptsize{c )-}" ///
>         "\begin{c -(}tablenotes{c )-}[normal,flushleft]" ///
>         "\begin{c -(}scriptsize{c )-}" ///
>         "\item \emph{c -(}Notes{c )-}:" /// 
>         "Reduced-form estimates of the potential mechanism. " ///
>         "Data comes from the Interregional survey of political phenomena (\textit{c -(}Enqu\^ete interr\'egionale des ph\'enomenes politiques{c )-}). " ///
>         "The unit of observation is an individual. " ///
>         "Standard errors clustered at year-of-birth $ \times $ region level in parentheses. " ///
>         "Column (1): Intergenerational transmission of attitudes; " ///
>         "Columns (2) and (3): Length of service and casualties; " ///
>         "Columns (4) to (6): Human capital and earnings; " ///
>         "Columns (7) and (8): Organizational skills. " ///
>         "Year-of-birth $ \times $ gender fixed effects are included in all specifications. " ///
>         "* significant at 10\%; ** at 5\%; *** at 1\%. " ///    
>         "\end{c -(}scriptsize{c )-}" ///
>         "\end{c -(}tablenotes{c )-}" ///
>         "\end{c -(}threeparttable{c )-} " ///
>         "\end{c -(}center{c )-}" ///
>         "\end{c -(}table{c )-}") //
{res}

\\
Individual controls &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Survey year FE      &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Year-of-birth FE    &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
\hline
Mean dep. variable  &       0.535   &       0.605   &       0.669   &       0.110   &       8.847   &       0.145   &       0.019   &       0.002   \\
Observations        &       1,935   &         167   &         166   &       1,086   &         816   &         981   &         525   &         525   \\
Clusters            &         133   &          34   &          35   &          40   &          40   &          40   &          37   &          37   \\
\hline
\hline
\end{c -(}tabular*{c )-}
\end{c -(}scriptsize{c )-}
\begin{c -(}tablenotes{c )-}[normal,flushleft]
\begin{c -(}scriptsize{c )-}
\item \emph{c -(}Notes{c )-}:
Reduced-form estimates of the potential mechanism. 
Data comes from the Interregional survey of political phenomena (\textit{c -(}Enqu\^ete interr\'egionale des ph\'enomenes politiques{c )-}). 
The unit of observation is an individual. 
Standard errors clustered at year-of-birth $ \times $ region level in parentheses. 
Column (1): Intergenerational transmission of attitudes; 
Columns (2) and (3): Length of service and casualties; 
Columns (4) to (6): Human capital and earnings; 
Columns (7) and (8): Organizational skills. 
Year-of-birth $ \times $ gender fixed effects are included in all specifications. 
* significant at 10\%; ** at 5\%; *** at 1\%. 
\end{c -(}scriptsize{c )-}
\end{c -(}tablenotes{c )-}
\end{c -(}threeparttable{c )-} 
\end{c -(}center{c )-}
\end{c -(}table{c )-}
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table9.tex"'})

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