{smcl}
{com}{sf}{ul off}{txt}
{com}. 
. **** REPLICATION OF TABLE 8 ****
. 
. 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. TRUST REGRESSIONS
>         2. VOTE REGRESSIONS
>         3. TABLE FOR PAPER
> */
. 
. ********************************
. ***** 1. TRUST 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}. 
. // Regressions
. 
. foreach i in 1 2 4 5 6 9 11{c -(} // 
{txt}  2{com}.         
.         ivreghdfe confiance`i' 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 G0confiance`i'
{txt}  4{com}.         qui sum confiance`i' if e(sample)
{txt}  5{com}.         scalar mean_y=r(mean)
{txt}  6{com}.         estadd scalar mean_y
{txt}  7{com}. 
. {c )-}
{res}{txt}(dropped 2 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 8 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}    36{txt}{col 55}Number of obs = {res}     174
{txt}{col 55}F(  2,    35) = {res}    2.67
{txt}{col 55}Prob > F      = {res}  0.0836
{txt}Total (centered) SS     = {res} 15.11437549{txt}{col 55}Centered R2   = {res}  0.0075
{txt}Total (uncentered) SS   = {res} 15.11437549{txt}{col 55}Uncentered R2 = {res}  0.0075
{txt}Residual SS             = {res} 15.00079659{txt}{col 55}Root MSE      = {res}   .3216

{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}               confiance1{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}-.0590875{col 39}{space 2} .0286229{col 50}{space 1}   -2.06{col 59}{space 3}0.046{col 67}{space 4} -.117195{col 80}{space 3}  -.00098
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0040121{col 39}{space 2} .1009413{col 50}{space 1}   -0.04{col 59}{space 3}0.969{col 67}{space 4}-.2089339{col 80}{space 3} .2009097
{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}       19{col 31}{space 1}        0{col 43}{result}{space 1}       19{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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.8908046
{txt}(dropped 2 {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}    35{txt}{col 55}Number of obs = {res}     174
{txt}{col 55}F(  2,    34) = {res}    1.19
{txt}{col 55}Prob > F      = {res}  0.3162
{txt}Total (centered) SS     = {res} 32.17197679{txt}{col 55}Centered R2   = {res}  0.0110
{txt}Total (uncentered) SS   = {res} 32.17197679{txt}{col 55}Uncentered R2 = {res}  0.0110
{txt}Residual SS             = {res} 31.81927824{txt}{col 55}Root MSE      = {res}   .4684

{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}               confiance2{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} .0982611{col 39}{space 2} .0672771{col 50}{space 1}    1.46{col 59}{space 3}0.153{col 67}{space 4}-.0384623{col 80}{space 3} .2349845
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0692525{col 39}{space 2} .2745366{col 50}{space 1}    0.25{col 59}{space 3}0.802{col 67}{space 4} -.488673{col 80}{space 3}  .627178
{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}       19{col 31}{space 1}        0{col 43}{result}{space 1}       19{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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.64367816
{txt}(dropped 2 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 8 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}    36{txt}{col 55}Number of obs = {res}     177
{txt}{col 55}F(  2,    35) = {res}    0.18
{txt}{col 55}Prob > F      = {res}  0.8327
{txt}Total (centered) SS     = {res} 21.58958291{txt}{col 55}Centered R2   = {res}  0.0004
{txt}Total (uncentered) SS   = {res} 21.58958291{txt}{col 55}Uncentered R2 = {res}  0.0004
{txt}Residual SS             = {res}  21.5818743{txt}{col 55}Root MSE      = {res}   .3819

{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}               confiance4{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} .0002902{col 39}{space 2} .0443795{col 50}{space 1}    0.01{col 59}{space 3}0.995{col 67}{space 4} -.089805{col 80}{space 3} .0903854
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0665334{col 39}{space 2}  .112204{col 50}{space 1}   -0.59{col 59}{space 3}0.557{col 67}{space 4}-.2943196{col 80}{space 3} .1612527
{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}       19{col 31}{space 1}        0{col 43}{result}{space 1}       19{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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.83050847
{txt}(dropped 2 {browse "http://scorreia.com/research/singletons.pdf":singleton observations})
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 8 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}    36{txt}{col 55}Number of obs = {res}     172
{txt}{col 55}F(  2,    35) = {res}    2.01
{txt}{col 55}Prob > F      = {res}  0.1495
{txt}Total (centered) SS     = {res} 22.90790835{txt}{col 55}Centered R2   = {res}  0.0036
{txt}Total (uncentered) SS   = {res} 22.90790835{txt}{col 55}Uncentered R2 = {res}  0.0036
{txt}Residual SS             = {res} 22.82448991{txt}{col 55}Root MSE      = {res}   .3995

{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}               confiance5{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}-.0478759{col 39}{space 2} .0400716{col 50}{space 1}   -1.19{col 59}{space 3}0.240{col 67}{space 4}-.1292257{col 80}{space 3} .0334738
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}  .130945{col 39}{space 2} .0695543{col 50}{space 1}    1.88{col 59}{space 3}0.068{col 67}{space 4}-.0102578{col 80}{space 3} .2721478
{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}       19{col 31}{space 1}        0{col 43}{result}{space 1}       19{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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.80232558
{txt}(dropped 2 {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}    36{txt}{col 55}Number of obs = {res}     179
{txt}{col 55}F(  2,    35) = {res}    2.60
{txt}{col 55}Prob > F      = {res}  0.0887
{txt}Total (centered) SS     = {res} 19.08051044{txt}{col 55}Centered R2   = {res}  0.0084
{txt}Total (uncentered) SS   = {res} 19.08051044{txt}{col 55}Uncentered R2 = {res}  0.0084
{txt}Residual SS             = {res} 18.91983068{txt}{col 55}Root MSE      = {res}   .3552

{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}               confiance6{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}-.0415196{col 39}{space 2} .0352897{col 50}{space 1}   -1.18{col 59}{space 3}0.247{col 67}{space 4}-.1131614{col 80}{space 3} .0301223
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.2047447{col 39}{space 2} .1402406{col 50}{space 1}   -1.46{col 59}{space 3}0.153{col 67}{space 4}-.4894483{col 80}{space 3} .0799589
{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}       19{col 31}{space 1}        0{col 43}{result}{space 1}       19{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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.8603352
{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 8 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}     168
{txt}{col 55}F(  2,    33) = {res}    1.76
{txt}{col 55}Prob > F      = {res}  0.1875
{txt}Total (centered) SS     = {res}    23.46503{txt}{col 55}Centered R2   = {res}  0.0105
{txt}Total (uncentered) SS   = {res}    23.46503{txt}{col 55}Uncentered R2 = {res}  0.0105
{txt}Residual SS             = {res} 23.21759576{txt}{col 55}Root MSE      = {res}   .4072

{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}               confiance9{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}-.0820483{col 39}{space 2} .0472279{col 50}{space 1}   -1.74{col 59}{space 3}0.092{col 67}{space 4}-.1781342{col 80}{space 3} .0140375
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.1004478{col 39}{space 2} .2099731{col 50}{space 1}   -0.48{col 59}{space 3}0.636{col 67}{space 4}-.5276413{col 80}{space 3} .3267457
{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}       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}        3{col 31}{space 1}        1{col 43}{result}{space 1}        2{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}.22619048
{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(  2,    33) = {res}   34.88
{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} 32.93738226{txt}{col 55}Root MSE      = {res}    .485

{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} .0967484{col 39}{space 2} .0610721{col 50}{space 1}    1.58{col 59}{space 3}0.123{col 67}{space 4}-.0275037{col 80}{space 3} .2210006
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.5488583{col 39}{space 2} .0821785{col 50}{space 1}   -6.68{col 59}{space 3}0.000{col 67}{space 4}-.7160517{col 80}{space 3} -.381665
{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}       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}added scalar:
             e(mean_y) =  {res}.60479042
{txt}
{com}. 
. // Fixed effects
. 
. estfe . G0confiance*, labels(nationalite_pop "Individual controls" annee "Survey year FE" birthyr#sexe "YoB $ \times $ gender 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}. 
. *******************************
. ***** 2. VOTE REGRESSIONS *****
. *******************************
. 
. // Vote
. 
. foreach var in proximiteFN proximiteRPR proximitePS proximiteUDF proximiteECO proximiteCOM proximiteNA{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 G0`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 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}    1092
{txt}{col 55}F(  2,    39) = {res}    4.15
{txt}{col 55}Prob > F      = {res}  0.0232
{txt}Total (centered) SS     = {res} 48.10045078{txt}{col 55}Centered R2   = {res}  0.0004
{txt}Total (uncentered) SS   = {res} 48.10045078{txt}{col 55}Uncentered R2 = {res}  0.0004
{txt}Residual SS             = {res} 48.08178458{txt}{col 55}Root MSE      = {res}   .2148

{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}              proximiteFN{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}-.0032114{col 39}{space 2} .0088962{col 50}{space 1}   -0.36{col 59}{space 3}0.720{col 67}{space 4}-.0212057{col 80}{space 3} .0147829
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0344558{col 39}{space 2} .0137564{col 50}{space 1}    2.50{col 59}{space 3}0.017{col 67}{space 4} .0066309{col 80}{space 3} .0622808
{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}.04945055
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    3.12
{txt}{col 55}Prob > F      = {res}  0.0553
{txt}Total (centered) SS     = {res} 169.9496787{txt}{col 55}Centered R2   = {res}  0.0047
{txt}Total (uncentered) SS   = {res} 169.9496787{txt}{col 55}Uncentered R2 = {res}  0.0047
{txt}Residual SS             = {res} 169.1580642{txt}{col 55}Root MSE      = {res}   .4029

{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}             proximiteRPR{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}-.0655898{col 39}{space 2} .0262708{col 50}{space 1}   -2.50{col 59}{space 3}0.017{col 67}{space 4}-.1187275{col 80}{space 3}-.0124522
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}  .057915{col 39}{space 2} .0524733{col 50}{space 1}    1.10{col 59}{space 3}0.276{col 67}{space 4}-.0482222{col 80}{space 3} .1640523
{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}.21336996
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    0.90
{txt}{col 55}Prob > F      = {res}  0.4153
{txt}Total (centered) SS     = {res} 163.9224171{txt}{col 55}Centered R2   = {res}  0.0009
{txt}Total (uncentered) SS   = {res} 163.9224171{txt}{col 55}Uncentered R2 = {res}  0.0009
{txt}Residual SS             = {res} 163.7779867{txt}{col 55}Root MSE      = {res}   .3965

{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}              proximitePS{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}-.0238657{col 39}{space 2}  .018559{col 50}{space 1}   -1.29{col 59}{space 3}0.206{col 67}{space 4}-.0614049{col 80}{space 3} .0136735
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0262717{col 39}{space 2} .1141574{col 50}{space 1}   -0.23{col 59}{space 3}0.819{col 67}{space 4}-.2571767{col 80}{space 3} .2046334
{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}.1959707
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    1.76
{txt}{col 55}Prob > F      = {res}  0.1859
{txt}Total (centered) SS     = {res} 162.5013838{txt}{col 55}Centered R2   = {res}  0.0019
{txt}Total (uncentered) SS   = {res} 162.5013838{txt}{col 55}Uncentered R2 = {res}  0.0019
{txt}Residual SS             = {res} 162.1866701{txt}{col 55}Root MSE      = {res}   .3945

{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}             proximiteUDF{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} .0391145{col 39}{space 2} .0214023{col 50}{space 1}    1.83{col 59}{space 3}0.075{col 67}{space 4}-.0041757{col 80}{space 3} .0824047
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0080526{col 39}{space 2} .0801555{col 50}{space 1}    0.10{col 59}{space 3}0.920{col 67}{space 4}-.1540772{col 80}{space 3} .1701824
{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}.19688645
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    0.88
{txt}{col 55}Prob > F      = {res}  0.4232
{txt}Total (centered) SS     = {res} 42.80987758{txt}{col 55}Centered R2   = {res}  0.0004
{txt}Total (uncentered) SS   = {res} 42.80987758{txt}{col 55}Uncentered R2 = {res}  0.0004
{txt}Residual SS             = {res}  42.7912405{txt}{col 55}Root MSE      = {res}   .2026

{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}             proximiteECO{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} .0049185{col 39}{space 2} .0152739{col 50}{space 1}    0.32{col 59}{space 3}0.749{col 67}{space 4}-.0259758{col 80}{space 3} .0358129
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} .0243927{col 39}{space 2} .0251474{col 50}{space 1}    0.97{col 59}{space 3}0.338{col 67}{space 4}-.0264728{col 80}{space 3} .0752582
{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}.04395604
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    1.62
{txt}{col 55}Prob > F      = {res}  0.2104
{txt}Total (centered) SS     = {res} 19.52883033{txt}{col 55}Centered R2   = {res}  0.0018
{txt}Total (uncentered) SS   = {res} 19.52883033{txt}{col 55}Uncentered R2 = {res}  0.0018
{txt}Residual SS             = {res} 19.49322173{txt}{col 55}Root MSE      = {res}   .1368

{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}             proximiteCOM{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}-.0104876{col 39}{space 2} .0086416{col 50}{space 1}   -1.21{col 59}{space 3}0.232{col 67}{space 4}-.0279669{col 80}{space 3} .0069917
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2} -.020416{col 39}{space 2} .0253367{col 50}{space 1}   -0.81{col 59}{space 3}0.425{col 67}{space 4}-.0716643{col 80}{space 3} .0308322
{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}.01923077
{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}    1092
{txt}{col 55}F(  2,    39) = {res}    4.42
{txt}{col 55}Prob > F      = {res}  0.0187
{txt}Total (centered) SS     = {res} 206.7595231{txt}{col 55}Centered R2   = {res}  0.0030
{txt}Total (uncentered) SS   = {res} 206.7595231{txt}{col 55}Uncentered R2 = {res}  0.0030
{txt}Residual SS             = {res} 206.1448855{txt}{col 55}Root MSE      = {res}   .4448

{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}              proximiteNA{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} .0574172{col 39}{space 2} .0194408{col 50}{space 1}    2.95{col 59}{space 3}0.005{col 67}{space 4} .0180945{col 80}{space 3} .0967399
{txt}{space 25} {c |}
c.born_1908_1913#c.alsace {c |}{col 27}{res}{space 2}-.0751345{col 39}{space 2}  .077073{col 50}{space 1}   -0.97{col 59}{space 3}0.336{col 67}{space 4}-.2310293{col 80}{space 3} .0807604
{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}.27747253
{txt}
{com}. 
. // Fixed effects
. 
. estfe . G0proximite*, labels(nationalite_pop "Individual controls" annee "Survey year FE" birthyr#sexe "YoB $ \times $ gender 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}. 
. ******************************
. ***** 5. TABLE FOR PAPER *****
. ******************************
. 
. // Labels
. 
. la var born_1908_1913 "[0.3em] Born 1908-1913" 
{txt}
{com}. 
. // Trust
. 
. estout G0proximiteCOM G0proximitePS G0proximiteECO G0proximiteUDF G0proximiteRPR G0proximiteFN G0proximiteNA ///
>         using "$results\\$program.tex", replace 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) ///
>         keep(alsace c.born_1908_1913#c.alsace) /// 
>         order(alsace c.born_1908_1913#c.alsace) ///
>         title(Party proximity and trust in institutions: Baseline results) ///
>         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 )-}\\" ///
>         "%\hline" ///
>         "\cmidrule(lr){c -(}2-8{c )-}" ///
>         "& \multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}Panel A: Party proximity. \textit{c -(}Which party do you feel closer to, or less distant from{c )-}{c )-}\\" ///
>         "%\hline" ///
>         "%\cmidrule(lr){c -(}1-8{c )-}" /// %\hline" ///
>         "%\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}                    &\multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Which party do you feel closer to{c )-}{c )-} \\" ///
>         "\cmidrule(lr){c -(}2-8{c )-}" ///  \cmidrule(lr){c -(}9-9{c )-}
>         "\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Communist{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Socialist{c )-}{c )-}         &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Ecologist{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Liberal{c )-}{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Conservative{c )-}{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Radical right{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}None{c )-}{c )-}              \\" ///
>         "\hline") ///
>         posthead("\\") ///
>         prefoot("\\" ///
>         "\hline") ///
>         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: Baseline results{c )-}\label{c -(}EEREV-D-21-00004_Table8{c )-}
\begin{c -(}tabular*{c )-}{c -(}\columnwidth{c )-}{c -(}@{c -(}\extracolsep{c -(}\stretch{c -(}1{c )-}{c )-}{c )-}l*{c -(}7{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 )-}\\
%\hline
\cmidrule(lr){c -(}2-8{c )-}
& \multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}Panel A: Party proximity. \textit{c -(}Which party do you feel closer to, or less distant from{c )-}{c )-}\\
%\hline
%\cmidrule(lr){c -(}1-8{c )-}
%\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}                    &\multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Which party do you feel closer to{c )-}{c )-} \\
\cmidrule(lr){c -(}2-8{c )-}
\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Communist{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Socialist{c )-}{c )-}         &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Ecologist{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Liberal{c )-}{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Conservative{c )-}{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Radical right{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}None{c )-}{c )-}              \\
\hline
\\
Alsace              &     --0.010   &     --0.024   &       0.005   &       0.039*  &     --0.066** &     --0.003   &       0.057***\\
                    &     (0.009)   &     (0.019)   &     (0.015)   &     (0.021)   &     (0.026)   &     (0.009)   &     (0.019)   \\
[0.3em] Born 1908-1913 $\times$ Alsace&     --0.020   &     --0.026   &       0.024   &       0.008   &       0.058   &       0.034** &     --0.075   \\
                    &     (0.025)   &     (0.114)   &     (0.025)   &     (0.080)   &     (0.052)   &     (0.014)   &     (0.077)   \\
\\
\hline
Mean dep. variable  &       0.019   &       0.196   &       0.044   &       0.197   &       0.213   &       0.049   &       0.277   \\
Observations        &       1,092   &       1,092   &       1,092   &       1,092   &       1,092   &       1,092   &       1,092   \\
Clusters            &          40   &          40   &          40   &          40   &          40   &          40   &          40   \\

{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table8.tex"'})

{com}. 
. // Vote
. 
. estout G0confiance1 G0confiance2 G0confiance4 G0confiance5 G0confiance6 G0confiance9 G0confiance11 /// 
>         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) ///
>         keep(alsace c.born_1908_1913#c.alsace) /// 
>         order(alsace c.born_1908_1913#c.alsace) /// 
>         prehead("\\") ///
>         posthead("%\hline" ///
>         "\cmidrule(lr){c -(}2-8{c )-}" /// 
>         "& \multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}Panel B: Trust in institutions. \textit{c -(}Would you say you rather trust or distrust in{c )-}{c )-}\\" ///
>         "%\hline" ///
>         "%\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}                    &\multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Would you say you rather trust or distrust{c )-}{c )-} \\" /// 
>         "\cmidrule(lr){c -(}2-8{c )-}" ///
>         "\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Schooling{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Judicial{c )-}{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Police{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Church{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Army{c )-}{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Political{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Elected{c )-}{c )-}           \\" /// 
>         "       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}system{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}system{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                                   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                                   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                           &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}parties{c )-}{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}politicians{c )-}{c )-}       \\" /// 
>         "\hline" ///
>         "\\") ///
>         prefoot("\\" ///
>         "\hline") //
{res}
\\
%\hline
\cmidrule(lr){c -(}2-8{c )-}
& \multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}Panel B: Trust in institutions. \textit{c -(}Would you say you rather trust or distrust in{c )-}{c )-}\\
%\hline
%\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}                    &\multicolumn{c -(}7{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Would you say you rather trust or distrust{c )-}{c )-} \\
\cmidrule(lr){c -(}2-8{c )-}
\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Schooling{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Judicial{c )-}{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Police{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Church{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Army{c )-}{c )-}      &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Political{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}Elected{c )-}{c )-}           \\
       &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}system{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}system{c )-}{c )-}    &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                                   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                                   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}{c )-}                           &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}parties{c )-}{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}\textit{c -(}politicians{c )-}{c )-}       \\
\hline
\\
Alsace              &     --0.059** &       0.098   &       0.000   &     --0.048   &     --0.042   &     --0.082*  &       0.097   \\
                    &     (0.029)   &     (0.067)   &     (0.044)   &     (0.040)   &     (0.035)   &     (0.047)   &     (0.061)   \\
[0.3em] Born 1908-1913 $\times$ Alsace&     --0.004   &       0.069   &     --0.067   &       0.131*  &     --0.205   &     --0.100   &     --0.549***\\
                    &     (0.101)   &     (0.275)   &     (0.112)   &     (0.070)   &     (0.140)   &     (0.210)   &     (0.082)   \\
\\
\hline
Mean dep. variable  &       0.891   &       0.644   &       0.831   &       0.802   &       0.860   &       0.226   &       0.605   \\
Observations        &         174   &         174   &         177   &         172   &         179   &         168   &         167   \\
Clusters            &          36   &          35   &          36   &          36   &          36   &          34   &          34   \\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table8.tex"'})

{com}. 
. estout G0confiance1 G0confiance2 G0confiance4 G0confiance5 G0confiance6 G0confiance9 G0confiance11 /// 
>         using "$results\\$program.tex", append 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) ///
>         drop(*) /// 
>         indicate( ///
>         "Individual controls = 0.nationalite_pop" ///   
>         "Survey year FE = 0.annee" ///  
>         "Year-of-birth FE = 0.birthyr#0.sexe" ///
>         , labels(Yes )) ///
>         prehead("") ///
>         posthead("\\" ///
>         "\hline") ///
>         prefoot("") ///
>         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 effect of Wehmacht eligibility on political preferences. " ///
>         "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. " ///
>         "Panel A: Results with party proximity as the outcome; " ///
>         "Panel B: Results with measures of trust in institutions are the outcome. " ///
>         "Each column presents the estimation for a different party/insitution. " ///
>         "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}

\\
\hline
Individual controls &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Survey year FE      &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Year-of-birth FE    &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\

\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 effect of Wehmacht eligibility on political preferences. 
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. 
Panel A: Results with party proximity as the outcome; 
Panel B: Results with measures of trust in institutions are the outcome. 
Each column presents the estimation for a different party/insitution. 
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_Table8.tex"'})

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