{smcl}
{com}{sf}{ul off}{txt}
{com}. 
. **** REPLICATION OF TABLE 2 ****
. 
. 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. CANTON REGRESSIONS
>         2. MUNICIPALITY REGRESSIONS
>         3. TABLE FOR PAPER
> */
. 
. *********************************
. ***** 1. CANTON REGRESSIONS *****
. *********************************
. 
. // Import Cantonal data
. 
. use $data\EEREV-D-21-00004_Data_Canton1936.dta, clear
{txt}
{com}. 
. // Controls vector
. 
. global rdd2             "rdd_lat rdd_lon rdd_lat_2 rdd_lon_2 rdd_lat_lon"
{txt}
{com}. global sigma            "lelevation_mean lelevation_std $rdd2 water ldist_de"
{txt}
{com}. 
. // Voting variables 
. 
. foreach party in perc_droi_1936_ins perc_abst_1936{c -(}
{txt}  2{com}. 
.         reg `party' alsace, cluster(ar_1936_enc)
{txt}  3{com}.         estimates store `party'_1
{txt}  4{com}.         qui sum `party' if e(sample)
{txt}  5{com}.         scalar mean_y=r(mean)
{txt}  6{com}.         estadd scalar mean_y
{txt}  7{com}. 
.         ivreghdfe `party' alsace $sigma, cluster(ar_1936_enc) a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt}  8{com}.         estimates store `party'_2
{txt}  9{com}.         qui sum `party' if e(sample)
{txt} 10{com}.         scalar mean_y=r(mean)
{txt} 11{com}.         estadd scalar mean_y
{txt} 12{com}. 
.         ivreghdfe `party' alsace $sigma if sample==1, robust a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt} 13{com}.         estimates store `party'_3
{txt} 14{com}.         qui sum `party' if e(sample)
{txt} 15{com}.         scalar mean_y=r(mean)
{txt} 16{com}.         estadd scalar mean_y
{txt} 17{com}.         
. {c )-}

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     0.08
                                                {txt}Prob > F          = {res}    0.7801
                                                {txt}R-squared         = {res}    0.0025
                                                {txt}Root MSE          =    {res} 14.983

{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in ar_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_droi_~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-1.543244{col 26}{space 2} 5.461444{col 37}{space 1}   -0.28{col 46}{space 3}0.780{col 54}{space 4}-12.86958{col 67}{space 3} 9.783097
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 62.70046{col 26}{space 2} 4.791932{col 37}{space 1}   13.08{col 46}{space 3}0.000{col 54}{space 4} 52.76261{col 67}{space 3} 72.63832
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}61.727549
{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 ar_1936_enc

Number of clusters (ar_1936_enc) = {col 33}{res}    23{txt}{col 55}Number of obs = {res}      92
{txt}{col 55}F(  1,    22) = {res}    1.03
{txt}{col 55}Prob > F      = {res}  0.3208
{txt}Total (centered) SS     = {res} 11777.32078{txt}{col 55}Centered R2   = {res}  0.0095
{txt}Total (uncentered) SS   = {res} 11777.32078{txt}{col 55}Uncentered R2 = {res}  0.0095
{txt}Residual SS             = {res} 11665.04087{txt}{col 55}Root MSE      = {res}   12.64

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_droi_~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-6.194948{col 26}{space 2}  6.09867{col 37}{space 1}   -1.02{col 46}{space 3}0.321{col 54}{space 4}-18.84282{col 67}{space 3}  6.45292
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}61.727549
{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 4 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}      31
{txt}{col 55}F(  1,    16) = {res}    0.03
{txt}{col 55}Prob > F      = {res}  0.8712
{txt}Total (centered) SS     = {res}  2701.52627{txt}{col 55}Centered R2   = {res}  0.0018
{txt}Total (uncentered) SS   = {res}  2701.52627{txt}{col 55}Uncentered R2 = {res}  0.0018
{txt}Residual SS             = {res} 2696.543621{txt}{col 55}Root MSE      = {res}   12.98

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_droi_~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} 1.577648{col 26}{space 2} 9.572976{col 37}{space 1}    0.16{col 46}{space 3}0.871{col 54}{space 4}-18.71615{col 67}{space 3} 21.87145
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}62.774574

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     0.68
                                                {txt}Prob > F          = {res}    0.4172
                                                {txt}R-squared         = {res}    0.0152
                                                {txt}Root MSE          =    {res} 10.764

{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in ar_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_ab~1936{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-2.736982{col 26}{space 2} 3.309967{col 37}{space 1}   -0.83{col 46}{space 3}0.417{col 54}{space 4}-9.601434{col 67}{space 3}  4.12747
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 20.43394{col 26}{space 2} 2.677815{col 37}{space 1}    7.63{col 46}{space 3}0.000{col 54}{space 4}  14.8805{col 67}{space 3} 25.98739
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}18.708455
{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 ar_1936_enc

Number of clusters (ar_1936_enc) = {col 33}{res}    23{txt}{col 55}Number of obs = {res}      92
{txt}{col 55}F(  1,    22) = {res}    0.04
{txt}{col 55}Prob > F      = {res}  0.8499
{txt}Total (centered) SS     = {res} 7306.121725{txt}{col 55}Centered R2   = {res}  0.0002
{txt}Total (uncentered) SS   = {res} 7306.121725{txt}{col 55}Uncentered R2 = {res}  0.0002
{txt}Residual SS             = {res} 7304.880821{txt}{col 55}Root MSE      = {res}      10

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_ab~1936{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-.6512621{col 26}{space 2} 3.401419{col 37}{space 1}   -0.19{col 46}{space 3}0.850{col 54}{space 4}-7.705374{col 67}{space 3}  6.40285
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}18.708455
{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 4 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}      31
{txt}{col 55}F(  1,    16) = {res}    0.35
{txt}{col 55}Prob > F      = {res}  0.5638
{txt}Total (centered) SS     = {res} 1914.316345{txt}{col 55}Centered R2   = {res}  0.0221
{txt}Total (uncentered) SS   = {res} 1914.316345{txt}{col 55}Uncentered R2 = {res}  0.0221
{txt}Residual SS             = {res} 1872.075402{txt}{col 55}Root MSE      = {res}   10.82

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}perc_ab~1936{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-4.593533{col 26}{space 2} 7.793883{col 37}{space 1}   -0.59{col 46}{space 3}0.564{col 54}{space 4}-21.11583{col 67}{space 3} 11.92876
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}20.701382
{txt}
{com}. 
. // Nazi insitutions
. 
. foreach party in displaced_all_1946 displaced_intern_1946 displaced_enroled_1946{c -(} 
{txt}  2{com}. 
.         reg `party' alsace, cluster(ar_1936_enc) 
{txt}  3{com}.         estimates store `party'_1
{txt}  4{com}.         qui sum `party' if e(sample)
{txt}  5{com}.         scalar mean_y=r(mean)
{txt}  6{com}.         estadd scalar mean_y
{txt}  7{com}. 
.         ivreghdfe `party' alsace $sigma, cluster(ar_1936_enc) a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt}  8{com}.         estimates store `party'_2
{txt}  9{com}.         qui sum `party' if e(sample)
{txt} 10{com}.         scalar mean_y=r(mean)
{txt} 11{com}.         estadd scalar mean_y
{txt} 12{com}. 
.         ivreghdfe `party' alsace $sigma if sample==1, robust a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt} 13{com}.         estimates store `party'_3
{txt} 14{com}.         qui sum `party' if e(sample)
{txt} 15{com}.         scalar mean_y=r(mean)
{txt} 16{com}.         estadd scalar mean_y
{txt} 17{com}.         
. {c )-}

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     6.97
                                                {txt}Prob > F          = {res}    0.0149
                                                {txt}R-squared         = {res}    0.3188
                                                {txt}Root MSE          =    {res} 14.388

{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in ar_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~l_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-20.16937{col 26}{space 2} 7.637898{col 37}{space 1}   -2.64{col 46}{space 3}0.015{col 54}{space 4} -36.0094{col 67}{space 3} -4.32934
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 23.29265{col 26}{space 2} 7.629891{col 37}{space 1}    3.05{col 46}{space 3}0.006{col 54}{space 4} 7.469222{col 67}{space 3} 39.11607
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}10.577174
{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 ar_1936_enc

Number of clusters (ar_1936_enc) = {col 33}{res}    23{txt}{col 55}Number of obs = {res}      92
{txt}{col 55}F(  1,    22) = {res}    3.31
{txt}{col 55}Prob > F      = {res}  0.0824
{txt}Total (centered) SS     = {res} 5105.470807{txt}{col 55}Centered R2   = {res}  0.1127
{txt}Total (uncentered) SS   = {res} 5105.470807{txt}{col 55}Uncentered R2 = {res}  0.1127
{txt}Residual SS             = {res} 4530.325216{txt}{col 55}Root MSE      = {res}   7.878

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~l_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}-14.02089{col 26}{space 2} 7.705074{col 37}{space 1}   -1.82{col 46}{space 3}0.082{col 54}{space 4}-30.00023{col 67}{space 3} 1.958457
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}10.577174
{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 4 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}      31
{txt}{col 55}F(  1,    16) = {res}    2.03
{txt}{col 55}Prob > F      = {res}  0.1735
{txt}Total (centered) SS     = {res} 550.3019137{txt}{col 55}Centered R2   = {res}  0.0968
{txt}Total (uncentered) SS   = {res} 550.3019137{txt}{col 55}Uncentered R2 = {res}  0.0968
{txt}Residual SS             = {res} 497.0241695{txt}{col 55}Root MSE      = {res}   5.574

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~l_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} -5.15885{col 26}{space 2} 3.621492{col 37}{space 1}   -1.42{col 46}{space 3}0.174{col 54}{space 4}-12.83607{col 67}{space 3} 2.518369
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}8.5064516

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}    22.58
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.1873
                                                {txt}Root MSE          =    {res} .19546

{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in ar_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~n_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} .1922921{col 26}{space 2} .0404677{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .1083672{col 67}{space 3}  .276217
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2352941{col 26}{space 2} .0222057{col 37}{space 1}   10.60{col 46}{space 3}0.000{col 54}{space 4} .1892424{col 67}{space 3} .2813458
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}.35652174
{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 ar_1936_enc

Number of clusters (ar_1936_enc) = {col 33}{res}    23{txt}{col 55}Number of obs = {res}      92
{txt}{col 55}F(  1,    22) = {res}    1.08
{txt}{col 55}Prob > F      = {res}  0.3108
{txt}Total (centered) SS     = {res} 2.819148457{txt}{col 55}Centered R2   = {res}  0.0059
{txt}Total (uncentered) SS   = {res} 2.819148457{txt}{col 55}Uncentered R2 = {res}  0.0059
{txt}Residual SS             = {res} 2.802594433{txt}{col 55}Root MSE      = {res}   .1959

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~n_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} .0752209{col 26}{space 2} .0724989{col 37}{space 1}    1.04{col 46}{space 3}0.311{col 54}{space 4}-.0751325{col 67}{space 3} .2255743
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}.35652174
{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 4 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}      31
{txt}{col 55}F(  1,    16) = {res}    1.45
{txt}{col 55}Prob > F      = {res}  0.2455
{txt}Total (centered) SS     = {res} .3141012904{txt}{col 55}Centered R2   = {res}  0.0433
{txt}Total (uncentered) SS   = {res} .3141012904{txt}{col 55}Uncentered R2 = {res}  0.0433
{txt}Residual SS             = {res} .3004897996{txt}{col 55}Root MSE      = {res}    .137

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~n_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}  .082458{col 26}{space 2} .0684014{col 37}{space 1}    1.21{col 46}{space 3}0.246{col 54}{space 4}-.0625465{col 67}{space 3} .2274625
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}.31774194

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}    36.40
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.5599
                                                {txt}Root MSE          =    {res} 1.5351

{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in ar_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~d_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} 3.548377{col 26}{space 2} .5881012{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4}  2.32873{col 67}{space 3} 4.768024
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.420588{col 26}{space 2} .4746751{col 37}{space 1}    7.21{col 46}{space 3}0.000{col 54}{space 4} 2.436172{col 67}{space 3} 4.405004
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}5.6576087
{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 ar_1936_enc

Number of clusters (ar_1936_enc) = {col 33}{res}    23{txt}{col 55}Number of obs = {res}      92
{txt}{col 55}F(  1,    22) = {res}   26.92
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 100.5289761{txt}{col 55}Centered R2   = {res}  0.1832
{txt}Total (uncentered) SS   = {res} 100.5289761{txt}{col 55}Uncentered R2 = {res}  0.1832
{txt}Residual SS             = {res} 82.11049514{txt}{col 55}Root MSE      = {res}   1.061

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~d_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} 2.509074{col 26}{space 2} .4835614{col 37}{space 1}    5.19{col 46}{space 3}0.000{col 54}{space 4} 1.506229{col 67}{space 3} 3.511919
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}5.6576087
{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 4 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity

{col 55}Number of obs = {res}      31
{txt}{col 55}F(  1,    16) = {res}   40.38
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 15.92745754{txt}{col 55}Centered R2   = {res}  0.5975
{txt}Total (uncentered) SS   = {res} 15.92745754{txt}{col 55}Uncentered R2 = {res}  0.5975
{txt}Residual SS             = {res} 6.410296653{txt}{col 55}Root MSE      = {res}    .633

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}displ~d_1946{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} 2.180388{col 26}{space 2} .3431311{col 37}{space 1}    6.35{col 46}{space 3}0.000{col 54}{space 4} 1.452982{col 67}{space 3} 2.907793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}5.5451613
{txt}
{com}. 
. ***************************************
. ***** 2. MUNICIPALITY REGRESSIONS *****
. ***************************************
. 
. // Import Municipality data
. 
. use $data\EEREV-D-21-00004_Data_Municipalities.dta, clear
{txt}
{com}. 
. keep if annee==1965
{txt}(11,053 observations deleted)

{com}. 
. // Conscription
. 
. foreach party in perc_i_full{c -(}
{txt}  2{com}. 
.         reg `party' alsace, cluster(ct_1936_enc) 
{txt}  3{com}.         estimates store `party'_1
{txt}  4{com}.         qui sum `party' if e(sample)
{txt}  5{com}.         scalar mean_y=r(mean)
{txt}  6{com}.         estadd scalar mean_y
{txt}  7{com}. 
.         ivreghdfe `party' alsace $sigma, cluster(ct_1936_enc) a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt}  8{com}.         estimates store `party'_2
{txt}  9{com}.         qui sum `party' if e(sample)
{txt} 10{com}.         scalar mean_y=r(mean)
{txt} 11{com}.         estadd scalar mean_y
{txt} 12{com}.         
.         ivreghdfe `party' alsace $sigma if sample==1, cluster(ct_1936_enc) a(dialect_lux dialect_mos dialect_rhe dialect_pal dialect_als dialect_hau dialect_lor dialect_fra borderfe25) partial($sigma)
{txt} 13{com}.         estimates store `party'_3
{txt} 14{com}.         qui sum `party' if e(sample)
{txt} 15{com}.         scalar mean_y=r(mean)
{txt} 16{com}.         estadd scalar mean_y
{txt} 17{com}.         
. {c )-}

{txt}Linear regression                               Number of obs     = {res}     1,579
                                                {txt}F(1, 92)          =  {res}   102.69
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2978
                                                {txt}Root MSE          =    {res} 3.8856

{txt}{ralign 78:(Std. err. adjusted for {res:93} clusters in ct_1936_enc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} perc_i_full{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}   5.0864{col 26}{space 2}  .501925{col 37}{space 1}   10.13{col 46}{space 3}0.000{col 54}{space 4} 4.089534{col 67}{space 3} 6.083267
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.233357{col 26}{space 2} .4279883{col 37}{space 1}    9.89{col 46}{space 3}0.000{col 54}{space 4} 3.383335{col 67}{space 3} 5.083378
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added scalar:
             e(mean_y) =  {res}7.0455337
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 10 iterations)
{res}
{txt}OLS estimation
{hline 14}

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on ct_1936_enc

Number of clusters (ct_1936_enc) = {col 33}{res}    93{txt}{col 55}Number of obs = {res}    1579
{txt}{col 55}F(  1,    92) = {res}  101.09
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 20563.29385{txt}{col 55}Centered R2   = {res}  0.0697
{txt}Total (uncentered) SS   = {res} 20563.29385{txt}{col 55}Uncentered R2 = {res}  0.0697
{txt}Residual SS             = {res} 19129.54988{txt}{col 55}Root MSE      = {res}    3.51

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} perc_i_full{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2} 4.890294{col 26}{space 2} .4863857{col 37}{space 1}   10.05{col 46}{space 3}0.000{col 54}{space 4}  3.92429{col 67}{space 3} 5.856298
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        2{col 27}{space 1}        0{col 39}{result}{space 1}        2{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        8{col 27}{space 1}        1{col 39}{result}{space 1}        7{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}7.0455337
{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 ct_1936_enc

Number of clusters (ct_1936_enc) = {col 33}{res}    32{txt}{col 55}Number of obs = {res}     462
{txt}{col 55}F(  1,    31) = {res}   63.07
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 6590.165826{txt}{col 55}Centered R2   = {res}  0.0868
{txt}Total (uncentered) SS   = {res} 6590.165826{txt}{col 55}Uncentered R2 = {res}  0.0868
{txt}Residual SS             = {res}  6017.87723{txt}{col 55}Root MSE      = {res}   3.698

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} perc_i_full{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}alsace {c |}{col 14}{res}{space 2}   3.6777{col 26}{space 2} .4631075{col 37}{space 1}    7.94{col 46}{space 3}0.000{col 54}{space 4} 2.733186{col 67}{space 3} 4.622214
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Included instruments:{col 23}alsace
{help ivreg2##partial:Partialled-out}:{col 23}lelevation_mean lelevation_std rdd_lat rdd_lon rdd_lat_2
{col 23}rdd_lon_2 rdd_lat_lon water ldist_de _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 13}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text} Absorbed FE{col 14}{c |} Categories{col 27} - Redundant{col 39}  = Num. Coefs{col 54}{c |}
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text} dialect_lux{col 14}{c |}{space 1}        1{col 27}{space 1}        0{col 39}{result}{space 1}        1{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_mos{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text} {col 54}{c |}
{res}{col 1}{text} dialect_rhe{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_pal{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_als{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_hau{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_lor{col 14}{c |}{space 1}        2{col 27}{space 1}        1{col 39}{result}{space 1}        1{col 53}{text}?{col 54}{c |}
{res}{col 1}{text} dialect_fra{col 14}{c |}{space 1}        1{col 27}{space 1}        1{col 39}{result}{space 1}        0{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}  borderfe25{col 14}{c |}{space 1}        8{col 27}{space 1}        1{col 39}{result}{space 1}        7{col 53}{text}?{col 54}{c |}
{res}{col 1}{text}{hline 13}{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}7.7381999
{txt}
{com}. 
. ******************************
. ***** 3. TABLE FOR PAPER *****
. ******************************
. 
. // Unconditional 
. 
. estout perc_droi_1936_ins_1 perc_abst_1936_1 displaced_all_1946_1 displaced_intern_1946_1 displaced_enroled_1946_1 perc_i_full_1 ///
>         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(2 %9.0fc 0)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(alsace) ///
>         order(alsace) ///
>         title(Estimation strategy: Pre-War voting and Nazi policies) ///
>         prehead( ///
>         "\begin{c -(}table{c )-}[t!]" ///
>         "\begin{c -(}center{c )-}" ///
>         "\begin{c -(}threeparttable{c )-}" ///
>         "\begin{c -(}scriptsize{c )-}" ///
>         "\caption{c -(}@title\label{c -(}$program{c )-}{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 -(}2{c )-}{c -(}c{c )-}{c -(}Pre-War voting (1936){c )-} &\multicolumn{c -(}3{c )-}{c -(}c{c )-}{c -(}Nazi policies \citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Municipality-level{c )-}\\" ///
>         "\cmidrule(lr){c -(}2-3{c )-} \cmidrule(lr){c -(}4-6{c )-} \cmidrule(lr){c -(}7-7{c )-}" ///
>         "\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Right-wing{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Abstention{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Deported{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Incarcerated{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Wehrmacht{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Wehrmacht{c )-}   \\" /// 
>         "\cmidrule(lr){c -(}2-7{c )-}" ///
>         "& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel A: Unconditional differences{c )-}\\") ///
>         posthead("\hline" ///
>         "\\") ///
>         prefoot("\\") ///
>         postfoot("\\")
{res}
\begin{c -(}table{c )-}[t!]
\begin{c -(}center{c )-}
\begin{c -(}threeparttable{c )-}
\begin{c -(}scriptsize{c )-}
\caption{c -(}Estimation strategy: Pre-War voting and Nazi policies\label{c -(}EEREV-D-21-00004_Table2{c )-}{c )-}
\begin{c -(}tabular*{c )-}{c -(}\columnwidth{c )-}{c -(}@{c -(}\extracolsep{c -(}\stretch{c -(}1{c )-}{c )-}{c )-}l*{c -(}6{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 -(}2{c )-}{c -(}c{c )-}{c -(}Pre-War voting (1936){c )-} &\multicolumn{c -(}3{c )-}{c -(}c{c )-}{c -(}Nazi policies \citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Municipality-level{c )-}\\
\cmidrule(lr){c -(}2-3{c )-} \cmidrule(lr){c -(}4-6{c )-} \cmidrule(lr){c -(}7-7{c )-}
\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Right-wing{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Abstention{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Deported{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Incarcerated{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Wehrmacht{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Wehrmacht{c )-}   \\
\cmidrule(lr){c -(}2-7{c )-}
& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel A: Unconditional differences{c )-}\\
\hline
\\
Alsace dummy        &     --1.543   &     --2.737   &    --20.169** &       0.192***&       3.548***&       5.086***\\
                    &     (5.461)   &     (3.310)   &     (7.638)   &     (0.040)   &     (0.588)   &     (0.502)   \\
\\
Mean dep. variable  &       61.73   &       18.71   &       10.58   &        0.36   &        5.66   &        7.05   \\
Observations        &          92   &          92   &          92   &          92   &          92   &       1,579   \\
Clusters            &          23   &          23   &          23   &          23   &          23   &          93   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table2.tex"'})

{com}. 
. // Conditional
. 
. estout perc_droi_1936_ins_2 perc_abst_1936_2 displaced_all_1946_2 displaced_intern_1946_2 displaced_enroled_1946_2 perc_i_full_2 ///
>         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(2 %9.0fc 0)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(alsace) /// 
>         order(alsace) /// 
>         prehead("%\hline" ///
>         "\cmidrule(lr){c -(}2-7{c )-}" ///
>         "& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel B: Conditional on geography and dialect{c )-}\\") ///
>         posthead("\hline" ///
>         "\\") ///
>         prefoot("\\") ///
>         postfoot("\\")
{res}
%\hline
\cmidrule(lr){c -(}2-7{c )-}
& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel B: Conditional on geography and dialect{c )-}\\
\hline
\\
Alsace dummy        &     --6.195   &     --0.651   &    --14.021*  &       0.075   &       2.509***&       4.890***\\
                    &     (6.099)   &     (3.401)   &     (7.705)   &     (0.072)   &     (0.484)   &     (0.486)   \\
\\
Mean dep. variable  &       61.73   &       18.71   &       10.58   &        0.36   &        5.66   &        7.05   \\
Observations        &          92   &          92   &          92   &          92   &          92   &       1,579   \\
Clusters            &          23   &          23   &          23   &          23   &          23   &          93   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_Table2.tex"'})

{com}. 
. // Conditional: Estimation sample
. 
. estout perc_droi_1936_ins_3 perc_abst_1936_3 displaced_all_1946_3 displaced_intern_1946_3 displaced_enroled_1946_3 perc_i_full_3 ///
>         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(2 %9.0fc 0)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(alsace) /// 
>         order(alsace) /// 
>         prehead("%\hline" ///
>         "\cmidrule(lr){c -(}2-7{c )-}" ///
>         "& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel C: Conditional on geography and dialect (estimation sample){c )-}\\") /// 
>         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 )-}:" /// 
>         "Differences in pre-War voting, Nazi policies, and ferility between Alsace and Moselle. " ///
>         "In Columns (1) to (5) the unit of observation is a 1936 canton; in Column (6) it is a municipality. " ///
>         "Panel A: Unconditional differences; " ///
>         "Panel B: Differences conditional on geography and dialects; " ///
>         "Panel C: Differences conditional on geography and dialects (estimation sample). " ///
>         "Column (1): Vote for right-wing parties in the 1936 parliament election. " ///
>         "Column (2): Abstention in the 1936 parliament election. " ///
>         "Column (3): Fraction of 1936 population that was deported. " ///       
>         "Column (4): that was incarcerated. " ///
>         "Column (5): that was conscripted to the Wehrmacht \citep{c -(}maitre1958aspects{c )-}. " ///
>         "Column (6): that was conscripted to the Wehrmacht \citep{c -(}ministere1945basrhin{c )-}. " ///  
>         "Geography and dialects controls included in Panels B and C: " ///
>         "access to waterways (binary), elevation (log mean, log std.dev.), distance to Germany (log km), 25km border segment fixed-effects, " ///
>         "historical dialect fixed effects, and a quadratic polynomial in latitude and longitude ($ x+y+x^2+y^2+xy $). " ///
>         "Standard errors clustered at the arrondissement level in parentheses in Columns (1) to (5) of Panels A and B; " ///
>         "robust standard errors in Columns (1) to (5) of Panel C; " ///
>         "standard errors clustered at the canton level in Column (6). " ///
>         "* 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
\cmidrule(lr){c -(}2-7{c )-}
& \multicolumn{c -(}6{c )-}{c -(}c{c )-}{c -(}Panel C: Conditional on geography and dialect (estimation sample){c )-}\\
\hline
\\
Alsace dummy        &       1.578   &     --4.594   &     --5.159   &       0.082   &       2.180***&       3.678***\\
                    &     (9.573)   &     (7.794)   &     (3.621)   &     (0.068)   &     (0.343)   &     (0.463)   \\
\\
Mean dep. variable  &       62.77   &       20.70   &        8.51   &        0.32   &        5.55   &        7.74   \\
Observations        &          31   &          31   &          31   &          31   &          31   &         462   \\
Clusters            &               &               &               &               &               &          32   \\
\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 )-}:
Differences in pre-War voting, Nazi policies, and ferility between Alsace and Moselle. 
In Columns (1) to (5) the unit of observation is a 1936 canton; in Column (6) it is a municipality. 
Panel A: Unconditional differences; 
Panel B: Differences conditional on geography and dialects; 
Panel C: Differences conditional on geography and dialects (estimation sample). 
Column (1): Vote for right-wing parties in the 1936 parliament election. 
Column (2): Abstention in the 1936 parliament election. 
Column (3): Fraction of 1936 population that was deported. 
Column (4): that was incarcerated. 
Column (5): that was conscripted to the Wehrmacht \citep{c -(}maitre1958aspects{c )-}. 
Column (6): that was conscripted to the Wehrmacht \citep{c -(}ministere1945basrhin{c )-}. 
Geography and dialects controls included in Panels B and C: 
access to waterways (binary), elevation (log mean, log std.dev.), distance to Germany (log km), 25km border segment fixed-effects, 
historical dialect fixed effects, and a quadratic polynomial in latitude and longitude ($ x+y+x^2+y^2+xy $). 
Standard errors clustered at the arrondissement level in parentheses in Columns (1) to (5) of Panels A and B; 
robust standard errors in Columns (1) to (5) of Panel C; 
standard errors clustered at the canton level in Column (6). 
* 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_Table2.tex"'})

{com}. 
. cap log close
{smcl}
{com}{sf}{ul off}