{smcl}
{com}{sf}{ul off}{txt}
{com}. 
. **** REPLICATION OF TABLE C1 ****
. 
. 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. 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}. 
. // Regressions
. 
. foreach party in profe_agric_1907 profe_indus_1907 perc_fore_1936 perc_male_1936 perc_prot_1936 perc_jewi_1936 perc_fran_1936 perc_dial_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}     0.11
                                                {txt}Prob > F          = {res}    0.7470
                                                {txt}R-squared         = {res}    0.0018
                                                {txt}Root MSE          =    {res} 20.689

{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}profe_a~1907{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} 1.802817{col 26}{space 2} 5.518684{col 37}{space 1}    0.33{col 46}{space 3}0.747{col 54}{space 4}-9.642234{col 67}{space 3} 13.24787
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 42.79412{col 26}{space 2} 4.496733{col 37}{space 1}    9.52{col 46}{space 3}0.000{col 54}{space 4} 33.46846{col 67}{space 3} 52.11977
{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}43.930676
{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}    0.01
{txt}{col 55}Prob > F      = {res}  0.9262
{txt}Total (centered) SS     = {res} 24304.66356{txt}{col 55}Centered R2   = {res}  0.0001
{txt}Total (uncentered) SS   = {res} 24304.66356{txt}{col 55}Uncentered R2 = {res}  0.0001
{txt}Residual SS             = {res} 24303.13128{txt}{col 55}Root MSE      = {res}   18.25

{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}profe_a~1907{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} .7236947{col 26}{space 2}  7.72388{col 37}{space 1}    0.09{col 46}{space 3}0.926{col 54}{space 4}-15.29465{col 67}{space 3} 16.74204
{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}43.930676
{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}    0.70
{txt}{col 55}Prob > F      = {res}  0.4154
{txt}Total (centered) SS     = {res} 4445.952412{txt}{col 55}Centered R2   = {res}  0.0488
{txt}Total (uncentered) SS   = {res} 4445.952412{txt}{col 55}Uncentered R2 = {res}  0.0488
{txt}Residual SS             = {res}  4229.08624{txt}{col 55}Root MSE      = {res}   16.26

{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}profe_a~1907{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} 10.40821{col 26}{space 2} 12.44702{col 37}{space 1}    0.84{col 46}{space 3}0.415{col 54}{space 4}-15.97829{col 67}{space 3} 36.79471
{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}47.741935

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     0.02
                                                {txt}Prob > F          = {res}    0.9031
                                                {txt}R-squared         = {res}    0.0004
                                                {txt}Root MSE          =    {res} 18.382

{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}profe_i~1907{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}-.7619168{col 26}{space 2} 6.186887{col 37}{space 1}   -0.12{col 46}{space 3}0.903{col 54}{space 4}-13.59274{col 67}{space 3}  12.0689
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 35.95588{col 26}{space 2}  5.49044{col 37}{space 1}    6.55{col 46}{space 3}0.000{col 54}{space 4} 24.56941{col 67}{space 3} 47.34236
{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}35.475543
{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}    0.00
{txt}{col 55}Prob > F      = {res}  0.9743
{txt}Total (centered) SS     = {res} 16569.12746{txt}{col 55}Centered R2   = {res}  0.0000
{txt}Total (uncentered) SS   = {res} 16569.12746{txt}{col 55}Uncentered R2 = {res}  0.0000
{txt}Residual SS             = {res} 16569.03267{txt}{col 55}Root MSE      = {res}   15.07

{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}profe_i~1907{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}-.1799988{col 26}{space 2} 5.515409{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-11.61826{col 67}{space 3} 11.25826
{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}35.475543
{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}    1.16
{txt}{col 55}Prob > F      = {res}  0.2973
{txt}Total (centered) SS     = {res} 2437.861956{txt}{col 55}Centered R2   = {res}  0.0723
{txt}Total (uncentered) SS   = {res} 2437.861956{txt}{col 55}Uncentered R2 = {res}  0.0723
{txt}Residual SS             = {res} 2261.576795{txt}{col 55}Root MSE      = {res}   11.89

{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}profe_i~1907{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}-9.383997{col 26}{space 2}  8.70959{col 37}{space 1}   -1.08{col 46}{space 3}0.297{col 54}{space 4} -27.8475{col 67}{space 3} 9.079509
{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}32.419355

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     5.08
                                                {txt}Prob > F          = {res}    0.0345
                                                {txt}R-squared         = {res}    0.2065
                                                {txt}Root MSE          =    {res} 4.0216

{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_fo~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}-4.204375{col 26}{space 2} 1.865875{col 37}{space 1}   -2.25{col 46}{space 3}0.035{col 54}{space 4}-8.073964{col 67}{space 3}-.3347864
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.629006{col 26}{space 2} 1.850493{col 37}{space 1}    3.04{col 46}{space 3}0.006{col 54}{space 4} 1.791318{col 67}{space 3} 9.466694
{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}2.978422
{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}    0.00
{txt}{col 55}Prob > F      = {res}  0.9652
{txt}Total (centered) SS     = {res} 584.1897052{txt}{col 55}Centered R2   = {res}  0.0000
{txt}Total (uncentered) SS   = {res} 584.1897052{txt}{col 55}Uncentered R2 = {res}  0.0000
{txt}Residual SS             = {res} 584.1727269{txt}{col 55}Root MSE      = {res}   2.829

{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_fo~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}-.0761787{col 26}{space 2} 1.724711{col 37}{space 1}   -0.04{col 46}{space 3}0.965{col 54}{space 4}-3.653009{col 67}{space 3} 3.500652
{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}2.978422
{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}    0.62
{txt}{col 55}Prob > F      = {res}  0.4429
{txt}Total (centered) SS     = {res} 74.71012022{txt}{col 55}Centered R2   = {res}  0.0121
{txt}Total (uncentered) SS   = {res} 74.71012022{txt}{col 55}Uncentered R2 = {res}  0.0121
{txt}Residual SS             = {res}  73.8094465{txt}{col 55}Root MSE      = {res}   2.148

{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_fo~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}-.6707547{col 26}{space 2} .8525983{col 37}{space 1}   -0.79{col 46}{space 3}0.443{col 54}{space 4}-2.478182{col 67}{space 3} 1.136673
{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}2.1048448

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     6.57
                                                {txt}Prob > F          = {res}    0.0177
                                                {txt}R-squared         = {res}    0.0810
                                                {txt}Root MSE          =    {res} 3.1833

{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_ma~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}-1.936119{col 26}{space 2} .7552254{col 37}{space 1}   -2.56{col 46}{space 3}0.018{col 54}{space 4}-3.502361{col 67}{space 3}-.3698773
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}   51.277{col 26}{space 2} .6837839{col 37}{space 1}   74.99{col 46}{space 3}0.000{col 54}{space 4} 49.85892{col 67}{space 3} 52.69508
{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}50.056401
{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}    1.07
{txt}{col 55}Prob > F      = {res}  0.3113
{txt}Total (centered) SS     = {res} 701.3969406{txt}{col 55}Centered R2   = {res}  0.0116
{txt}Total (uncentered) SS   = {res} 701.3969406{txt}{col 55}Uncentered R2 = {res}  0.0116
{txt}Residual SS             = {res} 693.2950286{txt}{col 55}Root MSE      = {res}   3.082

{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_ma~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}-1.664104{col 26}{space 2} 1.605601{col 37}{space 1}   -1.04{col 46}{space 3}0.311{col 54}{space 4}-4.993916{col 67}{space 3} 1.665709
{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}50.056401
{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}    0.07
{txt}{col 55}Prob > F      = {res}  0.7928
{txt}Total (centered) SS     = {res} 368.4710008{txt}{col 55}Centered R2   = {res}  0.0037
{txt}Total (uncentered) SS   = {res} 368.4710008{txt}{col 55}Uncentered R2 = {res}  0.0037
{txt}Residual SS             = {res} 367.1069278{txt}{col 55}Root MSE      = {res}    4.79

{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_ma~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}-.8254653{col 26}{space 2} 3.090101{col 37}{space 1}   -0.27{col 46}{space 3}0.793{col 54}{space 4}-7.376187{col 67}{space 3} 5.725256
{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}50.075465

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}    11.85
                                                {txt}Prob > F          = {res}    0.0023
                                                {txt}R-squared         = {res}    0.1816
                                                {txt}Root MSE          =    {res} 16.204

{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_pr~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} 15.64024{col 26}{space 2}  4.54345{col 37}{space 1}    3.44{col 46}{space 3}0.002{col 54}{space 4} 6.217697{col 67}{space 3} 25.06278
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.906832{col 26}{space 2} .8723246{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} 2.097741{col 67}{space 3} 5.715922
{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}13.766981
{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}   23.32
{txt}{col 55}Prob > F      = {res}  0.0001
{txt}Total (centered) SS     = {res} 16919.16614{txt}{col 55}Centered R2   = {res}  0.4073
{txt}Total (uncentered) SS   = {res} 16919.16614{txt}{col 55}Uncentered R2 = {res}  0.4073
{txt}Residual SS             = {res}  10028.3014{txt}{col 55}Root MSE      = {res}   11.72

{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_pr~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} 48.53147{col 26}{space 2} 10.05007{col 37}{space 1}    4.83{col 46}{space 3}0.000{col 54}{space 4}  27.6889{col 67}{space 3} 69.37405
{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}13.766981
{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}   27.65
{txt}{col 55}Prob > F      = {res}  0.0001
{txt}Total (centered) SS     = {res} 9427.999395{txt}{col 55}Centered R2   = {res}  0.7349
{txt}Total (uncentered) SS   = {res} 9427.999395{txt}{col 55}Uncentered R2 = {res}  0.7349
{txt}Residual SS             = {res} 2499.519795{txt}{col 55}Root MSE      = {res}    12.5

{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_pr~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} 58.83002{col 26}{space 2} 11.18835{col 37}{space 1}    5.26{col 46}{space 3}0.000{col 54}{space 4} 35.11178{col 67}{space 3} 82.54825
{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}20.498645

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     0.68
                                                {txt}Prob > F          = {res}    0.4188
                                                {txt}R-squared         = {res}    0.0088
                                                {txt}Root MSE          =    {res} .81255

{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_je~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} .1564375{col 26}{space 2} .1898714{col 37}{space 1}    0.82{col 46}{space 3}0.419{col 54}{space 4}-.2373316{col 67}{space 3} .5502066
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8308217{col 26}{space 2} .1486778{col 37}{space 1}    5.59{col 46}{space 3}0.000{col 54}{space 4} .5224827{col 67}{space 3} 1.139161
{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}.9294453
{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}    1.06
{txt}{col 55}Prob > F      = {res}  0.3147
{txt}Total (centered) SS     = {res} 40.67803236{txt}{col 55}Centered R2   = {res}  0.0040
{txt}Total (uncentered) SS   = {res} 40.67803236{txt}{col 55}Uncentered R2 = {res}  0.0040
{txt}Residual SS             = {res} 40.51507809{txt}{col 55}Root MSE      = {res}    .745

{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_je~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} .2360041{col 26}{space 2}  .229382{col 37}{space 1}    1.03{col 46}{space 3}0.315{col 54}{space 4}-.2397052{col 67}{space 3} .7117133
{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}.9294453
{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}    0.64
{txt}{col 55}Prob > F      = {res}  0.4371
{txt}Total (centered) SS     = {res}  4.43220747{txt}{col 55}Centered R2   = {res}  0.0256
{txt}Total (uncentered) SS   = {res}  4.43220747{txt}{col 55}Uncentered R2 = {res}  0.0256
{txt}Residual SS             = {res} 4.318950458{txt}{col 55}Root MSE      = {res}   .5196

{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_je~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} .2378551{col 26}{space 2} .2984015{col 37}{space 1}    0.80{col 46}{space 3}0.437{col 54}{space 4}-.3947277{col 67}{space 3}  .870438
{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}.8788718

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}    10.87
                                                {txt}Prob > F          = {res}    0.0033
                                                {txt}R-squared         = {res}    0.2864
                                                {txt}Root MSE          =    {res} 14.795

{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_fr~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}-19.20398{col 26}{space 2} 5.823408{col 37}{space 1}   -3.30{col 46}{space 3}0.003{col 54}{space 4}-31.28099{col 67}{space 3}-7.126976
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 25.79883{col 26}{space 2} 5.480736{col 37}{space 1}    4.71{col 46}{space 3}0.000{col 54}{space 4} 14.43248{col 67}{space 3} 37.16518
{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}13.691973
{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}    0.94
{txt}{col 55}Prob > F      = {res}  0.3419
{txt}Total (centered) SS     = {res} 3068.810542{txt}{col 55}Centered R2   = {res}  0.0223
{txt}Total (uncentered) SS   = {res} 3068.810542{txt}{col 55}Uncentered R2 = {res}  0.0223
{txt}Residual SS             = {res} 3000.353771{txt}{col 55}Root MSE      = {res}   6.411

{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_fr~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}-4.837209{col 26}{space 2} 4.979634{col 37}{space 1}   -0.97{col 46}{space 3}0.342{col 54}{space 4}-15.16434{col 67}{space 3}  5.48992
{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}13.691973
{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}    2.00
{txt}{col 55}Prob > F      = {res}  0.1762
{txt}Total (centered) SS     = {res} 831.9511843{txt}{col 55}Centered R2   = {res}  0.1040
{txt}Total (uncentered) SS   = {res} 831.9511843{txt}{col 55}Uncentered R2 = {res}  0.1040
{txt}Residual SS             = {res}  745.459818{txt}{col 55}Root MSE      = {res}   6.826

{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_fr~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}-6.573041{col 26}{space 2} 4.644935{col 37}{space 1}   -1.42{col 46}{space 3}0.176{col 54}{space 4}-16.41986{col 67}{space 3}  3.27378
{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}11.952314

{txt}Linear regression                               Number of obs     = {res}        92
                                                {txt}F(1, 22)          =  {res}     6.77
                                                {txt}Prob > F          = {res}    0.0163
                                                {txt}R-squared         = {res}    0.1731
                                                {txt}Root MSE          =    {res} 4.2224

{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_di~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} 3.958059{col 26}{space 2} 1.521138{col 37}{space 1}    2.60{col 46}{space 3}0.016{col 54}{space 4} .8034121{col 67}{space 3} 7.112705
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 9.122956{col 26}{space 2} 1.288564{col 37}{space 1}    7.08{col 46}{space 3}0.000{col 54}{space 4} 6.450638{col 67}{space 3} 11.79527
{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}11.618254
{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}    1.16
{txt}{col 55}Prob > F      = {res}  0.2928
{txt}Total (centered) SS     = {res} 701.4587878{txt}{col 55}Centered R2   = {res}  0.0144
{txt}Total (uncentered) SS   = {res} 701.4587878{txt}{col 55}Uncentered R2 = {res}  0.0144
{txt}Residual SS             = {res} 691.3775372{txt}{col 55}Root MSE      = {res}   3.077

{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_di~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}-1.856281{col 26}{space 2} 1.722414{col 37}{space 1}   -1.08{col 46}{space 3}0.293{col 54}{space 4}-5.428349{col 67}{space 3} 1.715786
{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}11.618254
{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}    0.00
{txt}{col 55}Prob > F      = {res}  0.9942
{txt}Total (centered) SS     = {res} 96.68526333{txt}{col 55}Centered R2   = {res}  0.0000
{txt}Total (uncentered) SS   = {res} 96.68526333{txt}{col 55}Uncentered R2 = {res}  0.0000
{txt}Residual SS             = {res}  96.6849483{txt}{col 55}Root MSE      = {res}   2.458

{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_di~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}-.0125446{col 26}{space 2} 1.685108{col 37}{space 1}   -0.01{col 46}{space 3}0.994{col 54}{space 4}-3.584813{col 67}{space 3} 3.559724
{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}10.811941
{txt}
{com}. 
. ******************************
. ***** 2. TABLE FOR PAPER *****
. ******************************
. 
. // Unconditional 
. 
. estout profe_agric_1907_1 profe_indus_1907_1 perc_fore_1936_1 perc_male_1936_1 perc_prot_1936_1 perc_jewi_1936_1 perc_fran_1936_1 perc_dial_1936_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 comparability) ///
>         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 -(}1{c )-}{c -(}c{c )-}{c -(}(7){c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(8){c )-} \\" ///    
>         "       &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Occupation 1907{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Population 1936{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Religion 1936{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Language 1936{c )-}\\" ///
>         "       &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}rosse1938elsass{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-}\\" ///
>         "\cmidrule(lr){c -(}2-3{c )-} \cmidrule(lr){c -(}4-5{c )-} \cmidrule(lr){c -(}6-7{c )-} \cmidrule(lr){c -(}8-9{c )-}" ///
>         "\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Agriculture{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Industry{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Foreign{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Male{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Protestant{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Jewish{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}French{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Dialect{c )-}   \\" ///
>         "\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{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 comparability\label{c -(}EEREV-D-21-00004_TableC1{c )-}{c )-}
\begin{c -(}tabular*{c )-}{c -(}\columnwidth{c )-}{c -(}@{c -(}\extracolsep{c -(}\stretch{c -(}1{c )-}{c )-}{c )-}l*{c -(}8{c )-}{c -(}c{c )-}@{c -(}{c )-}{c )-}
\hline
\hline
&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(1){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(2){c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(3){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(4){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(5){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(6){c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(7){c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}(8){c )-} \\
       &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Occupation 1907{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Population 1936{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Religion 1936{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}Language 1936{c )-}\\
       &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}rosse1938elsass{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-} &\multicolumn{c -(}2{c )-}{c -(}c{c )-}{c -(}\citep{c -(}maitre1958aspects{c )-}{c )-}\\
\cmidrule(lr){c -(}2-3{c )-} \cmidrule(lr){c -(}4-5{c )-} \cmidrule(lr){c -(}6-7{c )-} \cmidrule(lr){c -(}8-9{c )-}
\multicolumn{c -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Dep. Variable{c )-}{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Agriculture{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Industry{c )-}     &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Foreign{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Male{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Protestant{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Jewish{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}French{c )-}   &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}Dialect{c )-}   \\
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel A: Unconditional differences{c )-}\\
\hline
\\
Alsace dummy        &       1.803   &     --0.762   &     --4.204** &     --1.936** &      15.640***&       0.156   &    --19.204***&       3.958** \\
                    &     (5.519)   &     (6.187)   &     (1.866)   &     (0.755)   &     (4.543)   &     (0.190)   &     (5.823)   &     (1.521)   \\
\\
Mean dep. variable  &       43.93   &       35.48   &        2.98   &       50.06   &       13.77   &        0.93   &       13.69   &       11.62   \\
Observations        &          92   &          92   &          92   &          92   &          92   &          92   &          92   &          92   \\
Clusters            &          23   &          23   &          23   &          23   &          23   &          23   &          23   &          23   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableC1.tex"'})

{com}. 
. // Conditional
. 
. estout profe_agric_1907_2 profe_indus_1907_2 perc_fore_1936_2 perc_male_1936_2 perc_prot_1936_2 perc_jewi_1936_2 perc_fran_1936_2 perc_dial_1936_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("\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel B: Conditional on geography and dialect{c )-}\\") ///
>         posthead("\hline" ///
>         "\\") ///
>         prefoot("\\") ///
>         postfoot("\\")
{res}
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel B: Conditional on geography and dialect{c )-}\\
\hline
\\
Alsace dummy        &       0.724   &     --0.180   &     --0.076   &     --1.664   &      48.531***&       0.236   &     --4.837   &     --1.856   \\
                    &     (7.724)   &     (5.515)   &     (1.725)   &     (1.606)   &    (10.050)   &     (0.229)   &     (4.980)   &     (1.722)   \\
\\
Mean dep. variable  &       43.93   &       35.48   &        2.98   &       50.06   &       13.77   &        0.93   &       13.69   &       11.62   \\
Observations        &          92   &          92   &          92   &          92   &          92   &          92   &          92   &          92   \\
Clusters            &          23   &          23   &          23   &          23   &          23   &          23   &          23   &          23   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableC1.tex"'})

{com}. 
. // Conditional: Estimation sample
. 
. estout profe_agric_1907_3 profe_indus_1907_3 perc_fore_1936_3 perc_male_1936_3 perc_prot_1936_3 perc_jewi_1936_3 perc_fran_1936_3 perc_dial_1936_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, lab("Mean dep. variable" "Observations") fmt(2 %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) ///
>         order(alsace) ///
>         prehead("\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{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 occupation, population, religions, and language between Alsace and Moselle. " ///
>         "The unit of observation is a 1936 canton. " ///
>         "Panel A: Unconditional differences; " ///
>         "Panel B: Differences conditional on geography and dialects; " ///
>         "Panel C: Differences conditional on geography and dialects (estimation sample). " ///
>         "Column (1): Share of the population occupied in agriculture in 1907; " ///
>         "Column (2): Share of the population occupied in industry in 1907; " ///
>         "Column (3): Share of foreign population in 1936; " ///
>         "Column (4): Share of male population in 1936; " ///
>         "Column (5): Share of population of protestant confession in 1936; " ///
>         "Column (6): Share of population of jewish confession in 1936; " ///    
>         "Column (7): Share of french-speaking population in 1936; " ///
>         "Column (8): Share of dialect-speaking population in 1936; " ///
>         "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 Panels A and B; " ///
>         "robust standard errors in Panel C. " ///
>         "* 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}
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel C: Conditional on geography and dialect (estimation sample){c )-}\\
\hline
\\
Alsace dummy        &      10.408   &     --9.384   &     --0.671   &     --0.825   &      58.830***&       0.238   &     --6.573   &     --0.013   \\
                    &    (12.447)   &     (8.710)   &     (0.853)   &     (3.090)   &    (11.188)   &     (0.298)   &     (4.645)   &     (1.685)   \\
\\
Mean dep. variable  &       47.74   &       32.42   &        2.10   &       50.08   &       20.50   &        0.88   &       11.95   &       10.81   \\
Observations        &          31   &          31   &          31   &          31   &          31   &          31   &          31   &          31   \\
\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 occupation, population, religions, and language between Alsace and Moselle. 
The unit of observation is a 1936 canton. 
Panel A: Unconditional differences; 
Panel B: Differences conditional on geography and dialects; 
Panel C: Differences conditional on geography and dialects (estimation sample). 
Column (1): Share of the population occupied in agriculture in 1907; 
Column (2): Share of the population occupied in industry in 1907; 
Column (3): Share of foreign population in 1936; 
Column (4): Share of male population in 1936; 
Column (5): Share of population of protestant confession in 1936; 
Column (6): Share of population of jewish confession in 1936; 
Column (7): Share of french-speaking population in 1936; 
Column (8): Share of dialect-speaking population in 1936; 
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 Panels A and B; 
robust standard errors in Panel C. 
* 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_TableC1.tex"'})

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