{smcl}
{com}{sf}{ul off}{txt}
{com}. 
. **** REPLICATION OF TABLE A3 ****
. 
. 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. PREPARE DATA
>         2. REGRESSIONS
>         3. TABLE FOR PAPER
> */
. 
. ***************************
. ***** 1. PREPARE DATA *****
. ***************************
. 
. // Import Municipality data
. 
. use $data\EEREV-D-21-00004_Data_Municipalities.dta, clear
{txt}
{com}. 
. // Reshape
. 
. drop if sample==0
{txt}(8,936 observations deleted)

{com}. drop sample
{txt}
{com}. 
. reshape long voix_, i(com dep annee) j(candidate) string
{txt}(j = AbstBlancs Arthaud Asselineau Balladur Barbu Bayrou Besancenot Boutin Bove Buffet ChabanDelmas Cheminade Chevenement Chirac DeGaulle DeVilliers Defferre Ducatel Duclos Dumont DupontAignan Fillon GiscarddEstaing Gluckstein Hamon Heraud Hollande Hue Joly Jospin Krivine Laguiller Lassalle LePen Lecanuet Lepage Macron Madelin Mamere Marcilhacy Megret Melenchon Mitterrand Muller Nihous Poher Pompidou Poutou Renouvin Rocard Royal Royer SaintJosse Sarkozy Schivardi Sebag Taubira TixierVignancour Voynet)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       3,696   {txt}->   {res}218,064     
{txt}Number of variables        {res}         159   {txt}->   {res}102         
{txt}j variable (59 values)                    ->   {res}candidate
{txt}xij variables:
{res}voix_AbstBlancs voix_Arthaud ... voix_Voynet{txt}-> {res}voix_
{txt}{hline 77}

{com}. drop if voix_==.
{txt}(176,022 observations deleted)

{com}. rename voix_ voix
{res}{txt}
{com}. 
. // Merge with Candidate data
. 
. merge m:1 annee candidate using $data\EEREV-D-21-00004_Data_Candidates
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}           3,696
{txt}{col 9}from master{col 30}{res}           3,696{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          38,346{txt}  (_merge==3)
{col 5}{hline 41}

{com}. tab candidate annee if _merge==1

                 {txt}{c |}                                      Election year
           ANNEE {c |}      1965       1969       1974       1995       2002       2007       2012       2017 {c |}     Total
{hline 17}{c +}{hline 88}{c +}{hline 10}
      AbstBlancs {c |}{res}       462        462        462        462        462        462        462        462 {txt}{c |}{res}     3,696 
{txt}{hline 17}{c +}{hline 88}{c +}{hline 10}
           Total {c |}{res}       462        462        462        462        462        462        462        462 {txt}{c |}{res}     3,696 
{txt}
{com}. rename _merge sample
{res}{txt}
{com}. 
. // Generate vote shares
. 
. g perc_exp=(voix*100)/exprimes 
{txt}
{com}. g perc_ins=(voix*100)/inscrits
{txt}
{com}. format perc_* %9.2f
{txt}
{com}. 
. // Keep sample
. 
. keep if sample==3
{txt}(3,696 observations deleted)

{com}. 
. // Macros
. 
. global rdd2_cand                        "i.cand_year#c.rdd_lat i.cand_year#c.rdd_lon i.cand_year#c.rdd_lat_2 i.cand_year#c.rdd_lon_2 i.cand_year#c.rdd_lat_lon"
{txt}
{com}. 
. **************************
. ***** 2. REGRESSIONS *****
. **************************
. 
. // Two-stage least-squares
. 
. foreach var in aut nat rle abs{c -(} 
{txt}  2{com}. 
.         global geogr_endow_std          "c.water#c.`var'_std c.lelevation_mean#c.`var'_std c.lelevation_std#c.`var'_std c.ldist_de#c.`var'_std"
{txt}  3{com}.         global pop_std                          "c.perc_b_1928_1945#c.`var'_std c.perc_b_1946_1964#c.`var'_std c.perc_b_1965_1980#c.`var'_std c.perc_b_1981_1996#c.`var'_std c.perc_male_counter#c.`var'_std"
{txt}  4{com}.         global demog_chara_std          "c.lpop_counter#c.`var'_std c.perc_fore#c.`var'_std $pop_std"
{txt}  5{com}.         global socec_aspec_std          "c.perc_bacplus#c.`var'_std c.perc_bluecollar#c.`var'_std c.perc_chom#c.`var'_std c.lrev_median#c.`var'_std c.lrev_sd#c.`var'_std"
{txt}  6{com}.         global sigma_std                        "c.perc_prot_church#c.`var'_std c.pop_jew#c.`var'_std c.p_french_name#c.`var'_std c.lpop_1936#c.`var'_std c.perc_fore_1936#c.`var'_std c.perc_dial_1936#c.`var'_std c.perc_disp_1946#c.`var'_std $geogr_endow_std c.perc_droi_1936_ins#c.`var'_std"
{txt}  7{com}.         global w_std                            "$demog_chara_std $socec_aspec_std"
{txt}  8{com}. 
.         foreach year in 1965 1969 1974 1995 2002 2007 2012 2017{c -(}
{txt}  9{com}.                         
.                 sum annee if annee==`year'
{txt} 10{com}.                 
.                 ivreghdfe perc_ins ///
>                         (perc_i_full c.perc_i_full#c.`var'_std = perc_e_1908_1927 c.perc_e_1908_1927#c.`var'_std) ///
>                         perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932 /// 
>                         c.perc_b_1903_1912#c.`var'_std c.perc_b_1913_1922#c.`var'_std c.perc_b_1923_1932#c.`var'_std /// 
>                         $sigma_std $w_std $rdd2_cand ///
>                         if annee==`year' & sample==3, cluster(com) ///
>                         a(i.cand_year#i.borderfe i.dialect#i.cand_year) partial($sigma_std $w_std $rdd2_cand) //
{txt} 11{com}.                 estimates store P`year'T1_`var'_R, title(`year'R1)
{txt} 12{com}.                 scalar f = e(rkf)
{txt} 13{com}.                 estadd scalar F_fs=f
{txt} 14{com}.                 scalar f_under=e(idstat)
{txt} 15{com}.                 estadd scalar F_u=f_under
{txt} 16{com}.                 qui sum perc_ins if e(sample)
{txt} 17{com}.                 scalar mean_y=r(mean)
{txt} 18{com}.                 scalar sd_y=r(sd)
{txt} 19{com}.                 estadd scalar mean_y
{txt} 20{com}.         
.                 sum `var'_std if e(sample) [w=poll], d
{txt} 21{com}.                 estadd scalar max=r(max)
{txt} 22{com}.                 estadd scalar p50=r(p50)
{txt} 23{com}.                 
.         {c )-}
{txt} 24{com}. {c )-}

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      2,772        1965           0       1965       1965
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.aut_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    2772
{txt}{col 55}F(  8,   461) = {res}    4.16
{txt}{col 55}Prob > F      = {res}  0.0001
{txt}Total (centered) SS     = {res} 69962.36309{txt}{col 55}Centered R2   = {res} -0.1250
{txt}Total (uncentered) SS   = {res} 69962.36309{txt}{col 55}Uncentered R2 = {res} -0.1250
{txt}Residual SS             = {res}  78709.5753{txt}{col 55}Root MSE      = {res}   5.455

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .5288199{col 42}{space 2} .1362433{col 53}{space 1}    3.88{col 62}{space 3}0.000{col 70}{space 4} .2610851{col 83}{space 3} .7965547
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2}-.9687539{col 42}{space 2} .2886056{col 53}{space 1}   -3.36{col 62}{space 3}0.001{col 70}{space 4}  -1.5359{col 83}{space 3}-.4016083
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} -.065149{col 42}{space 2} .0515464{col 53}{space 1}   -1.26{col 62}{space 3}0.207{col 70}{space 4}-.1664441{col 83}{space 3}  .036146
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0138749{col 42}{space 2} .0880717{col 53}{space 1}    0.16{col 62}{space 3}0.875{col 70}{space 4}-.1591968{col 83}{space 3} .1869466
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.2291021{col 42}{space 2} .0775905{col 53}{space 1}   -2.95{col 62}{space 3}0.003{col 70}{space 4} -.381577{col 83}{space 3}-.0766273
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2} .1078393{col 42}{space 2} .0817277{col 53}{space 1}    1.32{col 62}{space 3}0.188{col 70}{space 4}-.0527657{col 83}{space 3} .2684444
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0677845{col 42}{space 2} .1400975{col 53}{space 1}   -0.48{col 62}{space 3}0.629{col 70}{space 4}-.3430933{col 83}{space 3} .2075243
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .4472288{col 42}{space 2} .1232779{col 53}{space 1}    3.63{col 62}{space 3}0.000{col 70}{space 4} .2049725{col 83}{space 3}  .689485
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.539
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  42.299
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.455
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_male_counter#c.aut_std
{col 23}c.perc_bacplus#c.aut_std c.perc_bluecollar#c.aut_std
{col 23}c.perc_chom#c.aut_std c.lrev_median#c.aut_std
{col 23}c.lrev_sd#c.aut_std 5b.cand_year#c.rdd_lat
{col 23}51.cand_year#c.rdd_lat 56.cand_year#c.rdd_lat
{col 23}57.cand_year#c.rdd_lat 78.cand_year#c.rdd_lat
{col 23}81.cand_year#c.rdd_lat 5b.cand_year#c.rdd_lon
{col 23}51.cand_year#c.rdd_lon 56.cand_year#c.rdd_lon
{col 23}57.cand_year#c.rdd_lon 78.cand_year#c.rdd_lon
{col 23}81.cand_year#c.rdd_lon 5b.cand_year#c.rdd_lat_2
{col 23}51.cand_year#c.rdd_lat_2 56.cand_year#c.rdd_lat_2
{col 23}57.cand_year#c.rdd_lat_2 78.cand_year#c.rdd_lat_2
{col 23}81.cand_year#c.rdd_lat_2 5b.cand_year#c.rdd_lon_2
{col 23}51.cand_year#c.rdd_lon_2 56.cand_year#c.rdd_lon_2
{col 23}57.cand_year#c.rdd_lon_2 78.cand_year#c.rdd_lon_2
{col 23}81.cand_year#c.rdd_lon_2 5b.cand_year#c.rdd_lat_lon
{col 23}51.cand_year#c.rdd_lat_lon 56.cand_year#c.rdd_lat_lon
{col 23}57.cand_year#c.rdd_lat_lon 78.cand_year#c.rdd_lat_lon
{col 23}81.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.aut_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       48{col 38}{space 1}        0{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       24{col 38}{space 1}        6{col 50}{result}{space 1}       18{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.4549835

{txt}added scalar:
                e(F_u) =  {res}12.539211

{txt}added scalar:
             e(mean_y) =  {res}14.120275

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,772
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res}  .752899                      {txt}Mean          {res} 1.118558
                        {txt}Largest       Std. dev.     {res} .9130356
{txt}75%    {res} 2.122289       2.122289
{txt}90%    {res} 2.122289       2.122289       {txt}Variance      {res} .8336341
{txt}95%    {res} 2.122289       2.122289       {txt}Skewness      {res} .0283719
{txt}99%    {res} 2.122289       2.122289       {txt}Kurtosis      {res} 1.273097

{txt}added scalar:
                e(max) =  {res}2.1222892

{txt}added scalar:
                e(p50) =  {res}.75289896

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      3,234        1969           0       1969       1969
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.aut_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    3234
{txt}{col 55}F(  8,   461) = {res}    2.40
{txt}{col 55}Prob > F      = {res}  0.0153
{txt}Total (centered) SS     = {res} 60416.30509{txt}{col 55}Centered R2   = {res} -0.0514
{txt}Total (uncentered) SS   = {res} 60416.30509{txt}{col 55}Uncentered R2 = {res} -0.0514
{txt}Residual SS             = {res} 63520.11204{txt}{col 55}Root MSE      = {res}   4.533

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .2920541{col 42}{space 2} .0999784{col 53}{space 1}    2.92{col 62}{space 3}0.004{col 70}{space 4} .0955841{col 83}{space 3} .4885241
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2}-.5044763{col 42}{space 2} .2018698{col 53}{space 1}   -2.50{col 62}{space 3}0.013{col 70}{space 4}-.9011754{col 83}{space 3}-.1077773
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} -.030673{col 42}{space 2} .0350759{col 53}{space 1}   -0.87{col 62}{space 3}0.382{col 70}{space 4}-.0996015{col 83}{space 3} .0382555
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0138415{col 42}{space 2} .0586267{col 53}{space 1}    0.24{col 62}{space 3}0.813{col 70}{space 4}-.1013671{col 83}{space 3} .1290502
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0450574{col 42}{space 2} .0611131{col 53}{space 1}   -0.74{col 62}{space 3}0.461{col 70}{space 4}-.1651521{col 83}{space 3} .0750374
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2} .0364822{col 42}{space 2} .0566791{col 53}{space 1}    0.64{col 62}{space 3}0.520{col 70}{space 4}-.0748992{col 83}{space 3} .1478635
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0480022{col 42}{space 2} .0940819{col 53}{space 1}   -0.51{col 62}{space 3}0.610{col 70}{space 4}-.2328848{col 83}{space 3} .1368804
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .1793091{col 42}{space 2} .0950262{col 53}{space 1}    1.89{col 62}{space 3}0.060{col 70}{space 4} -.007429{col 83}{space 3} .3660473
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.133
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  53.572
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.751
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_male_counter#c.aut_std
{col 23}c.perc_bacplus#c.aut_std c.perc_bluecollar#c.aut_std
{col 23}c.perc_chom#c.aut_std c.lrev_median#c.aut_std
{col 23}c.lrev_sd#c.aut_std 21b.cand_year#c.rdd_lat
{col 23}22.cand_year#c.rdd_lat 23.cand_year#c.rdd_lat
{col 23}38.cand_year#c.rdd_lat 64.cand_year#c.rdd_lat
{col 23}65.cand_year#c.rdd_lat 69.cand_year#c.rdd_lat
{col 23}21b.cand_year#c.rdd_lon 22.cand_year#c.rdd_lon
{col 23}23.cand_year#c.rdd_lon 38.cand_year#c.rdd_lon
{col 23}64.cand_year#c.rdd_lon 65.cand_year#c.rdd_lon
{col 23}69.cand_year#c.rdd_lon 21b.cand_year#c.rdd_lat_2
{col 23}22.cand_year#c.rdd_lat_2 23.cand_year#c.rdd_lat_2
{col 23}38.cand_year#c.rdd_lat_2 64.cand_year#c.rdd_lat_2
{col 23}65.cand_year#c.rdd_lat_2 69.cand_year#c.rdd_lat_2
{col 23}21b.cand_year#c.rdd_lon_2 22.cand_year#c.rdd_lon_2
{col 23}23.cand_year#c.rdd_lon_2 38.cand_year#c.rdd_lon_2
{col 23}64.cand_year#c.rdd_lon_2 65.cand_year#c.rdd_lon_2
{col 23}69.cand_year#c.rdd_lon_2 21b.cand_year#c.rdd_lat_lon
{col 23}22.cand_year#c.rdd_lat_lon 23.cand_year#c.rdd_lat_lon
{col 23}38.cand_year#c.rdd_lat_lon 64.cand_year#c.rdd_lat_lon
{col 23}65.cand_year#c.rdd_lat_lon 69.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
Dropped collinear:{col 23}c.perc_b_1981_1996#c.aut_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       56{col 38}{space 1}        0{col 50}{result}{space 1}       56{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       28{col 38}{space 1}        7{col 50}{result}{space 1}       21{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.7509461

{txt}added scalar:
                e(F_u) =  {res}13.133181

{txt}added scalar:
             e(mean_y) =  {res}10.910314

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .0697899              0
{txt} 5%    {res} .1395799              0
{txt}10%    {res} .1395799              0       {txt}Obs         {res}      3,234
{txt}25%    {res} .7841696              0       {txt}Sum of wgt. {res} 461.999991

{txt}50%    {res} .8002532                      {txt}Mean          {res} 1.409956
                        {txt}Largest       Std. dev.     {res} .9259632
{txt}75%    {res} 2.516922       2.516922
{txt}90%    {res} 2.516922       2.516922       {txt}Variance      {res} .8574079
{txt}95%    {res} 2.516922       2.516922       {txt}Skewness      {res} .2459254
{txt}99%    {res} 2.516922       2.516922       {txt}Kurtosis      {res} 1.321683

{txt}added scalar:
                e(max) =  {res}2.5169218

{txt}added scalar:
                e(p50) =  {res}.80025315

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        1974           0       1974       1974
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.aut_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    1.39
{txt}{col 55}Prob > F      = {res}  0.1980
{txt}Total (centered) SS     = {res} 92211.48852{txt}{col 55}Centered R2   = {res} -0.0054
{txt}Total (uncentered) SS   = {res} 92211.48852{txt}{col 55}Uncentered R2 = {res} -0.0054
{txt}Residual SS             = {res} 92708.41919{txt}{col 55}Root MSE      = {res}   4.174

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0360163{col 42}{space 2} .0387829{col 53}{space 1}   -0.93{col 62}{space 3}0.354{col 70}{space 4}-.1122295{col 83}{space 3} .0401968
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2}-.0494003{col 42}{space 2} .0366518{col 53}{space 1}   -1.35{col 62}{space 3}0.178{col 70}{space 4}-.1214255{col 83}{space 3}  .022625
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0049306{col 42}{space 2} .0141018{col 53}{space 1}    0.35{col 62}{space 3}0.727{col 70}{space 4}-.0227812{col 83}{space 3} .0326423
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0059058{col 42}{space 2} .0237095{col 53}{space 1}   -0.25{col 62}{space 3}0.803{col 70}{space 4}-.0524979{col 83}{space 3} .0406863
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0185138{col 42}{space 2} .0217785{col 53}{space 1}    0.85{col 62}{space 3}0.396{col 70}{space 4}-.0242836{col 83}{space 3} .0613113
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2}-.0021437{col 42}{space 2}  .010424{col 53}{space 1}   -0.21{col 62}{space 3}0.837{col 70}{space 4}-.0226281{col 83}{space 3} .0183407
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}  .009715{col 42}{space 2}  .018041{col 53}{space 1}    0.54{col 62}{space 3}0.590{col 70}{space 4}-.0257378{col 83}{space 3} .0451678
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2}-.0018363{col 42}{space 2} .0159519{col 53}{space 1}   -0.12{col 62}{space 3}0.908{col 70}{space 4}-.0331838{col 83}{space 3} .0295111
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.956
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  95.402
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.991
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_male_counter#c.aut_std
{col 23}c.perc_bacplus#c.aut_std c.perc_bluecollar#c.aut_std
{col 23}c.perc_chom#c.aut_std c.lrev_median#c.aut_std
{col 23}c.lrev_sd#c.aut_std 14b.cand_year#c.rdd_lat
{col 23}24.cand_year#c.rdd_lat 28.cand_year#c.rdd_lat
{col 23}31.cand_year#c.rdd_lat 39.cand_year#c.rdd_lat
{col 23}40.cand_year#c.rdd_lat 45.cand_year#c.rdd_lat
{col 23}58.cand_year#c.rdd_lat 59.cand_year#c.rdd_lat
{col 23}68.cand_year#c.rdd_lat 71.cand_year#c.rdd_lat
{col 23}76.cand_year#c.rdd_lat 14b.cand_year#c.rdd_lon
{col 23}24.cand_year#c.rdd_lon 28.cand_year#c.rdd_lon
{col 23}31.cand_year#c.rdd_lon 39.cand_year#c.rdd_lon
{col 23}40.cand_year#c.rdd_lon 45.cand_year#c.rdd_lon
{col 23}58.cand_year#c.rdd_lon 59.cand_year#c.rdd_lon
{col 23}68.cand_year#c.rdd_lon 71.cand_year#c.rdd_lon
{col 23}76.cand_year#c.rdd_lon 14b.cand_year#c.rdd_lat_2
{col 23}24.cand_year#c.rdd_lat_2 28.cand_year#c.rdd_lat_2
{col 23}31.cand_year#c.rdd_lat_2 39.cand_year#c.rdd_lat_2
{col 23}40.cand_year#c.rdd_lat_2 45.cand_year#c.rdd_lat_2
{col 23}58.cand_year#c.rdd_lat_2 59.cand_year#c.rdd_lat_2
{col 23}68.cand_year#c.rdd_lat_2 71.cand_year#c.rdd_lat_2
{col 23}76.cand_year#c.rdd_lat_2 14b.cand_year#c.rdd_lon_2
{col 23}24.cand_year#c.rdd_lon_2 28.cand_year#c.rdd_lon_2
{col 23}31.cand_year#c.rdd_lon_2 39.cand_year#c.rdd_lon_2
{col 23}40.cand_year#c.rdd_lon_2 45.cand_year#c.rdd_lon_2
{col 23}58.cand_year#c.rdd_lon_2 59.cand_year#c.rdd_lon_2
{col 23}68.cand_year#c.rdd_lon_2 71.cand_year#c.rdd_lon_2
{col 23}76.cand_year#c.rdd_lon_2 14b.cand_year#c.rdd_lat_lon
{col 23}24.cand_year#c.rdd_lat_lon 28.cand_year#c.rdd_lat_lon
{col 23}31.cand_year#c.rdd_lat_lon 39.cand_year#c.rdd_lat_lon
{col 23}40.cand_year#c.rdd_lat_lon 45.cand_year#c.rdd_lat_lon
{col 23}58.cand_year#c.rdd_lat_lon 59.cand_year#c.rdd_lat_lon
{col 23}68.cand_year#c.rdd_lat_lon 71.cand_year#c.rdd_lat_lon
{col 23}76.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.aut_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.990841

{txt}added scalar:
                e(F_u) =  {res}12.956384

{txt}added scalar:
             e(mean_y) =  {res}6.9111711

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .3376027              0
{txt}10%    {res} .3376027              0       {txt}Obs         {res}      5,544
{txt}25%    {res} .3376027              0       {txt}Sum of wgt. {res} 462.000001

{txt}50%    {res} .9194183                      {txt}Mean          {res} 1.012292
                        {txt}Largest       Std. dev.     {res} .9575134
{txt}75%    {res} 1.004979        3.50637
{txt}90%    {res} 3.041819        3.50637       {txt}Variance      {res}  .916832
{txt}95%    {res} 3.041819        3.50637       {txt}Skewness      {res} 1.399307
{txt}99%    {res} 3.041819        3.50637       {txt}Kurtosis      {res} 3.545032

{txt}added scalar:
                e(max) =  {res}3.5063696

{txt}added scalar:
                e(p50) =  {res}.91941833

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,158        1995           0       1995       1995
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4158
{txt}{col 55}F(  8,   461) = {res}    2.09
{txt}{col 55}Prob > F      = {res}  0.0358
{txt}Total (centered) SS     = {res} 43191.42654{txt}{col 55}Centered R2   = {res} -0.0285
{txt}Total (uncentered) SS   = {res} 43191.42654{txt}{col 55}Uncentered R2 = {res} -0.0285
{txt}Residual SS             = {res} 44424.35606{txt}{col 55}Root MSE      = {res}    3.34

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1690489{col 42}{space 2} .0516759{col 53}{space 1}   -3.27{col 62}{space 3}0.001{col 70}{space 4}-.2705984{col 83}{space 3}-.0674994
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2} .1639942{col 42}{space 2} .0507162{col 53}{space 1}    3.23{col 62}{space 3}0.001{col 70}{space 4} .0643307{col 83}{space 3} .2636578
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0126374{col 42}{space 2} .0197893{col 53}{space 1}   -0.64{col 62}{space 3}0.523{col 70}{space 4}-.0515259{col 83}{space 3} .0262511
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}  .000364{col 42}{space 2} .0341515{col 53}{space 1}    0.01{col 62}{space 3}0.992{col 70}{space 4}-.0667479{col 83}{space 3} .0674759
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0303663{col 42}{space 2} .0281547{col 53}{space 1}    1.08{col 62}{space 3}0.281{col 70}{space 4}-.0249612{col 83}{space 3} .0856937
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2} .0017693{col 42}{space 2} .0143058{col 53}{space 1}    0.12{col 62}{space 3}0.902{col 70}{space 4}-.0263433{col 83}{space 3} .0298819
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0014953{col 42}{space 2} .0258258{col 53}{space 1}   -0.06{col 62}{space 3}0.954{col 70}{space 4}-.0522461{col 83}{space 3} .0492555
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2}-.0141694{col 42}{space 2} .0200751{col 53}{space 1}   -0.71{col 62}{space 3}0.481{col 70}{space 4}-.0536194{col 83}{space 3} .0252807
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.358
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  67.289
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.904
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_b_1981_1996#c.aut_std
{col 23}c.perc_male_counter#c.aut_std c.perc_bacplus#c.aut_std
{col 23}c.perc_bluecollar#c.aut_std c.perc_chom#c.aut_std
{col 23}c.lrev_median#c.aut_std c.lrev_sd#c.aut_std
{col 23}4b.cand_year#c.rdd_lat 15.cand_year#c.rdd_lat
{col 23}19.cand_year#c.rdd_lat 33.cand_year#c.rdd_lat
{col 23}36.cand_year#c.rdd_lat 41.cand_year#c.rdd_lat
{col 23}46.cand_year#c.rdd_lat 79.cand_year#c.rdd_lat
{col 23}82.cand_year#c.rdd_lat 4b.cand_year#c.rdd_lon
{col 23}15.cand_year#c.rdd_lon 19.cand_year#c.rdd_lon
{col 23}33.cand_year#c.rdd_lon 36.cand_year#c.rdd_lon
{col 23}41.cand_year#c.rdd_lon 46.cand_year#c.rdd_lon
{col 23}79.cand_year#c.rdd_lon 82.cand_year#c.rdd_lon
{col 23}4b.cand_year#c.rdd_lat_2 15.cand_year#c.rdd_lat_2
{col 23}19.cand_year#c.rdd_lat_2 33.cand_year#c.rdd_lat_2
{col 23}36.cand_year#c.rdd_lat_2 41.cand_year#c.rdd_lat_2
{col 23}46.cand_year#c.rdd_lat_2 79.cand_year#c.rdd_lat_2
{col 23}82.cand_year#c.rdd_lat_2 4b.cand_year#c.rdd_lon_2
{col 23}15.cand_year#c.rdd_lon_2 19.cand_year#c.rdd_lon_2
{col 23}33.cand_year#c.rdd_lon_2 36.cand_year#c.rdd_lon_2
{col 23}41.cand_year#c.rdd_lon_2 46.cand_year#c.rdd_lon_2
{col 23}79.cand_year#c.rdd_lon_2 82.cand_year#c.rdd_lon_2
{col 23}4b.cand_year#c.rdd_lat_lon 15.cand_year#c.rdd_lat_lon
{col 23}19.cand_year#c.rdd_lat_lon 33.cand_year#c.rdd_lat_lon
{col 23}36.cand_year#c.rdd_lat_lon 41.cand_year#c.rdd_lat_lon
{col 23}46.cand_year#c.rdd_lat_lon 79.cand_year#c.rdd_lat_lon
{col 23}82.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       72{col 38}{space 1}        0{col 50}{result}{space 1}       72{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       36{col 38}{space 1}        9{col 50}{result}{space 1}       27{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.9038785

{txt}added scalar:
                e(F_u) =  {res}13.357566

{txt}added scalar:
             e(mean_y) =  {res}8.9124834

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .1516099              0
{txt}10%    {res} .1516099              0       {txt}Obs         {res}      4,158
{txt}25%    {res}  1.06703              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.490697                      {txt}Mean          {res} 1.554561
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 1.809285       3.196707
{txt}90%    {res} 3.114619       3.196707       {txt}Variance      {res} .8891026
{txt}95%    {res} 3.196707       3.196707       {txt}Skewness      {res} .3895525
{txt}99%    {res} 3.196707       3.196707       {txt}Kurtosis      {res} 2.419917

{txt}added scalar:
                e(max) =  {res}3.1967075

{txt}added scalar:
                e(p50) =  {res}1.4906973

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      7,392        2002           0       2002       2002
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    7392
{txt}{col 55}F(  8,   461) = {res}    0.94
{txt}{col 55}Prob > F      = {res}  0.4805
{txt}Total (centered) SS     = {res} 30730.04444{txt}{col 55}Centered R2   = {res} -0.0203
{txt}Total (uncentered) SS   = {res} 30730.04444{txt}{col 55}Uncentered R2 = {res} -0.0203
{txt}Residual SS             = {res} 31355.37509{txt}{col 55}Root MSE      = {res}   2.101

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0106743{col 42}{space 2} .0257389{col 53}{space 1}   -0.41{col 62}{space 3}0.679{col 70}{space 4}-.0612544{col 83}{space 3} .0399058
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2} .0561179{col 42}{space 2} .0350821{col 53}{space 1}    1.60{col 62}{space 3}0.110{col 70}{space 4}-.0128228{col 83}{space 3} .1250586
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0106281{col 42}{space 2} .0113287{col 53}{space 1}    0.94{col 62}{space 3}0.349{col 70}{space 4}-.0116343{col 83}{space 3} .0328905
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0177923{col 42}{space 2} .0190213{col 53}{space 1}    0.94{col 62}{space 3}0.350{col 70}{space 4}-.0195869{col 83}{space 3} .0551714
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0071299{col 42}{space 2} .0204234{col 53}{space 1}   -0.35{col 62}{space 3}0.727{col 70}{space 4}-.0472644{col 83}{space 3} .0330046
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2}-.0132423{col 42}{space 2} .0105192{col 53}{space 1}   -1.26{col 62}{space 3}0.209{col 70}{space 4}-.0339138{col 83}{space 3} .0074291
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0178974{col 42}{space 2} .0157521{col 53}{space 1}   -1.14{col 62}{space 3}0.256{col 70}{space 4}-.0488522{col 83}{space 3} .0130573
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .0060106{col 42}{space 2} .0159675{col 53}{space 1}    0.38{col 62}{space 3}0.707{col 70}{space 4}-.0253676{col 83}{space 3} .0373888
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.314
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71} 113.354
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.377
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_b_1981_1996#c.aut_std
{col 23}c.perc_male_counter#c.aut_std c.perc_bacplus#c.aut_std
{col 23}c.perc_bluecollar#c.aut_std c.perc_chom#c.aut_std
{col 23}c.lrev_median#c.aut_std c.lrev_sd#c.aut_std
{col 23}6b.cand_year#c.rdd_lat 9.cand_year#c.rdd_lat
{col 23}11.cand_year#c.rdd_lat 18.cand_year#c.rdd_lat
{col 23}20.cand_year#c.rdd_lat 29.cand_year#c.rdd_lat
{col 23}34.cand_year#c.rdd_lat 37.cand_year#c.rdd_lat
{col 23}42.cand_year#c.rdd_lat 47.cand_year#c.rdd_lat
{col 23}52.cand_year#c.rdd_lat 54.cand_year#c.rdd_lat
{col 23}55.cand_year#c.rdd_lat 60.cand_year#c.rdd_lat
{col 23}72.cand_year#c.rdd_lat 77.cand_year#c.rdd_lat
{col 23}6b.cand_year#c.rdd_lon 9.cand_year#c.rdd_lon
{col 23}11.cand_year#c.rdd_lon 18.cand_year#c.rdd_lon
{col 23}20.cand_year#c.rdd_lon 29.cand_year#c.rdd_lon
{col 23}34.cand_year#c.rdd_lon 37.cand_year#c.rdd_lon
{col 23}42.cand_year#c.rdd_lon 47.cand_year#c.rdd_lon
{col 23}52.cand_year#c.rdd_lon 54.cand_year#c.rdd_lon
{col 23}55.cand_year#c.rdd_lon 60.cand_year#c.rdd_lon
{col 23}72.cand_year#c.rdd_lon 77.cand_year#c.rdd_lon
{col 23}6b.cand_year#c.rdd_lat_2 9.cand_year#c.rdd_lat_2
{col 23}11.cand_year#c.rdd_lat_2 18.cand_year#c.rdd_lat_2
{col 23}20.cand_year#c.rdd_lat_2 29.cand_year#c.rdd_lat_2
{col 23}34.cand_year#c.rdd_lat_2 37.cand_year#c.rdd_lat_2
{col 23}42.cand_year#c.rdd_lat_2 47.cand_year#c.rdd_lat_2
{col 23}52.cand_year#c.rdd_lat_2 54.cand_year#c.rdd_lat_2
{col 23}55.cand_year#c.rdd_lat_2 60.cand_year#c.rdd_lat_2
{col 23}72.cand_year#c.rdd_lat_2 77.cand_year#c.rdd_lat_2
{col 23}6b.cand_year#c.rdd_lon_2 9.cand_year#c.rdd_lon_2
{col 23}11.cand_year#c.rdd_lon_2 18.cand_year#c.rdd_lon_2
{col 23}20.cand_year#c.rdd_lon_2 29.cand_year#c.rdd_lon_2
{col 23}34.cand_year#c.rdd_lon_2 37.cand_year#c.rdd_lon_2
{col 23}42.cand_year#c.rdd_lon_2 47.cand_year#c.rdd_lon_2
{col 23}52.cand_year#c.rdd_lon_2 54.cand_year#c.rdd_lon_2
{col 23}55.cand_year#c.rdd_lon_2 60.cand_year#c.rdd_lon_2
{col 23}72.cand_year#c.rdd_lon_2 77.cand_year#c.rdd_lon_2
{col 23}6b.cand_year#c.rdd_lat_lon 9.cand_year#c.rdd_lat_lon
{col 23}11.cand_year#c.rdd_lat_lon 18.cand_year#c.rdd_lat_lon
{col 23}20.cand_year#c.rdd_lat_lon 29.cand_year#c.rdd_lat_lon
{col 23}34.cand_year#c.rdd_lat_lon 37.cand_year#c.rdd_lat_lon
{col 23}42.cand_year#c.rdd_lat_lon 47.cand_year#c.rdd_lat_lon
{col 23}52.cand_year#c.rdd_lat_lon 54.cand_year#c.rdd_lat_lon
{col 23}55.cand_year#c.rdd_lat_lon 60.cand_year#c.rdd_lat_lon
{col 23}72.cand_year#c.rdd_lat_lon 77.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}      128{col 38}{space 1}        0{col 50}{result}{space 1}      128{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       64{col 38}{space 1}       16{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.3769918

{txt}added scalar:
                e(F_u) =  {res}12.314243

{txt}added scalar:
             e(mean_y) =  {res}4.4757881

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .1071888              0
{txt} 5%    {res} .3229645              0
{txt}10%    {res} .3797047              0       {txt}Obs         {res}      7,392
{txt}25%    {res} .4183535              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.991057                      {txt}Mean          {res} 1.673692
                        {txt}Largest       Std. dev.     {res} .9683114
{txt}75%    {res} 2.144001       2.818247
{txt}90%    {res} 2.818247       2.818247       {txt}Variance      {res} .9376269
{txt}95%    {res} 2.818247       2.818247       {txt}Skewness      {res}  -.38626
{txt}99%    {res} 2.818247       2.818247       {txt}Kurtosis      {res}  1.59255

{txt}added scalar:
                e(max) =  {res}2.8182471

{txt}added scalar:
                e(p50) =  {res}1.9910566

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        2007           0       2007       2007
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    0.58
{txt}{col 55}Prob > F      = {res}  0.7960
{txt}Total (centered) SS     = {res} 37224.76534{txt}{col 55}Centered R2   = {res}  0.0036
{txt}Total (uncentered) SS   = {res} 37224.76534{txt}{col 55}Uncentered R2 = {res}  0.0036
{txt}Residual SS             = {res} 37089.29757{txt}{col 55}Root MSE      = {res}    2.64

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .0396013{col 42}{space 2} .0359923{col 53}{space 1}    1.10{col 62}{space 3}0.272{col 70}{space 4} -.031128{col 83}{space 3} .1103306
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2}-.0513326{col 42}{space 2} .0435295{col 53}{space 1}   -1.18{col 62}{space 3}0.239{col 70}{space 4}-.1368734{col 83}{space 3} .0342081
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}  .011178{col 42}{space 2} .0137987{col 53}{space 1}    0.81{col 62}{space 3}0.418{col 70}{space 4}-.0159382{col 83}{space 3} .0382942
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0104599{col 42}{space 2}  .027776{col 53}{space 1}    0.38{col 62}{space 3}0.707{col 70}{space 4}-.0441234{col 83}{space 3} .0650432
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0188972{col 42}{space 2} .0258688{col 53}{space 1}   -0.73{col 62}{space 3}0.465{col 70}{space 4}-.0697327{col 83}{space 3} .0319383
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2}-.0116252{col 42}{space 2} .0135771{col 53}{space 1}   -0.86{col 62}{space 3}0.392{col 70}{space 4}-.0383058{col 83}{space 3} .0150554
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0080762{col 42}{space 2} .0241226{col 53}{space 1}   -0.33{col 62}{space 3}0.738{col 70}{space 4}  -.05548{col 83}{space 3} .0393276
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .0214003{col 42}{space 2} .0220806{col 53}{space 1}    0.97{col 62}{space 3}0.333{col 70}{space 4}-.0219909{col 83}{space 3} .0647914
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.337
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  94.915
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   7.025
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_b_1981_1996#c.aut_std
{col 23}c.perc_male_counter#c.aut_std c.perc_bacplus#c.aut_std
{col 23}c.perc_bluecollar#c.aut_std c.perc_chom#c.aut_std
{col 23}c.lrev_median#c.aut_std c.lrev_sd#c.aut_std
{col 23}7b.cand_year#c.rdd_lat 10.cand_year#c.rdd_lat
{col 23}12.cand_year#c.rdd_lat 13.cand_year#c.rdd_lat
{col 23}43.cand_year#c.rdd_lat 48.cand_year#c.rdd_lat
{col 23}63.cand_year#c.rdd_lat 70.cand_year#c.rdd_lat
{col 23}73.cand_year#c.rdd_lat 75.cand_year#c.rdd_lat
{col 23}80.cand_year#c.rdd_lat 83.cand_year#c.rdd_lat
{col 23}7b.cand_year#c.rdd_lon 10.cand_year#c.rdd_lon
{col 23}12.cand_year#c.rdd_lon 13.cand_year#c.rdd_lon
{col 23}43.cand_year#c.rdd_lon 48.cand_year#c.rdd_lon
{col 23}63.cand_year#c.rdd_lon 70.cand_year#c.rdd_lon
{col 23}73.cand_year#c.rdd_lon 75.cand_year#c.rdd_lon
{col 23}80.cand_year#c.rdd_lon 83.cand_year#c.rdd_lon
{col 23}7b.cand_year#c.rdd_lat_2 10.cand_year#c.rdd_lat_2
{col 23}12.cand_year#c.rdd_lat_2 13.cand_year#c.rdd_lat_2
{col 23}43.cand_year#c.rdd_lat_2 48.cand_year#c.rdd_lat_2
{col 23}63.cand_year#c.rdd_lat_2 70.cand_year#c.rdd_lat_2
{col 23}73.cand_year#c.rdd_lat_2 75.cand_year#c.rdd_lat_2
{col 23}80.cand_year#c.rdd_lat_2 83.cand_year#c.rdd_lat_2
{col 23}7b.cand_year#c.rdd_lon_2 10.cand_year#c.rdd_lon_2
{col 23}12.cand_year#c.rdd_lon_2 13.cand_year#c.rdd_lon_2
{col 23}43.cand_year#c.rdd_lon_2 48.cand_year#c.rdd_lon_2
{col 23}63.cand_year#c.rdd_lon_2 70.cand_year#c.rdd_lon_2
{col 23}73.cand_year#c.rdd_lon_2 75.cand_year#c.rdd_lon_2
{col 23}80.cand_year#c.rdd_lon_2 83.cand_year#c.rdd_lon_2
{col 23}7b.cand_year#c.rdd_lat_lon 10.cand_year#c.rdd_lat_lon
{col 23}12.cand_year#c.rdd_lat_lon 13.cand_year#c.rdd_lat_lon
{col 23}43.cand_year#c.rdd_lat_lon 48.cand_year#c.rdd_lat_lon
{col 23}63.cand_year#c.rdd_lat_lon 70.cand_year#c.rdd_lat_lon
{col 23}73.cand_year#c.rdd_lat_lon 75.cand_year#c.rdd_lat_lon
{col 23}80.cand_year#c.rdd_lat_lon 83.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}7.0252252

{txt}added scalar:
                e(F_u) =  {res}13.337417

{txt}added scalar:
             e(mean_y) =  {res}6.9392575

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .4357336              0
{txt}10%    {res} .6880289              0       {txt}Obs         {res}      5,544
{txt}25%    {res} 1.094393              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res}  2.89684                      {txt}Mean          {res} 2.161467
                        {txt}Largest       Std. dev.     {res} .9575134
{txt}75%    {res} 3.038616       3.038616
{txt}90%    {res} 3.038616       3.038616       {txt}Variance      {res}  .916832
{txt}95%    {res} 3.038616       3.038616       {txt}Skewness      {res}-.6454098
{txt}99%    {res} 3.038616       3.038616       {txt}Kurtosis      {res} 2.005536

{txt}added scalar:
                e(max) =  {res}3.0386157

{txt}added scalar:
                e(p50) =  {res}2.8968399

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,620        2012           0       2012       2012
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4620
{txt}{col 55}F(  8,   461) = {res}    0.87
{txt}{col 55}Prob > F      = {res}  0.5452
{txt}Total (centered) SS     = {res} 38247.30756{txt}{col 55}Centered R2   = {res} -0.0562
{txt}Total (uncentered) SS   = {res} 38247.30756{txt}{col 55}Uncentered R2 = {res} -0.0562
{txt}Residual SS             = {res} 40398.14563{txt}{col 55}Root MSE      = {res}    3.02

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} -.095766{col 42}{space 2} .0555977{col 53}{space 1}   -1.72{col 62}{space 3}0.086{col 70}{space 4}-.2050223{col 83}{space 3} .0134903
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2} .1698994{col 42}{space 2} .0748681{col 53}{space 1}    2.27{col 62}{space 3}0.024{col 70}{space 4} .0227744{col 83}{space 3} .3170244
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0082348{col 42}{space 2} .0209088{col 53}{space 1}    0.39{col 62}{space 3}0.694{col 70}{space 4}-.0328535{col 83}{space 3} .0493231
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0718733{col 42}{space 2} .0453109{col 53}{space 1}    1.59{col 62}{space 3}0.113{col 70}{space 4}-.0171682{col 83}{space 3} .1609148
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0883462{col 42}{space 2} .0583106{col 53}{space 1}   -1.52{col 62}{space 3}0.130{col 70}{space 4}-.2029337{col 83}{space 3} .0262414
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2}-.0148834{col 42}{space 2} .0200131{col 53}{space 1}   -0.74{col 62}{space 3}0.457{col 70}{space 4}-.0542116{col 83}{space 3} .0244448
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2}-.0674545{col 42}{space 2} .0444328{col 53}{space 1}   -1.52{col 62}{space 3}0.130{col 70}{space 4}-.1547704{col 83}{space 3} .0198614
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .0740357{col 42}{space 2} .0507288{col 53}{space 1}    1.46{col 62}{space 3}0.145{col 70}{space 4}-.0256526{col 83}{space 3} .1737241
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.698
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  73.636
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.590
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_b_1981_1996#c.aut_std
{col 23}c.perc_male_counter#c.aut_std c.perc_bacplus#c.aut_std
{col 23}c.perc_bluecollar#c.aut_std c.perc_chom#c.aut_std
{col 23}c.lrev_median#c.aut_std c.lrev_sd#c.aut_std
{col 23}1b.cand_year#c.rdd_lat 8.cand_year#c.rdd_lat
{col 23}16.cand_year#c.rdd_lat 25.cand_year#c.rdd_lat
{col 23}32.cand_year#c.rdd_lat 35.cand_year#c.rdd_lat
{col 23}49.cand_year#c.rdd_lat 61.cand_year#c.rdd_lat
{col 23}66.cand_year#c.rdd_lat 74.cand_year#c.rdd_lat
{col 23}1b.cand_year#c.rdd_lon 8.cand_year#c.rdd_lon
{col 23}16.cand_year#c.rdd_lon 25.cand_year#c.rdd_lon
{col 23}32.cand_year#c.rdd_lon 35.cand_year#c.rdd_lon
{col 23}49.cand_year#c.rdd_lon 61.cand_year#c.rdd_lon
{col 23}66.cand_year#c.rdd_lon 74.cand_year#c.rdd_lon
{col 23}1b.cand_year#c.rdd_lat_2 8.cand_year#c.rdd_lat_2
{col 23}16.cand_year#c.rdd_lat_2 25.cand_year#c.rdd_lat_2
{col 23}32.cand_year#c.rdd_lat_2 35.cand_year#c.rdd_lat_2
{col 23}49.cand_year#c.rdd_lat_2 61.cand_year#c.rdd_lat_2
{col 23}66.cand_year#c.rdd_lat_2 74.cand_year#c.rdd_lat_2
{col 23}1b.cand_year#c.rdd_lon_2 8.cand_year#c.rdd_lon_2
{col 23}16.cand_year#c.rdd_lon_2 25.cand_year#c.rdd_lon_2
{col 23}32.cand_year#c.rdd_lon_2 35.cand_year#c.rdd_lon_2
{col 23}49.cand_year#c.rdd_lon_2 61.cand_year#c.rdd_lon_2
{col 23}66.cand_year#c.rdd_lon_2 74.cand_year#c.rdd_lon_2
{col 23}1b.cand_year#c.rdd_lat_lon 8.cand_year#c.rdd_lat_lon
{col 23}16.cand_year#c.rdd_lat_lon 25.cand_year#c.rdd_lat_lon
{col 23}32.cand_year#c.rdd_lat_lon 35.cand_year#c.rdd_lat_lon
{col 23}49.cand_year#c.rdd_lat_lon 61.cand_year#c.rdd_lat_lon
{col 23}66.cand_year#c.rdd_lat_lon 74.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       80{col 38}{space 1}        0{col 50}{result}{space 1}       80{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       40{col 38}{space 1}       10{col 50}{result}{space 1}       30{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.5900872

{txt}added scalar:
                e(F_u) =  {res}12.697908

{txt}added scalar:
             e(mean_y) =  {res}8.058661

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,158
{txt}25%    {res} 1.435501              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.776685                      {txt}Mean          {res} 1.688409
                        {txt}Largest       Std. dev.     {res} .9429225
{txt}75%    {res} 2.263901       2.836686
{txt}90%    {res} 2.836686       2.836686       {txt}Variance      {res} .8891028
{txt}95%    {res} 2.836686       2.836686       {txt}Skewness      {res}-.4929063
{txt}99%    {res} 2.836686       2.836686       {txt}Kurtosis      {res} 2.219202

{txt}added scalar:
                e(max) =  {res}2.8366861

{txt}added scalar:
                e(p50) =  {res}1.7766852

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,082        2017           0       2017       2017
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5082
{txt}{col 55}F(  8,   461) = {res}    0.45
{txt}{col 55}Prob > F      = {res}  0.8927
{txt}Total (centered) SS     = {res} 35008.03194{txt}{col 55}Centered R2   = {res} -0.0014
{txt}Total (uncentered) SS   = {res} 35008.03194{txt}{col 55}Uncentered R2 = {res} -0.0014
{txt}Residual SS             = {res} 35057.71913{txt}{col 55}Root MSE      = {res}   2.682

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0184405{col 42}{space 2} .0300142{col 53}{space 1}   -0.61{col 62}{space 3}0.539{col 70}{space 4}-.0774221{col 83}{space 3} .0405411
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.aut_std {c |}{col 30}{res}{space 2} .0138499{col 42}{space 2}  .051427{col 53}{space 1}    0.27{col 62}{space 3}0.788{col 70}{space 4}-.0872105{col 83}{space 3} .1149104
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0061441{col 42}{space 2}  .011345{col 53}{space 1}    0.54{col 62}{space 3}0.588{col 70}{space 4}-.0161502{col 83}{space 3} .0284384
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0109481{col 42}{space 2} .0254681{col 53}{space 1}    0.43{col 62}{space 3}0.667{col 70}{space 4}   -.0391{col 83}{space 3} .0609961
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0157646{col 42}{space 2} .0303084{col 53}{space 1}   -0.52{col 62}{space 3}0.603{col 70}{space 4}-.0753242{col 83}{space 3}  .043795
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.aut_std {c |}{col 30}{res}{space 2} .0048533{col 42}{space 2} .0136555{col 53}{space 1}    0.36{col 62}{space 3}0.722{col 70}{space 4}-.0219814{col 83}{space 3}  .031688
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.aut_std {c |}{col 30}{res}{space 2} -.011244{col 42}{space 2} .0265412{col 53}{space 1}   -0.42{col 62}{space 3}0.672{col 70}{space 4}-.0634007{col 83}{space 3} .0409127
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.aut_std {c |}{col 30}{res}{space 2} .0183204{col 42}{space 2} .0300015{col 53}{space 1}    0.61{col 62}{space 3}0.542{col 70}{space 4}-.0406363{col 83}{space 3} .0772771
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.982
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0005
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  79.621
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.285
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.aut_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.aut_std c.perc_b_1913_1922#c.aut_std
{col 23}c.perc_b_1923_1932#c.aut_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.aut_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.aut_std c.pop_jew#c.aut_std
{col 23}c.p_french_name#c.aut_std c.lpop_1936#c.aut_std
{col 23}c.perc_fore_1936#c.aut_std c.perc_dial_1936#c.aut_std
{col 23}c.perc_disp_1946#c.aut_std c.water#c.aut_std
{col 23}c.lelevation_mean#c.aut_std c.lelevation_std#c.aut_std
{col 23}c.ldist_de#c.aut_std c.perc_droi_1936_ins#c.aut_std
{col 23}c.lpop_counter#c.aut_std c.perc_fore#c.aut_std
{col 23}c.perc_b_1928_1945#c.aut_std c.perc_b_1946_1964#c.aut_std
{col 23}c.perc_b_1965_1980#c.aut_std c.perc_b_1981_1996#c.aut_std
{col 23}c.perc_male_counter#c.aut_std c.perc_bacplus#c.aut_std
{col 23}c.perc_bluecollar#c.aut_std c.perc_chom#c.aut_std
{col 23}c.lrev_median#c.aut_std c.lrev_sd#c.aut_std
{col 23}2b.cand_year#c.rdd_lat 3.cand_year#c.rdd_lat
{col 23}17.cand_year#c.rdd_lat 26.cand_year#c.rdd_lat
{col 23}27.cand_year#c.rdd_lat 30.cand_year#c.rdd_lat
{col 23}44.cand_year#c.rdd_lat 50.cand_year#c.rdd_lat
{col 23}53.cand_year#c.rdd_lat 62.cand_year#c.rdd_lat
{col 23}67.cand_year#c.rdd_lat 2b.cand_year#c.rdd_lon
{col 23}3.cand_year#c.rdd_lon 17.cand_year#c.rdd_lon
{col 23}26.cand_year#c.rdd_lon 27.cand_year#c.rdd_lon
{col 23}30.cand_year#c.rdd_lon 44.cand_year#c.rdd_lon
{col 23}50.cand_year#c.rdd_lon 53.cand_year#c.rdd_lon
{col 23}62.cand_year#c.rdd_lon 67.cand_year#c.rdd_lon
{col 23}2b.cand_year#c.rdd_lat_2 3.cand_year#c.rdd_lat_2
{col 23}17.cand_year#c.rdd_lat_2 26.cand_year#c.rdd_lat_2
{col 23}27.cand_year#c.rdd_lat_2 30.cand_year#c.rdd_lat_2
{col 23}44.cand_year#c.rdd_lat_2 50.cand_year#c.rdd_lat_2
{col 23}53.cand_year#c.rdd_lat_2 62.cand_year#c.rdd_lat_2
{col 23}67.cand_year#c.rdd_lat_2 2b.cand_year#c.rdd_lon_2
{col 23}3.cand_year#c.rdd_lon_2 17.cand_year#c.rdd_lon_2
{col 23}26.cand_year#c.rdd_lon_2 27.cand_year#c.rdd_lon_2
{col 23}30.cand_year#c.rdd_lon_2 44.cand_year#c.rdd_lon_2
{col 23}50.cand_year#c.rdd_lon_2 53.cand_year#c.rdd_lon_2
{col 23}62.cand_year#c.rdd_lon_2 67.cand_year#c.rdd_lon_2
{col 23}2b.cand_year#c.rdd_lat_lon 3.cand_year#c.rdd_lat_lon
{col 23}17.cand_year#c.rdd_lat_lon 26.cand_year#c.rdd_lat_lon
{col 23}27.cand_year#c.rdd_lat_lon 30.cand_year#c.rdd_lat_lon
{col 23}44.cand_year#c.rdd_lat_lon 50.cand_year#c.rdd_lat_lon
{col 23}53.cand_year#c.rdd_lat_lon 62.cand_year#c.rdd_lat_lon
{col 23}67.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       88{col 38}{space 1}        0{col 50}{result}{space 1}       88{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       44{col 38}{space 1}       11{col 50}{result}{space 1}       33{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.2849287

{txt}added scalar:
                e(F_u) =  {res}11.982442

{txt}added scalar:
             e(mean_y) =  {res}7.2146001

               {txt}Authoritarianism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} 2.61e-17       2.61e-17
{txt} 5%    {res} 2.61e-17       2.61e-17
{txt}10%    {res} .0272537       2.61e-17       {txt}Obs         {res}      4,158
{txt}25%    {res} .0272537       2.61e-17       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.811416                      {txt}Mean          {res} 1.586722
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res}  2.24054       2.392051
{txt}90%    {res} 2.392051       2.392051       {txt}Variance      {res} .8891027
{txt}95%    {res} 2.392051       2.392051       {txt}Skewness      {res}-.9546856
{txt}99%    {res} 2.392051       2.392051       {txt}Kurtosis      {res} 2.112538

{txt}added scalar:
                e(max) =  {res}2.3920507

{txt}added scalar:
                e(p50) =  {res}1.811416

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      2,772        1965           0       1965       1965
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.nat_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    2772
{txt}{col 55}F(  8,   461) = {res}    5.12
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 70733.79778{txt}{col 55}Centered R2   = {res} -0.0705
{txt}Total (uncentered) SS   = {res} 70733.79778{txt}{col 55}Uncentered R2 = {res} -0.0705
{txt}Residual SS             = {res} 75718.61013{txt}{col 55}Root MSE      = {res}    5.35

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .4310714{col 42}{space 2} .0964248{col 53}{space 1}    4.47{col 62}{space 3}0.000{col 70}{space 4} .2415848{col 83}{space 3}  .620558
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2}-.6413425{col 42}{space 2} .1787257{col 53}{space 1}   -3.59{col 62}{space 3}0.000{col 70}{space 4}-.9925606{col 83}{space 3}-.2901244
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0534977{col 42}{space 2} .0343075{col 53}{space 1}   -1.56{col 62}{space 3}0.120{col 70}{space 4}-.1209162{col 83}{space 3} .0139207
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0189825{col 42}{space 2} .0640421{col 53}{space 1}   -0.30{col 62}{space 3}0.767{col 70}{space 4} -.144833{col 83}{space 3}  .106868
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.1565045{col 42}{space 2} .0575439{col 53}{space 1}   -2.72{col 62}{space 3}0.007{col 70}{space 4}-.2695852{col 83}{space 3}-.0434237
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0776303{col 42}{space 2} .0477511{col 53}{space 1}    1.63{col 62}{space 3}0.105{col 70}{space 4}-.0162064{col 83}{space 3}  .171467
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2}-.0167679{col 42}{space 2} .0852211{col 53}{space 1}   -0.20{col 62}{space 3}0.844{col 70}{space 4}-.1842378{col 83}{space 3}  .150702
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2} .2862141{col 42}{space 2} .0751279{col 53}{space 1}    3.81{col 62}{space 3}0.000{col 70}{space 4} .1385785{col 83}{space 3} .4338496
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.539
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  42.299
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.455
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_male_counter#c.nat_std
{col 23}c.perc_bacplus#c.nat_std c.perc_bluecollar#c.nat_std
{col 23}c.perc_chom#c.nat_std c.lrev_median#c.nat_std
{col 23}c.lrev_sd#c.nat_std 5b.cand_year#c.rdd_lat
{col 23}51.cand_year#c.rdd_lat 56.cand_year#c.rdd_lat
{col 23}57.cand_year#c.rdd_lat 78.cand_year#c.rdd_lat
{col 23}81.cand_year#c.rdd_lat 5b.cand_year#c.rdd_lon
{col 23}51.cand_year#c.rdd_lon 56.cand_year#c.rdd_lon
{col 23}57.cand_year#c.rdd_lon 78.cand_year#c.rdd_lon
{col 23}81.cand_year#c.rdd_lon 5b.cand_year#c.rdd_lat_2
{col 23}51.cand_year#c.rdd_lat_2 56.cand_year#c.rdd_lat_2
{col 23}57.cand_year#c.rdd_lat_2 78.cand_year#c.rdd_lat_2
{col 23}81.cand_year#c.rdd_lat_2 5b.cand_year#c.rdd_lon_2
{col 23}51.cand_year#c.rdd_lon_2 56.cand_year#c.rdd_lon_2
{col 23}57.cand_year#c.rdd_lon_2 78.cand_year#c.rdd_lon_2
{col 23}81.cand_year#c.rdd_lon_2 5b.cand_year#c.rdd_lat_lon
{col 23}51.cand_year#c.rdd_lat_lon 56.cand_year#c.rdd_lat_lon
{col 23}57.cand_year#c.rdd_lat_lon 78.cand_year#c.rdd_lat_lon
{col 23}81.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.nat_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       48{col 38}{space 1}        0{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       24{col 38}{space 1}        6{col 50}{result}{space 1}       18{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.4549835

{txt}added scalar:
                e(F_u) =  {res}12.539211

{txt}added scalar:
             e(mean_y) =  {res}14.120275

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,772
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.397626                      {txt}Mean          {res} 1.229639
                        {txt}Largest       Std. dev.     {res} .9130356
{txt}75%    {res} 2.108802       2.108802
{txt}90%    {res} 2.108802       2.108802       {txt}Variance      {res} .8336341
{txt}95%    {res} 2.108802       2.108802       {txt}Skewness      {res}-.3936496
{txt}99%    {res} 2.108802       2.108802       {txt}Kurtosis      {res} 1.396842

{txt}added scalar:
                e(max) =  {res}2.1088018

{txt}added scalar:
                e(p50) =  {res}1.3976262

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      3,234        1969           0       1969       1969
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.nat_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    3234
{txt}{col 55}F(  8,   461) = {res}    2.33
{txt}{col 55}Prob > F      = {res}  0.0187
{txt}Total (centered) SS     = {res} 60691.18715{txt}{col 55}Centered R2   = {res} -0.0334
{txt}Total (uncentered) SS   = {res} 60691.18715{txt}{col 55}Uncentered R2 = {res} -0.0334
{txt}Residual SS             = {res} 62718.29246{txt}{col 55}Root MSE      = {res}   4.505

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .1532165{col 42}{space 2} .0505516{col 53}{space 1}    3.03{col 62}{space 3}0.003{col 70}{space 4} .0538764{col 83}{space 3} .2525566
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2}  -.32029{col 42}{space 2} .1351806{col 53}{space 1}   -2.37{col 62}{space 3}0.018{col 70}{space 4}-.5859366{col 83}{space 3}-.0546434
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0151355{col 42}{space 2} .0183081{col 53}{space 1}   -0.83{col 62}{space 3}0.409{col 70}{space 4}-.0511131{col 83}{space 3} .0208421
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0047244{col 42}{space 2} .0305993{col 53}{space 1}   -0.15{col 62}{space 3}0.877{col 70}{space 4}-.0648557{col 83}{space 3} .0554069
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0513128{col 42}{space 2} .0315735{col 53}{space 1}   -1.63{col 62}{space 3}0.105{col 70}{space 4}-.1133587{col 83}{space 3}  .010733
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0213816{col 42}{space 2} .0340425{col 53}{space 1}    0.63{col 62}{space 3}0.530{col 70}{space 4}-.0455162{col 83}{space 3} .0882793
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2} -.022194{col 42}{space 2} .0573176{col 53}{space 1}   -0.39{col 62}{space 3}0.699{col 70}{space 4}-.1348301{col 83}{space 3} .0904421
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2} .1596288{col 42}{space 2}  .054875{col 53}{space 1}    2.91{col 62}{space 3}0.004{col 70}{space 4} .0517926{col 83}{space 3} .2674651
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.133
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  53.572
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.751
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_male_counter#c.nat_std
{col 23}c.perc_bacplus#c.nat_std c.perc_bluecollar#c.nat_std
{col 23}c.perc_chom#c.nat_std c.lrev_median#c.nat_std
{col 23}c.lrev_sd#c.nat_std 21b.cand_year#c.rdd_lat
{col 23}22.cand_year#c.rdd_lat 23.cand_year#c.rdd_lat
{col 23}38.cand_year#c.rdd_lat 64.cand_year#c.rdd_lat
{col 23}65.cand_year#c.rdd_lat 69.cand_year#c.rdd_lat
{col 23}21b.cand_year#c.rdd_lon 22.cand_year#c.rdd_lon
{col 23}23.cand_year#c.rdd_lon 38.cand_year#c.rdd_lon
{col 23}64.cand_year#c.rdd_lon 65.cand_year#c.rdd_lon
{col 23}69.cand_year#c.rdd_lon 21b.cand_year#c.rdd_lat_2
{col 23}22.cand_year#c.rdd_lat_2 23.cand_year#c.rdd_lat_2
{col 23}38.cand_year#c.rdd_lat_2 64.cand_year#c.rdd_lat_2
{col 23}65.cand_year#c.rdd_lat_2 69.cand_year#c.rdd_lat_2
{col 23}21b.cand_year#c.rdd_lon_2 22.cand_year#c.rdd_lon_2
{col 23}23.cand_year#c.rdd_lon_2 38.cand_year#c.rdd_lon_2
{col 23}64.cand_year#c.rdd_lon_2 65.cand_year#c.rdd_lon_2
{col 23}69.cand_year#c.rdd_lon_2 21b.cand_year#c.rdd_lat_lon
{col 23}22.cand_year#c.rdd_lat_lon 23.cand_year#c.rdd_lat_lon
{col 23}38.cand_year#c.rdd_lat_lon 64.cand_year#c.rdd_lat_lon
{col 23}65.cand_year#c.rdd_lat_lon 69.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
Dropped collinear:{col 23}c.perc_b_1981_1996#c.nat_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       56{col 38}{space 1}        0{col 50}{result}{space 1}       56{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       28{col 38}{space 1}        7{col 50}{result}{space 1}       21{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.7509461

{txt}added scalar:
                e(F_u) =  {res}13.133181

{txt}added scalar:
             e(mean_y) =  {res}10.910314

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res} .0325876              0       {txt}Obs         {res}      3,234
{txt}25%    {res} .4761245              0       {txt}Sum of wgt. {res} 461.999991

{txt}50%    {res} 2.052956                      {txt}Mean          {res}  1.70822
                        {txt}Largest       Std. dev.     {res} .9259633
{txt}75%    {res} 2.463623       2.463623
{txt}90%    {res} 2.463623       2.463623       {txt}Variance      {res} .8574081
{txt}95%    {res} 2.463623       2.463623       {txt}Skewness      {res}-.8749975
{txt}99%    {res} 2.463623       2.463623       {txt}Kurtosis      {res} 2.006001

{txt}added scalar:
                e(max) =  {res}2.463623

{txt}added scalar:
                e(p50) =  {res}2.0529561

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        1974           0       1974       1974
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.nat_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    2.15
{txt}{col 55}Prob > F      = {res}  0.0305
{txt}Total (centered) SS     = {res} 91643.31693{txt}{col 55}Centered R2   = {res} -0.0199
{txt}Total (uncentered) SS   = {res} 91643.31693{txt}{col 55}Uncentered R2 = {res} -0.0199
{txt}Residual SS             = {res} 93463.37309{txt}{col 55}Root MSE      = {res}   4.191

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .1441824{col 42}{space 2} .0433634{col 53}{space 1}    3.32{col 62}{space 3}0.001{col 70}{space 4} .0589679{col 83}{space 3} .2293968
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2}-.1361579{col 42}{space 2} .0360401{col 53}{space 1}   -3.78{col 62}{space 3}0.000{col 70}{space 4}-.2069812{col 83}{space 3}-.0653347
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0137034{col 42}{space 2} .0137639{col 53}{space 1}   -1.00{col 62}{space 3}0.320{col 70}{space 4}-.0407511{col 83}{space 3} .0133444
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0078799{col 42}{space 2} .0242757{col 53}{space 1}   -0.32{col 62}{space 3}0.746{col 70}{space 4}-.0555846{col 83}{space 3} .0398249
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0034578{col 42}{space 2} .0219075{col 53}{space 1}   -0.16{col 62}{space 3}0.875{col 70}{space 4}-.0465088{col 83}{space 3} .0395931
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0082474{col 42}{space 2} .0095122{col 53}{space 1}    0.87{col 62}{space 3}0.386{col 70}{space 4}-.0104453{col 83}{space 3} .0269402
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2} .0065111{col 42}{space 2} .0172662{col 53}{space 1}    0.38{col 62}{space 3}0.706{col 70}{space 4} -.027419{col 83}{space 3} .0404413
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2} .0110079{col 42}{space 2} .0144796{col 53}{space 1}    0.76{col 62}{space 3}0.448{col 70}{space 4}-.0174463{col 83}{space 3} .0394622
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.956
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  95.402
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.991
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_male_counter#c.nat_std
{col 23}c.perc_bacplus#c.nat_std c.perc_bluecollar#c.nat_std
{col 23}c.perc_chom#c.nat_std c.lrev_median#c.nat_std
{col 23}c.lrev_sd#c.nat_std 14b.cand_year#c.rdd_lat
{col 23}24.cand_year#c.rdd_lat 28.cand_year#c.rdd_lat
{col 23}31.cand_year#c.rdd_lat 39.cand_year#c.rdd_lat
{col 23}40.cand_year#c.rdd_lat 45.cand_year#c.rdd_lat
{col 23}58.cand_year#c.rdd_lat 59.cand_year#c.rdd_lat
{col 23}68.cand_year#c.rdd_lat 71.cand_year#c.rdd_lat
{col 23}76.cand_year#c.rdd_lat 14b.cand_year#c.rdd_lon
{col 23}24.cand_year#c.rdd_lon 28.cand_year#c.rdd_lon
{col 23}31.cand_year#c.rdd_lon 39.cand_year#c.rdd_lon
{col 23}40.cand_year#c.rdd_lon 45.cand_year#c.rdd_lon
{col 23}58.cand_year#c.rdd_lon 59.cand_year#c.rdd_lon
{col 23}68.cand_year#c.rdd_lon 71.cand_year#c.rdd_lon
{col 23}76.cand_year#c.rdd_lon 14b.cand_year#c.rdd_lat_2
{col 23}24.cand_year#c.rdd_lat_2 28.cand_year#c.rdd_lat_2
{col 23}31.cand_year#c.rdd_lat_2 39.cand_year#c.rdd_lat_2
{col 23}40.cand_year#c.rdd_lat_2 45.cand_year#c.rdd_lat_2
{col 23}58.cand_year#c.rdd_lat_2 59.cand_year#c.rdd_lat_2
{col 23}68.cand_year#c.rdd_lat_2 71.cand_year#c.rdd_lat_2
{col 23}76.cand_year#c.rdd_lat_2 14b.cand_year#c.rdd_lon_2
{col 23}24.cand_year#c.rdd_lon_2 28.cand_year#c.rdd_lon_2
{col 23}31.cand_year#c.rdd_lon_2 39.cand_year#c.rdd_lon_2
{col 23}40.cand_year#c.rdd_lon_2 45.cand_year#c.rdd_lon_2
{col 23}58.cand_year#c.rdd_lon_2 59.cand_year#c.rdd_lon_2
{col 23}68.cand_year#c.rdd_lon_2 71.cand_year#c.rdd_lon_2
{col 23}76.cand_year#c.rdd_lon_2 14b.cand_year#c.rdd_lat_lon
{col 23}24.cand_year#c.rdd_lat_lon 28.cand_year#c.rdd_lat_lon
{col 23}31.cand_year#c.rdd_lat_lon 39.cand_year#c.rdd_lat_lon
{col 23}40.cand_year#c.rdd_lat_lon 45.cand_year#c.rdd_lat_lon
{col 23}58.cand_year#c.rdd_lat_lon 59.cand_year#c.rdd_lat_lon
{col 23}68.cand_year#c.rdd_lat_lon 71.cand_year#c.rdd_lat_lon
{col 23}76.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.nat_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.990841

{txt}added scalar:
                e(F_u) =  {res}12.956384

{txt}added scalar:
             e(mean_y) =  {res}6.9111711

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}  .751029              0
{txt}10%    {res}  .751029              0       {txt}Obs         {res}      5,544
{txt}25%    {res}  .751029              0       {txt}Sum of wgt. {res} 462.000001

{txt}50%    {res} 1.780313                      {txt}Mean          {res} 1.606522
                        {txt}Largest       Std. dev.     {res} .9575135
{txt}75%    {res} 2.045483       4.772965
{txt}90%    {res} 3.038171       4.772965       {txt}Variance      {res} .9168321
{txt}95%    {res} 3.038171       4.772965       {txt}Skewness      {res} .5051454
{txt}99%    {res} 3.046432       4.772965       {txt}Kurtosis      {res} 2.335087

{txt}added scalar:
                e(max) =  {res}4.7729654

{txt}added scalar:
                e(p50) =  {res}1.7803133

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,158        1995           0       1995       1995
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4158
{txt}{col 55}F(  8,   461) = {res}    2.14
{txt}{col 55}Prob > F      = {res}  0.0311
{txt}Total (centered) SS     = {res} 42113.79665{txt}{col 55}Centered R2   = {res} -0.0859
{txt}Total (uncentered) SS   = {res} 42113.79665{txt}{col 55}Uncentered R2 = {res} -0.0859
{txt}Residual SS             = {res} 45732.25687{txt}{col 55}Root MSE      = {res}   3.389

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.2148939{col 42}{space 2} .0565682{col 53}{space 1}   -3.80{col 62}{space 3}0.000{col 70}{space 4}-.3260574{col 83}{space 3}-.1037304
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2} .3837308{col 42}{space 2}   .10823{col 53}{space 1}    3.55{col 62}{space 3}0.000{col 70}{space 4} .1710455{col 83}{space 3} .5964161
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0059667{col 42}{space 2} .0236968{col 53}{space 1}   -0.25{col 62}{space 3}0.801{col 70}{space 4}-.0525339{col 83}{space 3} .0406005
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0199102{col 42}{space 2} .0450796{col 53}{space 1}    0.44{col 62}{space 3}0.659{col 70}{space 4}-.0686769{col 83}{space 3} .1084972
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0197007{col 42}{space 2} .0353699{col 53}{space 1}    0.56{col 62}{space 3}0.578{col 70}{space 4}-.0498055{col 83}{space 3} .0892069
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0073856{col 42}{space 2} .0308333{col 53}{space 1}    0.24{col 62}{space 3}0.811{col 70}{space 4}-.0532057{col 83}{space 3} .0679769
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2}-.0278176{col 42}{space 2}  .060951{col 53}{space 1}   -0.46{col 62}{space 3}0.648{col 70}{space 4}-.1475939{col 83}{space 3} .0919586
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2}-.0146439{col 42}{space 2} .0468565{col 53}{space 1}   -0.31{col 62}{space 3}0.755{col 70}{space 4}-.1067227{col 83}{space 3} .0774349
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.358
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  67.289
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.904
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_b_1981_1996#c.nat_std
{col 23}c.perc_male_counter#c.nat_std c.perc_bacplus#c.nat_std
{col 23}c.perc_bluecollar#c.nat_std c.perc_chom#c.nat_std
{col 23}c.lrev_median#c.nat_std c.lrev_sd#c.nat_std
{col 23}4b.cand_year#c.rdd_lat 15.cand_year#c.rdd_lat
{col 23}19.cand_year#c.rdd_lat 33.cand_year#c.rdd_lat
{col 23}36.cand_year#c.rdd_lat 41.cand_year#c.rdd_lat
{col 23}46.cand_year#c.rdd_lat 79.cand_year#c.rdd_lat
{col 23}82.cand_year#c.rdd_lat 4b.cand_year#c.rdd_lon
{col 23}15.cand_year#c.rdd_lon 19.cand_year#c.rdd_lon
{col 23}33.cand_year#c.rdd_lon 36.cand_year#c.rdd_lon
{col 23}41.cand_year#c.rdd_lon 46.cand_year#c.rdd_lon
{col 23}79.cand_year#c.rdd_lon 82.cand_year#c.rdd_lon
{col 23}4b.cand_year#c.rdd_lat_2 15.cand_year#c.rdd_lat_2
{col 23}19.cand_year#c.rdd_lat_2 33.cand_year#c.rdd_lat_2
{col 23}36.cand_year#c.rdd_lat_2 41.cand_year#c.rdd_lat_2
{col 23}46.cand_year#c.rdd_lat_2 79.cand_year#c.rdd_lat_2
{col 23}82.cand_year#c.rdd_lat_2 4b.cand_year#c.rdd_lon_2
{col 23}15.cand_year#c.rdd_lon_2 19.cand_year#c.rdd_lon_2
{col 23}33.cand_year#c.rdd_lon_2 36.cand_year#c.rdd_lon_2
{col 23}41.cand_year#c.rdd_lon_2 46.cand_year#c.rdd_lon_2
{col 23}79.cand_year#c.rdd_lon_2 82.cand_year#c.rdd_lon_2
{col 23}4b.cand_year#c.rdd_lat_lon 15.cand_year#c.rdd_lat_lon
{col 23}19.cand_year#c.rdd_lat_lon 33.cand_year#c.rdd_lat_lon
{col 23}36.cand_year#c.rdd_lat_lon 41.cand_year#c.rdd_lat_lon
{col 23}46.cand_year#c.rdd_lat_lon 79.cand_year#c.rdd_lat_lon
{col 23}82.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       72{col 38}{space 1}        0{col 50}{result}{space 1}       72{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       36{col 38}{space 1}        9{col 50}{result}{space 1}       27{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.9038785

{txt}added scalar:
                e(F_u) =  {res}13.357566

{txt}added scalar:
             e(mean_y) =  {res}8.9124834

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .1068615              0
{txt}10%    {res} .1068615              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .2961051              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} .3035268                      {txt}Mean          {res} .7223142
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} .4011176       2.978381
{txt}90%    {res} 2.978381       2.978381       {txt}Variance      {res} .8891027
{txt}95%    {res} 2.978381       2.978381       {txt}Skewness      {res} 1.833935
{txt}99%    {res} 2.978381       2.978381       {txt}Kurtosis      {res} 4.648611

{txt}added scalar:
                e(max) =  {res}2.9783814

{txt}added scalar:
                e(p50) =  {res}.30352685

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      7,392        2002           0       2002       2002
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    7392
{txt}{col 55}F(  8,   461) = {res}    1.61
{txt}{col 55}Prob > F      = {res}  0.1202
{txt}Total (centered) SS     = {res} 30759.05284{txt}{col 55}Centered R2   = {res} -0.0349
{txt}Total (uncentered) SS   = {res} 30759.05284{txt}{col 55}Uncentered R2 = {res} -0.0349
{txt}Residual SS             = {res} 31833.04229{txt}{col 55}Root MSE      = {res}   2.117

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1069837{col 42}{space 2} .0363664{col 53}{space 1}   -2.94{col 62}{space 3}0.003{col 70}{space 4}-.1784481{col 83}{space 3}-.0355192
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2}  .140179{col 42}{space 2} .0440908{col 53}{space 1}    3.18{col 62}{space 3}0.002{col 70}{space 4} .0535352{col 83}{space 3} .2268228
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0040477{col 42}{space 2} .0137425{col 53}{space 1}    0.29{col 62}{space 3}0.768{col 70}{space 4} -.022958{col 83}{space 3} .0310534
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0056961{col 42}{space 2} .0239758{col 53}{space 1}   -0.24{col 62}{space 3}0.812{col 70}{space 4}-.0528114{col 83}{space 3} .0414193
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0089993{col 42}{space 2} .0243076{col 53}{space 1}    0.37{col 62}{space 3}0.711{col 70}{space 4}-.0387682{col 83}{space 3} .0567668
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2}-.0007469{col 42}{space 2} .0127322{col 53}{space 1}   -0.06{col 62}{space 3}0.953{col 70}{space 4}-.0257673{col 83}{space 3} .0242734
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2} .0078782{col 42}{space 2}  .020797{col 53}{space 1}    0.38{col 62}{space 3}0.705{col 70}{space 4}-.0329904{col 83}{space 3} .0487468
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2}-.0165459{col 42}{space 2} .0214672{col 53}{space 1}   -0.77{col 62}{space 3}0.441{col 70}{space 4}-.0587316{col 83}{space 3} .0256399
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.314
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71} 113.354
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.377
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_b_1981_1996#c.nat_std
{col 23}c.perc_male_counter#c.nat_std c.perc_bacplus#c.nat_std
{col 23}c.perc_bluecollar#c.nat_std c.perc_chom#c.nat_std
{col 23}c.lrev_median#c.nat_std c.lrev_sd#c.nat_std
{col 23}6b.cand_year#c.rdd_lat 9.cand_year#c.rdd_lat
{col 23}11.cand_year#c.rdd_lat 18.cand_year#c.rdd_lat
{col 23}20.cand_year#c.rdd_lat 29.cand_year#c.rdd_lat
{col 23}34.cand_year#c.rdd_lat 37.cand_year#c.rdd_lat
{col 23}42.cand_year#c.rdd_lat 47.cand_year#c.rdd_lat
{col 23}52.cand_year#c.rdd_lat 54.cand_year#c.rdd_lat
{col 23}55.cand_year#c.rdd_lat 60.cand_year#c.rdd_lat
{col 23}72.cand_year#c.rdd_lat 77.cand_year#c.rdd_lat
{col 23}6b.cand_year#c.rdd_lon 9.cand_year#c.rdd_lon
{col 23}11.cand_year#c.rdd_lon 18.cand_year#c.rdd_lon
{col 23}20.cand_year#c.rdd_lon 29.cand_year#c.rdd_lon
{col 23}34.cand_year#c.rdd_lon 37.cand_year#c.rdd_lon
{col 23}42.cand_year#c.rdd_lon 47.cand_year#c.rdd_lon
{col 23}52.cand_year#c.rdd_lon 54.cand_year#c.rdd_lon
{col 23}55.cand_year#c.rdd_lon 60.cand_year#c.rdd_lon
{col 23}72.cand_year#c.rdd_lon 77.cand_year#c.rdd_lon
{col 23}6b.cand_year#c.rdd_lat_2 9.cand_year#c.rdd_lat_2
{col 23}11.cand_year#c.rdd_lat_2 18.cand_year#c.rdd_lat_2
{col 23}20.cand_year#c.rdd_lat_2 29.cand_year#c.rdd_lat_2
{col 23}34.cand_year#c.rdd_lat_2 37.cand_year#c.rdd_lat_2
{col 23}42.cand_year#c.rdd_lat_2 47.cand_year#c.rdd_lat_2
{col 23}52.cand_year#c.rdd_lat_2 54.cand_year#c.rdd_lat_2
{col 23}55.cand_year#c.rdd_lat_2 60.cand_year#c.rdd_lat_2
{col 23}72.cand_year#c.rdd_lat_2 77.cand_year#c.rdd_lat_2
{col 23}6b.cand_year#c.rdd_lon_2 9.cand_year#c.rdd_lon_2
{col 23}11.cand_year#c.rdd_lon_2 18.cand_year#c.rdd_lon_2
{col 23}20.cand_year#c.rdd_lon_2 29.cand_year#c.rdd_lon_2
{col 23}34.cand_year#c.rdd_lon_2 37.cand_year#c.rdd_lon_2
{col 23}42.cand_year#c.rdd_lon_2 47.cand_year#c.rdd_lon_2
{col 23}52.cand_year#c.rdd_lon_2 54.cand_year#c.rdd_lon_2
{col 23}55.cand_year#c.rdd_lon_2 60.cand_year#c.rdd_lon_2
{col 23}72.cand_year#c.rdd_lon_2 77.cand_year#c.rdd_lon_2
{col 23}6b.cand_year#c.rdd_lat_lon 9.cand_year#c.rdd_lat_lon
{col 23}11.cand_year#c.rdd_lat_lon 18.cand_year#c.rdd_lat_lon
{col 23}20.cand_year#c.rdd_lat_lon 29.cand_year#c.rdd_lat_lon
{col 23}34.cand_year#c.rdd_lat_lon 37.cand_year#c.rdd_lat_lon
{col 23}42.cand_year#c.rdd_lat_lon 47.cand_year#c.rdd_lat_lon
{col 23}52.cand_year#c.rdd_lat_lon 54.cand_year#c.rdd_lat_lon
{col 23}55.cand_year#c.rdd_lat_lon 60.cand_year#c.rdd_lat_lon
{col 23}72.cand_year#c.rdd_lat_lon 77.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}      128{col 38}{space 1}        0{col 50}{result}{space 1}      128{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       64{col 38}{space 1}       16{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.3769918

{txt}added scalar:
                e(F_u) =  {res}12.314243

{txt}added scalar:
             e(mean_y) =  {res}4.4757881

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .4461176              0
{txt}10%    {res} .4461176              0       {txt}Obs         {res}      7,392
{txt}25%    {res} .4461176              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} .6225861                      {txt}Mean          {res} 1.089036
                        {txt}Largest       Std. dev.     {res} .9683113
{txt}75%    {res} 1.300991       3.007468
{txt}90%    {res} 2.793256       3.007468       {txt}Variance      {res} .9376268
{txt}95%    {res} 3.007468       3.007468       {txt}Skewness      {res} 1.154988
{txt}99%    {res} 3.007468       3.007468       {txt}Kurtosis      {res}   2.6025

{txt}added scalar:
                e(max) =  {res}3.0074685

{txt}added scalar:
                e(p50) =  {res}.62258607

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        2007           0       2007       2007
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    0.99
{txt}{col 55}Prob > F      = {res}  0.4429
{txt}Total (centered) SS     = {res} 37248.22544{txt}{col 55}Centered R2   = {res} -0.0014
{txt}Total (uncentered) SS   = {res} 37248.22544{txt}{col 55}Uncentered R2 = {res} -0.0014
{txt}Residual SS             = {res} 37300.59215{txt}{col 55}Root MSE      = {res}   2.648

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0614259{col 42}{space 2} .0407983{col 53}{space 1}   -1.51{col 62}{space 3}0.133{col 70}{space 4}-.1415997{col 83}{space 3} .0187478
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2} .0099677{col 42}{space 2}  .067081{col 53}{space 1}    0.15{col 62}{space 3}0.882{col 70}{space 4}-.1218548{col 83}{space 3} .1417901
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0089993{col 42}{space 2} .0163999{col 53}{space 1}   -0.55{col 62}{space 3}0.583{col 70}{space 4}-.0412272{col 83}{space 3} .0232286
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0043978{col 42}{space 2} .0330967{col 53}{space 1}    0.13{col 62}{space 3}0.894{col 70}{space 4}-.0606413{col 83}{space 3} .0694369
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}  .015464{col 42}{space 2} .0333038{col 53}{space 1}    0.46{col 62}{space 3}0.643{col 70}{space 4}-.0499819{col 83}{space 3}   .08091
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0182166{col 42}{space 2} .0189064{col 53}{space 1}    0.96{col 62}{space 3}0.336{col 70}{space 4}-.0189369{col 83}{space 3}   .05537
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2} .0047178{col 42}{space 2}  .040565{col 53}{space 1}    0.12{col 62}{space 3}0.907{col 70}{space 4}-.0749975{col 83}{space 3} .0844331
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2}-.0252396{col 42}{space 2} .0426468{col 53}{space 1}   -0.59{col 62}{space 3}0.554{col 70}{space 4}-.1090459{col 83}{space 3} .0585666
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.337
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  94.915
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   7.025
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_b_1981_1996#c.nat_std
{col 23}c.perc_male_counter#c.nat_std c.perc_bacplus#c.nat_std
{col 23}c.perc_bluecollar#c.nat_std c.perc_chom#c.nat_std
{col 23}c.lrev_median#c.nat_std c.lrev_sd#c.nat_std
{col 23}7b.cand_year#c.rdd_lat 10.cand_year#c.rdd_lat
{col 23}12.cand_year#c.rdd_lat 13.cand_year#c.rdd_lat
{col 23}43.cand_year#c.rdd_lat 48.cand_year#c.rdd_lat
{col 23}63.cand_year#c.rdd_lat 70.cand_year#c.rdd_lat
{col 23}73.cand_year#c.rdd_lat 75.cand_year#c.rdd_lat
{col 23}80.cand_year#c.rdd_lat 83.cand_year#c.rdd_lat
{col 23}7b.cand_year#c.rdd_lon 10.cand_year#c.rdd_lon
{col 23}12.cand_year#c.rdd_lon 13.cand_year#c.rdd_lon
{col 23}43.cand_year#c.rdd_lon 48.cand_year#c.rdd_lon
{col 23}63.cand_year#c.rdd_lon 70.cand_year#c.rdd_lon
{col 23}73.cand_year#c.rdd_lon 75.cand_year#c.rdd_lon
{col 23}80.cand_year#c.rdd_lon 83.cand_year#c.rdd_lon
{col 23}7b.cand_year#c.rdd_lat_2 10.cand_year#c.rdd_lat_2
{col 23}12.cand_year#c.rdd_lat_2 13.cand_year#c.rdd_lat_2
{col 23}43.cand_year#c.rdd_lat_2 48.cand_year#c.rdd_lat_2
{col 23}63.cand_year#c.rdd_lat_2 70.cand_year#c.rdd_lat_2
{col 23}73.cand_year#c.rdd_lat_2 75.cand_year#c.rdd_lat_2
{col 23}80.cand_year#c.rdd_lat_2 83.cand_year#c.rdd_lat_2
{col 23}7b.cand_year#c.rdd_lon_2 10.cand_year#c.rdd_lon_2
{col 23}12.cand_year#c.rdd_lon_2 13.cand_year#c.rdd_lon_2
{col 23}43.cand_year#c.rdd_lon_2 48.cand_year#c.rdd_lon_2
{col 23}63.cand_year#c.rdd_lon_2 70.cand_year#c.rdd_lon_2
{col 23}73.cand_year#c.rdd_lon_2 75.cand_year#c.rdd_lon_2
{col 23}80.cand_year#c.rdd_lon_2 83.cand_year#c.rdd_lon_2
{col 23}7b.cand_year#c.rdd_lat_lon 10.cand_year#c.rdd_lat_lon
{col 23}12.cand_year#c.rdd_lat_lon 13.cand_year#c.rdd_lat_lon
{col 23}43.cand_year#c.rdd_lat_lon 48.cand_year#c.rdd_lat_lon
{col 23}63.cand_year#c.rdd_lat_lon 70.cand_year#c.rdd_lat_lon
{col 23}73.cand_year#c.rdd_lat_lon 75.cand_year#c.rdd_lat_lon
{col 23}80.cand_year#c.rdd_lat_lon 83.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}7.0252252

{txt}added scalar:
                e(F_u) =  {res}13.337417

{txt}added scalar:
             e(mean_y) =  {res}6.9392575

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0576122              0
{txt}10%    {res} .0576122              0       {txt}Obs         {res}      5,544
{txt}25%    {res} .0576122              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} .3692117                      {txt}Mean          {res} .7876572
                        {txt}Largest       Std. dev.     {res} .9575134
{txt}75%    {res} 1.098879       3.002055
{txt}90%    {res} 3.002055       3.002055       {txt}Variance      {res}  .916832
{txt}95%    {res} 3.002055       3.002055       {txt}Skewness      {res}  1.48873
{txt}99%    {res} 3.002055       3.002055       {txt}Kurtosis      {res} 3.896928

{txt}added scalar:
                e(max) =  {res}3.0020554

{txt}added scalar:
                e(p50) =  {res}.36921167

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,620        2012           0       2012       2012
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4620
{txt}{col 55}F(  8,   461) = {res}    1.07
{txt}{col 55}Prob > F      = {res}  0.3807
{txt}Total (centered) SS     = {res}  37804.0653{txt}{col 55}Centered R2   = {res} -0.0038
{txt}Total (uncentered) SS   = {res}  37804.0653{txt}{col 55}Uncentered R2 = {res} -0.0038
{txt}Residual SS             = {res} 37946.78832{txt}{col 55}Root MSE      = {res}   2.927

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0826979{col 42}{space 2} .0368018{col 53}{space 1}   -2.25{col 62}{space 3}0.025{col 70}{space 4} -.155018{col 83}{space 3}-.0103778
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2} .1778535{col 42}{space 2} .0921514{col 53}{space 1}    1.93{col 62}{space 3}0.054{col 70}{space 4}-.0032353{col 83}{space 3} .3589422
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0053833{col 42}{space 2} .0158133{col 53}{space 1}   -0.34{col 62}{space 3}0.734{col 70}{space 4}-.0364584{col 83}{space 3} .0256917
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0276619{col 42}{space 2} .0374027{col 53}{space 1}    0.74{col 62}{space 3}0.460{col 70}{space 4}-.0458391{col 83}{space 3} .1011629
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} -.022847{col 42}{space 2} .0253004{col 53}{space 1}   -0.90{col 62}{space 3}0.367{col 70}{space 4}-.0725655{col 83}{space 3} .0268715
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0091081{col 42}{space 2} .0250949{col 53}{space 1}    0.36{col 62}{space 3}0.717{col 70}{space 4}-.0402065{col 83}{space 3} .0584228
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2}-.0203868{col 42}{space 2} .0492682{col 53}{space 1}   -0.41{col 62}{space 3}0.679{col 70}{space 4}-.1172049{col 83}{space 3} .0764314
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2} .0236136{col 42}{space 2}  .037794{col 53}{space 1}    0.62{col 62}{space 3}0.532{col 70}{space 4}-.0506564{col 83}{space 3} .0978835
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.698
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  73.636
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.590
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_b_1981_1996#c.nat_std
{col 23}c.perc_male_counter#c.nat_std c.perc_bacplus#c.nat_std
{col 23}c.perc_bluecollar#c.nat_std c.perc_chom#c.nat_std
{col 23}c.lrev_median#c.nat_std c.lrev_sd#c.nat_std
{col 23}1b.cand_year#c.rdd_lat 8.cand_year#c.rdd_lat
{col 23}16.cand_year#c.rdd_lat 25.cand_year#c.rdd_lat
{col 23}32.cand_year#c.rdd_lat 35.cand_year#c.rdd_lat
{col 23}49.cand_year#c.rdd_lat 61.cand_year#c.rdd_lat
{col 23}66.cand_year#c.rdd_lat 74.cand_year#c.rdd_lat
{col 23}1b.cand_year#c.rdd_lon 8.cand_year#c.rdd_lon
{col 23}16.cand_year#c.rdd_lon 25.cand_year#c.rdd_lon
{col 23}32.cand_year#c.rdd_lon 35.cand_year#c.rdd_lon
{col 23}49.cand_year#c.rdd_lon 61.cand_year#c.rdd_lon
{col 23}66.cand_year#c.rdd_lon 74.cand_year#c.rdd_lon
{col 23}1b.cand_year#c.rdd_lat_2 8.cand_year#c.rdd_lat_2
{col 23}16.cand_year#c.rdd_lat_2 25.cand_year#c.rdd_lat_2
{col 23}32.cand_year#c.rdd_lat_2 35.cand_year#c.rdd_lat_2
{col 23}49.cand_year#c.rdd_lat_2 61.cand_year#c.rdd_lat_2
{col 23}66.cand_year#c.rdd_lat_2 74.cand_year#c.rdd_lat_2
{col 23}1b.cand_year#c.rdd_lon_2 8.cand_year#c.rdd_lon_2
{col 23}16.cand_year#c.rdd_lon_2 25.cand_year#c.rdd_lon_2
{col 23}32.cand_year#c.rdd_lon_2 35.cand_year#c.rdd_lon_2
{col 23}49.cand_year#c.rdd_lon_2 61.cand_year#c.rdd_lon_2
{col 23}66.cand_year#c.rdd_lon_2 74.cand_year#c.rdd_lon_2
{col 23}1b.cand_year#c.rdd_lat_lon 8.cand_year#c.rdd_lat_lon
{col 23}16.cand_year#c.rdd_lat_lon 25.cand_year#c.rdd_lat_lon
{col 23}32.cand_year#c.rdd_lat_lon 35.cand_year#c.rdd_lat_lon
{col 23}49.cand_year#c.rdd_lat_lon 61.cand_year#c.rdd_lat_lon
{col 23}66.cand_year#c.rdd_lat_lon 74.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       80{col 38}{space 1}        0{col 50}{result}{space 1}       80{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       40{col 38}{space 1}       10{col 50}{result}{space 1}       30{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.5900872

{txt}added scalar:
                e(F_u) =  {res}12.697908

{txt}added scalar:
             e(mean_y) =  {res}8.058661

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .0408273              0
{txt}10%    {res} .0408273              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .1078492              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} .1278218                      {txt}Mean          {res} .7416607
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} .9044974       2.666475
{txt}90%    {res} 2.666475       2.666475       {txt}Variance      {res} .8891027
{txt}95%    {res} 2.666475       2.666475       {txt}Skewness      {res} 1.258783
{txt}99%    {res} 2.666475       2.666475       {txt}Kurtosis      {res} 3.092452

{txt}added scalar:
                e(max) =  {res}2.6664751

{txt}added scalar:
                e(p50) =  {res}.12782179

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,082        2017           0       2017       2017
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5082
{txt}{col 55}F(  8,   461) = {res}    1.24
{txt}{col 55}Prob > F      = {res}  0.2738
{txt}Total (centered) SS     = {res} 34396.49755{txt}{col 55}Centered R2   = {res} -0.0069
{txt}Total (uncentered) SS   = {res} 34396.49755{txt}{col 55}Uncentered R2 = {res} -0.0069
{txt}Residual SS             = {res} 34633.49205{txt}{col 55}Root MSE      = {res}   2.666

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1105431{col 42}{space 2} .0597414{col 53}{space 1}   -1.85{col 62}{space 3}0.065{col 70}{space 4}-.2279423{col 83}{space 3} .0068561
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.nat_std {c |}{col 30}{res}{space 2} .1151823{col 42}{space 2} .0847722{col 53}{space 1}    1.36{col 62}{space 3}0.175{col 70}{space 4}-.0514054{col 83}{space 3}   .28177
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0050505{col 42}{space 2} .0211809{col 53}{space 1}   -0.24{col 62}{space 3}0.812{col 70}{space 4}-.0466736{col 83}{space 3} .0365726
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0065192{col 42}{space 2} .0371961{col 53}{space 1}    0.18{col 62}{space 3}0.861{col 70}{space 4}-.0665758{col 83}{space 3} .0796142
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0346975{col 42}{space 2}  .034479{col 53}{space 1}   -1.01{col 62}{space 3}0.315{col 70}{space 4}-.1024529{col 83}{space 3} .0330579
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.nat_std {c |}{col 30}{res}{space 2} .0183271{col 42}{space 2} .0222241{col 53}{space 1}    0.82{col 62}{space 3}0.410{col 70}{space 4} -.025346{col 83}{space 3} .0620001
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.nat_std {c |}{col 30}{res}{space 2}-.0086138{col 42}{space 2} .0369331{col 53}{space 1}   -0.23{col 62}{space 3}0.816{col 70}{space 4}-.0811919{col 83}{space 3} .0639644
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.nat_std {c |}{col 30}{res}{space 2} .0353207{col 42}{space 2} .0364914{col 53}{space 1}    0.97{col 62}{space 3}0.334{col 70}{space 4}-.0363894{col 83}{space 3} .1070308
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.982
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0005
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  79.621
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.285
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.nat_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.nat_std c.perc_b_1913_1922#c.nat_std
{col 23}c.perc_b_1923_1932#c.nat_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.nat_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.nat_std c.pop_jew#c.nat_std
{col 23}c.p_french_name#c.nat_std c.lpop_1936#c.nat_std
{col 23}c.perc_fore_1936#c.nat_std c.perc_dial_1936#c.nat_std
{col 23}c.perc_disp_1946#c.nat_std c.water#c.nat_std
{col 23}c.lelevation_mean#c.nat_std c.lelevation_std#c.nat_std
{col 23}c.ldist_de#c.nat_std c.perc_droi_1936_ins#c.nat_std
{col 23}c.lpop_counter#c.nat_std c.perc_fore#c.nat_std
{col 23}c.perc_b_1928_1945#c.nat_std c.perc_b_1946_1964#c.nat_std
{col 23}c.perc_b_1965_1980#c.nat_std c.perc_b_1981_1996#c.nat_std
{col 23}c.perc_male_counter#c.nat_std c.perc_bacplus#c.nat_std
{col 23}c.perc_bluecollar#c.nat_std c.perc_chom#c.nat_std
{col 23}c.lrev_median#c.nat_std c.lrev_sd#c.nat_std
{col 23}2b.cand_year#c.rdd_lat 3.cand_year#c.rdd_lat
{col 23}17.cand_year#c.rdd_lat 26.cand_year#c.rdd_lat
{col 23}27.cand_year#c.rdd_lat 30.cand_year#c.rdd_lat
{col 23}44.cand_year#c.rdd_lat 50.cand_year#c.rdd_lat
{col 23}53.cand_year#c.rdd_lat 62.cand_year#c.rdd_lat
{col 23}67.cand_year#c.rdd_lat 2b.cand_year#c.rdd_lon
{col 23}3.cand_year#c.rdd_lon 17.cand_year#c.rdd_lon
{col 23}26.cand_year#c.rdd_lon 27.cand_year#c.rdd_lon
{col 23}30.cand_year#c.rdd_lon 44.cand_year#c.rdd_lon
{col 23}50.cand_year#c.rdd_lon 53.cand_year#c.rdd_lon
{col 23}62.cand_year#c.rdd_lon 67.cand_year#c.rdd_lon
{col 23}2b.cand_year#c.rdd_lat_2 3.cand_year#c.rdd_lat_2
{col 23}17.cand_year#c.rdd_lat_2 26.cand_year#c.rdd_lat_2
{col 23}27.cand_year#c.rdd_lat_2 30.cand_year#c.rdd_lat_2
{col 23}44.cand_year#c.rdd_lat_2 50.cand_year#c.rdd_lat_2
{col 23}53.cand_year#c.rdd_lat_2 62.cand_year#c.rdd_lat_2
{col 23}67.cand_year#c.rdd_lat_2 2b.cand_year#c.rdd_lon_2
{col 23}3.cand_year#c.rdd_lon_2 17.cand_year#c.rdd_lon_2
{col 23}26.cand_year#c.rdd_lon_2 27.cand_year#c.rdd_lon_2
{col 23}30.cand_year#c.rdd_lon_2 44.cand_year#c.rdd_lon_2
{col 23}50.cand_year#c.rdd_lon_2 53.cand_year#c.rdd_lon_2
{col 23}62.cand_year#c.rdd_lon_2 67.cand_year#c.rdd_lon_2
{col 23}2b.cand_year#c.rdd_lat_lon 3.cand_year#c.rdd_lat_lon
{col 23}17.cand_year#c.rdd_lat_lon 26.cand_year#c.rdd_lat_lon
{col 23}27.cand_year#c.rdd_lat_lon 30.cand_year#c.rdd_lat_lon
{col 23}44.cand_year#c.rdd_lat_lon 50.cand_year#c.rdd_lat_lon
{col 23}53.cand_year#c.rdd_lat_lon 62.cand_year#c.rdd_lat_lon
{col 23}67.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       88{col 38}{space 1}        0{col 50}{result}{space 1}       88{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       44{col 38}{space 1}       11{col 50}{result}{space 1}       33{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.2849287

{txt}added scalar:
                e(F_u) =  {res}11.982442

{txt}added scalar:
             e(mean_y) =  {res}7.2146001

                 {txt}Nationalism (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .1111746              0
{txt} 5%    {res} .2223493              0
{txt}10%    {res} .4380405              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .4380405              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} .9235747                      {txt}Mean          {res} 1.325641
                        {txt}Largest       Std. dev.     {res} .9429225
{txt}75%    {res}  1.32407       2.924496
{txt}90%    {res} 2.924496       2.924496       {txt}Variance      {res} .8891029
{txt}95%    {res} 2.924496       2.924496       {txt}Skewness      {res} .7937406
{txt}99%    {res} 2.924496       2.924496       {txt}Kurtosis      {res} 2.241278

{txt}added scalar:
                e(max) =  {res}2.9244962

{txt}added scalar:
                e(p50) =  {res}.92357475

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      2,772        1965           0       1965       1965
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.rle_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    2772
{txt}{col 55}F(  8,   461) = {res}    4.16
{txt}{col 55}Prob > F      = {res}  0.0001
{txt}Total (centered) SS     = {res} 73555.86718{txt}{col 55}Centered R2   = {res} -0.0448
{txt}Total (uncentered) SS   = {res} 73555.86718{txt}{col 55}Uncentered R2 = {res} -0.0448
{txt}Residual SS             = {res} 76854.84488{txt}{col 55}Root MSE      = {res}    5.39

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .3284242{col 42}{space 2} .0895004{col 53}{space 1}    3.67{col 62}{space 3}0.000{col 70}{space 4} .1525448{col 83}{space 3} .5043036
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2}-.4056733{col 42}{space 2} .1168959{col 53}{space 1}   -3.47{col 62}{space 3}0.001{col 70}{space 4}-.6353882{col 83}{space 3}-.1759583
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0482484{col 42}{space 2} .0326101{col 53}{space 1}   -1.48{col 62}{space 3}0.140{col 70}{space 4}-.1123312{col 83}{space 3} .0158344
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0325095{col 42}{space 2} .0589328{col 53}{space 1}   -0.55{col 62}{space 3}0.581{col 70}{space 4}-.1483197{col 83}{space 3} .0833008
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0859623{col 42}{space 2} .0554356{col 53}{space 1}   -1.55{col 62}{space 3}0.122{col 70}{space 4}-.1949001{col 83}{space 3} .0229754
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2}  .055911{col 42}{space 2} .0320238{col 53}{space 1}    1.75{col 62}{space 3}0.081{col 70}{space 4}-.0070197{col 83}{space 3} .1188417
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2} .0071847{col 42}{space 2} .0565525{col 53}{space 1}    0.13{col 62}{space 3}0.899{col 70}{space 4} -.103948{col 83}{space 3} .1183174
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .1458176{col 42}{space 2} .0499182{col 53}{space 1}    2.92{col 62}{space 3}0.004{col 70}{space 4} .0477221{col 83}{space 3}  .243913
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.539
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  42.299
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.455
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_male_counter#c.rle_std
{col 23}c.perc_bacplus#c.rle_std c.perc_bluecollar#c.rle_std
{col 23}c.perc_chom#c.rle_std c.lrev_median#c.rle_std
{col 23}c.lrev_sd#c.rle_std 5b.cand_year#c.rdd_lat
{col 23}51.cand_year#c.rdd_lat 56.cand_year#c.rdd_lat
{col 23}57.cand_year#c.rdd_lat 78.cand_year#c.rdd_lat
{col 23}81.cand_year#c.rdd_lat 5b.cand_year#c.rdd_lon
{col 23}51.cand_year#c.rdd_lon 56.cand_year#c.rdd_lon
{col 23}57.cand_year#c.rdd_lon 78.cand_year#c.rdd_lon
{col 23}81.cand_year#c.rdd_lon 5b.cand_year#c.rdd_lat_2
{col 23}51.cand_year#c.rdd_lat_2 56.cand_year#c.rdd_lat_2
{col 23}57.cand_year#c.rdd_lat_2 78.cand_year#c.rdd_lat_2
{col 23}81.cand_year#c.rdd_lat_2 5b.cand_year#c.rdd_lon_2
{col 23}51.cand_year#c.rdd_lon_2 56.cand_year#c.rdd_lon_2
{col 23}57.cand_year#c.rdd_lon_2 78.cand_year#c.rdd_lon_2
{col 23}81.cand_year#c.rdd_lon_2 5b.cand_year#c.rdd_lat_lon
{col 23}51.cand_year#c.rdd_lat_lon 56.cand_year#c.rdd_lat_lon
{col 23}57.cand_year#c.rdd_lat_lon 78.cand_year#c.rdd_lat_lon
{col 23}81.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.rle_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       48{col 38}{space 1}        0{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       24{col 38}{space 1}        6{col 50}{result}{space 1}       18{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.4549835

{txt}added scalar:
                e(F_u) =  {res}12.539211

{txt}added scalar:
             e(mean_y) =  {res}14.120275

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,772
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.806934                      {txt}Mean          {res} 1.401724
                        {txt}Largest       Std. dev.     {res} .9130357
{txt}75%    {res} 2.130145       2.276327
{txt}90%    {res} 2.130145       2.276327       {txt}Variance      {res} .8336341
{txt}95%    {res} 2.276327       2.276327       {txt}Skewness      {res}-.7335317
{txt}99%    {res} 2.276327       2.276327       {txt}Kurtosis      {res} 1.789897

{txt}added scalar:
                e(max) =  {res}2.2763271

{txt}added scalar:
                e(p50) =  {res}1.806934

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      3,234        1969           0       1969       1969
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.rle_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    3234
{txt}{col 55}F(  8,   461) = {res}    1.81
{txt}{col 55}Prob > F      = {res}  0.0735
{txt}Total (centered) SS     = {res}  59114.1923{txt}{col 55}Centered R2   = {res} -0.0335
{txt}Total (uncentered) SS   = {res}  59114.1923{txt}{col 55}Uncentered R2 = {res} -0.0335
{txt}Residual SS             = {res} 61095.67166{txt}{col 55}Root MSE      = {res}   4.446

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .0891293{col 42}{space 2} .0449349{col 53}{space 1}    1.98{col 62}{space 3}0.048{col 70}{space 4} .0008267{col 83}{space 3} .1774318
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2}-.3522109{col 42}{space 2} .1721522{col 53}{space 1}   -2.05{col 62}{space 3}0.041{col 70}{space 4} -.690511{col 83}{space 3}-.0139107
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0082597{col 42}{space 2} .0162866{col 53}{space 1}   -0.51{col 62}{space 3}0.612{col 70}{space 4} -.040265{col 83}{space 3} .0237455
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0007837{col 42}{space 2}  .026719{col 53}{space 1}    0.03{col 62}{space 3}0.977{col 70}{space 4}-.0517224{col 83}{space 3} .0532897
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0267638{col 42}{space 2} .0297504{col 53}{space 1}   -0.90{col 62}{space 3}0.369{col 70}{space 4} -.085227{col 83}{space 3} .0316994
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2} .0195442{col 42}{space 2} .0426925{col 53}{space 1}    0.46{col 62}{space 3}0.647{col 70}{space 4}-.0643518{col 83}{space 3} .1034403
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0330174{col 42}{space 2} .0704671{col 53}{space 1}   -0.47{col 62}{space 3}0.640{col 70}{space 4}-.1714939{col 83}{space 3} .1054592
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .1851752{col 42}{space 2} .0708086{col 53}{space 1}    2.62{col 62}{space 3}0.009{col 70}{space 4} .0460275{col 83}{space 3} .3243229
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.133
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  53.572
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.751
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_male_counter#c.rle_std
{col 23}c.perc_bacplus#c.rle_std c.perc_bluecollar#c.rle_std
{col 23}c.perc_chom#c.rle_std c.lrev_median#c.rle_std
{col 23}c.lrev_sd#c.rle_std 21b.cand_year#c.rdd_lat
{col 23}22.cand_year#c.rdd_lat 23.cand_year#c.rdd_lat
{col 23}38.cand_year#c.rdd_lat 64.cand_year#c.rdd_lat
{col 23}65.cand_year#c.rdd_lat 69.cand_year#c.rdd_lat
{col 23}21b.cand_year#c.rdd_lon 22.cand_year#c.rdd_lon
{col 23}23.cand_year#c.rdd_lon 38.cand_year#c.rdd_lon
{col 23}64.cand_year#c.rdd_lon 65.cand_year#c.rdd_lon
{col 23}69.cand_year#c.rdd_lon 21b.cand_year#c.rdd_lat_2
{col 23}22.cand_year#c.rdd_lat_2 23.cand_year#c.rdd_lat_2
{col 23}38.cand_year#c.rdd_lat_2 64.cand_year#c.rdd_lat_2
{col 23}65.cand_year#c.rdd_lat_2 69.cand_year#c.rdd_lat_2
{col 23}21b.cand_year#c.rdd_lon_2 22.cand_year#c.rdd_lon_2
{col 23}23.cand_year#c.rdd_lon_2 38.cand_year#c.rdd_lon_2
{col 23}64.cand_year#c.rdd_lon_2 65.cand_year#c.rdd_lon_2
{col 23}69.cand_year#c.rdd_lon_2 21b.cand_year#c.rdd_lat_lon
{col 23}22.cand_year#c.rdd_lat_lon 23.cand_year#c.rdd_lat_lon
{col 23}38.cand_year#c.rdd_lat_lon 64.cand_year#c.rdd_lat_lon
{col 23}65.cand_year#c.rdd_lat_lon 69.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
Dropped collinear:{col 23}c.perc_b_1981_1996#c.rle_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       56{col 38}{space 1}        0{col 50}{result}{space 1}       56{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       28{col 38}{space 1}        7{col 50}{result}{space 1}       21{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.7509461

{txt}added scalar:
                e(F_u) =  {res}13.133181

{txt}added scalar:
             e(mean_y) =  {res}10.910314

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      3,234
{txt}25%    {res} .0722411              0       {txt}Sum of wgt. {res} 461.999991

{txt}50%    {res} 1.511993                      {txt}Mean          {res} 1.387314
                        {txt}Largest       Std. dev.     {res} .9259633
{txt}75%    {res} 2.276228       2.276228
{txt}90%    {res} 2.276228       2.276228       {txt}Variance      {res}  .857408
{txt}95%    {res} 2.276228       2.276228       {txt}Skewness      {res}-.5650728
{txt}99%    {res} 2.276228       2.276228       {txt}Kurtosis      {res} 1.693448

{txt}added scalar:
                e(max) =  {res}2.2762277

{txt}added scalar:
                e(p50) =  {res}1.5119934

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        1974           0       1974       1974
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.rle_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    2.51
{txt}{col 55}Prob > F      = {res}  0.0113
{txt}Total (centered) SS     = {res} 89711.14773{txt}{col 55}Centered R2   = {res} -0.0448
{txt}Total (uncentered) SS   = {res} 89711.14773{txt}{col 55}Uncentered R2 = {res} -0.0448
{txt}Residual SS             = {res}  93728.8337{txt}{col 55}Root MSE      = {res}   4.197

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .1850701{col 42}{space 2} .0500102{col 53}{space 1}    3.70{col 62}{space 3}0.000{col 70}{space 4} .0867939{col 83}{space 3} .2833462
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2}-.2781621{col 42}{space 2} .0788506{col 53}{space 1}   -3.53{col 62}{space 3}0.000{col 70}{space 4}-.4331132{col 83}{space 3}-.1232109
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0212997{col 42}{space 2} .0153717{col 53}{space 1}   -1.39{col 62}{space 3}0.167{col 70}{space 4} -.051507{col 83}{space 3} .0089077
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0101171{col 42}{space 2}  .029057{col 53}{space 1}   -0.35{col 62}{space 3}0.728{col 70}{space 4}-.0672177{col 83}{space 3} .0469834
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0113489{col 42}{space 2} .0248387{col 53}{space 1}   -0.46{col 62}{space 3}0.648{col 70}{space 4}-.0601599{col 83}{space 3} .0374621
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2} .0184908{col 42}{space 2} .0193757{col 53}{space 1}    0.95{col 62}{space 3}0.340{col 70}{space 4}-.0195849{col 83}{space 3} .0565665
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2} .0080826{col 42}{space 2} .0358654{col 53}{space 1}    0.23{col 62}{space 3}0.822{col 70}{space 4}-.0623975{col 83}{space 3} .0785626
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .0316044{col 42}{space 2} .0292823{col 53}{space 1}    1.08{col 62}{space 3}0.281{col 70}{space 4} -.025939{col 83}{space 3} .0891478
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.956
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  95.402
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.991
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_male_counter#c.rle_std
{col 23}c.perc_bacplus#c.rle_std c.perc_bluecollar#c.rle_std
{col 23}c.perc_chom#c.rle_std c.lrev_median#c.rle_std
{col 23}c.lrev_sd#c.rle_std 14b.cand_year#c.rdd_lat
{col 23}24.cand_year#c.rdd_lat 28.cand_year#c.rdd_lat
{col 23}31.cand_year#c.rdd_lat 39.cand_year#c.rdd_lat
{col 23}40.cand_year#c.rdd_lat 45.cand_year#c.rdd_lat
{col 23}58.cand_year#c.rdd_lat 59.cand_year#c.rdd_lat
{col 23}68.cand_year#c.rdd_lat 71.cand_year#c.rdd_lat
{col 23}76.cand_year#c.rdd_lat 14b.cand_year#c.rdd_lon
{col 23}24.cand_year#c.rdd_lon 28.cand_year#c.rdd_lon
{col 23}31.cand_year#c.rdd_lon 39.cand_year#c.rdd_lon
{col 23}40.cand_year#c.rdd_lon 45.cand_year#c.rdd_lon
{col 23}58.cand_year#c.rdd_lon 59.cand_year#c.rdd_lon
{col 23}68.cand_year#c.rdd_lon 71.cand_year#c.rdd_lon
{col 23}76.cand_year#c.rdd_lon 14b.cand_year#c.rdd_lat_2
{col 23}24.cand_year#c.rdd_lat_2 28.cand_year#c.rdd_lat_2
{col 23}31.cand_year#c.rdd_lat_2 39.cand_year#c.rdd_lat_2
{col 23}40.cand_year#c.rdd_lat_2 45.cand_year#c.rdd_lat_2
{col 23}58.cand_year#c.rdd_lat_2 59.cand_year#c.rdd_lat_2
{col 23}68.cand_year#c.rdd_lat_2 71.cand_year#c.rdd_lat_2
{col 23}76.cand_year#c.rdd_lat_2 14b.cand_year#c.rdd_lon_2
{col 23}24.cand_year#c.rdd_lon_2 28.cand_year#c.rdd_lon_2
{col 23}31.cand_year#c.rdd_lon_2 39.cand_year#c.rdd_lon_2
{col 23}40.cand_year#c.rdd_lon_2 45.cand_year#c.rdd_lon_2
{col 23}58.cand_year#c.rdd_lon_2 59.cand_year#c.rdd_lon_2
{col 23}68.cand_year#c.rdd_lon_2 71.cand_year#c.rdd_lon_2
{col 23}76.cand_year#c.rdd_lon_2 14b.cand_year#c.rdd_lat_lon
{col 23}24.cand_year#c.rdd_lat_lon 28.cand_year#c.rdd_lat_lon
{col 23}31.cand_year#c.rdd_lat_lon 39.cand_year#c.rdd_lat_lon
{col 23}40.cand_year#c.rdd_lat_lon 45.cand_year#c.rdd_lat_lon
{col 23}58.cand_year#c.rdd_lat_lon 59.cand_year#c.rdd_lat_lon
{col 23}68.cand_year#c.rdd_lat_lon 71.cand_year#c.rdd_lat_lon
{col 23}76.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.rle_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.990841

{txt}added scalar:
                e(F_u) =  {res}12.956384

{txt}added scalar:
             e(mean_y) =  {res}6.9111711

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}   .08792              0
{txt}10%    {res}   .08792              0       {txt}Obs         {res}      5,544
{txt}25%    {res}   .08792              0       {txt}Sum of wgt. {res} 462.000001

{txt}50%    {res} .6684368                      {txt}Mean          {res} 1.024859
                        {txt}Largest       Std. dev.     {res} .9575135
{txt}75%    {res} 1.721779       2.934236
{txt}90%    {res} 2.324766       2.934236       {txt}Variance      {res} .9168322
{txt}95%    {res} 2.324766       2.934236       {txt}Skewness      {res} .1615545
{txt}99%    {res} 2.324766       2.934236       {txt}Kurtosis      {res} 1.259379

{txt}added scalar:
                e(max) =  {res}2.9342356

{txt}added scalar:
                e(p50) =  {res}.66843677

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,158        1995           0       1995       1995
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4158
{txt}{col 55}F(  8,   461) = {res}    2.10
{txt}{col 55}Prob > F      = {res}  0.0341
{txt}Total (centered) SS     = {res} 43032.39163{txt}{col 55}Centered R2   = {res} -0.0450
{txt}Total (uncentered) SS   = {res} 43032.39163{txt}{col 55}Uncentered R2 = {res} -0.0450
{txt}Residual SS             = {res} 44969.05741{txt}{col 55}Root MSE      = {res}   3.361

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.2026653{col 42}{space 2} .0632221{col 53}{space 1}   -3.21{col 62}{space 3}0.001{col 70}{space 4}-.3269045{col 83}{space 3}-.0784262
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2} .2020769{col 42}{space 2} .0613907{col 53}{space 1}    3.29{col 62}{space 3}0.001{col 70}{space 4} .0814366{col 83}{space 3} .3227171
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0204455{col 42}{space 2} .0218417{col 53}{space 1}   -0.94{col 62}{space 3}0.350{col 70}{space 4}-.0633672{col 83}{space 3} .0224761
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0254249{col 42}{space 2} .0374972{col 53}{space 1}    0.68{col 62}{space 3}0.498{col 70}{space 4}-.0482618{col 83}{space 3} .0991115
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0071304{col 42}{space 2} .0349738{col 53}{space 1}    0.20{col 62}{space 3}0.839{col 70}{space 4}-.0615974{col 83}{space 3} .0758582
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2} .0036782{col 42}{space 2} .0164304{col 53}{space 1}    0.22{col 62}{space 3}0.823{col 70}{space 4}-.0286097{col 83}{space 3}  .035966
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0182155{col 42}{space 2} .0301587{col 53}{space 1}   -0.60{col 62}{space 3}0.546{col 70}{space 4} -.077481{col 83}{space 3}   .04105
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .0014796{col 42}{space 2} .0266336{col 53}{space 1}    0.06{col 62}{space 3}0.956{col 70}{space 4}-.0508588{col 83}{space 3}  .053818
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.358
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  67.289
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.904
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_b_1981_1996#c.rle_std
{col 23}c.perc_male_counter#c.rle_std c.perc_bacplus#c.rle_std
{col 23}c.perc_bluecollar#c.rle_std c.perc_chom#c.rle_std
{col 23}c.lrev_median#c.rle_std c.lrev_sd#c.rle_std
{col 23}4b.cand_year#c.rdd_lat 15.cand_year#c.rdd_lat
{col 23}19.cand_year#c.rdd_lat 33.cand_year#c.rdd_lat
{col 23}36.cand_year#c.rdd_lat 41.cand_year#c.rdd_lat
{col 23}46.cand_year#c.rdd_lat 79.cand_year#c.rdd_lat
{col 23}82.cand_year#c.rdd_lat 4b.cand_year#c.rdd_lon
{col 23}15.cand_year#c.rdd_lon 19.cand_year#c.rdd_lon
{col 23}33.cand_year#c.rdd_lon 36.cand_year#c.rdd_lon
{col 23}41.cand_year#c.rdd_lon 46.cand_year#c.rdd_lon
{col 23}79.cand_year#c.rdd_lon 82.cand_year#c.rdd_lon
{col 23}4b.cand_year#c.rdd_lat_2 15.cand_year#c.rdd_lat_2
{col 23}19.cand_year#c.rdd_lat_2 33.cand_year#c.rdd_lat_2
{col 23}36.cand_year#c.rdd_lat_2 41.cand_year#c.rdd_lat_2
{col 23}46.cand_year#c.rdd_lat_2 79.cand_year#c.rdd_lat_2
{col 23}82.cand_year#c.rdd_lat_2 4b.cand_year#c.rdd_lon_2
{col 23}15.cand_year#c.rdd_lon_2 19.cand_year#c.rdd_lon_2
{col 23}33.cand_year#c.rdd_lon_2 36.cand_year#c.rdd_lon_2
{col 23}41.cand_year#c.rdd_lon_2 46.cand_year#c.rdd_lon_2
{col 23}79.cand_year#c.rdd_lon_2 82.cand_year#c.rdd_lon_2
{col 23}4b.cand_year#c.rdd_lat_lon 15.cand_year#c.rdd_lat_lon
{col 23}19.cand_year#c.rdd_lat_lon 33.cand_year#c.rdd_lat_lon
{col 23}36.cand_year#c.rdd_lat_lon 41.cand_year#c.rdd_lat_lon
{col 23}46.cand_year#c.rdd_lat_lon 79.cand_year#c.rdd_lat_lon
{col 23}82.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       72{col 38}{space 1}        0{col 50}{result}{space 1}       72{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       36{col 38}{space 1}        9{col 50}{result}{space 1}       27{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.9038785

{txt}added scalar:
                e(F_u) =  {res}13.357566

{txt}added scalar:
             e(mean_y) =  {res}8.9124834

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .3202856              0
{txt} 5%    {res} .3202856              0
{txt}10%    {res} .3909419              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .8566263              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.815808                      {txt}Mean          {res} 1.690686
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res}  2.21371       3.429569
{txt}90%    {res} 3.429569       3.429569       {txt}Variance      {res} .8891027
{txt}95%    {res} 3.429569       3.429569       {txt}Skewness      {res} .3532975
{txt}99%    {res} 3.429569       3.429569       {txt}Kurtosis      {res} 2.357841

{txt}added scalar:
                e(max) =  {res}3.429569

{txt}added scalar:
                e(p50) =  {res}1.8158076

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      7,392        2002           0       2002       2002
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    7392
{txt}{col 55}F(  8,   461) = {res}    2.02
{txt}{col 55}Prob > F      = {res}  0.0421
{txt}Total (centered) SS     = {res} 30831.31148{txt}{col 55}Centered R2   = {res} -0.0271
{txt}Total (uncentered) SS   = {res} 30831.31148{txt}{col 55}Uncentered R2 = {res} -0.0271
{txt}Residual SS             = {res} 31665.89501{txt}{col 55}Root MSE      = {res}   2.111

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0876887{col 42}{space 2} .0362073{col 53}{space 1}   -2.42{col 62}{space 3}0.016{col 70}{space 4}-.1588406{col 83}{space 3}-.0165368
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2} .0796893{col 42}{space 2} .0279242{col 53}{space 1}    2.85{col 62}{space 3}0.005{col 70}{space 4} .0248148{col 83}{space 3} .1345638
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0121425{col 42}{space 2} .0142824{col 53}{space 1}    0.85{col 62}{space 3}0.396{col 70}{space 4}-.0159241{col 83}{space 3} .0402091
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0132624{col 42}{space 2} .0225489{col 53}{space 1}    0.59{col 62}{space 3}0.557{col 70}{space 4} -.031049{col 83}{space 3} .0575739
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0387312{col 42}{space 2} .0251882{col 53}{space 1}    1.54{col 62}{space 3}0.125{col 70}{space 4}-.0107667{col 83}{space 3} .0882291
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2}-.0078934{col 42}{space 2} .0086632{col 53}{space 1}   -0.91{col 62}{space 3}0.363{col 70}{space 4}-.0249176{col 83}{space 3} .0091309
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0080233{col 42}{space 2} .0133175{col 53}{space 1}   -0.60{col 62}{space 3}0.547{col 70}{space 4}-.0341939{col 83}{space 3} .0181472
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2}-.0224295{col 42}{space 2} .0153537{col 53}{space 1}   -1.46{col 62}{space 3}0.145{col 70}{space 4}-.0526015{col 83}{space 3} .0077424
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.314
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71} 113.354
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.377
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_b_1981_1996#c.rle_std
{col 23}c.perc_male_counter#c.rle_std c.perc_bacplus#c.rle_std
{col 23}c.perc_bluecollar#c.rle_std c.perc_chom#c.rle_std
{col 23}c.lrev_median#c.rle_std c.lrev_sd#c.rle_std
{col 23}6b.cand_year#c.rdd_lat 9.cand_year#c.rdd_lat
{col 23}11.cand_year#c.rdd_lat 18.cand_year#c.rdd_lat
{col 23}20.cand_year#c.rdd_lat 29.cand_year#c.rdd_lat
{col 23}34.cand_year#c.rdd_lat 37.cand_year#c.rdd_lat
{col 23}42.cand_year#c.rdd_lat 47.cand_year#c.rdd_lat
{col 23}52.cand_year#c.rdd_lat 54.cand_year#c.rdd_lat
{col 23}55.cand_year#c.rdd_lat 60.cand_year#c.rdd_lat
{col 23}72.cand_year#c.rdd_lat 77.cand_year#c.rdd_lat
{col 23}6b.cand_year#c.rdd_lon 9.cand_year#c.rdd_lon
{col 23}11.cand_year#c.rdd_lon 18.cand_year#c.rdd_lon
{col 23}20.cand_year#c.rdd_lon 29.cand_year#c.rdd_lon
{col 23}34.cand_year#c.rdd_lon 37.cand_year#c.rdd_lon
{col 23}42.cand_year#c.rdd_lon 47.cand_year#c.rdd_lon
{col 23}52.cand_year#c.rdd_lon 54.cand_year#c.rdd_lon
{col 23}55.cand_year#c.rdd_lon 60.cand_year#c.rdd_lon
{col 23}72.cand_year#c.rdd_lon 77.cand_year#c.rdd_lon
{col 23}6b.cand_year#c.rdd_lat_2 9.cand_year#c.rdd_lat_2
{col 23}11.cand_year#c.rdd_lat_2 18.cand_year#c.rdd_lat_2
{col 23}20.cand_year#c.rdd_lat_2 29.cand_year#c.rdd_lat_2
{col 23}34.cand_year#c.rdd_lat_2 37.cand_year#c.rdd_lat_2
{col 23}42.cand_year#c.rdd_lat_2 47.cand_year#c.rdd_lat_2
{col 23}52.cand_year#c.rdd_lat_2 54.cand_year#c.rdd_lat_2
{col 23}55.cand_year#c.rdd_lat_2 60.cand_year#c.rdd_lat_2
{col 23}72.cand_year#c.rdd_lat_2 77.cand_year#c.rdd_lat_2
{col 23}6b.cand_year#c.rdd_lon_2 9.cand_year#c.rdd_lon_2
{col 23}11.cand_year#c.rdd_lon_2 18.cand_year#c.rdd_lon_2
{col 23}20.cand_year#c.rdd_lon_2 29.cand_year#c.rdd_lon_2
{col 23}34.cand_year#c.rdd_lon_2 37.cand_year#c.rdd_lon_2
{col 23}42.cand_year#c.rdd_lon_2 47.cand_year#c.rdd_lon_2
{col 23}52.cand_year#c.rdd_lon_2 54.cand_year#c.rdd_lon_2
{col 23}55.cand_year#c.rdd_lon_2 60.cand_year#c.rdd_lon_2
{col 23}72.cand_year#c.rdd_lon_2 77.cand_year#c.rdd_lon_2
{col 23}6b.cand_year#c.rdd_lat_lon 9.cand_year#c.rdd_lat_lon
{col 23}11.cand_year#c.rdd_lat_lon 18.cand_year#c.rdd_lat_lon
{col 23}20.cand_year#c.rdd_lat_lon 29.cand_year#c.rdd_lat_lon
{col 23}34.cand_year#c.rdd_lat_lon 37.cand_year#c.rdd_lat_lon
{col 23}42.cand_year#c.rdd_lat_lon 47.cand_year#c.rdd_lat_lon
{col 23}52.cand_year#c.rdd_lat_lon 54.cand_year#c.rdd_lat_lon
{col 23}55.cand_year#c.rdd_lat_lon 60.cand_year#c.rdd_lat_lon
{col 23}72.cand_year#c.rdd_lat_lon 77.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}      128{col 38}{space 1}        0{col 50}{result}{space 1}      128{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       64{col 38}{space 1}       16{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.3769918

{txt}added scalar:
                e(F_u) =  {res}12.314243

{txt}added scalar:
             e(mean_y) =  {res}4.4757881

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .5256224              0
{txt}10%    {res} .7134705              0       {txt}Obs         {res}      7,392
{txt}25%    {res}  1.25833              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 2.526222                      {txt}Mean          {res} 2.015382
                        {txt}Largest       Std. dev.     {res} .9683113
{txt}75%    {res}  2.65987       3.235621
{txt}90%    {res} 3.122966       3.235621       {txt}Variance      {res} .9376269
{txt}95%    {res} 3.235621       3.235621       {txt}Skewness      {res}-.3983284
{txt}99%    {res} 3.235621       3.235621       {txt}Kurtosis      {res} 1.770362

{txt}added scalar:
                e(max) =  {res}3.2356215

{txt}added scalar:
                e(p50) =  {res}2.526222

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        2007           0       2007       2007
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    0.69
{txt}{col 55}Prob > F      = {res}  0.7011
{txt}Total (centered) SS     = {res}  37372.6757{txt}{col 55}Centered R2   = {res}  0.0015
{txt}Total (uncentered) SS   = {res}  37372.6757{txt}{col 55}Uncentered R2 = {res}  0.0015
{txt}Residual SS             = {res} 37316.23219{txt}{col 55}Root MSE      = {res}   2.648

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1058878{col 42}{space 2} .0608686{col 53}{space 1}   -1.74{col 62}{space 3}0.083{col 70}{space 4}-.2255022{col 83}{space 3} .0137265
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2} .0492264{col 42}{space 2} .0351599{col 53}{space 1}    1.40{col 62}{space 3}0.162{col 70}{space 4}-.0198672{col 83}{space 3} .1183199
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0208349{col 42}{space 2} .0227132{col 53}{space 1}   -0.92{col 62}{space 3}0.359{col 70}{space 4}-.0654691{col 83}{space 3} .0237994
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0352124{col 42}{space 2} .0343014{col 53}{space 1}    1.03{col 62}{space 3}0.305{col 70}{space 4} -.032194{col 83}{space 3} .1026188
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} -.017361{col 42}{space 2} .0371247{col 53}{space 1}   -0.47{col 62}{space 3}0.640{col 70}{space 4}-.0903156{col 83}{space 3} .0555937
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2} .0087128{col 42}{space 2} .0117436{col 53}{space 1}    0.74{col 62}{space 3}0.459{col 70}{space 4}-.0143647{col 83}{space 3} .0317904
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0169403{col 42}{space 2}  .017802{col 53}{space 1}   -0.95{col 62}{space 3}0.342{col 70}{space 4}-.0519234{col 83}{space 3} .0180428
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .0099789{col 42}{space 2} .0189343{col 53}{space 1}    0.53{col 62}{space 3}0.598{col 70}{space 4}-.0272294{col 83}{space 3} .0471871
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.337
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  94.915
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   7.025
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_b_1981_1996#c.rle_std
{col 23}c.perc_male_counter#c.rle_std c.perc_bacplus#c.rle_std
{col 23}c.perc_bluecollar#c.rle_std c.perc_chom#c.rle_std
{col 23}c.lrev_median#c.rle_std c.lrev_sd#c.rle_std
{col 23}7b.cand_year#c.rdd_lat 10.cand_year#c.rdd_lat
{col 23}12.cand_year#c.rdd_lat 13.cand_year#c.rdd_lat
{col 23}43.cand_year#c.rdd_lat 48.cand_year#c.rdd_lat
{col 23}63.cand_year#c.rdd_lat 70.cand_year#c.rdd_lat
{col 23}73.cand_year#c.rdd_lat 75.cand_year#c.rdd_lat
{col 23}80.cand_year#c.rdd_lat 83.cand_year#c.rdd_lat
{col 23}7b.cand_year#c.rdd_lon 10.cand_year#c.rdd_lon
{col 23}12.cand_year#c.rdd_lon 13.cand_year#c.rdd_lon
{col 23}43.cand_year#c.rdd_lon 48.cand_year#c.rdd_lon
{col 23}63.cand_year#c.rdd_lon 70.cand_year#c.rdd_lon
{col 23}73.cand_year#c.rdd_lon 75.cand_year#c.rdd_lon
{col 23}80.cand_year#c.rdd_lon 83.cand_year#c.rdd_lon
{col 23}7b.cand_year#c.rdd_lat_2 10.cand_year#c.rdd_lat_2
{col 23}12.cand_year#c.rdd_lat_2 13.cand_year#c.rdd_lat_2
{col 23}43.cand_year#c.rdd_lat_2 48.cand_year#c.rdd_lat_2
{col 23}63.cand_year#c.rdd_lat_2 70.cand_year#c.rdd_lat_2
{col 23}73.cand_year#c.rdd_lat_2 75.cand_year#c.rdd_lat_2
{col 23}80.cand_year#c.rdd_lat_2 83.cand_year#c.rdd_lat_2
{col 23}7b.cand_year#c.rdd_lon_2 10.cand_year#c.rdd_lon_2
{col 23}12.cand_year#c.rdd_lon_2 13.cand_year#c.rdd_lon_2
{col 23}43.cand_year#c.rdd_lon_2 48.cand_year#c.rdd_lon_2
{col 23}63.cand_year#c.rdd_lon_2 70.cand_year#c.rdd_lon_2
{col 23}73.cand_year#c.rdd_lon_2 75.cand_year#c.rdd_lon_2
{col 23}80.cand_year#c.rdd_lon_2 83.cand_year#c.rdd_lon_2
{col 23}7b.cand_year#c.rdd_lat_lon 10.cand_year#c.rdd_lat_lon
{col 23}12.cand_year#c.rdd_lat_lon 13.cand_year#c.rdd_lat_lon
{col 23}43.cand_year#c.rdd_lat_lon 48.cand_year#c.rdd_lat_lon
{col 23}63.cand_year#c.rdd_lat_lon 70.cand_year#c.rdd_lat_lon
{col 23}73.cand_year#c.rdd_lat_lon 75.cand_year#c.rdd_lat_lon
{col 23}80.cand_year#c.rdd_lat_lon 83.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}7.0252252

{txt}added scalar:
                e(F_u) =  {res}13.337417

{txt}added scalar:
             e(mean_y) =  {res}6.9392575

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} 1.165565              0
{txt}10%    {res} 1.630724              0       {txt}Obs         {res}      5,544
{txt}25%    {res} 1.674607              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 2.067506                      {txt}Mean          {res}  2.46462
                        {txt}Largest       Std. dev.     {res} .9575134
{txt}75%    {res} 3.119869        4.09299
{txt}90%    {res}  4.09299        4.09299       {txt}Variance      {res} .9168319
{txt}95%    {res}  4.09299        4.09299       {txt}Skewness      {res} .0058823
{txt}99%    {res}  4.09299        4.09299       {txt}Kurtosis      {res} 2.571278

{txt}added scalar:
                e(max) =  {res}4.0929899

{txt}added scalar:
                e(p50) =  {res}2.0675058

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,620        2012           0       2012       2012
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4620
{txt}{col 55}F(  8,   461) = {res}    1.15
{txt}{col 55}Prob > F      = {res}  0.3311
{txt}Total (centered) SS     = {res} 38454.04377{txt}{col 55}Centered R2   = {res} -0.0254
{txt}Total (uncentered) SS   = {res} 38454.04377{txt}{col 55}Uncentered R2 = {res} -0.0254
{txt}Residual SS             = {res} 39428.91315{txt}{col 55}Root MSE      = {res}   2.984

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1701267{col 42}{space 2}  .081142{col 53}{space 1}   -2.10{col 62}{space 3}0.037{col 70}{space 4}-.3295808{col 83}{space 3}-.0106727
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2} .1346687{col 42}{space 2}  .055742{col 53}{space 1}    2.42{col 62}{space 3}0.016{col 70}{space 4} .0251288{col 83}{space 3} .2442086
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}-.0143647{col 42}{space 2} .0284075{col 53}{space 1}   -0.51{col 62}{space 3}0.613{col 70}{space 4} -.070189{col 83}{space 3} .0414595
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0828865{col 42}{space 2} .0548444{col 53}{space 1}    1.51{col 62}{space 3}0.131{col 70}{space 4}-.0248896{col 83}{space 3} .1906626
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0857056{col 42}{space 2} .0506867{col 53}{space 1}   -1.69{col 62}{space 3}0.092{col 70}{space 4}-.1853111{col 83}{space 3} .0138999
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2} .0041606{col 42}{space 2} .0165938{col 53}{space 1}    0.25{col 62}{space 3}0.802{col 70}{space 4}-.0284483{col 83}{space 3} .0367695
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0464476{col 42}{space 2} .0311802{col 53}{space 1}   -1.49{col 62}{space 3}0.137{col 70}{space 4}-.1077206{col 83}{space 3} .0148253
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2} .0475206{col 42}{space 2} .0301647{col 53}{space 1}    1.58{col 62}{space 3}0.116{col 70}{space 4}-.0117567{col 83}{space 3} .1067979
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.698
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  73.636
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.590
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_b_1981_1996#c.rle_std
{col 23}c.perc_male_counter#c.rle_std c.perc_bacplus#c.rle_std
{col 23}c.perc_bluecollar#c.rle_std c.perc_chom#c.rle_std
{col 23}c.lrev_median#c.rle_std c.lrev_sd#c.rle_std
{col 23}1b.cand_year#c.rdd_lat 8.cand_year#c.rdd_lat
{col 23}16.cand_year#c.rdd_lat 25.cand_year#c.rdd_lat
{col 23}32.cand_year#c.rdd_lat 35.cand_year#c.rdd_lat
{col 23}49.cand_year#c.rdd_lat 61.cand_year#c.rdd_lat
{col 23}66.cand_year#c.rdd_lat 74.cand_year#c.rdd_lat
{col 23}1b.cand_year#c.rdd_lon 8.cand_year#c.rdd_lon
{col 23}16.cand_year#c.rdd_lon 25.cand_year#c.rdd_lon
{col 23}32.cand_year#c.rdd_lon 35.cand_year#c.rdd_lon
{col 23}49.cand_year#c.rdd_lon 61.cand_year#c.rdd_lon
{col 23}66.cand_year#c.rdd_lon 74.cand_year#c.rdd_lon
{col 23}1b.cand_year#c.rdd_lat_2 8.cand_year#c.rdd_lat_2
{col 23}16.cand_year#c.rdd_lat_2 25.cand_year#c.rdd_lat_2
{col 23}32.cand_year#c.rdd_lat_2 35.cand_year#c.rdd_lat_2
{col 23}49.cand_year#c.rdd_lat_2 61.cand_year#c.rdd_lat_2
{col 23}66.cand_year#c.rdd_lat_2 74.cand_year#c.rdd_lat_2
{col 23}1b.cand_year#c.rdd_lon_2 8.cand_year#c.rdd_lon_2
{col 23}16.cand_year#c.rdd_lon_2 25.cand_year#c.rdd_lon_2
{col 23}32.cand_year#c.rdd_lon_2 35.cand_year#c.rdd_lon_2
{col 23}49.cand_year#c.rdd_lon_2 61.cand_year#c.rdd_lon_2
{col 23}66.cand_year#c.rdd_lon_2 74.cand_year#c.rdd_lon_2
{col 23}1b.cand_year#c.rdd_lat_lon 8.cand_year#c.rdd_lat_lon
{col 23}16.cand_year#c.rdd_lat_lon 25.cand_year#c.rdd_lat_lon
{col 23}32.cand_year#c.rdd_lat_lon 35.cand_year#c.rdd_lat_lon
{col 23}49.cand_year#c.rdd_lat_lon 61.cand_year#c.rdd_lat_lon
{col 23}66.cand_year#c.rdd_lat_lon 74.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       80{col 38}{space 1}        0{col 50}{result}{space 1}       80{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       40{col 38}{space 1}       10{col 50}{result}{space 1}       30{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.5900872

{txt}added scalar:
                e(F_u) =  {res}12.697908

{txt}added scalar:
             e(mean_y) =  {res}8.058661

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .9378467       .9378467
{txt} 5%    {res} .9378467       .9378467
{txt}10%    {res} .9378467       .9378467       {txt}Obs         {res}      4,158
{txt}25%    {res} 1.421029       .9378467       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.955355                      {txt}Mean          {res} 2.161561
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 3.114293       3.417522
{txt}90%    {res} 3.417522       3.417522       {txt}Variance      {res} .8891027
{txt}95%    {res} 3.417522       3.417522       {txt}Skewness      {res} .1661202
{txt}99%    {res} 3.417522       3.417522       {txt}Kurtosis      {res} 1.334357

{txt}added scalar:
                e(max) =  {res}3.417522

{txt}added scalar:
                e(p50) =  {res}1.9553552

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,082        2017           0       2017       2017
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5082
{txt}{col 55}F(  8,   461) = {res}    0.48
{txt}{col 55}Prob > F      = {res}  0.8671
{txt}Total (centered) SS     = {res}  35019.2628{txt}{col 55}Centered R2   = {res} -0.0046
{txt}Total (uncentered) SS   = {res}  35019.2628{txt}{col 55}Uncentered R2 = {res} -0.0046
{txt}Residual SS             = {res} 35179.32354{txt}{col 55}Root MSE      = {res}   2.687

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0190175{col 42}{space 2} .0431623{col 53}{space 1}   -0.44{col 62}{space 3}0.660{col 70}{space 4}-.1038367{col 83}{space 3} .0658018
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.rle_std {c |}{col 30}{res}{space 2} .0278562{col 42}{space 2}  .041108{col 53}{space 1}    0.68{col 62}{space 3}0.498{col 70}{space 4} -.052926{col 83}{space 3} .1086385
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0075028{col 42}{space 2} .0178942{col 53}{space 1}    0.42{col 62}{space 3}0.675{col 70}{space 4}-.0276615{col 83}{space 3}  .042667
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0054968{col 42}{space 2} .0304119{col 53}{space 1}    0.18{col 62}{space 3}0.857{col 70}{space 4}-.0542663{col 83}{space 3}   .06526
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0074237{col 42}{space 2} .0354543{col 53}{space 1}    0.21{col 62}{space 3}0.834{col 70}{space 4}-.0622483{col 83}{space 3} .0770957
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.rle_std {c |}{col 30}{res}{space 2}-.0015468{col 42}{space 2} .0129375{col 53}{space 1}   -0.12{col 62}{space 3}0.905{col 70}{space 4}-.0269705{col 83}{space 3} .0238769
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.rle_std {c |}{col 30}{res}{space 2}-.0067222{col 42}{space 2} .0217432{col 53}{space 1}   -0.31{col 62}{space 3}0.757{col 70}{space 4}-.0494503{col 83}{space 3} .0360059
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.rle_std {c |}{col 30}{res}{space 2}-.0006911{col 42}{space 2} .0247389{col 53}{space 1}   -0.03{col 62}{space 3}0.978{col 70}{space 4}-.0493061{col 83}{space 3}  .047924
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.982
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0005
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  79.621
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.285
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.rle_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.rle_std c.perc_b_1913_1922#c.rle_std
{col 23}c.perc_b_1923_1932#c.rle_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.rle_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.rle_std c.pop_jew#c.rle_std
{col 23}c.p_french_name#c.rle_std c.lpop_1936#c.rle_std
{col 23}c.perc_fore_1936#c.rle_std c.perc_dial_1936#c.rle_std
{col 23}c.perc_disp_1946#c.rle_std c.water#c.rle_std
{col 23}c.lelevation_mean#c.rle_std c.lelevation_std#c.rle_std
{col 23}c.ldist_de#c.rle_std c.perc_droi_1936_ins#c.rle_std
{col 23}c.lpop_counter#c.rle_std c.perc_fore#c.rle_std
{col 23}c.perc_b_1928_1945#c.rle_std c.perc_b_1946_1964#c.rle_std
{col 23}c.perc_b_1965_1980#c.rle_std c.perc_b_1981_1996#c.rle_std
{col 23}c.perc_male_counter#c.rle_std c.perc_bacplus#c.rle_std
{col 23}c.perc_bluecollar#c.rle_std c.perc_chom#c.rle_std
{col 23}c.lrev_median#c.rle_std c.lrev_sd#c.rle_std
{col 23}2b.cand_year#c.rdd_lat 3.cand_year#c.rdd_lat
{col 23}17.cand_year#c.rdd_lat 26.cand_year#c.rdd_lat
{col 23}27.cand_year#c.rdd_lat 30.cand_year#c.rdd_lat
{col 23}44.cand_year#c.rdd_lat 50.cand_year#c.rdd_lat
{col 23}53.cand_year#c.rdd_lat 62.cand_year#c.rdd_lat
{col 23}67.cand_year#c.rdd_lat 2b.cand_year#c.rdd_lon
{col 23}3.cand_year#c.rdd_lon 17.cand_year#c.rdd_lon
{col 23}26.cand_year#c.rdd_lon 27.cand_year#c.rdd_lon
{col 23}30.cand_year#c.rdd_lon 44.cand_year#c.rdd_lon
{col 23}50.cand_year#c.rdd_lon 53.cand_year#c.rdd_lon
{col 23}62.cand_year#c.rdd_lon 67.cand_year#c.rdd_lon
{col 23}2b.cand_year#c.rdd_lat_2 3.cand_year#c.rdd_lat_2
{col 23}17.cand_year#c.rdd_lat_2 26.cand_year#c.rdd_lat_2
{col 23}27.cand_year#c.rdd_lat_2 30.cand_year#c.rdd_lat_2
{col 23}44.cand_year#c.rdd_lat_2 50.cand_year#c.rdd_lat_2
{col 23}53.cand_year#c.rdd_lat_2 62.cand_year#c.rdd_lat_2
{col 23}67.cand_year#c.rdd_lat_2 2b.cand_year#c.rdd_lon_2
{col 23}3.cand_year#c.rdd_lon_2 17.cand_year#c.rdd_lon_2
{col 23}26.cand_year#c.rdd_lon_2 27.cand_year#c.rdd_lon_2
{col 23}30.cand_year#c.rdd_lon_2 44.cand_year#c.rdd_lon_2
{col 23}50.cand_year#c.rdd_lon_2 53.cand_year#c.rdd_lon_2
{col 23}62.cand_year#c.rdd_lon_2 67.cand_year#c.rdd_lon_2
{col 23}2b.cand_year#c.rdd_lat_lon 3.cand_year#c.rdd_lat_lon
{col 23}17.cand_year#c.rdd_lat_lon 26.cand_year#c.rdd_lat_lon
{col 23}27.cand_year#c.rdd_lat_lon 30.cand_year#c.rdd_lat_lon
{col 23}44.cand_year#c.rdd_lat_lon 50.cand_year#c.rdd_lat_lon
{col 23}53.cand_year#c.rdd_lat_lon 62.cand_year#c.rdd_lat_lon
{col 23}67.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       88{col 38}{space 1}        0{col 50}{result}{space 1}       88{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       44{col 38}{space 1}       11{col 50}{result}{space 1}       33{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.2849287

{txt}added scalar:
                e(F_u) =  {res}11.982442

{txt}added scalar:
             e(mean_y) =  {res}7.2146001

         {txt}Right-wing \citep[][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .6316419       .3530807
{txt} 5%    {res}  .910203       .3530807
{txt}10%    {res}  .910203       .3530807       {txt}Obs         {res}      4,158
{txt}25%    {res}  1.16684       .3530807       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 2.592971                      {txt}Mean          {res} 2.290581
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 2.719445       3.534603
{txt}90%    {res} 3.534603       3.534603       {txt}Variance      {res} .8891027
{txt}95%    {res} 3.534603       3.534603       {txt}Skewness      {res}-.3747599
{txt}99%    {res} 3.534603       3.534603       {txt}Kurtosis      {res} 1.817851

{txt}added scalar:
                e(max) =  {res}3.5346031

{txt}added scalar:
                e(p50) =  {res}2.5929713

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      2,772        1965           0       1965       1965
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.abs_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    2772
{txt}{col 55}F(  8,   461) = {res}    5.17
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 76614.24155{txt}{col 55}Centered R2   = {res} -0.0712
{txt}Total (uncentered) SS   = {res} 76614.24155{txt}{col 55}Uncentered R2 = {res} -0.0712
{txt}Residual SS             = {res} 82071.45506{txt}{col 55}Root MSE      = {res}    5.57

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.7108286{col 42}{space 2}  .148515{col 53}{space 1}   -4.79{col 62}{space 3}0.000{col 70}{space 4}-1.002679{col 83}{space 3}-.4189783
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .3062119{col 42}{space 2}  .081262{col 53}{space 1}    3.77{col 62}{space 3}0.000{col 70}{space 4} .1465222{col 83}{space 3} .4659017
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .1195167{col 42}{space 2} .0476202{col 53}{space 1}    2.51{col 62}{space 3}0.012{col 70}{space 4} .0259371{col 83}{space 3} .2130962
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0788779{col 42}{space 2}  .091475{col 53}{space 1}    0.86{col 62}{space 3}0.389{col 70}{space 4}-.1008818{col 83}{space 3} .2586376
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .1143849{col 42}{space 2} .0822581{col 53}{space 1}    1.39{col 62}{space 3}0.165{col 70}{space 4}-.0472623{col 83}{space 3} .2760322
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2}-.0525022{col 42}{space 2}  .024624{col 53}{space 1}   -2.13{col 62}{space 3}0.034{col 70}{space 4}-.1008914{col 83}{space 3}-.0041129
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0585203{col 42}{space 2} .0471533{col 53}{space 1}   -1.24{col 62}{space 3}0.215{col 70}{space 4}-.1511823{col 83}{space 3} .0341418
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.0300934{col 42}{space 2} .0471183{col 53}{space 1}   -0.64{col 62}{space 3}0.523{col 70}{space 4}-.1226867{col 83}{space 3} .0624998
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.539
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  42.299
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.455
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_male_counter#c.abs_std
{col 23}c.perc_bacplus#c.abs_std c.perc_bluecollar#c.abs_std
{col 23}c.perc_chom#c.abs_std c.lrev_median#c.abs_std
{col 23}c.lrev_sd#c.abs_std 5b.cand_year#c.rdd_lat
{col 23}51.cand_year#c.rdd_lat 56.cand_year#c.rdd_lat
{col 23}57.cand_year#c.rdd_lat 78.cand_year#c.rdd_lat
{col 23}81.cand_year#c.rdd_lat 5b.cand_year#c.rdd_lon
{col 23}51.cand_year#c.rdd_lon 56.cand_year#c.rdd_lon
{col 23}57.cand_year#c.rdd_lon 78.cand_year#c.rdd_lon
{col 23}81.cand_year#c.rdd_lon 5b.cand_year#c.rdd_lat_2
{col 23}51.cand_year#c.rdd_lat_2 56.cand_year#c.rdd_lat_2
{col 23}57.cand_year#c.rdd_lat_2 78.cand_year#c.rdd_lat_2
{col 23}81.cand_year#c.rdd_lat_2 5b.cand_year#c.rdd_lon_2
{col 23}51.cand_year#c.rdd_lon_2 56.cand_year#c.rdd_lon_2
{col 23}57.cand_year#c.rdd_lon_2 78.cand_year#c.rdd_lon_2
{col 23}81.cand_year#c.rdd_lon_2 5b.cand_year#c.rdd_lat_lon
{col 23}51.cand_year#c.rdd_lat_lon 56.cand_year#c.rdd_lat_lon
{col 23}57.cand_year#c.rdd_lat_lon 78.cand_year#c.rdd_lat_lon
{col 23}81.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.abs_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       48{col 38}{space 1}        0{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       24{col 38}{space 1}        6{col 50}{result}{space 1}       18{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.4549835

{txt}added scalar:
                e(F_u) =  {res}12.539211

{txt}added scalar:
             e(mean_y) =  {res}14.120275

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      2,772
{txt}25%    {res}  .744028              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res}  .744028                      {txt}Mean          {res} 1.078005
                        {txt}Largest       Std. dev.     {res} .9130356
{txt}75%    {res} 2.453104       2.453104
{txt}90%    {res} 2.453104       2.453104       {txt}Variance      {res}  .833634
{txt}95%    {res} 2.453104       2.453104       {txt}Skewness      {res} .5736763
{txt}99%    {res} 2.453104       2.453104       {txt}Kurtosis      {res} 1.877171

{txt}added scalar:
                e(max) =  {res}2.4531038

{txt}added scalar:
                e(p50) =  {res}.74402797

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      3,234        1969           0       1969       1969
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.abs_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    3234
{txt}{col 55}F(  8,   461) = {res}    4.39
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 69313.70985{txt}{col 55}Centered R2   = {res} -0.1582
{txt}Total (uncentered) SS   = {res} 69313.70985{txt}{col 55}Uncentered R2 = {res} -0.1582
{txt}Residual SS             = {res} 80277.87909{txt}{col 55}Root MSE      = {res}   5.096

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-1.298548{col 42}{space 2} .2826373{col 53}{space 1}   -4.59{col 62}{space 3}0.000{col 70}{space 4}-1.853965{col 83}{space 3}-.7431305
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .5284022{col 42}{space 2} .1242683{col 53}{space 1}    4.25{col 62}{space 3}0.000{col 70}{space 4} .2841997{col 83}{space 3} .7726046
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .1609236{col 42}{space 2} .0934167{col 53}{space 1}    1.72{col 62}{space 3}0.086{col 70}{space 4}-.0226517{col 83}{space 3} .3444988
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}  .100903{col 42}{space 2} .1783767{col 53}{space 1}    0.57{col 62}{space 3}0.572{col 70}{space 4}-.2496291{col 83}{space 3} .4514352
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .3196348{col 42}{space 2} .1523292{col 53}{space 1}    2.10{col 62}{space 3}0.036{col 70}{space 4} .0202892{col 83}{space 3} .6189805
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2}-.0653498{col 42}{space 2} .0411298{col 53}{space 1}   -1.59{col 62}{space 3}0.113{col 70}{space 4}-.1461751{col 83}{space 3} .0154754
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0441652{col 42}{space 2} .0771657{col 53}{space 1}   -0.57{col 62}{space 3}0.567{col 70}{space 4}-.1958053{col 83}{space 3} .1074749
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.1438003{col 42}{space 2} .0683562{col 53}{space 1}   -2.10{col 62}{space 3}0.036{col 70}{space 4}-.2781286{col 83}{space 3} -.009472
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.133
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  53.572
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.751
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_male_counter#c.abs_std
{col 23}c.perc_bacplus#c.abs_std c.perc_bluecollar#c.abs_std
{col 23}c.perc_chom#c.abs_std c.lrev_median#c.abs_std
{col 23}c.lrev_sd#c.abs_std 21b.cand_year#c.rdd_lat
{col 23}22.cand_year#c.rdd_lat 23.cand_year#c.rdd_lat
{col 23}38.cand_year#c.rdd_lat 64.cand_year#c.rdd_lat
{col 23}65.cand_year#c.rdd_lat 69.cand_year#c.rdd_lat
{col 23}21b.cand_year#c.rdd_lon 22.cand_year#c.rdd_lon
{col 23}23.cand_year#c.rdd_lon 38.cand_year#c.rdd_lon
{col 23}64.cand_year#c.rdd_lon 65.cand_year#c.rdd_lon
{col 23}69.cand_year#c.rdd_lon 21b.cand_year#c.rdd_lat_2
{col 23}22.cand_year#c.rdd_lat_2 23.cand_year#c.rdd_lat_2
{col 23}38.cand_year#c.rdd_lat_2 64.cand_year#c.rdd_lat_2
{col 23}65.cand_year#c.rdd_lat_2 69.cand_year#c.rdd_lat_2
{col 23}21b.cand_year#c.rdd_lon_2 22.cand_year#c.rdd_lon_2
{col 23}23.cand_year#c.rdd_lon_2 38.cand_year#c.rdd_lon_2
{col 23}64.cand_year#c.rdd_lon_2 65.cand_year#c.rdd_lon_2
{col 23}69.cand_year#c.rdd_lon_2 21b.cand_year#c.rdd_lat_lon
{col 23}22.cand_year#c.rdd_lat_lon 23.cand_year#c.rdd_lat_lon
{col 23}38.cand_year#c.rdd_lat_lon 64.cand_year#c.rdd_lat_lon
{col 23}65.cand_year#c.rdd_lat_lon 69.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
Dropped collinear:{col 23}c.perc_b_1981_1996#c.abs_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       56{col 38}{space 1}        0{col 50}{result}{space 1}       56{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       28{col 38}{space 1}        7{col 50}{result}{space 1}       21{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.7509461

{txt}added scalar:
                e(F_u) =  {res}13.133181

{txt}added scalar:
             e(mean_y) =  {res}10.910314

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      3,234
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res} 461.999991

{txt}50%    {res} .6530598                      {txt}Mean          {res} .9328926
                        {txt}Largest       Std. dev.     {res} .9259633
{txt}75%    {res} 2.237125        2.36593
{txt}90%    {res}  2.36593        2.36593       {txt}Variance      {res}  .857408
{txt}95%    {res}  2.36593        2.36593       {txt}Skewness      {res}  .645063
{txt}99%    {res}  2.36593        2.36593       {txt}Kurtosis      {res} 1.786063

{txt}added scalar:
                e(max) =  {res}2.3659296

{txt}added scalar:
                e(p50) =  {res}.65305984

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        1974           0       1974       1974
{txt}Warning - collinearities detected
Vars dropped:{col 21}c.perc_b_1981_1996#c.abs_std
{res}{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    4.55
{txt}{col 55}Prob > F      = {res}  0.0000
{txt}Total (centered) SS     = {res} 92002.53712{txt}{col 55}Centered R2   = {res} -0.2228
{txt}Total (uncentered) SS   = {res} 92002.53712{txt}{col 55}Uncentered R2 = {res} -0.2228
{txt}Residual SS             = {res}   112503.46{txt}{col 55}Root MSE      = {res}   4.598

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-1.222656{col 42}{space 2} .2359996{col 53}{space 1}   -5.18{col 62}{space 3}0.000{col 70}{space 4}-1.686425{col 83}{space 3} -.758888
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .7893847{col 42}{space 2} .1641751{col 53}{space 1}    4.81{col 62}{space 3}0.000{col 70}{space 4} .4667604{col 83}{space 3} 1.112009
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .1482993{col 42}{space 2} .0741056{col 53}{space 1}    2.00{col 62}{space 3}0.046{col 70}{space 4} .0026728{col 83}{space 3} .2939259
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0954717{col 42}{space 2} .1499875{col 53}{space 1}    0.64{col 62}{space 3}0.525{col 70}{space 4}-.1992721{col 83}{space 3} .3902155
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .1117809{col 42}{space 2} .1233802{col 53}{space 1}    0.91{col 62}{space 3}0.365{col 70}{space 4}-.1306765{col 83}{space 3} .3542382
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2} -.087721{col 42}{space 2} .0468905{col 53}{space 1}   -1.87{col 62}{space 3}0.062{col 70}{space 4}-.1798665{col 83}{space 3} .0044246
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0472447{col 42}{space 2} .0954605{col 53}{space 1}   -0.49{col 62}{space 3}0.621{col 70}{space 4}-.2348364{col 83}{space 3} .1403469
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.0783958{col 42}{space 2} .0775657{col 53}{space 1}   -1.01{col 62}{space 3}0.313{col 70}{space 4} -.230822{col 83}{space 3} .0740304
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.956
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  95.402
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.991
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_male_counter#c.abs_std
{col 23}c.perc_bacplus#c.abs_std c.perc_bluecollar#c.abs_std
{col 23}c.perc_chom#c.abs_std c.lrev_median#c.abs_std
{col 23}c.lrev_sd#c.abs_std 14b.cand_year#c.rdd_lat
{col 23}24.cand_year#c.rdd_lat 28.cand_year#c.rdd_lat
{col 23}31.cand_year#c.rdd_lat 39.cand_year#c.rdd_lat
{col 23}40.cand_year#c.rdd_lat 45.cand_year#c.rdd_lat
{col 23}58.cand_year#c.rdd_lat 59.cand_year#c.rdd_lat
{col 23}68.cand_year#c.rdd_lat 71.cand_year#c.rdd_lat
{col 23}76.cand_year#c.rdd_lat 14b.cand_year#c.rdd_lon
{col 23}24.cand_year#c.rdd_lon 28.cand_year#c.rdd_lon
{col 23}31.cand_year#c.rdd_lon 39.cand_year#c.rdd_lon
{col 23}40.cand_year#c.rdd_lon 45.cand_year#c.rdd_lon
{col 23}58.cand_year#c.rdd_lon 59.cand_year#c.rdd_lon
{col 23}68.cand_year#c.rdd_lon 71.cand_year#c.rdd_lon
{col 23}76.cand_year#c.rdd_lon 14b.cand_year#c.rdd_lat_2
{col 23}24.cand_year#c.rdd_lat_2 28.cand_year#c.rdd_lat_2
{col 23}31.cand_year#c.rdd_lat_2 39.cand_year#c.rdd_lat_2
{col 23}40.cand_year#c.rdd_lat_2 45.cand_year#c.rdd_lat_2
{col 23}58.cand_year#c.rdd_lat_2 59.cand_year#c.rdd_lat_2
{col 23}68.cand_year#c.rdd_lat_2 71.cand_year#c.rdd_lat_2
{col 23}76.cand_year#c.rdd_lat_2 14b.cand_year#c.rdd_lon_2
{col 23}24.cand_year#c.rdd_lon_2 28.cand_year#c.rdd_lon_2
{col 23}31.cand_year#c.rdd_lon_2 39.cand_year#c.rdd_lon_2
{col 23}40.cand_year#c.rdd_lon_2 45.cand_year#c.rdd_lon_2
{col 23}58.cand_year#c.rdd_lon_2 59.cand_year#c.rdd_lon_2
{col 23}68.cand_year#c.rdd_lon_2 71.cand_year#c.rdd_lon_2
{col 23}76.cand_year#c.rdd_lon_2 14b.cand_year#c.rdd_lat_lon
{col 23}24.cand_year#c.rdd_lat_lon 28.cand_year#c.rdd_lat_lon
{col 23}31.cand_year#c.rdd_lat_lon 39.cand_year#c.rdd_lat_lon
{col 23}40.cand_year#c.rdd_lat_lon 45.cand_year#c.rdd_lat_lon
{col 23}58.cand_year#c.rdd_lat_lon 59.cand_year#c.rdd_lat_lon
{col 23}68.cand_year#c.rdd_lat_lon 71.cand_year#c.rdd_lat_lon
{col 23}76.cand_year#c.rdd_lat_lon _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
Dropped collinear:{col 23}c.perc_b_1981_1996#c.abs_std
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.990841

{txt}added scalar:
                e(F_u) =  {res}12.956384

{txt}added scalar:
             e(mean_y) =  {res}6.9111711

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      5,544
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res} 462.000001

{txt}50%    {res} 1.229567                      {txt}Mean          {res} 1.031771
                        {txt}Largest       Std. dev.     {res} .9575135
{txt}75%    {res} 1.989659       2.136937
{txt}90%    {res} 1.989659       2.136937       {txt}Variance      {res} .9168321
{txt}95%    {res} 1.989659       2.136937       {txt}Skewness      {res}-.0226642
{txt}99%    {res} 2.136937       2.136937       {txt}Kurtosis      {res} 1.040414

{txt}added scalar:
                e(max) =  {res}2.1369369

{txt}added scalar:
                e(p50) =  {res}1.2295669

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,158        1995           0       1995       1995
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4158
{txt}{col 55}F(  8,   461) = {res}    1.21
{txt}{col 55}Prob > F      = {res}  0.2898
{txt}Total (centered) SS     = {res} 43298.17357{txt}{col 55}Centered R2   = {res} -0.0160
{txt}Total (uncentered) SS   = {res} 43298.17357{txt}{col 55}Uncentered R2 = {res} -0.0160
{txt}Residual SS             = {res}  43989.6719{txt}{col 55}Root MSE      = {res}   3.324

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.2495011{col 42}{space 2} .0874786{col 53}{space 1}   -2.85{col 62}{space 3}0.005{col 70}{space 4}-.4214074{col 83}{space 3}-.0775948
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .1588771{col 42}{space 2} .0571901{col 53}{space 1}    2.78{col 62}{space 3}0.006{col 70}{space 4} .0464916{col 83}{space 3} .2712626
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0072437{col 42}{space 2}  .036643{col 53}{space 1}    0.20{col 62}{space 3}0.843{col 70}{space 4}-.0647643{col 83}{space 3} .0792517
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0386784{col 42}{space 2} .0770882{col 53}{space 1}    0.50{col 62}{space 3}0.616{col 70}{space 4}-.1128094{col 83}{space 3} .1901663
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0236884{col 42}{space 2}  .066619{col 53}{space 1}    0.36{col 62}{space 3}0.722{col 70}{space 4}-.1072262{col 83}{space 3} .1546029
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2} -.001105{col 42}{space 2} .0206093{col 53}{space 1}   -0.05{col 62}{space 3}0.957{col 70}{space 4}-.0416049{col 83}{space 3} .0393948
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0225766{col 42}{space 2} .0449514{col 53}{space 1}   -0.50{col 62}{space 3}0.616{col 70}{space 4}-.1109116{col 83}{space 3} .0657584
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.0092423{col 42}{space 2}  .038295{col 53}{space 1}   -0.24{col 62}{space 3}0.809{col 70}{space 4}-.0844967{col 83}{space 3}  .066012
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.358
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  67.289
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.904
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_b_1981_1996#c.abs_std
{col 23}c.perc_male_counter#c.abs_std c.perc_bacplus#c.abs_std
{col 23}c.perc_bluecollar#c.abs_std c.perc_chom#c.abs_std
{col 23}c.lrev_median#c.abs_std c.lrev_sd#c.abs_std
{col 23}4b.cand_year#c.rdd_lat 15.cand_year#c.rdd_lat
{col 23}19.cand_year#c.rdd_lat 33.cand_year#c.rdd_lat
{col 23}36.cand_year#c.rdd_lat 41.cand_year#c.rdd_lat
{col 23}46.cand_year#c.rdd_lat 79.cand_year#c.rdd_lat
{col 23}82.cand_year#c.rdd_lat 4b.cand_year#c.rdd_lon
{col 23}15.cand_year#c.rdd_lon 19.cand_year#c.rdd_lon
{col 23}33.cand_year#c.rdd_lon 36.cand_year#c.rdd_lon
{col 23}41.cand_year#c.rdd_lon 46.cand_year#c.rdd_lon
{col 23}79.cand_year#c.rdd_lon 82.cand_year#c.rdd_lon
{col 23}4b.cand_year#c.rdd_lat_2 15.cand_year#c.rdd_lat_2
{col 23}19.cand_year#c.rdd_lat_2 33.cand_year#c.rdd_lat_2
{col 23}36.cand_year#c.rdd_lat_2 41.cand_year#c.rdd_lat_2
{col 23}46.cand_year#c.rdd_lat_2 79.cand_year#c.rdd_lat_2
{col 23}82.cand_year#c.rdd_lat_2 4b.cand_year#c.rdd_lon_2
{col 23}15.cand_year#c.rdd_lon_2 19.cand_year#c.rdd_lon_2
{col 23}33.cand_year#c.rdd_lon_2 36.cand_year#c.rdd_lon_2
{col 23}41.cand_year#c.rdd_lon_2 46.cand_year#c.rdd_lon_2
{col 23}79.cand_year#c.rdd_lon_2 82.cand_year#c.rdd_lon_2
{col 23}4b.cand_year#c.rdd_lat_lon 15.cand_year#c.rdd_lat_lon
{col 23}19.cand_year#c.rdd_lat_lon 33.cand_year#c.rdd_lat_lon
{col 23}36.cand_year#c.rdd_lat_lon 41.cand_year#c.rdd_lat_lon
{col 23}46.cand_year#c.rdd_lat_lon 79.cand_year#c.rdd_lat_lon
{col 23}82.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       72{col 38}{space 1}        0{col 50}{result}{space 1}       72{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       36{col 38}{space 1}        9{col 50}{result}{space 1}       27{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.9038785

{txt}added scalar:
                e(F_u) =  {res}13.357566

{txt}added scalar:
             e(mean_y) =  {res}8.9124834

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .2252232              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.041352                      {txt}Mean          {res} 1.067009
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 2.059227       3.139874
{txt}90%    {res} 2.359559       3.139874       {txt}Variance      {res} .8891026
{txt}95%    {res} 2.374557       3.139874       {txt}Skewness      {res} .2366332
{txt}99%    {res} 2.374557       3.139874       {txt}Kurtosis      {res} 1.500107

{txt}added scalar:
                e(max) =  {res}3.1398735

{txt}added scalar:
                e(p50) =  {res}1.041352

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      7,392        2002           0       2002       2002
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    7392
{txt}{col 55}F(  8,   461) = {res}    2.22
{txt}{col 55}Prob > F      = {res}  0.0246
{txt}Total (centered) SS     = {res}  31025.4866{txt}{col 55}Centered R2   = {res} -0.0122
{txt}Total (uncentered) SS   = {res}  31025.4866{txt}{col 55}Uncentered R2 = {res} -0.0122
{txt}Residual SS             = {res} 31402.70962{txt}{col 55}Root MSE      = {res}   2.102

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .1580722{col 42}{space 2} .0484128{col 53}{space 1}    3.27{col 62}{space 3}0.001{col 70}{space 4}  .062935{col 83}{space 3} .2532093
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2}-.0404863{col 42}{space 2} .0130391{col 53}{space 1}   -3.10{col 62}{space 3}0.002{col 70}{space 4}-.0661098{col 83}{space 3}-.0148628
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} -.022338{col 42}{space 2} .0219066{col 53}{space 1}   -1.02{col 62}{space 3}0.308{col 70}{space 4}-.0653872{col 83}{space 3} .0207111
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} -.029276{col 42}{space 2} .0301158{col 53}{space 1}   -0.97{col 62}{space 3}0.332{col 70}{space 4}-.0884572{col 83}{space 3} .0299052
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0126146{col 42}{space 2} .0318592{col 53}{space 1}   -0.40{col 62}{space 3}0.692{col 70}{space 4}-.0752218{col 83}{space 3} .0499925
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2} .0065541{col 42}{space 2} .0058489{col 53}{space 1}    1.12{col 62}{space 3}0.263{col 70}{space 4}-.0049397{col 83}{space 3} .0180479
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2} .0090601{col 42}{space 2} .0079766{col 53}{space 1}    1.14{col 62}{space 3}0.257{col 70}{space 4} -.006615{col 83}{space 3} .0247352
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2} .0033696{col 42}{space 2}  .008563{col 53}{space 1}    0.39{col 62}{space 3}0.694{col 70}{space 4}-.0134578{col 83}{space 3}  .020197
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.314
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71} 113.354
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.377
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_b_1981_1996#c.abs_std
{col 23}c.perc_male_counter#c.abs_std c.perc_bacplus#c.abs_std
{col 23}c.perc_bluecollar#c.abs_std c.perc_chom#c.abs_std
{col 23}c.lrev_median#c.abs_std c.lrev_sd#c.abs_std
{col 23}6b.cand_year#c.rdd_lat 9.cand_year#c.rdd_lat
{col 23}11.cand_year#c.rdd_lat 18.cand_year#c.rdd_lat
{col 23}20.cand_year#c.rdd_lat 29.cand_year#c.rdd_lat
{col 23}34.cand_year#c.rdd_lat 37.cand_year#c.rdd_lat
{col 23}42.cand_year#c.rdd_lat 47.cand_year#c.rdd_lat
{col 23}52.cand_year#c.rdd_lat 54.cand_year#c.rdd_lat
{col 23}55.cand_year#c.rdd_lat 60.cand_year#c.rdd_lat
{col 23}72.cand_year#c.rdd_lat 77.cand_year#c.rdd_lat
{col 23}6b.cand_year#c.rdd_lon 9.cand_year#c.rdd_lon
{col 23}11.cand_year#c.rdd_lon 18.cand_year#c.rdd_lon
{col 23}20.cand_year#c.rdd_lon 29.cand_year#c.rdd_lon
{col 23}34.cand_year#c.rdd_lon 37.cand_year#c.rdd_lon
{col 23}42.cand_year#c.rdd_lon 47.cand_year#c.rdd_lon
{col 23}52.cand_year#c.rdd_lon 54.cand_year#c.rdd_lon
{col 23}55.cand_year#c.rdd_lon 60.cand_year#c.rdd_lon
{col 23}72.cand_year#c.rdd_lon 77.cand_year#c.rdd_lon
{col 23}6b.cand_year#c.rdd_lat_2 9.cand_year#c.rdd_lat_2
{col 23}11.cand_year#c.rdd_lat_2 18.cand_year#c.rdd_lat_2
{col 23}20.cand_year#c.rdd_lat_2 29.cand_year#c.rdd_lat_2
{col 23}34.cand_year#c.rdd_lat_2 37.cand_year#c.rdd_lat_2
{col 23}42.cand_year#c.rdd_lat_2 47.cand_year#c.rdd_lat_2
{col 23}52.cand_year#c.rdd_lat_2 54.cand_year#c.rdd_lat_2
{col 23}55.cand_year#c.rdd_lat_2 60.cand_year#c.rdd_lat_2
{col 23}72.cand_year#c.rdd_lat_2 77.cand_year#c.rdd_lat_2
{col 23}6b.cand_year#c.rdd_lon_2 9.cand_year#c.rdd_lon_2
{col 23}11.cand_year#c.rdd_lon_2 18.cand_year#c.rdd_lon_2
{col 23}20.cand_year#c.rdd_lon_2 29.cand_year#c.rdd_lon_2
{col 23}34.cand_year#c.rdd_lon_2 37.cand_year#c.rdd_lon_2
{col 23}42.cand_year#c.rdd_lon_2 47.cand_year#c.rdd_lon_2
{col 23}52.cand_year#c.rdd_lon_2 54.cand_year#c.rdd_lon_2
{col 23}55.cand_year#c.rdd_lon_2 60.cand_year#c.rdd_lon_2
{col 23}72.cand_year#c.rdd_lon_2 77.cand_year#c.rdd_lon_2
{col 23}6b.cand_year#c.rdd_lat_lon 9.cand_year#c.rdd_lat_lon
{col 23}11.cand_year#c.rdd_lat_lon 18.cand_year#c.rdd_lat_lon
{col 23}20.cand_year#c.rdd_lat_lon 29.cand_year#c.rdd_lat_lon
{col 23}34.cand_year#c.rdd_lat_lon 37.cand_year#c.rdd_lat_lon
{col 23}42.cand_year#c.rdd_lat_lon 47.cand_year#c.rdd_lat_lon
{col 23}52.cand_year#c.rdd_lat_lon 54.cand_year#c.rdd_lat_lon
{col 23}55.cand_year#c.rdd_lat_lon 60.cand_year#c.rdd_lat_lon
{col 23}72.cand_year#c.rdd_lat_lon 77.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}      128{col 38}{space 1}        0{col 50}{result}{space 1}      128{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       64{col 38}{space 1}       16{col 50}{result}{space 1}       48{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.3769918

{txt}added scalar:
                e(F_u) =  {res}12.314243

{txt}added scalar:
             e(mean_y) =  {res}4.4757881

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res} .9915991              0
{txt} 5%    {res} 1.983198              0
{txt}10%    {res} 2.890931              0       {txt}Obs         {res}      7,392
{txt}25%    {res} 2.977696              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 3.088771                      {txt}Mean          {res} 3.376755
                        {txt}Largest       Std. dev.     {res} .9683114
{txt}75%    {res} 3.495522       6.452561
{txt}90%    {res} 4.746397       6.452561       {txt}Variance      {res}  .937627
{txt}95%    {res} 4.962035       6.452561       {txt}Skewness      {res} .9094984
{txt}99%    {res} 6.452561       6.452561       {txt}Kurtosis      {res} 6.038699

{txt}added scalar:
                e(max) =  {res}6.4525609

{txt}added scalar:
                e(p50) =  {res}3.0887713

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,544        2007           0       2007       2007
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5544
{txt}{col 55}F(  8,   461) = {res}    0.57
{txt}{col 55}Prob > F      = {res}  0.8020
{txt}Total (centered) SS     = {res} 37707.31285{txt}{col 55}Centered R2   = {res} -0.0043
{txt}Total (uncentered) SS   = {res} 37707.31285{txt}{col 55}Uncentered R2 = {res} -0.0043
{txt}Residual SS             = {res} 37869.78533{txt}{col 55}Root MSE      = {res}   2.668

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.0581397{col 42}{space 2} .0389278{col 53}{space 1}   -1.49{col 62}{space 3}0.136{col 70}{space 4}-.1346377{col 83}{space 3} .0183582
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .0142314{col 42}{space 2} .0179954{col 53}{space 1}    0.79{col 62}{space 3}0.429{col 70}{space 4}-.0211317{col 83}{space 3} .0495946
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0081395{col 42}{space 2}  .014537{col 53}{space 1}    0.56{col 62}{space 3}0.576{col 70}{space 4}-.0204275{col 83}{space 3} .0367064
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0124606{col 42}{space 2} .0374332{col 53}{space 1}    0.33{col 62}{space 3}0.739{col 70}{space 4}-.0611002{col 83}{space 3} .0860214
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2}-.0020076{col 42}{space 2} .0399986{col 53}{space 1}   -0.05{col 62}{space 3}0.960{col 70}{space 4}-.0806098{col 83}{space 3} .0765945
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2}-.0035202{col 42}{space 2} .0062587{col 53}{space 1}   -0.56{col 62}{space 3}0.574{col 70}{space 4}-.0158193{col 83}{space 3} .0087789
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0033612{col 42}{space 2} .0154198{col 53}{space 1}   -0.22{col 62}{space 3}0.828{col 70}{space 4} -.033663{col 83}{space 3} .0269407
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2} .0027905{col 42}{space 2} .0164794{col 53}{space 1}    0.17{col 62}{space 3}0.866{col 70}{space 4}-.0295936{col 83}{space 3} .0351745
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  13.337
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0003
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  94.915
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   7.025
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_b_1981_1996#c.abs_std
{col 23}c.perc_male_counter#c.abs_std c.perc_bacplus#c.abs_std
{col 23}c.perc_bluecollar#c.abs_std c.perc_chom#c.abs_std
{col 23}c.lrev_median#c.abs_std c.lrev_sd#c.abs_std
{col 23}7b.cand_year#c.rdd_lat 10.cand_year#c.rdd_lat
{col 23}12.cand_year#c.rdd_lat 13.cand_year#c.rdd_lat
{col 23}43.cand_year#c.rdd_lat 48.cand_year#c.rdd_lat
{col 23}63.cand_year#c.rdd_lat 70.cand_year#c.rdd_lat
{col 23}73.cand_year#c.rdd_lat 75.cand_year#c.rdd_lat
{col 23}80.cand_year#c.rdd_lat 83.cand_year#c.rdd_lat
{col 23}7b.cand_year#c.rdd_lon 10.cand_year#c.rdd_lon
{col 23}12.cand_year#c.rdd_lon 13.cand_year#c.rdd_lon
{col 23}43.cand_year#c.rdd_lon 48.cand_year#c.rdd_lon
{col 23}63.cand_year#c.rdd_lon 70.cand_year#c.rdd_lon
{col 23}73.cand_year#c.rdd_lon 75.cand_year#c.rdd_lon
{col 23}80.cand_year#c.rdd_lon 83.cand_year#c.rdd_lon
{col 23}7b.cand_year#c.rdd_lat_2 10.cand_year#c.rdd_lat_2
{col 23}12.cand_year#c.rdd_lat_2 13.cand_year#c.rdd_lat_2
{col 23}43.cand_year#c.rdd_lat_2 48.cand_year#c.rdd_lat_2
{col 23}63.cand_year#c.rdd_lat_2 70.cand_year#c.rdd_lat_2
{col 23}73.cand_year#c.rdd_lat_2 75.cand_year#c.rdd_lat_2
{col 23}80.cand_year#c.rdd_lat_2 83.cand_year#c.rdd_lat_2
{col 23}7b.cand_year#c.rdd_lon_2 10.cand_year#c.rdd_lon_2
{col 23}12.cand_year#c.rdd_lon_2 13.cand_year#c.rdd_lon_2
{col 23}43.cand_year#c.rdd_lon_2 48.cand_year#c.rdd_lon_2
{col 23}63.cand_year#c.rdd_lon_2 70.cand_year#c.rdd_lon_2
{col 23}73.cand_year#c.rdd_lon_2 75.cand_year#c.rdd_lon_2
{col 23}80.cand_year#c.rdd_lon_2 83.cand_year#c.rdd_lon_2
{col 23}7b.cand_year#c.rdd_lat_lon 10.cand_year#c.rdd_lat_lon
{col 23}12.cand_year#c.rdd_lat_lon 13.cand_year#c.rdd_lat_lon
{col 23}43.cand_year#c.rdd_lat_lon 48.cand_year#c.rdd_lat_lon
{col 23}63.cand_year#c.rdd_lat_lon 70.cand_year#c.rdd_lat_lon
{col 23}73.cand_year#c.rdd_lat_lon 75.cand_year#c.rdd_lat_lon
{col 23}80.cand_year#c.rdd_lat_lon 83.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       96{col 38}{space 1}        0{col 50}{result}{space 1}       96{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       48{col 38}{space 1}       12{col 50}{result}{space 1}       36{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}7.0252252

{txt}added scalar:
                e(F_u) =  {res}13.337417

{txt}added scalar:
             e(mean_y) =  {res}6.9392575

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res} .8002077              0
{txt}10%    {res} .8002077              0       {txt}Obs         {res}      5,544
{txt}25%    {res} .9191423              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.657898                      {txt}Mean          {res} 1.809617
                        {txt}Largest       Std. dev.     {res} .9575134
{txt}75%    {res}  2.53373       5.313539
{txt}90%    {res}  2.53373       5.313539       {txt}Variance      {res}  .916832
{txt}95%    {res} 3.079344       5.313539       {txt}Skewness      {res} 1.029687
{txt}99%    {res} 5.313539       5.313539       {txt}Kurtosis      {res} 5.329437

{txt}added scalar:
                e(max) =  {res}5.313539

{txt}added scalar:
                e(p50) =  {res}1.6578984

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      4,620        2012           0       2012       2012
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    4620
{txt}{col 55}F(  8,   461) = {res}    1.79
{txt}{col 55}Prob > F      = {res}  0.0768
{txt}Total (centered) SS     = {res} 39008.64033{txt}{col 55}Centered R2   = {res} -0.0154
{txt}Total (uncentered) SS   = {res} 39008.64033{txt}{col 55}Uncentered R2 = {res} -0.0154
{txt}Residual SS             = {res} 39610.26742{txt}{col 55}Root MSE      = {res}   2.991

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2} .0271924{col 42}{space 2} .0662972{col 53}{space 1}    0.41{col 62}{space 3}0.682{col 70}{space 4}-.1030898{col 83}{space 3} .1574745
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2}-.0679635{col 42}{space 2} .0356135{col 53}{space 1}   -1.91{col 62}{space 3}0.057{col 70}{space 4}-.1379484{col 83}{space 3} .0020213
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2}   .02746{col 42}{space 2} .0224999{col 53}{space 1}    1.22{col 62}{space 3}0.223{col 70}{space 4}-.0167551{col 83}{space 3}  .071675
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2}-.0088189{col 42}{space 2} .0384381{col 53}{space 1}   -0.23{col 62}{space 3}0.819{col 70}{space 4}-.0843546{col 83}{space 3} .0667168
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0500678{col 42}{space 2}  .036652{col 53}{space 1}    1.37{col 62}{space 3}0.173{col 70}{space 4}-.0219579{col 83}{space 3} .1220936
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2}-.0091973{col 42}{space 2} .0107416{col 53}{space 1}   -0.86{col 62}{space 3}0.392{col 70}{space 4}-.0303058{col 83}{space 3} .0119113
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2} .0135806{col 42}{space 2} .0179968{col 53}{space 1}    0.75{col 62}{space 3}0.451{col 70}{space 4}-.0217853{col 83}{space 3} .0489465
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.0235294{col 42}{space 2}  .017879{col 53}{space 1}   -1.32{col 62}{space 3}0.189{col 70}{space 4}-.0586639{col 83}{space 3}  .011605
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  12.698
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0004
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  73.636
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.590
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_b_1981_1996#c.abs_std
{col 23}c.perc_male_counter#c.abs_std c.perc_bacplus#c.abs_std
{col 23}c.perc_bluecollar#c.abs_std c.perc_chom#c.abs_std
{col 23}c.lrev_median#c.abs_std c.lrev_sd#c.abs_std
{col 23}1b.cand_year#c.rdd_lat 8.cand_year#c.rdd_lat
{col 23}16.cand_year#c.rdd_lat 25.cand_year#c.rdd_lat
{col 23}32.cand_year#c.rdd_lat 35.cand_year#c.rdd_lat
{col 23}49.cand_year#c.rdd_lat 61.cand_year#c.rdd_lat
{col 23}66.cand_year#c.rdd_lat 74.cand_year#c.rdd_lat
{col 23}1b.cand_year#c.rdd_lon 8.cand_year#c.rdd_lon
{col 23}16.cand_year#c.rdd_lon 25.cand_year#c.rdd_lon
{col 23}32.cand_year#c.rdd_lon 35.cand_year#c.rdd_lon
{col 23}49.cand_year#c.rdd_lon 61.cand_year#c.rdd_lon
{col 23}66.cand_year#c.rdd_lon 74.cand_year#c.rdd_lon
{col 23}1b.cand_year#c.rdd_lat_2 8.cand_year#c.rdd_lat_2
{col 23}16.cand_year#c.rdd_lat_2 25.cand_year#c.rdd_lat_2
{col 23}32.cand_year#c.rdd_lat_2 35.cand_year#c.rdd_lat_2
{col 23}49.cand_year#c.rdd_lat_2 61.cand_year#c.rdd_lat_2
{col 23}66.cand_year#c.rdd_lat_2 74.cand_year#c.rdd_lat_2
{col 23}1b.cand_year#c.rdd_lon_2 8.cand_year#c.rdd_lon_2
{col 23}16.cand_year#c.rdd_lon_2 25.cand_year#c.rdd_lon_2
{col 23}32.cand_year#c.rdd_lon_2 35.cand_year#c.rdd_lon_2
{col 23}49.cand_year#c.rdd_lon_2 61.cand_year#c.rdd_lon_2
{col 23}66.cand_year#c.rdd_lon_2 74.cand_year#c.rdd_lon_2
{col 23}1b.cand_year#c.rdd_lat_lon 8.cand_year#c.rdd_lat_lon
{col 23}16.cand_year#c.rdd_lat_lon 25.cand_year#c.rdd_lat_lon
{col 23}32.cand_year#c.rdd_lat_lon 35.cand_year#c.rdd_lat_lon
{col 23}49.cand_year#c.rdd_lat_lon 61.cand_year#c.rdd_lat_lon
{col 23}66.cand_year#c.rdd_lat_lon 74.cand_year#c.rdd_lat_lon
{col 23}_cons
{col 23}nb: total SS, model F and R2s are after partialling-out;
{col 23}    any {help ivreg2##s_small:small-sample adjustments} include partialled-out
{col 23}    variables in regressor count K
{hline 78}
{res}
{txt}Absorbed degrees of freedom:
{res}{col 1}{text}{hline 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       80{col 38}{space 1}        0{col 50}{result}{space 1}       80{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       40{col 38}{space 1}       10{col 50}{result}{space 1}       30{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.5900872

{txt}added scalar:
                e(F_u) =  {res}12.697908

{txt}added scalar:
             e(mean_y) =  {res}8.058661

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .5995982              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.223232                      {txt}Mean          {res} 1.353119
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 2.025447       2.750876
{txt}90%    {res} 2.750876       2.750876       {txt}Variance      {res} .8891027
{txt}95%    {res} 2.750876       2.750876       {txt}Skewness      {res}-.0162406
{txt}99%    {res} 2.750876       2.750876       {txt}Kurtosis      {res} 1.632679

{txt}added scalar:
                e(max) =  {res}2.7508757

{txt}added scalar:
                e(p50) =  {res}1.2232318

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}annee {c |}{res}      5,082        2017           0       2017       2017
{txt}({browse "http://scorreia.com/research/hdfe.pdf":MWFE estimator} converged in 5 iterations)
{res}
{txt}IV (2SLS) estimation
{hline 20}

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

Number of clusters (com) = {col 33}{res}   462{txt}{col 55}Number of obs = {res}    5082
{txt}{col 55}F(  8,   461) = {res}    1.84
{txt}{col 55}Prob > F      = {res}  0.0684
{txt}Total (centered) SS     = {res} 35528.67976{txt}{col 55}Centered R2   = {res} -0.0110
{txt}Total (uncentered) SS   = {res} 35528.67976{txt}{col 55}Uncentered R2 = {res} -0.0110
{txt}Residual SS             = {res}  35919.4313{txt}{col 55}Root MSE      = {res}   2.715

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                    perc_ins{col 30}{c |} Coefficient{col 42}  std. err.{col 54}      t{col 62}   P>|t|{col 70}     [95% con{col 83}f. interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}perc_i_full {c |}{col 30}{res}{space 2}-.1823942{col 42}{space 2} .0635214{col 53}{space 1}   -2.87{col 62}{space 3}0.004{col 70}{space 4}-.3072216{col 83}{space 3}-.0575669
{txt}{space 28} {c |}
{space 5}c.perc_i_full#c.abs_std {c |}{col 30}{res}{space 2} .0649425{col 42}{space 2} .0251609{col 53}{space 1}    2.58{col 62}{space 3}0.010{col 70}{space 4} .0154983{col 83}{space 3} .1143868
{txt}{space 28} {c |}
{space 12}perc_b_1903_1912 {c |}{col 30}{res}{space 2} .0272904{col 42}{space 2} .0221237{col 53}{space 1}    1.23{col 62}{space 3}0.218{col 70}{space 4}-.0161854{col 83}{space 3} .0707662
{txt}{space 12}perc_b_1913_1922 {c |}{col 30}{res}{space 2} .0299085{col 42}{space 2} .0377071{col 53}{space 1}    0.79{col 62}{space 3}0.428{col 70}{space 4}-.0441906{col 83}{space 3} .1040076
{txt}{space 12}perc_b_1923_1932 {c |}{col 30}{res}{space 2} .0192767{col 42}{space 2} .0343042{col 53}{space 1}    0.56{col 62}{space 3}0.574{col 70}{space 4}-.0481353{col 83}{space 3} .0866887
{txt}{space 28} {c |}
c.perc_b_1903_1912#c.abs_std {c |}{col 30}{res}{space 2}-.0074461{col 42}{space 2} .0083872{col 53}{space 1}   -0.89{col 62}{space 3}0.375{col 70}{space 4}-.0239281{col 83}{space 3} .0090358
{txt}{space 28} {c |}
c.perc_b_1913_1922#c.abs_std {c |}{col 30}{res}{space 2}-.0149131{col 42}{space 2} .0141265{col 53}{space 1}   -1.06{col 62}{space 3}0.292{col 70}{space 4}-.0426735{col 83}{space 3} .0128472
{txt}{space 28} {c |}
c.perc_b_1923_1932#c.abs_std {c |}{col 30}{res}{space 2}-.0021813{col 42}{space 2} .0129789{col 53}{space 1}   -0.17{col 62}{space 3}0.867{col 70}{space 4}-.0276864{col 83}{space 3} .0233237
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{help ivreg2##idtest:Underidentification test} (Kleibergen-Paap rk LM statistic):{res}{col 71}  11.982
{txt}{col 52}Chi-sq({res}1{txt}) P-val =  {res}{col 73}0.0005
{txt}{hline 78}
{help ivreg2##widtest:Weak identification test} (Cragg-Donald Wald F statistic):{res}{col 71}  79.621
{txt}                         (Kleibergen-Paap rk Wald F statistic):{res}{col 71}   6.285
{txt}Stock-Yogo weak ID test critical values:{res}{txt}{col 42}10% maximal IV size{res}{col 73}  7.03
{txt}{col 42}15% maximal IV size{res}{col 73}  4.58
{txt}{col 42}20% maximal IV size{res}{col 73}  3.95
{txt}{col 42}25% maximal IV size{res}{col 73}  3.63
{txt}Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
{hline 78}
{help ivreg2##overidtests:Hansen J statistic} (overidentification test of all instruments):{res}{col 71}   0.000
{txt}{col 50}(equation exactly identified)
{hline 78}
Instrumented:{col 23}perc_i_full c.perc_i_full#c.abs_std
Included instruments:{col 23}perc_b_1903_1912 perc_b_1913_1922 perc_b_1923_1932
{col 23}c.perc_b_1903_1912#c.abs_std c.perc_b_1913_1922#c.abs_std
{col 23}c.perc_b_1923_1932#c.abs_std
Excluded instruments:{col 23}perc_e_1908_1927 c.perc_e_1908_1927#c.abs_std
{help ivreg2##partial:Partialled-out}:{col 23}c.perc_prot_church#c.abs_std c.pop_jew#c.abs_std
{col 23}c.p_french_name#c.abs_std c.lpop_1936#c.abs_std
{col 23}c.perc_fore_1936#c.abs_std c.perc_dial_1936#c.abs_std
{col 23}c.perc_disp_1946#c.abs_std c.water#c.abs_std
{col 23}c.lelevation_mean#c.abs_std c.lelevation_std#c.abs_std
{col 23}c.ldist_de#c.abs_std c.perc_droi_1936_ins#c.abs_std
{col 23}c.lpop_counter#c.abs_std c.perc_fore#c.abs_std
{col 23}c.perc_b_1928_1945#c.abs_std c.perc_b_1946_1964#c.abs_std
{col 23}c.perc_b_1965_1980#c.abs_std c.perc_b_1981_1996#c.abs_std
{col 23}c.perc_male_counter#c.abs_std c.perc_bacplus#c.abs_std
{col 23}c.perc_bluecollar#c.abs_std c.perc_chom#c.abs_std
{col 23}c.lrev_median#c.abs_std c.lrev_sd#c.abs_std
{col 23}2b.cand_year#c.rdd_lat 3.cand_year#c.rdd_lat
{col 23}17.cand_year#c.rdd_lat 26.cand_year#c.rdd_lat
{col 23}27.cand_year#c.rdd_lat 30.cand_year#c.rdd_lat
{col 23}44.cand_year#c.rdd_lat 50.cand_year#c.rdd_lat
{col 23}53.cand_year#c.rdd_lat 62.cand_year#c.rdd_lat
{col 23}67.cand_year#c.rdd_lat 2b.cand_year#c.rdd_lon
{col 23}3.cand_year#c.rdd_lon 17.cand_year#c.rdd_lon
{col 23}26.cand_year#c.rdd_lon 27.cand_year#c.rdd_lon
{col 23}30.cand_year#c.rdd_lon 44.cand_year#c.rdd_lon
{col 23}50.cand_year#c.rdd_lon 53.cand_year#c.rdd_lon
{col 23}62.cand_year#c.rdd_lon 67.cand_year#c.rdd_lon
{col 23}2b.cand_year#c.rdd_lat_2 3.cand_year#c.rdd_lat_2
{col 23}17.cand_year#c.rdd_lat_2 26.cand_year#c.rdd_lat_2
{col 23}27.cand_year#c.rdd_lat_2 30.cand_year#c.rdd_lat_2
{col 23}44.cand_year#c.rdd_lat_2 50.cand_year#c.rdd_lat_2
{col 23}53.cand_year#c.rdd_lat_2 62.cand_year#c.rdd_lat_2
{col 23}67.cand_year#c.rdd_lat_2 2b.cand_year#c.rdd_lon_2
{col 23}3.cand_year#c.rdd_lon_2 17.cand_year#c.rdd_lon_2
{col 23}26.cand_year#c.rdd_lon_2 27.cand_year#c.rdd_lon_2
{col 23}30.cand_year#c.rdd_lon_2 44.cand_year#c.rdd_lon_2
{col 23}50.cand_year#c.rdd_lon_2 53.cand_year#c.rdd_lon_2
{col 23}62.cand_year#c.rdd_lon_2 67.cand_year#c.rdd_lon_2
{col 23}2b.cand_year#c.rdd_lat_lon 3.cand_year#c.rdd_lat_lon
{col 23}17.cand_year#c.rdd_lat_lon 26.cand_year#c.rdd_lat_lon
{col 23}27.cand_year#c.rdd_lat_lon 30.cand_year#c.rdd_lat_lon
{col 23}44.cand_year#c.rdd_lat_lon 50.cand_year#c.rdd_lat_lon
{col 23}53.cand_year#c.rdd_lat_lon 62.cand_year#c.rdd_lat_lon
{col 23}67.cand_year#c.rdd_lat_lon _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 24}{c TT}{hline 12}{hline 12}{hline 14}{hline 1}{c TRC}
{col 1}{text}            Absorbed FE{col 25}{c |} Categories{col 38} - Redundant{col 50}  = Num. Coefs{col 65}{c |}
{res}{col 1}{text}{hline 24}{c +}{hline 12}{hline 12}{hline 14}{hline 1}{c RT}
{col 1}{text}   cand_year#borderfe25{col 25}{c |}{space 1}       88{col 38}{space 1}        0{col 50}{result}{space 1}       88{col 64}{text} {col 65}{c |}
{res}{col 1}{text}      dialect#cand_year{col 25}{c |}{space 1}       44{col 38}{space 1}       11{col 50}{result}{space 1}       33{col 64}{text} {col 65}{c |}
{res}{col 1}{text}{hline 24}{c BT}{hline 12}{hline 12}{hline 14}{hline 1}{c BRC}
{res}
{txt}added scalar:
               e(F_fs) =  {res}6.2849287

{txt}added scalar:
                e(F_u) =  {res}11.982442

{txt}added scalar:
             e(mean_y) =  {res}7.2146001

      {txt}Extremist \citep[absolute][scale]{c -(}budge2016party{c )-}
                       (standardized)
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      4,158
{txt}25%    {res} .9294725              0       {txt}Sum of wgt. {res}        462

{txt}50%    {res} 1.580887                      {txt}Mean          {res} 1.313314
                        {txt}Largest       Std. dev.     {res} .9429224
{txt}75%    {res} 2.211803        3.47144
{txt}90%    {res} 2.542537        3.47144       {txt}Variance      {res} .8891026
{txt}95%    {res} 2.542537        3.47144       {txt}Skewness      {res}-.0478069
{txt}99%    {res} 3.006988        3.47144       {txt}Kurtosis      {res} 1.829364

{txt}added scalar:
                e(max) =  {res}3.4714396

{txt}added scalar:
                e(p50) =  {res}1.5808868
{txt}
{com}. 
. ******************************
. ***** 3. TABLE FOR PAPER *****
. ******************************
. 
. // Fixed effects
. 
. estfe . P*, labels(dialect_lux "Historical dialects")
{res}{txt}
{com}. return list

{txt}macros:
        r(indicate_fe) : "{res} "cand_year#borderfe25=0.cand_year#0.borderfe25" "dialect#cand_year=0.dialect#0.cand_year"{txt}"

{com}. 
. // Nationalism
. 
. estout P1965T1_nat_R P1969T1_nat_R P1974T1_nat_R P1995T1_nat_R P2002T1_nat_R P2007T1_nat_R P2012T1_nat_R P2017T1_nat_R ///
>         using "$results\\$program.tex", replace style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///
>         stats(F_fs F_u, lab("\\ First-stage $ F $-statistic" "Underidentification $ F $-statistic") fmt(2 2)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(perc_i_full c.perc_i_full#c.nat_std) ///
>         varlabels(perc_i_full "\\ Conscription proxy (\%)" c.perc_i_full#c.nat_std "[0.3em] Conscription (\%) $ \times $ Nationalism [std]") ////
>         title(Radical and moderate candidates: Other policy positions) ///
>         prehead( ///
>         "\begin{c -(}table{c )-}[h!]" ///
>         "\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 -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Election year{c )-}{c )-}&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1965{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1969{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1974{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1995{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2002{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2007{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2012{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2017{c )-}\\" ///
>         "\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel A: 2SLS Estimates. Nationalist discourse{c )-}\\") ///
>         posthead("\hline") ///
>         prefoot("") ///
>         postfoot("\\")
{res}
\begin{c -(}table{c )-}[h!]
\begin{c -(}center{c )-}
\begin{c -(}threeparttable{c )-}
\begin{c -(}scriptsize{c )-}
\caption{c -(}Radical and moderate candidates: Other policy positions\label{c -(}EEREV-D-21-00004_TableA3{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 -(}1{c )-}{c -(}l{c )-}{c -(}\textit{c -(}Election year{c )-}{c )-}&\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1965{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1969{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1974{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}1995{c )-}  &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2002{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2007{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2012{c )-} &\multicolumn{c -(}1{c )-}{c -(}c{c )-}{c -(}2017{c )-}\\
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel A: 2SLS Estimates. Nationalist discourse{c )-}\\
\hline
\\ Conscription proxy (\%)&       0.431***&       0.153***&       0.144***&     --0.215***&     --0.107***&     --0.061   &     --0.083** &     --0.111*  \\
                    &     (0.096)   &     (0.051)   &     (0.043)   &     (0.057)   &     (0.036)   &     (0.041)   &     (0.037)   &     (0.060)   \\
[0.3em] Conscription (\%) $ \times $ Nationalism [std]&     --0.641***&     --0.320** &     --0.136***&       0.384***&       0.140***&       0.010   &       0.178*  &       0.115   \\
                    &     (0.179)   &     (0.135)   &     (0.036)   &     (0.108)   &     (0.044)   &     (0.067)   &     (0.092)   &     (0.085)   \\

\\ First-stage $ F $-statistic&        6.45   &        6.75   &        6.99   &        6.90   &        6.38   &        7.03   &        6.59   &        6.28   \\
Underidentification $ F $-statistic&       12.54   &       13.13   &       12.96   &       13.36   &       12.31   &       13.34   &       12.70   &       11.98   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableA3.tex"'})

{com}. 
. // Authoritarian
. 
. estout P1965T1_aut_R P1969T1_aut_R P1974T1_aut_R P1995T1_aut_R P2002T1_aut_R P2007T1_aut_R P2012T1_aut_R P2017T1_aut_R ///
>         using "$results\\$program.tex", append style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///       
>         stats(F_fs F_u, lab("\\ First-stage $ F $-statistic" "Underidentification $ F $-statistic") fmt(2 2)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(perc_i_full c.perc_i_full#c.aut_std) ///
>         varlabels(perc_i_full "\\ Conscription proxy (\%)" c.perc_i_full#c.aut_std "[0.3em] Conscription (\%) $ \times $ Authoritarian [std]") ////
>         prehead("%\hline" ///
>         "\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel B: 2SLS Estimates. Authoritarian discourse{c )-}\\") ///
>         posthead("\hline") ///
>         prefoot("") ///
>         postfoot("\\")
{res}
%\hline
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel B: 2SLS Estimates. Authoritarian discourse{c )-}\\
\hline
\\ Conscription proxy (\%)&       0.529***&       0.292***&     --0.036   &     --0.169***&     --0.011   &       0.040   &     --0.096*  &     --0.018   \\
                    &     (0.136)   &     (0.100)   &     (0.039)   &     (0.052)   &     (0.026)   &     (0.036)   &     (0.056)   &     (0.030)   \\
[0.3em] Conscription (\%) $ \times $ Authoritarian [std]&     --0.969***&     --0.504** &     --0.049   &       0.164***&       0.056   &     --0.051   &       0.170** &       0.014   \\
                    &     (0.289)   &     (0.202)   &     (0.037)   &     (0.051)   &     (0.035)   &     (0.044)   &     (0.075)   &     (0.051)   \\

\\ First-stage $ F $-statistic&        6.45   &        6.75   &        6.99   &        6.90   &        6.38   &        7.03   &        6.59   &        6.28   \\
Underidentification $ F $-statistic&       12.54   &       13.13   &       12.96   &       13.36   &       12.31   &       13.34   &       12.70   &       11.98   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableA3.tex"'})

{com}. 
. // Rile
. 
. estout P1965T1_rle_R P1969T1_rle_R P1974T1_rle_R P1995T1_rle_R P2002T1_rle_R P2007T1_rle_R P2012T1_rle_R P2017T1_rle_R ///
>         using "$results\\$program.tex", append style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///       
>         stats(F_fs F_u, lab("\\ First-stage $ F $-statistic" "Underidentification $ F $-statistic") fmt(2 2)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(perc_i_full c.perc_i_full#c.rle_std) ///
>         varlabels(perc_i_full "\\ Conscription proxy (\%)" c.perc_i_full#c.rle_std "[0.3em] Conscription (\%) $ \times $ Right-wing [std]") ////
>         prehead("%\hline" ///
>         "\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel C: 2SLS Estimates. Right-wing discourse \citep[][scale]{c -(}budge2016party{c )-}{c )-}\\") ///
>         posthead("\hline") ///
>         prefoot("") ///
>         postfoot("\\")
{res}
%\hline
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel C: 2SLS Estimates. Right-wing discourse \citep[][scale]{c -(}budge2016party{c )-}{c )-}\\
\hline
\\ Conscription proxy (\%)&       0.328***&       0.089** &       0.185***&     --0.203***&     --0.088** &     --0.106*  &     --0.170** &     --0.019   \\
                    &     (0.090)   &     (0.045)   &     (0.050)   &     (0.063)   &     (0.036)   &     (0.061)   &     (0.081)   &     (0.043)   \\
[0.3em] Conscription (\%) $ \times $ Right-wing [std]&     --0.406***&     --0.352** &     --0.278***&       0.202***&       0.080***&       0.049   &       0.135** &       0.028   \\
                    &     (0.117)   &     (0.172)   &     (0.079)   &     (0.061)   &     (0.028)   &     (0.035)   &     (0.056)   &     (0.041)   \\

\\ First-stage $ F $-statistic&        6.45   &        6.75   &        6.99   &        6.90   &        6.38   &        7.03   &        6.59   &        6.28   \\
Underidentification $ F $-statistic&       12.54   &       13.13   &       12.96   &       13.36   &       12.31   &       13.34   &       12.70   &       11.98   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableA3.tex"'})

{com}. 
. // Extremism
. 
. estout P1965T1_abs_R P1969T1_abs_R P1974T1_abs_R P1995T1_abs_R P2002T1_abs_R P2007T1_abs_R P2012T1_abs_R P2017T1_abs_R ///
>         using "$results\\$program.tex", append style(tex) type label ///
>         cells(b(star fmt(%9.3f)) se(par fmt(%9.3f)))  ///       
>         stats(F_fs F_u, lab("\\ First-stage $ F $-statistic" "Underidentification $ F $-statistic") fmt(2 2)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         keep(perc_i_full c.perc_i_full#c.abs_std) ///
>         varlabels(perc_i_full "\\ Conscription proxy (\%)" c.perc_i_full#c.abs_std "[0.3em] Conscription (\%) $ \times $ Extremism [std]") ////
>         prehead("%\hline" ///
>         "\cmidrule(lr){c -(}2-9{c )-}" ///
>         "& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel D: 2SLS Estimates. Extremist discourse \citep[absolute][scale]{c -(}budge2016party{c )-}{c )-}\\") ///
>         posthead("\hline") ///
>         prefoot("") ///
>         postfoot("\\")
{res}
%\hline
\cmidrule(lr){c -(}2-9{c )-}
& \multicolumn{c -(}8{c )-}{c -(}c{c )-}{c -(}Panel D: 2SLS Estimates. Extremist discourse \citep[absolute][scale]{c -(}budge2016party{c )-}{c )-}\\
\hline
\\ Conscription proxy (\%)&     --0.711***&     --1.299***&     --1.223***&     --0.250***&       0.158***&     --0.058   &       0.027   &     --0.182***\\
                    &     (0.149)   &     (0.283)   &     (0.236)   &     (0.087)   &     (0.048)   &     (0.039)   &     (0.066)   &     (0.064)   \\
[0.3em] Conscription (\%) $ \times $ Extremism [std]&       0.306***&       0.528***&       0.789***&       0.159***&     --0.040***&       0.014   &     --0.068*  &       0.065** \\
                    &     (0.081)   &     (0.124)   &     (0.164)   &     (0.057)   &     (0.013)   &     (0.018)   &     (0.036)   &     (0.025)   \\

\\ First-stage $ F $-statistic&        6.45   &        6.75   &        6.99   &        6.90   &        6.38   &        7.03   &        6.59   &        6.28   \\
Underidentification $ F $-statistic&       12.54   &       13.13   &       12.96   &       13.36   &       12.31   &       13.34   &       12.70   &       11.98   \\
\\
{txt}(output written to {browse  `"D:\Dropbox\SVLACHOS\Research\Malgre_nous\Replication_files\Results\EEREV-D-21-00004_TableA3.tex"'})

{com}. 
. estout P1965T1_abs_R P1969T1_abs_R P1974T1_abs_R P1995T1_abs_R P2002T1_abs_R P2007T1_abs_R P2012T1_abs_R P2017T1_abs_R ///
>         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 dependent variable" "Observations" "Clusters") fmt(2 %9.0fc %9.0fc)) ///
>         collabels(none) ///
>         mlabels(, span none prefix(\multicolumn{c -(}@span{c )-}{c -(}c{c )-}{c -(}) suffix({c )-})) ///
>         msign(--) starlevels(* .1 ** .05 *** .01) ///
>         drop(*) ///     
>         indicate( ///   
>         "Candidate-border segment FE = 0.cand_year#0.borderfe25" ///
>         "Candidate-dialect FE = 0.dialect#0.cand_year" ///
>         "Full set of controls $ \times $ Position [std] = perc_b_1903_1912" ///
>         , labels(Yes )) ///
>         prehead("Candidate-lat-lon polynomial & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd \\ ") ///
>         posthead("") ///
>         prefoot("\hline") ///
>         postfoot("\hline" "\hline" ///
>         "\end{c -(}tabular*{c )-}" /// 
>         "\end{c -(}scriptsize{c )-}" ///
>         "\begin{c -(}tablenotes{c )-}[normal,flushleft]" ///
>         "\begin{c -(}scriptsize{c )-}" ///
>         "\item \emph{c -(}Notes{c )-}:" /// 
>         "2SLS estimates of the effect of conscription into the Wehrmacht on support for radical and moderate candidates. " ///
>         "The unit of observation is a municipality $ \times $ candidate. " ///
>         "Standard errors clustered at the municipality level in parentheses. " ///
>         "Panel A: 2SLS estimates with respect to the degree of nationalist discourse; " ///
>         "Panel B: 2SLS estimates with respect to the degree of authoritarian discourse; " ///
>         "Panel C: 2SLS estimates with respect to the degree of right-wing discourse using the \cite{c -(}budge2016party{c )-} index; " ///
>         "Panel D: 2SLS estimates with respect to the degree of extremist discourse using the absolute value of the \cite{c -(}budge2016party{c )-} index. " ///
>         "Each column presents the estimation for a different election. " ///
>         "All specifications include a candidate-specific quadratic polynomial in latitude and longitude ($ x+y+x^2+y^2+xy $). " ///
>         "First-stage F-statistic is the Kleibergen-Paap rk Wald F-statistic of the first-stage estimation with eligible births as the exogenous instrument. " ///
>         "* 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}
Candidate-lat-lon polynomial & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd & 2nd \\ 

Candidate-border segment FE &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Candidate-dialect FE &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
Full set of controls $ \times $ Position [std] &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   &         Yes   \\
\hline
Mean dependent variable&       14.12   &       10.91   &        6.91   &        8.91   &        4.48   &        6.94   &        8.06   &        7.21   \\
Observations        &       2,772   &       3,234   &       5,544   &       4,158   &       7,392   &       5,544   &       4,620   &       5,082   \\
Clusters            &         462   &         462   &         462   &         462   &         462   &         462   &         462   &         462   \\
\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 )-}:
2SLS estimates of the effect of conscription into the Wehrmacht on support for radical and moderate candidates. 
The unit of observation is a municipality $ \times $ candidate. 
Standard errors clustered at the municipality level in parentheses. 
Panel A: 2SLS estimates with respect to the degree of nationalist discourse; 
Panel B: 2SLS estimates with respect to the degree of authoritarian discourse; 
Panel C: 2SLS estimates with respect to the degree of right-wing discourse using the \cite{c -(}budge2016party{c )-} index; 
Panel D: 2SLS estimates with respect to the degree of extremist discourse using the absolute value of the \cite{c -(}budge2016party{c )-} index. 
Each column presents the estimation for a different election. 
All specifications include a candidate-specific quadratic polynomial in latitude and longitude ($ x+y+x^2+y^2+xy $). 
First-stage F-statistic is the Kleibergen-Paap rk Wald F-statistic of the first-stage estimation with eligible births as the exogenous instrument. 
* 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_TableA3.tex"'})

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