* Replace "DIRECTORY" in the global "user" with own directory global user "DIRECTORY" global dataset "DIRECTORY\dataset" global output "DIRECTORY\output" /// GENERAL DATASET DEFINITION use "$dataset\census 2000original.dta", clear label define spralabel 110 "German" 120 "French" 130 "Italian" 140 "Romansh" 210 "English" 220 "Dutch" 230 "Scandinavian" 240 "Greek" 250 "Spanish" 270 "Slavic" 290 "Czech and Slovak" 284 "Hungarian" 300 "Other languages" label values spra spralabel * Definition of languages categories gen spklang="German" if spra==110 replace spklang="French" if spra==120 replace spklang="Italian" if spra==130 replace spklang="Romansh" if spra==140 replace spklang="English" if spra==210 replace spklang="Dutch" if spra==220 replace spklang="Swedish" if spra==230 & gort==8234 replace spklang="Danish" if spra==230 & gort==8206 replace spklang="Greek" if spra==240 replace spklang="Spanish" if spra==250 replace spklang="Czech" if spra==290 replace spklang="Hungarian" if spra==284 replace spklang="Yugoslavian" if spra==270 & gort==8220 ///identification of mother tongue by census' "other languages" category and birthplace variable replace spklang="Portuguese" if spra==300 & (gort==8231|nati==8231) replace spklang="Finnish" if spra==300 & (gort==8211|nati==8211) replace spklang="Romanian" if spra==300 & (gort==8232|nati==8232) replace spklang="Turkish" if spra==300 & (gort==8239|nati==8239) replace spklang="Mandarin" if spra==300 & (gort==8508|nati==8508) replace spklang="Japanese" if spra==300 & (gort==8515|nati==8515) replace spklang="Arabic" if spra==300 & (gort==8523|gort==8304|gort==8357|nati==8523|nati==8304|nati==8357) replace spklang="Vietnamese" if spra==300 & (gort==8545|nati==8545) replace spklang="Persian" if spra==300 & (gort==8513|nati==8513) replace spklang="Hebrew" if spra==300 & (gort==8514|nati==8514) * merging with Chen's dataset on languages' future reference merge m:1 spklang using "$dataset\chen language ftr.dta", gen(merge_ftr) drop if merge_ftr==2 drop merge_ftr *generation of age squared variable gen altj2=(altj)^2 * generation of female dummy (0 = male; 1 = female) gen double female=0 if gesl==1 replace female=1 if gesl==2 * generation of labour market participation dummy gen double active=0 replace active=1 if ams<=20 * generation of labour market participation dummy gen double employed=0 replace employed=1 if ams<=14 * generation of sector of occupation dummies (1 = agriculture; 2 = industry; 3 = services) gen double worksector=1 if wseko==1 replace worksector=2 if wseko==2 replace worksector=3 if wseko==3 * generation of tertiary education dummy (0 = below tertiary education; 1 = tertiary education) gen tertedu=0 replace tertedu=1 if if (habgh==3|habgh==4) *generation of self-employment dummy (0 = non self-employed; 1 = self-employed) gen self_employed=0 replace self_employed=1 if sthb==1|sthb==2 *generation of religion dummies (see the generation of labels below for the definition of religion categories) gen religion=1 if rel==120|rel==130 replace religion=2 if rel==111|rel==112|rel==113|rel==115 replace religion=3 if rel==300 replace religion=4 if rel==114 replace religion=5 if rel==200 replace religion=6 if rel==140 replace religion=7 if rel==150 replace religion=8 if rel==400 replace religion=9 if rel==500 label define religionlabel 1 "Catholic" 2 "Protestant" 3 "Muslim" 4 "Jeovah" 5 "Jew" 6 "Orthodox" 7 "Other Christians" 8 "Other religions" 9 "Atheist" label values religion religionlabel ** generation of the immigration status categoric variable "imm" (0 = swiss native; 1 = first gen. immigrant; 2 = second gen. imm.) * identification of Swiss natives (imm=0) gen double imm=0 * identification of first generation immigrants as the individuals who are born abroad (imm = 1) replace imm=1 if gort>=8204 *identification of fathers as the male-gendered individuals who live in a household with children and being either the household head or the partner of the household head (0 = not father in the household; 1 = father in the household) gen double father=0 replace father=1 if (female==0 & hhtp>=2211 & hhtp<=2320 & (rphh==1|rphh==2)) *identification of mothers as the female-gendered individuals who live in a household with children and being either the household head or the partner of the household head (0 = not mother in the household; 1 = mother in the household) gen double mother=0 replace mother=1 if (female==1 & hhtp>=2211 & hhtp<=2320 & (rphh==1|rphh==2)) *identification of first-gen. imm. fathers as the first gen. imm. male-gendered individuals who live in a household with children and being either the household head or the partner of the household head (0 = not father in the household; 1 = father in the household) gen double imm_father=0 replace imm_father=1 if (female==0 & imm==1 & hhtp>=2211 & hhtp<=2320 & (rphh==1|rphh==2)) *identification of first-gen. imm. mothers as the first-gen. imm. female-gendered individuals who live in a household with children and being either the household head or the partner of the household head (0 = not mother in the household; 1 = mother in the household) gen double imm_mother=0 replace imm_mother=1 if (female==1 & imm==1 & hhtp>=2211 & hhtp<=2320 & (rphh==1|rphh==2)) sort hhnr * identification of immigrant households (0 = no first-gen. imm. parent; 1 = one of the parents is a first-gen. imm.; 2 = both parents are first-gen. imm.) by hhnr: egen imm_householdfath=sum(imm_father) by hhnr: egen imm_householdmoth=sum(imm_mother) gen imm_household= imm_householdfath + imm_householdmoth * indentification of second generation immigrants (imm=2) as those born in Switzerland and living in a household with both parents who are first-gen. immigrants replace imm=2 if ((gort<8204 & gort!=0 & imm_household==2 & sthh==2)| (gort<8204 & gort!=0 & imm_household==1 & sthh==2 & (hhtp==2310|hhtp==2320))|(gort<8204 & gort!=0 & hmat==2)) * generation of swiss native dummy (if variable imm = 0) gen double native=0 replace native=1 if imm==0 * generation of first-generation immigrants dummy (if variable imm = 1) gen double firstg_imm=0 replace firstg_imm=1 if imm==1 * generation of second-generation immigrants dummy (if variable imm = 2) gen double secg_imm=0 replace secg_imm=1 if imm==2 * generation of immigrants dummy (if variable imm = 1 or imm = 2) gen immigrant=0 replace immigrant=1 if (firstg_imm==1|secg_imm==1) * generation of Swiss nationality dummy (0 = foreing nationality; 1 = swiss nationality) gen double swissnational=0 replace swissnational=1 if hmat==1 * generation of dummy indicating whether the individual has lived in Switzerland for more than 5 years (0 = more than 5 years ; 1 = less than 5 years) gen double years5abroad=0 replace years5abroad=1 if wor5>=8204 replace years5abroad=0 if native==1|secg_imm==1 * generation of child dummy gen double child=0 replace child=1 if sthh==2 * generation of variable for number of children in the household sort hhnr by hhnr: egen household_nchild=sum(child) gen hh_child=0 if household_nchild==0 replace hh_child=1 if household_nchild>0 * generation of marriage status dummy (0 = not married; 1 = married) gen double married=0 replace married=1 if zivl>1 * generation of dummies identifying Swiss native by spoken language foreach i in German French Italian Romansh{ gen `i'_native=0 replace `i'_native=1 if spklang=="`i'" & native==1 } * generation of dummy identifying speakers of one of Swiss native languages gen natspk=0 replace natspk=1 if spklang=="German"|spklang=="French"|spklang=="Italian"|spklang=="Romansh" * generation of dummy identifying first-gen. imm. speaking of one of Swiss native languages gen natspk_imm=0 if firstg_imm==1 replace natspk_imm=1 if firstg_imm==1 & (spklang=="German"|spklang=="French"|spklang=="Italian"|spklang=="Romansh") * generation of dummy identifying second-gen. imm. speaking of one of Swiss native languages gen natspk_secgimm=0 if secg_imm==1 replace natspk_secgimm=1 if secg_imm==1 & (spklang=="German"|spklang=="French"|spklang=="Italian"|spklang=="Romansh") * generation of continent of birth dummies gen continent=1 if gort>=8204 & gort<=8290 replace continent=2 if gort>=8304 & gort<=8390 replace continent=3 if (gort==8423|gort==8439) replace continent=4 if (gort==8407|gort==8490) replace continent=5 if gort>=8508 & gort<=8590 replace continent=6 if gort==8690 gen continent_nat=1 if nati>=8204 & nati<=8290 replace continent_nat=2 if nati>=8304 & nati<=8390 replace continent_nat=3 if (nati==8423|nati==8439) replace continent_nat=4 if (nati==8407|nati==8490) replace continent_nat=5 if nati>=8508 & nati<=8590 replace continent_nat=6 if nati==8690 * generation of elementary occupations dummy (0 = rest of occupations; 1 = elementary occupations) gen lowsk_occ=0 replace lowsk_occ=1 if (isco>=5000 & isco<=5220)|(isco>=9000 & isco<=9330) * recode from prediction future reference (Chen, 2013) to weak future reference - it's just a differet way to label future reference gen weak_ftr=0 if prediction_ftr==1 replace weak_ftr=1 if prediction_ftr==0 *gen variable indicating area of birth gen aor1=gort if firstg_imm==1 replace aor1=nati if secg_imm==1 replace aor1=district_gort if native==1 * generation of dummy identifying individuals living in multilingual cantons gen multil_kant=0 replace multil_kant=1 if kant==2|kant==10|kant==18|kant==23 * generation of dummy identifying individuals living in multilingual cantons (Grabunden excluded) gen multil_kant_nograb=0 replace multil_kant_nograb=1 if kant==2|kant==10|kant==23 * drop unnecessary variables drop zjhr fbnr zgdo ams sgde wo5k gortk hmat aort sort zivl rel rphh sthh hhtp aper kams erlb pber sthb pens wart wseko refo sopk awegz awegh avemi swegz swegh svemi habgh habag habgb habgn rart zkrs1 mua name_mua BEZIRK KT area _merge replace weak_ftr=0 if spklang=="Yugoslavian" replace weak_ftr=1 if spklang=="Danish" /// if you want to get statistics on linguistic majorities and export to a separate dataset, execute the do-file below (used for Qgis analysis) *preserve * do "`user'\do files paper language dropbox\language majorities zgde dis.do" *restore /// merge with dataset of linguistic majorities in municipality of birth (gort) * Merging with datasets created on Qgis to identify those administrative units at linsuistic border or their distance from bordering countries merge m:1 district using "$dataset\districts at linguistic border 60tr multiling cantons.dta", nogenerate merge m:1 zgde using "$dataset\municipalities at linguistic border 60tr multiling cantons.dta", nogenerate merge m:1 zgde using "$dataset\municipalities at linguistic border 50tr multiling cantons.dta", nogenerate * Merging with WALS dataset on language features merge m:1 spklang using "$dataset\language features.dta", gen(merge_feat) drop if missing(spklang) * generate new dataset with the new variables that have been created save "$output\temp.dta", replace * Generation of variable indicating the linguistic majority by municipality bys zgde: egen natpop_zgde=total(native==1) bys zgde: egen germspk_natpop_zgde=total(native==1 & spklang=="German") bys zgde: egen frenspk_natpop_zgde=total(native==1 & spklang=="French") bys zgde: egen italspk_natpop_zgde=total(native==1 & spklang=="Italian") bys zgde: egen romaspk_natpop_zgde=total(native==1 & spklang=="Romansh") foreach i in germ fren ital roma{ gen sh_`i'spk_natpop_zgde=`i'spk_natpop_zgde/natpop_zgde } gen majspklang_nat_zgde=1 if germspk_natpop_zgde>frenspk_natpop_zgde & germspk_natpop_zgde>italspk_natpop_zgde & germspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=2 if frenspk_natpop_zgde>germspk_natpop_zgde & frenspk_natpop_zgde>italspk_natpop_zgde & frenspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=3 if italspk_natpop_zgde>germspk_natpop_zgde & italspk_natpop_zgde>frenspk_natpop_zgde & italspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=4 if romaspk_natpop_zgde>germspk_natpop_zgde & romaspk_natpop_zgde>frenspk_natpop_zgde & romaspk_natpop_zgde>italspk_natpop_zgde * keep first generation immigrants keep if firstg_imm==1 replace lowsk_occ = . if isco==9999 * generation of language stayers and switchers dummies gen switcher = natspk == 1 gen stayer = switcher == 0 * definition of Swiss lingustic areas categorical variable gen germanarea = (kant!=21 & kant!=18 & kant!=10 & (kant<22 | kant>26)) gen frencharea = (kant==26 | kant==24 | kant==22 | kant==23 | kant==25 | kant== 10) gen italianarea = (kant==21 | kant==18) * generation of variable indicating modal language by country (only among language stayers) bysort aor1 switcher: egen modal_lang = mode(spklang), maxmode * remove linguistic outliers (i.e., if language stayers but not speaking the modal language in the country) drop if spklang != modal_lang & switcher == 0 label var self_employed "Self empl." label var weak_ftr "Weak FTR" label var switcher "Switcher" label var stayer "Stayer" label var altj "Age" label var household_nchild "# childs" label var tertedu "High skilled" label var years5abroad "Less than 5 years in SWZ" label var swissnational "Citizen" label define ftr 0 "strong-FTR" 1 "weak-FTR" label values weak_ftr ftr * keep if age between 25 and 70 years old keep if altj>=25 & altj<=70 * generation of std-errors cluster variable (i.e., country of birth by Swiss linguistic areas) egen aor1_maj=group(aor1 majspklang_nat_zgde) * drop speakers of Yugoslavian and Danish language (because WALS linguistic features are not available) drop if spklang=="Yugoslavian"|spklang=="Danish" drop if gort==8290 /* Drop observations from European country of birth without further indication */ drop if aor1 == 8999 /* Drop observations from foreign country of birth without further indication */ * drop observations from countries where one of Swiss languages is an official language (for example, Germany, France, etc. - see in the main text for the complete list) drop if aor1==8218|aor1==8222 | aor1==8229 | aor1==8207|aor1==8212 | aor1==8204 | aor1==8304 | aor1==8357 | aor1==8423 * drop observations living in Switzerland for less than 5 years drop if years5abroad == 1 gen other_africa = aor1== 8390 gen other_america = aor1== 8490 gen other_asia = aor1== 8590 gen oceania = aor1== 8690 * generation of interaction between weak_FTR and switcher indicator gen weak_FTR_switcher = weak_ftr == 1 & switcher ==1 label var weak_FTR_switcher "Weak FTR $ \times $ Switcher" * We here remove 38 more linguistic outliers. These are 33 individuals born in Poland, and 5 born in Cambodia, who speak English (Their inclusion does not alter the results) drop if switcher == 0 & (aor1 == 8230 | aor1 == 8518) * save new dataset with the new variables that have been created containing first generation immigrants only save "$output\temp_firstgen.dta", replace global sdcluster = "aor1_maj" use "$output\temp_firstgen.dta", clear ********************** * Summary Statistics * ********************** preserve keep if weak_ftr!=. reghdfe self_employed weak_ftr switcher $contr if employed==1, absorb(district aor1) vce(cluster $sdcluster) keep if e(sample)==1 gen constant = "Total" label define t 1 "Total" gen constant2 = 1 label values constant2 t * Table 1 - The sample encode spklang, gen(LLL) * Generation of descriptive statistics of languages in the sample across type of future reference (weak vs strong ftr) bysort weak_ftr : tab spklang self_employed tabout spklang if weak_ftr==0 using "$output\tab1A.tex", replace /// c(count LLL mean self_employed) f(0c 2c) h1(nil) h2(nil) h3(nil) ptotal(none) style(tex) lines(none) sum tabout spklang if weak_ftr==1 using "$output\tab1B.tex", replace /// c(count LLL mean self_employed) f(0c 2c) h1(nil) h2(nil) h3(nil) ptotal(none) style(tex) lines(none) sum tabout constant2 using "$output\tab1C.tex", replace /// c(count constant2 mean self_employed) f(0c 2c) h1(nil) h2(nil) h3(nil) ptotal(none) style(tex) lines(none) sum * Table 2 - Summary statistics * Generation of descriptive statistics of outcome and control variables across type of future reference (weak vs strong ftr) tab religion, gen(R) tabout weak_ftr using "$output\tab2A.tex", replace /// c(mean self_employed mean female mean altj mean tertedu mean swissnational) /// f(2c) sum h1(nil) h2(nil) h3(nil) style(tex) lines(none) tabout weak_ftr using "$output\tab2B.tex", replace /// c(mean married mean household_nchild /// mean R1 mean R2 mean R9) f(2c) sum h1(nil) h2(nil) h3(nil) style(tex) lines(none) restore /// Estimates on first generation immigrants ************ * Analysis * ************ use "$output\temp_firstgen.dta", clear global indcontr = "female altj altj2 i.religion swissnational married household_nchild tertedu" global langcontr = "gend_dist polit_dist past_tense perfect_tense" global gps_se "patience risktaking posrecip negrecip altruism trust self_emp_cb" global sdcluster = "aor1_maj" *** Table 3 - Baseline estimates on subsamples of stayers and switchers, plus pooled sample with interaction between FTR and switcher status dummy preserve use "$dataset\GPS_Dataset\GPS_dataset_country_level\country_gort.dta", clear foreach var in patience risktaking posrecip negrecip altruism trust{ sum `var' local m_`var' = r(mean) } restore preserve use "$dataset\self employed in country of birth_wb.dta", clear sum self_emp_cb local m_self_emp_cb = r(mean) restore * Merging with country-of-birth-level GPS variables merge m:1 gort using "$dataset\GPS_Dataset\GPS_dataset_country_level\country_gort.dta", gen(merge_gps) foreach var in patience risktaking posrecip negrecip altruism trust{ replace `var' = `m_`var'' if merge_gps==1 } * Merging with country-of-birth-level self-employment (World Bank data) merge m:1 gort using "$dataset\self employed in country of birth_wb.dta", gen(merge_se) replace self_emp_cb = `m_self_emp_cb' if merge_se==1 *** Table 3, column 1 - stayers only - GPS controls and no country of origin fixed effects global contr = "$indcontr $langcontr $gps_se" preserve keep if stayer == 1 reghdfe self_employed weak_ftr $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline", nocons keep(weak_ftr) ctitle("Stayers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel replace nonotes restore *** Table 3, column 2 - switchers only - GPS controls and no country of origin fixed effects global contr = "$indcontr $gps_se" preserve keep if switcher == 1 reghdfe self_employed weak_ftr $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline", nocons keep(weak_ftr*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table 3, column 3 - switchers only with country of origin fixed effects global contr = "$indcontr" reghdfe self_employed weak_ftr $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\baseline", nocons keep(weak_ftr*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes restore *** Table 3, column 4 - pooled sample - GPS controls and no country of origin fixed effects global contr = "$indcontr $langcontr $gps_se" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table 3, column 5 - pooled sample - country of origin fixed effects global contr = "$indcontr $langcontr" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes *** Table 4 - estimates on multilingual cantons *** Table 4, column 1 - baseline estimates on pooled sample from the whole Switzerland (column 5, Table 3) global contr = "$indcontr $langcontr" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\multilingual_cantons", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Baseline") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel replace nonotes *** Table 4, column 2 - pooled sample from multilingual cantons (Grabunded excluded) reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 & multil_kant_nograb==1), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\multilingual_cantons", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Multilingual cantons") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes *** Table 4, column 2 - pooled sample from districts at the linguistic border in multilingual cantons (Grabunded excluded) reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 & multil_kant_nograb==1 & ling_border_dis60tr==1), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\multilingual_cantons", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Linguistic Border") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes *** Table 5 - estimates excluding low-skilled occupations, excluding agricultural sector and by education - pooled sample global contr = "$indcontr $langcontr" *** Table 5, column 1 - estimates excluding low skill occupations reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (lowsk_occ==0 & altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\skills", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Low-skilled occupations excluded") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel replace nonotes *** Table 5, column 2 - estimates excluding agriculture gen worksector2 = worksector replace worksector2 = 1 if worksector!=. & ((isco>=6000 & isco<7000) | (isco>=9200 & isco<9300)) replace worksector2 = 3 if worksector!=. & ((isco>=5000 & isco<6000) | (isco>=4000 & isco<5000) | (isco>=8300 & isco<=8400) | (isco>=9100 & isco<9200) | (isco>=9400 & isco<9700)) replace worksector2 = 2 if worksector!=. & ((isco>=7000 & isco<8300) | (isco>=9300 & isco<9400)) gen worksector3 = worksector replace worksector3 = . if isco==9999 gen worksector4 = 1 if worksector== 1 | ((isco>=6000 & isco<7000) | (isco>=9200 & isco<9300)) replace worksector4 = 23 if (worksector==2 | worksector==3) & !((isco>=6000 & isco<7000) | (isco>=9200 & isco<9300)) replace worksector4 = 23 if (worksector==2 | worksector==3) & (((isco>=5000 & isco<6000) | (isco>=4000 & isco<5000) | (isco>=8300 & isco<=8400) | (isco>=9100 & isco<9200) | (isco>=9400 & isco<9700))) replace worksector4 = 23 if (worksector==2 | worksector==3) & ((isco>=7000 & isco<8300) | (isco>=9300 & isco<9400)) reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (worksector4==23 & altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\skills", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Agriculture excluded") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes *** Table 5, column 3 and 4 - estimates by level of education (non tertiary vs tertiary education) reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (tertedu==0 & altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\skills", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Secondary education or less") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (tertedu==1 & altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\skills", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Tertiary education") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes *** Table 6 - estimates by gender and age - pooled sample global contr "$indcontr $langcontr" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (female==0 & altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\agegender", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Males") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel replace nonotes reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (female==1 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\agegender", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Females") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & altj<36 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\agegender", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Age 25-35") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=36 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\agegender", nocons keep(weak_ftr switcher weak_FTR_switcher) ctitle("Age over 35") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, District FE, YES, Country of origin FE, YES) tex(frag) excel append nonotes *** Appendix Tables *** *** Table A3 - Baseline estimates with non inflectional future dummy rather than weak ftr dummy use "$output\temp_firstgen.dta", clear global indcontr = "female altj altj2 i.religion swissnational married household_nchild tertedu" global langcontr = "gend_dist polit_dist past_tense perfect_tense" global gps_se "patience risktaking posrecip negrecip altruism trust self_emp_cb" egen aor1_maj_spklang = group(aor1_maj spklang) global sdcluster = "aor1_maj_spklang" *** Baseline estimates with non inflectional future dummy rather than weak ftr dummy preserve use "$dataset\GPS_Dataset\GPS_dataset_country_level\country_gort.dta", clear foreach var in patience risktaking posrecip negrecip altruism trust{ sum `var' local m_`var' = r(mean) } restore preserve use "$dataset\self employed in country of birth_wb.dta", clear sum self_emp_cb local m_self_emp_cb = r(mean) restore merge m:1 gort using "$dataset\GPS_Dataset\GPS_dataset_country_level\country_gort.dta", gen(merge_gps) foreach var in patience risktaking posrecip negrecip altruism trust{ replace `var' = `m_`var'' if merge_gps==1 } merge m:1 gort using "$dataset\self employed in country of birth_wb.dta", gen(merge_se) replace self_emp_cb = `m_self_emp_cb' if merge_se==1 gen non_inflectional = inflectional_ftr == 0 replace non_inflectional = 1 if spklang == "Portuguese" label var non_inflectional "Non inflectional future" gen non_infl_switcher = non_inflectional == 1 & switcher ==1 label var non_infl_switcher "Non inflectional $ \times $ Switcher" *** Table 3, column 1 - stayers only - GPS controls and no country of origin fixed effects global contr = "$indcontr $langcontr $gps_se" preserve keep if stayer == 1 reghdfe self_employed non_inflectional $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_non_inflectional_new", nocons keep(non_inflectional) ctitle("Stayers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel replace nonotes restore *** Table 3, column 2 - switchers only - GPS controls and no country of origin fixed effects global contr = "$indcontr $gps_se" preserve keep if switcher == 1 reghdfe self_employed non_inflectional $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_non_inflectional_new", nocons keep(non_inflectional*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table 3, column 3 - switchers only with country of origin fixed effects global contr = "$indcontr" reghdfe self_employed non_inflectional $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\baseline_non_inflectional_new", nocons keep(non_inflectional*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes restore *** Table 3, column 4 - pooled sample - GPS controls and no country of origin fixed effects global contr = "$indcontr $langcontr $gps_se" reghdfe self_employed non_inflectional switcher non_infl_switcher $contr if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_non_inflectional_new", nocons keep(non_inflectional* switcher non_infl_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, GPS cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table 3, column 5 - pooled sample - country of origin fixed effects global contr = "$indcontr $langcontr" reghdfe self_employed non_inflectional switcher non_infl_switcher $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_non_inflectional_new", nocons keep(non_inflectional* switcher non_infl_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes *** Table A4 - descriptive statistics by country of origin use "$output\temp_firstgen.dta", clear gen weakftraor = 0 replace weakftraor = 1 if aor1 == 8206 replace weakftraor = 1 if aor1 == 8211 replace weakftraor = 1 if aor1 == 8227 replace weakftraor = 1 if aor1 == 8234 replace weakftraor = 1 if aor1 == 8508 replace weakftraor = 1 if aor1 == 8515 replace weakftraor = . if aor1 == 8390|aor1 == 8490|aor1 == 8590 label define weakftraor_l 0 "Strong" 1 "Weak" label values weakftraor weakftraor_l gen switcher_weakftr = switcher == 1 & weak_ftr == 1 gen switcher_strongftr = switcher == 1 & weak_ftr == 0 label define coo 8204 "Belgium" 8206 "Denmark" 8207 "Germany" 8211 "Finland" 8212 "France" 8214 "Greece" 8215 "UK" 8218 "Italy" 8220 "Former Yugoslavia" 8222 "Liechtenstein" 8227 "Netherlands" 8229 "Austria" 8230 "Poland" 8231 "Portugal" 8232 "Romania" 8234 "Sweden" 8236 "Spain" 8238 "former Czechoslovakia" 8239 "Turkey" 8240 "Hungary" 8290 "Other European" 8304 "Algeria" 8357 "Tunisia" 8390 "Other Africa" 8407 "Chile" 8423 "Canada" 8439 "United States" 8490 "Other America" 8508 "China" 8510 "India" 8513 "Iran" 8514 "Israel" 8515 "Japan" 8518 "Cambodia" 8523 "Lebanon" 8545 "Vietnam" 8590 "Other Asia" 8690 "Oceania" label values gort coo gen cb_name="Belgium" if gort==8204 replace cb_name="Denmark" if gort==8206 replace cb_name="Germany" if gort==8207 replace cb_name="Finland" if gort==8211 replace cb_name="France" if gort==8212 replace cb_name="Greece" if gort==8214 replace cb_name="United Kingdom" if gort==8215 replace cb_name="Italy" if gort==8218 replace cb_name="Former Yugoslavia" if gort==8220 replace cb_name="Liechtenstein" if gort==8222 replace cb_name="Netherlands" if gort==8227 replace cb_name="Austria" if gort==8229 replace cb_name="Poland" if gort==8230 replace cb_name="Portugal" if gort==8231 replace cb_name="Romania" if gort==8232 replace cb_name="Sweden" if gort==8234 replace cb_name="Spain" if gort==8236 replace cb_name="former Czechoslovakia" if gort==8238 replace cb_name="Turkey" if gort==8239 replace cb_name="Hungary" if gort==8240 replace cb_name="Algeria" if gort==8304 replace cb_name="Tunisia" if gort==8357 replace cb_name="Other Africa" if gort==8390 replace cb_name="Chile" if gort==8407 replace cb_name="Canada" if gort==8423 replace cb_name="United States" if gort==8439 replace cb_name="Other America" if gort==8490 replace cb_name="China" if gort==8508 replace cb_name="India" if gort==8510 replace cb_name="Iran" if gort==8513 replace cb_name="Israel" if gort==8514 replace cb_name="Japan" if gort==8515 replace cb_name="Cambodia" if gort==8518 replace cb_name="Lebanon" if gort==8523 replace cb_name="Vietnam" if gort==8545 replace cb_name="Other Asia" if gort==8590 replace cb_name="Oceania" if gort==8690 gen lowsk_occ_se=0 if lowsk_occ==1 replace lowsk_occ_se=1 if lowsk_occ==1 & self_employed==1 gen higsk_occ_se=0 if lowsk_occ==0 replace higsk_occ_se=1 if lowsk_occ==0 & self_employed==1 keep if firstg_imm==1 & female!=. & weak_ftr!=. & switcher!=. & altj!=. & altj2!=. & religion!=. & years5abroad!=. & swissnational!=. & married!=. & household_nchild!=. & tertedu!=. & gend_dist!=. & polit_dist!=. & past_tense!=. & perfect_tense!=. & altj>=25 & employed==1 & district!=. tabout cb_name using "$output\tabApp_stat_by_country.tex", replace /// c(count gort mean weakftraor mean switcher_weakftr mean switcher_strongftr mean tertedu mean lowsk_occ mean self_employed mean lowsk_occ_se mean higsk_occ_se ) f(0c 2c 2c) sum h1(nil) h2(nil) h3(nil) style(tex) lines(none) *** Table A5 and A6 - estimates of the country-level correlation between GPS variables (Table A5) and Hofstede's dimensions (Table A6) with Weak FTR of the official language use "$dataset\hofstede cultural dimensions and language dataset.dta", clear gen isocode=country_code replace isocode = ctr if missing(country_code) merge m:1 isocode using "$dataset\GPS_Dataset\GPS_dataset_country_level\country.dta", generate(_merge1) keep if _merge1==3 gen weak_ftr = 0 if prediction_ftr == 1 replace weak_ftr = 1 if prediction_ftr == 0 label var weak_ftr "Weak FTR" local geocontr pdiv_aa abslat suitavg elevavg arable distcr temp precip i.legor i.oldw local GPS patience risktaking posrecip negrecip altruism trust local hofstede ltowvs uai idv pdi mas ivr local append replace label var pdi "Power distance" label var idv "Individualism" label var mas "Masculinity" label var uai "Uncertainty avoidance" label var ltowvs "Long-term orientation" label var ivr "Indulgence vs restraint" foreach var in `GPS'{ reg weak_ftr `var' `geocontr' i.continent_n, robust outreg2 using "$output\GPS_weakFTR_country_level", nocons keep(`var') tex(pretty landscape) excel label ctitle("Weak FTR") addtext(Continent FE, YES, Geograph. and Instit. controls, YES) `append' local append append } reg weak_ftr `GPS' `geocontr' i.continent_n, robust outreg2 using "$output\GPS_weakFTR_country_level", nocons keep(`GPS') tex(pretty landscape) excel label ctitle("Weak FTR") addtext(Continent FE, YES, Geograph. and Instit. controls, YES) append local append replace foreach var in `hofstede'{ reg weak_ftr `var' `geocontr' i.continent_n, robust outreg2 using "$output\hofstede_weakFTR_country_level", nocons keep(`var') tex(pretty landscape) excel label ctitle("Weak FTR") addtext(Continent FE, YES, Geograph. and Instit. controls, YES) `append' local append append } reg weak_ftr `hofstede' `geocontr' i.continent_n, robust outreg2 using "$output\hofstede_weakFTR_country_level", nocons keep(`hofstede') tex(pretty landscape) excel label ctitle("Weak FTR") addtext(Continent FE, YES, Geograph. and Instit. controls, YES) append *** Table A7 - Baseline estimates with Hofstede's cultural dimensions (rather than GPS) on subsamples of stayers and switchers, plus pooled sample with interaction between FTR and switcher status dummy use "$output\temp_firstgen.dta", clear global indcontr = "female altj altj2 i.religion swissnational married household_nchild tertedu" global langcontr = "gend_dist polit_dist past_tense perfect_tense" global gps_se "patience risktaking posrecip negrecip altruism trust self_emp_cb" egen aor1_maj_spklang = group(aor1_maj spklang) global sdcluster = "aor1_maj_spklang" use "$output\temp_firstgen.dta", clear preserve use "$dataset\hofstede cultural dimensions by country of birth.dta", clear foreach var in pdi idv mas uai ltowvs ivr{ sum `var' local m_`var' = r(mean) } restore preserve use "$dataset\self employed in country of birth_wb.dta", clear sum self_emp_cb local m_self_emp_cb = r(mean) restore merge m:1 gort using "$dataset\hofstede cultural dimensions by country of birth.dta", gen(merge_hof) foreach var in pdi idv mas uai ltowvs ivr{ replace `var' = `m_`var'' if merge_hof==1 } merge m:1 gort using "$dataset\self employed in country of birth_wb.dta", gen(merge_se) replace self_emp_cb = `m_self_emp_cb' if merge_se==1 global indcontr = "female altj altj2 i.religion swissnational married household_nchild tertedu" global langcontr = "gend_dist polit_dist past_tense perfect_tense" global hof_se "pdi idv mas uai ltowvs ivr self_emp_cb" global sdcluster = "aor1_maj" *** Table A7, column 1 - stayers only - Hofstede's controls and no country of origin fixed effects global contr = "$indcontr $langcontr $hof_se" preserve keep if stayer == 1 reghdfe self_employed weak_ftr $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_hofstede", nocons keep(weak_ftr) ctitle("Stayers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, Hofstede's cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel replace nonotes restore *** Table A7, column 2 - switchers only - Hofstede's controls and no country of origin fixed effects global contr = "$indcontr $hof_se" preserve keep if switcher == 1 reghdfe self_employed weak_ftr $contr other_africa other_america other_asia oceania if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_hofstede", nocons keep(weak_ftr*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Hofstede's cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table A7, column 3 - switchers only with country of origin fixed effects global contr = "$indcontr" reghdfe self_employed weak_ftr $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\baseline_hofstede", nocons keep(weak_ftr*) ctitle("Switchers") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes restore *** Table A7, column 4 - pooled sample - Hofstede's controls and no country of origin fixed effects global contr = "$indcontr $langcontr $hof_se" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 ), absorb(district) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_hofstede", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, Hofstede's cult. dimensions, YES, Self-empl. in birth-country, YES, District FE, YES) tex(frag) excel append nonotes *** Table A7, column 5 - pooled sample - country of origin fixed effects global contr = "$indcontr $langcontr" reghdfe self_employed weak_ftr switcher weak_FTR_switcher $contr if (altj>=25 & employed==1 ), absorb(district aor1) vce(cluster $sdcluster) sum self_employed if e(sample)==1 local self_employed = string(r(mean), "%9.2f") outreg2 using "$output\baseline_hofstede", nocons keep(weak_ftr* switcher weak_FTR_switcher) ctitle("Pooled") label addtext(Mean dep. var., `self_employed' , Individual controls, YES, Religion dummies, YES, Linguistic features, YES, Country of origin FE, YES, District FE, YES) tex(frag) excel append nonotes *** Table A8 - estimates on the population of Swiss natives use "$output\temp.dta", clear bys zgde: egen natpop_zgde=total(native==1) bys zgde: egen germspk_natpop_zgde=total(native==1 & spklang=="German") bys zgde: egen frenspk_natpop_zgde=total(native==1 & spklang=="French") bys zgde: egen italspk_natpop_zgde=total(native==1 & spklang=="Italian") bys zgde: egen romaspk_natpop_zgde=total(native==1 & spklang=="Romansh") foreach i in germ fren ital roma{ gen sh_`i'spk_natpop_zgde=`i'spk_natpop_zgde/natpop_zgde } gen majspklang_nat_zgde=1 if germspk_natpop_zgde>frenspk_natpop_zgde & germspk_natpop_zgde>italspk_natpop_zgde & germspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=2 if frenspk_natpop_zgde>germspk_natpop_zgde & frenspk_natpop_zgde>italspk_natpop_zgde & frenspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=3 if italspk_natpop_zgde>germspk_natpop_zgde & italspk_natpop_zgde>frenspk_natpop_zgde & italspk_natpop_zgde>romaspk_natpop_zgde replace majspklang_nat_zgde=4 if romaspk_natpop_zgde>germspk_natpop_zgde & romaspk_natpop_zgde>frenspk_natpop_zgde & romaspk_natpop_zgde>italspk_natpop_zgde keep if native == 1 & natspk==1 keep if altj>=25 & altj<=70 label var self_employed "Self empl." label var weak_ftr "Weak FTR" label var altj "Age" label var household_nchild "# childs" label var tertedu "High skilled" label var years5abroad "Less than 5 years in SWZ" label var swissnational "Citizen" label define ftr 0 "strong-FTR" 1 "weak-FTR" label values weak_ftr ftr egen aor1_dis=group(aor1 district) egen aor1_lng=group(aor1 spklang) egen aor1_maj=group(aor1 majspklang_nat_zgde) global indcontr = "female altj altj2 i.religion swissnational married household_nchild tertedu" global contr ="$indcontr" global sdcluster = "aor1_maj" *** Table A8, column 1 - estimates on Swiss natives from the whole Switzerland reghdfe self_employed weak_ftr $contr if (altj>=25 & employed==1), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\natives", nocons keep(weak_ftr*) ctitle("Switzerland") label addtext(Individual controls, YES, Religion dummies, YES, District FE, YES, District of Birth FE, YES) tex(frag) excel replace nonotes *** Table A8, column 2 - estimates on Swiss natives in multilingual cantons (Grabunded excluded) reghdfe self_employed weak_ftr $contr if (altj>=25 & employed==1 & multil_kant_nograb==1), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\natives", nocons keep(weak_ftr*) ctitle("Multilingual Cantons") label addtext(Individual controls, YES, Religion dummies, YES, District FE, YES, District of Birth FE, YES) tex(frag) excel append nonotes *** Table A8, column 3 - estimates on Swiss natives in districts at the linguistic border in multilingual cantons (Grabunded excluded) reghdfe self_employed weak_ftr $contr if (altj>=25 & employed==1 & multil_kant_nograb==1 & ling_border_dis60tr==1), absorb(district aor1) vce(cluster $sdcluster) outreg2 using "$output\natives", nocons keep(weak_ftr*) ctitle("Linguistic Border") label addtext(Individual controls, YES, Religion dummies, YES, District FE, YES, District of Birth FE, YES) tex(frag) excel append nonotes