* Set Directory
clear
set more off
set scheme s1mono

cd "$path"
global data_files "$path/Data"
global out_files "$path/output"

**==============================================================================
* Table 1-2
// Conley
use "$data_files/firm_enf.dta", clear
keep if min_dist<50 & starty<=2010
drop if revenue == .
drop if key == .

label var min_dist_10 "Mon\$\_{<10km}\$"
label var any_air "Any Enforcement (0/1)"
label var post "Post"
label var key "High Pollution"

gen min_dist_10_post1 = c.min_dist_10#c.post1
egen ind_time = group(industry prov_id time)

reg2hdfespatial any_air min_dist_10_post1, lat(lat) lon(lon) timevar(time) panelvar(id) altfetime(ind_time) distcutoff(100) lagcutoff(20)
scalar Conley1 = _se[min_dist_10_post1]

reg2hdfespatial any_air_shutdown min_dist_10_post1, lat(lat) lon(lon) timevar(time) panelvar(id) altfetime(ind_time) distcutoff(100) lagcutoff(20) 
scalar Conley2 = _se[min_dist_10_post1]

reg2hdfespatial any_air_renovate min_dist_10_post1, lat(lat) lon(lon) timevar(time) panelvar(id) altfetime(ind_time) distcutoff(100) lagcutoff(20) 
scalar Conley3 = _se[min_dist_10_post1]

reg2hdfespatial any_air_fine min_dist_10_post1, lat(lat) lon(lon) timevar(time) panelvar(id) altfetime(ind_time) distcutoff(100) lagcutoff(20) 
scalar Conley4 = _se[min_dist_10_post1]

reg2hdfespatial any_air_warning min_dist_10_post1, lat(lat) lon(lon) timevar(time) panelvar(id) altfetime(ind_time) distcutoff(100) lagcutoff(20) 
scalar Conley5 = _se[min_dist_10_post1]

use "$data_files/firm_enf.dta", clear
drop if revenue == .
drop if key == .

label var min_dist_10 "Mon\$\_{<10km}\$"
label var any_air "Any Enforcement (0/1)"
label var post "Post"
label var key "High Pollution"

eststo clear
reghdfe any_air c.min_dist_10#c.post1 if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo A
estadd ysumm, mean
estadd scalar EN = e(N_full)
estadd local Conley =  "[`: di %9.5f Conley1']"
reghdfe any_air_shutdown c.min_dist_10#c.post1 if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo B
estadd ysumm, mean
estadd scalar EN = e(N_full)
estadd local Conley = "[`: di %9.5f Conley2']"
reghdfe any_air_renovate c.min_dist_10#c.post1 if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo C
estadd ysumm, mean
estadd scalar EN = e(N_full)
estadd local Conley = "[`: di %9.5f Conley3']"
reghdfe any_air_fine c.min_dist_10#c.post1 if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo D
estadd ysumm, mean
estadd scalar EN = e(N_full)
estadd local Conley = "[`:di %9.5f Conley4']"
reghdfe any_air_warning c.min_dist_10#c.post1 if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo E
estadd ysumm, mean
estadd scalar EN = e(N_full)
estadd local Conley = "[`:di %9.5f Conley5']"
esttab A B C D E using "$out_files/Table1a.tex", replace b(a2) noconstant se(a2) nolines nogaps compress fragment nonumbers label mlabels(none) collabels() keep(c.min_dist_10*) stats(ymean EN Conley, labels("Mean Outcome" "Observations" "Conley SE")) starlevels(* 0.10 ** 0.05 *** 0.01) substitute(\_ _) tex

* Table 1b
use "$data_files/firm_enf.dta", clear
drop if revenue == .
drop if key == .

label var min_dist_10 "Mon\$\_{<10km}\$"
label var any_air "Any Enforcement (0/1)"
label var post "Post"
label var key "High Pollution"

eststo clear
reghdfe air c.min_dist_10#c.post1##c.key if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo A
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe air_1 c.min_dist_10#c.post1##c.key if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo B
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe air_2 c.min_dist_10#c.post1##c.key if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo C
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe leni c.min_dist_10#c.post1##c.key if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo D
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe stri c.min_dist_10#c.post1##c.key if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo E
estadd ysumm, mean
estadd scalar EN = e(N_full)
esttab A B C D E using "$out_files/Table1b.tex", replace b(a2) noconstant se(a2) nolines nogaps compress fragment nonumbers label mlabels(none) collabels() keep(c.min_dist_10*) stats(ymean EN, labels("Mean Outcome" "Observations")) starlevels(* 0.10 ** 0.05 *** 0.01) substitute(\_ _) tex

* Table 2
gen Shock = high_pre
eststo clear
reghdfe any_air c.min_dist_10#c.post1##c.Shock tem_mean if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo A
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe any_air c.min_dist_10##c.post1##c.Shock tem_mean if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo B
estadd ysumm, mean
estadd scalar EN = e(N_full)
replace Shock = upwd
reghdfe any_air c.min_dist_10#c.post1##c.Shock tem_mean if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo C
estadd ysumm, mean
estadd scalar EN = e(N_full)
reghdfe any_air c.min_dist_10##c.post1##c.Shock tem_mean if min_dist<50 & starty<=2010, absorb(time id industry#time prov_id#time) cluster(city_id)
eststo D
estadd ysumm, mean
estadd scalar EN = e(N_full)
esttab A B C D using "$out_files/Table2.tex", replace b(a2) noconstant se(a2) nolines nogaps compress fragment nonumbers label mlabels(none) collabels() keep() drop(_cons tem_mean post1 min_dist_10) order(Shock c.min_dist_10#c.post1 c.min_dist_10#c.Shock c.min_dist_10#c.post1#c.Shock) stats(ymean EN, labels("Mean Outcome" "Observations")) starlevels(* 0.10 ** 0.05 *** 0.01) substitute(\_ _) tex

**==============================================================================
* Table 3
use "$data_files/city_pm.dta", clear
label variable post1 "Post" 
label variable number "\# Mon"
label variable number_iv "Min \# Mon"

gen RD_Estimate = c.post1#c.number

eststo clear
reghdfe pm25 RD_Estimate c.post#c.area c.post#c.pop, a(city_id year#month pred tem_meand age_year incentive2#time) cluster(city_id)
estadd scalar EN = e(N_full)
eststo A
ivreghdfe pm25 c.post#c.area c.post#c.pop (RD_Estimate=c.post1#c.number_iv), a(city_id year#month pred tem_meand age_year incentive2#time) cluster(city_id)
estadd scalar EN = e(N_full)
eststo B

use "$data_files/city_pm_rd.dta", clear

gen dist1 = area - 20 if cutoff == 1
replace dist1 = area - 50 if cutoff == 2

gen bench = pm25 if year < 2012
bys city_id cutoff: egen mean_bench = mean(bench)

gen above = dist1 > 0
gen RD_Estimate = c.post1#c.above

rdrobust pm25 dist1 if year>=2015, fuzzy(number) p(1) h(11.3) covs(cutoff mean_bench year month) kernel(uni) vce(cluster city_id)
estadd scalar EN = e(N_h_l) + e(N_h_r)
estadd scalar band = e(h_l)
eststo C
reghdfe pm25 RD_Estimate post1 above dist1 c.post1#c.dist1 c.above#c.dist1 c.post#c.above#c.dist1 cutoff if abs(dist1) < 11.3, a(time) cl(city_id)
estadd scalar EN = e(N)
estadd scalar band = 11.3
eststo D
esttab A B C D using "$out_files/Table3a.tex", keep(RD_Estimate) transform(@/1.21 1/1.21, pattern(0 0 0 1)) replace b(a2) se(a2) label noconstant nolines nogaps compress fragment nonumbers mlabels(none) coeflabels(RD_Estimate "\# Monitors") stats(EN, labels( "Observations")) starlevels(* 0.10 ** 0.05 *** 0.01) tex

use "$data_files/city_enf.dta", clear

label variable post1 "Post"
label variable number "\# Mon"
label variable number_iv "Min \# Mon"

gen RD_Estimate = c.post1#c.number

eststo clear
reghdfe log_any_air RD_Estimate c.post#c.area c.post#c.pop, a(city_id year#quarter pred tem_meand age_year incentive2#time) cluster(city_id)
estadd scalar EN = e(N_full)
eststo A
ivreghdfe log_any_air c.post#c.area c.post#c.pop (RD_Estimate=c.post1#c.number_iv), a(city_id year#quarter pred tem_meand age_year incentive2#time) cluster(city_id)
estadd scalar EN = e(N_full)
eststo B

use "$data_files/city_enf_rd.dta", clear

gen dist1 = area - 20 if cutoff == 1
replace dist1 = area - 50 if cutoff == 2

gen bench = log_any_air if year < 2012
bys city_id cutoff: egen mean_bench = mean(bench)

gen above = dist1 > 0
gen RD_Estimate = c.post1#c.above

rdrobust log_any_air dist1 if year>=2015, fuzzy(number) p(1) h(11.3) covs(cutoff mean_bench year quarter) kernel(uni) vce(cluster city_id)
estadd scalar EN = e(N_h_l) + e(N_h_r)
estadd scalar band = e(h_l)
eststo C
reghdfe log_any_air RD_Estimate post1 above dist1 c.post1#c.dist1 c.above#c.dist1 c.post1#c.above#c.dist1 cutoff if abs(dist1) < 11.3, a(time) cl(city_id)
estadd scalar EN = e(N)
estadd scalar band = 11.3
eststo D
esttab A B C D using "$out_files/Table3b.tex", tex keep(RD_Estimate) transform(@/1.21 1/1.21, pattern(0 0 0 1)) replace b(a2) se(a2) label noconstant nolines nogaps compress fragment nonumbers mlabels(none) coeflabels(RD_Estimate "\# Monitors") stats(EN, labels("Observations")) starlevels(* 0.10 ** 0.05 *** 0.01)

replace RD_Estimate = number_iv

eststo clear
regress number number_iv if year>=2015, vce(cluster city_id)
eststo A
regress number RD_Estimate pop area if year>=2015, vce(cluster city_id)
eststo B
rdrobust number dist1 if year>=2015, p(1) h(11.3) covs(cutoff) kernel(uni) vce(cluster city_id)
eststo C
estadd local kern = "Uniform"
estadd scalar band = 11.3
rdrobust number dist1 if year>=2015, p(1) h(11.3) covs(cutoff) kernel(uni) vce(cluster city_id)
eststo D
estadd local kern = "Uniform"
estadd scalar band = 11.3
esttab A B C D using "$out_files/Table3c.tex", tex replace b(a2) se(a2) label noconstant nolines nogaps compress fragment nonumbers noobs mlabels(none) keep(RD_Estimate) coeflabels(RD_Estimate "Estimate") stats(kern band, labels("Kernel" "Bandwidth")) starlevels(* 0.10 ** 0.05 *** 0.01)


**==============================================================================
* Table 4
use "$data_files/monitor_api.dta", clear

gen log_pm25api = log(pm25api)
gen log_pm10api = log(pm10api)

gen reassign = (year == 2017)
replace reassign = 1 if year == 2016 & month >= 11

rename pm25 AOD
label variable AOD "AOD"
label variable reassign "Reassigned"

eststo clear
reghdfe log_pm25api AOD pre tem_mean, a(monitor_id year#month) cl(city_id)
eststo A
estadd ysumm, mean
reghdfe log_pm25api AOD pre tem_mean if ~compare, a(monitor_id year#month) cl(city_id)
eststo B
estadd ysumm, mean
reghdfe log_pm25api AOD c.AOD#c.reassign pre tem_mean if ~compare, a(monitor_id year#month) cl(city_id)
eststo C
estadd ysumm, mean
reghdfe log_pm25api AOD pre tem_mean if compare, a(monitor_id year#month) cl(city_id)
eststo D
estadd ysumm, mean
reghdfe log_pm25api AOD c.AOD#c.reassign pre tem_mean if compare, a(monitor_id year#month) cl(city_id)
eststo E
estadd ysumm, mean

use "$data_files/city_pm.dta", clear

label variable post1 "Post" 
label variable number "\# Mon"

gen reassign = (year == 2017)
replace reassign = 1 if year == 2016 & month >= 10
label variable reassign "Reassigned"

reghdfe pm25 c.post1#c.number c.post1#c.number#c.reassign c.post1#c.area c.post1#c.pop, a(city_id year#month pred tem_meand age_year incentive2#time) cluster(city_id)
eststo F
estadd ysumm, mean

use "$data_files/city_enf.dta", clear

label variable post1 "Post" 
label variable number "\# Mon"

gen reassign = (year == 2017)
replace reassign = 1 if year == 2016 & quarter == 4
label variable reassign "Reassigned"

reghdfe log_any_air c.post1#c.number c.post1#c.number#c.reassign c.post1#c.area c.post1#c.pop, a(city_id year#quarter pred tem_meand age_year incentive2#time) cluster(city_id)
eststo G
estadd ysumm, mean
esttab A B C D E F G using "$out_files/Table4.tex", replace b(a2) noconstant se(a2) nolines nogaps compress fragment nonumbers label mlabels(none) collabels() keep(AOD c.AOD#c.reassign c.post1#c.number c.post1#c.number#c.reassign) drop() coeflabels(c.AOD#c.reassign "AOD $\times$ Reassigned" c.post1#c.number "\# Monitors" c.post1#c.number#c.reassign "\# Monitors $\times$ Reassigned") stats(ymean N, labels("Mean Outcome" "Observations")) starlevels(* 0.10 ** 0.05 *** 0.01) substitute(\_ _) tex