*** main specs *** *cd /Users/Tom/Dropbox/AdaptationGap/AAAFeb14Revisions use "disall_new", clear drop ln_dis_deaths gen ln_dis_deaths=ln(1+dis_deaths) label var ln_dis_deaths "ln(1+dis_deaths)" save disall_new, replace **************** ***floods*** ***************** ** drop non-flood observations ** keep if dis_subevent=="gf" *** if we want to estimate this as a panel, need to set panel variables *** to use time variation, will have to run disaster types separately (because different events occur in same country-year) *encode dis_country, gen(country) xtset country year *** damages *** set more off reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year, vce(cluster dis_country) estimates store flood_damages1 reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured, vce(cluster dis_country) estimates store flood_damages2 reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured lngovexp, vce(cluster dis_country) estimates store flood_damages3 xtreg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured lngovexp, fe vce(cluster dis_country) estimates store flood_damages4 estout flood_damages* using flood_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** deaths *** set more off reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year, vce(cluster dis_country) estimates store flood_fatalities1 reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured, vce(cluster dis_country) estimates store flood_fatalities2 reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured lngovexp, vce(cluster dis_country) estimates store flood_fatalities3 xtreg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured lngovexp, fe vce(cluster dis_country) estimates store flood_fatalities4 nbreg dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year, vce(cluster dis_country) estimates store flood_fatalities5 nbreg dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured lngovexp, vce(cluster dis_country) estimates store flood_fatalities6 estout flood_fatalities* using flood_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) **************** ***tropical cyclones*** ***************** use "disall_new", clear ** drop non-flood observations ** keep if dis_subevent=="tc" *** if we want to estimate this as a panel, need to set panel variables *** to use time variation, will have to run disaster types separately (because different events occur in same country-year) *encode dis_country, gen(country) xtset country year *** damages *** set more off reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year, vce(cluster dis_country) estimates store tc_damages1 reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured, vce(cluster dis_country) estimates store tc_damages2 reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured lngovexp, vce(cluster dis_country) estimates store tc_damages3 xtreg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured lngovexp, fe vce(cluster dis_country) estimates store tc_damages4 estout tc_damages* using tc_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** deaths *** set more off reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year, vce(cluster dis_country) estimates store tc_fatalities1 reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured, vce(cluster dis_country) estimates store tc_fatalities2 reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured lngovexp, vce(cluster dis_country) estimates store tc_fatalities3 xtreg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured lngovexp, fe vce(cluster dis_country) estimates store tc_fatalities4 nbreg dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year, vce(cluster dis_country) estimates store tc_fatalities5 nbreg dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured lngovexp, vce(cluster dis_country) estimates store tc_fatalities6 estout tc_fatalities* using tc_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) ******** *** OECD vs non-OECD ******** use "disall_new", clear **************** ***floods*** ***************** ** drop non-flood observations ** keep if dis_subevent=="gf" *** if we want to estimate this as a panel, need to set panel variables *** to use time variation, will have to run disaster types separately (because different events occur in same country-year) *encode dis_country, gen(country) xtset country year *** damages *** *** OECD and other high income *** set more off reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year if inc_high_oecd==1 | inc_high==1 , vce(cluster dis_country) estimates store oecd_flood_damages1 reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==1 | inc_high==1 , vce(cluster dis_country) estimates store oecd_flood_damages2 xtreg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==1 | inc_high==1 , fe vce(cluster dis_country) estimates store oecd_flood_damages3 estout oecd_flood_damages* using oecd_flood_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** non-OECD, non-high income *** set more off reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_flood_damages1 reg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_flood_damages2 xtreg lndis_loss_overall_usd1995 lnsum_precip_abs_pos lngdp lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==0 & inc_high==0 , fe vce(cluster dis_country) estimates store nonoecd_flood_damages3 estout nonoecd_flood_damages* using nonoecd_flood_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** deaths *** *** OECD and other high income *** set more off reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_flood_fatalities1 reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_flood_fatalities2 xtreg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==1 | inc_high==1, fe vce(cluster dis_country) estimates store oecd_flood_fatalities3 nbreg dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_flood_fatalities4 estout oecd_flood_fatalities* using oecd_flood_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** non-OECD, non-high income *** set more off reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_flood_fatalities1 reg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_flood_fatalities2 xtreg ln_dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==0 & inc_high==0, fe vce(cluster dis_country) estimates store nonoecd_flood_fatalities3 nbreg dis_deaths lnsum_precip_abs_pos lnpop lnareakm2 lngdppc lnprop_sum_precip_abs_pos year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_flood_fatalities4 estout nonoecd_flood_fatalities* using nonoecd_flood_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) **************** ***tropical cyclones*** ***************** use disall_new, clear ** drop non-flood observations ** keep if dis_subevent=="tc" *** if we want to estimate this as a panel, need to set panel variables *** to use time variation, will have to run disaster types separately (because different events occur in same country-year) *encode dis_country, gen(country) xtset country year *** damages *** *** oecd and other high income *** set more off reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_tc_damages1 reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_tc_damages2 xtreg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==1 | inc_high==1, fe vce(cluster dis_country) estimates store oecd_tc_damages3 estout oecd_tc_damages* using oecd_tc_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** non-oecd, non-high income *** set more off reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_tc_damages1 reg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_tc_damages2 xtreg lndis_loss_overall_usd1995 lnt3_top_wind_speed lngdp lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==0 & inc_high==0, fe vce(cluster dis_country) estimates store nonoecd_tc_damages3 estout nonoecd_tc_damages* using nonoecd_tc_damages.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** deaths *** *** oecd and other high income *** set more off reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_tc_fatalities1 reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_tc_fatalities2 xtreg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==1 | inc_high==1, fe vce(cluster dis_country) estimates store oecd_tc_fatalities3 nbreg dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==1 | inc_high==1, vce(cluster dis_country) estimates store oecd_tc_fatalities4 estout oecd_tc_fatalities* using oecd_tc_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq)) *** non-oecd, non-high income set more off reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_tc_fatalities1 reg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_tc_fatalities2 xtreg ln_dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==0 & inc_high==0, fe vce(cluster dis_country) estimates store nonoecd_tc_fatalities3 nbreg dis_deaths lnt3_top_wind_speed lnpop lnareakm2 lngdppc lnprop_t3_top_wind_speed year ln_insured if inc_high_oecd==0 & inc_high==0, vce(cluster dis_country) estimates store nonoecd_tc_fatalities4 estout nonoecd_tc_fatalities* using nonoecd_tc_fatalities.doc, replace cells(b(star fmt(3)) se(par fmt(2))) legend label varlabels(_cons constant) starlevels(* 0.10 ** 0.05 *** 0.01) stats(N N_clust r2_a, fmt(0 0 ) label(Obs. Countries Adj.R-Sq))