数据和代码可下载供复制结果！空间(地理)断点设计RDD发(小)JPE的文章！

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今天，展示一下这篇文章的复制数据和代码“那个很早就发AER的中国青年学者, 通过巧秒的空间(地理)断点设计RDD发了(小)JPE！”。有需要的学者，可以自行前往文章主页下载，以便对照着文章进行复制结果，从而掌握RDD的详细操作过程。

*当然也将这些结果完整打包放到了社群里，群友可自行下载。

表格结果及代码：

******Table3******

use data_si_chong,clear

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50,cluster(county)

gen x2=xcenter^2

gen y2=ycenter^2

gen xy=xcenter*ycenter

reg ln_diff_2013_1996 new xcenter ycenter x2y2 xy if distance<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter x2y2 xy if distance<=50,cluster(county)

gen x3=xcenter^3

gen y3=ycenter^3

gen x2y=x2*ycenter

gen xy2=xcenter*y2

reg ln_diff_2013_1996 new x3 y3 x2y xy2 x2y2 xy xcenter ycenter,cluster(county)

gen x4=xcenter^4

gen x3y=xcenter^3*ycenter

gen x2y2=xcenter^2*ycenter^2

gen xy3=xcenter*ycenter^3

gen y4=ycenter^4

reg ln_diff_2013_1996 new x4 x3y x2y2 xy3 y4x3 y3 x2y xy2 x2 y2 xy xcenter ycenter,cluster(county)

******Table 5******

use data_si_chong,clear

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter if10<=distance & distance<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter if5<=distance & distance<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter if10<=distance & distance<=50,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter if5<=distance & distance<=50,cluster(county)

******Table 6******

use data_si_chong,clear

geodist ycenter xcenter seg01_y seg01_x ,gen(dist_seg01)

geodist ycenter xcenter seg02_y seg02_x ,gen(dist_seg02)

geodist ycenter xcenter seg03_y seg03_x ,gen(dist_seg03)

geodist ycenter xcenter seg11_y seg11_x ,gen(dist_seg11)

geodist ycenter xcenter seg12_y seg12_x ,gen(dist_seg12)

geodist ycenter xcenter seg13_y seg13_x ,gen(dist_seg13)

reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg11<=30 | dist_seg12<=30 |dist_seg13<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg11<=50 | dist_seg12<=50 |dist_seg13<=50,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg01<=30 | dist_seg02<=30 |dist_seg03<=30,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg01<=50 | dist_seg02<=50 |dist_seg03<=50,cluster(county)

******Table 9(Panel A)******

use data_si_chong,clear

gen l_output=ln(output)

gen l_output_pp=ln(output/popu+0.01)

gen urbrate=popu_urb/popu

gen nonfarmemploy_ratio=employ_23/popu

gen l_road_local_pp=ln(road_local/popu+0.01)

genl_road_national_pp=ln(road_national/popu+0.01)

gen l_school_pp=ln(school/popu+0.01)

gen l_hospital_pp=ln(hospital/popu+0.01)

winsor2 l_output_pp if distance<=30, trimcuts(1 99)

reg l_output_pp_tr new xcenter ycenter ifdistance<=30,cluster(county)

reg urbrate new xcenter ycenter ifdistance<=30,cluster(county)

reg nonfarmemploy_ratio new xcenter ycenterif distance<=30,cluster(county)

reg l_road_local_pp new xcenter ycenter ifdistance<=30,cluster(county)

reg l_road_national_pp new xcenter ycenterif distance<=30,cluster(county)

reg l_school_pp new xcenter ycenter ifdistance<=30,cluster(county)

reg l_hospital_pp new xcenter ycenter ifdistance<=30,cluster(county)

******Table 9(Panel B)******

*(1)

use data_enterprise.dta,clear

gen x=_n

bys town: egen num2008_a1997=count(x) if birthyr>=1997

bys town: egencom_2008_a1997=max(num2008_a1997)

egen position=concat(xcenter ycenter)

duplicates drop position,force

merge 1:1 position using"data_si_chong.dta"

drop if _merge==1

drop _merge

replace com_2008_a1997=0 if com_2008_a1997==.

gen lcom_2008_a1997=ln(com_2008_a1997+0.01)

reg lcom_2008 new xcenter ycenter ifdistance<=30,cluster(county)

*(2)

use data_enterprise.dta,clear

gen new=0

replace new=1 if province=="重庆市"

gen r_prof=profit/salemain

winsor2 r_prof ,cuts(0.25 99.75) trim

gen industry=int(indcode/100)

geodist latq lngq near_cy near_cx ,gen(cdistance)

reg r_prof_tr new lngq latq i.industry ifcdistance<=30,cluster(county)

*(3)(4)

use data_cencus2005_30km.dta,clear

keep if age>=18 & age<=60

reg water new xcenterycenter,cluster(county)

reg nonfarm_emp new xcenterycenter,cluster(county)

******Table 11******

use data_si_chong,clear

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30 & inundated==0,cluster(county)

reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50 & inundated==0,cluster(county)

******Table 12******

use data_townlight_hubei.dta,clear

reg ln_diff_2013_1996 yichang xcenterycenter if distance<=30

reg ln_diff_2013_1996 yichang xcenterycenter if distance<=50

use data_townlight_sichuan.dta,clear

reg ln_diff_2013_1996 chengdu xcenterycenter if dist_cdborder<=30,cluster(county)

reg ln_diff_2013_1996 chengdu xcenterycenter if dist_cdborder<=50,cluster(county)

图结果及代码：

******Figure 3******

use data_si_chong,clear

gen dist_g=distance

replace dist_g=-distance if new==0

keep if distance<=100

gen group=-100

forvalues i=1(1)20{

replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)

}

egen distance_mean=mean(dist_g),by(group)

egen mean1996_mean=mean(mean1996),by(group)

egen elevation_mean=mean(elevation),by(group)

egen slope_mean=mean(slope),by(group)

reg mean1996 new dist_g

predict mean1996_p

predict se1,stdp

gen mean1996_low = mean1996_p - 1.96 * se1

gen mean1996_high = mean1996_p + 1.96 * se1

bygroup, sort : egen float count = count(mean1996_p)

twoway (scatter mean1996_mean distance_mean if distance<=100,mcolor(black) ) /*

*/(line mean1996_p mean1996_low mean1996_high dist_g if dist_g<0 &dist_g>=-100, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line mean1996_p mean1996_low mean1996_high dist_g if dist_g>0 &dist_g<=100, pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ ytitle(Light intensity of 1996) xtitle(Distance to cutoff) legend(off)xline(0,lpattern(dash)) text(1.7 50"Chongqing",size(medlarge)) text(1.7 -50"Sichuan",size(medlarge)) scheme(s1color)

******Figure 4******

use dynamic_1992-2013,clear

graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off) scheme(s1color) ylabel(-1(0.5)2)

******Figure 5******

use data_si_chong,clear

gen dist_g=distance

replace dist_g=-distance if new==0

gen group=-100 if distance<=100

forvalues i=1(1)20{

replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)

}

gen growthrate=ln(mean2013+0.01)-ln(mean1996+0.01)

egen growthrate_mean=mean(growthrate),by(group)

egen distance_mean=mean(dist_g),by(group)

qui reg growthrate new dist_g if distance<=100

predict growthrate_p

predict se,stdp

gen growthrate_low = growthrate_p - 1.96 * se

gen growthrate_high = growthrate_p + 1.96 * se

bygroup, sort : egen float count = count(growthrate_p)

twoway (scatter growthrate_mean distance_mean if distance<=100,mcolor(black)) /*

*/(line growthrate_p growthrate_low growthrate_high dist_g if dist_g<0 &dist_g>=-100, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line growthrate_p growthrate_low growthrate_high dist_g if dist_g>0 &dist_g<=100, pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ ytitle(Night light growth rate 1996-2013) xtitle(Distance to cutoff)legend(off) xline(0,lpattern(dash)) text(0.75 50 "Chongqing",size(medlarge)) text(0.75 -50"Sichuan",size(medlarge)) scheme(s1color)

******Figure 6 panel A_Linear******

use data_si_chong,clear

matrix A=J(9,2,0)

forvalues i=1/9{

qui reg ln_diff_2013_1996 new xcenter ycenterif distance<=(`i'+1)*10,cluster(county)

matrix a`i'=e(b)

matrix b`i'=e(V)

scalar A`i'1=a`i'[1,1]

scalar A`i'2=sqrt(b`i'[1,1])

matrix A[`i',1]=A`i'1

matrix A[`i',2]=A`i'2

}

mat2txt, matrix(A) saving (matrixa) replace

insheet using matrixa.txt,clear

rename c1 coff

rename c2 se

gen low=coff-1.96*se

gen high=coff+1.96*se

egen v2=group(v1)

gen bw=(v2+1)*10

graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0, lpattern(dash)lcolor(black)) legend(off)xtitle("Brandwidth") xlabel(20(10)100) ylabel(-0.5(0.5)2)scheme(s1color)

******Figure 6 panel B_Quadratic******

use data_si_chong,clear

gen x2=xcenter^2

gen y2=ycenter^2

gen xy=xcenter*ycenter

matrix B=J(9,2,0)

forvalues i=1/9{

qui reg ln_diff_2013_1996 new xcenter ycenterx2 y2 xy if distance<=(`i'+1)*10,cluster(county)

matrix a`i'=e(b)

matrix b`i'=e(V)

scalar B`i'1=a`i'[1,1]

scalar B`i'2=sqrt(b`i'[1,1])

matrix B[`i',1]=B`i'1

matrix B[`i',2]=B`i'2

}

mat2txt, matrix(B) saving (matrixb) replace

insheet using matrixb.txt,clear

rename c1 coff

rename c2 se

gen low=coff-1.96*se

gen high=coff+1.96*se

egen v2=group(v1)

gen bw=(v2+1)*10

******Figure 6 panel C_Control******

use data_si_chong,clear

matrix C=J(9,2,0)

forvalues i=1/9{

qui reg ln_diff_2013_1996 new xcenter ycenterslope elevation seg1 seg2 seg3 seg4 if distance<=(`i'+1)*10,cluster(county)

matrix a`i'=e(b)

matrix b`i'=e(V)

scalar C`i'1=a`i'[1,1]

scalar C`i'2=sqrt(b`i'[1,1])

matrix C[`i',1]=C`i'1

matrix C[`i',2]=C`i'2

}

mat2txt, matrix(C) saving (matrixc) replace

insheet using matrixc.txt,clear

rename c1 coff

rename c2 se

gen low=coff-1.96*se

gen high=coff+1.96*se

egen v2=group(v1)

gen bw=(v2+1)*10

graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0,lpattern(dash) lcolor(black)) legend(off) xtitle("Brandwidth") xlabel(20(10)100)ylabel(-0.5(0.5)2) scheme(s1color)

******Figure 6 panel D_Weight******

use data_si_chong,clear

matrix D=J(9,2,0)

forvalue i=1/9{

gen weight`i'=1-distance/(`i'+1)/10 if distance<=(`i'+1)*10

}

forvalues i=1/9{

qui reg ln_diff_2013_1996 new xcenter ycenterif distance<=(`i'+1)*10 [weight=weight`i'],cluster(county)

matrix a`i'=e(b)

matrix b`i'=e(V)

scalar D`i'1=a`i'[1,1]

scalar D`i'2=sqrt(b`i'[1,1])

matrix D[`i',1]=D`i'1

matrix D[`i',2]=D`i'2

}

mat2txt, matrix(D) saving (matrixd) replace

insheet using matrixd.txt,clear

rename c1 coff

rename c2 se

gen low=coff-1.96*se

gen high=coff+1.96*se

egen v2=group(v1)

gen bw=(v2+1)*10

graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0,lpattern(dash) lcolor(black)) legend(off)xtitle("Brandwidth") xlabel(20(10)100) ylabel(-0.5(0.5)2) scheme(s1color)

******Figure 9******

use dynamic_1992-2013_si_gansu,clear

use dynamic_1992-2013_si_guizhou,clear

use dynamic_1992-2013_si_qinghai,clear

graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year) ,yline(0,lpattern(dash))xline(1996,lpattern(dash)) legend(off) scheme(s1color) ylabel(-1(1)2)

use dynamic_1992-2013_si_shaanxi,clear

use dynamic_1992-2013_si_yunnan,clear

******Figure 9******

use data_si_chong,clear

keep if distance<=100

gen dist_g=distance

replace dist_g=-distance if new==0

gen group=-100

forvalues i=1(1)20{

replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)

}

gen l_road_local_pp=ln(road_local/popu*10+1)

gen l_road_national_pp=ln(road_national/popu*10+1)

gen l_school_pp=ln(school/popu*10+1)

gen l_hospital_pp=ln(hospital/popu*10+1)

egen distance_mean=mean(dist_g),by(group)

egen l_road_local_pp_mean=mean(l_road_local_pp),by(group)

egen l_road_national_pp_mean=mean(l_road_national_pp),by(group)

egen l_school_pp_mean=mean(l_school_pp),by(group)

egen l_hospital_pp_mean=mean(l_hospital_pp),by(group)

reg l_road_local_pp new dist_g

predict l_road_local_pp_p

predict se1,stdp

gen l_road_local_pp_low = l_road_local_pp_p - 1.96 * se1

gen l_road_local_pp_high = l_road_local_pp_p + 1.96 * se1

twoway (scatter l_road_local_pp_mean distance_mean , mcolor(black)) /*

*/(line l_road_local_pp_p l_road_local_pp_low l_road_local_pp_high dist_g ifdist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line l_road_local_pp_p l_road_local_pp_low l_road_local_pp_high dist_g ifdist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ title("Length of local roads per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash)) text(1.5 50 "Chongqing",size(med)) text(1.5 -50"Sichuan",size(med)) scheme(s1color)

reg l_road_national_pp new dist_g

predict l_road_national_pp_p

predict se2,stdp

gen l_road_national_pp_low = l_road_national_pp_p - 1.96 * se2

gen l_road_national_pp_high = l_road_national_pp_p + 1.96 * se2

twoway (scatter l_road_national_pp_mean distance_mean , mcolor(black))/*

*/(line l_road_national_pp_p l_road_national_pp_low l_road_national_pp_highdist_g if dist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line l_road_national_pp_p l_road_national_pp_low l_road_national_pp_high dist_gif dist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ title("Length of national roads per capita") xtitle(Distanceto cutoff) legend(off) xline(0,lpattern(dash)) text(0 50 "Chongqing",size(med)) text(0 -50"Sichuan",size(med)) scheme(s1color)

reg l_school_pp new dist_g

predict l_school_pp_p

predict se4,stdp

gen l_school_pp_low = l_school_pp_p - 1.96 * se4

gen l_school_pp_high = l_school_pp_p + 1.96 * se4

twoway (scatter l_school_pp_mean distance_mean , mcolor(black) ) /*

*/(line l_school_pp_p l_school_pp_low l_school_pp_high dist_g if dist_g<0,pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line l_school_pp_p l_school_pp_low l_school_pp_high dist_g if dist_g>0,pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ title("Number of schools per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash)) text(0.650 "Chongqing",size(med)) text(0.6 -50 "Sichuan",size(med))scheme(s1color)

reg l_hospital_pp new dist_g

predict l_hospital_pp_p

predict se5,stdp

gen l_hospital_pp_low = l_hospital_pp_p - 1.96 * se5

gen l_hospital_pp_high = l_hospital_pp_p + 1.96 * se5

twoway (scatter l_hospital_pp_mean distance_mean , mcolor(black) ) /*

*/(line l_hospital_pp_p l_hospital_pp_low l_hospital_pp_high dist_g ifdist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*

*/(line l_hospital_pp_p l_hospital_pp_low l_hospital_pp_high dist_g ifdist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /*

*/ title("Number of hospitals per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash)) text(0.5 50 "Chongqing",size(med)) text(0.5 -50"Sichuan",size(med)) scheme(s1color)

******Figure 10******

usedata_transfer_si_chong.dta,clear

genyear1=year-0.2

genyear2=year+0.2

twoway (bar trans_p_si year1,barw(0.4)) (bar trans_p_chong year2,barw(0.4)), xlabel(1994(1)2013, angle(45)) xtitle("YEAR")ytitle("Transfer as percentage of GDP(%)") scheme(s1color)

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