title: “SCORE: Fielding-Miller et al. (2020) replication” |
subtitle: “Analysis of 5% sample” |
author: “Radoslaw Panczak” |
date: 23 Nov 2020 |
Read data
C:\external\SCORE_Fielding-Miller_covid_R3pV\data Contains data from merged_covid_usa_prepared_original_sample.dta obs: 125 vars: 18 23 Nov 2020 11:22 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── storage display value variable name type format label variable label ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── date str8 %9s nonurban byte %9.0g nonurban Non-urban counties flag county str33 %33s state str20 %20s fips long %12.0g cases long %8.0g Cases deaths int %8.0g Deaths nonenglish double %10.0g % nonenglish speaking hh farmwork float %9.0g % engaged in farmwork uninsured float %9.0g % uninsured under 65 poverty double %10.0g % below poverty line older double %10.0g % aged 65 and above pop_dens float %9.0g Pop density date_case1 int %td.. Date of case 1 time_case1 byte %9.0g Days since case 1 in county sip_effect int %td.. SIP effect start date date_case100 int %td.. Date of case 100 time_case100 byte %9.0g Days between case 100 in county and SIP in state ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Sorted by:
. mdesc Variable │ Missing Total Percent Missing ────────────────┼─────────────────────────────────────────────── date │ 0 125 0.00 nonurban │ 0 125 0.00 county │ 0 125 0.00 state │ 0 125 0.00 fips │ 0 125 0.00 cases │ 0 125 0.00 deaths │ 0 125 0.00 nonenglish │ 0 125 0.00 farmwork │ 3 125 2.40 uninsured │ 0 125 0.00 poverty │ 1 125 0.80 older │ 0 125 0.00 pop_dens │ 0 125 0.00 date_case1 │ 0 125 0.00 time_case1 │ 0 125 0.00 sip_effect │ 5 125 4.00 date_case100 │ 86 125 68.80 time_case100 │ 0 125 0.00 ────────────────┼───────────────────────────────────────────────
. ta state, m state │ Freq. Percent Cum. ─────────────────────┼─────────────────────────────────── Arizona │ 12 9.60 9.60 California │ 6 4.80 14.40 Colorado │ 52 41.60 56.00 Kansas │ 11 8.80 64.80 Nebraska │ 3 2.40 67.20 New Mexico │ 20 16.00 83.20 Oklahoma │ 3 2.40 85.60 Texas │ 5 4.00 89.60 Utah │ 11 8.80 98.40 Wyoming │ 2 1.60 100.00 ─────────────────────┼─────────────────────────────────── Total │ 125 100.00
. ta nonurban, m Non-urban │ counties │ flag │ Freq. Percent Cum. ────────────┼─────────────────────────────────── urban │ 4 3.20 3.20 non-urban │ 121 96.80 100.00 ────────────┼─────────────────────────────────── Total │ 125 100.00
. univar deaths, dec(0) ────────────── Quantiles ────────────── Variable n Mean S.D. Min .25 Mdn .75 Max ─────────────────────────────────────────────────────────────────────────────── deaths 125 11 26 0 0 0 4 132 ─────────────────────────────────────────────────────────────────────────────── . * hist deaths, width(10)
Largely based on Stata’s SP manual.
. spshape2dta cb_2018_us_county_20m_prep_sample.shp, replace (importing .shp file) (importing .dbf file) (creating _ID spatial-unit id) (creating _CX coordinate) (creating _CY coordinate) file cb_2018_us_county_20m_prep_sample_shp.dta created file cb_2018_us_county_20m_prep_sample.dta created . * spshape2dta cb_2018_us_county_20m_prep.shp, replace . . u cb_2018_us_county_20m_prep_sample, clear . d Contains data from cb_2018_us_county_20m_prep_sample.dta obs: 157 vars: 5 23 Nov 2020 11:22 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── storage display value variable name type format label variable label ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── _ID int %12.0g Spatial-unit ID _CX double %10.0g x-coordinate of area centroid _CY double %10.0g y-coordinate of area centroid fips long %9.0f fips state_code str2 %9s state_code ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Sorted by: _ID . spset fips, modify replace (_shp.dta file saved) (data in memory saved) Sp dataset cb_2018_us_county_20m_prep_sample.dta data: cross sectional spatial-unit id: _ID (equal to fips) coordinates: _CX, _CY (planar) linked shapefile: cb_2018_us_county_20m_prep_sample_shp.dta . spset, modify coordsys(latlong, miles) Sp dataset cb_2018_us_county_20m_prep_sample.dta data: cross sectional spatial-unit id: _ID (equal to fips) coordinates: _CY, _CX (latitude-and-longitude, miles) linked shapefile: cb_2018_us_county_20m_prep_sample_shp.dta . sa, replace file cb_2018_us_county_20m_prep_sample.dta saved
In order to test focal hypothesis only nonurban counties are used.
Counties with missing information on farmwork
and poverty
are also excluded since models will not run in situations in which counties exist in prepared spatial data but are excluded from regression (default Stata behaviour afaik).
. u "merged_covid_usa_prepared_original_sample.dta" , clear . *u "merged_covid_usa_prepared_original.dta" , clear . keep if nonurban (4 observations deleted) . . * stata deletes cases with missing obs . * then the dataset doesnt match the spatial matrix . * either fix missings or drop . drop if mi(farmwork) (3 observations deleted) . drop if mi(poverty) (1 observation deleted)
. merge 1:1 fips using cb_2018_us_county_20m_prep_sample Result # of obs. ───────────────────────────────────────── not matched 40 from master 0 (_merge==1) from using 40 (_merge==2) matched 117 (_merge==3) ───────────────────────────────────────── . *merge 1:1 fips using cb_2018_us_county_20m_prep . assert _merge != 1 . keep if _merge == 3 (40 observations deleted) . drop _merge . . * grmap deaths
Using gs2sls
option (instead of ml
) to guard against potential heteroskedasticity.
. spmatrix create contiguity W, replace weighting matrix in W contains 3 islands . . spregress deaths nonenglish farmwork uninsured poverty older pop_dens time_case1 time_case100, gs2sls dvarlag(W) (117 observations) (117 observations (places) used) (weighting matrix defines 117 places) Spatial autoregressive model Number of obs = 117 GS2SLS estimates Wald chi2(9) = 165.72 Prob > chi2 = 0.0000 Pseudo R2 = 0.5895 ─────────────┬──────────────────────────────────────────────────────────────── deaths │ Coef. Std. Err. z P>|z| [95% Conf. Interval] ─────────────┼──────────────────────────────────────────────────────────────── deaths │ nonenglish │ -.098869 .484726 -0.20 0.838 -1.048914 .8511764 farmwork │ .2569663 .3432206 0.75 0.454 -.4157337 .9296663 uninsured │ .0136608 .183338 0.07 0.941 -.3456751 .3729968 poverty │ .1671235 .2698939 0.62 0.536 -.3618588 .6961057 older │ -.4547352 .2922553 -1.56 0.120 -1.027545 .1180746 pop_dens │ .0704497 .0117454 6.00 0.000 .0474291 .0934702 time_case1 │ .5964849 .1571643 3.80 0.000 .2884485 .9045213 time_case100 │ -.3877583 .2244585 -1.73 0.084 -.8276889 .0521724 _cons │ -10.85542 9.045094 -1.20 0.230 -28.58348 6.872641 ─────────────┼──────────────────────────────────────────────────────────────── W │ deaths │ .2020525 .1471494 1.37 0.170 -.0863551 .4904601 ─────────────┴──────────────────────────────────────────────────────────────── Wald test of spatial terms: chi2(1) = 1.89 Prob > chi2 = 0.1697 . . * estat impact . . est sto m_original_queen .
rook
contiguity. spmatrix create contiguity W, rook replace weighting matrix in W contains 3 islands . . spregress deaths nonenglish farmwork uninsured poverty older pop_dens time_case1 time_case100, gs2sls dvarlag(W) (117 observations) (117 observations (places) used) (weighting matrix defines 117 places) Spatial autoregressive model Number of obs = 117 GS2SLS estimates Wald chi2(9) = 166.11 Prob > chi2 = 0.0000 Pseudo R2 = 0.5899 ─────────────┬──────────────────────────────────────────────────────────────── deaths │ Coef. Std. Err. z P>|z| [95% Conf. Interval] ─────────────┼──────────────────────────────────────────────────────────────── deaths │ nonenglish │ -.1192228 .4839862 -0.25 0.805 -1.067818 .8293727 farmwork │ .2588012 .3429208 0.75 0.450 -.4133113 .9309137 uninsured │ .0157909 .1833139 0.09 0.931 -.3434978 .3750796 poverty │ .1832614 .2698682 0.68 0.497 -.3456705 .7121933 older │ -.4681431 .2925293 -1.60 0.110 -1.04149 .1052039 pop_dens │ .0704416 .0117314 6.00 0.000 .0474485 .0934347 time_case1 │ .5941377 .1572338 3.78 0.000 .2859651 .9023103 time_case100 │ -.3911039 .224273 -1.74 0.081 -.8306708 .0484631 _cons │ -10.76116 9.031736 -1.19 0.233 -28.46304 6.940713 ─────────────┼──────────────────────────────────────────────────────────────── W │ deaths │ .1964226 .1401691 1.40 0.161 -.0783038 .4711489 ─────────────┴──────────────────────────────────────────────────────────────── Wald test of spatial terms: chi2(1) = 1.96 Prob > chi2 = 0.1611 . . est sto m_original_rook . . est tab m_original_queen m_original_rook , b(%6.3f) p(%6.3f) ─────────────┬──────────────────── Variable │ m_ori~n m_ori~k ─────────────┼──────────────────── deaths │ nonenglish │ -0.099 -0.119 │ 0.838 0.805 farmwork │ 0.257 0.259 │ 0.454 0.450 uninsured │ 0.014 0.016 │ 0.941 0.931 poverty │ 0.167 0.183 │ 0.536 0.497 older │ -0.455 -0.468 │ 0.120 0.110 pop_dens │ 0.070 0.070 │ 0.000 0.000 time_case1 │ 0.596 0.594 │ 0.000 0.000 time_case100 │ -0.388 -0.391 │ 0.084 0.081 _cons │ -10.855 -10.761 │ 0.230 0.233 ─────────────┼──────────────────── W │ deaths │ 0.202 0.196 │ 0.170 0.161 ─────────────┴──────────────────── legend: b/p