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_extended_sample.dta obs: 150 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 int %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 int %9.0g Days between case 100 in county and SIP in state ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Sorted by:
. mdesc Variable │ Missing Total Percent Missing ────────────────┼─────────────────────────────────────────────── date │ 0 150 0.00 nonurban │ 0 150 0.00 county │ 0 150 0.00 state │ 0 150 0.00 fips │ 0 150 0.00 cases │ 0 150 0.00 deaths │ 0 150 0.00 nonenglish │ 0 150 0.00 farmwork │ 4 150 2.67 uninsured │ 0 150 0.00 poverty │ 1 150 0.67 older │ 0 150 0.00 pop_dens │ 0 150 0.00 date_case1 │ 0 150 0.00 time_case1 │ 0 150 0.00 sip_effect │ 12 150 8.00 date_case100 │ 89 150 59.33 time_case100 │ 0 150 0.00 ────────────────┼───────────────────────────────────────────────
. ta state, m state │ Freq. Percent Cum. ─────────────────────┼─────────────────────────────────── Arizona │ 12 8.00 8.00 California │ 6 4.00 12.00 Colorado │ 57 38.00 50.00 Kansas │ 21 14.00 64.00 Nebraska │ 10 6.67 70.67 New Mexico │ 21 14.00 84.67 Oklahoma │ 3 2.00 86.67 Texas │ 5 3.33 90.00 Utah │ 13 8.67 98.67 Wyoming │ 2 1.33 100.00 ─────────────────────┼─────────────────────────────────── Total │ 150 100.00
. ta nonurban, m Non-urban │ counties │ flag │ Freq. Percent Cum. ────────────┼─────────────────────────────────── urban │ 4 2.67 2.67 non-urban │ 146 97.33 100.00 ────────────┼─────────────────────────────────── Total │ 150 100.00
. univar deaths, dec(0) ────────────── Quantiles ────────────── Variable n Mean S.D. Min .25 Mdn .75 Max ─────────────────────────────────────────────────────────────────────────────── deaths 150 43 136 0 0 1 12 1311 ─────────────────────────────────────────────────────────────────────────────── . * 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_extended_sample.dta" , clear . * u "merged_covid_usa_prepared_extended.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 . mdesc Variable │ Missing Total Percent Missing ────────────────┼─────────────────────────────────────────────── date │ 0 146 0.00 nonurban │ 0 146 0.00 county │ 0 146 0.00 state │ 0 146 0.00 fips │ 0 146 0.00 cases │ 0 146 0.00 deaths │ 0 146 0.00 nonenglish │ 0 146 0.00 farmwork │ 4 146 2.74 uninsured │ 0 146 0.00 poverty │ 1 146 0.68 older │ 0 146 0.00 pop_dens │ 0 146 0.00 date_case1 │ 0 146 0.00 time_case1 │ 0 146 0.00 sip_effect │ 12 146 8.22 date_case100 │ 89 146 60.96 time_case100 │ 0 146 0.00 ────────────────┼─────────────────────────────────────────────── . drop if mi(farmwork) (4 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 16 from master 0 (_merge==1) from using 16 (_merge==2) matched 141 (_merge==3) ───────────────────────────────────────── . * merge 1:1 fips using cb_2018_us_county_20m_prep . assert _merge != 1 . keep if _merge == 3 (16 observations deleted) . drop _merge . . * grmap deaths
Using gs2sls
option (instead of ml
) to guard against potential heteroskedasticity.
. spmatrix create contiguity W, replace . . spregress deaths nonenglish farmwork uninsured poverty older pop_dens time_case1 time_case100, gs2sls dvarlag(W) (141 observations) (141 observations (places) used) (weighting matrix defines 141 places) Spatial autoregressive model Number of obs = 141 GS2SLS estimates Wald chi2(9) = 88.76 Prob > chi2 = 0.0000 Pseudo R2 = 0.3184 ─────────────┬──────────────────────────────────────────────────────────────── deaths │ Coef. Std. Err. z P>|z| [95% Conf. Interval] ─────────────┼──────────────────────────────────────────────────────────────── deaths │ nonenglish │ -.7747344 2.671196 -0.29 0.772 -6.010183 4.460714 farmwork │ 1.437903 1.769458 0.81 0.416 -2.030171 4.905977 uninsured │ .3591921 1.028018 0.35 0.727 -1.655687 2.374071 poverty │ 2.456317 1.613196 1.52 0.128 -.7054892 5.618123 older │ -1.734486 1.758349 -0.99 0.324 -5.180786 1.711815 pop_dens │ .3879637 .0647953 5.99 0.000 .2609672 .5149602 time_case1 │ .6737964 .3885301 1.73 0.083 -.0877086 1.435301 time_case100 │ -.7328983 .3591166 -2.04 0.041 -1.436754 -.0290428 _cons │ -77.00937 62.33612 -1.24 0.217 -199.1859 45.16719 ─────────────┼──────────────────────────────────────────────────────────────── W │ deaths │ .4377769 .1956335 2.24 0.025 .0543424 .8212114 ─────────────┴──────────────────────────────────────────────────────────────── Wald test of spatial terms: chi2(1) = 5.01 Prob > chi2 = 0.0252 . . * estat impact . . est sto m_extended_queen .
rook
contiguity. spmatrix create contiguity W, rook replace . . spregress deaths nonenglish farmwork uninsured poverty older pop_dens time_case1 time_case100, gs2sls dvarlag(W) (141 observations) (141 observations (places) used) (weighting matrix defines 141 places) Spatial autoregressive model Number of obs = 141 GS2SLS estimates Wald chi2(9) = 89.27 Prob > chi2 = 0.0000 Pseudo R2 = 0.3190 ─────────────┬──────────────────────────────────────────────────────────────── deaths │ Coef. Std. Err. z P>|z| [95% Conf. Interval] ─────────────┼──────────────────────────────────────────────────────────────── deaths │ nonenglish │ -.9039196 2.670872 -0.34 0.735 -6.138733 4.330894 farmwork │ 1.447637 1.767351 0.82 0.413 -2.016307 4.91158 uninsured │ .3879726 1.027182 0.38 0.706 -1.625267 2.401212 poverty │ 2.523433 1.60896 1.57 0.117 -.6300706 5.676937 older │ -1.82808 1.763444 -1.04 0.300 -5.284366 1.628207 pop_dens │ .3871985 .0647255 5.98 0.000 .2603389 .5140582 time_case1 │ .6675897 .3880934 1.72 0.085 -.0930594 1.428239 time_case100 │ -.7407261 .3589477 -2.06 0.039 -1.444251 -.0372015 _cons │ -75.96434 62.27776 -1.22 0.223 -198.0265 46.09782 ─────────────┼──────────────────────────────────────────────────────────────── W │ deaths │ .4337577 .1878628 2.31 0.021 .0655534 .8019621 ─────────────┴──────────────────────────────────────────────────────────────── Wald test of spatial terms: chi2(1) = 5.33 Prob > chi2 = 0.0209 . . est sto m_extended_rook . . est tab m_extended_queen m_extended_rook , b(%6.3f) p(%6.3f) ─────────────┬──────────────────── Variable │ m_ext~n m_ext~k ─────────────┼──────────────────── deaths │ nonenglish │ -0.775 -0.904 │ 0.772 0.735 farmwork │ 1.438 1.448 │ 0.416 0.413 uninsured │ 0.359 0.388 │ 0.727 0.706 poverty │ 2.456 2.523 │ 0.128 0.117 older │ -1.734 -1.828 │ 0.324 0.300 pop_dens │ 0.388 0.387 │ 0.000 0.000 time_case1 │ 0.674 0.668 │ 0.083 0.085 time_case100 │ -0.733 -0.741 │ 0.041 0.039 _cons │ -77.009 -75.964 │ 0.217 0.223 ─────────────┼──────────────────── W │ deaths │ 0.438 0.434 │ 0.025 0.021 ─────────────┴──────────────────── legend: b/p