Ragdad Cani Miranti
Suggested citation: Miranti, Ragdad Cani. 2020. Regional Poverty, Convergence,and Spatial Effects: A Spatial Econometric Approach https://rpubs.com/canimiranti/stata_spatial_panel_514districts
This work is licensed under the Creative Commons Attribution-Share Alike
4.0 International License. 
All data are derived from the Indonesia Central Bureau of Statistics (Badan Pusat Statistik Republik of Indonesia). https://www.bps.go.id/
In this case, data3.dta is the panel ( long data) form
sysuse data3end of do-file
sysuse data3
label variable fips "District ID"
label variable district "District name"
label variable pov " Poverty Rate"
label variable gap " Poverty Gap Index"
label variable sev " Poverty Severity Index"
label variable agr " Total GRDP of Agriculture sector at district-i"
label variable ind "Total GRDP of Industry sector at district-i"
label variable gpov "Growth of Poverty Rate"
label variable gsev "Growth of Poverty Severity Index"
label variable ggap "Growth of Poverty Gap Index"
label variable mys "Mean Year School"
label variable shr_agr "Share of Agricultural sector to total GRDP"
label variable unemp "Unemployment Rate"
label variable gdpgr "Economic growth"
label variable shr_ind "Share of industry sector to total GRDP"
label variable subs_rice "Percentage of poor purchase subsidized rice"
label variable inv_shr "Share of Public investment to GDP"
label variable gdi "Gender Development Index"
describe
summarizeContains data from ./data3.dta
obs: 4,626
vars: 27 27 Jan 2021 09:42
size: 670,770
-------------------------------------------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------------------------------------------------------------
fips int %8.0g District ID
year int %8.0g
district str26 %26s District name
service float %9.0g
pov float %9.0g Poverty Rate
gap float %9.0g Poverty Gap Index
sev float %9.0g Poverty Severity Index
mys float %9.0g Mean Year School
agr float %9.0g Total GRDP of Agriculture sector at district-i
unemp float %9.0g Unemployment Rate
gdpgr float %9.0g Economic growth
inv double %10.0g
ind float %9.0g Total GRDP of Industry sector at district-i
subs_rice float %9.0g Percentage of poor purchase subsidized rice
gdi float %9.0g Gender Development Index
gdp long %12.0g
island str19 %19s
gpov float %9.0g Growth of Poverty Rate
ggap float %9.0g Growth of Poverty Gap Index
gsev float %9.0g Growth of Poverty Severity Index
shr_ind float %9.0g Share of industry sector to total GRDP
shr_agr float %9.0g Share of Agricultural sector to total GRDP
ln_pov float %9.0g
ln_gap float %9.0g
ln_sev float %9.0g
inv_shr float %9.0g Share of Public investment to GDP
ln_inv float %9.0g
-------------------------------------------------------------------------------------------------------------------------------------
Sorted by:
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
fips | 4,626 4574.257 2678.595 1101 9471
year | 4,626 2014 2.582268 2010 2018
district | 0
service | 4,626 6.614797 9.538464 0 64.74
pov | 4,626 14.22667 8.705273 1.67 49.58
-------------+---------------------------------------------------------
gap | 4,626 2.374658 2.300813 .05 19.16
sev | 4,626 .7474189 .97928 .01 10.15
mys | 4,626 7.645441 1.717653 .25 12.6
agr | 4,626 2863.28 3989.058 23.53 62445
unemp | 4,626 5.343531 3.081055 0 19.84
-------------+---------------------------------------------------------
gdpgr | 4,626 5.997218 2.943957 -14.49 107.07
inv | 4,626 1.59e+09 1.37e+10 407000 2.22e+11
ind | 4,626 4914.623 15559.28 0 238957.1
subs_rice | 4,626 57.89956 25.12048 0 116.5955
gdi | 4,626 88.12807 7.235699 24.1 99.75
-------------+---------------------------------------------------------
gdp | 4,626 2.09e+07 3.64e+07 174740 3.65e+08
island | 0
gpov | 4,626 -.0517686 .0535961 -.3567133 .1496517
ggap | 4,626 -.0500954 .2633248 -1.241657 1.161183
gsev | 4,626 -.0745803 .3329664 -1.778151 1.342423
-------------+---------------------------------------------------------
shr_ind | 4,626 6.462646 10.54983 0 88.27
shr_agr | 4,626 6.618044 6.446088 .01 51.65
ln_pov | 4,626 2.483652 .5925594 .5128236 3.903588
ln_gap | 4,626 .5225219 .8400528 -2.995732 2.952825
ln_sev | 4,626 -.7647654 .9530818 -4.60517 2.317474
-------------+---------------------------------------------------------
inv_shr | 4,626 .3126588 4.260859 .0000299 149.0742
ln_inv | 4,626 19.24801 .9199063 12.91657 26.12594
##save myPANEL data
sysuse data3
save, replacefile ./data3.dta saved
Import and translate to stata shapa file
spshape2dta INDO_KAB_2016, replace
* NOTE: Two stata files will be created
* INDO_KAB_2016_shp.dta
* INDO_KAB_2016.dta
*Explore my spatial data: myMAP
use INDO_KAB_2016
*Describe and summarize myMAP.dta
describe
summarize
*Generate new spatial-unit id: fips
destring IDKAB, generate(fips)
save, replace
*Change the spatial-unit id from _ID to fips
spset fips, modify replace
*Modify the coordinate system from planar to latlong
spset, modify coordsys(latlong, miles)
*Check spatial ID and coordinate system
spset (importing .dbf file)
(creating _ID spatial-unit id)
(creating _CX coordinate)
(creating _CY coordinate)
file INDO_KAB_2016_shp.dta created
file INDO_KAB_2016.dta created
Contains data from INDO_KAB_2016.dta
obs: 522
vars: 12 10 Feb 2021 11:45
size: 52,722
-------------------------------------------------------------------------------------------------------------------------------------
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
PROVNO str2 %9s PROVNO
KABKOTNO str2 %9s KABKOTNO
PROVINSI str26 %26s PROVINSI
KABKOT str26 %26s KABKOT
IDKAB str4 %9s IDKAB
TAHUN str4 %9s TAHUN
SUMBER str3 %9s SUMBER
COORD_X double %18.7f COORD_X
COORD_Y double %18.7f COORD_Y
-------------------------------------------------------------------------------------------------------------------------------------
Sorted by: _ID
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
_ID | 522 261.5 150.8327 1 522
_CX | 522 113.2422 10.98185 95.3056 140.8089
_CY | 522 -3.223506 3.866893 -10.72604 5.837818
PROVNO | 0
KABKOTNO | 0
-------------+---------------------------------------------------------
PROVINSI | 0
KABKOT | 0
IDKAB | 0
TAHUN | 0
SUMBER | 0
-------------+---------------------------------------------------------
COORD_X | 522 113.2427 10.97469 95.27568 140.7202
COORD_Y | 522 -3.238102 3.875886 -10.69717 5.85758
IDKAB: all characters numeric; fips generated as int
file INDO_KAB_2016.dta saved
(_shp.dta file saved)
(data in memory saved)
Sp dataset INDO_KAB_2016.dta
data: cross sectional
spatial-unit id: _ID (equal to fips)
coordinates: _CX, _CY (planar)
linked shapefile: INDO_KAB_2016_shp.dta
Sp dataset INDO_KAB_2016.dta
data: cross sectional
spatial-unit id: _ID (equal to fips)
coordinates: _CY, _CX (latitude-and-longitude, miles)
linked shapefile: INDO_KAB_2016_shp.dta
Sp dataset INDO_KAB_2016.dta
data: cross sectional
spatial-unit id: _ID (equal to fips)
coordinates: _CY, _CX (latitude-and-longitude, miles)
linked shapefile: INDO_KAB_2016_shp.dta
sysuse data3
xtset fips year
spbalance
merge m:1 fips using INDO_KAB_2016
keep if _merge==3
drop _merge
tset
**Save the merge of my map and panel data
save mymap_and_panel,replace panel variable: fips (strongly balanced)
time variable: year, 2010 to 2018
delta: 1 unit
(data strongly balanced)
Result # of obs.
-----------------------------------------
not matched 8
from master 0 (_merge==1)
from using 8 (_merge==2)
matched 4,626 (_merge==3)
-----------------------------------------
(8 observations deleted)
panel variable: fips (strongly balanced)
time variable: year, 2010 to 2018
delta: 1 unit
file mymap_and_panel.dta saved
This is my mymap_and_panel.dta ( the merge between MAP and panel data)
sysuse mymap_and_panel
describeContains data from ./mymap_and_panel.dta
obs: 4,626
vars: 39 10 Feb 2021 11:45
size: 1,137,996
-------------------------------------------------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------------------------------------------------------------
fips int %8.0g Spatial-unit ID
year int %8.0g
district str26 %26s District name
service float %9.0g
pov float %9.0g
gap float %9.0g
sev float %9.0g
mys float %9.0g Mean Year School
agr float %9.0g
unemp float %9.0g Unemployment Rate
gdpgr float %9.0g Economic growth
inv double %10.0g
ind float %9.0g
subs_rice float %9.0g Percentage of poor receiving subsidized rice
gdi float %9.0g Gender Development Index
gdp long %12.0g
island str19 %19s
gpov float %9.0g Growth of Poverty Rate
ggap float %9.0g Growth of Poverty Gap Index
gsev float %9.0g Growth of Poverty Severity Index
shr_ind float %9.0g Share of Manufacturing sector GRDP
shr_agr float %9.0g Share of Agricultural sector GRDP
ln_pov float %9.0g
ln_gap float %9.0g
ln_sev float %9.0g
inv_shr float %9.0g Public investment to GDP
ln_inv float %9.0g
_ID int %10.0g Spatial-unit ID
_CX double %10.0g x-coordinate of area centroid
_CY double %10.0g y-coordinate of area centroid
PROVNO str2 %9s PROVNO
KABKOTNO str2 %9s KABKOTNO
PROVINSI str26 %26s PROVINSI
KABKOT str26 %26s KABKOT
IDKAB str4 %9s IDKAB
TAHUN str4 %9s TAHUN
SUMBER str3 %9s SUMBER
COORD_X double %18.7f COORD_X
COORD_Y double %18.7f COORD_Y
-------------------------------------------------------------------------------------------------------------------------------------
Sorted by: fips year
To create weight matrix in panel model, firstly we must create weight matrix in the cross-sectional (wide) data containing COORD (X) and COORD (Y) and spatial-ID. In this case, I rename my cross-sectional data with datacross.dta. I use inverse distance matrix as an example.
sysuse datacross.dta
spmat idistance datacross coord_x coord_y, id(fips) normalize (row)
spmat export datacross using WaWa already exists
r(498);
end of do-file
r(498);
We use similar syntax for poverty rate, poverty gap, and poverty severity index
**OLS Fixed Effect
sysuse mymap_and_panel
*Poverty rate
quietly xtreg gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, fe
gen speed1 = - (log(1+_b[ln_pov])/8)
gen halfLife1 = log(2)/speed1
quietly estat ic
eststo model1
*The same for poverty gap
quietly xtreg ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, fe
gen speed2 = - (log(1+_b[ln_gap])/8)
gen halfLife2 = log(2)/speed2
quietly estat ic
eststo model2
*The same for poverty severity
quietly xtreg gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, fe
gen speed3 = - (log(1+_b[ln_sev])/8)
gen halfLife3 = log(2)/speed3
quietly estat ic
eststo model3
esttab, r2 aic bic
estimates store ols_fe (1) (2) (3)
gpov ggap gsev
------------------------------------------------------------
ln_pov -0.0249***
(-14.02)
mys -0.00375*** -0.0258*** -0.0381***
(-6.25) (-10.81) (-11.08)
gdpgr -0.000843*** 0.0000283 0.000111
(-3.73) (0.03) (0.08)
unemp 0.0000740 0.000496 -0.00183
(0.28) (0.46) (-1.17)
subs_rice 0.000140*** 0.000554*** 0.00104***
(4.23) (4.07) (5.29)
gdi -0.0000637 0.000477 0.00111
(-0.53) (0.96) (1.54)
inv_shr 0.0000279 0.000430 0.00117
(0.15) (0.58) (1.07)
shr_agr 0.000400*** 0.000851 -0.00214**
(3.50) (1.80) (-3.11)
shr_ind -0.000389*** 0.000435 0.00113**
(-6.04) (1.64) (2.91)
ln_gap -0.145***
(-28.96)
ln_sev -0.205***
(-35.80)
_cons 0.0407*** 0.138*** -0.0834
(3.49) (3.34) (-1.41)
------------------------------------------------------------
N 4626 4626 4626
R-sq 0.060 0.175 0.247
AIC -17629.0 -4510.3 -999.0
BIC -17564.6 -4445.9 -934.6
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
In spatial panel model,there are three types of fixed-effect options: time-fixed effect, region-fixed effect and time and region fixed-effect. In this tutorail, I use region (district) fixed effect.
SLM stands for Spatial Lag Model using Fixed Effect with Region (District) Fixed Effect and SAR Random Effect Model. Import our weight matrix Wa into our panel dataset. I rename with Wi.
This syntax generates direct and indirect effects as well by adding code ‘effects’ into model.
**SAR Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) fe type (ind) effects
gen speed4 = - (log(1+_b[ln_pov])/8)
gen halfLife4 = log(2)/speed4
quietly estat ic
eststo model4
** The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) fe type (ind) effects
gen speed5 = - (log(1+_b[ln_gap])/8)
gen halfLife5 = log(2)/speed5
quietly estat ic
eststo model5
** The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) fe type (ind) effects
gen speed6 = - (log(1+_b[ln_sev])/8)
gen halfLife6 = log(2)/speed6
quietly estat ic
eststo model6
esttab, r2 aic bic
estimates store sar_fe
** SAR Random Effect
*Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) re
gen speed7 = - (log(1+_b[ln_pov])/8)
gen halfLife7 = log(2)/speed7
quietly estat ic
eststo model7
*The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) re
gen speed8 = - (log(1+_b[ln_gap])/8)
gen halfLife8 = log(2)/speed8
quietly estat ic
eststo model8
*The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sar) re
gen speed9 = - (log(1+_b[ln_sev])/8)
gen halfLife9 = log(2)/speed9
quietly estat ic
eststo model9
esttab, r2 aic bic
estimates store sar_re
** Conducting Hausman Test
hausman sar_fe sar_re (1) (2) (3)
gpov ggap gsev
------------------------------------------------------------
Main
ln_pov -0.0249***
(-14.94)
mys -0.00375*** -0.0259*** -0.0381***
(-6.64) (-11.49) (-11.77)
gdpgr -0.000841*** 0.0000338 0.000114
(-3.96) (0.04) (0.09)
unemp 0.0000732 0.000500 -0.00183
(0.30) (0.50) (-1.24)
subs_rice 0.000141*** 0.000553*** 0.00104***
(4.54) (4.31) (5.62)
gdi -0.0000579 0.000477 0.00111
(-0.51) (1.02) (1.63)
inv_shr 0.0000106 0.000428 0.00116
(0.06) (0.61) (1.13)
shr_agr 0.000394*** 0.000852 -0.00214***
(3.67) (1.92) (-3.30)
shr_ind -0.000380*** 0.000435 0.00113**
(-6.29) (1.74) (3.09)
ln_gap -0.145***
(-30.76)
ln_sev -0.205***
(-38.02)
------------------------------------------------------------
Spatial
rho -0.449*** 0.0514 0.0194
(-3.97) (0.54) (0.20)
------------------------------------------------------------
Variance
sigma2_e 0.00128*** 0.0220*** 0.0470***
(48.05) (48.09) (48.09)
------------------------------------------------------------
LR_Direct
ln_pov -0.0249***
(-14.54)
mys -0.00377*** -0.0259*** -0.0382***
(-6.91) (-11.97) (-12.27)
gdpgr -0.000821*** 0.000126 0.000249
(-4.04) (0.15) (0.20)
unemp 0.0000754 0.000508 -0.00182
(0.32) (0.51) (-1.26)
subs_rice 0.000141*** 0.000552*** 0.00104***
(4.68) (4.44) (5.77)
gdi -0.0000504 0.000509 0.00116
(-0.44) (1.09) (1.70)
inv_shr 0.0000103 0.000427 0.00116
(0.06) (0.59) (1.09)
shr_agr 0.000392*** 0.000840* -0.00216***
(3.82) (1.98) (-3.49)
shr_ind -0.000376*** 0.000457 0.00116**
(-6.34) (1.87) (3.25)
ln_gap -0.145***
(-29.91)
ln_sev -0.205***
(-36.98)
------------------------------------------------------------
LR_Indirect
ln_pov 0.00758***
(5.15)
mys 0.00115*** -0.00181 -0.00132
(4.20) (-0.61) (-0.32)
gdpgr 0.000250*** 0.00000391 0.00000175
(3.31) (0.03) (0.01)
unemp -0.0000236 0.0000322 -0.0000696
(-0.31) (0.22) (-0.26)
subs_rice -0.0000432*** 0.0000377 0.0000354
(-3.48) (0.60) (0.31)
gdi 0.0000155 0.0000369 0.0000420
(0.44) (0.43) (0.28)
inv_shr -0.00000294 0.0000287 0.0000355
(-0.05) (0.27) (0.21)
shr_agr -0.000119** 0.0000610 -0.0000717
(-3.17) (0.52) (-0.30)
shr_ind 0.000114*** 0.0000305 0.0000384
(4.22) (0.52) (0.30)
ln_gap -0.0101
(-0.62)
ln_sev -0.00710
(-0.32)
------------------------------------------------------------
LR_Total
ln_pov -0.0173***
(-9.08)
mys -0.00262*** -0.0277*** -0.0395***
(-6.02) (-7.35) (-7.49)
gdpgr -0.000571*** 0.000130 0.000250
(-3.76) (0.14) (0.20)
unemp 0.0000518 0.000540 -0.00189
(0.31) (0.51) (-1.25)
subs_rice 0.0000982*** 0.000590*** 0.00108***
(4.41) (4.08) (4.98)
gdi -0.0000349 0.000546 0.00120
(-0.44) (1.07) (1.65)
inv_shr 0.00000736 0.000456 0.00120
(0.06) (0.59) (1.09)
shr_agr 0.000273*** 0.000901 -0.00223**
(3.55) (1.89) (-3.27)
shr_ind -0.000261*** 0.000487 0.00120**
(-5.46) (1.84) (3.10)
ln_gap -0.155***
(-8.83)
ln_sev -0.212***
(-9.07)
------------------------------------------------------------
N 4626 4626 4626
R-sq 0.007 0.168 0.209
AIC -17607.3 -4472.6 -961.1
BIC -17420.5 -4285.9 -774.3
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
------------------------------------------------------------------------------------------------------------
(1) (2) (3) (4) (5) (6)
gpov ggap gsev gpov ggap gsev
------------------------------------------------------------------------------------------------------------
Main
ln_pov -0.0249*** -0.0201***
(-14.94) (-11.97)
mys -0.00375*** -0.0259*** -0.0381*** -0.00322*** -0.0260*** -0.0387***
(-6.64) (-11.49) (-11.77) (-5.50) (-11.11) (-11.64)
gdpgr -0.000841*** 0.0000338 0.000114 -0.000952*** -0.000102 0.000161
(-3.96) (0.04) (0.09) (-4.29) (-0.11) (0.12)
unemp 0.0000732 0.000500 -0.00183 -0.000255 -0.0000866 -0.00236
(0.30) (0.50) (-1.24) (-1.01) (-0.08) (-1.57)
subs_rice 0.000141*** 0.000553*** 0.00104*** 0.0000781* 0.000448*** 0.00107***
(4.54) (4.31) (5.62) (2.45) (3.42) (5.77)
gdi -0.0000579 0.000477 0.00111 -0.0000137 0.000315 0.000494
(-0.51) (1.02) (1.63) (-0.12) (0.65) (0.71)
inv_shr 0.0000106 0.000428 0.00116 -0.0000590 0.000269 0.000619
(0.06) (0.61) (1.13) (-0.34) (0.38) (0.60)
shr_agr 0.000394*** 0.000852 -0.00214*** 0.000400*** 0.000526 -0.00271***
(3.67) (1.92) (-3.30) (3.59) (1.14) (-4.08)
shr_ind -0.000380*** 0.000435 0.00113** -0.000381*** 0.000420 0.00116**
(-6.29) (1.74) (3.09) (-5.95) (1.59) (3.02)
ln_gap -0.145*** -0.143***
(-30.76) (-30.54)
ln_sev -0.205*** -0.198***
(-38.02) (-37.17)
_cons 0.0499*** 0.197*** 0.00518
(4.10) (4.80) (0.09)
------------------------------------------------------------------------------------------------------------
Spatial
rho -0.449*** 0.0514 0.0194 0.440*** 0.560*** 0.245**
(-3.97) (0.54) (0.20) (5.37) (8.05) (2.75)
------------------------------------------------------------------------------------------------------------
Variance
sigma2_e 0.00128*** 0.0220*** 0.0470*** 0.00147*** 0.0248*** 0.0529***
(48.05) (48.09) (48.09) (45.05) (45.29) (45.29)
lgt_theta -0.636*** -0.836*** -0.465***
(-11.47) (-16.98) (-8.48)
------------------------------------------------------------------------------------------------------------
LR_Direct
ln_pov -0.0249***
(-14.54)
mys -0.00377*** -0.0259*** -0.0382***
(-6.91) (-11.97) (-12.27)
gdpgr -0.000821*** 0.000126 0.000249
(-4.04) (0.15) (0.20)
unemp 0.0000754 0.000508 -0.00182
(0.32) (0.51) (-1.26)
subs_rice 0.000141*** 0.000552*** 0.00104***
(4.68) (4.44) (5.77)
gdi -0.0000504 0.000509 0.00116
(-0.44) (1.09) (1.70)
inv_shr 0.0000103 0.000427 0.00116
(0.06) (0.59) (1.09)
shr_agr 0.000392*** 0.000840* -0.00216***
(3.82) (1.98) (-3.49)
shr_ind -0.000376*** 0.000457 0.00116**
(-6.34) (1.87) (3.25)
ln_gap -0.145***
(-29.91)
ln_sev -0.205***
(-36.98)
------------------------------------------------------------------------------------------------------------
LR_Indirect
ln_pov 0.00758***
(5.15)
mys 0.00115*** -0.00181 -0.00132
(4.20) (-0.61) (-0.32)
gdpgr 0.000250*** 0.00000391 0.00000175
(3.31) (0.03) (0.01)
unemp -0.0000236 0.0000322 -0.0000696
(-0.31) (0.22) (-0.26)
subs_rice -0.0000432*** 0.0000377 0.0000354
(-3.48) (0.60) (0.31)
gdi 0.0000155 0.0000369 0.0000420
(0.44) (0.43) (0.28)
inv_shr -0.00000294 0.0000287 0.0000355
(-0.05) (0.27) (0.21)
shr_agr -0.000119** 0.0000610 -0.0000717
(-3.17) (0.52) (-0.30)
shr_ind 0.000114*** 0.0000305 0.0000384
(4.22) (0.52) (0.30)
ln_gap -0.0101
(-0.62)
ln_sev -0.00710
(-0.32)
------------------------------------------------------------------------------------------------------------
LR_Total
ln_pov -0.0173***
(-9.08)
mys -0.00262*** -0.0277*** -0.0395***
(-6.02) (-7.35) (-7.49)
gdpgr -0.000571*** 0.000130 0.000250
(-3.76) (0.14) (0.20)
unemp 0.0000518 0.000540 -0.00189
(0.31) (0.51) (-1.25)
subs_rice 0.0000982*** 0.000590*** 0.00108***
(4.41) (4.08) (4.98)
gdi -0.0000349 0.000546 0.00120
(-0.44) (1.07) (1.65)
inv_shr 0.00000736 0.000456 0.00120
(0.06) (0.59) (1.09)
shr_agr 0.000273*** 0.000901 -0.00223**
(3.55) (1.89) (-3.27)
shr_ind -0.000261*** 0.000487 0.00120**
(-5.46) (1.84) (3.10)
ln_gap -0.155***
(-8.83)
ln_sev -0.212***
(-9.07)
------------------------------------------------------------------------------------------------------------
N 4626 4626 4626 4626 4626 4626
R-sq 0.007 0.168 0.209 0.014 0.188 0.212
AIC -17607.3 -4472.6 -961.1 -15923.9 -2696.8 542.1
BIC -17420.5 -4285.9 -774.3 -15840.1 -2613.1 625.8
------------------------------------------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| sar_fe sar_re Difference S.E.
-------------+----------------------------------------------------------------
ln_sev | -.2054804 -.1976194 -.007861 .0009758
mys | -.0380943 -.0386616 .0005673 .
gdpgr | .0001138 .0001607 -.0000469 .
unemp | -.0018342 -.0023628 .0005286 .
subs_rice | .001043 .001069 -.000026 9.42e-06
gdi | .0011141 .0004944 .0006197 .
inv_shr | .0011612 .0006186 .0005426 .0000556
shr_agr | -.0021405 -.0027115 .000571 .
shr_ind | .0011279 .0011587 -.0000308 .
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xsmle
B = inconsistent under Ha, efficient under Ho; obtained from xsmle
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 133.24
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
SLM stands for Spatial Error Model using Fixed Effect with Region (District) Fixed Effect. Import our weight matrix Wa into our panel dataset. In the SEM mode, the direct and indirect effects can not be generated.
*SEM Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, emat (Wi) model (sem) fe type (ind)
gen speed10 = - (log(1+_b[ln_pov])/8)
gen halfLife10 = log(2)/speed10
quietly estat ic
eststo model10
**The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, emat (Wi) model (sem) fe type (ind) effects
gen speed11 = - (log(1+_b[ln_gap])/8)
gen halfLife11 = log(2)/speed11
quietly estat ic
eststo model11
**The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, emat (Wi) model (sem) fe type (ind) effects
gen speed12 = - (log(1+_b[ln_sev])/8)
gen halfLife12 = log(2)/speed12
quietly estat ic
eststo model12
esttab, r2 aic bic
estimates store sem_fe (1) (2) (3)
gpov ggap gsev
------------------------------------------------------------
Main
ln_pov -0.0249***
(-14.97)
mys -0.00372*** -0.0259*** -0.0382***
(-6.60) (-11.50) (-11.80)
gdpgr -0.000841*** 0.0000369 0.000124
(-3.96) (0.04) (0.10)
unemp 0.0000787 0.000499 -0.00188
(0.32) (0.49) (-1.27)
subs_rice 0.000143*** 0.000552*** 0.00105***
(4.62) (4.30) (5.64)
gdi -0.0000560 0.000486 0.00114
(-0.50) (1.04) (1.66)
inv_shr 0.0000114 0.000409 0.00111
(0.07) (0.58) (1.07)
shr_agr 0.000394*** 0.000845 -0.00216***
(3.68) (1.90) (-3.33)
shr_ind -0.000372*** 0.000436 0.00113**
(-6.14) (1.74) (3.11)
ln_gap -0.145***
(-30.76)
ln_sev -0.206***
(-38.04)
------------------------------------------------------------
Spatial
lambda -0.439*** 0.0681 0.0905
(-3.80) (0.68) (0.88)
------------------------------------------------------------
Variance
sigma2_e 0.00128*** 0.0220*** 0.0470***
(48.05) (48.09) (48.09)
------------------------------------------------------------
N 4626 4626 4626
R-sq 0.007 0.167 0.209
AIC -17641.9 -4508.8 -997.8
BIC -17571.1 -4437.9 -927.0
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
SAC stands for Spatial Autoregressive Combined using Fixed Effect with Region (District) Import our weight matrix Wa into our panel dataset
*SAC Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) emat (Wi) model (sac) fe type (ind) effects
gen speed13 = - (log(1+_b[ln_pov])/8)
gen halfLife13 = log(2)/speed13
quietly estat ic
eststo model13
**The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) emat (Wi) model (sac) fe type (ind) effects
gen speed14 = - (log(1+_b[ln_gap])/8)
gen halfLife14 = log(2)/speed14
quietly estat ic
eststo model14
**The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) emat (Wi) model (sac) fe type (ind) effects
gen speed15 = - (log(1+_b[ln_sev])/8)
gen halfLife15 = log(2)/speed15
quietly estat ic
eststo model15
esttab, r2 aic bic
estimates store sac_fe (1) (2) (3)
gpov ggap gsev
------------------------------------------------------------
Main
ln_pov -0.0249***
(-14.91)
mys -0.00375*** -0.0260*** -0.0384***
(-6.64) (-11.50) (-11.87)
gdpgr -0.000841*** 0.0000382 0.000113
(-3.96) (0.04) (0.09)
unemp 0.0000728 0.000495 -0.00195
(0.30) (0.49) (-1.32)
subs_rice 0.000141*** 0.000551*** 0.00105***
(4.52) (4.29) (5.67)
gdi -0.0000581 0.000497 0.00118
(-0.51) (1.06) (1.73)
inv_shr 0.0000110 0.000385 0.00106
(0.06) (0.53) (0.99)
shr_agr 0.000394*** 0.000835 -0.00219***
(3.68) (1.88) (-3.37)
shr_ind -0.000381*** 0.000436 0.00114**
(-6.27) (1.75) (3.13)
ln_gap -0.145***
(-30.72)
ln_sev -0.205***
(-37.97)
------------------------------------------------------------
Spatial
rho -0.483 -0.0949 -0.232
(-1.25) (-0.31) (-1.15)
lambda 0.0320 0.157 0.285
(0.09) (0.53) (1.55)
------------------------------------------------------------
Variance
sigma2_e 0.00144*** 0.0247*** 0.0527***
(53.31) (53.95) (53.84)
------------------------------------------------------------
LR_Direct
ln_pov -0.0249***
(-14.55)
mys -0.00377*** -0.0261*** -0.0385***
(-6.91) (-11.98) (-12.36)
gdpgr -0.000821*** 0.000131 0.000248
(-4.04) (0.16) (0.20)
unemp 0.0000751 0.000503 -0.00193
(0.31) (0.51) (-1.34)
subs_rice 0.000141*** 0.000551*** 0.00105***
(4.66) (4.42) (5.82)
gdi -0.0000506 0.000530 0.00123
(-0.45) (1.13) (1.80)
inv_shr 0.0000107 0.000384 0.00105
(0.06) (0.51) (0.96)
shr_agr 0.000392*** 0.000824 -0.00221***
(3.82) (1.93) (-3.56)
shr_ind -0.000377*** 0.000458 0.00117***
(-6.31) (1.88) (3.29)
ln_gap -0.145***
(-29.88)
ln_sev -0.205***
(-37.10)
------------------------------------------------------------
LR_Indirect
ln_pov 0.00633
(0.92)
mys 0.000966 -0.000740 0.00617
(0.91) (-0.06) (1.02)
gdpgr 0.000209 -0.00000270 -0.0000479
(0.93) (-0.01) (-0.17)
unemp -0.0000191 0.0000103 0.000308
(-0.18) (0.02) (0.69)
subs_rice -0.0000358 0.0000171 -0.000169
(-0.86) (0.06) (-1.00)
gdi 0.0000141 0.00000736 -0.000200
(0.33) (0.02) (-0.82)
inv_shr -0.00000363 0.0000433 -0.000162
(-0.05) (0.11) (-0.53)
shr_agr -0.0000991 0.0000474 0.000360
(-0.82) (0.09) (0.98)
shr_ind 0.0000971 0.00000974 -0.000190
(0.91) (0.04) (-0.95)
ln_gap -0.00455
(-0.07)
ln_sev 0.0327
(1.02)
------------------------------------------------------------
LR_Total
ln_pov -0.0186**
(-2.60)
mys -0.00281* -0.0268* -0.0324***
(-2.51) (-2.22) (-4.99)
gdpgr -0.000613* 0.000128 0.000200
(-2.24) (0.12) (0.19)
unemp 0.0000560 0.000513 -0.00163
(0.28) (0.42) (-1.28)
subs_rice 0.000106* 0.000568 0.000882***
(2.20) (1.88) (4.03)
gdi -0.0000366 0.000537 0.00103
(-0.41) (0.95) (1.68)
inv_shr 0.00000711 0.000427 0.000893
(0.05) (0.49) (0.94)
shr_agr 0.000293* 0.000871 -0.00185**
(2.00) (1.23) (-3.04)
shr_ind -0.000280* 0.000468 0.000981**
(-2.49) (1.41) (2.85)
ln_gap -0.150*
(-2.21)
ln_sev -0.173***
(-5.24)
------------------------------------------------------------
N 4626 4626 4626
R-sq 0.007 0.165 0.210
AIC -17605.3 -4470.9 -961.1
BIC -17412.1 -4277.7 -767.9
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
SDM stands for Spatial Durbin Model using Fixed Effect with Region (District) Fixed Effect and SDM Random Effect Model. Import our weight matrix Wa into our panel dataset
*SDM Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) fe type (ind) effects
gen speed16 = - (log(1+_b[ln_pov])/8)
gen halfLife16 = log(2)/speed16
quietly estat ic
eststo model16
**The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) fe type (ind) effects
gen speed17 = - (log(1+_b[ln_gap])/8)
gen halfLife17 = log(2)/speed17
quietly estat ic
eststo model17
**The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) fe type (ind) effects
gen speed18 = - (log(1+_b[ln_sev])/8)
gen halfLife18 = log(2)/speed18
quietly estat ic
eststo model18
esttab, r2 aic bic
estimates store sdm_fe
*SDM Random Effect Model
**Poverty rate
quietly xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) re
gen speed19 = - (log(1+_b[ln_pov])/8)
gen halfLife19 = log(2)/speed19
quietly estat ic
eststo model19
**The same for poverty gap
quietly xsmle ggap ln_gap mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) re
gen speed20 = - (log(1+_b[ln_gap])/8)
gen halfLife20 = log(2)/speed20
quietly estat ic
eststo model20
**The same for poverty severity
quietly xsmle gsev ln_sev mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, wmat (Wi) model (sdm) re
gen speed21 = - (log(1+_b[ln_sev])/8)
gen halfLife21 = log(2)/speed21
quietly estat ic
eststo model21
esttab, r2 aic bic
estimates store sdm_re
**Conducting Hausman Test
hausman sdm_fe sdm_re (1) (2) (3)
gpov ggap gsev
------------------------------------------------------------
Main
ln_pov -0.0250***
(-15.01)
mys -0.00370*** -0.0258*** -0.0379***
(-6.56) (-11.45) (-11.73)
gdpgr -0.000857*** 0.000000752 -0.00000877
(-4.04) (0.00) (-0.01)
unemp 0.0000549 0.000516 -0.00177
(0.23) (0.51) (-1.20)
subs_rice 0.000135*** 0.000553*** 0.00106***
(4.33) (4.31) (5.71)
gdi -0.0000575 0.000503 0.00120
(-0.51) (1.08) (1.77)
inv_shr 0.0000426 0.0000264 0.000378
(0.23) (0.03) (0.33)
shr_agr 0.000403*** 0.000853 -0.00224***
(3.75) (1.92) (-3.46)
shr_ind -0.000382*** 0.000406 0.00110**
(-6.32) (1.62) (3.01)
ln_gap -0.146***
(-30.87)
ln_sev -0.206***
(-38.23)
------------------------------------------------------------
Wx
ln_pov -0.0178
(-0.97)
mys 0.00492 0.0769** 0.134***
(0.73) (2.84) (3.42)
gdpgr -0.000764 -0.0108 -0.0128
(-0.29) (-1.00) (-0.82)
unemp 0.000302 -0.00105 0.0324*
(0.11) (-0.09) (2.01)
subs_rice 0.000359 0.00212 -0.00118
(1.14) (1.66) (-0.64)
gdi 0.00101 -0.00883 -0.0135
(0.81) (-1.71) (-1.81)
inv_shr -0.0000950 0.00294 0.00517
(-0.14) (1.07) (1.30)
shr_agr -0.00000770 0.00591 0.0109
(-0.01) (1.28) (1.63)
shr_ind 0.00182** -0.00141 -0.00514
(2.74) (-0.51) (-1.28)
ln_gap 0.00384
(0.07)
ln_sev 0.0807
(1.34)
------------------------------------------------------------
Spatial
rho -0.523*** 0.0273 0.0446
(-4.45) (0.27) (0.43)
------------------------------------------------------------
Variance
sigma2_e 0.00128*** 0.0219*** 0.0467***
(48.03) (48.09) (48.09)
------------------------------------------------------------
LR_Direct
ln_pov -0.0249***
(-14.47)
mys -0.00375*** -0.0258*** -0.0380***
(-6.86) (-11.92) (-12.20)
gdpgr -0.000834*** 0.0000898 0.000120
(-4.10) (0.11) (0.10)
unemp 0.0000563 0.000523 -0.00175
(0.24) (0.53) (-1.21)
subs_rice 0.000133*** 0.000553*** 0.00106***
(4.40) (4.43) (5.84)
gdi -0.0000545 0.000532 0.00124
(-0.48) (1.14) (1.82)
inv_shr 0.0000429 0.0000265 0.000380
(0.22) (0.03) (0.32)
shr_agr 0.000401*** 0.000843* -0.00226***
(3.90) (1.98) (-3.64)
shr_ind -0.000386*** 0.000427 0.00113**
(-6.51) (1.75) (3.16)
ln_gap -0.146***
(-30.03)
ln_sev -0.206***
(-37.23)
------------------------------------------------------------
LR_Indirect
ln_pov -0.00267
(-0.22)
mys 0.00447 0.0784** 0.139**
(1.04) (2.80) (3.27)
gdpgr -0.0000864 -0.0104 -0.0124
(-0.05) (-0.88) (-0.70)
unemp 0.000112 -0.00155 0.0335*
(0.06) (-0.14) (1.98)
subs_rice 0.000194 0.00223 -0.00116
(0.91) (1.66) (-0.58)
gdi 0.000666 -0.00924 -0.0145
(0.83) (-1.77) (-1.84)
inv_shr -0.0000777 0.00305 0.00550
(-0.16) (1.04) (1.26)
shr_agr -0.0000972 0.00643 0.0119
(-0.12) (1.26) (1.57)
shr_ind 0.00133** -0.00153 -0.00551
(2.85) (-0.51) (-1.23)
ln_gap 0.00236
(0.05)
ln_sev 0.0781
(1.27)
------------------------------------------------------------
LR_Total
ln_pov -0.0276*
(-2.31)
mys 0.000720 0.0525 0.101*
(0.17) (1.87) (2.37)
gdpgr -0.000920 -0.0103 -0.0123
(-0.50) (-0.86) (-0.69)
unemp 0.000168 -0.00102 0.0317
(0.10) (-0.09) (1.86)
subs_rice 0.000327 0.00278* -0.000107
(1.54) (2.06) (-0.05)
gdi 0.000612 -0.00871 -0.0132
(0.76) (-1.66) (-1.67)
inv_shr -0.0000348 0.00308 0.00588
(-0.09) (1.15) (1.48)
shr_agr 0.000303 0.00727 0.00963
(0.39) (1.41) (1.27)
shr_ind 0.000944* -0.00110 -0.00438
(2.01) (-0.36) (-0.97)
ln_gap -0.143**
(-2.81)
ln_sev -0.128*
(-2.08)
------------------------------------------------------------
N 4626 4626 4626
R-sq 0.013 0.168 0.211
AIC -17610.2 -4467.9 -973.3
BIC -17365.5 -4223.2 -728.6
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
------------------------------------------------------------------------------------------------------------
(1) (2) (3) (4) (5) (6)
gpov ggap gsev gpov ggap gsev
------------------------------------------------------------------------------------------------------------
Main
ln_pov -0.0250*** -0.0196***
(-15.01) (-11.72)
mys -0.00370*** -0.0258*** -0.0379*** -0.00318*** -0.0258*** -0.0387***
(-6.56) (-11.45) (-11.73) (-5.43) (-11.01) (-11.68)
gdpgr -0.000857*** 0.000000752 -0.00000877 -0.000962*** -0.000146 0.000121
(-4.04) (0.00) (-0.01) (-4.35) (-0.16) (0.09)
unemp 0.0000549 0.000516 -0.00177 -0.000266 -0.0000567 -0.00219
(0.23) (0.51) (-1.20) (-1.05) (-0.05) (-1.46)
subs_rice 0.000135*** 0.000553*** 0.00106*** 0.0000801* 0.000469*** 0.00113***
(4.33) (4.31) (5.71) (2.52) (3.58) (6.08)
gdi -0.0000575 0.000503 0.00120 -0.0000253 0.000272 0.000554
(-0.51) (1.08) (1.77) (-0.22) (0.56) (0.80)
inv_shr 0.0000426 0.0000264 0.000378 -0.0000230 0.0000536 0.000307
(0.23) (0.03) (0.33) (-0.12) (0.07) (0.27)
shr_agr 0.000403*** 0.000853 -0.00224*** 0.000370*** 0.000419 -0.00291***
(3.75) (1.92) (-3.46) (3.32) (0.91) (-4.37)
shr_ind -0.000382*** 0.000406 0.00110** -0.000353*** 0.000465 0.00118**
(-6.32) (1.62) (3.01) (-5.50) (1.76) (3.07)
ln_gap -0.146*** -0.142***
(-30.87) (-30.40)
ln_sev -0.206*** -0.198***
(-38.23) (-37.21)
_cons -0.254* -0.138 0.0109
(-2.06) (-0.31) (0.02)
------------------------------------------------------------------------------------------------------------
Wx
ln_pov -0.0178 0.00925
(-0.97) (0.52)
mys 0.00492 0.0769** 0.134*** 0.0175** 0.107*** 0.158***
(0.73) (2.84) (3.42) (2.60) (3.86) (4.06)
gdpgr -0.000764 -0.0108 -0.0128 -0.00518* -0.0302** -0.00997
(-0.29) (-1.00) (-0.82) (-1.97) (-2.78) (-0.65)
unemp 0.000302 -0.00105 0.0324* -0.00638* -0.0212 0.00732
(0.11) (-0.09) (2.01) (-2.49) (-1.96) (0.49)
subs_rice 0.000359 0.00212 -0.00118 -0.000959** -0.00188 -0.00336*
(1.14) (1.66) (-0.64) (-3.28) (-1.54) (-1.98)
gdi 0.00101 -0.00883 -0.0135 0.00279* -0.00126 -0.0101
(0.81) (-1.71) (-1.81) (2.25) (-0.24) (-1.38)
inv_shr -0.0000950 0.00294 0.00517 -0.000242 0.00248 0.00226
(-0.14) (1.07) (1.30) (-0.36) (0.88) (0.56)
shr_agr -0.00000770 0.00591 0.0109 -0.000362 0.00506 0.00670
(-0.01) (1.28) (1.63) (-0.31) (1.05) (0.97)
shr_ind 0.00182** -0.00141 -0.00514 0.00182* -0.00118 -0.00493
(2.74) (-0.51) (-1.28) (2.57) (-0.41) (-1.16)
ln_gap 0.00384 0.00522
(0.07) (0.11)
ln_sev 0.0807 0.158**
(1.34) (2.68)
------------------------------------------------------------------------------------------------------------
Spatial
rho -0.523*** 0.0273 0.0446 0.200* 0.430*** 0.267**
(-4.45) (0.27) (0.43) (2.17) (5.23) (2.79)
------------------------------------------------------------------------------------------------------------
Variance
sigma2_e 0.00128*** 0.0219*** 0.0467*** 0.00147*** 0.0248*** 0.0526***
(48.03) (48.09) (48.09) (45.02) (45.20) (45.25)
lgt_theta -0.543*** -0.781*** -0.449***
(-9.68) (-15.63) (-8.10)
------------------------------------------------------------------------------------------------------------
LR_Direct
ln_pov -0.0249***
(-14.47)
mys -0.00375*** -0.0258*** -0.0380***
(-6.86) (-11.92) (-12.20)
gdpgr -0.000834*** 0.0000898 0.000120
(-4.10) (0.11) (0.10)
unemp 0.0000563 0.000523 -0.00175
(0.24) (0.53) (-1.21)
subs_rice 0.000133*** 0.000553*** 0.00106***
(4.40) (4.43) (5.84)
gdi -0.0000545 0.000532 0.00124
(-0.48) (1.14) (1.82)
inv_shr 0.0000429 0.0000265 0.000380
(0.22) (0.03) (0.32)
shr_agr 0.000401*** 0.000843* -0.00226***
(3.90) (1.98) (-3.64)
shr_ind -0.000386*** 0.000427 0.00113**
(-6.51) (1.75) (3.16)
ln_gap -0.146***
(-30.03)
ln_sev -0.206***
(-37.23)
------------------------------------------------------------------------------------------------------------
LR_Indirect
ln_pov -0.00267
(-0.22)
mys 0.00447 0.0784** 0.139**
(1.04) (2.80) (3.27)
gdpgr -0.0000864 -0.0104 -0.0124
(-0.05) (-0.88) (-0.70)
unemp 0.000112 -0.00155 0.0335*
(0.06) (-0.14) (1.98)
subs_rice 0.000194 0.00223 -0.00116
(0.91) (1.66) (-0.58)
gdi 0.000666 -0.00924 -0.0145
(0.83) (-1.77) (-1.84)
inv_shr -0.0000777 0.00305 0.00550
(-0.16) (1.04) (1.26)
shr_agr -0.0000972 0.00643 0.0119
(-0.12) (1.26) (1.57)
shr_ind 0.00133** -0.00153 -0.00551
(2.85) (-0.51) (-1.23)
ln_gap 0.00236
(0.05)
ln_sev 0.0781
(1.27)
------------------------------------------------------------------------------------------------------------
LR_Total
ln_pov -0.0276*
(-2.31)
mys 0.000720 0.0525 0.101*
(0.17) (1.87) (2.37)
gdpgr -0.000920 -0.0103 -0.0123
(-0.50) (-0.86) (-0.69)
unemp 0.000168 -0.00102 0.0317
(0.10) (-0.09) (1.86)
subs_rice 0.000327 0.00278* -0.000107
(1.54) (2.06) (-0.05)
gdi 0.000612 -0.00871 -0.0132
(0.76) (-1.66) (-1.67)
inv_shr -0.0000348 0.00308 0.00588
(-0.09) (1.15) (1.48)
shr_agr 0.000303 0.00727 0.00963
(0.39) (1.41) (1.27)
shr_ind 0.000944* -0.00110 -0.00438
(2.01) (-0.36) (-0.97)
ln_gap -0.143**
(-2.81)
ln_sev -0.128*
(-2.08)
------------------------------------------------------------------------------------------------------------
N 4626 4626 4626 4626 4626 4626
R-sq 0.013 0.168 0.211 0.118 0.261 0.225
AIC -17610.2 -4467.9 -973.3 -15982.1 -2722.8 525.2
BIC -17365.5 -4223.2 -728.6 -15840.4 -2581.1 666.9
------------------------------------------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| sdm_fe sdm_re Difference S.E.
-------------+----------------------------------------------------------------
ln_sev | -.2062572 -.1975043 -.008753 .0009642
mys | -.0379153 -.0387483 .000833 .
gdpgr | -8.77e-06 .000121 -.0001298 .
unemp | -.0017744 -.0021902 .0004158 .
subs_rice | .0010581 .0011287 -.0000706 .
gdi | .0012048 .0005539 .0006509 .
inv_shr | .0003783 .0003071 .0000713 .
shr_agr | -.0022435 -.0029054 .0006619 .
shr_ind | .0010984 .0011754 -.000077 .
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xsmle
B = inconsistent under Ha, efficient under Ho; obtained from xsmle
Test: Ho: difference in coefficients not systematic
chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 77.17
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
Belotti, Federico and Hughes, Gordon and Piano Mortari, Andrea, Spatial Panel Data Models Using Stata (March 25, 2016). CEIS Working Paper No. 373, Available at <https://ssrn.com/abstract=2754703 or http://dx.doi.org/10.2139/ssrn.2754703>