Regional Poverty, Convergence, and Spatial Effects:
A Spatial Econometric Approach
Ragdad Cani Miranti
Suggested citation: >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>
This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License. 
1 Original data source
All data are derived from the Indonesia Central Bureau of Statistics (Badan Pusat Statistik Republik of Indonesia). https://www.bps.go.id/
2 Explore my non-spatial data: datapanel
In this case, data3.dta is the panel ( long data) form
end of do-file2.1 Label the variables
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 30 Dec 2020 23:29
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
file ./data3.dta saved3 Explore my Map
3.1 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 30 Dec 2020 23:31
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.dta3.2 Merge with myPANEL data : data3.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 saved4 Describe the new dataset
This is my mymap_and_panel.dta ( the merge between MAP and panel data)
Contains data from ./mymap_and_panel.dta
obs: 4,626
vars: 39 30 Dec 2020 23:31
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 year5 Create our spatial weights matrix Wa generated from STATA (for cross-sectional dataset)
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);6 Run the OLS: Non spatial model
The similar syntax for poverty rate, poverty gap, and poverty severity index
**OLS Fixed Effect
sysuse mymap_and_panel
*Poverty rate
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
estat ic
*The same for poverty gap
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
estat ic
*The same for poverty severity
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
estat ic
estimates store ols_feFixed-effects (within) regression Number of obs = 4,626
Group variable: fips Number of groups = 514
R-sq: Obs per group:
within = 0.0602 min = 9
between = 0.0045 avg = 9.0
overall = 0.0071 max = 9
F(9,4103) = 29.23
corr(u_i, Xb) = -0.2102 Prob > F = 0.0000
------------------------------------------------------------------------------
gpov | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_pov | -.0248764 .0017738 -14.02 0.000 -.028354 -.0213988
mys | -.0037537 .0006006 -6.25 0.000 -.0049312 -.0025763
gdpgr | -.0008429 .000226 -3.73 0.000 -.001286 -.0003997
unemp | .000074 .0002596 0.28 0.776 -.000435 .000583
subs_rice | .0001402 .0000331 4.23 0.000 .0000752 .0002051
gdi | -.0000637 .0001201 -0.53 0.596 -.0002992 .0001719
inv_shr | .0000279 .0001806 0.15 0.877 -.0003262 .000382
shr_agr | .0003998 .0001141 3.50 0.000 .0001761 .0006235
shr_ind | -.0003887 .0000643 -6.04 0.000 -.0005148 -.0002625
_cons | .040727 .0116697 3.49 0.000 .0178481 .063606
-------------+----------------------------------------------------------------
sigma_u | .04045799
sigma_e | .03813812
rho | .52949061 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(513, 4103) = 8.92 Prob > F = 0.0000
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 8680.788 8824.509 10 -17629.02 -17564.62
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Fixed-effects (within) regression Number of obs = 4,626
Group variable: fips Number of groups = 514
R-sq: Obs per group:
within = 0.1750 min = 9
between = 0.1622 avg = 9.0
overall = 0.1671 max = 9
F(9,4103) = 96.71
corr(u_i, Xb) = -0.0112 Prob > F = 0.0000
------------------------------------------------------------------------------
ggap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_gap | -.1451411 .0050111 -28.96 0.000 -.1549656 -.1353166
mys | -.025839 .0023898 -10.81 0.000 -.0305242 -.0211537
gdpgr | .0000283 .0009331 0.03 0.976 -.0018011 .0018578
unemp | .0004958 .0010725 0.46 0.644 -.0016069 .0025984
subs_rice | .0005541 .0001361 4.07 0.000 .0002873 .0008208
gdi | .0004766 .0004963 0.96 0.337 -.0004964 .0014497
inv_shr | .0004305 .0007455 0.58 0.564 -.0010311 .0018921
shr_agr | .0008507 .0004713 1.80 0.071 -.0000734 .0017748
shr_ind | .000435 .0002651 1.64 0.101 -.0000847 .0009547
_cons | .1378133 .0412592 3.34 0.001 .056923 .2187036
-------------+----------------------------------------------------------------
sigma_u | .1892742
sigma_e | .15745587
rho | .59100053 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(513, 4103) = 12.56 Prob > F = 0.0000
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 1820.185 2265.161 10 -4510.321 -4445.927
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Fixed-effects (within) regression Number of obs = 4,626
Group variable: fips Number of groups = 514
R-sq: Obs per group:
within = 0.2466 min = 9
between = 0.1735 avg = 9.0
overall = 0.2091 max = 9
F(9,4103) = 149.21
corr(u_i, Xb) = -0.1243 Prob > F = 0.0000
------------------------------------------------------------------------------
gsev | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ln_sev | -.2054647 .0057387 -35.80 0.000 -.2167156 -.1942138
mys | -.0380807 .0034354 -11.08 0.000 -.0448159 -.0313455
gdpgr | .0001107 .0013638 0.08 0.935 -.002563 .0027844
unemp | -.001831 .0015667 -1.17 0.243 -.0049026 .0012407
subs_rice | .0010426 .000197 5.29 0.000 .0006565 .0014287
gdi | .0011136 .0007254 1.54 0.125 -.0003085 .0025357
inv_shr | .0011671 .0010894 1.07 0.284 -.0009687 .0033029
shr_agr | -.0021391 .0006884 -3.11 0.002 -.0034888 -.0007894
shr_ind | .0011273 .0003873 2.91 0.004 .000368 .0018866
_cons | -.0834493 .0590254 -1.41 0.158 -.1991711 .0322726
-------------+----------------------------------------------------------------
sigma_u | .20349282
sigma_e | .23013405
rho | .43879288 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(513, 4103) = 6.76 Prob > F = 0.0000
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 -145.3874 509.5162 10 -999.0324 -934.638
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
7 Run the spatial model: SAR Fixed Effect with Region (District) Fixed Effect and SAR Random Effect Model
7.1 Import our weight matrix Wa into our panel dataset
This syntax generates direct and indirect effects as well by adding code ‘effects’ into model.
**SAR Fixed Effect Model
sysuse mymap_and_panel
**Import Wa ( our weight matrix) into our panel model. I rename with Wi
spmat import Wi using Wa
**Poverty rate
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
estat ic
** The same for poverty gap
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
estat ic
** The same for poverty severity
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
estat ic
estimates store sar_fe
** SAR Random Effect
*Poverty rate
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
estat ic
*The same for poverty gap
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
estat ic
*The same for poverty severity
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
estat ic
estimates store sar_re
** Conducting Hausman Test
hausman sar_fe sar_reIteration 0: Log-likelihood = 8809.3045
Iteration 1: Log-likelihood = 8832.5646
Iteration 2: Log-likelihood = 8832.6339
Iteration 3: Log-likelihood = 8832.6339
Computing marginal effects standard errors using MC simulation...
SAR with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0605
between = 0.0046
overall = 0.0071
Mean of fixed-effects = 0.0162
Log-likelihood = 8832.6339
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.0248986 .001666 -14.94 0.000 -.0281639 -.0216332
mys | -.0037459 .0005641 -6.64 0.000 -.0048515 -.0026403
gdpgr | -.0008415 .0002123 -3.96 0.000 -.0012576 -.0004254
unemp | .0000732 .0002439 0.30 0.764 -.0004048 .0005511
subs_rice | .0001414 .0000311 4.54 0.000 .0000804 .0002023
gdi | -.0000579 .0001128 -0.51 0.608 -.0002791 .0001632
inv_shr | .0000106 .0001697 0.06 0.950 -.000322 .0003431
shr_agr | .0003938 .0001072 3.67 0.000 .0001837 .0006038
shr_ind | -.0003803 .0000605 -6.29 0.000 -.0004988 -.0002618
-------------+----------------------------------------------------------------
Spatial |
rho | -.449106 .1130602 -3.97 0.000 -.6706999 -.2275122
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0012831 .0000267 48.05 0.000 .0012308 .0013354
-------------+----------------------------------------------------------------
LR_Direct |
ln_pov | -.0248851 .001712 -14.54 0.000 -.0282406 -.0215296
mys | -.0037675 .0005449 -6.91 0.000 -.0048355 -.0026995
gdpgr | -.0008207 .0002033 -4.04 0.000 -.0012193 -.0004221
unemp | .0000754 .0002388 0.32 0.752 -.0003926 .0005435
subs_rice | .0001414 .0000302 4.68 0.000 .0000821 .0002006
gdi | -.0000504 .0001132 -0.44 0.656 -.0002722 .0001715
inv_shr | .0000103 .0001761 0.06 0.953 -.0003348 .0003554
shr_agr | .0003917 .0001025 3.82 0.000 .0001908 .0005926
shr_ind | -.0003757 .0000593 -6.34 0.000 -.0004919 -.0002596
-------------+----------------------------------------------------------------
LR_Indirect |
ln_pov | .0075849 .001472 5.15 0.000 .0046997 .0104701
mys | .0011489 .0002734 4.20 0.000 .0006131 .0016847
gdpgr | .0002497 .0000754 3.31 0.001 .0001019 .0003974
unemp | -.0000236 .0000751 -0.31 0.754 -.0001707 .0001236
subs_rice | -.0000432 .0000124 -3.48 0.001 -.0000675 -.0000188
gdi | .0000155 .0000348 0.44 0.657 -.0000528 .0000837
inv_shr | -2.94e-06 .0000554 -0.05 0.958 -.0001115 .0001056
shr_agr | -.0001191 .0000376 -3.17 0.002 -.0001929 -.0000454
shr_ind | .0001144 .0000271 4.22 0.000 .0000612 .0001675
-------------+----------------------------------------------------------------
LR_Total |
ln_pov | -.0173002 .001906 -9.08 0.000 -.0210358 -.0135645
mys | -.0026186 .0004352 -6.02 0.000 -.0034716 -.0017656
gdpgr | -.000571 .0001521 -3.76 0.000 -.0008691 -.000273
unemp | .0000518 .0001659 0.31 0.755 -.0002733 .000377
subs_rice | .0000982 .0000222 4.41 0.000 .0000546 .0001418
gdi | -.0000349 .0000793 -0.44 0.660 -.0001903 .0001205
inv_shr | 7.36e-06 .000122 0.06 0.952 -.0002318 .0002465
shr_agr | .0002725 .0000767 3.55 0.000 .0001222 .0004228
shr_ind | -.0002614 .0000478 -5.46 0.000 -.0003551 -.0001676
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 8832.634 11 -17643.27 -17572.43
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 2261.7899
Iteration 1: Log-likelihood = 2265.2939
Iteration 2: Log-likelihood = 2265.3048
Iteration 3: Log-likelihood = 2265.3048
Computing marginal effects standard errors using MC simulation...
SAR with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1750
between = 0.1640
overall = 0.1682
Mean of fixed-effects = 0.1409
Log-likelihood = 2265.3048
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1451404 .0047192 -30.76 0.000 -.1543898 -.1358911
mys | -.0258585 .0022508 -11.49 0.000 -.03027 -.021447
gdpgr | .0000338 .0008788 0.04 0.969 -.0016887 .0017563
unemp | .0005002 .00101 0.50 0.620 -.0014795 .0024798
subs_rice | .0005527 .0001282 4.31 0.000 .0003016 .0008039
gdi | .0004772 .0004674 1.02 0.307 -.0004389 .0013934
inv_shr | .0004283 .0007021 0.61 0.542 -.0009478 .0018044
shr_agr | .0008521 .0004439 1.92 0.055 -.0000179 .0017221
shr_ind | .0004351 .0002496 1.74 0.081 -.0000542 .0009244
-------------+----------------------------------------------------------------
Spatial |
rho | .0513628 .0953995 0.54 0.590 -.1356167 .2383423
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0219874 .0004572 48.09 0.000 .0210914 .0228835
-------------+----------------------------------------------------------------
LR_Direct |
ln_gap | -.1449875 .0048472 -29.91 0.000 -.1544878 -.1354872
mys | -.0259323 .0021664 -11.97 0.000 -.0301783 -.0216863
gdpgr | .0001263 .0008406 0.15 0.881 -.0015213 .0017738
unemp | .0005078 .0009886 0.51 0.607 -.0014298 .0024454
subs_rice | .000552 .0001244 4.44 0.000 .0003081 .0007958
gdi | .0005092 .0004684 1.09 0.277 -.0004089 .0014272
inv_shr | .0004273 .0007274 0.59 0.557 -.0009984 .0018529
shr_agr | .00084 .0004244 1.98 0.048 8.28e-06 .0016717
shr_ind | .0004566 .0002439 1.87 0.061 -.0000215 .0009347
-------------+----------------------------------------------------------------
LR_Indirect |
ln_gap | -.0101095 .0163689 -0.62 0.537 -.0421919 .021973
mys | -.0018062 .0029437 -0.61 0.539 -.0075757 .0039633
gdpgr | 3.91e-06 .0001182 0.03 0.974 -.0002277 .0002356
unemp | .0000322 .0001455 0.22 0.825 -.0002529 .0003173
subs_rice | .0000377 .0000629 0.60 0.549 -.0000856 .0001611
gdi | .0000369 .0000852 0.43 0.665 -.00013 .0002038
inv_shr | .0000287 .0001056 0.27 0.786 -.0001784 .0002358
shr_agr | .000061 .0001177 0.52 0.605 -.0001698 .0002917
shr_ind | .0000305 .000059 0.52 0.605 -.0000851 .0001462
-------------+----------------------------------------------------------------
LR_Total |
ln_gap | -.155097 .0175685 -8.83 0.000 -.1895306 -.1206633
mys | -.0277385 .0037718 -7.35 0.000 -.0351311 -.0203459
gdpgr | .0001302 .0009078 0.14 0.886 -.001649 .0019094
unemp | .00054 .0010595 0.51 0.610 -.0015366 .0026166
subs_rice | .0005897 .0001447 4.08 0.000 .0003061 .0008733
gdi | .0005461 .0005115 1.07 0.286 -.0004566 .0015487
inv_shr | .0004559 .000777 0.59 0.557 -.001067 .0019788
shr_agr | .000901 .0004764 1.89 0.059 -.0000328 .0018347
shr_ind | .0004871 .000265 1.84 0.066 -.0000323 .0010066
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 2265.305 11 -4508.61 -4437.776
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 505.24135
Iteration 1: Log-likelihood = 509.52649
Iteration 2: Log-likelihood = 509.536
Iteration 3: Log-likelihood = 509.536
Computing marginal effects standard errors using MC simulation...
SAR with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2465
between = 0.1735
overall = 0.2091
Mean of fixed-effects = -0.0819
Log-likelihood = 509.5360
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.2054804 .0054051 -38.02 0.000 -.2160742 -.1948867
mys | -.0380943 .0032361 -11.77 0.000 -.0444369 -.0317517
gdpgr | .0001138 .0012844 0.09 0.929 -.0024037 .0026312
unemp | -.0018342 .0014756 -1.24 0.214 -.0047263 .0010579
subs_rice | .001043 .0001855 5.62 0.000 .0006794 .0014065
gdi | .0011141 .0006831 1.63 0.103 -.0002248 .002453
inv_shr | .0011612 .0010264 1.13 0.258 -.0008505 .0031728
shr_agr | -.0021405 .0006484 -3.30 0.001 -.0034112 -.0008697
shr_ind | .0011279 .0003648 3.09 0.002 .000413 .0018428
-------------+----------------------------------------------------------------
Spatial |
rho | .0194291 .0975725 0.20 0.842 -.1718094 .2106676
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0469734 .0009767 48.09 0.000 .0450591 .0488877
-------------+----------------------------------------------------------------
LR_Direct |
ln_sev | -.2053052 .0055512 -36.98 0.000 -.2161854 -.1944251
mys | -.0382116 .0031143 -12.27 0.000 -.0443155 -.0321077
gdpgr | .0002487 .0012286 0.20 0.840 -.0021594 .0026567
unemp | -.0018214 .0014419 -1.26 0.207 -.0046475 .0010047
subs_rice | .0010423 .0001807 5.77 0.000 .0006882 .0013964
gdi | .0011607 .0006844 1.70 0.090 -.0001808 .0025022
inv_shr | .0011599 .0010638 1.09 0.276 -.0009252 .003245
shr_agr | -.0021579 .000619 -3.49 0.000 -.0033711 -.0009447
shr_ind | .0011592 .0003561 3.25 0.001 .0004612 .0018572
-------------+----------------------------------------------------------------
LR_Indirect |
ln_sev | -.0071022 .0221457 -0.32 0.748 -.0505069 .0363025
mys | -.001318 .0041405 -0.32 0.750 -.0094334 .0067973
gdpgr | 1.75e-06 .0001498 0.01 0.991 -.0002918 .0002953
unemp | -.0000696 .0002626 -0.26 0.791 -.0005843 .0004451
subs_rice | .0000354 .0001127 0.31 0.754 -.0001856 .0002563
gdi | .000042 .0001473 0.28 0.776 -.0002468 .0003308
inv_shr | .0000355 .0001715 0.21 0.836 -.0003006 .0003716
shr_agr | -.0000717 .0002377 -0.30 0.763 -.0005375 .0003941
shr_ind | .0000384 .0001293 0.30 0.766 -.000215 .0002918
-------------+----------------------------------------------------------------
LR_Total |
ln_sev | -.2124074 .0234105 -9.07 0.000 -.2582912 -.1665236
mys | -.0395296 .0052745 -7.49 0.000 -.0498674 -.0291919
gdpgr | .0002504 .0012829 0.20 0.845 -.002264 .0027648
unemp | -.001891 .0015167 -1.25 0.212 -.0048636 .0010817
subs_rice | .0010777 .0002162 4.98 0.000 .0006539 .0015014
gdi | .0012027 .0007302 1.65 0.100 -.0002285 .0026339
inv_shr | .0011955 .0010996 1.09 0.277 -.0009596 .0033506
shr_agr | -.0022296 .000681 -3.27 0.001 -.0035644 -.0008948
shr_ind | .0011976 .0003864 3.10 0.002 .0004402 .0019551
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 509.536 11 -997.072 -926.2381
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 7283.2829 (not concave)
Iteration 1: Log-likelihood = 7822.3184
Iteration 2: Log-likelihood = 7945.5979
Iteration 3: Log-likelihood = 7973.996
Iteration 4: Log-likelihood = 7974.9238
Iteration 5: Log-likelihood = 7974.926
Iteration 6: Log-likelihood = 7974.926
SAR with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0576
between = 0.0000
overall = 0.0143
Log-likelihood = 7974.9260
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.0201417 .0016821 -11.97 0.000 -.0234387 -.0168448
mys | -.0032205 .000586 -5.50 0.000 -.0043691 -.002072
gdpgr | -.0009522 .0002219 -4.29 0.000 -.0013872 -.0005172
unemp | -.0002552 .0002536 -1.01 0.314 -.0007524 .0002419
subs_rice | .0000781 .0000319 2.45 0.014 .0000156 .0001406
gdi | -.0000137 .0001163 -0.12 0.906 -.0002417 .0002143
inv_shr | -.000059 .0001727 -0.34 0.733 -.0003975 .0002796
shr_agr | .0003998 .0001113 3.59 0.000 .0001817 .0006179
shr_ind | -.0003812 .0000641 -5.95 0.000 -.0005068 -.0002557
_cons | .0499395 .0121872 4.10 0.000 .026053 .0738261
-------------+----------------------------------------------------------------
Spatial |
rho | .4404706 .0820365 5.37 0.000 .2796819 .6012593
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.6363097 .0554577 -11.47 0.000 -.7450047 -.5276147
sigma2_e | .001468 .0000326 45.05 0.000 .0014041 .0015319
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 7974.926 13 -15923.85 -15840.14
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 724.02894 (not concave)
Iteration 1: Log-likelihood = 1163.1261
Iteration 2: Log-likelihood = 1324.9129
Iteration 3: Log-likelihood = 1360.4176
Iteration 4: Log-likelihood = 1361.3834
Iteration 5: Log-likelihood = 1361.3859
Iteration 6: Log-likelihood = 1361.3859
SAR with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1738
between = 0.1964
overall = 0.1876
Log-likelihood = 1361.3859
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1431656 .0046879 -30.54 0.000 -.1523537 -.1339774
mys | -.0259901 .0023389 -11.11 0.000 -.0305743 -.0214059
gdpgr | -.0001017 .0009173 -0.11 0.912 -.0018996 .0016962
unemp | -.0000866 .0010488 -0.08 0.934 -.0021422 .0019689
subs_rice | .0004479 .0001309 3.42 0.001 .0001913 .0007045
gdi | .0003153 .0004828 0.65 0.514 -.000631 .0012616
inv_shr | .0002694 .0007178 0.38 0.707 -.0011374 .0016762
shr_agr | .0005259 .0004613 1.14 0.254 -.0003782 .00143
shr_ind | .0004202 .0002635 1.59 0.111 -.0000962 .0009365
_cons | .1969698 .0410418 4.80 0.000 .1165294 .2774103
-------------+----------------------------------------------------------------
Spatial |
rho | .5604701 .0696162 8.05 0.000 .4240247 .6969154
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.8362968 .0492492 -16.98 0.000 -.9328234 -.7397702
sigma2_e | .0248169 .000548 45.29 0.000 .0237429 .0258909
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 1361.386 13 -2696.772 -2613.059
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = -504.7949
Iteration 1: Log-likelihood = -348.0637
Iteration 2: Log-likelihood = -259.59283
Iteration 3: Log-likelihood = -258.05935
Iteration 4: Log-likelihood = -258.05275
Iteration 5: Log-likelihood = -258.05275
SAR with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2451
between = 0.1797
overall = 0.2123
Log-likelihood = -258.0528
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.1976194 .0053163 -37.17 0.000 -.2080391 -.1871997
mys | -.0386616 .0033222 -11.64 0.000 -.045173 -.0321502
gdpgr | .0001607 .0013248 0.12 0.903 -.0024358 .0027573
unemp | -.0023628 .0015074 -1.57 0.117 -.0053173 .0005917
subs_rice | .001069 .0001853 5.77 0.000 .0007059 .0014321
gdi | .0004944 .0006932 0.71 0.476 -.0008642 .001853
inv_shr | .0006186 .0010249 0.60 0.546 -.0013901 .0026273
shr_agr | -.0027115 .0006647 -4.08 0.000 -.0040144 -.0014086
shr_ind | .0011587 .0003832 3.02 0.002 .0004076 .0019097
_cons | .0051845 .0573022 0.09 0.928 -.1071258 .1174949
-------------+----------------------------------------------------------------
Spatial |
rho | .2452659 .0892069 2.75 0.006 .0704235 .4201083
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.4650608 .0548417 -8.48 0.000 -.5725485 -.3575731
sigma2_e | .0529378 .001169 45.29 0.000 .0506466 .055229
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . -258.0528 13 542.1055 625.8183
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
---- 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)8 Run the spatial model; SEM Fixed Effect with Region (District) Fixed Effect
8.1 Import our weight matrix Wa into our panel dataset
*SEM Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, emat (Wi) model (sem) fe type (ind) effects
gen speed10 = - (log(1+_b[ln_pov])/8)
gen halfLife10 = log(2)/speed10
estat ic
**The same for poverty gap
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
estat ic
**The same for poverty severity
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
estat ic
estimates store sem_feWarning: Option -effects- is redundant
Iteration 0: Log-likelihood = 8820.6157
Iteration 1: Log-likelihood = 8831.9436
Iteration 2: Log-likelihood = 8831.9583
Iteration 3: Log-likelihood = 8831.9583
SEM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0602
between = 0.0046
overall = 0.0069
Mean of fixed-effects = 0.0396
Log-likelihood = 8831.9583
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.0249179 .001665 -14.97 0.000 -.0281811 -.0216546
mys | -.0037214 .0005638 -6.60 0.000 -.0048265 -.0026163
gdpgr | -.000841 .0002122 -3.96 0.000 -.001257 -.0004251
unemp | .0000787 .000244 0.32 0.747 -.0003996 .000557
subs_rice | .0001434 .0000311 4.62 0.000 .0000825 .0002042
gdi | -.000056 .0001128 -0.50 0.619 -.000277 .000165
inv_shr | .0000114 .0001598 0.07 0.943 -.0003017 .0003246
shr_agr | .000394 .000107 3.68 0.000 .0001842 .0006037
shr_ind | -.0003725 .0000606 -6.14 0.000 -.0004913 -.0002537
-------------+----------------------------------------------------------------
Spatial |
lambda | -.4385749 .1154665 -3.80 0.000 -.6648852 -.2122647
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0012836 .0000267 48.05 0.000 .0012312 .0013359
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 8831.958 11 -17641.92 -17571.08
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: Option -effects- is redundant
Iteration 0: Log-likelihood = 2265.3368
Iteration 1: Log-likelihood = 2265.3873
Iteration 2: Log-likelihood = 2265.3874
SEM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1750
between = 0.1623
overall = 0.1672
Mean of fixed-effects = 0.1376
Log-likelihood = 2265.3874
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1451348 .0047187 -30.76 0.000 -.1543832 -.1358863
mys | -.0259097 .0022527 -11.50 0.000 -.0303249 -.0214945
gdpgr | .0000369 .0008788 0.04 0.966 -.0016855 .0017593
unemp | .0004986 .0010097 0.49 0.621 -.0014804 .0024777
subs_rice | .0005518 .0001282 4.30 0.000 .0003006 .0008031
gdi | .0004859 .0004676 1.04 0.299 -.0004306 .0014023
inv_shr | .0004092 .0007088 0.58 0.564 -.0009801 .0017985
shr_agr | .0008452 .000444 1.90 0.057 -.000025 .0017153
shr_ind | .0004355 .0002496 1.74 0.081 -.0000537 .0009247
-------------+----------------------------------------------------------------
Spatial |
lambda | .0680783 .1006588 0.68 0.499 -.1292094 .265366
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0219862 .0004572 48.09 0.000 .0210902 .0228822
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 2265.387 11 -4508.775 -4437.941
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: Option -effects- is redundant
Iteration 0: Log-likelihood = 509.89632
Iteration 1: Log-likelihood = 509.90061
Iteration 2: Log-likelihood = 509.90061
SEM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2466
between = 0.1736
overall = 0.2092
Mean of fixed-effects = -0.0844
Log-likelihood = 509.9006
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.2055367 .0054029 -38.04 0.000 -.2161262 -.1949471
mys | -.0382404 .0032394 -11.80 0.000 -.0445896 -.0318913
gdpgr | .0001235 .0012842 0.10 0.923 -.0023934 .0026404
unemp | -.001879 .0014759 -1.27 0.203 -.0047716 .0010137
subs_rice | .001047 .0001856 5.64 0.000 .0006833 .0014108
gdi | .0011382 .0006836 1.66 0.096 -.0002016 .002478
inv_shr | .0011141 .0010394 1.07 0.284 -.0009231 .0031512
shr_agr | -.0021597 .0006487 -3.33 0.001 -.0034311 -.0008882
shr_ind | .0011334 .0003647 3.11 0.002 .0004186 .0018481
-------------+----------------------------------------------------------------
Spatial |
lambda | .0905081 .1025432 0.88 0.377 -.1104728 .291489
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0469621 .0009765 48.09 0.000 .0450482 .0488761
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 509.9006 11 -997.8012 -926.9673
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
9 Run the spatial model: SAC Fixed Effect with Region (District) Fixed Effect
9.1 Import our weight matrix Wa into our panel dataset
*SAC Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
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
estat ic
**The same for poverty gap
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
estat ic
**The same for poverty severity
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
estat ic
estimates store sac_feIteration 0: Log-likelihood = 8815.76
Iteration 1: Log-likelihood = 8832.4596
Iteration 2: Log-likelihood = 8832.5845
Iteration 3: Log-likelihood = 8832.6375
Iteration 4: Log-likelihood = 8832.6379
Computing marginal effects standard errors using MC simulation...
SAC with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0606
between = 0.0046
overall = 0.0071
Mean of fixed-effects = 0.0144
Log-likelihood = 8832.6379
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.0248898 .0016688 -14.91 0.000 -.0281606 -.021619
mys | -.0037469 .0005641 -6.64 0.000 -.0048526 -.0026413
gdpgr | -.0008413 .0002123 -3.96 0.000 -.0012573 -.0004252
unemp | .0000728 .0002438 0.30 0.765 -.0004052 .0005507
subs_rice | .0001411 .0000312 4.52 0.000 .0000799 .0002023
gdi | -.0000581 .0001128 -0.51 0.607 -.0002793 .0001631
inv_shr | .000011 .0001704 0.06 0.949 -.000323 .000345
shr_agr | .0003938 .0001072 3.68 0.000 .0001838 .0006038
shr_ind | -.0003808 .0000607 -6.27 0.000 -.0004999 -.0002618
-------------+----------------------------------------------------------------
Spatial |
rho | -.4826244 .3846835 -1.25 0.210 -1.23659 .2713414
lambda | .032039 .3491747 0.09 0.927 -.652331 .7164089
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0014431 .0000271 53.31 0.000 .00139 .0014961
-------------+----------------------------------------------------------------
LR_Direct |
ln_pov | -.0249067 .0017114 -14.55 0.000 -.0282609 -.0215524
mys | -.0037733 .0005461 -6.91 0.000 -.0048437 -.0027029
gdpgr | -.0008215 .0002034 -4.04 0.000 -.0012202 -.0004227
unemp | .0000751 .0002392 0.31 0.754 -.0003937 .0005438
subs_rice | .0001413 .0000303 4.66 0.000 .0000818 .0002008
gdi | -.0000506 .0001133 -0.45 0.655 -.0002726 .0001714
inv_shr | .0000107 .0001771 0.06 0.952 -.0003364 .0003579
shr_agr | .0003922 .0001027 3.82 0.000 .000191 .0005934
shr_ind | -.0003767 .0000597 -6.31 0.000 -.0004937 -.0002598
-------------+----------------------------------------------------------------
LR_Indirect |
ln_pov | .006334 .0068908 0.92 0.358 -.0071717 .0198396
mys | .0009662 .0010611 0.91 0.362 -.0011135 .003046
gdpgr | .0002089 .0002252 0.93 0.354 -.0002325 .0006502
unemp | -.0000191 .0001067 -0.18 0.858 -.0002282 .0001899
subs_rice | -.0000358 .0000417 -0.86 0.391 -.0001175 .0000459
gdi | .0000141 .0000423 0.33 0.740 -.0000688 .0000969
inv_shr | -3.63e-06 .0000665 -0.05 0.956 -.000134 .0001267
shr_agr | -.0000991 .000121 -0.82 0.413 -.0003363 .0001381
shr_ind | .0000971 .0001066 0.91 0.362 -.0001119 .000306
-------------+----------------------------------------------------------------
LR_Total |
ln_pov | -.0185727 .0071461 -2.60 0.009 -.0325788 -.0045666
mys | -.0028071 .001119 -2.51 0.012 -.0050003 -.0006138
gdpgr | -.0006126 .0002739 -2.24 0.025 -.0011494 -.0000758
unemp | .000056 .0001991 0.28 0.779 -.0003343 .0004462
subs_rice | .0001055 .000048 2.20 0.028 .0000114 .0001996
gdi | -.0000366 .0000902 -0.41 0.685 -.0002133 .0001402
inv_shr | 7.11e-06 .000136 0.05 0.958 -.0002594 .0002736
shr_agr | .0002931 .0001468 2.00 0.046 5.32e-06 .0005809
shr_ind | -.0002796 .0001123 -2.49 0.013 -.0004998 -.0000594
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 8832.638 12 -17641.28 -17564
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 2264.4047
Iteration 1: Log-likelihood = 2265.4132
Iteration 2: Log-likelihood = 2265.4355
Iteration 3: Log-likelihood = 2265.4356
Computing marginal effects standard errors using MC simulation...
SAC with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1750
between = 0.1593
overall = 0.1653
Mean of fixed-effects = 0.1317
Log-likelihood = 2265.4356
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1450847 .0047231 -30.72 0.000 -.1543417 -.1358277
mys | -.0259623 .0022568 -11.50 0.000 -.0303856 -.021539
gdpgr | .0000382 .0008786 0.04 0.965 -.0016837 .0017601
unemp | .0004949 .0010093 0.49 0.624 -.0014832 .002473
subs_rice | .0005508 .0001283 4.29 0.000 .0002994 .0008022
gdi | .0004971 .0004688 1.06 0.289 -.0004218 .0014159
inv_shr | .000385 .0007204 0.53 0.593 -.001027 .001797
shr_agr | .0008355 .0004449 1.88 0.060 -.0000366 .0017075
shr_ind | .000436 .0002495 1.75 0.081 -.0000531 .000925
-------------+----------------------------------------------------------------
Spatial |
rho | -.09494 .3075249 -0.31 0.758 -.6976777 .5077977
lambda | .1573699 .2953601 0.53 0.594 -.4215252 .736265
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0247263 .0004583 53.95 0.000 .0238279 .0256246
-------------+----------------------------------------------------------------
LR_Direct |
ln_gap | -.1450677 .004855 -29.88 0.000 -.1545833 -.1355521
mys | -.0260613 .002176 -11.98 0.000 -.0303262 -.0217964
gdpgr | .0001308 .0008412 0.16 0.876 -.0015179 .0017795
unemp | .0005028 .0009889 0.51 0.611 -.0014354 .0024411
subs_rice | .0005505 .0001246 4.42 0.000 .0003063 .0007948
gdi | .0005297 .0004702 1.13 0.260 -.000392 .0014513
inv_shr | .0003837 .0007474 0.51 0.608 -.0010813 .0018486
shr_agr | .000824 .0004261 1.93 0.053 -.0000112 .0016591
shr_ind | .0004579 .000244 1.88 0.061 -.0000204 .0009362
-------------+----------------------------------------------------------------
LR_Indirect |
ln_gap | -.0045494 .0669042 -0.07 0.946 -.1356792 .1265803
mys | -.0007399 .011977 -0.06 0.951 -.0242144 .0227347
gdpgr | -2.70e-06 .0005035 -0.01 0.996 -.0009895 .0009841
unemp | .0000103 .0006479 0.02 0.987 -.0012594 .0012801
subs_rice | .0000171 .0002714 0.06 0.950 -.0005148 .000549
gdi | 7.36e-06 .0003021 0.02 0.981 -.0005848 .0005995
inv_shr | .0000433 .0004019 0.11 0.914 -.0007444 .000831
shr_agr | .0000474 .0005012 0.09 0.925 -.000935 .0010297
shr_ind | 9.74e-06 .0002269 0.04 0.966 -.000435 .0004545
-------------+----------------------------------------------------------------
LR_Total |
ln_gap | -.1496171 .0676456 -2.21 0.027 -.2822001 -.0170341
mys | -.0268012 .0120959 -2.22 0.027 -.0505087 -.0030936
gdpgr | .0001281 .0010277 0.12 0.901 -.0018861 .0021423
unemp | .0005132 .0012145 0.42 0.673 -.0018672 .0028936
subs_rice | .0005676 .0003024 1.88 0.061 -.0000251 .0011603
gdi | .000537 .0005661 0.95 0.343 -.0005725 .0016465
inv_shr | .0004269 .0008708 0.49 0.624 -.0012797 .0021336
shr_agr | .0008713 .0007071 1.23 0.218 -.0005147 .0022573
shr_ind | .0004676 .0003307 1.41 0.157 -.0001805 .0011158
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 2265.436 12 -4506.871 -4429.598
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Iteration 0: Log-likelihood = 508.72518
Iteration 1: Log-likelihood = 510.54136
Iteration 2: Log-likelihood = 510.56009
Iteration 3: Log-likelihood = 510.56011
Computing marginal effects standard errors using MC simulation...
SAC with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2469
between = 0.1740
overall = 0.2097
Mean of fixed-effects = -0.1047
Log-likelihood = 510.5601
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.2052711 .0054055 -37.97 0.000 -.2158658 -.1946765
mys | -.0383951 .0032353 -11.87 0.000 -.0447362 -.032054
gdpgr | .0001131 .0012827 0.09 0.930 -.0024009 .0026272
unemp | -.0019451 .0014741 -1.32 0.187 -.0048342 .0009441
subs_rice | .001051 .0001855 5.67 0.000 .0006875 .0014145
gdi | .0011849 .0006838 1.73 0.083 -.0001553 .0025251
inv_shr | .0010557 .0010638 0.99 0.321 -.0010293 .0031407
shr_agr | -.0021866 .0006483 -3.37 0.001 -.0034573 -.0009159
shr_ind | .0011391 .0003641 3.13 0.002 .0004254 .0018528
-------------+----------------------------------------------------------------
Spatial |
rho | -.2322128 .2022482 -1.15 0.251 -.628612 .1641865
lambda | .2845535 .1831747 1.55 0.120 -.0744623 .6435693
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0527459 .0009796 53.84 0.000 .0508259 .0546659
-------------+----------------------------------------------------------------
LR_Direct |
ln_sev | -.2052474 .0055326 -37.10 0.000 -.2160911 -.1944037
mys | -.0385413 .0031179 -12.36 0.000 -.0446523 -.0324303
gdpgr | .000248 .0012278 0.20 0.840 -.0021585 .0026546
unemp | -.0019341 .0014416 -1.34 0.180 -.0047596 .0008915
subs_rice | .0010512 .0001807 5.82 0.000 .0006969 .0014054
gdi | .0012326 .0006856 1.80 0.072 -.0001111 .0025763
inv_shr | .0010547 .0011034 0.96 0.339 -.0011078 .0032173
shr_agr | -.0022058 .0006198 -3.56 0.000 -.0034206 -.0009909
shr_ind | .0011713 .0003559 3.29 0.001 .0004737 .0018688
-------------+----------------------------------------------------------------
LR_Indirect |
ln_sev | .032659 .0319562 1.02 0.307 -.0299741 .095292
mys | .006168 .0060757 1.02 0.310 -.0057401 .0180761
gdpgr | -.0000479 .0002829 -0.17 0.866 -.0006024 .0005066
unemp | .0003081 .0004458 0.69 0.489 -.0005656 .0011818
subs_rice | -.0001691 .0001685 -1.00 0.316 -.0004993 .0001612
gdi | -.0002001 .0002435 -0.82 0.411 -.0006773 .0002772
inv_shr | -.0001616 .0003044 -0.53 0.595 -.0007582 .0004349
shr_agr | .00036 .0003666 0.98 0.326 -.0003585 .0010785
shr_ind | -.0001899 .000199 -0.95 0.340 -.00058 .0002001
-------------+----------------------------------------------------------------
LR_Total |
ln_sev | -.1725885 .0329564 -5.24 0.000 -.2371817 -.1079952
mys | -.0323733 .0064825 -4.99 0.000 -.0450788 -.0196679
gdpgr | .0002001 .0010606 0.19 0.850 -.0018786 .0022789
unemp | -.0016259 .0012659 -1.28 0.199 -.0041071 .0008552
subs_rice | .0008821 .0002191 4.03 0.000 .0004526 .0013115
gdi | .0010325 .0006153 1.68 0.093 -.0001734 .0022384
inv_shr | .0008931 .0009548 0.94 0.350 -.0009784 .0027646
shr_agr | -.0018458 .0006082 -3.04 0.002 -.0030377 -.0006538
shr_ind | .0009813 .0003442 2.85 0.004 .0003066 .001656
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 510.5601 12 -997.1202 -919.8469
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
10 Run the spatial model; SDM Fixed Effect with Region (District) Fixed Effect and SDM Random Effect Model
10.1 Import our weight matrix Wa into our panel dataset
*SDM Fixed Effect Model
sysuse mymap_and_panel
spmat import Wi using Wa
**Poverty rate
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
estat ic
**The same for poverty gap
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
estat ic
**The same for poverty severity
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
estat ic
estimates store sdm_fe
*SDM Random Effect Model
**Poverty rate
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
estat ic
**The same for poverty gap
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
estat ic
**The same for poverty severity
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
estat ic
estimates store sdm_re
**Conducting Hausman Test
hausman sdm_fe sdm_reWarning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = 8816.9685
Iteration 1: Log-likelihood = 8842.9842
Iteration 2: Log-likelihood = 8843.1133
Iteration 3: Log-likelihood = 8843.1133
Computing marginal effects standard errors using MC simulation...
SDM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0631
between = 0.0002
overall = 0.0131
Mean of fixed-effects = -0.0990
Log-likelihood = 8843.1133
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.0249862 .0016641 -15.01 0.000 -.0282477 -.0217246
mys | -.0037 .0005638 -6.56 0.000 -.0048051 -.0025949
gdpgr | -.0008567 .0002121 -4.04 0.000 -.0012724 -.000441
unemp | .0000549 .0002436 0.23 0.822 -.0004224 .0005323
subs_rice | .0001349 .0000311 4.33 0.000 .0000739 .0001959
gdi | -.0000575 .0001127 -0.51 0.610 -.0002783 .0001633
inv_shr | .0000426 .0001891 0.23 0.822 -.0003281 .0004133
shr_agr | .0004026 .0001073 3.75 0.000 .0001923 .0006129
shr_ind | -.0003823 .0000605 -6.32 0.000 -.0005009 -.0002638
-------------+----------------------------------------------------------------
Wx |
ln_pov | -.0178354 .0183666 -0.97 0.332 -.0538332 .0181624
mys | .0049249 .0067209 0.73 0.464 -.0082478 .0180976
gdpgr | -.000764 .0026015 -0.29 0.769 -.0058628 .0043348
unemp | .0003019 .0026602 0.11 0.910 -.004912 .0055158
subs_rice | .0003587 .000314 1.14 0.253 -.0002567 .0009742
gdi | .0010064 .0012394 0.81 0.417 -.0014228 .0034357
inv_shr | -.000095 .0006647 -0.14 0.886 -.0013978 .0012078
shr_agr | -7.70e-06 .0011125 -0.01 0.994 -.0021882 .0021728
shr_ind | .0018242 .0006656 2.74 0.006 .0005196 .0031288
-------------+----------------------------------------------------------------
Spatial |
rho | -.5226486 .117485 -4.45 0.000 -.7529151 -.2923822
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0012765 .0000266 48.03 0.000 .0012244 .0013286
-------------+----------------------------------------------------------------
LR_Direct |
ln_pov | -.0249048 .0017208 -14.47 0.000 -.0282775 -.021532
mys | -.0037463 .0005458 -6.86 0.000 -.004816 -.0026766
gdpgr | -.0008336 .0002033 -4.10 0.000 -.0012321 -.0004351
unemp | .0000563 .0002384 0.24 0.813 -.0004111 .0005236
subs_rice | .0001332 .0000303 4.40 0.000 .0000738 .0001926
gdi | -.0000545 .0001134 -0.48 0.631 -.0002769 .0001678
inv_shr | .0000429 .0001981 0.22 0.828 -.0003453 .0004312
shr_agr | .0004005 .0001027 3.90 0.000 .0001992 .0006018
shr_ind | -.0003865 .0000594 -6.51 0.000 -.0005029 -.00027
-------------+----------------------------------------------------------------
LR_Indirect |
ln_pov | -.0026675 .012078 -0.22 0.825 -.02634 .0210049
mys | .0044666 .0043132 1.04 0.300 -.0039873 .0129204
gdpgr | -.0000864 .0018219 -0.05 0.962 -.0036572 .0034845
unemp | .000112 .0017346 0.06 0.949 -.0032878 .0035118
subs_rice | .0001938 .0002122 0.91 0.361 -.000222 .0006097
gdi | .0006664 .0008055 0.83 0.408 -.0009123 .0022451
inv_shr | -.0000777 .0004891 -0.16 0.874 -.0010364 .0008809
shr_agr | -.0000972 .0007845 -0.12 0.901 -.0016348 .0014403
shr_ind | .0013307 .0004666 2.85 0.004 .0004162 .0022452
-------------+----------------------------------------------------------------
LR_Total |
ln_pov | -.0275723 .0119176 -2.31 0.021 -.0509305 -.0042142
mys | .0007202 .0042969 0.17 0.867 -.0077015 .009142
gdpgr | -.00092 .0018229 -0.50 0.614 -.0044927 .0026528
unemp | .0001683 .0017401 0.10 0.923 -.0032423 .0035789
subs_rice | .0003271 .0002119 1.54 0.123 -.0000881 .0007423
gdi | .0006119 .0008 0.76 0.444 -.0009562 .0021799
inv_shr | -.0000348 .0004074 -0.09 0.932 -.0008334 .0007638
shr_agr | .0003033 .0007824 0.39 0.698 -.0012302 .0018368
shr_ind | .0009442 .0004697 2.01 0.044 .0000237 .0018648
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 8843.113 20 -17646.23 -17517.44
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = 2268.2397
Iteration 1: Log-likelihood = 2271.9225
Iteration 2: Log-likelihood = 2271.9288
Iteration 3: Log-likelihood = 2271.9288
Computing marginal effects standard errors using MC simulation...
SDM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1774
between = 0.1624
overall = 0.1681
Mean of fixed-effects = 0.2404
Log-likelihood = 2271.9288
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1457719 .0047219 -30.87 0.000 -.1550266 -.1365172
mys | -.0257834 .0022527 -11.45 0.000 -.0301987 -.0213681
gdpgr | 7.52e-07 .000879 0.00 0.999 -.001722 .0017235
unemp | .0005162 .0010101 0.51 0.609 -.0014637 .002496
subs_rice | .0005534 .0001284 4.31 0.000 .0003017 .000805
gdi | .000503 .0004673 1.08 0.282 -.0004129 .001419
inv_shr | .0000264 .0007837 0.03 0.973 -.0015095 .0015624
shr_agr | .0008533 .000445 1.92 0.055 -.0000188 .0017255
shr_ind | .0004064 .0002501 1.62 0.104 -.0000838 .0008965
-------------+----------------------------------------------------------------
Wx |
ln_gap | .0038393 .0515668 0.07 0.941 -.0972299 .1049084
mys | .0769483 .0270892 2.84 0.005 .0238545 .1300422
gdpgr | -.0107517 .0107324 -1.00 0.316 -.0317869 .0102834
unemp | -.0010465 .0110385 -0.09 0.924 -.0226815 .0205885
subs_rice | .0021184 .0012799 1.66 0.098 -.00039 .0046269
gdi | -.0088292 .0051529 -1.71 0.087 -.0189286 .0012702
inv_shr | .0029426 .0027502 1.07 0.285 -.0024476 .0083328
shr_agr | .0059087 .0046305 1.28 0.202 -.0031669 .0149843
shr_ind | -.0014087 .0027554 -0.51 0.609 -.0068093 .0039919
-------------+----------------------------------------------------------------
Spatial |
rho | .0272658 .1025017 0.27 0.790 -.1736339 .2281655
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .021925 .0004559 48.09 0.000 .0210315 .0228185
-------------+----------------------------------------------------------------
LR_Direct |
ln_gap | -.1455953 .0048476 -30.03 0.000 -.1550965 -.1360941
mys | -.0258319 .0021677 -11.92 0.000 -.0300804 -.0215833
gdpgr | .0000898 .0008423 0.11 0.915 -.0015611 .0017408
unemp | .0005234 .0009894 0.53 0.597 -.0014158 .0024626
subs_rice | .000553 .0001247 4.43 0.000 .0003085 .0007974
gdi | .0005325 .0004686 1.14 0.256 -.0003861 .001451
inv_shr | .0000265 .0008124 0.03 0.974 -.0015658 .0016188
shr_agr | .0008429 .0004253 1.98 0.047 9.42e-06 .0016764
shr_ind | .0004274 .0002446 1.75 0.081 -.000052 .0009068
-------------+----------------------------------------------------------------
LR_Indirect |
ln_gap | .0023616 .0509103 0.05 0.963 -.0974208 .102144
mys | .0783523 .027979 2.80 0.005 .0235144 .1331902
gdpgr | -.0104111 .0118844 -0.88 0.381 -.0337042 .0128819
unemp | -.0015474 .0113206 -0.14 0.891 -.0237354 .0206406
subs_rice | .0022258 .0013415 1.66 0.097 -.0004035 .004855
gdi | -.0092411 .0052249 -1.77 0.077 -.0194816 .0009995
inv_shr | .0030499 .0029356 1.04 0.299 -.0027039 .0088036
shr_agr | .0064284 .0051163 1.26 0.209 -.0035993 .016456
shr_ind | -.0015277 .0029963 -0.51 0.610 -.0074003 .0043448
-------------+----------------------------------------------------------------
LR_Total |
ln_gap | -.1432337 .0509602 -2.81 0.005 -.2431139 -.0433534
mys | .0525204 .0281079 1.87 0.062 -.0025701 .1076109
gdpgr | -.0103213 .0119711 -0.86 0.389 -.0337843 .0131417
unemp | -.001024 .011427 -0.09 0.929 -.0234204 .0213724
subs_rice | .0027787 .0013497 2.06 0.040 .0001333 .0054242
gdi | -.0087086 .0052424 -1.66 0.097 -.0189836 .0015664
inv_shr | .0030763 .0026699 1.15 0.249 -.0021566 .0083092
shr_agr | .0072713 .0051417 1.41 0.157 -.0028063 .0173488
shr_ind | -.0011003 .0030271 -0.36 0.716 -.0070334 .0048328
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 2271.929 20 -4503.858 -4375.069
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = 521.46387
Iteration 1: Log-likelihood = 524.63885
Iteration 2: Log-likelihood = 524.64484
Iteration 3: Log-likelihood = 524.64484
Computing marginal effects standard errors using MC simulation...
SDM with spatial fixed-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2514
between = 0.1731
overall = 0.2110
Mean of fixed-effects = 0.0722
Log-likelihood = 524.6448
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.2062572 .0053952 -38.23 0.000 -.2168316 -.1956828
mys | -.0379153 .0032325 -11.73 0.000 -.0442508 -.0315798
gdpgr | -8.77e-06 .0012822 -0.01 0.995 -.0025219 .0025044
unemp | -.0017744 .0014731 -1.20 0.228 -.0046617 .0011129
subs_rice | .0010581 .0001854 5.71 0.000 .0006947 .0014216
gdi | .0012048 .000682 1.77 0.077 -.000132 .0025416
inv_shr | .0003783 .0011434 0.33 0.741 -.0018627 .0026194
shr_agr | -.0022435 .0006485 -3.46 0.001 -.0035146 -.0009725
shr_ind | .0010984 .0003647 3.01 0.003 .0003836 .0018133
-------------+----------------------------------------------------------------
Wx |
ln_sev | .0806642 .060373 1.34 0.182 -.0376648 .1989931
mys | .1340604 .0391803 3.42 0.001 .0572684 .2108524
gdpgr | -.0128497 .0156897 -0.82 0.413 -.0436009 .0179015
unemp | .0324251 .0160923 2.01 0.044 .0008848 .0639655
subs_rice | -.0011843 .0018482 -0.64 0.522 -.0048068 .0024382
gdi | -.0135375 .007491 -1.81 0.071 -.0282196 .0011445
inv_shr | .0051749 .0039811 1.30 0.194 -.0026279 .0129777
shr_agr | .0109156 .0067001 1.63 0.103 -.0022163 .0240475
shr_ind | -.0051374 .0040263 -1.28 0.202 -.0130289 .002754
-------------+----------------------------------------------------------------
Spatial |
rho | .0446306 .1033237 0.43 0.666 -.1578802 .2471414
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0466668 .0009703 48.09 0.000 .044765 .0485686
-------------+----------------------------------------------------------------
LR_Direct |
ln_sev | -.2060217 .0055344 -37.23 0.000 -.2168688 -.1951745
mys | -.0379619 .003112 -12.20 0.000 -.0440613 -.0318626
gdpgr | .0001197 .0012286 0.10 0.922 -.0022884 .0025277
unemp | -.0017458 .0014395 -1.21 0.225 -.0045672 .0010756
subs_rice | .0010567 .0001808 5.84 0.000 .0007023 .0014111
gdi | .0012442 .0006841 1.82 0.069 -.0000967 .0025851
inv_shr | .0003795 .0011848 0.32 0.749 -.0019427 .0027018
shr_agr | -.0022552 .0006191 -3.64 0.000 -.0034686 -.0010418
shr_ind | .001127 .0003564 3.16 0.002 .0004285 .0018255
-------------+----------------------------------------------------------------
LR_Indirect |
ln_sev | .0781179 .0614649 1.27 0.204 -.0423511 .1985869
mys | .1390579 .0424721 3.27 0.001 .0558141 .2223018
gdpgr | -.0124243 .0176555 -0.70 0.482 -.0470284 .0221798
unemp | .0334941 .0169358 1.98 0.048 .0003006 .0666876
subs_rice | -.0011634 .0020175 -0.58 0.564 -.0051176 .0027909
gdi | -.0144744 .007877 -1.84 0.066 -.0299131 .0009643
inv_shr | .0055044 .0043627 1.26 0.207 -.0030463 .0140551
shr_agr | .01189 .007576 1.57 0.117 -.0029587 .0267386
shr_ind | -.0055068 .0044663 -1.23 0.218 -.0142605 .0032469
-------------+----------------------------------------------------------------
LR_Total |
ln_sev | -.1279038 .0615718 -2.08 0.038 -.2485823 -.0072252
mys | .101096 .0426821 2.37 0.018 .0174405 .1847514
gdpgr | -.0123046 .0177787 -0.69 0.489 -.0471502 .0225409
unemp | .0317483 .0170792 1.86 0.063 -.0017263 .0652229
subs_rice | -.0001067 .002032 -0.05 0.958 -.0040893 .0038759
gdi | -.0132302 .0079078 -1.67 0.094 -.0287293 .0022689
inv_shr | .005884 .0039795 1.48 0.139 -.0019157 .0136837
shr_agr | .0096348 .0076155 1.27 0.206 -.0052914 .024561
shr_ind | -.0043798 .0045122 -0.97 0.332 -.0132235 .0044639
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 524.6448 20 -1009.29 -880.5007
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = 7396.441 (not concave)
Iteration 1: Log-likelihood = 7923.7125
Iteration 2: Log-likelihood = 8011.0662
Iteration 3: Log-likelihood = 8013.0278
Iteration 4: Log-likelihood = 8013.0309
Iteration 5: Log-likelihood = 8013.0309
SDM with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.0531
between = 0.1780
overall = 0.1176
Log-likelihood = 8013.0309
------------------------------------------------------------------------------
gpov | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_pov | -.019622 .0016736 -11.72 0.000 -.0229022 -.0163418
mys | -.0031761 .0005845 -5.43 0.000 -.0043218 -.0020304
gdpgr | -.0009622 .0002214 -4.35 0.000 -.0013962 -.0005282
unemp | -.000266 .0002525 -1.05 0.292 -.0007609 .0002289
subs_rice | .0000801 .0000318 2.52 0.012 .0000178 .0001424
gdi | -.0000253 .0001159 -0.22 0.827 -.0002525 .0002018
inv_shr | -.000023 .0001932 -0.12 0.905 -.0004016 .0003557
shr_agr | .0003698 .0001112 3.32 0.001 .0001518 .0005879
shr_ind | -.0003532 .0000642 -5.50 0.000 -.0004791 -.0002274
_cons | -.2538549 .1232401 -2.06 0.039 -.495401 -.0123088
-------------+----------------------------------------------------------------
Wx |
ln_pov | .0092457 .0178024 0.52 0.604 -.0256464 .0441377
mys | .0175386 .0067446 2.60 0.009 .0043194 .0307579
gdpgr | -.0051751 .0026239 -1.97 0.049 -.0103179 -.0000322
unemp | -.0063801 .0025646 -2.49 0.013 -.0114066 -.0013537
subs_rice | -.0009586 .0002922 -3.28 0.001 -.0015314 -.0003858
gdi | .0027868 .0012377 2.25 0.024 .0003609 .0052127
inv_shr | -.0002416 .0006797 -0.36 0.722 -.0015737 .0010905
shr_agr | -.0003624 .0011551 -0.31 0.754 -.0026263 .0019015
shr_ind | .0018163 .0007059 2.57 0.010 .0004329 .0031998
-------------+----------------------------------------------------------------
Spatial |
rho | .1996357 .092009 2.17 0.030 .0193013 .3799701
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.5432103 .0561066 -9.68 0.000 -.6531773 -.4332433
sigma2_e | .0014661 .0000326 45.02 0.000 .0014023 .0015299
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 8013.031 22 -15982.06 -15840.39
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = 861.33864 (not concave)
Iteration 1: Log-likelihood = 1291.1812
Iteration 2: Log-likelihood = 1377.2866
Iteration 3: Log-likelihood = 1383.3368
Iteration 4: Log-likelihood = 1383.377
Iteration 5: Log-likelihood = 1383.377
SDM with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.1703
between = 0.3186
overall = 0.2608
Log-likelihood = 1383.3770
------------------------------------------------------------------------------
ggap | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_gap | -.1424265 .0046847 -30.40 0.000 -.1516084 -.1332446
mys | -.0257667 .0023406 -11.01 0.000 -.0303541 -.0211792
gdpgr | -.0001462 .000918 -0.16 0.873 -.0019453 .001653
unemp | -.0000567 .0010479 -0.05 0.957 -.0021106 .0019972
subs_rice | .0004695 .0001311 3.58 0.000 .0002125 .0007265
gdi | .0002721 .000483 0.56 0.573 -.0006745 .0012187
inv_shr | .0000536 .0008048 0.07 0.947 -.0015237 .001631
shr_agr | .0004188 .0004626 0.91 0.365 -.0004879 .0013255
shr_ind | .0004648 .0002643 1.76 0.079 -.0000531 .0009828
_cons | -.1378425 .4403658 -0.31 0.754 -1.000944 .7252586
-------------+----------------------------------------------------------------
Wx |
ln_gap | .0052243 .049096 0.11 0.915 -.0910022 .1014507
mys | .1067199 .0276546 3.86 0.000 .0525178 .160922
gdpgr | -.0301852 .0108678 -2.78 0.005 -.0514857 -.0088846
unemp | -.0211848 .0108305 -1.96 0.050 -.0424121 .0000425
subs_rice | -.0018836 .0012243 -1.54 0.124 -.0042832 .0005159
gdi | -.0012599 .0051719 -0.24 0.808 -.0113966 .0088769
inv_shr | .0024761 .002825 0.88 0.381 -.0030608 .008013
shr_agr | .0050637 .0048051 1.05 0.292 -.0043541 .0144815
shr_ind | -.0011804 .0029107 -0.41 0.685 -.0068852 .0045245
-------------+----------------------------------------------------------------
Spatial |
rho | .4303674 .0822257 5.23 0.000 .2692079 .5915269
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.7805233 .0499531 -15.63 0.000 -.8784297 -.682617
sigma2_e | .024837 .0005495 45.20 0.000 .0237601 .0259139
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . 1383.377 22 -2722.754 -2581.086
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
Warning: All regressors will be spatially lagged
Iteration 0: Log-likelihood = -479.80286
Iteration 1: Log-likelihood = -297.53837
Iteration 2: Log-likelihood = -241.00889
Iteration 3: Log-likelihood = -240.6194
Iteration 4: Log-likelihood = -240.61898
Iteration 5: Log-likelihood = -240.61898
SDM with random-effects Number of obs = 4626
Group variable: fips Number of groups = 514
Time variable: year Panel length = 9
R-sq: within = 0.2495
between = 0.2010
overall = 0.2247
Log-likelihood = -240.6190
------------------------------------------------------------------------------
gsev | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
ln_sev | -.1975043 .0053084 -37.21 0.000 -.2079085 -.1871001
mys | -.0387483 .0033165 -11.68 0.000 -.0452486 -.0322481
gdpgr | .000121 .0013229 0.09 0.927 -.0024718 .0027138
unemp | -.0021902 .0015042 -1.46 0.145 -.0051383 .0007579
subs_rice | .0011287 .0001857 6.08 0.000 .0007648 .0014926
gdi | .0005539 .0006928 0.80 0.424 -.0008041 .0019118
inv_shr | .0003071 .0011507 0.27 0.790 -.0019483 .0025624
shr_agr | -.0029054 .000665 -4.37 0.000 -.0042088 -.0016021
shr_ind | .0011754 .0003832 3.07 0.002 .0004242 .0019266
_cons | .0109046 .6071885 0.02 0.986 -1.179163 1.200972
-------------+----------------------------------------------------------------
Wx |
ln_sev | .1577857 .0589354 2.68 0.007 .0422744 .273297
mys | .1578271 .0388894 4.06 0.000 .0816054 .2340488
gdpgr | -.0099658 .0153617 -0.65 0.517 -.0400742 .0201425
unemp | .0073235 .0149551 0.49 0.624 -.021988 .0366349
subs_rice | -.0033627 .0017012 -1.98 0.048 -.006697 -.0000284
gdi | -.0101382 .0073284 -1.38 0.167 -.0245016 .0042252
inv_shr | .0022603 .0040126 0.56 0.573 -.0056043 .0101249
shr_agr | .0067015 .0068989 0.97 0.331 -.00682 .020223
shr_ind | -.0049252 .004229 -1.16 0.244 -.0132139 .0033634
-------------+----------------------------------------------------------------
Spatial |
rho | .2666974 .0956696 2.79 0.005 .0791885 .4542063
-------------+----------------------------------------------------------------
Variance |
lgt_theta | -.4494071 .0554867 -8.10 0.000 -.5581591 -.3406551
sigma2_e | .0526456 .0011634 45.25 0.000 .0503654 .0549259
------------------------------------------------------------------------------
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 4,626 . -240.619 22 525.238 666.9058
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
---- 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)---
title: "Regional Poverty, Convergence, and Spatial Effects:"
subtitle: "A Spatial Econometric Approach"
author: "Ragdad Cani Miranti"
output:
  html_document:
    code_download: true
    df_print: paged
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
    toc_depth: 4
    number_sections: true
    code_folding: "show"
    theme: "cosmo"
    highlight: "monochrome"
  pdf_document: default
  word_document: default
bibliography: biblio.bib
---

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h1.title {font-size: 18pt; color: DarkBlue;} 
body, h1, h2, h3, h4 {font-family: "Palatino", serif;}
body {font-size: 12pt;}
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```{r, echo=FALSE, message=FALSE}
library(Statamarkdown)
```


Suggested citation: 
>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>

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License. 
![](License.png)


# Original data source

All data are derived from the Indonesia Central Bureau of Statistics (Badan Pusat Statistik Republik of Indonesia). <https://www.bps.go.id/>

# Explore my non-spatial data: datapanel
In this case, data3.dta is the panel ( long data) form

```{stata}
sysuse data3
```

## Label the variables

```{stata}
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
summarize
```

##save myPANEL data

```{stata}

sysuse data3
save, replace
```

# Explore my Map

## Import and translate to stata shapa file

```{stata}
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
```

## Merge with myPANEL data : data3.dta

```{stata}
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 

```

# Describe the new dataset
This is my mymap_and_panel.dta ( the merge between MAP and panel data)

```{stata}

sysuse mymap_and_panel
describe
```


# Create our spatial weights matrix Wa generated from STATA (for cross-sectional dataset)

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.

```{stata}
sysuse datacross.dta
spmat idistance datacross coord_x coord_y, id(fips) normalize (row)
spmat export datacross using Wa

```

# Run the OLS: Non spatial model
The similar syntax for poverty rate, poverty gap, and poverty severity index

```{stata}
**OLS Fixed Effect
sysuse mymap_and_panel

*Poverty rate
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
estat ic

*The same for poverty gap
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
estat ic


*The same for poverty severity
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
estat ic

estimates store ols_fe

```

# Run the spatial model: SAR Fixed Effect with Region (District) Fixed Effect and SAR Random Effect Model

## Import our weight matrix Wa into our panel dataset

This syntax generates direct and indirect effects as well by adding code 'effects' into model.

```{stata}
**SAR Fixed Effect Model 
sysuse mymap_and_panel

**Import Wa ( our weight matrix) into our panel model. I rename with Wi

spmat import Wi using Wa

**Poverty rate
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
estat ic

** The same for poverty gap
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
estat ic

** The same for poverty severity
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
estat ic

estimates store sar_fe


** SAR Random Effect


*Poverty rate
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
estat ic

*The same for poverty gap
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
estat ic


*The same for poverty severity
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
estat ic

estimates store sar_re

** Conducting Hausman Test

hausman sar_fe sar_re

```

# Run the spatial model; SEM Fixed Effect with Region (District) Fixed Effect

## Import our weight matrix Wa into our panel dataset

```{stata}
*SEM Fixed Effect Model 
sysuse mymap_and_panel

spmat import Wi using Wa

**Poverty rate
xsmle gpov ln_pov mys gdpgr unemp subs_rice gdi inv_shr shr_agr shr_ind, emat (Wi) model (sem) fe type (ind) effects
gen speed10 = - (log(1+_b[ln_pov])/8)
gen halfLife10 = log(2)/speed10
estat ic


**The same for poverty gap
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
estat ic

**The same for poverty severity
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
estat ic

estimates store sem_fe

```


# Run the spatial model: SAC Fixed Effect with Region (District) Fixed Effect

## Import our weight matrix Wa into our panel dataset

```{stata}
*SAC Fixed Effect Model 
sysuse mymap_and_panel

spmat import Wi using Wa

**Poverty rate
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
estat ic


**The same for poverty gap
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
estat ic

**The same for poverty severity
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
estat ic

estimates store sac_fe



```

# Run the spatial model; SDM Fixed Effect with Region (District) Fixed Effect and SDM Random Effect Model

## Import our weight matrix Wa into our panel dataset

```{stata}
*SDM Fixed Effect Model 
sysuse mymap_and_panel

spmat import Wi using Wa

**Poverty rate
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
estat ic


**The same for poverty gap
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
estat ic

**The same for poverty severity
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
estat ic

estimates store sdm_fe


*SDM Random Effect Model 

**Poverty rate
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
estat ic


**The same for poverty gap
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
estat ic

**The same for poverty severity
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
estat ic

estimates store sdm_re

**Conducting Hausman Test

hausman sdm_fe sdm_re

```

