i am doing some research in DID with panel data. i want to see the effect of renewable energy in reducing welfare-recipient or people who receive social security insurance from the government.
my data is :
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long welfare_recipient float(renewable nongrid renewablexnongrid jr_sch mid_sch infra_health industry TVsignal Phonesignal) 469 0 0 0 0 0 1 1 0 0 157 0 0 0 1 0 1 0 0 1 474 0 0 0 1 0 1 0 0 1 661 0 0 0 1 0 1 1 0 0 342 0 0 0 1 0 1 0 0 1 100 0 0 0 1 0 1 0 0 1 279 0 0 0 0 1 1 1 0 0 301 0 0 0 1 0 1 0 0 1 376 0 0 0 1 0 1 0 0 1 249 0 0 0 0 1 1 1 0 0 285 0 0 0 0 1 1 1 0 1 287 0 0 0 0 1 1 0 0 1 115 0 0 0 0 0 0 0 1 0 228 0 0 0 0 0 1 1 0 1 210 0 0 0 0 0 1 0 0 1 185 0 0 0 1 0 1 1 1 0 262 0 0 0 1 0 1 1 0 1 200 0 0 0 1 0 1 0 0 1 160 0 0 0 1 0 0 0 1 0 423 0 0 0 1 0 1 1 0 1 419 0 0 0 1 0 1 1 0 1 510 0 0 0 1 0 1 1 0 0 583 0 0 0 1 0 1 0 0 0 120 0 0 0 1 0 1 0 0 1 527 0 0 0 1 0 1 1 0 0 218 0 0 0 1 1 1 0 0 1 308 0 0 0 1 1 1 0 0 1 198 0 0 0 0 0 1 1 0 0 202 0 0 0 0 0 1 0 0 1 209 0 0 0 0 0 1 0 0 1 269 0 0 0 1 0 1 1 1 0 1126 0 0 0 1 0 1 1 0 0 1000 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 0 1 1336 0 0 0 1 1 1 0 0 1 239 0 0 0 1 1 1 0 1 0 451 0 0 0 1 1 1 0 0 0 250 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 0 1 1311 0 0 0 1 1 1 0 0 1 458 0 0 0 1 1 1 0 1 0 1051 0 0 0 1 1 1 1 0 1 800 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 0 1 1440 0 0 0 1 1 1 0 0 1 277 0 0 0 1 0 1 0 1 0 334 0 0 0 1 0 1 1 0 1 85 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 0 0 1 585 0 0 0 1 0 1 0 0 1 356 0 0 0 1 0 1 1 1 1 0 0 0 0 1 0 1 1 0 1 400 0 0 0 1 0 1 1 0 1 750 0 0 0 1 0 1 1 0 1 900 0 0 0 1 0 1 0 0 1 52 0 0 0 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 1161 0 0 0 0 0 1 1 1 1 24 0 0 0 1 1 1 0 0 1 2000 0 0 0 1 1 1 1 0 1 1489 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 1 400 0 0 0 0 0 1 1 1 1 150 0 0 0 1 0 1 0 0 1 1950 0 0 0 1 0 0 1 0 1 617 0 0 0 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 2730 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 3100 0 0 0 1 1 1 1 0 1 1821 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 1 1 0 1 200 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1640 0 0 0 1 1 1 1 0 1 218 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 1200 0 0 0 1 1 1 1 1 1 50 0 0 0 1 1 1 0 0 1 2100 0 0 0 1 1 1 1 0 1 164 0 1 0 1 0 1 1 1 0 1024 0 0 0 1 0 1 1 0 1 730 0 0 0 1 0 1 1 1 1 393 0 0 0 1 0 1 0 0 1 948 0 0 0 1 0 1 1 0 1 385 0 1 0 1 0 1 1 1 0 426 0 0 0 1 0 1 0 0 1 499 0 0 0 1 0 1 0 0 1 372 0 0 0 1 0 1 1 0 0 520 0 0 0 1 0 1 0 0 1 390 0 0 0 1 0 1 0 0 1 709 0 0 0 1 1 1 0 1 0 1501 0 0 0 1 0 1 1 0 1 608 0 0 0 1 0 1 1 0 1 750 0 0 0 1 0 1 0 0 1 194 0 0 0 1 0 1 0 0 1 364 0 0 0 1 0 1 1 1 0 426 0 0 0 1 0 1 1 0 0 6 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 0 0 1 end
and yes, all my independent variable is dummy.
and my output is :
Code:
.
xtset id_desa year
panel variable: id_desa (unbalanced)
time variable: year, 2006 to 2018, but with gaps
delta: 1 unit
. xtreg Pen_Bantuan d_year_ebt d_PLN DD sd smp industri sinyalTV sinyalHP infra_kes i.year, fe cluster ( id_desa)
Fixed-effects (within) regression Number of obs = 18,062
Group variable: id_desa Number of groups = 5,205
R-sq: Obs per group:
within = 0.0428 min = 1
between = 0.0006 avg = 3.5
overall = 0.0102 max = 5
F(13,5204) = 34.85
corr(u_i, Xb) = -0.0797 Prob > F = 0.0000
(Std. Err. adjusted for 5,205 clusters in id_desa)
------------------------------------------------------------------------------
| Robust
Pen_Bantuan | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d_year_ebt | 320.5818 84.82165 3.78 0.000 154.2957 486.8678
d_PLN | 65.16589 17.55079 3.71 0.000 30.75897 99.57281
DD | -346.1853 84.1762 -4.11 0.000 -511.206 -181.1646
sd | -24.81595 12.6424 -1.96 0.050 -49.60035 -.0315438
smp | 63.10132 18.13105 3.48 0.001 27.55684 98.6458
industri | 25.61956 9.136575 2.80 0.005 7.708033 43.53108
sinyalTV | -34.88289 21.91833 -1.59 0.112 -77.85202 8.086237
sinyalHP | -18.75071 10.69288 -1.75 0.080 -39.71326 2.21183
infra_kes | .9282455 10.58314 0.09 0.930 -19.81915 21.67564
|
year |
2008 | -36.05412 14.96352 -2.41 0.016 -65.3889 -6.719336
2011 | -25.89031 15.40423 -1.68 0.093 -56.08907 4.308454
2014 | 131.7223 16.8642 7.81 0.000 98.66139 164.7832
2018 | 164.8634 18.79985 8.77 0.000 128.0078 201.719
|
_cons | 301.1723 20.57644 14.64 0.000 260.8339 341.5108
-------------+----------------------------------------------------------------
sigma_u | 501.7345
sigma_e | 513.91592
rho | .48800801 (fraction of variance due to u_i)
------------------------------------------------------------------------------
like we can see that i have high standard error. it is okay or not?
than i try to make it become balance panel, and the result, my standard error became more higher
Code:
. xtset id_desa year
panel variable: id_desa (strongly balanced)
time variable: year, 2006 to 2018, but with gaps
delta: 1 unit
. xtreg Pen_Bantuan d_year_ebt d_PLN DD sd smp industri sinyalTV sinyalHP infra_kes i.year, fe cluster ( id_desa)
Fixed-effects (within) regression Number of obs = 9,260
Group variable: id_desa Number of groups = 1,852
R-sq: Obs per group:
within = 0.0418 min = 5
between = 0.0099 avg = 5.0
overall = 0.0241 max = 5
F(13,1851) = 18.00
corr(u_i, Xb) = 0.0005 Prob > F = 0.0000
(Std. Err. adjusted for 1,852 clusters in id_desa)
------------------------------------------------------------------------------
| Robust
Pen_Bantuan | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
d_year_ebt | 413.0277 127.3231 3.24 0.001 163.3158 662.7396
d_PLN | 101.005 25.91229 3.90 0.000 50.18461 151.8254
DD | -462.132 127.1197 -3.64 0.000 -711.4451 -212.8189
sd | -50.50605 25.00024 -2.02 0.044 -99.53767 -1.474426
smp | 77.5842 24.66175 3.15 0.002 29.21642 125.952
industri | 43.06753 13.63826 3.16 0.002 16.31953 69.81552
sinyalTV | -14.28977 31.86309 -0.45 0.654 -76.78115 48.2016
sinyalHP | -17.9242 16.27599 -1.10 0.271 -49.84542 13.99702
infra_kes | 23.03077 21.26598 1.08 0.279 -18.67706 64.7386
|
year |
2008 | -57.11102 19.97784 -2.86 0.004 -96.2925 -17.92955
2011 | -24.54144 19.23714 -1.28 0.202 -62.27022 13.18734
2014 | 125.6676 21.58866 5.82 0.000 83.32692 168.0083
2018 | 187.306 24.15648 7.75 0.000 139.9292 234.6828
|
_cons | 388.3949 36.05687 10.77 0.000 317.6785 459.1114
-------------+----------------------------------------------------------------
sigma_u | 612.57098
sigma_e | 604.19824
rho | .5068808 (fraction of variance due to u_i)
------------------------------------------------------------------------------
many thanks with the help
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