Dear all,
I have a weekly panel dataset of crime categories per municipality. I am interested in exploring the relationship between the exposure to different covid related restrictions (red, orange, yellow) in the last 30 days and the crimes count per municipality. I am using a Poisson regression with the command ppmlhdfe from SSC in Stata 17 with municipality and week fixed effects.
My data looks like this:
I don't understand why, when studying robberies, if I include week in the abs() option ms_yellow is drop due to collinearity while if i include week as a factor variable ms_yellow doesn't drop and the coefficients are different (last week drops).
If i implement the same exercise for "drugs" the coefficients are the same in both specifications :
Hopefully someone can help me.
Andrea
I have a weekly panel dataset of crime categories per municipality. I am interested in exploring the relationship between the exposure to different covid related restrictions (red, orange, yellow) in the last 30 days and the crimes count per municipality. I am using a Poisson regression with the command ppmlhdfe from SSC in Stata 17 with municipality and week fixed effects.
My data looks like this:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float week double(Rapine Droga ms_red30 ms_orange30 ms_yellow30) 3164 0 0 0 0 0 3165 1 0 0 0 8 3166 0 0 0 0 15 3167 0 0 0 0 22 3168 0 0 0 0 28.571428571428573 3169 0 0 0 0 30 3170 0 0 0 0 30 3171 0 0 2.4444444444444446 .6666666666666666 26.88888888888889 3172 0 0 7.571428571428571 3.4285714285714284 19 3173 0 0 10 6.142857142857143 13.857142857142858 3174 0 0 10 13 7 3175 0 0 8 19.857142857142858 2.142857142857143 3176 0 0 4 23.142857142857142 2.857142857142857 3177 0 0 .14285714285714285 22.142857142857142 7.714285714285714 3178 0 1 0 16 14 3179 0 0 0 9 21 3180 0 0 0 2.142857142857143 27.857142857142858 3181 0 0 0 .8571428571428571 29.142857142857142 3182 0 0 .8571428571428571 6.142857142857143 23 3183 0 1 7 7 16 end format %tw week label var Rapine "Robberies" label var Droga "Drugs" label var ms_red30 "# of days under red zone in the last 30 days" label var ms_orange30 "# of days under orange zone in the last 30 days" label var ms_yellow30 "# of days under yellow zone in the last 30 days"
If i implement the same exercise for "drugs" the coefficients are the same in both specifications :
Code:
. ppmlhdfe Rapine ms_red30 ms_orange30 ms_yellow30 $controls, abs(city week) vce(cluster city_week)
(dropped 162492 observations that are either singletons or separated by a fixed effect)
(ReLU method dropped 4 separated observations in 1 iterations)
note: 1 variable omitted because of collinearity: ms_yellow30
Iteration 1: deviance = 5.7489e+03 eps = . iters = 4 tol = 1.0e-04 min(eta) = -3.44 P
Iteration 2: deviance = 4.8428e+03 eps = 1.87e-01 iters = 3 tol = 1.0e-04 min(eta) = -4.04
Iteration 3: deviance = 4.7653e+03 eps = 1.63e-02 iters = 3 tol = 1.0e-04 min(eta) = -4.35
Iteration 4: deviance = 4.7640e+03 eps = 2.89e-04 iters = 3 tol = 1.0e-04 min(eta) = -4.41
Iteration 5: deviance = 4.7640e+03 eps = 2.44e-07 iters = 2 tol = 1.0e-04 min(eta) = -4.41
Iteration 6: deviance = 4.7640e+03 eps = 5.11e-13 iters = 2 tol = 1.0e-05 min(eta) = -4.41 S
Iteration 7: deviance = 4.7640e+03 eps = 1.82e-16 iters = 2 tol = 1.0e-08 min(eta) = -4.41 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 7 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 16,410
Absorbing 2 HDFE groups Residual df = 16,003
Statistics robust to heteroskedasticity Wald chi2(5) = 17.64
Deviance = 4763.964214 Prob > chi2 = 0.0034
Log pseudolikelihood = -3514.408655 Pseudo R2 = 0.3932
Number of clusters (city_week)= 16,410
(Std. err. adjusted for 16,410 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Rapine | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | -.0226975 .0092091 -2.46 0.014 -.040747 -.004648
ms_orange30 | .0125123 .0081232 1.54 0.123 -.0034088 .0284335
ms_yellow30 | 0 (omitted)
pop_city | 1.14e-07 .0000194 0.01 0.995 -.000038 .0000382
male_1539 | -.0000613 .0000736 -0.83 0.405 -.0002055 .0000829
pop_imm | -.0000179 .0000348 -0.52 0.606 -.0000861 .0000502
_cons | 5.288924 8.805351 0.60 0.548 -11.96925 22.54709
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 357 0 357 |
week | 46 1 45 |
-----------------------------------------------------+
. ppmlhdfe Rapine ms_red30 ms_orange30 ms_yellow30 $controls, abs(city week) vce(cluster city_week) nolog
(dropped 162492 observations that are either singletons or separated by a fixed effect)
(ReLU method dropped 4 separated observations in 1 iterations)
note: 1 variable omitted because of collinearity: ms_yellow30
Converged in 7 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 16,410
Absorbing 2 HDFE groups Residual df = 16,003
Statistics robust to heteroskedasticity Wald chi2(5) = 17.64
Deviance = 4763.964214 Prob > chi2 = 0.0034
Log pseudolikelihood = -3514.408655 Pseudo R2 = 0.3932
Number of clusters (city_week)= 16,410
(Std. err. adjusted for 16,410 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Rapine | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | -.0226975 .0092091 -2.46 0.014 -.040747 -.004648
ms_orange30 | .0125123 .0081232 1.54 0.123 -.0034088 .0284335
ms_yellow30 | 0 (omitted)
pop_city | 1.14e-07 .0000194 0.01 0.995 -.000038 .0000382
male_1539 | -.0000613 .0000736 -0.83 0.405 -.0002055 .0000829
pop_imm | -.0000179 .0000348 -0.52 0.606 -.0000861 .0000502
_cons | 5.288924 8.805351 0.60 0.548 -11.96925 22.54709
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 357 0 357 |
week | 46 1 45 |
-----------------------------------------------------+
. ppmlhdfe Rapine ms_red30 ms_orange30 ms_yellow30 $controls, abs(city week) vce(cluster city_week)
(dropped 162492 observations that are either singletons or separated by a fixed effect)
(ReLU method dropped 4 separated observations in 1 iterations)
note: 1 variable omitted because of collinearity: ms_yellow30
Iteration 1: deviance = 5.7489e+03 eps = . iters = 4 tol = 1.0e-04 min(eta) = -3.44 P
Iteration 2: deviance = 4.8428e+03 eps = 1.87e-01 iters = 3 tol = 1.0e-04 min(eta) = -4.04
Iteration 3: deviance = 4.7653e+03 eps = 1.63e-02 iters = 3 tol = 1.0e-04 min(eta) = -4.35
Iteration 4: deviance = 4.7640e+03 eps = 2.89e-04 iters = 3 tol = 1.0e-04 min(eta) = -4.41
Iteration 5: deviance = 4.7640e+03 eps = 2.44e-07 iters = 2 tol = 1.0e-04 min(eta) = -4.41
Iteration 6: deviance = 4.7640e+03 eps = 5.11e-13 iters = 2 tol = 1.0e-05 min(eta) = -4.41 S
Iteration 7: deviance = 4.7640e+03 eps = 1.82e-16 iters = 2 tol = 1.0e-08 min(eta) = -4.41 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 7 iterations and 19 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 16,410
Absorbing 2 HDFE groups Residual df = 16,003
Statistics robust to heteroskedasticity Wald chi2(5) = 17.64
Deviance = 4763.964214 Prob > chi2 = 0.0034
Log pseudolikelihood = -3514.408655 Pseudo R2 = 0.3932
Number of clusters (city_week)= 16,410
(Std. err. adjusted for 16,410 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Rapine | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | -.0226975 .0092091 -2.46 0.014 -.040747 -.004648
ms_orange30 | .0125123 .0081232 1.54 0.123 -.0034088 .0284335
ms_yellow30 | 0 (omitted)
pop_city | 1.14e-07 .0000194 0.01 0.995 -.000038 .0000382
male_1539 | -.0000613 .0000736 -0.83 0.405 -.0002055 .0000829
pop_imm | -.0000179 .0000348 -0.52 0.606 -.0000861 .0000502
_cons | 5.288924 8.805351 0.60 0.548 -11.96925 22.54709
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 357 0 357 |
week | 46 1 45 |
-----------------------------------------------------+
. ppmlhdfe Rapine ms_red30 ms_orange30 ms_yellow30 i.week $controls, abs(city) vce(cluster city_week)
(dropped 162492 observations that are either singletons or separated by a fixed effect)
(simplex method dropped 4 separated observations)
note: 1 variable omitted because of collinearity: 3183bn.week
Iteration 1: deviance = 5.7489e+03 eps = . iters = 1 tol = 1.0e-04 min(eta) = -3.44 P
Iteration 2: deviance = 4.8428e+03 eps = 1.87e-01 iters = 1 tol = 1.0e-04 min(eta) = -4.04
Iteration 3: deviance = 4.7653e+03 eps = 1.63e-02 iters = 1 tol = 1.0e-04 min(eta) = -4.35
Iteration 4: deviance = 4.7640e+03 eps = 2.89e-04 iters = 1 tol = 1.0e-04 min(eta) = -4.41
Iteration 5: deviance = 4.7640e+03 eps = 2.44e-07 iters = 1 tol = 1.0e-04 min(eta) = -4.41
Iteration 6: deviance = 4.7640e+03 eps = 5.11e-13 iters = 1 tol = 1.0e-05 min(eta) = -4.41 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 6 iterations and 6 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 16,410
Absorbing 1 HDFE group Residual df = 16,003
Statistics robust to heteroskedasticity Wald chi2(50) = 137.08
Deviance = 4763.964214 Prob > chi2 = 0.0000
Log pseudolikelihood = -3514.408655 Pseudo R2 = 0.3932
Number of clusters (city_week)= 16,410
(Std. err. adjusted for 16,410 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Rapine | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | .0298577 .0146061 2.04 0.041 .0012302 .0584851
ms_orange30 | .0650675 .0124338 5.23 0.000 .0406978 .0894372
ms_yellow30 | .0525552 .0126576 4.15 0.000 .0277467 .0773636
|
week |
3139 | .8754687 .3511201 2.49 0.013 .1872861 1.563651
3140 | .9932518 .3632425 2.73 0.006 .2813095 1.705194
3141 | .7884574 .3749755 2.10 0.035 .053519 1.523396
3142 | 1.064711 .3661866 2.91 0.004 .3469982 1.782423
3143 | 1.410987 .3452933 4.09 0.000 .7342246 2.087749
3144 | .8754687 .3685572 2.38 0.018 .15311 1.597828
3145 | .7884574 .3900979 2.02 0.043 .0238794 1.553035
3146 | .9932518 .3725372 2.67 0.008 .2630922 1.723411
3147 | .8754687 .3752277 2.33 0.020 .1400359 1.610902
3148 | 1.223775 .3539036 3.46 0.001 .5301371 1.917414
3149 | 1.504077 .3373051 4.46 0.000 .8429716 2.165183
3150 | .9162907 .3528733 2.60 0.009 .2246719 1.60791
3151 | 1.131402 .3829937 2.95 0.003 .3807483 1.882056
3152 | 1.308333 .3583386 3.65 0.000 .6060021 2.010664
3153 | .9932518 .3676894 2.70 0.007 .2725937 1.71391
3154 | .9555114 .4048922 2.36 0.018 .1619374 1.749086
3155 | 1.064711 .3504744 3.04 0.002 .3777936 1.751628
3156 | 1.280934 .3412644 3.75 0.000 .6120679 1.9498
3157 | 1.458615 .3396505 4.29 0.000 .7929122 2.124318
3158 | 1.223775 .410377 2.98 0.003 .4194513 2.0281
3159 | 1.526056 .3439907 4.44 0.000 .8518469 2.200266
3160 | 1.193922 .3567544 3.35 0.001 .4946967 1.893148
3161 | .9555114 .4006547 2.38 0.017 .1702427 1.74078
3162 | 1.193922 .360692 3.31 0.001 .4869791 1.900866
3163 | 1.360977 .3413414 3.99 0.000 .6919596 2.029993
3164 | 1.163151 .3502992 3.32 0.001 .4765769 1.849725
3165 | .9285007 .2775702 3.35 0.001 .3844732 1.472528
3166 | -.1364457 .3175993 -0.43 0.667 -.7589289 .4860376
3167 | .0875085 .2584912 0.34 0.735 -.4191249 .594142
3168 | -.3776926 .2591558 -1.46 0.145 -.8856285 .1302434
3169 | -.6055099 .2392328 -2.53 0.011 -1.074398 -.1366222
3170 | -.6416722 .3143764 -2.04 0.041 -1.257839 -.0255058
3171 | -.4615336 .2528655 -1.83 0.068 -.9571409 .0340738
3172 | -.0268751 .3367053 -0.08 0.936 -.6868054 .6330551
3173 | .075262 .2417859 0.31 0.756 -.3986296 .5491536
3174 | -.0439122 .2387102 -0.18 0.854 -.5117756 .4239512
3175 | .0523969 .2549484 0.21 0.837 -.4472928 .5520867
3176 | -.2381298 .2435634 -0.98 0.328 -.7155052 .2392456
3177 | -.0684369 .2253688 -0.30 0.761 -.5101516 .3732778
3178 | -.0187477 .2187683 -0.09 0.932 -.4475257 .4100303
3179 | -.0529918 .238604 -0.22 0.824 -.520647 .4146634
3180 | -.0531046 .2305366 -0.23 0.818 -.5049482 .3987389
3181 | -.6612678 .2461883 -2.69 0.007 -1.143788 -.1787476
3182 | -.0768463 .2372416 -0.32 0.746 -.5418313 .3881386
3183 | 0 (omitted)
|
pop_city | 1.14e-07 .0000194 0.01 0.995 -.000038 .0000382
male_1539 | -.0000613 .0000736 -0.83 0.405 -.0002055 .0000829
pop_imm | -.0000179 .0000348 -0.52 0.606 -.0000861 .0000502
_cons | 4.022255 8.806262 0.46 0.648 -13.2377 21.28221
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 357 0 357 |
-----------------------------------------------------+
. ppmlhdfe Droga ms_red30 ms_orange30 ms_yellow30 $controls, abs(city week) vce(cluster city_week)
(dropped 141152 observations that are either singletons or separated by a fixed effect)
Iteration 1: deviance = 1.4431e+04 eps = . iters = 4 tol = 1.0e-04 min(eta) = -3.47 P
Iteration 2: deviance = 1.2203e+04 eps = 1.83e-01 iters = 3 tol = 1.0e-04 min(eta) = -4.12
Iteration 3: deviance = 1.1996e+04 eps = 1.72e-02 iters = 2 tol = 1.0e-04 min(eta) = -4.45
Iteration 4: deviance = 1.1981e+04 eps = 1.28e-03 iters = 2 tol = 1.0e-04 min(eta) = -4.52
Iteration 5: deviance = 1.1980e+04 eps = 9.72e-05 iters = 2 tol = 1.0e-04 min(eta) = -4.52
Iteration 6: deviance = 1.1980e+04 eps = 1.23e-06 iters = 2 tol = 1.0e-05 min(eta) = -4.52
Iteration 7: deviance = 1.1980e+04 eps = 2.56e-10 iters = 2 tol = 1.0e-06 min(eta) = -4.52 S
Iteration 8: deviance = 1.1980e+04 eps = 1.94e-16 iters = 2 tol = 1.0e-07 min(eta) = -4.52 S
Iteration 9: deviance = 1.1980e+04 eps = 0.00e+00 iters = 1 tol = 1.0e-09 min(eta) = -4.52 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 9 iterations and 20 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 37,754
Absorbing 2 HDFE groups Residual df = 36,882
Statistics robust to heteroskedasticity Wald chi2(6) = 6.78
Deviance = 11979.78293 Prob > chi2 = 0.3418
Log pseudolikelihood = -8965.445258 Pseudo R2 = 0.4611
Number of clusters (city_week)= 37,754
(Std. err. adjusted for 37,754 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Droga | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | .0126934 .0271877 0.47 0.641 -.0405936 .0659803
ms_orange30 | .0171888 .0270549 0.64 0.525 -.0358378 .0702155
ms_yellow30 | .0134935 .0269487 0.50 0.617 -.0393249 .0663119
pop_city | -.0000197 .0000122 -1.61 0.106 -.0000437 4.22e-06
male_1539 | .0000577 .000049 1.18 0.240 -.0000385 .0001538
pop_imm | -.0000421 .000022 -1.92 0.055 -.0000852 9.66e-07
_cons | 7.023082 4.023459 1.75 0.081 -.8627532 14.90892
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 821 0 821 |
week | 46 1 45 |
-----------------------------------------------------+
. ppmlhdfe Droga ms_red30 ms_orange30 ms_yellow30 i.week $controls, abs(city) vce(cluster city_week)
(dropped 141152 observations that are either singletons or separated by a fixed effect)
Iteration 1: deviance = 1.4431e+04 eps = . iters = 1 tol = 1.0e-04 min(eta) = -3.47 P
Iteration 2: deviance = 1.2203e+04 eps = 1.83e-01 iters = 1 tol = 1.0e-04 min(eta) = -4.12
Iteration 3: deviance = 1.1996e+04 eps = 1.72e-02 iters = 1 tol = 1.0e-04 min(eta) = -4.45
Iteration 4: deviance = 1.1981e+04 eps = 1.28e-03 iters = 1 tol = 1.0e-04 min(eta) = -4.52
Iteration 5: deviance = 1.1980e+04 eps = 9.72e-05 iters = 1 tol = 1.0e-04 min(eta) = -4.52
Iteration 6: deviance = 1.1980e+04 eps = 1.23e-06 iters = 1 tol = 1.0e-05 min(eta) = -4.52
Iteration 7: deviance = 1.1980e+04 eps = 2.56e-10 iters = 1 tol = 1.0e-06 min(eta) = -4.52 S O
------------------------------------------------------------------------------------------------------------
(legend: p: exact partial-out s: exact solver h: step-halving o: epsilon below tolerance)
Converged in 7 iterations and 7 HDFE sub-iterations (tol = 1.0e-08)
HDFE PPML regression No. of obs = 37,754
Absorbing 1 HDFE group Residual df = 36,882
Statistics robust to heteroskedasticity Wald chi2(51) = 309.71
Deviance = 11979.78293 Prob > chi2 = 0.0000
Log pseudolikelihood = -8965.445258 Pseudo R2 = 0.4611
Number of clusters (city_week)= 37,754
(Std. err. adjusted for 37,754 clusters in city_week)
------------------------------------------------------------------------------
| Robust
Droga | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ms_red30 | .0126934 .0271877 0.47 0.641 -.0405936 .0659803
ms_orange30 | .0171888 .0270549 0.64 0.525 -.0358378 .0702155
ms_yellow30 | .0134935 .0269486 0.50 0.617 -.0393249 .0663119
|
week |
3139 | -.0870114 .1592926 -0.55 0.585 -.3992192 .2251964
3140 | .0507723 .1539785 0.33 0.742 -.25102 .3525646
3141 | -.2205428 .1619966 -1.36 0.173 -.5380502 .0969646
3142 | .1805839 .1580223 1.14 0.253 -.1291342 .490302
3143 | .0703808 .1665922 0.42 0.673 -.256134 .3968956
3144 | -.0984401 .1520385 -0.65 0.517 -.3964301 .1995499
3145 | -.0645385 .1647794 -0.39 0.695 -.3875003 .2584232
3146 | .0990909 .1571578 0.63 0.528 -.2089328 .4071146
3147 | .0606246 .1702683 0.36 0.722 -.2730951 .3943443
3148 | .0103628 .1646738 0.06 0.950 -.3123918 .3331174
3149 | -.0104713 .2293761 -0.05 0.964 -.4600401 .4390975
3150 | -.3302417 .1803308 -1.83 0.067 -.6836836 .0232002
3151 | -.2205428 .1603323 -1.38 0.169 -.5347883 .0937027
3152 | -1.067841 .2244257 -4.76 0.000 -1.507707 -.6279743
3153 | -.4700036 .2033763 -2.31 0.021 -.8686139 -.0713933
3154 | -.3302417 .219605 -1.50 0.133 -.7606596 .1001762
3155 | -.0757118 .1726292 -0.44 0.661 -.4140588 .2626351
3156 | -2.74e-15 .1513658 -0.00 1.000 -.2966715 .2966715
3157 | -.1216969 .1613267 -0.75 0.451 -.4378915 .1944976
3158 | -.1823216 .1718081 -1.06 0.289 -.5190593 .1544162
3159 | .1978257 .1508679 1.31 0.190 -.0978699 .4935214
3160 | -.0210534 .1452417 -0.14 0.885 -.305722 .2636151
3161 | -.0870114 .1800608 -0.48 0.629 -.4399241 .2659014
3162 | -.2468601 .1580414 -1.56 0.118 -.5566156 .0628954
3163 | -.1216969 .1630139 -0.75 0.455 -.4411984 .1978045
3164 | -.0317487 .1599006 -0.20 0.843 -.3451482 .2816508
3165 | -.1451813 .2650406 -0.55 0.584 -.6646514 .3742887
3166 | -.3499935 .4323297 -0.81 0.418 -1.197344 .4973572
3167 | -.2394586 .6111949 -0.39 0.695 -1.437379 .9584615
3168 | -.4837699 .7857264 -0.62 0.538 -2.023765 1.056226
3169 | -.3742401 .8218253 -0.46 0.649 -1.984988 1.236508
3170 | -.528413 .8224956 -0.64 0.521 -2.140475 1.083649
3171 | -.8869515 .8294068 -1.07 0.285 -2.512559 .7386559
3172 | -1.037717 .8320618 -1.25 0.212 -2.668528 .5930944
3173 | -.2553729 .8244893 -0.31 0.757 -1.871342 1.360596
3174 | .0733849 .8195733 0.09 0.929 -1.532949 1.679719
3175 | -.2615349 .8208882 -0.32 0.750 -1.870446 1.347376
3176 | -.1447715 .8194019 -0.18 0.860 -1.75077 1.461227
3177 | .2621761 .8200977 0.32 0.749 -1.345186 1.869538
3178 | .1601439 .8199992 0.20 0.845 -1.447025 1.767313
3179 | -.1488362 .8213048 -0.18 0.856 -1.758564 1.460892
3180 | .0502398 .8201311 0.06 0.951 -1.557188 1.657667
3181 | -.1165959 .8209037 -0.14 0.887 -1.725538 1.492346
3182 | -.0481659 .8199865 -0.06 0.953 -1.65531 1.558978
3183 | .04592 .8025869 0.06 0.954 -1.527121 1.618962
|
pop_city | -.0000197 .0000122 -1.61 0.106 -.0000437 4.22e-06
male_1539 | .0000577 .000049 1.18 0.240 -.0000385 .0001538
pop_imm | -.0000421 .000022 -1.92 0.055 -.0000852 9.66e-07
_cons | 7.127697 4.005883 1.78 0.075 -.7236883 14.97908
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
city | 821 0 821 |
Andrea
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