You will see below that I am regressing an increase in the percent who are unemployed in a region (psum_unemployed_total_cont_y) on a person self reporting their health as "good" in a linear probability model.
The common way to describe these results seems to be:
Here an additional unit of regional unemployment decreases the probability of reporting good health by 1 percentage points.
However, regional unemployment is recorded from three to four percentage points, i.e.:
Code:
. sum psum_unemployed_total_cont_y
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
psum_unemp~y | 3,198 10.72768 5.007287 5.41 26.15I realise this may be a simple question, but I'm struggling with it so I appreciate any feedback,
Kindest regards,
John
Code:
. xtreg binary_health_y psum_unemployed_total_cont_y i.yrlycurrent_county_y1 i.year age_y i.maritalstatus_y if has_y0_questionna
> ire==1 & has_y5_questionnaire==1 | has_y0_questionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & has_y5_quest
> ionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==0 | has_y0_questionnair
> e==1 & cbmi_y10 !=. & has_y10_questionnaire==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==0 & cbmi_y10 !=
> . & has_y10_questionnaire==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 | has_y0_questionnaire==1 & cbm
> i_y10 !=. & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 & cbmi_y10 !=. & has_y1
> 0_questionnaire==1, cluster (current_county_y1) fe robust
note: 6.yrlycurrent_county_y1 omitted because of collinearity
note: 10.yrlycurrent_county_y1 omitted because of collinearity
note: 34.yrlycurrent_county_y1 omitted because of collinearity
note: 37.yrlycurrent_county_y1 omitted because of collinearity
note: 44.yrlycurrent_county_y1 omitted because of collinearity
note: 45.yrlycurrent_county_y1 omitted because of collinearity
note: 48.yrlycurrent_county_y1 omitted because of collinearity
note: 10.year omitted because of collinearity
Fixed-effects (within) regression Number of obs = 1581
Group variable: id Number of groups = 641
R-sq: within = 0.0502 Obs per group: min = 1
between = 0.0015 avg = 2.5
overall = 0.0000 max = 3
F(18,28) = .
corr(u_i, Xb) = -0.6875 Prob > F = .
(Std. Err. adjusted for 29 clusters in current_county_y1)
----------------------------------------------------------------------------------------------
| Robust
binary_health_y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
psum_unemployed_total_cont_y | -.0102855 .0048773 -2.11 0.044 -.0202762 -.0002947
|
yrlycurrent_county_y1 |
Cavan | -.018267 .2279823 -0.08 0.937 -.4852675 .4487335
Clare | -.5252115 .2027821 -2.59 0.015 -.9405917 -.1098313
Cork | .3906244 .2787621 1.40 0.172 -.1803939 .9616427
Donegal | 0 (omitted)
Dublin | .1718237 .2250034 0.76 0.451 -.2890749 .6327223
Dublin 10 | -.3292192 .3856107 -0.85 0.400 -1.119107 .4606685
Dublin 11 | 0 (omitted)
Dublin 12 | -.3186343 .4037992 -0.79 0.437 -1.145779 .5085107
Dublin 14 | -.1434118 .2760698 -0.52 0.608 -.7089152 .4220916
Dublin 15 | -.5067857 .4255742 -1.19 0.244 -1.378535 .3649634
Dublin 16 | .6643994 .4025214 1.65 0.110 -.1601284 1.488927
Dublin 18 | -.5712014 .3942685 -1.45 0.159 -1.378824 .236421
Dublin 22 | .1140665 .2243428 0.51 0.615 -.345479 .5736119
Dublin 24 | .0648586 .2443927 0.27 0.793 -.4357571 .5654742
Dublin 4 | .7202541 .3574293 2.02 0.054 -.0119065 1.452415
Dublin 6 | -.1533908 .2734907 -0.56 0.579 -.7136112 .4068296
Dublin 6W | .4818072 .1730873 2.78 0.010 .127254 .8363604
Dublin 7 | .41868 .2419016 1.73 0.094 -.0768329 .914193
Dublin 8 | .0001566 .2054587 0.00 0.999 -.4207065 .4210197
Dublin City | -.1254077 .2432794 -0.52 0.610 -.6237429 .3729275
Dún Laoghaire-Rathdown | -.0052935 .2089167 -0.03 0.980 -.4332399 .4226529
Fingal | -.6266997 .3963005 -1.58 0.125 -1.438484 .185085
Galway | -.1784478 .2249192 -0.79 0.434 -.639174 .2822783
Galway City | -.0723957 .2240071 -0.32 0.749 -.5312535 .3864621
Kerry | -.5674608 .3000446 -1.89 0.069 -1.182074 .0471527
Kildare | .5304143 .1807288 2.93 0.007 .1602081 .9006205
Kilkenny | 0 (omitted)
Laois | -.0744377 .0605301 -1.23 0.229 -.1984279 .0495525
Leitrim | .5543939 .1868227 2.97 0.006 .171705 .9370828
Limerick | 0 (omitted)
Longford | .1264136 .2223292 0.57 0.574 -.329007 .5818343
Louth | .2030122 .2291028 0.89 0.383 -.2662835 .672308
Mayo | .7999382 .224004 3.57 0.001 .3410868 1.25879
Meath | -.1253544 .3558635 -0.35 0.727 -.8543077 .6035989
Monaghan | -.2798706 .4098558 -0.68 0.500 -1.119422 .559681
Offaly | .0461998 .2170761 0.21 0.833 -.3984605 .49086
Roscommon | 0 (omitted)
Sligo | 0 (omitted)
South Dublin | .7029383 .3213452 2.19 0.037 .0446926 1.361184
Tipperary | -.0259743 .0451327 -0.58 0.570 -.1184245 .0664759
Tipperary North | 0 (omitted)
Waterford | -.639648 .222072 -2.88 0.008 -1.094542 -.1847542
Westmeath | .0048053 .2286901 0.02 0.983 -.4636452 .4732558
Wexford | .1496822 .2144573 0.70 0.491 -.2896137 .588978
Wicklow | .3035418 .2539436 1.20 0.242 -.2166381 .8237218
|
year |
5 | -.0829668 .032689 -2.54 0.017 -.1499272 -.0160065
10 | 0 (omitted)
|
age_y | .0071394 .0045045 1.58 0.124 -.0020876 .0163664
|
maritalstatus_y |
Cohabiting | .048518 .0302435 1.60 0.120 -.013433 .110469
Separated | -.0904769 .2361229 -0.38 0.704 -.5741527 .393199
Divorced | -.1477058 .0902324 -1.64 0.113 -.3325386 .0371269
Widowed | .0203674 .3064814 0.07 0.947 -.6074313 .6481661
Single/Never married | .0082783 .0634848 0.13 0.897 -.1217644 .138321
|
_cons | .5915398 .181058 3.27 0.003 .2206592 .9624204
-----------------------------+----------------------------------------------------------------
sigma_u | .48800391
sigma_e | .35656453
rho | .65194878 (fraction of variance due to u_i)
----------------------------------------------------------------------------------------------
0 Response to In regression analysis, is the unit of the predictor rounded in any way?
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