Poisson Regression

Look at these beautiful z-values!

I've been fighting with a badly skewed dependent variable--the
percentage of the municipal budget in forestry. It occurred to me that
the distribution resembles a poisson distribution. But I'm not sure
that this is right. Poisson regression is for count variables--number
of people who fall out of the back of Guatemalan pickup trucks, for
example. But this is a proportion... I don't think I have straight
budgetary size, which would be better...

This doesn't work as well with a personnel variable--the number of
people working in forestry.

Iteration 0: log pseudolikelihood = -164.60041
Iteration 1: log pseudolikelihood = -158.84706
Iteration 2: log pseudolikelihood = -158.82052
Iteration 3: log pseudolikelihood = -158.82052

Poisson regression Number of obs
= 50
Wald chi2(12) =
175.07
Prob > chi2 =
0.0000
Log pseudolikelihood = -158.82052 Pseudo R2 =
0.4930

------------------------------------------------------------------------------
| Robust
for_budge~08 | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
NGO_demands | .5640684 .189278 2.98 0.003 .1930903
.9350465
NGO_ec_suppo | -.3174905 .1405181 -2.26 0.024 -.5929009
-.0420801
rel_cit_dema | .6391908 .2348854 2.72 0.007 .1788238
1.099558
import_to_ci | -.3157467 .2260987 -1.40 0.163 -.7588921
.1273987
import_cent_ | .1148308 .1097506 1.05 0.295 -.1002765
.3299381
user_et~g_08 | .5093573 .3601709 1.41 0.157 -.1965647
1.215279
elite_educ | .0838478 .0705506 1.19 0.235 -.0544288
.2221245
pop_dens_01 | .0018278 .000772 2.37 0.018 .0003147
.003341
size | .000385 .0001025 3.76 0.000 .0001841
.000586
empl_per_~08 | 2.447348 3.064496 0.80 0.425 -3.558954
8.453651
pct_for_c~08 | .0180918 .0111026 1.63 0.103 -.0036688
.0398525
import_~v_08 | .4119408 .1828592 2.25 0.024 .0535434
.7703382
_cons | -1.051291 1.179759 -0.89 0.373 -3.363577
1.260995
------------------------------------------------------------------------------