Folks,
Perhaps someone could shed some light on the following predicament I have.
I'm looking to examine the influence bias in the distribution of capital grants. I utilise two dependent variables; the first being the natural logarithm of grant club i received. The second is the grant club i received as a proportion of the total amount it sought. In essence grant awarded/grant sought.
My explanatory variables include the natural logarithm of population, the natural logarithm of population per km2 (urbanisation). I also have, measured as a percentage, those in the age bracket of 0-19. The unemployment rate, and those who are employed as either managers, higher professionals or owners.
My bias variables is the inverse distance (km) between the Minister of Finance to club i.
What I'm wondering is there an issue with having such a variety of explanatory variables in different forms, logs, percentages and kms?
I don't see the logic in transforming my distance variables or percentage variables into logs.
However, running my model under OLS creates some difficult coefficients to analyse.
For example, a 1km decrease in the distance between the hometown of the President of the Gaelic Athletic Association and awards to multisport facilities, on average increases the log of grant per capita awarded by 5.4...(coefficient score).
Surely this isn't correct?
Perhaps someone could shed some light on the following predicament I have.
I'm looking to examine the influence bias in the distribution of capital grants. I utilise two dependent variables; the first being the natural logarithm of grant club i received. The second is the grant club i received as a proportion of the total amount it sought. In essence grant awarded/grant sought.
My explanatory variables include the natural logarithm of population, the natural logarithm of population per km2 (urbanisation). I also have, measured as a percentage, those in the age bracket of 0-19. The unemployment rate, and those who are employed as either managers, higher professionals or owners.
My bias variables is the inverse distance (km) between the Minister of Finance to club i.
What I'm wondering is there an issue with having such a variety of explanatory variables in different forms, logs, percentages and kms?
I don't see the logic in transforming my distance variables or percentage variables into logs.
However, running my model under OLS creates some difficult coefficients to analyse.
For example, a 1km decrease in the distance between the hometown of the President of the Gaelic Athletic Association and awards to multisport facilities, on average increases the log of grant per capita awarded by 5.4...(coefficient score).
Surely this isn't correct?
Code:
. regress loggrantcap logpop pop19p unemploymaterate logurban highearner infin insport ingaa inirfu infai, robust
Linear regression Number of obs = 1,296
F(44, 1251) = 26.99
Prob > F = 0.0000
R-squared = 0.4595
Root MSE = 1.1527
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| Robust
loggrantcap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
logpop | -.9300156 .0529773 -17.55 0.000 -1.03395 -.8260814
pop19p | 1.327534 .9115108 1.46 0.146 -.4607245 3.115792
unemploymaterate | -.2538378 1.160002 -0.22 0.827 -2.529602 2.021926
logurban | .0152337 .0364609 0.42 0.676 -.0562974 .0867649
highearner | .2345842 .6568613 0.36 0.721 -1.054087 1.523255
infin | .1498061 .0925063 1.62 0.106 -.0316785 .3312907
insport | .0757168 .0406931 1.86 0.063 -.0041176 .1555511
ingaa | 5.437885 2.508969 2.17 0.030 .5156333 10.36014
inirfu | -.4651907 .5890366 -0.79 0.430 -1.620799 .690418
infai | -.0305091 .0833021 -0.37 0.714 -.1939363 .1329181
_cons | 10.03631 .3955023 25.38 0.000 9.260384 10.81223
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