65
Table 5.8: Random Intercepts by Province
|
.. ■ Province |
Intercept |
|
BUENOS AIRES |
-0.23 |
|
CAP FEDERAL |
-6.60 |
|
Catamarca |
0.82 |
|
CHACO |
0.42 |
|
CHUBUT |
1.32 |
|
CORDOBA |
0.67 |
|
Corrientes |
0.99 |
|
ENTRE RIOS |
1.31 |
|
FORMOSA |
0.64 |
|
JUJUY |
1.03 |
|
LA PAMPA |
0.74 |
|
LA RIOJA |
0.74 |
|
MENDOZA |
-5.47 |
|
MISIONES |
-5.19 |
|
NEUQUEN |
0.99 |
|
RIO NEGRO |
1.47 |
|
S DEL ESTERO |
1.42 |
|
SALTA |
0.96 |
|
SANJUAN |
0.15 |
|
SAN LUIS |
1.48 |
|
SANTA CRUZ |
1.25 |
|
SANTAFE |
0.91 |
|
tdelfuego |
0.93 |
|
TUCUMAN |
0.35 |
GubernatorialResults
Empirical tests of the gubernatorial hypotheses show that the intuition was
globally correct, but without a notion of how powerful the effect was going to be.
Namely, my expectation was not to find any statistical significance in the coefficient of
previous governors. In the two estimated models, having a previous gubernatorial
background is strongly negatively correlated with the chances of submitting province-