Investor Presentaiton
3) Preliminary results (1)
- ICMS-E and municipal protected areas (PA)
Dependent variable: In of municipal protected area share in percent of total area
(mun 2)
2.904***
(0.556)
0.502***
-15.064
(13.988)
0.585***
(mun 3)
(0.187)
-1.200***
(0.391)
(0.184)
-1.317***
(0.343)
model:
variables
(mun 1)
icms_e
1.079***
(0.245)
0.677***
agr
(0.202)
-0.910*
ind
(0.466)
1.416
0.658
0.078
ser
(1.160)
(1.003)
(0.948)
2.233***
1.848**
1.779***
pop
(0.552)
(0.765)
(0.632)
3.138***
1.166**
1.242**
inc
(0.477)
(0.553)
0.431
1.047***
arpa
(0.337)
(0.370)
0.522***
fed
(0.110)
overall, there is a positive significant
correlation of ICMS-E with PA
there are on average higher PA
shares with ICMS-E than without
0.151
sta
(0.041)
-1.432***
fed*icms_e
(0.228)
(0.486)
-0.181
-0.611*
sta imcs_e
(0.145)
(0.320)
-0.458
agr*icms_e
ind*icms_e
ser*icms_e
pop*icms_e
inc*icms_e
(1.289)
intercept
biome D
effects
-23.472***
(5.100)
yes
-13.411***
(4.596)
yes
-10.925***
(3.891)
yes
individual re
adj. R²
0.31
individual re
0.39
individual re
0.41
(0.616)
1.086***
(0.320)
0.547***
(0.119)
0.075
(0.111)
-1.808***
(0.319)
1.299
(1.325)
2.799
(1.942)
-1.599***
(0.502)
3.464***
GDP per capita correlates positively
and significantly with PA
→ on average, richer states have
higher PA shares
the interaction of ICMS-E with GDP per
capita is positive ...
HELMHOLTZ
CENTRE FOR
ENVIRONMENTAL
RESEARCH - UFZ
The panel data sample is balanced with n=27, T=19, N=513. Robust standard errors are reported in parenthesis below the estimated
coefficients. Individual coefficients are indicated with *10%, **5% or ***1% significance levels. Biome D, represent dummy variables
for the different biomes. A individual random effects (re) model is employed.View entire presentation