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Appendix D: Delay Times and the Number of Grow-Ops
To get an estimate of the delay times we use data
from Plecas et al. for 32 regions. In the regression
we have the log of the time to bust, D, regressed
against the log of the number of busts, B. The
panel data are based on eight regions and four
years of data using a fixed effect model since the
regions do not change and may have individual
characteristics. The coefficient on D tells us the effect
of delay on the number of busts. In this case, a
Dependent Variable: LOG(B?)
Method: GLS (Cross Section Weights)
Sample: 1997 2000
Included observations: 4
Number of cross-sections used: 8
Total panel (unbalanced) observations: 31
One-step weighting matrix
PUBLIC POLICY SOURCES, NUMBER 74
White Heteroskedasticity-Consistent Standard Errors & Covariance
Appendix BCUC IR1 74.1
10 percent increase in the time of delay results in a
1.4 percent decrease in the number of busts. In
terms of the model, it suggests that the effect of
the number of grow ops measured is affected by
the number of grow ops. With more delay, fewer
grow-ops are discovered. Although there may be
many reasons for this, the subtleties of the model
in appendix C are clearly an issue that should be
investigated.
Variable Coefficient Std. Error t-Statistic Prob.
LOG(D?) -0.14 0.017 -8.48 0.0000
YEAR
Fixed Effects
0.22 0.013 16.7 0.0000
C—C 4.14
K—C 4.44
M—C 6.80
NC—C 2.70
T—C 5.40
V—C 5.95
NE—C 1.86
NK—C
Weighted Statistics
2.28
R-squared 0.998 Mean dependent var 6.73
Adjusted R-squared 0.997 S.D. dependent var 4.33
S.E. of regression 0.216 Sum squared resid 0.98
F-statistic 12060 Durbin-Watson stat 2.49
Prob(F-statistic)
Unweighted Statistics
0.00
R-squared 0.988 Mean dependent var. 4.45
Adjusted R-squared 0.98 S.D. dependent var. 1.66
S.E. of regression 0.218 Sum squared resid. 0.996
Durbin-Watson stat. 2.81
Marijuana Growth in British Columbia 38 The Fraser Institute