Geoscience Reference
In-Depth Information
X
51
i
¼1
Δ
CS
j
¼
ShipCost
ij
Δ
InvMtrxO
i
ð
4
:
12
Þ
where
Δ
CS
j
k
are decreased consumer expenditures at destination j and industry sector k
X
47
k
¼1
Δ
CS
j
are direct impacts by states; and
B
j
)
1
InvMtrxO
j
¼
47 inverse matrix, where
B
is the direct output-
based technical coefficients matrix in destination state
j
.
The reduced consumer expenditures associated with increased shipping costs
drive reductions in household final demand. We assume that there are no substitu-
tion effects and final demand is directly affected by the reduced consumer
expenditures. Equation (4.13) applies the demand-driven NIEMO to estimate the
state-by-state economic impacts resulting from these reductions in household final
demand.
(
I
is a 47
X
j
¼
LINV
NIEMO
Δ
CS
j
Δ
ð
4
:
13
Þ
where
Δ
X
j
k
are decreased final outputs in destination states j and industry sector k.
X
X
j
are total impacts by states and
X
47
51
X
j
are total impacts by sector.
¼1
Δ
1
Δ
k
j
¼
A simple equation in Berwick and Farooq (
2003
, Appendix A) was applied to
calculate truckers' labor cost per mile (
TRC
)as
ð
D
=
Speed
þ
Waittime
Þ
LRPH
TRC
¼
LR
þ
ð
4
:
14
Þ
D
where
LR
¼
Labor (Wage) Rate Per Mile, given as 0.493 ($/mile).
D
Trip Distance, given as 100 (miles),
Speed
¼
¼
65 (mile/h),
Waittime
¼
Wait Time, given as 1 (h), and
LRPH
Labor (Wage) Rate per Hour, given as 17 ($/h).
We modified the current
LRPH
is close to $17 while the literature assumed $10
per hour. Accordingly, we could obtain $0.9 per mile truckers' labor cost. Also,
from the literature, other variable costs were given as $0.48 per mile. Therefore, we
estimated labor cost to be 65 % of total variable cost as,
¼
