Refining the Isard Multiregional Input–Output Model: Theory, Operationality and Extensions - Regional Science Matters - page 45

T ij ¼ ShipCost ij Δ PTimeCost ij PLaborCost ð 4 : 9 Þ

where
Δ

T ij k are increased costs caused by the increased time of travel.
PTimeCost ij are the proportions of time changes calculated as total increased time
divided by total baseline time. Data for 114 MSAs by 114 MSAs flows are
aggregated to 49 states by 49 states.
PLaborCost is the proportion of labor costs in the operations of the transportation
industry (0.65).

D ij ¼ ShipCost ij

Δ PDistCost ij PVarCost ð 4 : 10 Þ

where
Δ D ij k are increased costs associated with increased shipping distance.

PDistCost ij are proportions of distance change calculated as total increased distance
divided by total baseline distance. Data for 114 MSAs by 114 MSAs are
aggregated to 49 states by 49 states.
PVarCost is the assumed proportion of variable costs in operation of the transpor-
tation industry (0.35).
Total increased shipping costs are estimated by adding the two increased costs,
time and distance. Equation (4.11) shows the procedure for estimating increased
shipping costs.

ShipCost ij ¼ Δ D ij þ Δ T ij Δ ð 4 : 11 Þ

where
Δ

ShipCost ij k are increased shipping costs from origin state i to destination state j for
industry sector k resulting from an event. In the short run, shipping costs are
assumed to be non-decreasing. In the event of an emergency, sellers can pass on
higher costs. They may cut prices because of competitive pressures, but probably
only in the longer run.
The increased shipping costs,

ShipCost ij k , are passed forward and lead to
increased prices at destinations resulting in lower consumer expenditures.
Dietzenbacher ( 1997 ) has shown that the supply-driven I-O model is more mean-
ingful for estimating price increases than the Leontief price I-O model when
absolute costs in value-added sectors are available. An application of a supply-
driven I-O model is summarized in equation (4.12).

Δ