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Q2 Z2,(Q2) Z2(Q2) - Institute for Water Resources - U.S. Army

Q2 Z2,(Q2) Z2(Q2) - Institute for Water Resources - U.S. Army

Q2 Z2,(Q2) Z2(Q2) - Institute for Water Resources - U.S. Army

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the quantity of commodity k transported from SMSA<br />

i to SMSA j by mode 1,<br />

Mk the distance in miles traveled by goods shipped between<br />

ij 1 area i and area j,<br />

P k the production of commodity k in area i, and<br />

i Ck the consumption of commodity k in area i.<br />

(i, j • 1, 2, ... 25)<br />

(k • 26, 29, 30, 32)<br />

(1 ■ rail, truck)<br />

The quantity figures were available directly from the census tape,<br />

whereas some processing of the data was necessary to obtain the other<br />

variables. The mileage values were obtained by dividing reported ton-<br />

miles by reported tons. Area production is the sum of an area's total<br />

exports and its reported internal shipments. Consumption is an area's<br />

total imports plus its reported internal shipments.<br />

The data were then divided into two groups according to whether<br />

the shipments traveled less than or greater than two hundred miles.<br />

This was to ascertain whether motor transport actually was dominant in<br />

the shorter shipments with rail transport becoming competitive at ap-<br />

proximately this distance. 17 For the aggregate regressions a dummy<br />

variable was utilized to identify the two groups. A second-degree<br />

term in distance was also added to the equation. This was an attempt<br />

to restrict the dummy variable to reporting definite breaks in the<br />

function as opposed to reflecting mild nonlinearities.<br />

Quantities by mode were first regressed on M, M2 , iP and j C <strong>for</strong><br />

each distance group and commodity. The groups were than combined and<br />

. 17. This is a common assertion. See, <strong>for</strong> example, Meyer, op. cit.<br />

132

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