exports the same commodity. This is a chronic problem in small regions that do not represent a functional economic area (Robison and Miller, 1988) but it is also problematic in larger regions (Kronenberg, 2009). The SLQ rules out the possibility of cross-hauling It a priori. presupposes that a region will import from other regions, yet not export to them, if SLQi < 1 but do the opposite if SLQi 1. The CILQ does not preclude cross-hauling, as some cells in ≥ a given row of the adjustment matrix can have CILQij < 1, while others can have CILQij 1. ≥ Hence imports and exports of commodity can occur simultaneously. The problem here is i that the CILQ does not make adequate allowance for cross-hauling, whereas the FLQ attempts to do so by taking regional size into account.24 Although it is obviously desirable to have as close to zero as possible, so as to μ1 eliminate any systematic tendency towards overstatement or understatement, = 0 could μ1 still arise if some very large positive and negative errors happened to average out to zero. Thus, when assessing accuracy, we do also need to look at measures such as and μ4 μ5. When = 0.25, the FLQ exhibits an average error of 11.9% for and 8.2% for The δ μ4 μ5. higher figure for is due to the fact that, with this measure, simulation errors are squared, μ4 which puts greater emphasis on larger errors. For instance, would treat an error of ±0.1 as μ5 10 times larger than an error of ±0.01, whereas would treat the former as 100 times larger μ4 than the latter. It is by no means clear that this extreme emphasis on avoiding larger errors is warranted. However, is an error of 8.2% unacceptably large? This is ultimately up to the analyst to decide, by weighing up the costs and benefits of increasing the degree of accuracy, e.g. the costs of surveying key sectors. It should also be borne in mind that the 8.2% average error arose as a result of using a single figure for this error could be reduced in many cases by δ; using a value tailored to the specific characteristics of the region under consideration. Many authors have suggested that measures using absolute values are the most
exports the same commodity. This is a chronic problem in small regions that do not represent a functional economic area (Robison and Miller, 1988) but it is also problematic in larger regions (Kronenberg, 2009). The SLQ rules out the possibility of cross-hauling a priori. It presupposes that a region will import from other regions, yet not export to them, if SLQi < 1 but do the opposite if SLQi ≥ 1. The CILQ does not preclude cross-hauling, as some cells in a given row of the adjustment matrix can have CILQij < 1, while others can have CILQij ≥ 1. Hence imports and exports of commodity i can occur simultaneously. The problem here is that the CILQ does not make adequate allowance for cross-hauling, whereas the FLQ attempts to do so by taking regional size into account.24 Although it is obviously desirable to have μ1 as close to zero as possible, so as to eliminate any systematic tendency towards overstatement or understatement, μ1 = 0 could still arise if some very large positive and negative errors happened to average out to zero. Thus, when assessing accuracy, we do also need to look at measures such as μ4 and μ5. When δ = 0.25, the FLQ exhibits an average error of 11.9% for μ4 and 8.2% for μ5. The higher figure for μ4 is due to the fact that, with this measure, simulation errors are squared, which puts greater emphasis on larger errors. For instance, μ5 would treat an error of ±0.1 as 10 times larger than an error of ±0.01, whereas μ4 would treat the former as 100 times larger than the latter. It is by no means clear that this extreme emphasis on avoiding larger errors is warranted. However, is an error of 8.2% unacceptably large? This is ultimately up to the analyst to decide, by weighing up the costs and benefits of increasing the degree of accuracy, e.g. the costs of surveying key sectors. It should also be borne in mind that the 8.2% average error arose as a result of using a single figure for δ; this error could be reduced in many cases by using a value tailored to the specific characteristics of the region under consideration. Many authors have suggested that measures using absolute values are the most