Limits on the precision of technical relationships within input–output frameworks have led to the use of stochastic analytical methods. The notion of stochastic analysis is developed in this paper to discern how the inherent imprecision effect, when aggregated data are utilised, affects the concomitant key sector analysis. Through a Monte Carlo based simulation, the stochastic key sector graph is introduced, with numerical expressions defined which quantify the association of the individual sectors to quadrants of the graph. The technical developments are benchmarked on a small problem, before a stochastic key sector analysis on an aggregated regional input–output table is reported. Comparisons are made between results when the aggregation of sectors is not employed. The paper reveals that aggregation in key sector analysis is inevitably a poor idea. However, it is argued that aggregation is often a practical necessity, so quantifying the uncertainty that is attendant on this aggregation is important, with the “association” expressions introduced potentially central to elucidate this uncertainty. The conclusions of this paper suggest that where analysts and decision makers are obliged to aggregate tables for analytical purposes then problems might be mitigated where marginal sectors are treated with care.
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Beynon, M.J., Munday, M. Stochastic key sector analysis: an application to a regional input–output framework. Ann Reg Sci 42, 863–877 (2008). https://doi.org/10.1007/s00168-007-0172-0