The Economic Field: Source, Channel, Dissipation and Position Where a subject can stand, and what each of three quantities actually moves

Abstract On how individuals and firms obtain economic returns, one popular taxonomy gives four paths: selling labour, building a distinctive strength, riding a wave, and creating a new paradigm. This paper argues the list is not exhaustive, and that the manner of its non-exhaustiveness is systematic: the four do not lie on one classificatory axis. The paper enumerates using the parts of a reaction-diffusion field: five parts times two ownerships gives ten cells, exhaustively. The four paths occupy one cell each; the remaining six are gaps, three of which are major classes (arbitrage intermediation, position rent, and selling reductions in others' dissipation). One further mechanism lies outside the ten cells because it is system-level: the aggregation of many weak sources into one strong source. Three computable quantities and their stratification are given, written out from the Green's function of the two-dimensional screened Poisson equation: raising source strength acts only logarithmically; raising the screening length moves the exchange rate; only raising the cut-off scale moves the ceiling. "Fit" is rewritten as a computable quantity taking a minimum rather than a weighted average. Finally the paper gives a quantitative prediction with zero adjustable parameters: the location of the market boundary between two centres. Its load-bearing content is not that the larger centre has the larger hinterland, but that the boundary's offset from the midpoint saturates with the separation of the two centres, whereas the retail gravity model gives linear growth. Numerically, with screening length one and source-strength ratio two, as the separation goes from three to forty screening lengths the offset predicted here rises from 0.27 to 0.34 and saturates, while the gravity model rises from 0.26 to 3.43, a factor of ten apart. A protocol for estimating the screening length independently of the boundary data is given in Section 7, so that the prediction's independent variable is not back-solved from the data being tested.