Sunday, June 17, 2018

Price Taking, Price Making and Economic Inclusion

One aspect of future labour force participation rates - given continued advances in automation and budget cutting realities - is how price taking and price making options affect potential employment levels. For instance, when firms are more willing to coordinate with other suppliers via price taking means, this often results in increased employment potential.

Hence one important consideration for future employment, is whether price taking or price making is more prevalent. Presently, the dominance of non tradable sector activity is also reflected in the fact price making has become the broader choice. All the more so, in non tradable sectors where time based product is a major part of output.

When price making occurs in tradable sector activity, much of it is due to temporary market advantages. The changing nature of these advantages, especially when tradable sector activity is regularly exposed to competition, contributes to organizational flexibility. Such flexibility helps to account for periods of tradable sector dominance that proved less problematic for class divisions and inequality, especially during the twentieth century.

On the other hand, due to price making, the degree to which economic inequality occurs in non tradable sector activity can become "baked into the cake" over time. While non tradable sectors do encourage organizational flexibility up to a point, much of this doesn't actually extend to core staff which works in a professional capacity. Consequently, price making at upper levels means that economic inequality is gradually becoming more entrenched as a societal reality. Of course, this is often an implicit choice which limits full time employment, wherever possible.

Consider also, how technological factors play into this scenario. There's a noticeably polarized discussion developing, with widely divergent viewpoints re whether AI deep learning will eventually displace many well paid professionals. Normally, automation often contributes to greater productivity, especially when it reduces the inputs necessary for aggregate output. However: This time around, deep learning AI creates problems for non tradable sector time based product, since much of it translates into potential skills substitution, instead of increased output. It's not much of a stretch to imagine deep learning AI as an existential threat, because this process actually makes unnecessary, some important elements of the reigning class structure. Ultimately, deep learning AI reduces the absolute need for extensive human capital investment as an input requirement for quality product. Perhaps this issue could be glossed over more readily, were it not for the fact entire societies are expected to carry much of the load for these additional costs.

Might extensive price making continue to appear as though warranted for quality high skill services? We don't yet know the answer. High skill services sector price making became more extensive in the twentieth century, due to human capital investment requirements which today comprise some of the most important wealth structures of our time. What would cause nations to turn around and deem much of this unnecessary? These expectations for quality product, have been carefully tended as long as anyone can remember. And given extensive debates re many supposedly "irrational" human capital investments ("stupid" student choices), some of these employment possibilities are treated as the most "rational" of all! Possibly, the only extent to which deep learning AI could make inroads in this general equilibrium reality in the near future, is at the margins where budgetary matters finally leave no choice.

If price taking services coordination becomes an economic option, the resulting time arbitrage would considerably reduce the pressures of budgetary constraints. Thus, it might be feasible in the near future for high skill knowledge providers to preserve the price making organizational capacity they prefer, in part by giving permission for defined equilibrium settings (alongside deep learning AI) to relieve general equilibrium budgetary pressure via price taking means. There are countless communities where physicians prefer not to practice - for instance - where AI could make it possible for local participants to create valuable services product via mid range skill sets. Such an economic strategy could help to prevent further political breakdown, as different factions struggle over whether to even incorporate deep learning automated intelligence as a part of high skill services generation.

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