Dynamic Firm Location Network Model with Anticipatory Scenarios for the Northeast Ohio Region

Abstract

Public policy and planning decisions require glimpses into the future, to assess how the social-ecological systems we plan for might evolve with or without policy intervention. To do so, one approach gaining currency is using anticipatory tools rather than predictions. Anticipation entails generating a range of possible systems futures (scenarios), instead of attempting to predict the one that will prevail. We use here a scenario-generating model, to anticipate where in a region businesses are likely to locate in time. Using data for Northeast Ohio, including the Cleveland-Akron-Lorain-Elyria, Ohio Combined Statistical Area, we estimate the model parameters. We evaluate its prediction accuracy against 2001-2015 regional data. To illustrate how policymakers could use the model, we generate three scenarios to explore what might happen to the spatial configuration of businesses if policies were implemented to attract businesses at specific locations or discourage them from locating in parts of the region.

Anticipating employment location patterns in economic regions: modeling complex dynamics

“…Moreover, our model has an economic rationale, while trends lack it and cannot be used to generate anticipatory policy scenarios, which is our objective. 6 We first had access to biennial data for the NEO region (Kaufman et al 2018). The biennial correlations for NEO corresponding to those displayed in Figure 5 declined monotonously, showing no reaction to the 2008 economic downturn.…”

“…We could also investigate how land conservation decisions affect employment in the places directly affected by restrictions. Kaufman et al (2018) explored three policies that either directly aimed at affecting the market potential of some NEO's cities or could impact market potential as a result of land conservation decisions. We compared the resulting anticipatory scenarios to the known "do-nothing" alternative represented by the actual data.…”

“…a. displays the change in economic activity in the NEO region from 2001 to 2015 in the absence of a policy intervention regarding employment location. In the first NEO policy scenario (Kaufman et al 2018), we assumed that Cleveland's ability to attract businesses from 2001 to 2007 10 was enhanced through taxation instruments. As a result, its share of the total regional employment rose by .04 every year.…”

“…Kaufman 2012, Simmie & Martin 2010. Instead, the longer the horizon for which we plan, the more important it is to make robust decision that remain wise for a wide range of possible futures (e.g., Chakraborty et al 2011;Kaufman et al 2018).…”

“…The study byKaufman et al (2018) the number of firms rather than number of jobs in order to update and compare results to the earlierKumar et al (2007) study. Outcomes using the employment data differ minimally from those using number of firms.…”