Input-output analysts are often confronted with requests for impacts assessments for economic shocks that stretch uncomfortably the assumptions of standard input-output modeling. This paper presents an approach to confronting a subset of these challenges straightforwardly in a way that ameliorates some of the more restrictive input-output assumptions, maintains the inter-industry detail of the input-output model, and enhances the representation of certain economic behaviors without the additional complexities of moving to more complex computable general equilibrium or conjoined econometric input-output models. We conclude with the observation that direct changes to the input-output framework most often necessitate further modifications requiring additional behavioral assumptions and decisions on the part of the modeler.
Practitioners and academics apply a range of regional economic models for impacts assessment. These models extend from a simple economic base through to input-output and econometric models and computable general equilibrium models. All such models have strengths and weaknesses. Dimensions of which impact assessment models are often compared include level of industry detail, data availability, and complexity of behaviour modelled. This chapter presents a model for Simulating Impacts on Regional Economies (SIRE) that occupies an intermediate position between Input-Output (IO), arguably the most widely used model for regional impacts assessments, and Computable General Equilibrium (CGE) models. With greater behavioural detail than the typical regional IO model, the SIRE model incorporates many of the features of CGE models without enforcing the strictly linear behavioural relationships of IO. Like most CGE models, the simulation framework presented here borrows a subset of parameters from an existing econometric model for the same region. The SIRE model falls short, however, of the complexity of capturing the full range of behaviours of CGE models.
Exended input–output (IO) models are increasingly prominent in regional economic analysis. Social accounting matrices and associated multiplier decompositions, IO econometric model hybrids and computable general equilibrium models are finding greater acceptance in contexts in which simple IO models once dominated. Although the extended regional models build primarily on the foundation of regional, interindustry accounting frameworks, the data from which these regional accounts are drawn are most commonly in the form of a national commodity-by-industry account. Despite this longstanding fact, the IO table adaptation literature has focused almost solely on methods of adapting national interindustry accounts to regional economies. This paper presents a method designed specifically to regionalize commodity-by-industry accounts, in the context of the US reporting system. The focus on commodity-by-industry data demands a confrontation with several important issues that otherwise might go unattended. Using a particular system and its accompanying classification scheme ensures a comprehensive and consistent regionalization method.
Past solar adoption literature has focused primarily on households without significant attention to the potential of commercial properties as sites for solar generation. We examine firms’ decisions to install solar panels on their properties using state and firm level data from the U.S. We are interested in the effects of state level characteristics, including policies and regulations, on firm decisions regarding solar investments. We find that state characteristics that influence the return-on-investment from solar installations, most notably solar intensity, are important for commercial adoption decisions. Further, the results suggest that certain state level policies, including solar carve-outs in renewable portfolio standards, financing programs and tax breaks, can incentivize firms to install solar panels. Across different model specifications, we observe that firm installation decisions are correlated with personal electric vehicle ownership rates. This may indicate a ‘green’ business marketing strategy, whereby firms install solar to improve their social responsibility image.
Shifts in populations and economic structure are as old as population settlements themselves.As technologies change and economies adapt, regional comparative advantages also evolve.Among the most consequential of recently developed new technologies is horizontal drilling, which has opened vast regions of the world to the extraction, development,and use of low-cost energy from shale gas.Economic transitions and structural change are inevitable.The challenge for regional economists, policymakers, and economic developers lies in the identifying and managing these transitions in ways that maximize benefits and minimize the costs that accompany them.Complicating this challenge is the recognition that the geographic distributions of costs and benefits of economic development often do not coincide.This spatial mismatch of costs and benefits has been prominent historically in regional resource boom and bust cycles, many of which have been fed specifically by energy resources.The resources of energy rich regions have often been exploited in ways that provide shortterm regional economic benefits and disruption, longer-term economic development often accompanied by environmental and physical infrastructural degradation.Recognizing that we are entering the early stages of one of the most substantial resource based shifts in economic structure in the history of energy resource development, we have the opportunity and the obligation to learn from successes and failures ofprevious economic transition management efforts, and to design strategies that will maximize the benefits and minimize the negative consequences of shale gas development.This contribution identifies and elaborates upon four critical dimensions of the transition management challenge.The first dimension includes the economic and environmental aspects of the extraction activities including drilling, materials assembly and usage including employment, income, capital equipment, and consumables, both manufactured and natural.The second dimension identifies the negative externalities of impacts on off-site physical infrastructure, with a special emphasis
