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There has been a recent resurgence of interest in biproportional adjustment methods for updating and interpreting change in matrix representations of regional structures, most commonly input-output accounts. Although the biproportional method, commonly called the RAS technique in the input-output literature, has been shown to have a number of theoretically appealing properties, various alternatives do exist. In this paper, we develop and empirically assess a number of alternatives, comparing performance and examining the attributes of these adjustment methods. Two of these are sign-preserving updating methods for use when tables contain both positive and negative entries. One of these is shown to generate less information gain than does a generalized RAS method that Junius & Oosterhaven (2003) formulated to deal with matrices with both positive and negative values. Overall, while the RAS method continues to be commonly used and its choice is often rational, alternative methods can perform as well or better along certain dimensions and in certain contexts.Updating, Biproportion, Ras, Distance Measures,

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.Regional accounts, input-output, commodity-by-industry,

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In this paper, we present a critical assessment of recent economic development policy directions centered on the concept of regional innovation clusters. We begin by investigating the rationale underlying the Obama administration's promotion of regional innovation clusters (RICs) and their introduction to the policy arena in its Strategy for American Innovation. The connections among RICs and existing research and policies in industry and occupational clusters, regional innovation systems and regional economic development are identified and analyzed to highlight those most critical challenges to conceptualizing and theorizing RICs. While we applaud the long overdue focus of economic development policies on sub-national regions, we identify several major conceptual shortcomings and programmatic difficulties associated with RICs as a centerpiece for economic development strategies.g row_546 111..124

The role of public capital in the economy is an emerging research area with theoretical significance and societal and policy relevance. Several academic disciplines and public institutions are regular contributors to this research field. The range of findings of these studies extends from virtually no role to a very strong role for public capital in the economy. Reconciling the varied and sometimes contradictory results has not been a high priority among researchers. The lack of attention to spatial forces, an issue gaining attention only relatively recently, also has been troublesome. This article reviews public capital research along three primary dimensions: the dependent variable, scale of analysis, and attention to spatial processes. The authors emphasize scale and space as among the most important but least discussed differences among public capital studies and then focus on the nature of the economic processes at work and detectable at different scales of analysis.

In 1988 Moghadam and Ballard first introduced the interindustry demand variable (IDV) as an innovative approach to integrate regional econometric and input-output models. Since then, several extensions of the IDV have been suggested in the literature, including adjustments for laborproductivity differentials across sectors to generate an interindustry employment demand variable (IEDV). In this paper we revisit the IEDV variable, and offer extensions that transform it from a static to a dynamic form. We implement the range of these specifications for the San Diego labor market, and evaluate their estimation properties and forecasting performance in the context of simulation and projection analyses. The results suggest that problems of multicollinearity plague the conventional specification, and have obscured the assessment of the value of adjustments for industry productivities and the nature of productivity specification. Based on an alternative specification, we find that dynamic labor-productivity adjustments lead to lower simulation errors. These adjustments also result in significantly lower impact multipliers relative to those from the IDV model.

This paper presents a probabilistic specification of coefficients in the inputoutput modeling framework. Although previous works on probabilistic input-output models attribute uncertainty to measurement and sampling errors, this specification derives from systematic variation directly attributable to industrial, institutional, and location factors. Experiments with national input-output data support the existence of such variation. Employing the specification not only yields a more flexible aggregate modeling framework capable of producing interval impact estimates, but also an alternative perspective on such issues as interregional differentiation and structural comparison, the identification of key industrial sectors, aggregation, and spatial variation in production.