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Toward a Public Policy Agenda for Regional Science: Planning Versus Measuring Impacts

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Regional Science Matters

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Abstract

Much of Walter Isard’s lifelong devotion to advancing the emerging disciplines of regional science and peace science focused on a better understanding of the forces shaping the social and economic development of geographic regions and the role that analysis can play formulating public policy. This paper explores generally the use of regional science tools applied to public policy questions in two fundamentally different ways: (1) to fashion steps directed at the most desirable outcome, however desirable is defined—an explicit planning objective or (2) to articulate the consequences of possible alternative courses of action—a perhaps more modest impact analysis objective. The paper illustrates the circumstances suggesting one approach versus the other with an example involving the use of optimization tools and input–output analysis.

Executive Director, Division on Engineering and Physical Sciences, National Research Council, National Academy of Sciences. Views expressed in this paper are the author’s and not necessarily those of the National Academy of Sciences. The principal examples used in this paper are drawn from Ronald E. Miller and Peter D. Blair, input–output Analysis: Foundations and Extensions, London: Cambridge University Press, 2009, and the updated materials located on the publisher’s website accompanying the text: http://www.cambridge.org/aus/catalogue/catalogue.asp?isbn=9780521517133

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Notes

  1. 1.

    In 1972 the United States Congress established the Office of Technology Assessment (OTA) as a small analytical agency to become better informed about implications of new and emerging technologies. The agency’s architects intended the reports and associated information it produced to be tuned specifically to the language and context of Congress. OTA’s principal products―technology assessments―were designed to inform Congressional deliberations and debates about issues that involved science and technology dimensions but without recommending specific policy actions. The agency’s unique governance by a bicameral and bipartisan board of House and Senate Members helped ensure that issues OTA addressed were relevant to the Congressional agenda and that assessments were undertaken with partisan and other stakeholder bias minimized. Over a span of 23 years OTA completed 755 reports on a wide range of topics including health, energy, defense, space, information technology, environment, and many others until Congress terminated the agency’s annual appropriation of funds to operate in 1995 (see Blair 2013).

  2. 2.

    Office of Technology Assessment, New Electric Power Technologies: Problems and Prospects for the 1990s, Washington, DC: U.S. Congress, Office of Technology Assessment, OTA-E-246, July 1985.

  3. 3.

    See Blair (2013) for additional details about the purpose, structure, and history of OTA technology assessments.

  4. 4.

    While the discussion of regressivity has been often absent from this policy debate, analysis of the topic is not, such as in Parry et al. (2005) or Oladosu and Rose (2007).

  5. 5.

    See Blair (1979) and Miller and Blair (2009), Chap. 10.

  6. 6.

    Beginning with Victor (1972) and many others over the past four decades; see Miller and Blair (2009), Chap. 10.

  7. 7.

    Measuring energy and environmental activities in input–output models in monetary units can create accounting inconsistencies as described in Miller and Blair (2009), but adopting hybrid units for such calculations, e.g., energy units for energy transactions, have limitations as well as described in Dietzenbacher and Sage (2006).

  8. 8.

    As examples, see Thoss (1976) and Blair (1979).

  9. 9.

    This formulation was originally applied by Just (1974) and Folk and Hannon (1974) to examine the impacts of new energy technologies. Other more recent applications are summarized in Forssell and Polenske (1998), including, in particular, Qayum (1991 and 1994), Schäfer and Stahmer (1989) and Lang (1998).

  10. 10.

    We will see later that the implicit objective function in an input–output model is to maximize the sum of all final demands or, equivalently, to minimize the sum of all value added inputs.

  11. 11.

    More extensive economic interpretations of the Leontief model as a linear programming problem are included in Dorfman et al. (1958) and Intriligator (1971). An important advantage of posing the input–output framework in this way is that we can include alternative objective functions and/or additional constraints as part of a planning problem.

  12. 12.

    Decision making with multiple objectives is another well-developed area in operations research with many approaches available. Surveys of such approaches are found in Cohen (1978), Cochrane and Zeleny (1973), Nijkamp and Rietveld (1976), Trzaskalik and Michnik (2002), and Tanino et al. (2003).

  13. 13.

    Constraints in LP are generally specified as inequalities, but by introduction of slack or surplus variables, they can be specified as equations.

  14. 14.

    This applies to the linear version of GP; the features of alternative GP formulations, such as developed in Lane (1970) or Cohen (1978), address some of the limitations of the linear approach that will be apparent in what follows, e.g., when problems are very tightly constrained.

  15. 15.

    As in Blair (1979) and Lee (1971, 1972, 1973). Other approaches are explored in Ijiri (1965), Cohen (1978), and Ignizio (1976).

  16. 16.

    This is the framework adopted in Blair (1979) and Miller and Blair (2009).

  17. 17.

    Illustrated and applied in Blair (1979) and Miller and Blair (2009) and summarized here.

  18. 18.

    The method of analytic hierarchies, often referred to as the Analytic Hierarchy Process, is a theory and method of decision-making based on deriving priorities from a matrix of pairwise comparisons of alternatives; see Saaty (1980); there are, of course, many other ways of deriving priorities as well.

  19. 19.

    Posed as a theorem in Blair (1979).

  20. 20.

    Also illustrated in Miller and Blair (2009) and Blair (1979). A similar example is given in Hipel (1992).

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Author information

Authors and Affiliations

  1. National Research Council, National Academy of Sciences, Washington, DC, 20001, USA

    Peter D. Blair

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  1. Peter D. Blair
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Correspondence to Peter D. Blair .

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Editors and Affiliations

  1. Department of Spatial Economics, VU University, Amsterdam, The Netherlands

    Peter Nijkamp

  2. University of Southern California Price School of Public Policy, Los Angeles, California, USA

    Adam Rose

  3. VU University Department of Spatial Economics, Amsterdam, The Netherlands

    Karima Kourtit

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Blair, P.D. (2015). Toward a Public Policy Agenda for Regional Science: Planning Versus Measuring Impacts. In: Nijkamp, P., Rose, A., Kourtit, K. (eds) Regional Science Matters. Springer, Cham. https://doi.org/10.1007/978-3-319-07305-7_13

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