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Data Construction: From IO Tables to Supply-Use Models

  • Jan OosterhavenEmail author
Chapter
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Part of the SpringerBriefs in Regional Science book series (BRIEFSREGION)

Abstract

An overview of non-survey construction methods for regional input–output tables (RIOTs) reveals a systematic overestimation of regional multipliers. The iterative bi-proportional scaling method RAS avoids this problem if it is fed with intra-regional row and column totals without a systematic bias. The Cell-Corrected RAS method, additionally, takes advantage of the multitude of survey-based RIOTs to improve the intra-regional cell estimates of unknown RIOTs. Next, it is shown how a semi-survey bi-regional IOT may be constructed with a double-entry construction method that requires only minimal survey data about the spatial destination of the sales by the regional industry. Finally, product-by-industry, national and interregional supply-use tables (SUTs) are introduced, along with the models based on them, and the assumptions needed to construct them.

Keywords

Location quotient methods Cross-hauling Cell-Corrected RAS Bi-regional input–output table Supply-use table Product technology assumption Industry sales structure assumption Interregional supply-use model International input–output tables 

References

  1. Bacharach M (1970) Biproportional matrices and input-output change. Cambridge University Press, CambridgeGoogle Scholar
  2. Batten D (1983) Spatial analysis of interacting economies. Kluwer-Nijhoff, BostonCrossRefGoogle Scholar
  3. Boomsma P, Oosterhaven J (1992) A double-entry method for the construction of bi-regional input-output tables. J Reg Sci 32:269–284CrossRefGoogle Scholar
  4. Bourque PJ, Conway RS (1977) The 1972 Washington input-output study. Graduate School of Business Administration, SeattleGoogle Scholar
  5. Bouwmeester MC (2014) Economics and environment—modelling global linkages. Dissertation, SOM Research School, University of GroningenGoogle Scholar
  6. Burford RL, Katz JL (1981) A method for estimation of input-output-type output multipliers when no I-O model exists. J Reg Sci 21:151–1621CrossRefGoogle Scholar
  7. Czamanski S, Malizia E (1969) Applicability and limitations in the use of national input-output tables for regional studies. Pap Reg Sci 23:65–78CrossRefGoogle Scholar
  8. de Mesnard L (2004) Understanding the shortcomings of commodity-based technology in input-output models: an economic circuit approach. J Reg Sci 44:125–141CrossRefGoogle Scholar
  9. de Mesnard L (2011) Negatives in symmetric input–output tables: the impossible quest for the Holy Grail. Ann Reg Sci 46:427–454CrossRefGoogle Scholar
  10. Dietzenbacher E, Los B, Stehrer R, Timmer M, de Vries G (2013) The construction of world input-output tables in the WIOD project. Econ Syst Res 25:71–98CrossRefGoogle Scholar
  11. Eurostat (2008) Eurostat manual on supply, use and input-output tables. European Communities, LuxemburgGoogle Scholar
  12. Flegg AT, Webber CB, Elliot MV (1995) On the appropriate use of location quotients in generating regional input-output tables. Reg Stud 29:547–561CrossRefGoogle Scholar
  13. Flegg AT, Huang Y, Tohmo T (2015) Using charm to adjust for cross-hauling: the case of the province of Hubei, China. Econ Syst Res 27:391–413CrossRefGoogle Scholar
  14. Gigantes T (1970) The representation of technology in input-output systems. In: Carter AP, Bródy A (eds) Contributions to input-output analysis. North-Holland, AmsterdamGoogle Scholar
  15. Hewings GJD (1977) Evaluating the possibilities for exchanging regional input-output coefficients. Environ Plan A 9:927–944CrossRefGoogle Scholar
  16. Hewings GJD, Janson BN (1980) Exchanging regional input-output coefficients: a reply and further comments. Environ Plan A 12:843–854CrossRefGoogle Scholar
  17. Hoen AR, Oosterhaven J (2006) On the measurement of comparative advantage. Ann Reg Sci 40:677–691CrossRefGoogle Scholar
  18. Isard W, Langford TW (1971) Regional input-output study: recollections, reflections and diverse notes on the Philadelphia experience. M.I.T Press, CambridgeGoogle Scholar
  19. Jackson RW, Schwarm WR (2011) Accounting foundations for interregional commodity-by-industry input-output models. Lett Spat Resour Sci 4:187–196CrossRefGoogle Scholar
  20. Jansen PK, ten Raa T (1990) The choice of model in the construction of input-output coefficients matrices. Int Econ Rev 31:31–45CrossRefGoogle Scholar
  21. Jensen RC, Hewings GJD (1985) Shortcut ‘input-output’ multipliers: a requiem. Environ Plan A 17:747–759CrossRefGoogle Scholar
  22. Junius T, Oosterhaven J (2003) The solution of updating or regionalizing a matrix with both positive and negative entries. Econ Syst Res 15:87–96CrossRefGoogle Scholar
  23. Kronenberg T (2009) Construction of regional input-output tables using nonsurvey methods: the role of cross-hauling. Int Reg Sci Rev 32:40–64CrossRefGoogle Scholar
  24. Kullback S (1959) Information theory and statistics. Wiley, New YorkGoogle Scholar
  25. Lahr ML (1993) A review of literature supporting the hybrid approach to constructing regional input-output models. Econ Syst Res 5:277–293CrossRefGoogle Scholar
  26. Lenzen M, Gallego B, Wood R (2009) Matrix balancing under conflicting information. Econ Syst Res 21:23–44CrossRefGoogle Scholar
  27. Lenzen M, Moran D, Kanemoto K, Geschke A (2013) Building EORA: a global multi-region input-output database at high country and sector resolution. Econ Syst Res 25:20–49CrossRefGoogle Scholar
  28. Madsen B, Jensen-Butler C (1999) Make and use approaches to regional and interregional accounts and models. Econ Syst Res 11:277–299CrossRefGoogle Scholar
  29. Miller RE, Blair PD (2009) Input-output analysis: foundations and extensions, 2nd edn. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  30. Minguez R, Oosterhaven J, Escobedo F (2009) Cell-Corrected RAS method (CRAS) for updating or regionalizing an input-output matrix. J Reg Sci 49:329–348CrossRefGoogle Scholar
  31. Oosterhaven J (1984) A family of square and rectangular interregional input-output tables and models. Reg Sci Urban Econ 14:565–582CrossRefGoogle Scholar
  32. Oosterhaven J, Escobedo-Cardeñoso F (2011) A new method to estimate input-output tables by means of structural lags, tested on Spanish regions. Pap Reg Sci 60:829–845CrossRefGoogle Scholar
  33. Oosterhaven J, Polenske KR, Hewings GJD (2019) Modern regional input-output and impact analysis. In: Capello R, Nijkamp P (eds) Handbook of regional growth and development theories: revised and extended, 2nd edn. Edward Elgar, CheltenhamGoogle Scholar
  34. Round JI (1983) Non-survey techniques: a critical review of the theory and the evidence. Int Reg Sci Rev 8:189–212CrossRefGoogle Scholar
  35. Rueda-Cantuche JM (2017) The construction of input-output coefficients. In: ten Raa T (ed) Handbook of input-output analysis. Edward Elgar, CheltenhamGoogle Scholar
  36. Rueda-Cantuche JM, ten Raa T (2009) The choice of model in the construction of industry input-output coefficient matrices. Econ Syst Res 21:363–376CrossRefGoogle Scholar
  37. Sawyer CH, Miller RE (1983) Experiments in the regionalization of national input-output table. Environ Plan A 15:1501–1520CrossRefGoogle Scholar
  38. Schaffer W, Chu K (1969) Nonsurvey techniques for constructing regional interindustry models. Pap Reg Sci 23:83–104CrossRefGoogle Scholar
  39. Stevens BH, Trainer GA (1980) Error generation in regional input-output analysis and its implications for nonsurvey models. In: Pleeter SP (ed) Economic impact analysis: methodology and applications. Martinus Nijhoff, BostonGoogle Scholar
  40. Stevens BH, Treyz GI, Lahr ML (1989) On the comparative accuracy of RPC estimation techniques. In: Miller RE, Polenske KR, Rose AZ (eds) Frontiers of input-output analysis. Oxford University Press, New YorkGoogle Scholar
  41. Stone R (1961) Input-output and national accounts. Organization for European Economic Cooperation, ParisGoogle Scholar
  42. Stone R, Brown A (1962) A computable model of economic growth. In: A programme for growth, vol. 1. Chapman and Hall, LondonGoogle Scholar
  43. Temurshoev U, Miller RE, Bouwmeester MC (2013) A note on the GRAS method. Econ Syst Res 25:361–367CrossRefGoogle Scholar
  44. ten Raa T, Rueda-Cantuche JM (2003) The construction of input-output coefficient matrices in an axiomatic context: some further considerations. Econ Syst Res 14:439–455Google Scholar
  45. Theil H (1967) Economics and information theory. North-Holland, AmsterdamGoogle Scholar
  46. Thomo T (2004) New developments in the use of location quotients to estimate regional input-output coefficients and multipliers. Reg Stud 38:43–54CrossRefGoogle Scholar
  47. Többen J (2017a) Effects of energy- and climate policy in Germany: a multiregional analysis. Dissertation, SOM research school, University of GroningenGoogle Scholar
  48. Többen J (2017b) On the simultaneous estimation of physical and monetary commodity flows. Econ Syst Res 29:1–24CrossRefGoogle Scholar
  49. Többen J, Kronenberg TH (2015) Construction of multi-regional input–output tables using the charm method. Econ Syst Res 27:487–507CrossRefGoogle Scholar
  50. Tukker A, De Koning A, Wood R, Hawkins T, Lutter S, Acosta J, Rueda-Cantuche JM, Bouwmeester MC, Oosterhaven J, Drosdowski T, Kuenen J (2013) Exiopol—development and illustrative analyses of a detailed global MR EE SUT/IOT. Econ Syst Res 25:50–70CrossRefGoogle Scholar
  51. van der Linden JA, Oosterhaven J (1995) European community intercountry input-output relations: construction method and main results for 1965–1985. Econ Syst Res 7:249–269CrossRefGoogle Scholar
  52. West GR (1990) Regional trade estimation: a hybrid approach. Int Reg Sc Rev 13:103–118CrossRefGoogle Scholar
  53. Willis KG (1987) Spatially disaggregated input-output tables: an evaluation and comparison of survey and non-survey results. Environ Plan A 19:107–116CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of GroningenGroningenThe Netherlands

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