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Data Construction: From IO Tables to Supply-Use Models

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Rethinking Input-Output Analysis

Part of the book series: SpringerBriefs in Regional Science ((BRIEFSREGION))

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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.

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Notes

  1. 1.

    If countries only have statistical information on total employment by regional industry, regional output may be replaced with regional employment times national output per unit of national employment by industry. Using employment this way implies making a strong additional assumption, namely that regional labour productivity by industry equals its national equivalent.

  2. 2.

    Stevens and Trainer (1980), who coined this term, show how RPCs may be estimated econometrically by means of secondary data. Stevens et al. (1989) show that this results in more reliable RPCs than those estimated by LQ-type non-survey methods.

  3. 3.

    The association between the two measures would be linear if the LQ would be measured in an additive way as \({\text{ALQ}}_{i}^{r} = x_{i}^{r} /x_{ \cdot }^{r} - x_{i}^{n} /x_{ \cdot }^{n}\) instead of the standard multiplicative definition [see Hoen and Oosterhaven (2006), for more reasons to use the additive definition].

  4. 4.

    In view of the poor performance of almost all these non-survey methods, we do not pay attention to shortcut multiplier estimation methods that do not even use a non-survey IOT to calculate regional multipliers. See Burford and Katz (1981) for a typical shortcut method and Jensen and Hewings (1985) for a critical evaluation of a series of such methods.

  5. 5.

    RAS only works with semi-positive base matrices and semi-positive margins. When the base IOT also has negative cells, such as subsidies or negative stocks changes, which have to be updated or regionalized to become consistent with margins that may also be negative, the generalized RAS method (GRAS) has to be used (Junius and Oosterhaven 2003). See Termurshoev et al. (2013) for the algorithm.

  6. 6.

    Unfortunately, in the past, it has been assumed that these margins were known exactly when applying RAS, instead of having to be estimated by say LQ methods. As a consequence, it was unjustly reported that RAS outperformed several LQ methods (Czamanski and Malizia 1969; Sawyer and Miller 1983). RAS and LQ methods, however, serve different purposes and may thus not be compared one-to-one.

  7. 7.

    See Többen (2017a) for a state of the art construction of a purchases only interregional SUT, using the KRAS algorithm of Lenzen et al. (2009). See Madsen and Jensen-Butler (1999) for a general discussion on constructing interregional SUTs and the actual construction of a Danish interregional SUT that has a more bottom-up character, due to the abundance of micro data in case of Denmark.

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Authors and Affiliations

  1. University of Groningen, Groningen, The Netherlands

    Jan Oosterhaven

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  1. Jan Oosterhaven
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Correspondence to Jan Oosterhaven .

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Oosterhaven, J. (2019). Data Construction: From IO Tables to Supply-Use Models. In: Rethinking Input-Output Analysis. SpringerBriefs in Regional Science. Springer, Cham. https://doi.org/10.1007/978-3-030-33447-5_3

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