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An alternative approach to the construction of multi-regional input–output tables of the Czech Republic: application of the CHARM method

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Abstract

The main scope of the paper is to review the construction method of (multi-)regional input–output tables (RIOTs) in the Czech Republic at the NUTS 3 regional level. We compare the estimation of regional IO tables based on a modified cross-hauling adjusted regionalisation method (CHARM) applied by the authors with application of the hybrid approach of (Sixta and Vltavská in Stat Stat Econ J 97:4–14, 2017) from the Department of Statistics at the University of Economics in Prague. The aim of the presented paper is to contribute to the discussion about the suitability of alternative methodological approaches from the perspective of quality and time. We assume that despite the lack of official, published RIOTs by state statistical offices, the existing methodology of the relatively simpler non-survey CHARM method represents an adequate option for the generation of regional input–output tables suitable for further analytical research.

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Fig. 1

Source: Transport Yearbook of the Czech Republic, Ministry of Transport, 2013

Fig. 2

Source: Authors, Vltavská and Sixta (2017), Czech Statistical Office

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Source: Authors, Vltavská and Sixta (2017)

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Source: Authors, Vltavská and Sixta (2017)

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Source: Authors, Vltavská and Sixta (2017)

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Source: Authors, Vltavská and Sixta (2017)

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Source: Authors, Vltavská and Sixta (2017)

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Source: Authors, Vltavská and Sixta (2017)

Fig. 9

Source: Authors, Vltavská and Sixta (2017)

Fig. 10

Source:Authors

Fig. 11

Source: Authors, Vltavská and Sixta (2017)

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Notes

  1. Generation of Regional Input–Output Tables; see Miller and Blair (2009).

  2. The respective tables are published at https://kest.vse.cz/veda-a-vyzkum/vysledky-vedecke-cinnosti/regionalizace-odhadu-hrubeho-domaciho-produktu-vydajovou-metodou/.

  3. The approximation of regional production in those specific sectors (by size) was smaller than the regional gross value added, estimated by Eq. (2). Coefficients representing the share of regional wages to national wages were rescaled to regional value added to the national value added for a given sector in a given region.

  4. Based on national data.

  5. There exist publicly available data on regional household consumption by use, structured by 10 groups of COICOP expenditure and the NUTS 2 level. This paper uses the regional structure on the NUTS 3 level and 11 groups of expenditure by product (CPA). There is the possibility to conduct further research using microdata from the HBS (household budget survey) in the future to obtain more precise estimates of household consumption, even though a limitation of these data was already discussed in the work (Kronenberg and Többen 2011).

  6. In some countries, regional external trade flows are covered by official statistics. However, trade statistics due to methodological differences provide slightly different information related to flows. Other proxies of inter-regional trade flows such as transportation statistics also should be utilised.

  7. A discussion about cross-hauling potential can also be found in Többen and Kronenberg (2015).

  8. In this paper, we use a modified CHARM formula, which partially resolves the problem related to the re-export of various commodities between regions. Some concerns in this respect remain and are discussed in Többen and Kronenberg (2015). However, in terms of re-exports, their only effect is related to the size of regional trade, because re-exports affect exports and imports similarly in particular regions; thus, they do not affect the regional trade balance.

  9. The RAS method is described in detail in Trinh and Phong (2013).

  10. Department of Economic Statistics, University of Economics, Prague (https://kest.vse.cz/english/science-research/results-of-the-funding-projects/regionalization-of-gdp-estimate-by-the-expenditure-approach/).

  11. “The main problem of the regional accounts by industry is that the source statistics are frequently designed for national and not for regional purposes. For instance, the Structural Business Statistics (SBS) are based on the statistical unit ‘enterprise’ and not the ‘local KAU’ upon which the national and regional accounts by industry should be based” (Eurostat 2013).

  12. In this analysis, we compare supply multipliers based on Type E input-output tables as discussed in Flegg and Tohmo (2013b). In this paper, authors argue the improper notation of these multipliers as output multipliers in previous papers, e.g. Kronenber and Többen (2011). Output multipliers are typically associated with type B IO tables.

  13. For details of the calculation, please consult, e.g., Cassar (2015).

  14. The Flegg and Tohmo (2013a) compared differences of output multipliers for Finland regions, while we compare the results of supply multipliers estimation in Czech regions.

  15. Products of sectors G–U.

  16. Transport Yearbook of the Czech Republic, Ministry of Transport (2013).

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Acknowledgement

The research was supported by the Slovak Research and Development Agency under contract no. APVV-16-0630 and 2/0135/17.

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  1. Institute of Economic Research, Slovak Academy of Sciences, Bratislava, Slovakia

    Marek Radvanský & Ivan Lichner

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  1. Marek Radvanský
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Correspondence to Marek Radvanský.

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Radvanský, M., Lichner, I. An alternative approach to the construction of multi-regional input–output tables of the Czech Republic: application of the CHARM method. Empirica 48, 1083–1111 (2021). https://doi.org/10.1007/s10663-020-09490-6

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