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An alternative approach to estimating regional input–output tables: the KFLQ method

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Abstract

This study proposes an alternative approach to constructing regional input–output models (RIOMs), which are efficient tools for establishing and evaluating regional economic policies. However, the usefulness of the RIOM does not diminish the complexity and high cost burden associated with the survey-based method, including the inordinate amount of time required to construct the model. Hence, many previous studies have actively studied nonsurvey techniques. However, the problems of overestimating regional input coefficients have been highlighted as a limitation of traditional nonsurvey methods. Therefore, this study proposes a modified Flegg’s location quotient formula (hereafter KFLQ) to address the limitations of previous nonsurvey methods and tests the validity of the KFLQ method through empirical analysis. In this regard, the empirical test focuses on how the distortion of input coefficients in the previous LQ-based formulas can be corrected. Ultimately, the results show that the KFLQ method yields more accurate results than those from previous LQ-based methods.

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Notes

  1. As of 2005 and 2010, the Bank of Korea included 16 metropolitan governments (sub-national regions) in the preparation of regional tables, and 17 metropolitan governments, including Sejong City, in the preparation of RIOT in 2015. However, there are 226 municipal governments (subregional regions) in South Korea for whom RIOTs are not prepared by the BOK.

  2. See Rokicki et al. (2021).

  3. This study compares the FLQ, which is the most representative method, with the KFLQ proposed in this paper to compare the suitability of the RIOMs.

  4. From the BOK’s preparation of the 2010 input–output tables, to the implementation of the revised System of National Accounts (2008 SNA), and the organization of “Supply and Use” tables, many changes have been made to the system and method of preparation. Therefore, careful attention is required when comparing with the 2005 Input–Output tables.

  5. For more details, see Kronenberg (2012).

  6. See Chapters 3, 7, and 8 of Miller and Blair (2009) for more detailed RIO modeling. Among these methods, survey-based methods are described in detail in each country’s input–output table.

  7. See more details in Kronenberg (2009), Flegg and Tohmo (2013).

  8. In addition to the methods introduced in this study, various nonsurvey methods are used. Here, we briefly summarize the most commonly used modeling method.

  9. There are 16 sub-national regions and 226 subregional regions in South Korea (2010 benchmark year). Therefore, it proves challenging to acquire consistent output or personal income data by sector at subregional level. See Miller and Blair (2009), Bonfiglio and Chelli (2008), and Kowalewski (2015).

  10. Concentrated is also expressed as localized. See Miller and Blair (2009).

  11. See Morrison and Smith (1974) and Jahn et al. (2020).

  12. This study compares the most typical nonsurvey method, the FLQ. See Flegg and Webber (2000) for a detailed description of the AFLQ method.

  13. See Round (1978).

  14. The KFLQ method may more allow for cross-hauling by applying \({SLQ}_{i}\) to the regionalization process instead of \({CILQ}_{ij}\), when \({SLQ}_{i}\) and \({SLQ}_{j}\) are less than one.

  15. The backward and forward linkages are used to indicate interconnection with a particular sector. Comparisons of the strengths of backward and forward linkages for the sectors in a single economy provide one mechanism for identifying key sectors in that economy. See Hirschman (1958).

  16. There are other methods of verifying the accuracy of the RIO model, such as MAD, MPE, WMPE, SDSD, MPAD, and RAD. See Bonfiglio and Chelli (2008) and Flegg and Tohmo (2013, 2016).

  17. See Myers and Well (2003), p. 508.

  18. https://ecos.bok.or.kr/#/SearchStat, or https://stat.kosis.kr/nsibsHtmlSvc/fileView/FileStbl/fileStblView.do?in_org_id=301&in_tbl_id=DT_FILE20101&tab_yn=N&conn_path=MT_ZTITLE.

  19. Although the rank correlation coefficient of SLQ is relatively higher than that of CILQ or FLQ methods, it is reasonable to compare with FLQ, which shows the errors of around 10% in terms of MAPE.

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Funding

This research was supported by the BK21 project funded by the Ministry of Education and National Research Foundation of Korea (Management number: 4199990214586).

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Authors and Affiliations

  1. Department of Economics, Kyungpook National University, Daegu, Republic of Korea

    Hana Kwon

  2. Institute of Social Research, Kyungpook National University, Daegu, Republic of Korea

    Sung-Goan Choi

  3. Emeritus Professor of Economics and Visiting Scholar of ARDI, Andong National University, Andong, Republic of Korea

    Sung-Goan Choi

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  1. Hana Kwon
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Correspondence to Sung-Goan Choi.

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Kwon, H., Choi, SG. An alternative approach to estimating regional input–output tables: the KFLQ method. Ann Reg Sci 72, 561–578 (2024). https://doi.org/10.1007/s00168-023-01211-8

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