Timo Tohmo (p)

This paper uses survey-based data for 16 South Korean regions to refine the application of the FLQ formula for estimating regional input coefficients. Especial attention is paid to the choice of a value for the unknown parameter δ in this this formula. Along with regional size, this value determines the size of the adjustment for regional imports in the FLQ formula. Our paper adds to the work of Flegg and Tohmo (2013a, 2016) and Flegg et al. (2016), the underlying aim of which is to find a cost-effective way of adapting national coefficients, so as to produce a satisfactory initial set of regional input coefficients.

Esteban Fernandez-Vazquez (p), Monica Serrano

The combination of consumption data from household surveys with the information contained in IO tables is a crucial step to conduct impact analysis related to the effects generated by consumption patterns on the generation of value added, CO2 emissions or energy uses, to mention just some examples. The point of departure of these analyses consist, basically, on connecting the information on consumption made by households with the final demand vector (or matrix) present in the IO tables, which is then conveniently modified to produce the multipliers of interest. This process requires the construction of a concordance or bridge matrix to make this connection possible, since several issues affect the combination of these two data sources: differences in price valuation between consumption surveys and IO tables, the influence of taxes and margins or the different product classifications between these two frameworks make this combination a challenge for the researcher.

In this paper we explore this challenge with a twofold purpose: (i) to investigate how important a “good” or “bad” conciliation of our consumption data between household surveys and IO tables affect our results in terms of impact analysis; and (ii) to propose a conciliation technique between both data structure, which using only minimal information provides a systematic way or reconciling them if detailed data are not at hand. This technique is based on entropy econometrics and it allows making statistical inference on the bridge matrix estimated. Both research objectives are illustrated by means of numerical simulation and by its application to real-world cases.

Sung-goan Choi, Hana Kwon (p)

This paper presents an overview of the data hybrid method (DHM) for the construction of regional input-output tables. A possibility of ‘jobless growth’ has been frequently suggested in recent studies. If this phenomenon exists, the construction of a regional input-output table (RIOT) that uses employment data as a proxy variable for output or value added in RIO modeling could distort the input coefficients by inappropriate use of the data for the location quotients. Since these distorted input coefficients may overestimate the output multipliers, a stable relationship should be required between output and employment for reliable RIOTs. In this research, we proposed the DHM as a more accurate method to estimate the RIO model and compared the empirical results with the previous methods, and found that DHM improved MAPE by 5.3~19.7% compared to the existing nonsurvey techniques. For the rank correlation coefficient, the results were improved significantly compared to those from the existing nonsurvey methods from the sample regions of South Korea. This study suggests that the DHM could be more efficient than the conventional nonsurvey methods.