Ali Asghar banouei; parisa mohajeri; fatemeh kalhori; zahra abdolmohammadi; zahra zabihi; sahar mohammad karimi; maryam parsa
Abstract
Since the 1950s, many types of non-survey methods have been introduced by regional input-output economists for the estimation of Regional Input-Output Coefficients (RIOCs) and Regional Input-Output Tables (RIOTs). On the one hand, there are different kinds of location quotient methods (for example, , ...
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Since the 1950s, many types of non-survey methods have been introduced by regional input-output economists for the estimation of Regional Input-Output Coefficients (RIOCs) and Regional Input-Output Tables (RIOTs). On the one hand, there are different kinds of location quotient methods (for example, , , , , , , and ) which focus on estimating RIOCs and balancing RIOTs and which require acceptance of 2 types of residuals: 1)exports of a region to other regions and the rest of the world, and 2)regional sectoral value added. On the other hand, there are Commodity Balance (CB) and Cross-Hauling Adjusted Regionalization Method (CHARM) which concentrate on estimating RIOTs and the regional sectoral value added which play a key role in balancing RIOTs. The application of the CHARM method is not appropriate for estimation of RIOTS in Iran. The main reason is that the use of CHARM method needs two residuals for balancing the RIOT. One of these residuals is the trade balance (exports- imports) and the second one is regional sectoral value added and the regional GDP. Using the former as a residual seems to be plausible for row balancing of RIOTS, whereas the latter unnecessarily adjusts the regional sectoral value added as well as regional GDP. This residual is not appropriate for countries like Iran which have regional accounts. Since 2000, the statistical center of Iran (SCI) has been providing regional accounts for 31 provinces. It comprises of 72 regional sectors which are comparable with national account classifications. The regional accounts of SCI provide us with sectoral intermediate inputs, sectoral value added and sectoral output for 72 sectors for these 31 provinces. If we use CHARM method for regionalizing NIOTs, the regional sectoral value added and the regional GDP given by the SCI have to be ignored due to the inevitability of residual regional sectoral value added.
In this paper, we have shown that the sectoral value added in regional accounts of Iran is adjusted involuntarily due to the use of CB and CHARM for estimation of RIOTs. This issue raises an important question: why should theofficial regional sectoral value added be unnecessarily adjusted? To tackle this problem, the new mixed CB-RAS and CHARM-RAS method is proposed. The new proposed mixed method only takes regional trade balance as a residual and uses the official regional sectoral value added of the regional accounts that have been provided by the statistical center of Iran.
For the application of CB, CHARM, CB-RAS and CHARM-RAS methods, the following data were utilized. 1) Based on the survey- based NIOT of 2001, we updated two NIOT for the year 2002. 2) The regional sectoral intermediate inputs, value added and outputs of Gilan provinces were directly taken from the regional accounts of the SCI. 3) The survey- based RIOTS of this province for the year 2002 were obtained. In order to make the applications of the two methods manageable, the data were aggregated into seven sectors: agriculture, mining, agro-based industries, other industries, water, electricity and gas, construction and services. Based on the CHARM and the proposed mixed CHARM-RAS methods, RIOTS for the province of Gilan for 2002 were generated. The results indicate that the official total value added (GDP) of Gilan province in 2002 was 23401590 billion rials, whereas the corresponding estimated figure derived from the CHARM and CB methods was 22847050 billion rials. This suggests that the CHARM and CB methods underestimate the GDP of Gilan province by 2.4 percentage. Moreover, The regional sectoral value added deviations were calculated. The minimum deviation for agro-based industries was -%9 and the maximum deviation was %+54.6 for mining. To fill this gap, we have proposed the new mixed CHARM-RAS and CB-RAS method to generate RIOTs for the this province. Applying these methods no longer requires the unnecessary adjustments , the regional GDP, and the use of official data. Therfore, the new mixed method eliminates the shortcomings of the previous methods. Moreover, five conventional statistical methods, namely MAD, RMSE, TIL, STPE and WAD were utilized for assessing statistical errors between supply multiplier matrixes which are derived from the new mixed CB-RAS and CHARM-RAS method corresponding to the official figures. The results show that the degree of accuracy of the supply multiplier matrix of the mixed CB-RAS and CHARM-RAS method in all of the five statistical methods is much closer to the official figures than the one gained using CB and CHARM methods independently. Hence, the statistical error of CB-RAS and CHARM-RAS method is much less than the statistical error of CB and CHARM method in supply multipliers.
