Identification of Superior Data and their Impact on the Statistical Reliability of the Regional Input-Output Table Using the New Mixed CHARM-RAS Method

Document Type : Research Article


allameh tabataba'i university


Regional input-output tables (RIOTs) are considered strong tools for planning and policy making in the regional level, so the calculation of regional Input-Output tables and regional Input-Output Coefficients (RIOCs) have attracted much attention of most of input-output analysts. Compiling of RIOTs by using the statistical methods is costly and time consuming. So, since the 1950s regional analysts have introduced non-survey based methods like Location Quotients (LQ, such as , , , , , ,  and ), Commodity Balances (CB) and Cross Hauling Adjusted Regionalization Method (CHARM) for estimating Regional Input-Output Coefficients (RIOCs) and RIOTs. These alternative methods motivated lively debates regarding the reliabilities and accuracies of regional output multipliers.
For eliminating the shortcomings of non-statistical methods, researchers have introduced hybrid methods or simultaneous approaches “from up to down and down to up” and they have been leaded to use statistical methods (which are collected from real statistics, interview with researchers and also resources) for improving the accuracy of tables. The accuracy of hybrid methods is higher than non-survey methods and some economist such as Richardson (1986) and Lahr (1992) concluded that the mechanical non-survey methods are unsatisfactory; the short cuts are ingenious, but probably unacceptable, and therefore, the forward of RIOTs lies with mixed survey/non-survey and other hybrid methods.
The departure point of hybrid methods in calculation of RIOTs is using one non-survey method. Since collecting superior data is expensive and time consuming, the question that crops up for analysts is to identify the cells in which the collection of statistics on those data bases plays a greater role in improving the statistical accuracy of estimating. So, identifying cells related to superior data have been noticed by researchers and some criteria have been introduced like important coefficients and key sectors. The first one is applied for identifying individual cells and the other is applied for identifying an entire rows and columns.
The main objective of this paper is to demonstrate that the use of superior data in the hybrid methods of estimating regional input-output tables (RIOTs) can improve the statistical validity of tables as well as coefficients. Calculation of this paper is based on two kinds of data; one national and regional (Gilan province) symmetric activity by activity input-output tables and second, regional accounts for 1381.
In this paper, the most appropriate criterion for identifying superior data in the RIOTs is selected, using the new CHARM-RAS mixed method and the seven criteria namely LARGE1 (the largest cells in the intermediate deliveries matrix), LARGE2 (the largest cells in the direct input coefficients) and INVIMP (inverse important coefficients) COLSUM (column-sums of the Leontief inverse Matrix), ROWSUM (row-sums of the Leontief inverse Matrix) and COLHYP (the impact of hypothetically extracting an entire column on the whole economy) and ROWHYP (the impact of hypothetically extracting an entire row on the whole economy).
Results of this paper indicate that, first of all, regardless of the criteria used to identifying superior data, the use of these data will improve the accuracy and the statistical validity of the tables. Second, the highest and the lowest improvements in accuracy are related to LARGE1 and ROWSUM, respectively. Third, while criteria in identifying of individual cells have the higher improvement in accuracy than row and column criteria, but with respect to the very high costs of collecting individual cells, there is a trade -off between the statistical credibility and the cost of collecting individual cells. Fourth, column and row criterion in the hypothetical extraction method is more applicable compared to traditional method. Hence, column criteria have less statistical errors than row criteria. Therefore, the most appropriate criterion for identifying superior data is the COLHYP criterion.


حساب‌های منطقه‌ای ایران، برگرفته از سایت مرکز آمار ایران.
بانویی، علی‌اصغر و بزازان، فاطمه (1385). «نقش و اهمیت ابعاد اقتصاد فضا در محاسبه جداول داده- ستانده منطقه‌ای: پدیده فراموش‌شده در ایران»، فصلنامه پژوهش‌های اقتصادی ایران، 8(27): 89-114.
بانویی، علی‌اصغر؛ مهاجری، پریسا؛ کلهری، فاطمه؛ محمد کریمی، سحر؛ ذبیحی، زهرا و مستعلی پارسا، مریم (1396). «روش‌های ترکیبی جدید CB-RAS و CHARM-RAS برای محاسبه جدول داده- ستانده منطقه‌ای و سنجش خطاهای آماری؛ مطالعه موردی استان گیلان»، دو فصل‌نامه اقتصاد و توسعه منطقه‌ای، 24(13):1-23.
بانویی، علی‌اصغر و مهاجری، پریسا (1396). ابعاد فضایی روش‌های غیرآماری محاسبه جداول داده-ستانده؛ مطالعه موردی استان گیلان، انتشارات سازمان مدیریت و برنامه‌ریزی استان گیلان ( زیر چاپ).
بانویی، علی‌اصغر؛ مهاجری، پریسا؛ صادقی، نرگس و شرکت، افسانه (1396). «یک روش ترکیبی جدید FLQ-RAS برای محاسبه جداول داده - ستانده منطقه‌ای؛ مطالعه موردی استان گیلان»، فصل‌نامه پژوهش‌های اقتصادی ایران (زیر چاپ).
صادقی، نرگس (1394). ماهیتبخش‌هایاقتصادایران1. مروریبرروش‌هایشناساییبخش‌هایکلیدیدراقتصاد، معاونت اقتصادی مرکز پژوهش‌های مجلس شورای اسلامی، شماره مسلسل 14726.
صادقی، نرگس و موسو‌ی نیک، سید هادی (1395). «بررسی تطبیقی روش‌های سنتی، بردار ویژه و حذف فرضی در سنجش بخش‌های کلیدی»، فصل‌نامه پژوهش‌های اقتصادی ایران، 21(69): 179-208.
عبدالمحمدی، زهرا؛ بانویی، علی‌اصغر و مهاجری، پریسا (1396). «سنجش اعتبار آماری روش‎های CB و CHARM در محاسبه جداول داده - ستانده منطقه‌ای؛ مطالعه موردی: استان هرمزگان»، فصل‌نامه علمی - پژوهشی مطالعات اقتصادی کاربردی ایران، 6(22): 33-58.
مجید نراقی، مهدی (۱۳۸1). بررسی اشتغالزایی بخش مسکن و نقش آن در توسعه اقتصاد استان اصفهان، پایان‌نامه، دانشکده علوم اداری و اقتصاد دانشگاه اصفهان.
مشفق، زهرا؛ رمضان‌زاده ولیس، گلروز؛ شرکت، افسانه؛ سلیمانی، محدثه و بانویی، علی‌اصغر (1393). «ارزیابی روش‌های RAS متعارف و RAS تعدیل‌شده در بهنگام سازی ضرایب داده - ستانده اقتصاد ایران با تأکید بر شقوق مختلف آمارهای برون‌زا»، فصل‌نامه پژوهش‌های اقتصادی ایران، 19(58): 117-152.
Bullard, C.W. and Sebald, A.V. (1997). “Effects of Parametric Uncertainty and Technological Change on Input-Output Models”, Review of Economics and Statistics, Vol. 59, No.1: 75-81.
Dietzenbacher, E. and Van der Linden, J.A. (1997). “Sectoral and Spatial Linkages in the EC Production Structure”, Journal of Regional Science, 37(2): 235-257.
Harrigan, F.; McGilvray, J. W. and McNicoll, I. H. (1981). “The Estimation of Interregional Trade, Flows”, Journal of Regional Sciences, 21(1): 65-77.
Hewings, Geoffrey .J.D. (1984). “The Role of Prior Information in Updating Input-Output Models”, Socio Economic Planning Science, 18(2): 319-339.
Hewings, G. J. D. and Romanos, M. C. (1981). “Simulating Less-Developed Regional Economies Under Conditions of Limited Information”, Geographical Analysis, 13(4): 373-390.
Hewings, G. .J. D. (1981). “Monitoring Change in a Regional Economy: An Input-Output Simulation Approach”, Modeling and Simulation, 12: 1043-1046.
Isard, W. (1953). “Regional Commodity Flows”, The American Economic Review, 43(2): 167-180.
Jackson, R. (2014). “Cross-Hauling Input-Output Tables: Comments on CHARM”, Regional Research Institute, Working Paper Series, 91(2): 275-297.
Jalili, A.R. (2000). “Comparison of Two Methods of Identifying Input-Output Coefficients for Exogenous Estimation”, Economic Systems Research, 12(1): 113-129.
Jensen, R.C. and West, G.R. (1980). “The Effects of Relative Coefficient Size on Input-Output multipliers”, Environment and Planning, 12(6): 659-670.
Jensen, R.C. (1980). “The Concept of Accuracy in Regional Input-Output Models”, International Regional Science Review, 5(2): 139-154.
Jiang, X.; Dietzenbacher, E. and Los, B. (2007). “Targeting the Collection of Superior Data for the Estimation of Regional Input- Output Table”, Environment and planning A(2010), 42(10): 2508-2526.
Kronenberg, G.T. (2009). “Construction of Regional Input-Output Tables using Non-survey Methods: the Role of Cross-Hauling”, International Regional Science Review, 5(1): 40-64.
Kronenberg, G.T. (2012). “Regional Input-Output Models and the Treatment of Imports in the European System of Account”, Jahrbuch fur Regional Wissenschaft, 32(1): 175-191.
Lahr, M. L. )1992(. An Investigation into Methods for Producing Hybrid Regional Input-Output Tables”, Unpublished Ph.D. dissertation, Regional Science Department, University of Pennsylvania.
Lahr, M.L. (1993). “A Review of the Literature Supporting the Hybrid Approach to Constructing Regional Input-Output Models”, Economic Systems Research, 16(3): 277-294.
Lahr, M.L. (1998). A Strategy for Producing Hybrid Regional Input-Output Tables, the 39th annual North American Meetings of the Regional Science Association in Chicago, November 13, 1992, and the 12th International Conference on Input-Output Techniques, in New York City, May 21, 1998.
Lahr, M. L. (2001). A Strategy for Producing Hybrid Regional Input-Output Tables”, in M. L. Lahr and E. Dietzenbacher (eds.), Input-Output Analysis: Frontiers and Extension, Palgrave, Great Britain, PP: 211-244.
Miller, R.E. and Blair, P.D. (2009). Input-Output Analysis: Foundations and Extensions(Englewood Cliffs, NJ: Prentice-Hall).
Miller, R. E. and Lahr, M. L. (2001). A Taxonomy of Extractions”, in: Michael L. Lahr and Ronald E. Miller (eds.), Regional Science Perspective in Economic Analysis, (Amsterdam: Elsevier Science), PP: 407-441.
Sherman. J. and Morrison. W. (1950). “Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix”, Annals of Mathematical Statistics, 21(1): 124-127.
Sonis, M. and Hewings, G.J.D. (1989). Error and Sensitivity Input-Output Analysis: a New Approach, in: R.E. Miller, K.R. Polenske and A.Z. Rose (eds.), Frontiers of Input-Output Analysis (New York: Oxford University Press), PP: 232-244.
Sonis, M. and Hewings, G.J.D. (1992). “Coefficient Change in Input-Output Models: Theory and Applications”, Economic Systems Research, 4(2): 143-157.
Richardson, H. W. (1985). “Input-Output and Economic Base Multipliers: Looking Backward and Forward”, Journal of Regional Science, Vol. 25, No. 4: 607-661