Multiregional input output table EORA

8/11/2019 Multiregional input output table EORA

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In RAS approach non-negative signs are preserved and zero is carried .However, this willinduce conflicts and non-convergence while updating the MRIO tables in which thesubsidies are usually reflected in negative sign while the change in inventory ranges fromnegative to positive besides the other elements of contingency table remain positive . Alsothe perpetuance of zero might lead to a sparse MRIO table which would result in non-convergence(Lenzen, Kanemoto et al. 2012)

In generating a MRIO table the raw data dealt have various constraints. They may beincomplete, unreliable, uncertain and conflicting.While adopting a RAS based approach ,it has been mandated that the following requirements are met(Lenzen, Gallego et al.2009).1) incorporation of constraints on arbitrarily sized and shaped subsets of matrix

elements( i.e. allowing aggregated, disaggregated and partial data coefficients )2) consideration of reliability of the initial estimate and external constraints(consideration of

standard deviation for the data and its magnitude(confidence interval))3) handling of negative values and the preservation of it.4) handling the conflicting external data ( i.,e ability to choose a prospective solution if two

different values exist for a single matrix entry).

In the following sections , the two techniques that were used in generating EoraMRIO bysatisfying the above conditions are summarised.

1) KRAS approach &2) Van der Plogg least square method.

KRAS Approach:Many variants of RAS method have been developed such as MRAS, TRAS and GRAS in

the past 4 decades to deal with the conflicting raw data in national IO tables. However,many national statisticians resolve conflicts manually in updating IO tables while usingRAS methodology. The key problem lies in the inability of handling the conflicting externaldata by existing RAS based approaches(Lenzen, Gallego et al. 2009). While constrainedoptimization methods could handle these constraints, the simplicity of the RASmethodology is the factor behind the national statistics departments adopting RAS basedapproaches in updating IO tables.

The (Konfliktfreies RAS) KRAS is based on the derivation of the GRAS method of Juniusand Oosterhaven(Junius and Oosterhaven 2003) which has been proposed by Lenzen etal(2009) to resolve the constraints and the reconciliation of contingency tables. In the first

step, the GRAS method is extended to allow constraints on arbitrarily sized and shapedsubsets of matrix elements by subjecting it to constraints equation Ga=c3. Then it isgeneralized to handle any real number (non-unity coefficients) by subjecting it toLagrangian for first-order minima and solved iteratively using Bregmans balancingmethod. Finally, the GRAS method is modified so that reliability is included and theexternal data is handled4.

Vander Ploegs least square method(Van der Ploeg 1988):

EORA MRIO - A SUMMARY MAGHIMAI MARCUS ARULRAJ

3G is the vectorized coeffieicnts of constraints, a is vectorized elements of matrix and c is the raw

data.Constraints violation is applied here in which the target data is allowed to deviate from the original datato a smaller extent and convergence is stipulated for ||Ga-c||

4by subjecting it to standard deviation constraints


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It involves bayesian or least square method to optimize constraints in developing MRIOtables. It is an alternative to the RAS variants. According to the linear programmingprinciples, the problem of solving the MRIO table can be formulated as ,

mina (t,t0): !i Tij = xj & !j Tij = xi. subject to Gt=c.Byron (Byron 1978) introduces a variance "and solved it for first order Lagrange equation

for Lagrange muliplier #5 . Van der Ploeg introduces a slack variable $where $= Gt-c6..The distance between the base table and the desired table (gt-c) is collated with theelements of the existing contingency table through variable p. The relation is explained as

with mean po and variance ". The objective is to minimise the slack variable $ to be a

best fit. p=[t| e]. After the slack variable is collated , the constraints in H is nullified byextending to G , where g=h-I and I is the identity matrix.Then the problembecomes,Minimize (p,p0, ") subject to GP=c7. In order to preserve signs, the elementsof t are set between boundary conditions , l%t%u8. .

General structure of Eora MRIOtable :

The Eora MRIO table developed with the above optimization methods is the mostcomprehensive MRIO developed so far. It has been considered as a detailed time-seriesof data known for its transparency, reliability and timeliness. Having the base year as2000, the table is constructed for a time series of 1990-2009 with a simultaneous

forecasting and backcasting approach. The initial estimates for year 2000 is scaled usinginteryear ratios in an eight dimensional hierarchy for five different valuations and solvedusing a software called AISHA9 . The comprehensiveness of EORA can be explained bythe availability of the transactions table for 187 countries , 15909 sectors , and 35environmental indicators with a time lag of 1-2years from the current year10.


5The objective function is= (t-t0)"-1(t-t0) + #(Gt-c) ,solvingfor1storderLagrange,#=(G

"G)-1(Gt0-c) & t=t0-"G#

6The above approach is called constraint violation and it allows the targeted solutions to deviate from itsprescribed values. It has been observed that the introduction of variance and disturbance allows the handlingof conflicting and unreliable information

7The objective function is (p,p0)=(p-p0)!"-1(p-p0). When solved for the first order langrangean , the solutions are #=(G"G!)-1(Gp0-c) & p=p0-"G #

8l is the lower bound and u is the upper bound and the ranges are given in bracket as follows:subsidies[-$,0],change in inventories[-$, + $], other MRIO elements[0,+ $]

98 dimensional hierarchy which includes time, valuation, region, entity and sector classification for both origin

and destination. Five different valuations included are basic price, producers price(transport margin), purchasersprice(trade margin), tax and subsidies.

10Most comprehensive when compared to other IO tables such IDE-Jetro, GTAP, Exiopol and WIOD


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Applications:Eora MRIO tables have multivariate applications that spread across different domainssuch as economy, environment and energy, etc. It has the potential to be a key policydriver by providing substantial information for analysis on international trade, climatechange ,endangered species, and specifically on sustainable development goals.. Whencomes to environmental analysis, the extended environmental database can be used to

calculate the carbon, water and energy footprint embedded in global trade and also forhybrid life cycle assessments. Individual supply chain and networks can be tracked usingStructural Path Analysis(SPA) whereas the changes in technology and economy can beanalysed using Structural Decomposition Analysis(SDA)

Application to Industrial ecology and future prospects.The Eora MRIO table have opened new frontiers in environmental input-output analysis.Currently its purpose is tended for footprint and hotspot analysis. By solving the majordrawback of conflicting data constraints, it provides a framework in which severalapplications for industrial ecology could be developed. Such model can be replicated fordeveloping international waste IO tables which would map the flow of secondarymaterials across boundaries. By extending the MRIO to material flow analysis andcombining with markov chain models, the hidden trend in material consumption can befigured out.

Conclusion:

Sustainable development needs robust policies . Robust policies can takes its stand onlywhen substantial information is available. Eora MRIO is one such initiative developed withsustainability as its core agenda. When combined with analytical tools , it provides keyinsights into past trends and paves way for forecasting future trends. By providing detailed,

transparent and reliable data, it overcomes data gaps and alleviates concerns onnumbers behind policy making. It has potential to evolve as a key driver in sustainablepolicy making in the global arena.

BibliographyBacharach, M. (1970). Biproportional Matrices and Input-Output Change, Cambridge

University Press.Byron, R. P. (1978). "The Estimation of Large Social Account Matrices." Journal of the

Royal Statistical Society Series A 141: 359-357.Junius, T. and J. Oosterhaven (2003). "The Solution for Updating or Regionalizing a Matrix

with Both Positive and negative Entries." Economic Systems Research 15: 87-96.Lenzen, M., B. Gallego, et al. (2009). "Matrix balancing under conflicting information."

Economics Systems Research 21(1): 23-44.Lenzen, M., K. Kanemoto , et al. (2012). "Mapping the structure of the World Economy."

Environmental Science & Technology 46: 8374-8381.Miernyk, W., H. (2004). Leontief and dynamic regional models. Wassily Leontief and Input-

Output Economics. E. Dietzenbacher and M. L. Lahr. United Kingdom, CambridgeUniversity Press: 90-101.

Stone, R. (1961). Input-Output and National Accounts. Paris, Organization for EuropeanEconomic Cooperation.

Van der Ploeg, F. (1988). "Balancing Large Systems of National Accounts." ComputerScience in Economics and Management 1: 31-39.


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Multiregional input output table EORA