TESTING ASSUMPTIONS MADE IN THE CONSTRUCTION OF INPUT–OUTPUT TABLES

Product input–output (IO) tables are mainly constructed on the basis of product and/or industry technology assumptions. The choice is not trivial and deserves empirical analysis using input and output data at the level of establishments. This paper offers input–output compilers econometric tests to facilitate the construction of tailored hybrid technology-based product IO tables. We provide weighted likelihood ratios of the product and industry technology assumptions. Although the proposed econometric tests are aimed to be used ex ante, we construct four variants of hybrid technology-based product IO tables using establishment data from Andalusia (Spain) and contrast them to the official product IO table and the pure product and industry technology-based tables. Our econometric tests are not valid for industry IO tables.     

ANALYSING THE STRUCTURE OF THE ECONOMY USING PHYSICAL INPUT–OUTPUT TABLES

Analyses using physical input–output tables (PIOTs) are key to understanding the physical metabolism of economies, since they relate production to the generation of emissions and use of resources. Two methods have been developed to calculate the primary resources and emissions associated with a given final demand. However, one of these alters the PIOT, revealing different technical coefficients and Leontief inverse matrices. Which method should be used for structural analysis? In this paper, I compare both methods, explain the structural differences between them and illustrate the latter through a backward linkage analysis. I find that only one method is suited to the analysis of the physical structure of the economy, since it comprehends both the production of goods and associated emissions. The method is identified as a new model capable of tracing by-products as final outputs. Finally, I generalise both methods to analyse PIOTs including several emission types.     

CONSTRUCTING AN ENVIRONMENTALLY-EXTENDED MULTI-REGIONAL INPUT–OUTPUT TABLE USING THE GTAP DATABASE

The use of Multi-Regional Input–Output Analysis (MRIOA) for understanding global environmental problems is growing rapidly. Renewed interest in MRIOA has led to several large research projects focused on constructing detailed and accurate MRIOTs. However, very few researchers have made use of the already available and regularly updated database produced by the Global Trade Analysis Project (GTAP). We demonstrate and discuss how the GTAP database can be converted into an MRIOT without the need for additional balancing. An illustrative example uses the GTAP-MRIO to reallocate carbon dioxide emissions from producing to consuming countries. We suggest that an MRIOT that treats international transport exogenously is adequate until more reliable data on international transport margins and emissions are available. To focus resources and refine methods, a concerted research effort is needed to compare the results of the GTAP-MRIO model with the new MRIO datasets under development.     

THE ‘REST OF THE WORLD’ – ESTIMATING THE ECONOMIC STRUCTURE OF MISSING REGIONS IN GLOBAL MULTI-REGIONAL INPUT–OUTPUT TABLES

Incomplete data for the economic structure of numerous countries hamper the compilation of global multi-regional input–output (MRIO) tables. By themselves, most of these countries are of only limited importance for the global economy and incumbent environmental issues. Hence, in most recent global MRIO tables these countries are either roughly estimated or summarised in one rest of the world (RoW) region. Combining a wide range of countries, this RoW region may play a significant role in global economic and environmental accounts. We conceptualise the importance of RoW in several environmental footprint accounts and present algorithms to estimate the structure of RoW. The approach utilises the information of the economic structure within known parts of the MRIO table to estimate the unknown structure. Using this method, global warming potential and employment footprints remain stable irrespective of the chosen initial estimates, whereas natural land use footprints and individual product impacts vary significantly.     

REGIONAL AND SECTORAL DISAGGREGATION OF MULTI-REGIONAL INPUT–OUTPUT TABLES – A FLEXIBLE ALGORITHM

A common shortcoming of available multi-regional input–output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.     

FROM INPUT–OUTPUT TABLES TO SUPPLY-AND-USE TABLES

In 1991, Statistics Netherlands introduced the supply-and-use tables as part of the national accounts. Since then, the supply-and-use tables have been the main statistics on the production structure of the Dutch economy. They form the basis from which input–output tables are derived. The time series of supply-and-use tables starts in 1987. However, there is a need for a time series since 1970 because benchmark revisions of the Dutch national accounts would become far easier if such time series were available. Therefore, a method has been developed to derive supply-and-use tables from existing input–output tables. This article presents the algorithm.     

INVESTIGATING ALTERNATIVE APPROACHES TO HARMONISE MULTI-REGIONAL INPUT–OUTPUT DATA

Over recent years a small number of global multi-regional input–output (MRIO) databases were developed to describe the entire global economy at high sector detail. We investigate the differences that arise out of applying different construction procedures for two global MRIO databases: The EXIOBASE database, developed as part of the EU FP6 & 7 programs and the Eora database developed at the University of Sydney. The procedures used in EXIOBASE involve a high degree of interrogation and adjustment throughout the construction of the data set, whilst the Eora MRIO relies on single-step mathematical programming techniques and high-performance computing. We unravel the effect of the different approaches taken to develop the databases by undertaking a number of combinatorial experiments in which we exchange parts of the construction process between the EXIOBASE and Eora build pipelines. We conclude that Eora's highly automated data reconciliation approach produces MRIO databases that are of comparable quality to those constructed with EXIOBASE's multi-step approach. However, the reliability and robustness of the resulting MRIO database largely depend on the level of detail and reliability of the underlying raw data.     

AN INPUT–OUTPUT STUDY OF THE SINGAPORE INFORMATION SECTOR

The development and use of information and communication technologies is one of the key drivers of the ‘knowledge economy.’ In this paper, we investigate the impact of information technology on the output growth of the Singapore economy using the input–output framework. The input–output framework allows us to understand the impact of information sector in an integrated framework in terms of its linkages to the manufacturing and service sectors. In particular, we adopt the input–output approach to shed light on both production and diffusion activities of the information sector on the Singapore economy. The results indicate that the ICT sector provided the key linkages for the expansion of high-value added manufacturing activities and electronics export for the Singapore economy.     

THE COMPILATION OF CHINA'S INTERREGIONAL INPUT–OUTPUT MODEL 2002

The increasing economic interaction among various regions in China makes the construction of an interregional input–output table relevant for economic studies. This paper elaborates the model compilation procedure of the China Interregional Input–output model 2002. The key features of the model compilation include: (1) using representative commodities to estimate the interregional commodity flows of the primary industries; (2) adopting functions to estimate the decreasing interregional transportation of manufacturing sectors in relation to distance and (3) selecting appropriate indicators to estimate the interregional commodity flows of non-material sectors. This study is an initial attempt in interregional input–output modelling and might be helpful for economic studies at the levels of micro-regions.     

CONSTRUCTION,STABILITY AND PREDICTABILITY OF AN INPUT–OUTPUT TIME-SERIES FOR AUSTRALIA

This paper documents the development of a time series of Australian input?output tables. It describes the construction techniques employed in order to overcome the major issues encountered. Environmentally important processes were delineated using a range of detailed commodity data, thus expanding the original tables from roughly 100 industries into a temporally consistent 344 industries. Data confidentiality and inconsistency were overcome using an iterative constrained optimisation method called KRAS???a recent modification of RAS (Lenzen et al. 2006; 2007; 2009). The article concludes by analysing the stability of input?output coefficients over time similar to work in Dietzenbacher and Hoen (2006). The issue of stability of coefficients and multipliers was investigated under the Leontief and Ghosh models of supply/demand. Finally, the predictability of the models was examined under updated final demand or primary inputs and over varying time scales.     

ESTIMATION OF SYMMETRIC INPUT–OUTPUT TABLES: AN EXTENSION TO BOHLIN AND WIDELL

This paper presents two optimisation models for use in the production of symmetric input–output tables (SIOTs) based on data contained within supply-use tables (SUTs). The first model produces commodity-by-commodity SIOTs derived from the selection of appropriate technology assumptions, while the second produces industry-by-industry SIOTs derived through the selection of appropriate sales structure assumptions. Both models address the problem of negative coefficients and also permit the use of rectangular SUTs as base input data. Additionally, this paper explores the development of a ‘comprehensive model’ enabling production of both commodity-by-commodity and industry-by-industry SIOTs that are conceptually and mathematically consistent.     

AGGREGATION VERSUS DISAGGREGATION IN INPUT–OUTPUT ANALYSIS OF THE ENVIRONMENT

Analysts carrying out input–output analyses of environmental issues are often plagued by environmental and input–output data existing in different classifications, with environmentally sensitive sectors sometimes being aggregated in the economic input–output database. In principle there are two alternatives for dealing with such misalignment: either environmental data have to be aggregated into the input–output classification, which entails an undesirable loss of information, or input–output data have to be disaggregated based on fragmentary information. In this article, I show that disaggregation of input–output data, even if based on few real data points, is superior to aggregating environmental data in determining input–output multipliers. This is especially true if the disaggregated sectors are heterogeneous with respect to their economic and environmental characteristics. The results of this work may help analysts in understanding that disaggregation based on even a small amount of proxy information can improve the accuracy of input–output multipliers significantly. Perhaps, these results will also provide encouragement for preferring model disaggregation to aggregation in future work.     

REFLECTIONS ON THE INOPERABILITY INPUT–OUTPUT MODEL

We argue that the inoperability input–output model is a straightforward – albeit potentially very relevant – application of the standard input–output model. In addition, we propose two less standard input–output approaches as alternatives to take into consideration when analyzing the effects of disasters or disruptions.     

INPUT–OUTPUT ANALYSIS: THE NEXT 25 YEARS

This year marks the 25th anniversary of the International Input–Output Association and the 25th volume of Economic Systems Research. To celebrate this anniversary, a group of eight experts provide their views on the future of input–output. Looking forward, they foresee progress in terms of data collections, methods, theory testing, and focus and scope.     

REGIONAL WATER FOOTPRINTS OF THE YANGTZE RIVER: AN INTERREGIONAL INPUT–OUTPUT APPROACH

Recently, researchers have applied the multi-regional input–output (MRIO) approach to water footprint (WF) analysis. The concept of interregional input–output (R-MRIO) was developed to analyse regional issues. Researchers have concentrated on the development of global or international input–output (N-MRIO) tables. Using the N-MRIO and the R-MRIO approach allows the study of global and regional issues, respectively. The WF is an indicator influenced by trade among nations and regions. However, the treatment of imports in an R-MRIO approach differs in whether international imports are separated or combined. We evaluate the effects of the difference between these models and discuss policy implications for the Yangtze River, China. The WF calculated using the combined type model is 11% larger than that by the separated type model. This difference can be ascribed to international imports, mainly internal consumption and interregional trade. We find that this difference affects social equity in water-abundant areas.     

USING TIME-SERIES TO MEASURE UNCERTAINTY IN ENVIRONMENTAL INPUT–OUTPUT ANALYSIS

Environmental Input-Output Analysis (EIOA) is a tool for environmental analysis of broad classes of sectoral activities, taking into account indirect effects in other sectors in the supply chain. The core of EIOA is an input–output table (IOT) and national accounting matrix including environmental accounts (NAMEA) for a fixed base-year. We evaluate the uncertainty in EIOA using a time series of current-price IOT and NAMEA for 13 years from 1990 to 2002. We find annual variations in the current-price IOT and NAMEA, which may represent either realistic changes in production or measurement error. We assume the changes are errors and apply a regression analysis to remove the trends from the underlying data and estimate the uncertainty in the raw IOT. We then calculate the emissions for various final users and sectors to estimate the uncertainties from typical EIOA investigations. Using Monte Carlo analysis, we then investigate how well the variations in the current-price IOT and NAMEA over time may represent uncertainties. The results of this work have several implications for both statistical offices and the analyst. Statistical offices can provide details on data sources, methodologies, and estimates of annual variations. Analysts can incorporate this uncertainty information to understand the implications of uncertainty on their calculations and ultimately the policy recommendations derived from their studies.     

INPUT–OUTPUT ANALYSIS FOR BUSINESS PLANNING: A CASE STUDY OF THE UNIVERSITY OF SYDNEY

We present a multi-region input–output (MRIO) model of the University of Sydney embedded in the Australian economy, which forms the centrepiece of a new data-driven framework for strategic forecasting and planning of the University's financial operations. This framework incorporates both Leontief's well-known demand-pull, as well as Ghosh's supply-push exercise. It is therefore able to estimate the immediate financial implications for the University, and the economy-wide flow-on effects, for example as a result of changes in demand for courses by students, or as a result of supply-side changes such as wage increases. We report on recent scenario studies on the financial performance of the teaching and research functions of the University, and the lessons learned for management practice.     

ON THE ENVIRONMENTAL IMPACT OF CONSUMER LIFESTYLES – USING A JAPANESE ENVIRONMENTAL INPUT–OUTPUT TABLE AND THE LINEAR EXPENDITURE SYSTEM DEMAND FUNCTION

In this study, we undertake some hypothetical experiments and predict the environmental effects of some changes in consumer behavior, using the Japanese Input–Output Table and the Family Income and Expenditure Survey for 2000. We estimate the demand function in a linear expenditure system (LES) and attempt to determine how changes in consumer behavior affect the environmental load induced by household consumption, using the ‘willingness to pay’ concept. Furthermore, we define an index to show the eco-efficiency of consumer behavior. Through such a study, we can determine what action is appropriate for a ‘sustainable consumption’ society. If some change of consumer behavior greatly improved utility while increasing the environmental load, then technological progress to reduce the environmental load must be stimulated. However, if other changes in consumer behavior increase the environmental load while not improving utility very much, then such changes should be strongly discouraged.     

DISAGGREGATING THE ELECTRICITY SECTOR OF CHINA'S INPUT–OUTPUT TABLE FOR IMPROVED ENVIRONMENTAL LIFE-CYCLE ASSESSMENT

Missing process detail of sectors in Input–Output (I–O) tables has been pointed out as a limitation of I–O analysis in environmental-economic life cycle assessment. Aggregation of resource-intensive sectors decreases the accuracy of the results. Often, economic sectors are compiled in a more aggregated form than environmental satellite accounts, and as [Lenzen, M. (2011) Aggregation Versus Disaggregation in Input–Output Analysis of the Environment. Economic Systems Research, 23, 73–89] asserts, it is superior for environmental analysis to disaggregate the I–O table, even if only partial information exists for the disaggregation. In this paper we present a methodology to disaggregate the electricity sector of the Chinese national I–O table by using regional information and cost data for operation and maintenance of power plants. The electricity sector is disaggregated into a transmission and distribution sector as well as eight sub-sectors representing different types of technology in power plants (subcritical coal, hydro, etc.). The electricity consumption mix of each industry is determined by using regional industry presence and regional electricity power mixes. The disaggregated I–O table offers refined results for calculating emissions embodied in international exports from China, a valuable contribution for estimating national greenhouse gases emissions inventories under the consumption-based approach for countries that rely heavily on imports of goods from China.