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.     

COMPLEMENTARITY IN INPUT–OUTPUT ANALYSIS AND STOCHASTICS

The complementarity between the quantity and value systems of input–output analysis is shown to be the basis of the complementarity problem approach to computable general equilibrium. The numerical superiority of the latter to the linear programming approach facilitates stochastic analysis of input–output scenarios. For the example where Kyoto targets are underachieved to uncertain degrees, confidence intervals are derived for the associated consumption reductions.     

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 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.     

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.     

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.     

REAL-FINANCIAL LINKAGES IN THE CANADIAN ECONOMY: AN INPUT–OUTPUT APPROACH

The recent financial crisis highlighted the importance of better understanding the interaction between macroeconomic and financial conditions. In this paper, we provide a financial social accounting matrix for the Canadian economy and use it to assess the strength of real-financial linkages by calculating and comparing multipliers with and without endogenous financial flows. It is found that taking into account financial flows increases the impact of a final demand shock on output by 4–11%. Moreover, between 2008 and 2009H1, the investment decisions of financial institutions together with the fact that non-financial institutions were unwilling or unable to increase their financial liabilities led to estimated declines in all GDP multipliers. The impact of a final demand shock on GDP declined 3–5%, while the impact of an increase in the availability of investment funds fell 30% and 55% for financial and non-financial corporations, respectively.? ^?The views expressed in this paper are those of the authors. No responsibility for them should be attributed to Statistics Canada. View all notes     

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.     

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.     

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.     

UNCERTAINTY ANALYSIS FOR MULTI-REGION INPUT–OUTPUT MODELS – A CASE STUDY OF THE UK'S CARBON FOOTPRINT

This paper reviews and demonstrates methods available for estimating standard deviations for carbon multipliers in a multi-regional input–output (MRIO) framework. We attempt to capture all possible variations of underlying data and calculation procedures in a global MRIO model constructed with particular focus on the UK. We consider these variations to be random, and determine the stochastic variation of the whole MRIO system using Monte Carlo techniques. 5000 simulation runs were carried out to determine the standard deviations of multipliers. From these, the standard deviations of components of the UK's carbon footprint were estimated using error propagation. We estimate an 89% probability that the UK's carbon footprint has increased between 1994 and 2004.     

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.     

OPTIMIZING PRODUCTION IN THE GREEK ECONOMY: EXPLORING THE INTERACTION BETWEEN GREENHOUSE GAS EMISSIONS AND SOLID WASTE VIA INPUT–OUTPUT ANALYSIS

We explore an input–output based framework for optimizing production in the Greek economy, under constraints relating to energy use, final demand, greenhouse gas emissions and solid waste. Using empirical data, we consider the effects on the maximum attainable gross value of production when imposing various pollution abatement targets. Our results quantify those effects as well as the magnitude of economic sacrifices required to achieve environmental goals, in a series of policy scenarios of practical importance. Because air pollution and solid waste are not produced independently of one another, we identify the settings in which it is meaningful to institute a separate policy for mitigating each pollutant, versus those in which only one pollutant needs to be actively addressed. The scenarios considered here represent a range of options that could be available to policy makers, depending on the country's international commitments and the effects on economic and environmental variables.     

CONSTRUCTION OF MULTI-REGIONAL INPUT–OUTPUT TABLES USING THE CHARM METHOD

Subnational multi-regional input–output tables (IOT) are important tools for studying interregional socio-economic and/or environmental interrelations that help to address a wide range of current societal, ecological and economic challenges. However, the lack of subnational input–output data is a major obstacle which leads to a wide use of non-survey methods. Like other non-survey methods, the cross-hauling adjusted regionalization method (CHARM) was originally developed for the construction of single-regional IOT. In this paper, we extend CHARM to the case of bi- and multi-regional IOT. We find that the original CHARM formula has two limitations that are also of great importance for the single-regional case: First, cross-hauling in interregional trade is implicitly set to zero and, second, accounting balances may be violated owing to structural differences between the regional and national economies. We present a modified formula addressing these issues and examine its performance in terms of a case study.     

THE EXTENDED ECONOMETRIC INPUT–OUTPUT MODEL WITH HETEROGENEOUS HOUSEHOLD DEMAND SYSTEM

This paper proposes an extension to the regional econometric input–output model (REIM) [Conway, R.S. (1990) The Washington Projection and Simulation Model: A Regional Interindustry Econometric Model. International Regional Science Review, 13, 141–165; Israilevich, P.R., G.J.D. Hewings, M. Sonis and G.R. Schindler (1997) Forecasting Structural Change with a Regional Econometric Input–Output Model. Journal of Regional Science, 37, 565–590]. We integrate a demand system with age and income parameters into the REIM. The extended model thus addresses concerns about the effects of household heterogeneity. The initial testing is conducted with a model for the Chicago metropolitan area. First, using aggregate expenditure data by income and age groups, the almost ideal demand system with group fixed effects is constructed. Next, the estimated demand system is linked to the REIM to reflect long-term changes in the age and income distribution of households. The long-range simulation from the extended model takes into account structural changes in expenditure type stemming from changing demographic composition. The extended model further broadens the scope of impact analysis under various scenarios associated with age and income changes.     

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.     

PROBABILISTIC MODELING OF WORKFORCE-BASED DISRUPTIONS AND INPUT–OUTPUT ANALYSIS OF INTERDEPENDENT RIPPLE EFFECTS

This paper extends the formulation of the input–output model to account for events that cause time varying and probabilistic workforce disruptions. One example of such an event is a pandemic, because the rates with which it affects the working population vary from period to period and are coupled with uncertainties. To address such complexities, the paper develops two extensions: (i) a method of translating unavailable workforce into a measure of sector productivity disruption, and (ii) a simulation framework to account for the possible variations in economic output losses. These extensions are implemented via a MATLAB program to simulate a pandemic scenario in the Commonwealth of Virginia.     

DEVELOPING A MULTI-SCALE MULTI-REGION INPUT–OUTPUT MODEL

Many efforts have recently been devoted to developing global multi-region input–output (GMRIO) models. Unfortunately, the scales of GMRIO models do not allow them to capture the heterogeneity of regions within a single country. Multi-scale models can provide more comprehensive analyses capable of capturing the interdependencies of the global economy while preserving regional differences. The primary objective of this research is to develop methods for integrating multi-region input–output data sets from multiple spatial scales into multi-scale multi-region input–output (MSMRIO) models. These methods result in models that may have unusual features such as non-square trade coefficient matrices and a mix of industry-by-industry and commodity-by-commodity technical coefficients. To demonstrate the feasibility of MSMRIO modelling, a Canada-centric model was developed. This model includes 47 countries and Canada's 13 subnational regions. A MSMRIO model provides a tool to analyse global issues with a more spatially detailed focus.     

DISAGGREGATING INPUT–OUTPUT MODELS WITH INCOMPLETE INFORMATION

Disaggregating a sector within the Leontief input–output (IO) framework is not a straightforward task since there is more than one possibility for the unknown technical coefficients of the disaggregated IO table, and more information than what is embodied in the aggregated IO table is thus required. This paper presents a methodology for disaggregating sectors into an arbitrary number of new sectors when the only available information about the newly formed sectors is their output weights. A random walk algorithm is used to explore the polytope containing the admissible combinations for the unknown technical coefficients of the disaggregated IO table. These combinations are then used to construct the probability distribution of the coefficients of the inverse Leontief matrix. The methodology is illustrated by disaggregating the electricity production sector of China's 2007 IO table and by looking at the probability distribution of the CO₂ emission intensity factors of the sectors of the economy.     

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.