Hypothetical extractions from a global perspective

ABSTRACT

The hypothetical extraction method (HEM) has been widely used to measure interindustry linkages and the importance of industries. HEM considers the hypothetical situation in which a certain industry is no longer operational. HEM was developed for national economies, using national input–output tables. When performing HEM, it is assumed (often implicitly) that the input requirements that were originally provided by the extracted industry are met by additional imports in the post-extraction situation. Applying HEM to global multiregional input–output tables then causes serious problems. It is no longer sufficient to assume that the required inputs are imported. Instead, it is necessary to indicate explicitly how much is imported from each origin to replace the original inputs. Our adaptation of HEM is the global extraction method (GEM). As an illustration, GEM is applied to the extraction of the motor vehicle industry in China, the US, and Germany, using the 2014 WIOD input–output table.     

Adjustment of Input–Output Tables from Two Initial Matrices

The compilation of the information required to construct survey-based input–output (I–O) tables consumes resources and time to statistical agencies. Consequently, a number of non-survey techniques have been developed in the last decades to estimate I–O tables. These techniques usually depart from observable information on the row and column margins, and then the cells of the matrix are adjusted using as a priori information a matrix from a past period (updating) or an I–O table from the same time period (regionalization). This paper proposes the use of a composite cross-entropy approach that allows for introducing both types of a priori information. The suggested methodology is suitable to be applied only to matrices with semi-positive interior cells and margins. Numerical simulations and an empirical application are carried out, where an I–O table for the Euro Area is estimated with this method and the result is compared with the traditional projection techniques.     

Reconciling Domestication Techniques,the Notion of Re-exports and Some Comments on Regional Accounting

Recent work by Jackson (1998) subtly pointed out a means of forming direct input coefficient matrices from national technology that is different from that published elsewhere. In this paper, I rationalize his approach and also point out that prior approaches may still be useful in certain applications where the phenomenon of re-exports (imports that satisfy exports) are explicit in exports accounts. In the second half of this paper, I show some means of developing regional accounts, currently being used in the US, that are more elaborate than those Jackson discussed. For example, I substitute regional shares of employment with earnings shares to obtain productivity adjusted regional output. I also suggest using available regional value added and regional labour income when producing regional Use matrices.     

HOW MUCH DO EXPORTS CONTRIBUTE TO CHINA'S INCOME GROWTH?

It is a widespread belief that exports, in particular of ‘high-tech’ products, contribute much to China's income growth. This study addresses this issue by applying a structural decomposition analysis to input–output (I–O) data. We employ two extended I–O tables that distinguish processing trade from ordinary exports. The contribution of exports to the value-added growth from 2002 to 2007 is found to be overestimated by 32% when standard I–O tables are used rather than the extended I–O tables. Even more strikingly, the value-added growth that may be attributed to the exports of ‘high-tech’ telecommunication products is overestimated by no less than 63%. A serious overestimation of the contribution to income growth of certain products (such as high-tech products) sends out misleading signals to policymakers. When measured correctly, the true contribution appears to be substantially smaller than is generally believed to be.     

A Synthetic Approach to Projecting Input–Output Tables

This paper describes an approach used in the Canadian input–output (IO) accounts, which seeks to enhance the timeliness of the tables. It combines traditional updating methods, balancing techniques and the most recent data. To assess the performance of this approach, aggregate estimates from the synthetic accounts are presented for two years, and compared with estimates from benchmarks and with estimates obtained from a mechanical estimation technique. The results show that most IO components can be estimated with a relatively small estimation error and that substantial accuracy is gained from using the synthetic approach compared with a mechanical technique. Results based on data which are two years away from IO benchmarks are obtained at the cost of large errors. Synthetic estimates of the IO accounts improve the timeliness problem by at least a full year.     

INCOME DISTRIBUTIONS IN INPUT-OUTPUT MODELS

The analysis of income distribution (ID) has traditionally been of prime importance for economists and policy-makers. However, the standard input–output (I–O) model is not particularly well equipped for studying current issues such as the consequences of decreasing access to primary inputs or the effects of specific redistributive policies. This paper addresses this gap in the existing literature. We propose that IDs can excellently be studied by restructuring the I–O relations. A new coefficients matrix is defined, the so-called augmented input coefficients matrix. This matrix is the sum of the intermediate input coefficients matrix and newly constructed matrices of sector-specific input coefficients that represent the existing distribution of income. We show that shifts in the distribution can be modelled by attributing weights to these matrices and vary these according to system-specific rules. Numerical illustrations based on the existing literature are given throughout the paper.     

A Test for a Functioning Market Economy: Seton'S Eigenprices of Turkey

We use Seton's eigenprices to see if some evidence can be found in support of the European Commission's official statement that the Turkish economy can be considered as a functioning market economy. Given an input–output flows matrix, there is a unique set of prices for outputs and production factors compatible with final demand, generating demand for factors. The findings based on Turkey's most recent I–O table and comparable I–O matrices for Romania and Poland (two EU members) in 2005 show that price distortions were on average five times larger in Turkey. Hence, based on price distortions alone, there was no solid evidence in support of the statement that Turkey had a functioning market economy.     

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.     

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.     

THE BIAS OF THE MULTIPLIER MATRIX WHEN SUPPLY AND USE TABLES ARE STOCHASTIC

The literature on stochastic input–output (I–O) analysis has paid considerable attention to the bias in the Leontief inverse. This paper extends previous studies by assuming supply and use tables (SUTs rather than I–O tables or input coefficients matrices) to be stochastic. This is a natural starting point because SUTs have become the basic data sources for I–O applications. In a Monte Carlo simulation experiment, a given SUT is randomized in two different ways and the effects are determined for eight different multiplier matrices. The analysis is carried out for Spain, Italy, the Netherlands, Germany and Finland, using their SUTs for 2006. The findings indicate that, in general, biases are statistically significant but negligibly small. This corroborates earlier findings obtained for stochastic I–O tables.     

Process System Modelling of Production Technology Alternatives using Input– Output Tables with Sector Specific Units

Input–output analysis is usually based on tables of accounts expressed in uniform monetary or physical units. However, from a process system modelling perspective, tables of accounts in sector specific units may be more useful for evaluating the effectiveness of new production technologies on reducing pollutant emissions. Using the sector specific unit conceptualization of an IO table, one can consider the effect of changes in direct input coefficients for a particular sector on the complete set of total input coefficients independently from the other direct input coefficients. A process system modelling based method for calculating the total industrial outputs from a new technology matrix together with the new relative prices for each sector output is presented. The method is then used to study the effect of technology changes in the steel making industry in Liaoning Province, China on prices and pollutant emissions.     

Building a global database: consequences for the national I–O data

ABSTRACT

Global economic analysis requires consistent and balanced data, which necessitates the reconciliation of datasets from both national and international sources. In the case of the Global Trade Analysis Project Data Base, datasets supplied by international sources are considered preferable to national input–output (I–O) tables. As a result, the national I–O data can experience significant adjustments during the reconciliation process due to differences between the national and international datasets. The purpose of this paper is to examine the extent to which national I–O data change during reconciliation. The results demonstrate that the I–O data are altered by the construction process, particularly from the reconciliation of the national I–O data to the international trade and energy datasets. Closer examination reveals potential issues with both the trade and energy datasets, as well as the national I–O data – illustrating the challenges associated with reconciling data from multiple sources.     

Updating Input–Output Tables with Benchmark Table Series

Numerous methods have been proposed to update input–output (I–O) tables. They rely on the assumption that the economic structure will not change significantly during the interpolation period. However, this assumption may not always hold, particularly for countries experiencing rapid development. This study attempts to combine forecasting with a matrix transformation technique (MTT) to provide a new perspective on updating I–O tables. Under the assumption that changes in the trend of an economic structure are statistically significant, the method extrapolates I–O tables by combining time series models with an MTT and proceeds with only the total value added during the target years. A simulation study and empirical analysis are conducted to compare the forecasting performance of the MTT to the Generalized RAS (GRAS) and Kuroda methods. The results show that the comprehensive performance of the MTT is better than the performance of the GRAS and Kuroda methods, as measured by the Standardized Total Percentage Error, Theil's U and Mean Absolute Percentage Error indices.     

STRUCTURAL DECOMPOSITION ANALYSIS APPLIED TO ENERGY AND EMISSIONS: AGGREGATION ISSUES

With the introduction of the environmentally extended input–output (I–O) framework, traditional economic I–O modeling and analysis can be conveniently adopted in energy and emission studies. Based on such an extended framework, many empirical studies investigating the driving forces of energy consumption and emission changes using structural decomposition analysis have been reported. Three aggregation issues are inherent in such decomposition studies, namely sector aggregation, spatial aggregation and temporal aggregation. This study, as an extension of our previous work on the first two issues, focuses on the third or temporal aggregation. An empirical study using the emission data of China from 1997 to 2007 is presented to illustrate the problems involved. How to deal with temporal aggregation and its possible interactions with the other two aggregations is also discussed.     

Regime shifts in stock–flow I(2)–I(1) systems: the case of US fiscal sustainability

In the last two decades, fiscal sustainability has been tested through the use of non‐stationary time series analysis. Two different approximations can be found in the literature: first, a univariate approach that has focused on the stochastic properties of the stock of debt and, second, a multivariate one that has focused on the long‐run properties of the flows of expenditures and revenues, i.e., in the stochastic properties of the deficit. In this paper we unify these approaches considering the stock–flow system that fiscal variables configure. Our approach involves working in an I(2) stochastic processes framework. Given the possibility of the existence of regime shifts in the sustainability of US deficit that the literature has pointed out, we develop a new statistic that can be applied to test several types of I(2) cointegration and multicointegration relationships allowing for regime shifts. To test for these kinds of changing long‐run relationships we propose the use of a residual‐based Dickey–Fuller class of statistic that accounts for one structural break. We show that consistent estimates of the break fraction can be obtained through the minimization of the sum of squared residuals when there is I(2) cointegration. The finite sample performance of the proposed statistic is investigated by Monte Carlo simulations. The econometric methodology is applied to assess whether the US fiscal deficit and debt are sustainable. Copyright © 2010 John Wiley & Sons, Ltd.     

Econometric estimation of distance functions and associated measures of productivity and efficiency change

Multi-input multi-output production technologies can be represented using distance functions. Econometric estimation of these functions typically involves factoring out one of the outputs or inputs and estimating the resulting equation using maximum likelihood methods. A problem with this approach is that the outputs or inputs that are not factored out may be correlated with the composite error term. Fernandez et al. (J Econ 98:47–79, 2000) show how to solve this so-called ‘endogeneity problem’ using Bayesian methods. In this paper I use the approach to estimate an output distance function and an associated index of total factor productivity (TFP) change. The TFP index is a new index that satisfies most, if not all, economically-relevant axioms from index number theory. It can also be exhaustively decomposed into a measure of technical change and various measures of efficiency change. I illustrate the methodology using state-level data on U.S. agricultural input and output quantities (no prices are needed). Results are summarized in terms of the characteristics (e.g., means) of estimated probability density functions for measures of TFP change, technical change and efficiency change.     

PROCESSING TRADE BIASES THE MEASUREMENT OF VERTICAL SPECIALIZATION IN CHINA

Vertical specialization (VS) is often measured by the import contents of the exports, using an input–output (I–O) framework. Half of China's exports are processing exports, which largely depend on imported intermediate inputs and tie up upstream as well as downstream trade partners. Thus, one would expect to find strong VS for China. Using the ‘ordinary’ I–O tables, however, this is not the case. Because the production of processing exports is only a small part of total production, the average input structure in the I–O table hides the typical features of processing exports. Using adapted, tripartite I–O tables (for 2002 and 2007) in which the processing exports have been singled out, indeed reveals the expected strong VS in China.     

Missing links in key sector analysis

In general terms, key sectors analysis aims at identifying and quantifying the economic impact of a sector in a given economy. For a sector, we mean here either an industry or a region, or even a cluster of them. Quite a few measures and methodologies of varied complexity have been proposed in the literature, from multiplier sums to extraction methods, but not without debate about their properties and information content. All of them, to our knowledge, focus exclusively on the interdependence effects that result from an input–output structure of the economy. By so doing the approach misses critical links beyond the interindustry ones. A productive sector's role is that of producing but also that of generating and distributing income among primary factors and households as a result of production. Thus, when measuring a sector's role, the income generating process should not be omitted if we want to elucidate the sector's true economic impact. A simple way to make the missing income links explicit is to use the SAM (Social Accounting Matrix) facility. Extending an extraction methodology to the SAM we compare lost output with and without the missing links. We observe that substantial differences in sectoral lost gross output arise but, even more important, we capture the implied shifting in the rank ordering of sectors.     

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.