Why has China’s vertical specialization declined?

Vertical specialization (VS) is quantified by the VS share, which measures the average import content per dollar of exports. A characteristic of China’s export trade is its strong dependence on assembly and processing activities. To take proper account of this, China’s VS shares should explicitly distinguish processing export production from other production. We estimate China’s annual VS shares from 2000 to 2012—the latest year for which a special input–output table is available that makes such an explicit distinction. We find that VS shares increased from 2000 to 2004 and subsequently started to decrease. To explore why it has declined, we introduce a new structural decomposition approach. We find that the decrease of the VS share appears to have been driven mainly by the substitution of imported intermediates by domestic products. This occurred in particular in the production of exports, which implies an upgrading of China’s position in global value chains.     

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.     

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.     

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.     

Improving footprint calculations of small open economies: combining local with multi-regional input–output tables

In a small, open and resource-poor economy, import and export dependency have an ever-growing impact on local policy decisions, which makes local (environmental) policy-makers increasingly depend on global data. This increases the interest in models that link local production and consumption data to global production, trade and environmental data. The recent increase in availability of global environmentally extended multi-regional input-output tables (EE-MRIO tables) provides an opportunity to link them with existing local environmentally extended input-output tables (EE-RIO tables). These combined tables make it possible (1) to analyse the links between local and global production and consumption and (2) to study global value chains, material use and environmental impacts simultaneously. However, estimations using input-output (I–O) analyses contain errors due to imperfect databases. In this article the magnitude of specification, aggregation and time errors are estimated and compared. The results show the need to combine local datasets with multi-regional ones and show that highest detailed (country and sector levels) as well as time series of I–O tables are the way forward for using I–O analyses in local policy-making. The paper provides guidance on trading off investments in model adoption and/or extension and the reliability of estimation results.     

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.     

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.     

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.     

STRUCTURAL DECOMPOSITION ANALYSIS OF GREENHOUSE GAS EMISSIONS IN NORWAY 1990–2002

The goal of this study is twofold: first, to quantify the economic factors driving greenhouse gas emissions in Norway, and second, to assess if random variations in the data affect the results. We use structural decomposition analysis (SDA) with chained constant price input–output tables and environmental extensions. We construct three sets of constant-price data using a smoothing algorithm to remove random variations from the data, and find that the results of the SDA are relatively robust to these variations. The production of exports was responsible for around 70% of the growth in greenhouse gas emissions from 1990 to 2002, household consumption of domestically produced products for about 15%, government 10%, with the remainder due to gross capital formation. The dominance of exports in the emissions growth may make future greenhouse gas mitigation challenging in Norway, particularly considering that the exports are dominated by oil and gas production.     

产业联系与北京优势产业及其演变

根据经济基础理论,城市经济增长源于城市外部需求.一个城市的比较优势体现在服务于外部市场的产业中.论文基于北京市投入-产出表,依据产业净流出,分析了北京市产业的比较优势及其变化.过去15年来,北京的劳动密集型和原料型制造业的优势逐渐丧失,以电子通信设备、化学工业和机械设备制造业为代表的资本和技术密集型产业正成为优势产业;服务业中,传统服务业从20世纪80年代的优势地位退居劣势地位,取而代之的是知识密集型的生产者服务业,如金融业、软件和计算机服务业、信息传输服务业、广告业、房地产开发业、商务服务、科技交流和推广服务业等.产业联系是北京产业比较优势的显著影响因素,资本、知识和技术是北京市产业比较优势的投入支撑.     

The Evolution of Production Structures,Analyzed by a Multi-layer Procedure

In this paper, a new method of so-called qualitative input–output analysis is outlined, which is called minimal flow analysis (MFA). It extracts the characteristic production structure given in an input–output table, on the basis of anendogenized threshold value. Formally, this is achieved by the binarization of the entries of different table layers which are reformulated according to the Eulerian sequence. The condensed characteristic structure of the economy is then obtained by means of graph theoretical methods. The new method is able to uncover production structures, even in highly aggregated tables. If applied to a chronological sequence of tables, as shown for German tables 1978–88 in detail, the MFA method can disclose the evoluton of sectoral structures.     

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.     

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.     

Input–Output Anatomy of China's Energy Use Changes in the 1980s

China significantly reduced the energy intensity of its economy in the 1980s. In this paper, we conduct a structural decomposition analysis to explain China's energy use changes between 1981 and 1987—the years for which we have input–output tables. We find that China's energy saving during this period came about primarily by changes in how to produce (production technology changes) rather than changes in what to consume (final demand shifts). The driving force of the energy intensity decline was energy efficiency improvements, which were multiplied across the entire economy through inter-industry input–output linkages.     

A NEW SUT CONSOLIDATION METHOD TESTED BY A DECOMPOSITION OF VALUE ADDED AND CO2 EMBODIED IN EU27 EXPORTS

This paper develops a method to consolidate national supply–use tables (SUTs) into a single supra-regional SUT. The method deals with mirror trade statistics problems, such as the different valuation of imports and exports, and it corrects for double-counting re-exports. The method is tested by means of a decomposition of value added and CO₂ emissions embodied in EU27 exports to third countries. When the national SUTs for the period 2000–2007 are used, neglecting intra-European Union spillover and feedback effects results in an underestimation of the embodied value added of 12–15%. Not consolidating the national tables properly leads to a further underestimation of 11–16%. With these underestimations removed, EU27 foreign exports still only explain around 11% of EU27 Gross Domestic Product, whereas they explain 17% of the EU27 CO₂ emissions. Hence, the income benefits of these exports are, in relative terms, considerably smaller than their CO₂ emission cost.     

Alternative Approaches of Physical Input–Output Analysis to Estimate Primary Material Inputs of Production and Consumption Activities

In the last few years, a number of studies have been presented that link material flow accounting and input–output analysis (based on monetary input–output tables) for the calculation of direct and indirect resource inputs for production and consumption activities. The compilation of the first physical input–output tables for some European countries in the 1990s opened new possibilities for linking physical accounting and input– output analysis. Physical input–output analysis has so far only been applied for selected materials, but it has not been used for comprehensive assessments of material requirements of economic activities. In this paper, possibilities and limits of this new input–output approach are clarified. We present and discuss a procedure similar to monetary input– output analysis and develop an alternative approach to account for primary inputs and waste otherwise not included in the analysis. Based on aggregated input–output tables for Germany, we present numerical examples intended to compare the alternative approaches of physical input–output analysis.     

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.     

Productivity change in a multisectoral economic system

We estimate productivity growth without recourse to data on factor input shares or prices. In the proposed model, the economy is represented by the Leontief input–output model, which is extended by the constraints of primary inputs. A Luenberger productivity indicator is proposed to estimate productivity change; this is then decomposed in a way that enables us to examine the contributions of individual production factors and individual commodities to productivity change. The results allow for the identification of inputs or outputs that are the drivers of the overall productivity change. Their contributions are then decomposed into efficiency change and technical change components. Using input–output tables of the US economy for the period 1977–2006, we show that technical progress has been the main source of productivity change. Technical progress was mostly driven by capital, whereas low-skilled labour contributed negatively.     

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.     

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.