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.     

Decomposing Input–Output Tables to Establish the Effects of Temporal Changes in Demographic and Economic Characteristics

This paper reports on an analysis of economic and demographic changes in the UK during the period 1981–91, using an extended input–output (IO) formulation designed to measure impacts of such changes in economic and demographic systems. We derive a simple temporal change decomposition method and apply it to the extended IO formulation. This is then linked to a labour market account decomposition, and the conjoint decomposition applied to two labour market accounts and two IO tables for the UK. We are able to identify the impacts on the demographic and economic systems caused by changes in the economy, economic activity of the population, natural population change, migration and coefficient shifts of different types.     

A disaster response model driven by spatial–temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts.     

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.     

A Spatial Structural Decomposition Analysis of Chinese and Japanese Energy Demand: 1985–1990

This paper proposes a spatial structural decomposition analysis to measure the effects of the changes in intra- and inter-country linkages on the embodied energy demand in the concerned country. For the empirical analysis, we have used the China- Japan inter-country input–output tables for 1985 and 1990, expressed in constant prices of 1990. The empirical results reveal that (1) at least for the period between 1985 and 1990, the effects of the non-competitive input structural changes in China on the primary energy requirements of Japan were negligible, and (2) the contribution of the Japanese final demand shifts on the total change in Chinese primary energy demand was 40 times larger than that of the Chinese final demand shifts on the primary energy requirements of Japan. The Japanese policy makers should concentrate on the energy impacts of the changes in the domestic production structure rather than the changes in the Chinese production structure.     

Dimensionality reduction in forecasting with temporal hierarchies

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.     

TEMPORAL AGGREGATION OF UNIVARIATE AND MULTIVARIATE TIME SERIES MODELS: A SURVEY

Abstract.  We present a unified and up-to-date overview of temporal aggregation techniques for univariate and multivariate time series models explaining in detail, although intuitively, the technical machinery behind the results. Some empirical applications illustrate the main issues.     

Has agricultural labor restructuring improved agricultural labor productivity in China? A decomposition approach

This paper presents a framework for analyzing the changes in agricultural labor productivity with regards to the structural, land intensity, and land productivity effects. This approach allows for the residual-free decomposition of data from different levels of aggregation. The logarithmic mean Divisia index was applied for the analysis and a data envelopment analysis model was constructed to identify potential gains in agricultural labor productivity due to the optimization of input use and output production. The proposed approach was applied to the case of China over the period of 1997–2017. Province-level data were used to identify the major driving factors behind agricultural labor productivity change. Land productivity change appeared to be the major source of agricultural labor productivity gains in China. The structural change was rather negligible, suggesting that the reallocation of the agricultural labor force did not add to the agricultural labor productivity growth in China. A frontier analysis indicated that agricultural labor productivity could increase by some 45% on average in case full technical efficiency is achieved.     

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 forecasting by estimating time series structural components across multiple frequencies

Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.     

Integrating decomposition approaches for the analysis of temporal changes in economic structure: an application to Chicago's economy from 1980 to 2000

This paper presents an attempt to integrate two flow decomposition methods to analyse temporal changes in a region's economic structure. The two methods of structural analysis are push–pull decomposition analysis and structural Q-analysis. Push–pull analysis presents a quasi-optimization decomposition of a set of matrices with actual intersectoral economic flows into a weighted set of matrices, while structural Q-analysis provides a form in which the structure of these decomposed flows can be considered. The paper provides an expository application to Chicago's economic structure over the period of 1980 to 2000, to reveal a complementary perspective of hollowing-out the production process in the Chicago economy that was identified in previous studies.     

MULTIPLICATIVE DECOMPOSITION AND INDEX NUMBER THEORY: AN EMPIRICAL APPLICATION OF THE SATO–VARTIA DECOMPOSITION

In de Boer (2008), additive decompositions of aggregate changes in a variable into its factors were considered. We proposed using the ‘ideal’ Montgomery decomposition, developed in index number theory as an alternative to the commonly used methods in structural decomposition analysis, and applied it to the example analyzed by Dietzenbacher and Los (1998) (D&L). In this paper we consider multiplicative decompositions and show that the method proposed by D&L of taking the geometric mean of all elementary decompositions is ‘ideal’. However, it requires the computation of an ever-increasing number of decompositions when the number of factors increases. As an alternative, we propose using the Sato–Vartia decomposition, which is also ‘ideal’, but requires the computation of only one decomposition. Application to the example of D&L reveals that the two methods yield results that are very close to each other.     

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.     

Competing Networks,Spatial and Industrial Concentration in the US Airline Industry

Abstract

The paper uses Gini decomposition analysis to evaluate changes in the spatial distribution and industry shares of total US air traffic, as well as analysing the decomposition components for individual airlines and airports for the period 1990–2002. The paper develops explicit relationships between two of the main decomposition schemes used in the income inequality literature and shows the insights that such analysis may provide for evaluation and examination of air transport networks and traffic distributions. A multi-dimensional Gini and its decomposition are derived using an adjustment method derived from the relationship between the two Gini decomposition schemes.     

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.     

Changes in China’s production-source CO2 emissions: insights from structural decomposition analysis and linkage analysis

This paper presents an input–output based methodology – structural decomposition analysis (SDA) plus linkage analysis, for identifying the key factors and sectors that affected production-source CO₂ emissions in China. The proposed methodology extends the SDA to account for the import substitution effect within an open economy such as China and incorporates the emission linkage by which the effect of the input mix on CO₂ emissions can be understood in depth. Empirical results indicate that, between 2005 and 2010, improving emission intensity and input intensity had helped to reduce CO₂ emissions; meanwhile, capital investment explained the majority of the increases in CO₂ emissions brought about by final demand, and import substitution was also observed to increase CO₂ emissions. Moreover, nine key emission sectors have been identified, and in this regard, domestic inputs became more CO₂-intensive in 2010 than it was in 2005.     

Mixing oil and water? Using hybrid input-output tables in a Structural decomposition analysis

Input-output studies of energy use frequently adopt the so-called hybrid table, where the rows corresponding to energy sectors are in energy units rather than in monetary units. However, we show that, in structural decomposition analyses, this hybrid approach may induce arbitrary results that depend on the choice of units, rather than on changes in economic structure. This is because an economically meaningless sum of monetary and energy units enters the calculations. Our proposed solution to this problem is based on using a sum of monetary units instead, thus avoiding this attempt to mix oil and water.     

The Extended Generalized Gamma Model and its Special Cases: Applications to Modeling Marriage Durations

The paper demonstrates how various parametric models for duration data such as the exponential, Weibull, and log-normal may be embedded in a single framework, and how such competing models may be assessed relative to a more comprehensive one. To illustrate the issues addressed, the survival patterns of marriages among 1203 Swedish men born 1936–1964 are studied by parametric and non-parametric survival methods. In particular, we study the sensitivity of model-choice with respect to level of aggregation of the time variable; and of covariate-effects with respect to the model chosen. In accordance with previous works our empirical results indicate that the choice of a parametric model for the duration variable is affected by the level of time aggregation. In contrast to previous results, however, our analysis shows that estimates of covariate effects are not always robust to distributional assumptions for the duration variable.     

Structural production layer decomposition: a new method to measure differences between MRIO databases for footprint assessments

Recent empirical assessments revealed that footprint indicators calculated with various multi-regional input–output (MRIO) databases deliver deviating results. In this paper, we propose a new method, called structural production layer decomposition (SPLD), which complements existing structural decomposition approaches. SPLD enables differentiating between effects stemming from specific parts in the technology matrix, e.g. trade blocks vs. domestic blocks, while still allowing to link the various effects to the total region footprint. Using the carbon footprint of the EU-28 in 2011 as an example, we analyse the differences between EXIOBASE, Eora, GTAP and WIOD. Identical environmental data are used across all MRIO databases. In all model comparisons, variations in domestic blocks have a more significant impact on the carbon footprint than variations in trade blocks. The results provide a wealth of information for MRIO developers and are relevant for policy makers designing climate policy measures targeted to specific stages along product supply chains.     

A general weighted least squares approach for the projection of input–output tables

In the context of input–output analysis, it is often necessary to update a matrix for a date when only the sum of its columns and rows are known. This projection problem is quite similar to temporal disaggregation. I borrow from this literature a class of solutions for which the exact result can be implemented without iteration. These solutions minimize the adjustment made to the out-of-date matrix and as such can be said optimal according to a chosen criteria. The framework I expose is flexible enough to encompass many of the existing methods and develop new ones. I propose one of such methods to project a matrix between two given benchmarks. I exemplify the technique on 35 years of input–output tables for France and show in particular that the issue of negative cells can be avoided.