A Fuzzy clustering approach to the key sectors of the Spanish economy

The search for key sectors in an economy has been and still is one of the more recurrent themes in input–output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input–output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.     

Interinstitutional linkage analysis: a social accounting matrix multiplier approach for the Mexican economy

By analogy to inter-industry analysis, this paper addresses the Inter-institutional linkage analysis through an accounting multiplier approach and a social accounting matrix framework that has a flow of funds. Such an analysis provides a useful tool to identify and quantify structural features of an economy in terms of productive and financial backward and forward linkage indices, which are estimated from the accounting multipliers. As an empirical application of this analysis, we identify and measure the accounting multipliers and the respective linkage indices for the Mexican economy. The results may help to improve the quality of policy decisions by detecting key linkage and partial linkage accounts (production sectors or institutions) and by providing a better understanding of how an impact of an initial exogenous injection runs within a complex economic structure.     

Ranking sectors using fuzzy output multipliers

Using input–output analysis to model the effects of changes in industry final demands is fraught with problems, many of which relate to the fundamental limitations of the concomitant linear framework. A further issue concerns the accuracy of the results, a consequence of the uncertainty surrounding the values of multipliers. Such uncertainty can create problems where the values of output multipliers are used to inform resource directions. This paper utilizes (and develops) a fuzzy input–output model and investigates the ranking of industries based on fuzzy output multipliers. The non-triviality of the fuzzy model is exposited in a general problem, where imprecision is defined by a proportional level of imprecision (fuzziness) in the technical coefficients. Through a nascent method for ranking fuzzy numbers, comparisons are made between the fuzzy and more traditional (non-fuzzy) analysis.     

A model of the economy based on an interpolated growth matrix

This paper deals with direct and indirect growth rates of sectors and with the interpolated growth matrix of the economy. Its purpose is to determine the model based on the interpolated growth matrix which enables a new approach in the planning of the growth of the economy. The formulation of such a model is useful for the expression of specific structural problems as well as for the extrapolation of system elements into the future. It is characterized by the use of lower and upper projections of growth and mutual relations of sector growths as a basis for programming future sector changes. In contrast to methods in which the direct growth rates of sectors are given, in this case such trends are determined as endogenous variables. At the same time, we use a large amount of data by means of the indirect growth rates in order to describe more completely the dependences among the sectors.     

Enhancing the green efficiency of fundamental sectors in China’s industrial system: A spatial-temporal analysis

Due to sectoral interactions in the economy, the overall green efficiency (GE) of China’s industrial system relies heavily on fundamental sectors that contribute substantial energy to the supply chain production of other sectors but shows low sectoral GE. For the three fundamental sectors in China’s industrial systems, namely the smelting and pressing of nonferrous metals (SPNFM), the processing of petroleum, coking, and nuclear fuel (PPCNF); and the manufacturing of nonmetallic mineral products (MNMMP), we employed a three-stage data envelopment analysis (DEA) model to measure GE in the fundamental sectors in 30 provinces from 2010 to 2015. We then adopted a stochastic frontier analysis (SFA) model to evaluate the influence of technological innovation (TI), industrial agglomeration (IA), environmental regulation (ER), and intraindustry competition (IC). The results showed that GE in the three fundamental sectors varied spatially. Specifically, TI promoted GE in MNMMP, but the effect was not obvious in the SPNFM and PPCNF sectors. Moreover, ER had positive impacts on GE in the fundamental sectors. The effects of IA and IC on GE in the fundamental sectors varied in direction and strength. After eliminating the impacts of environmental effects and statistical noise, the real GE in the three fundamental sectors varied significantly compared to the comprehensive GE. Policy opportunities for enhancing GE in the fundamental sectors mainly lie in region-specific policy and regulations that avoid a “one-size-fits-all” governance approach.     

An input-output approach to the measurement of the information economy

This study presents a set of methods based on input-output analysis to measure the size and structure of a country's information economy. Published current national data bases do not identify even the broad contours of a country's information economy. The methodology which is stylised algebraically using input-output modelling unravels the latent information economy. Both the primary or marketed information activities and the non-marketed or secondary information activities are measured. The interdependencies of the primary and secondary information sectors with the non-information sectors such as agriculture and manufacturing are examined. Linkages and multipliers are also calculated using consistent approaches. Finally, the methodology is empirically validated using Australia as a case-study and the results are discussed.The author acknowledges with thanks useful comments by referees of the journal on an earlier draft of this paper. The author is also grateful to Professor D.Mc Lamberton of the University of Queensland and Professor M. Jussawalla of the East-West Center, Hawaii, for their encouragement and support given to this study.     

ANALYSING IMPLICATIONS OF LIMITED WATER AVAILABILITY FOR GREAT BARRIER REEF CATCHMENTS

Dependence on water is one of the factors that can determine regional vulnerability in Australia. Climate change is predicted to change rainfall patterns in the Great Barrier Reef (GBR) region, and scarce water resources have the potential to make regional Queensland economies increasingly vulnerable. Understanding which economic sectors depend on water as an input factor helps in understanding sectoral and regional vulnerability, and thus in guiding regional policy aimed at structural change. Using a regional Queensland Input–Output (IO) model, this paper integrates water consumption of the GBR region and then compares monetary IO multipliers with water consumption multipliers. We argue that these IO multipliers can inform regional decision makers about potential future regional vulnerability by taking into account limited water resources.     

Extracting cover sets from free fuzzy sorting data

Assignment of items to multiple categories requires suitable statistical methods. The present paper provides a new approach to solve this task. The concept of fuzzy sets is extended to cover sets (sets of overlapping clusters) in a simple manner introducing a vector of item membership sums. The application of the new concept is exemplified by modifying the fuzzy cluster analysis algorithm of Kaufman and Rousseeuw (Finding groups in data: an introduction to cluster analysis, 1990) to cover set cluster analysis appropriately. Wide equivalence of the numerical problems is demonstrated from Lagrange multipliers and Karush-Kuhn-Tucker conditions. Additionally, some extensions are introduced to the algorithm to improve its behavior for suboptimal large or small numbers of clusters. The adapted algorithm in most cases reproduces single sortings for correct numbers of clusters. Two applications to empirical free fuzzy sorting data sets are provided. Limitations of the algorithm are discussed.     

An Aggregated Regional Economic Input–Output Analysis within a Fuzzy Environment

Abstract

Imprecision within economic input–output frameworks can be a problem, particularly when investigating the characteristics of defined industry sectors. This study undertakes an aggregated regional economic input–output analysis, within a fuzzy environment. The dearth of studies encompassing a fuzzy approach within input–output analysis largely concerns the problematic representation of imprecision. Results pertaining to the fuzzy output multipliers associated with each sector group are described, including possibilistic mean and variance; also identified is a specific ranking of the sector groups. Where appropriate, results are compared with those from a Monte Carlo simulation based stochastic analysis.     

Inefficiency Spillovers in Five OECD Countries: An Interindustry Analysis

Empirical studies have widely demonstrated that real-world activities are rarely on their production frontier. Hence, an obvious concern arises towards the detection of inefficiencies affecting sectoral performances. The current literature and practice have widely explored the sources of inefficiency internal to decision-making units. This paper argues that a major role is played by external effects due to inefficiency spillovers propagating through interindustry transactions. In order to take this mechanism into account, the paper suggests assessing sectoral performances by a system approach that makes use of shadow prices of intermediate inputs. Our approach is able to disentangle sectoral inefficiencies into internal sectoral inefficiencies and inefficiencies imported from other sectors. The latter component is due to inefficiency spillovers that appear to be empirically relevant in all sectors of five OECD countries.     

Business Services as a Production Factor

Based on the assumption that business services are influential production factors, an empirical analysis of the relation between business service inputs and production output in user sectors is carried out. Danish data from 52 manufacturing and service sectors covering the period 1970-95 are applied in the analysis. A sectoral dimension is introduced by dividing the 52 sectors into eight sectoral groupings and then allowing the slopes of the different variables included in the model to vary between the eight groups. The empirical analysis offers some support to the assumption that business services can have an effect comparable to traditional production factors, although this only applies to service sectors, and, partly, to low-knowledge manufacturing.     

Measuring Credit Risk of a Bank's Corporate Loan Portfolio Using Advanced Internal Ratings Base Approach

Abstract

The use of technical and advanced approaches in the measurement of credit risk of banks' portfolios has nowadays become a very hot issue. The most recent technical report issued by the Basel Committee in May 2003 has concentrated heavily on the measurement of credit risk using either foundation or advanced Internal Ratings Base (IRB) approaches. This empirical research study attempts to measure credit risk of a bank's corporate loan portfolio, including firms from 10 different Turkish sectors. The monthly observations of the total amount of corporate loans and the total amount of corporate loans at default across various sectors are downloaded from the web page of Central Bank of Turkey (CBT) in a period of 1999-2002. This period covers 47 monthly observations since CBT has captured sectoral corporate loans beginning of 1999. Therefore, the observed sectoral default rates are needed to be simulated to obtain a nicely shaped distribution. Monte Carlo simulation is applied for 1,000 times. Based on the simulated default rates, the expected sectoral default rates are computed. Next, a credit quality rating scale is fitted into sectoral default rates distributions. Finally, the sectoral weights in the whole loan portfolio are multiplied by the expected sectoral default rates matrix, considering cross-sectoral correlations to get the total amount of the bank's credit risk and capital requirement. It is assumed that sectoral monthly default rates are a good representative of the default risk of a sample bank's corporate loan portfolio since no publicly available data on any particular bank's corporate loan portfolio composition exists. Nevertheless, this research may be a good application for measuring the credit risk of banks' corporate loan portfolios using advanced IRB approach.     

Impact assessment based sectoral balancing in public R&D project portfolio selection

Government funding agencies spend significant amounts of R&D funds through funding programs. While allocating funds among sectors or scientific disciplines, the Decision Maker (DM) wants to maximize the total impact by supporting R&D activities in those sectors with higher scientific, social and economic return. On the other hand, the DM wants to balance the funding budget over sectors or disciplines. In this study, we incorporate the results of “sectoral impact assessments” into the public R&D project portfolio selection (RDPPS) problem. We develop a two-stage model. In the first stage, we make sectoral budget allocation decisions to maximize the total impact of the budget while ensuring a relative balance among sectors. In the second stage, we maximize the total score of supported projects under allocated sectoral budgets. We illustrate the proposed approach on an example problem. We show the value of the proposed approach by comparing our results with alternative policy options.     

Conceptualizing and measuring “industry resilience”: Composite indicators for postshock industrial policy decision-making

Can resilience be a relevant concept for industrial policy? Resilience is usually described as the ability of a socioeconomic system to recover from unexpected shocks. While this concept has caught the attention of regional economics researchers seeking to understand the different patterns behind regional recovery after a disruption, it is increasingly recognized that resilience can have policy-relevant conceptual applications in many other regards. In this paper, we apply it to industries and define the “industry resilience” concept and measurements. Our contribution is twofold. Theoretically, we frame industry resilience as a useful conceptual framework for policy-making to support the selection of industrial policy targets that are more capable of recovering after unexpected shocks. In addition, industry resilience can mitigate government failures by supporting decision-makers in promoting both economically and socially sustainable structural change. Methodologically, building on post-2008 U.S. data, we develop two composite indicators (CIs) to separately analyze quantitative and qualitative postshock variations in sectoral employment. Such CIs support policy-makers in visualizing sectoral performances dynamically and multidimensionally and can be used to compare each sector both to other sectors and to its counterfactual. Our results highlight that sectors react heterogeneously to shocks. This points to the relevance of tailoring vertical industrial policies according to sector features and the aims of industrial policy initiatives.     

Sectoral co-movements of employment growth at regional level

This paper presents a comprehensive study of sectoral co-movements of employment growth in the entire Dutch economy. We construct different macro typologies according to manufacturing versus services, innovativeness, labour skills and position in the value chain, thus expanding the list of potential logics of sectoral interrelations. Using a vector autoregression model, we assess whether and how growth in a macro-sector, and in a given region, can predict growth in the same or other macro-sectors, in the same or in other regions. Our findings bring to light the inter-regional nature of intersectoral linkages, as well as the existence of complementarities between sectors. Supporting the growth of innovative firms could have positive externality effects, especially in the Knowledge-Intensive Business Services sector which is associated with the growth of the entire economy.     

A New Image of Classical Key Sector Analysis: Minimum Information Decomposition of the Leontief Inverse

This paper provides a theoretical framework for the Rasmussen-Hirschman key sector analysis based on a minimum information approach. This approach introduces a separation of information about regional economic structure into two parts. In the first part, knowledge about economic structure, extracted on the basis of minimum information included in the row and column multipliers, is extracted from the Leontief inverse matrix. The second part presents the specifics of synergetic interactions between different sectors of the economy. A corresponding intensity matrix represents the strength of the fields of influence of simultaneous multiple changes. From this formulation, a minimum information decomposition of the Leontief inverse is shown to exist and applied to Chinese input-output tables for 1987 and 1990.     

An International Comparison of Leontief Input-Output Coefficients and its Application to Structural Growth Patterns

Empirical sectoral analyses of a standard development pattern have focused upon changes in the value added, disregarding the structural changes in intermediate input. In a more comprehensive approach to the production function, the present paper analyses both sectoral intermediate inputs and the value added by using 45 input-output tables to discover a standard pattern of the changes in the input-output coefficients as an economy develops. The major findings are first a U-shape pattern of the average value added ratio and, conversely, an inverse U-shape pattern of the average intermediate input ratio. Secondly, as compared with principal input coefficients that are broadly stable, supplementary input coefficients exhibit the non-linear trends of an inverse U shape contributed by a rising trend in agriculture in the early stages, and a growing energy cost in most sectors, although this is partly offset by mild U shapes of transport and distribution costs. A similar inverse U-shape pattern is implied for the Leontief multiplier.     

Forecasting macroeconomic variables using disaggregate survey data

We construct factor models based on disaggregate survey data for forecasting national aggregate macroeconomic variables. Our methodology applies regional and sectoral factor models to Norges Bank’s regional survey and to the Swedish Business Tendency Survey. The analysis identifies which of the pieces of information extracted from the individual regions in Norges Bank’s survey and the sectors for the two surveys perform particularly well at forecasting different variables at various horizons. The results show that several factor models beat an autoregressive benchmark in forecasting inflation and the unemployment rate. However, the factor models are most successful at forecasting GDP growth. Forecast combinations using the past performances of regional and sectoral factor models yield the most accurate forecasts in the majority of the cases.     

一个改进的金融CGE模型及在分析存款准备金率中的应用

本文对传统的CGE模型进行了扩展和改进,通过加入商业银行和中央银行两个部门以及对储蓄—投资的转化机制较为细致和系统的设定,构建了一个金融可计算一般均衡分析框架。在此分析框架下,我们建立了金融—社会核算矩阵（FSAM）,并以此为数据基础,模拟了存款准备金率变动对我国宏观经济、部门经济的冲击效果。模拟结果表明,存款准备金率的调整在一定程度上能够调控宏观经济,并且下调准备金率的政策效果比上调准备金率的政策效果更为明显。从调整存款准备金率对部门产出的影响看,在居民的固定投资方向仅为建筑业的假设下,建筑业对存款准备金率的反应最为敏感,其次为服务业和工业,而农业部门的政策反应相对较小。     

A fuzzy-QFD approach to supplier selection

This article suggests a new method that transfers the house of quality (HOQ) approach typical of quality function deployment (QFD) problems to the supplier selection process. To test its efficacy, the method is applied to a supplier selection process for a medium-to-large industry that manufactures complete clutch couplings.The study starts by identifying the features that the purchased product should have (internal variables “WHAT”) in order to satisfy the company's needs, then it seeks to establish the relevant supplier assessment criteria (external variables “HOW”) in order to come up with a final ranking based on the fuzzy suitability index (FSI). The whole procedure was implemented using fuzzy numbers; the application of a fuzzy algorithm allowed the company to define by means of linguistic variables the relative importance of the “WHAT”, the “HOW”–“WHAT” correlation scores, the resulting weights of the “HOW” and the impact of each potential supplier.Special attention is paid to the various subjective assessments in the HOQ process, and symmetrical triangular fuzzy numbers are suggested to capture the vagueness in people's verbal assessments.