On the limited usability of the inoperability IO model*

This note shows that the inoperability input–output model (IIM) estimates only a part of mainly the negative indirect economic impacts of disasters, whereas it neglects most of the positive indirect impacts. This means that the IIM is not suited to prioritize industries for policy interventions that aim at reducing the negative impacts of such disasters. Besides, this note shows that the application of the IIM is problematic and tends to overestimate the subset of impacts that the model is able to quantify. Finally, we identify two approaches that much better capture the variety of different disaster impacts.     

EVALUATING UNCERTAINTY IN RISK-BASED INTERDEPENDENCY MODELING WITH INTERVAL ARITHMETIC

Several sources of uncertainty exist in the effort to quantify the efficacy of preparedness decision-making in interdependent systems. For the Inoperability Input–Output Model (IIM), a risk-based extension of the traditional Leontief model, which describes the propagation of inoperability throughout interconnected economic sectors, uncertainty is manifested in parameters describing the strength of interdependencies among sectors and in parameters describing the adverse impacts of a disruptive event, among others. As the model is used to evaluate preparedness options to reduce the impact of these disruptive events, such uncertainty can impact decision-making efforts. This paper introduces interval arithmetic as an approach for dealing with uncertainties in the IIM when probability distributions are not known and only variable bounds are available. Illustrative examples highlight the use of the approach as well as a means to improve the evaluation and comparison of risk management strategies in interdependent systems when only intervals are known.     

Using a Demand System to Estimate Extended Input–Output Multipliers

The interface between household income and expenditure has always been considered to be a key component in the construction of input–output models. However, it can be argued that households are too often treated as if they were just another in dustry in the input–output table. In this paper, we seek to address this problem by developing a new modelling framework in which a micro demand system is used to estim ate the relationship between income and expenditure. This demand system is conjoined with an input–output table for the UK economy, and the system as a whole is solved as a computable general equilibrium model. Comparisons are made between the Jacobian multipliers generated by this model and those derived from a more traditional input–output model in which the income-expenditure linkage is estimated using static coefficients.     

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.     

Queensland State Impact and Projection Model: The Household Sector

The Queensland Impact and Projection model is an integrated input–output econometric model of the Queensland economy. Its purpose is to complement a conventional input–output model for analyzing economic impacts at the state level. This paper provides an overview of some of the methods used to model the household sector in an input–output framework, before describing the approach taken in the Queensland model. Some results which support the empirical performance of the model are also provided. It is demonstrated that the integrated model is a viable alternative and improvement on the conventional input–output model. The results are consistent with the static input–output model and conform to expectations about how the economy responds in real impact situations.     

Choosing among alternative technologies: conditions for assuring the feasibility of an input–output database or scenario

The conceptual reach of the basic input–output modeling framework is substantially extended by new models that incorporate the economic logic of comparative advantage as the basis for the endogenous choice among alternative production technologies. This paper establishes procedures that define the conditions under which the database used for scenario analysis in this extended framework assures the existence of an economically feasible solution. We provide a criterion for structural feasibility, the property established by the Hawkins–Simon condition for the basic input–output model, and introduce a criterion for scale feasibility. The logic underlying the tests is illustrated by numerical examples based on the Rectangular Choice-of-Technology model and database. These procedures can be particularly useful for incorporating engineering and other technical sources of information into multi-regional input–output databases; they can also provide substantial underlying detail about individual technologies, sectors, and factors of production for both feasible and infeasible scenarios.     

Dynamic inoperability input-output modeling for economic losses estimation in industries during flooding

Among all natural disasters, flood stands as a recurrently happening disaster. It holds the aptitude to disrupt the organizations and to cause absenteeism of the workforce in industries. As the workforce is directly involve in the functioning of industries, work force absenteeism can cause reduced production and inoperability which outcomes in financial losses of industrial sectors. This research objects to estimate inoperability of industries due to distraction of workers by incorporating Dynamic Inoperability Input-Output Model (DIIM). Economic losses are determined from inoperability. Industrial area which is selected for the research includes local industries in Peshawar, Khyber-Pakhtunkhwa, Pakistan. Various industries are chosen and are ordered according to inoperability and economic losses. Industries having highest financial damages are: (i) Agriculture; (ii) Sugar mills; and (iii) Marble industry. These three industries hold liable for 40.6% of the overall financial losses of fifteen industries. Industries suffering from highest inoperability include (i) Sugar mills; (ii) Agriculture and (iii) Marble industry. A risk analysis frame work has also been developed to help industrial sectors to recover after a disaster. Besides, data of three different floods has also been taken for the above mentioned critical sectors to plot probability distributions for predicting economic losses of most frequent floods. Furthermore, this research methodology has been applied to flooding but it can be applied to any other disaster, everywhere.     

Disaggregating input–output tables in time: the temporal input–output framework

The input–output framework has evolved dramatically since its initial formulation. New analytical techniques and extensions have allowed a more comprehensive assessment of the economy and expanded its applicability. Nonetheless, the core of the framework has remained unchanged: an annually compiled input–output table, which conveys monetary flows between sectors in a region in a particular year. Hence, the technical coefficients derived from it are ‘average’ input compositions, neglecting fluctuations in production capacity, seasonality and temporal shocks within that period. This paper develops a consistent methodology to disaggregate the annual input–output table in its time dimension in order to estimate intra-year input–output matrices with distinct technical structures for a particular year. The main advantages in relation to the annual model are to allow seasonal effects to be studied within the input–output framework, to better understand the process of coefficient change and to offer a more comprehensive dynamic view of production.     

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.     

Inclusion of Environmental Goods in National Income Accounting

National income statistics take into account the values of market goods and completely ignore the environmental goods. The purpose of this paper is to suggest the integration of environmental goods into the framework of Leontiefs input–output analysis. It is suggested that environmental goods, such as clean air, fresh water, quiet, etc., be treated as sectors in the same way as market goods sectors. The final deliveries of the sectors may be positive, zero or negative, depending on the scarcity of the environ mental goods and political decisions. The paper gives a numerical illustration of how the environmental goods can be valued from an input–output analysis. It is recommended that a system should be devised to adjust the national income estimators of individual countries for the changes in the environmental goods caused by the economic activities of these countries.     

EXPANDING EXTRACTIONS

In this paper, we generalize hypothetical extraction techniques. We suggest that the effect of certain economic phenomena can be measured by removing them from an input–output (I–O) table and by rebalancing the set of I–O accounts. The difference between the two sets of accounts yields the phenomenon's effect (or importance). We suggest that the approach can be used to measure the effect of changes in intermediate output, which are otherwise not easily rationalized within a Leontief framework. Of course, it can also be used to estimate the possible effects of the shutdown of a particular establishment or other identifiable segment of an economy. We demonstrate some properties and potential of the approach using the annual 2006 US I–O accounts.     

EMPIRICAL ESTIMATION OF NON-LINEAR INPUT–OUTPUT MODELS: AN ENTROPY ECONOMETRICS APPROACH

Despite theoretical advances, non-linear input–output models have been empirically applied only to a limited extent. This is mainly due to the fact that the number of parameters to be estimated is much higher than the number of available data points. Taking advantage of the recent proliferation of input–output databases and by applying an estimation strategy that relies on entropy econometrics, this paper suggests a way to estimate the parameters that characterize non-linear relationships between inputs and output. This non-linear modelling allows for considering time-specific input coefficients, instead of fixed ones. Several types of multipliers can be derived from this non-linear model, and the proposed generalized maximum entropy (GME) estimator allows estimating them from time series or cross-sectional datasets of input–output tables. The proposed GME technique is illustrated by means of an empirical application that estimates the parameters that characterize a non-linear input–output model for the Spanish economy over the period 1995–2011.     

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.     

Compilation of Input–Output Data from the National Accounts

In this paper, a new method is presented to derive an input–output table from a system of make and use tables. The method, which we call ‘activity technology’, is mathematically equivalent to the well-known commodity technology, but chooses another unit, i.e. the activity. We will argue that, in the activity technology model, negatives can only arise from causes such as heterogeneity and errors in the data. To apply the activity technology, very detailed make and use matrices are required, as well as additional data on the input structures of certain activities. We will describe a method that can incorporate this additional data within the activity technology framework. Statistics Netherlands has adopted the method     

A POST-KEYNESIAN AGE MODEL TO FORECAST ENERGY DEMAND IN SPAIN

This paper develops an extended input–output model for the estimation of energy demand and related issues. It is built on the last Spanish Symmetric Input–Output Table (IOT, 2005). It has been tested for the period 2005–2008 and used for forecasting energy demand for the years 2009–2012 under different economic scenarios. The model shares some traits of the computable and applied general equilibrium models where quantity and price systems are interwoven. The differences lie in the theories explaining output and prices. Our quantity system is based on Keynes’ principle of effective demand (broad energy multipliers are derived). The price system is based on the classical (Sraffian) theory of prices of production, akin to post-Keynesian full-cost prices. The general price system can be manipulated to account for the specificities of energy prices. Historical trends of energy coefficients are computed by extrapolation of past IOTs and calibration.     

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.     

Some Applications of Input–Output Analysis in a Gold Mine

In this paper, some applications of a mixed physical-financial input–output model for a large gold mine in China are described. This mixed input–output model is a basic part of the mine's decision-making system, called an interactive and hierarchical multi-objective feedback decision-making system of the gold mine's production and management. The model is used for the following: (1) to analyze quantitatively how the changes in the economic, technical and geological factors affect the mine's production and management; (2) to search for a series of bottleneck components in the mine when the capacities of some components of the mine change; (3) to optimize mine planning with the help of mathematical programming (linear programming, 0–1 programming) and computer simulation.     

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.     

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.     

The Grad Field of Input–Output Models and the Nature of Coefficients

The grad field concept of input–output models is proposed to investigate the nature of parameter change on input–output models. Two kinds of grad field are explored. One is VB, which represents the multiplier potential and can be used to judge the influence of coeficient change on B; and the other is VX, which represents the gross output potential and can be used to judge the influence of the change of coefficients and final demand on X. The sensitivity and important coefficient concepts of input–output models are further examined in terms of VB and VX. Four kinds of sensitivity functions and importance functions are suggested.