Input–Output Model

PurposeThe purpose of this study is to examine the total economic impact of tourism at the regional level in Central Finland. This paper aims to clarify the extent to which tourism contributes to regional output, employment, income and taxes in tourism-related sectors.Design/methodology/approachThis study is based on a regional input–output analysis. The author calculates the effects of tourism on regional output, demand, wages, employment and national and regional taxes in Central Finland.FindingsThe author’s regional input–output analysis reveals that tourism has a substantial impact on production in Central Finland (including the direct and indirect effects of consumption by tourists in different sectors). Moreover, the effects of tourism expenditures on employment and residents’ incomes in tourism-related sectors in Central Finland are quite significant.Research limitations/implicationsMany limitations of this study stem from the assumptions of the input–output model. Other limitations relate to the analysis of the impacts of tourism on household taxes, savings, consumption and net income. Our study uses average figures, which may overstate the effect of tourist expenditures on taxes because tourism jobs are often low paying.Practical implicationsThe study yields results that can be used to frame regional policy. The results may be useful for policymakers in planning for tourist attractions. Furthermore, local authorities may use the results to guide decisions regarding infrastructure investments or improvements to the operating environment of tourism industries.Originality/valueMany studies analyse the economic impact of events at the regional level using input–output analysis. National-level tourism impact studies using the input–output technique have also been conducted. Studies focussed on the economic impact of tourism at the regional level typically examine the macroeconomic (income, output and employment) effects of tourism. Consequently, these studies have focussed on estimating output, employment and income multipliers (Mazumder et al., 2012). The author’s contribution is a regional input–output analysis of direct and indirect impacts of tourism expenditures on production, demand, wages, income and employment in the whole economy at the regional level (in Central Finland). The author also analyses the impacts of tourism on national and regional taxes. The results of this study could be used by planners and policymakers involved in regional planning and development.

The main problems of constructing multiregional input-output (IO) models used for the development of spatial and sectoral long-term national economy forecasts are caused by the absence of proper statistics. Therefore, it is necessary to make up the input-output tables (IOTs) based on a limited set of direct and indirect indicators that measure spatial patterns of production, consumption, capital formation, etc., sufficiently accurate for filling up multiregional IO models. The paper discusses Russian IOT spatial mapping problems and proposes an approach to partial automation of the procedures necessary for regional IOT construction (by federal districts). The regionalization was carried out using a static IO model with the bounding sum control. As a result, a set of consistent regional tables for 2015 was developed, with the eight IOT sum equalling Russia’s IOT. Based on the estimated regional IOTs, a static multiregional input-output model (OMIOM) across federal districts for 2015 was made up. The model allows us to proceed to the follow-up phase, i. e., developing a semi-dynamic model for long-term national economic projection computations

Input–output analysis involves all aspects of the national accounts related to goods and services, including expenditure aggregates. Input–output analysis provides the opportunity to reconcile supply and use of goods and services, as well as reconcile GDP and expenditure on GDP. One of the goals of this analysis is to eliminate the statistical discrepancy. This is also a requirement for deriving downstream input–output tables. Compiling regional input–output table, not only identify the quantity of products in inter-regional trade, but also determine the trade flows among departments. Moreover, in the inter-regional trade, it is also necessary to distinguish how much of intermediate inputs used in the production sector and how much used in final consumption. Therefore, compiling inter-regional input–output tables require high quality data, but so far, apart from a small part of developed countries, the vast majority of countries cannot meet the need of basic data requirements compiling inter-regional input–output tables in the existing statistical system, because of a lot of manpower and material resources to carry out surveys and collect data, which makes considerable difficult to compile inter-regional input–output table at present. It requires compiling inter-regional input–output tables when the data resources are relatively low.

This study aims to look at the estimated structural changes in the input output table for DKI Jakarta Province in 2024. Estimates for changes in the structure are obtained by making projections of the input-output table for DKI Jakarta Province in 2024 using the Dynamic Input Output (DIO) model, namely by embedding the econometric model into the input output model, using the 2016 based year, 52 economic sectors, and 22 data series from 2000 to 2021.DIO is a hybrid model that places more emphasis on non-survey aspects that combines macro-econometric equations with identity equations in input-output analysis. This model has many dynamic equations consisting of 425 equation model. The parameters value of the equation are estimated using a combination of three estimation methods: (1) Ordinary Least Square, (2) First Order of Autoregressive, and (3) Second Order of Autoregressive. The general balance value in the DIO model is formulated using the Gauss-Seidel iteration "RAS" method.The results of the study show that in 2024 the corporate services sector, the chemical, pharmaceutical and traditional medicine industry sector, and the transportation equipment industry sector are the dominant sectors in shaping changes in the structure of intermediate demand. The Construction Sector, the Corporate Services sector, and the transportation equipment industry sector are the dominant sectors shaping changes in the structure of intermediate input. The construction sector, the real estate sector, and the wholesale and retail trade sector, not cars and motorcycles, are the dominant sectors in shaping changes in the structure of final demand. For the gross value added component, the Wholesale and Retail Trade sector, Not Cars and Motorcycles, the Private Information and Communication sector, and the Financial Intermediary Services sector other than the Central Bank are the largest sectors in the formation of structural changes.

Applying physical input–output analysis to estimate land appropriation (ecological footprints) of international trade activities

The construction of regional input-output tables presents several challenges. Ideally, a detailed survey of the regional economy would be conducted to determine the strength of relationships among sectors. However, a major limitation arises due to the large volume of data required, which may not always be readily available and entails high costs and lengthy delays. One potential solution to this issue is the adaptation of national input-output coefficients using methods such as location quotients (LQs) or constrained matrix-balancing techniques. This paper focuses on location quotient methods and proposes a new formula for estimating regional input coefficients. This explicit formula enables the measurement of the impact of primary inputs (including imports) on the coefficient estimation. To validate the proposed method, the multi-regional input-output table for Korea in 2015 and the Figaro database were used. The results of the investigation confirm the effectiveness of the approach.

On the grounds of the long discussion in the literature for the construction of regional input–output (I/O) tables, the present study endeavors to identify the best performing method among the most applied location quotient based non-survey techniques. The analysis uses actual data—in particular, the EU input–output tables for the years 2010 and 2014—and by employing three different statistics, it compares the tables created by the quotients FLQ, AFLQ, SLQ, CILQ and RLQ. Comparison is made in both the technical coefficient tables and the Leontief ones. The results indicate that the AFLQ and FLQ provide better results for values of δ from 0.1 to 0.3, whereas for values of δ from 0.4 to 0.9, the results of the two quotients are not satisfactory. At the same time, using another statistic, the SLQ–CILQ technique seemed to yield satisfactory results. Similar results are also presented for the Leontief tables. Finally, using a linear regression model, the imports do not appear to be related to the value of δ in the FLQ.

Non-Survey Method is a method of indirectly estimating the input-output table in the region where the table is not prepared. This method is eventually to estimate the input-output table of a small region which is included in the large region of which input output table is available. The majority of Non Survey Methods which have been already proposed and researched use input output coefficient as an index which describes a industrial relationship. However, it has not been explained how tightthe relationships between input-output coefficients of the large region and the small regions are. In this study, input-output tables of a large region and small regions were compared in terms of their input-output coefficients and sharing coefficients. It were conducted for the input output tables in 1990 of the whole country, Kinki District, and four prefectures. The results of analyses admitted that the degree of correlation between input-output coefficients of these regions was high. On the other hand, the sharing coefficients which included in out flow (import, export, shipping in and out) did not show high correlation between these regions. However, the analyses excludingthe in-out flow showed very high correlation between these regions. From these analyses, it was understood that inner region flows in the large region might be the largest cause of this phenomena.

FROM the early days of input-output analysis its practitioners have recognized the importance of the aggregation problem and the fact that the results of the analysis depend upon the particular aggregation procedures used to combine industries. Recently, an increasing number of writers have become interested in theoretical as well as practical aspects of the problem.' The potential user of input-output analysis often needs to reduce a given table to smaller size. table say 200 by 200 or even 50 by 50is cumbersome for many purposes. The theorist finds it too detailed to understand, and the forecaster finds it too extensive to use in making further numerical computations. Of course some information is always lost by aggregation. But the considerations that lead to the computations of averages and index numbers also lead to the consolidation of inputoutput tables: ease of comprehension and economy of manipulation. The following question has been posed in the literature: 2 given a detailed input-output table in which many industries appear, and given the desire of the research worker to have only a few large industries to deal with, when is it possible to consolidate the small industries into industries, and still obtain the results of the analysis or predictions as would have been obtained by using the many small industries in the first place? The answer to this question has been shown to be almost never. The special cases where it is possible have indicated the type of conditions needed for what might be called good aggregation procedures meaning procedures which give approximately the same results as given by the detailed model. The need to develop criteria for good aggregation has been recognized, but few specific suggestions have been made.3 The purpose of this paper is to suggest specific criteria and procedures for good aggregation of a given input-output table. First, reasonable criteria are developed, based on the usual objectives of input-output analysis and on some special assumptions concerning final demand. Some approximations to these criteria are then proposed that are closely related to previous suggestions to aggregate on the basis of similarity of coefficients or homogeneity of input structure. The criteria are then illustrated and the usefulness of the approximations tested by numerical experiments involving a * The author gratefully acknowledges the assistance given him by the Social Science Research Council, which awarded him a Faculty Research Fellowship; the Bureau of Research of Kansas State College; Professor William A. Neiswanger, of the Department of Economics of the University of Illinois; Professor Thomas E. Hull, of the Department of Mathematics of the University of British Columbia; Patricia Kollman, graduate research assistant in the Department of Economics and Sociology of Kansas State College. 'J. B. Balderston and T. M. Whitin, in the Input-Output Model in Oskar Morgenstern, ed., Economic Activity (New York, I954), 79-I28; Tibor Barna, and in Input-Output Analysis in Tibor Barna, ed., The Structural Interdependence of the Economy (Proceedings of an International Conference on Input-Output Varenna, I954), ch. 7; John C. H. Fei, A Fundamental Theorem for the Problem of Input-Output Analysis, Econometrica, xxiv (October I956), 400-I2; M. Hatanaka, Note on Consolidation Within a Leontief System, Econometrica, xx (April 1952), 30I-303; Mathilda Holzman, of Classification and Aggregation in Wassily Leontief, ed., Studies in the Structure of the American Economy (New York, I953), ch. 9; John McCarthy, in the Open Leontief Model (paper presented to the Econometric Society at Cleveland, Ohio, December 1956); M. McManus, General Consistent in Leontief Models, Yorkshire Bulletin, vIII (June I956), 28-48; Edmond Malinvaud, Problems in Input-Output Models in Tibor Barna ed., op. cit., ch. 8; Oskar Morgenstern and Thomson M. Whitin, Comments in National Bureau of Economic Research ed., Input-Output Analysis: An Appraisal (Studies in Income and Wealth, Vol. i8, Princeton, I955), I28-35; Herbert A. Simon and Albert Ando, of Variables in Dynamic Systems (paper presented to the Econometric Society at Cleveland, Ohio, December I956); H. Theil, Linear in Input-Output Analysis, Econometrica, xxv (January I957), III-22. 2See works cited of Balderston and Whitin, Hatanaka, Malinvaud, McManus, Theil. 3Some preliminary suggestions have been made by McCarthy and by Simon and Ando in their papers cited.

The recent increasing attention to the economic and policy analysis of the food systems from international fora, public institutions and academia calls for the availability of information and data capable of informing about the interrelations across economic sectors and within value chains. The international policy agenda is pushing for a more effective application of measures at country and regional level in line with the recommendations of the 2030 Agenda and its Sustainable Development Goals, for which more systematic and integrated data about economic, social and environmental impacts of policies are requested. The Food Value Chain Domain recently published in FAOSTAT responds to this call. Its data and information shed light on the distribution of final domestic food expenditures across industries (Agriculture, Food Processing, Wholesale, Retail, Accommodations and Food Services) and primary factors (e.g.: Labour, Gross Operating Surplus) on the relative food value chain. The FAOSTAT Domain offers therefore robust and granular information on both the farm and the post-farm gate component of the Food Value Chain. The applied Global Food Dollar methodology, that FAO is contributing to upscale at global level, is based on Leontief decomposition approach on the Input-Output tables. Moreover, whenever the Input-Output table are not available, it is now possible to impute them from Supply-Use tables by applying a conversion methodology, developed by FAO in compliance with European (EUROSTAT), United Nations (UNSD) and international statistical standards as the System of National Accounts. This allows to extend the analysis to several African, Asian, and Latin American countries that produce on regular basis only Supply and Use Tables, and not Industry by industry Input Output Tables. The potential time and data coverage of the methodology is therefore significantly expanded. The aim of this paper is to describe the conceptual framework of the conversion methodology of Supply-Use Tables into Input-Output Tables of the Global Food Dollar methodology, and the potential implementation scope of these methodologies. Preliminary analytical findings of the applied methodologies are presented as well. The new methods and data presented in this paper, being based on data compliant with the International Statistical Standards, as the System of National Accounts, and therefore comparable across countries, associated to larger data availability, have the potential to effectively support food policies at international, regional and national level, as well as contribute to a decision making in line with the 2030 Agenda.

: The preparation of input output tables aims to present a picture of the interrelationships and interconnections among sector units in the economy as a whole, so that the input output model is a complete and comprehensive analysis tool. For the purposes of planning and evaluating the overall development outcomes of both national and smaller scale (district / city level), the regional development planning approach model can use an input-output analysis model. Through the analysis of the location quention can be seen the diversity of the economic base of a region's society. Against the areas analyzed can be classified sectors that are able to compete in their own territory or that can compete with other regions. Based on the analysis of 40 economic sectors in Bandung, as many as 16 sectors able to compete with other regions, and the rest can only compete in the area of Bandung . Keywords : O utput I nput M odel, Location Quotient, I ndustry C lassification, O utput, L abor

The nature and significance of input–output analysis or interindustry analysis, which was initiated by W. Leontief, can be interpreted in many different ways. It can be seen as the generalization of Keynesian multiplier theory, or as the modern version of Quesnay’s Tableau economique or Marx’s reproduction scheme, or as a degenerated linear programming model. From our point of view, however, it can be considered as an empirically oriented simplified version of the general equilibrium theory. While the importance of the analysis of interindustry input–output relations is duly emphasized, consumer demands are assumed to be given by outside information. The optimization and substitutability in production are assumed away by the use of the no-joint-output constant-coefficient production functions which are, however, numerically estimated by the data available from input–output tables. The input–output analysis can, therefore, provide numerical answers for a number of interesting problems of the general equilibrium of an economy.

1. Introduction In recent years there has been an increased interest in input-output analysis and its multiple applications to regional and interregional analysis. The purpose of this study is to provide a cursory description of the results of estimating trade coefficients and interregional multipliers by means of a statistical implementation of the Chenery--Moses model for Greece. In order to carry out the study in this field, the estimation of interregional trade coefficients by commodities is the fundamental work and is an important part for the compilation of interregional input-output table (Thalassinos 2007). But as most countries in the world, even developed countries, Greece does not have enough statistical data to build such matrix. The author has constructed by own forces an interregional model for the year 2010 of the Greek economy, which fill the gap of official regional statistics. The evaluation of the impacts of an exogenous change on a regional system would require a model that incorporates the industrial interrelations of a region with another region. The impacts were simulated considering three producing regions for the Greek economy, North Greece, Center Greece and Aegean Islands and Crete. The constructed interregional input-output matrix was used considering a set of three regions and ten aggregated sectors. From this matrix interregional-interdependence matrix was computing to show the direct and indirect relationships between producing sectors and regions. Finally, interregional multipliers were computed for each sector and region measured the impact on regions and on the economy as a whole. The above procedure revealed the major producing sector in the nations. In this regard the attempt of the present study to introduce regional input-output tables and regional technical coefficients would be beneficial to interregional model. The rest of the paper is organized as follows: In the second section a short overview of the literature review was provided. In section three we will describe the research methodology of interregional model. Section four is devoted to an empirical illustration. We apply the estimation procedures and data sources in the empirical implementation of the model. A concluding section finalized the paper. 2. Literature Review Isard (1951) was the first writer who developed the interregional input-output model with an analytical interregional trade coefficient matrix. This model requires an extent size of basic data. As a result one of the most powerful model was put forward by Chenery (1953) and then Moses (1955) independently and it is called Chenery-Moses model. The relative advantage of this model due to the fact that the technical input structure of production in each region and interregional trade structure of various products are separately stated. Therefore, the primary data needed to implement such a model, are usually less than the Isard model. Wassily Leontief in collaboration with Alan Strout evaluated a gravity trade model for different commodity shipments. New interregional input-output models have been constructed, Kilkenny and Rose (1995) and Hewings (1995). 3. The Model The Greek economy is divided into three regions: North, Middle and Aegean Islands and Crete. The model contains ten intermediate sectors after aggregation. The solution of the model can be obtained by using two sets of structural constants. The first set describes the structure of production or input requirements in each region. [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] A second category of structural constants is a set of trade coefficients. The trade coefficients may also be presented as a diagonal block matrix. From the above two sets of structural constants a new matrix is obtained named regional input-output coefficient matrix and is given by the matrix A. [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] In the case of Greece with three regions I, II, III the terms [A. …

Indonesian government's policy to promote downstream natural resources leads to tax incentives policy intended to encourage the downstream. This study focuses on corporate income tax incentives in the form of net income reduction (tax allowance according to Article 31A of the Income Tax Law) by 30 percent for 6 years or corporate income tax reduction (tax holiday according to Minister of Finance Regulation Number 130/PMK.010 /2020) by 100 percent or 50 percent. Despite the positive or negative impact of tax incentives enactment argued in the previous studies, this study will calculate the impact on the economy provided by the mentioned tax incentive schemes quantitatively and will analyze which tax incentive scheme gives the greater impact on the economy. The analysis was carried out by using input-output analysis method to calculate the impact from output approach on secondary data in the form of the latest input output table released by Badan Pusat Statistik i.e., 2016 input output table. The result of the study shows that tax incentive in the form of tax holiday with income tax reduction by 100 percent provides greater impact on the economy than the others do. The total impact is getting greater and shows comparable results as the tax incentive rate increases. However, the result of this study implies that the implementation of tax incentives still needs the right tax incentive policy design to gain the expected results.

This chapter reviews the available empirical evidence on the performance of Flegg’s location quotient (FLQ) and Kronenberg’s Cross-Hauling Adjusted Regionalization Method (CHARM), a relatively new non-survey technique that accounts explicitly for cross-hauling when constructing regional input–output tables. The performance of the FLQ and related formulae is evaluated using official data for 20 Finnish and 16 South Korean regions. The results confirm previous findings that the FLQ can produce far more accurate estimates of regional output multipliers than can simpler LQ-based formulae such as the SLQ and CILQ. We also explore possible ways of determining suitable values for the unknown parameter δ in the FLQ formula. Finally, we carry out a detailed investigation into an innovative new approach, Kowalewski’s sector-specific FLQ (SFLQ), but find that further work is required before the SFLQ can be recommended for routine use. Our assessment of CHARM employs official data for two contrasting regions: Uusimaa, the largest Finnish province, and the central Chinese province of Hubei. In the case of Uusimaa, detailed data for 26 regional sectors in 2002 are examined. CHARM is found to perform relatively well in terms of estimating exports, imports, the volume and balance of trade, and supply multipliers. The results are particularly encouraging for manufacturing sectors, which typically produce heterogeneous commodities and where cross-hauling is rife. As regards Hubei, CHARM is used to construct a detailed regional input–output table with 42 sectors, including 17 diverse types of manufacturing. The analysis makes use of official published national and regional data for 2007. However, in this instance, CHARM does not generate realistic estimates of Hubei’s sectoral exports, imports, volume of trade, and supply multipliers. This outcome is attributed to the difficulty of estimating regional technical coefficients, the heterogeneity of commodities and final demand for this data set. This problem is linked to the relatively small size of Hubei, which generates around 4% of China’s GDP. By contrast, Uusimaa produced 34.6% of Finland’s national output in 2002. These findings highlight the crucial importance, especially in relatively small regions, of adjusting for any known divergence between regional and national technologies, heterogeneity and final demand.