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Structural Changes in Less Developed Areas: AnInput- Output FrameworkClaudia Ciobanu a , Konstadinos Mattas b & Dimitris Psaltopoulos ca Department of Agricultural Economics , Aristotle University of Thessaloniki , PO Box225, Thessaloniki, GR-54 006, Greeceb Department of Agricultural Economics , Aristotle University of Thessaloniki , PO Box225, Thessaloniki, GR-54 006, Greece E-mail:c Department of Economics , University of Patras , University Campus, Rio, Patras, GR-26 500, GreecePublished online: 18 Aug 2010.
To cite this article: Claudia Ciobanu , Konstadinos Mattas & Dimitris Psaltopoulos (2004) Structural Changes in LessDeveloped Areas: An Input- Output Framework, Regional Studies, 38:6, 603-614, DOI: 10.1080/003434042000240914
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Regional Studies, Vol. 38.6, pp. 603–614, August 2004
Structural Changes in Less Developed Areas:An Input–Output Framework
CLAUDIA CIOBANU*, KONSTADINOS MATTAS* andDIMITRIS PSALTOPOULOS†
*Department of Agricultural Economics, Aristotle University of Thessaloniki, PO Box 225, GR-54 006, Thessaloniki,Greece. Email: [email protected]
†Department of Economics, University of Patras, University Campus, Rio, GR-26 500, Patras, Greece
(Received July 2002: in revised form December 2003)
C C., M K. and P D. (2004) Structural changes in less developed areas: an input–outputframework, Regional Studies 38, 603–614. This paper uses an input–output framework to investigate structural changes withina time span of 17 years in the regional economy of East Macedonia and Thrace in North East Greece, which has gone throughconsiderable changes after Greece’s accession to the EU in 1981. The Generation of Regional Input–Output Tables procedureis followed for the construction of the relevant regional models. Then, changes in the structure of the regional economy areestimated using a series of indicators and structural decomposition analysis. Results reveal that between 1980 and 1997, theeconomy of the selected region has undergone significant transformations affecting both producing and consuming sectors.Also, final demand effects on gross production were more important than changes in technical coefficients, while employmentrequirements were significantly reduced. Finally, empirical findings provide an indication of the future trends in the structureof the regional economy.
Input–output analysis Changes Economic structure Region Indicators Agricultural sectors
C C., M K. et P D. (2004) Des changements structurels des zones en voie de developpement:un tableau d’echanges interindustriels, Regional Studies 38, 603–614. Cet article cherche a employer un tableau d’echangesinterindustriels afin d’examiner des changements structurels sur une periode de 17 annees pour ce qui est de l’economieregionale de la Macedoine de l’est et de la Thrace, situee dans le nord-est de la Grece et qui a subi de profondes transformationsdepuis l’entree de la Grece dans l’Ue en 1981. On suit les demarches d’usage quant a la production des tableaux d’echangesinterindustriels afin de construire des modeles regionaux correspondants. A partir d’une serie d’indicateurs et d’une anlyse pardecomposition structurelle, on estime les changements de la structure de l’economie regionale. Les resultats laissent voir que,de 1980 a 1997, l’economie de la region en question a subi de profondes transformations qui touchent non seulement lessecteurs qui produisent, mais aussi ceux qui consomment. En outre, les effets de la demande finale sur la production brute sesont averes plus importants que le changement des coefficients techniques, alors que les offres d’emploi se sont sensiblementreduites. Finalement, des preuves empiriques indiquent les tendances futures quant a la structure de l’economie regionale.
Tableau d’echanges interindustriels Changements Structure economique Region IndicateursSecteurs agricoles
C C., M K. und P D. (2004) Struktureller Wandel in weniger entwickelten Gebieten: einAufwand-Ertragsmodell, Regional Studies 38, 603–614. Dieser Aufsatz benutzt einen Aufwands-Ertragsmodellrahmen zurUntersuchung von strukturellem Wandel in einem Zeitraum von 17 Jahren in der Regionalwirtschaft von Ostmazedonien undThrakien in Nordostgriechenland, die sich seit dem Beitritt Griechenlands zur EU im Jahre 1981 betrachtlich gewandelt hat.Die Erstellung regionaler Aufswands-Ertragstabellen dient zur Konstruktion relevanter Regionalmodelle. Danach werdenVeranderungen in der Struktur der regionalen Wirtschaft mit Hilfe einer Indikatorenserie und einer Strukturzerlegungsanalyseberechnet. Die Ergebnisse zeigen, daß die Wirtschaft der betroffenen Region im Zeitraum 1980–97 signifikante Veranderungendurchgemacht hat, die sowohl die Produktions-wie auch die Verbrauchersektoren betreffen. Es zeigt sich auch, daß Endnachfra-geauswirkungen auf die Bruttoproduktion wichtiger waren als Umstellungen in technischen Koeffizienten, wahrend Bewer-bungen fur Arbeitsplatze deutlich zuruckgingen. Schließlich liefern empirisiche Befunde einen Anhalt fur zukunftige Tendenzenin der Struktur der regionalen Wirtschaft.
Anfwands-Ertragsanalyse Umstellungen Wirtschaftsstruktur Region IndikatorenLandwirtschaftliche Sektoren
0034-3404 print/1360-0591 online/04/060603-12 ©2004 Regional Studies Association DOI: 10.1080/0034340042000240914
http://www.regional-studies-assoc.ac.uk
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604 Claudia Ciobanu et al.
C C., M K. y P D. (2004) Cambios estructurales en areas menos desarrolladas: un marco deinput–output, Regional Studies 38, 603–614. Este artıculo utiliza un marco de input–output para investigar cambios estructuralesdurante un periodo de 17 anos en la economıa regional de Macedonia del Este y de Tracia en el Noroeste de Grecia, la cualha experimentado considerables cambios a partir de la entrada de Grecia en la Union Europea en 1981. Se sigue el procedimientode Generacion de Tablas Input–Output regionales para la elaboracion de los modelos regionales relevantes. A continuacion seestiman cambios en la estructura de la economıa regional utilizando una serie de indicadores y un analisis de descomposicionestructural. Los resultados muestran que, entre 1980 y 1997, la economıa de las regiones seleccionadas experimentotransformaciones significativas que afectaron tanto a los sectores de produccion como a los de consumo. Ademas, los efectosfinales de la demanda en la produccion en bruto fueron mas importantes que los cambios en los coeficientes tecnicos, mientrasque los requerimientos de empleo se redujeron significativamente. Por ultimo, los resultados empıricos proporcionan unindicativo sobre las futuras tendencias en la estructura de la economıa regional.
Analisis input–output Cambios Estructura economica Region Indicadores Sectores agrıcolas
JEL classifications: R10, R15, Q10, Q18
INTRODUCTION the structural analysis. Finally, concluding remarks arepresented in the fifth section.
Structural economic change, defined as temporalchanges in interactions among economic sectors( J et al., 1990), can be studied via the use of THE REGION UNDER STUDYvarious measures. The identification of methods that
Based on systematic analyses on the evolution ofestimate sectoral interdependence is an important issueregional inequalities in Greece since the 1980sin development planning, as policy-makers prefer to(M et al., 1996; G et al., 1997;‘target’ sectors with comparatively high interindustryP and S , 2000), the peripherallinks, which in turn can facilitate an extensive roundregion of EMT – located in the north-eastern part ofof economy-wide effects triggered by changes in finalGreece and consisting of five Prefectures (Drama,demand (D , 1974). A popular and effectiveKavala, Xanthi, Rodopi and Evros) – has been associ-way of analysing structural changes over time is the useated with a significant course of development andof an input–output (I–O) framework because of itsconvergence. The convergence process is particularlyuniquely rich representation of economic structureinteresting for less developed regions of Europe and(C, 1980; R and M , 1989).especially those classified as ‘Objective 1’ because itWithin this context, structural decomposition analysisillustrates the possibility of closing the gap between
provides an analytical tool for distinguishing amongprosperous and less-developed regions of the European
major sources of change in an economy (R and Union in a relatively short time, leading to the long-C, 1991; R and C, 1996). term policy objective of economic cohesion.1 Statistical
The present paper applies an I–O approach to the data show that the EMT gross regional product perintertemporal analysis of structural changes in the capita increased from 64.5% (1971) to 123.1% (1996)regional economy of East Macedonia and Thrace of the per capita Gross National Product, as the region(EMT) – a NUTS 2, less developed area of the developed more rapidly than any other region inEuropean Union, that is classified as ‘Objective 1’. To Greece. An important role of this trend seems toperform the relevant comparative analysis, I–O tables originate from structural policy funds (active for manyconstructed for 1980 and 1997 were used. In this years in the region), which have been spent on agricul-respect, one of the most widely used techniques for tural infrastructure, including land reclamation, roads,the construction of regional I–O tables, the ‘hybrid’ irrigation and support for cooperatives (KGeneration of Regional Input–Output Tables (GRIT) et al., 1999).procedure, is employed to generate the relevant Table 1 provides some recent indicators of the eco-regional models. Then, various indicators are used to nomic structure of the EMT region in comparisonmeasure the structural changes in the regional economy with Greece. The region possesses 10.6% of total Greekof EMT, which reveal the characteristics of the devel- agricultural area and 29.5% of its overall land andopment process in the region. Knowledge of these accounts for 5.3% of the national population. In termsmeasures is of interest to policy-makers in determining of employment, the region has a greater share ofthe development prospects of the region. employment in agriculture (40%) and a lower share of
The paper is organized as follows. A brief description employment in manufacturing (17.9%) and servicesof the region’s economy is presented in the second (42.1%) compared with the national level. The un-section, while the third section describes the measures employment rate of EMT (8.3%) is below the Greekemployed to investigate structural changes in an econ- average (9.6%). As EMT continues to modernize, it
may experience higher unemployment rates given theomy. The fourth section deals with the results of
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Structural Changes in Less Developed Areas: An Input–Output Framework 605
Table 1. Regional profile, 1997 in local agriculture (A , 1990). Althoughthe relative importance of the agricultural sector to the
East Macedoniaregional economy is expected to decline in the nearand Thrace Greecefuture, agriculture is expected to remain an important
Total area (km2) 14 145 131 957 source of income and employment (B ,Agricultural area (km2) 4186 39 422
1998).Population 561 632 10 498 838The region’s industrialization is quite recent, as mostEmployment 230 218 3 853 335
Agriculture (%) 40.0 19.8 enterprises were established after 1970. The main eco-Manufacture (%) 17.9 22.5 nomic stimulus originates from construction (dams forServices (%) 42.1 57.7 hydroelectric power, oil deposits, geothermal fields,Unemployment rate (%) 8.3 9.6
non-ferrous material, marble, sulphurous composites)Labour productivitya
and manufacturing (food and beverages, textiles, cloth-(European Unionó100) 56.0 72.0Gross Domestic Producta ing and footwear, furniture and metal products), which
(million drachmas) 1 189 857 26 554 500 accounts for 260 firms and a gross output of 263 036Agriculture (%) 19.0 8.5 million drachmas (Table 1).Manufacture (%) 24.5 21.0
The tertiary sector constitutes a significant source ofServices (%) 56.5 70.5economic activity for the region, with the majority ofGross Domestic Product per
capitaa (PPS)b (European firms specializing in wholesale and retail trade, tourism,Unionó100) 61.0 68.0 transport and communications. Other expanding
Firm size in industry, small service sectors in the region include banking, insurance,industry and construction
public administration, education and health.Number of manufacturingPublic investment for improvements in infrastructurefirms (more than ten
persons) 260 5407 and incentives for the establishment and modernizationTurnover (million drachmas) 263 036 6 315 029 of firms in all three sectors have supported the develop-
ment of the region in the last two decades. Since 1986,Source: E (various years); E C (1999).development frameworks that have been applied in theNotes: a Data refer to 1996.
bPPS, Purchasing Power Standard, is an artificial common region include the Integrated Mediterranean Pro-currency that equalizes the purchasing power of different grammes (1986–94), the Regional Developmentnational currencies. Programme and the Sectoral Programmes of the First
(1989–94) and Second (1994–99) Community SupportFramework, and the Community Initiatives such asENVIREG (1989–94), INTERREG I (1989–94)high share of population that is still employed in
agriculture (I and P , 2000). On and II (1994–99), and LEADER I and II. At present,regional policy support in EMT is mostly provided bythe other hand, the labour productivity in the region
(56%) is quite far from the national level (72%), which the Sectoral and Regional Development Programmesof the Third Community Support Framework, whilereflects the problematic performance of the region.
The region produces 4.5% of the Greek Gross the main development objective is to promote thediversification of the regional economy.Domestic Product (GDP). Nonetheless, with a per
capita GDP of about 61% of the European Unionaverage (compared with the Greek average of about68%), EMT is included among the poorest regions of METHODOLOGY AND DATAthe European Union. However, compared with other
Regional input–output modellingregions of Greece, it is classified somewhere in themiddle (K and S , 1998). In terms The formalized approaches to the construction of
regional I–O tables range from methods that include aof the sectoral distribution of EMT’s GDP, agricultureis considered as an important sector (19% of total GDP considerable survey element to methods based com-
pletely on published data. The terms ‘survey’ and ‘non-in 1996), while manufacturing and services account for24.5 and 56.5%, respectively, in the same year. The survey’ suggest the existence of two well-defined and
mutually exclusive groups, but in practice virtuallycorresponding national figures are 8.5, 21.0 and 70.5%,respectively. almost all I–O tables are ‘hybrid’ ones, constructed by
semi-survey techniques and employing primary andNine per cent of Greek agricultural output and 16%of the national production of cereals is produced in the secondary sources to a greater or a lesser extent
(R , 1983). Probably the most advanced of theseregion. Wheat, cotton, tobacco and tomato areamongst the main cultivations. The small size of the techniques is the GRIT technique originally
developed by J et al. (1979).agricultural holdings, the comparatively high cost ofagricultural inputs, the lack of technical support, the In the present paper, the GRIT technique was used
to generate regional I–O tables both for 1980 andlow level of farm skills and shortcomings in infra-structure are factors that contribute to low productivity 1997, which in turn were used to estimate various
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indicators of structural changes.2 The GRIT method change in an economy. The measures employed in thisstudy to investigate structural changes in the regionalestimates the regional intersectoral flows by applying
an employment-based Cross Industrial Location Quo- economy of EMT are described below.4
tient to corresponding elements of the national matrix.3
After deriving initial estimates of regional technicalInput–output multipliers
coefficients, the GRIT procedure allows for the inser-tion of superior information, as judged by the analyst, I–O multipliers measure the response of the economy
to an exogenous change in final demand. They areto replace mechanically derived estimates. The superiordata can come from survey data, published statistics and conceived as indicators of the importance of particular
sectors and measure the interdependence of the sectoralother sources (C and M, 1969;P and T, 1993; P- structure. In this paper, three of the most frequently
used multipliers are employed.5 , 1995).First, the output multiplier for a sector j is defined
as the total production in all sectors of the economyMeasurements of structural changes
necessary to satisfy a unit of final demand for sector j’soutput (M and B, 1985). It is estimated bySince the 1950s, I–O models have been extensively
used to compare the structure of production over time summing each column of the total requirements matrix:and across countries. Pioneer researchers in the fieldinclude L (1951), R (1956) and O.jó;
n
ió1zij jó1, 2, . . . , n (1)
C and W (1958). Interest in thestudy of structural change re-emerged in the 1980s where O.j is the output multiplier of sector j and zij is(R and M , 1989). the element of total requirements matrix.
In Greece, various economists have applied I–O Next, income and employment multipliers (M analysis for studies at national and regional levels. For and B, 1985) are estimated by dividing the directexample, S and M (1980) and and indirect income or employment effect by theS (1986) estimated and analysed a time corresponding direct effect. Thus, the direct incomeseries of I–O tables for 1958–77 and 1960–80, effect of sector j is defined as:respectively, for the Greek economy, while M
DIEjóHj/xj (2)(1989) described analytically the construction of thenational I–O table for 1980. M and S
where Hj is the income of sector j and xj is the total(1989) used I–O to estimate the contribution of theoutput of sector j.food sector to economic growth in Greece. Moreover,
The direct and indirect income effect of sector j isM and S (1991) suggested a newdefined as:approach – that of I–O elasticities that incorporate
both multiplier effects and the relative size of economicDIIEjó;
n
ió1zijDIEi (3)sectors – to identify the important economic sectors of
Greece. T and M (1995) exploredThe direct employment effect of sector j is defined as:the dynamics of the economic sectors in the Cretan
economy by comparing several impact indicators andDEEjóEj/xj (4)identified the regional key sectors. Using a regional
I–O model for Crete, the same authors investigated where Ej is the employment of sector j.the role of the tourism and agri-food sectors in the Finally, the direct and indirect employment effect isdevelopment prospects of the Cretan economy defined as:(T and M, 1999). In the sameframework, they also estimated the effects of Greece’s DIEEjó;
n
ió1zijDEEi (5)
accession to the European Union on its domesticeconomy (M and T, 1999). Inaddition, M et al. (1999) examined the dynamicsof the tobacco sector in the regional development of
Input–output elasticities
Although I–O multipliers are the most widely usedMacedonia and Thrace.Despite the above efforts, it seems there is a lack of measures for estimating the economy-wide impacts of
changes in final demand, they neglect the relative sizeliterature about changes in the structure of productionover time both at the national and regional levels. The of a sector in an economy. Therefore, M and
S (1991) have suggested I–O elasticities asonly relevant study on this subject is by S(1980), who examined structural changes in the Greek indicators for estimating sectoral potentials on the
growth of an economy. I–O elasticities reveal theeconomy between 1958 and 1970.The use of I–O models is a valuable tool for percentage change in total output, income or employ-
ment of the economy due to percentage changes inuncovering the important dimensions of structural
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Structural Changes in Less Developed Areas: An Input–Output Framework 607
the final demand of any sector. Such, elasticities provide output multipliers and focuses the analysis upon therelative influences of each sector on each other sector.better insights than multipliers of the impacts of sectoral
changes on the economy. Given two time periods, t and tò1, the correspond-ing transition matrices are assumed to be linked by theWithin the framework of I–O analysis, the output
elasticity for sector j is estimated as: formula:
Ktò1óCKt (10)OEjó;
n
ió1zij(yj/X) jó1, 2, . . . , n (6)
where Ktò1 and Kt are calculated according to equation(9), and C is the causative matrix, which is defined as
where zij is the element of the Leontief inverse,yj is the follows:final demand for the sector j and X is the total regional
CóKtò1Kñ1t (11)output.6
The corresponding income elasticity for sector j is Matrix C explains the change between the transitioncomputed as: matrices Kt and Ktò1 through the interpretation of the
elements and row sums of C.8 It is also called leftcausative matrix.9IEjó�;
n
ió1(Hi/xj)zij/(Hj/xj)](yj/X) jó1, 2, . . . , n
Matrix C may contain negative terms, where anegative Cik implies a reduction in sector i’s contri-(7)bution to sector j’s output multiplier due to the pres-ence of sector k. All column sums of C equal 1. Rowwhere Hi/xj is the direct income coefficient.sums less than 1 indicate smaller contributions to outputFinally, the employment elasticity of sector j is:multipliers, i.e. the corresponding sectors recordingsmaller impacts when final demands in other sectors
EEjó�;n
ió1(Ei/xj)zij/(Ej/xj)�(yj/X) jó1, 2, . . . , n change (and vice versa in the case of row sums greater
than 1).10 Negative deviations of the diagonal elements(8) of sectors from 1 imply decreased relative internaliza-
tion of their own final demand output impacts (andwhere Ei/xj is the direct employment coefficient. vice versa in the case of positive deviations of the
diagonal elements from 1).The causative matrix approach has the advantage ofCausative matrix
capturing both the direct changes in interactions andAs a final means of fulfilling the objective of this study, the relative changes due to the presence of otherthe causative matrix approach is used for measuring sectors.11
temporal changes. Applications of the causative matrixapproach to the analysis of structural changes at two
Decomposition of structural changedistinct points of time has been conducted in othercontexts, particularly in fields that employ Markov Differences in the structure of an economy betweenchain analysis. J et al. (1990) presented an two different points in time can be shown on pro-extension to I–O analysis of the causative matrix duction and employment data. More specifically, themethod to evaluate the change between two matrices. differences in output and employment levels and in theThis method identifies not only the contributions of structure of the economy can be depicted with theeconomic sectors with respect to the whole economy, help of the I–O model basic equation:but also focuses on the intersectoral interrelationships.
XóZy (12)Following the I–O notation specified above, thereare two possibilities: working on the technical coeffi- where all terms are as defined above.cients matrix, A, or the inverse matrix.7 J et al. If the difference in gross outputs between two(1990) choose the second and compute the transition different years, t and tò1, are expressed by equationmatrix (standardized Leontief inverse), K, by the (12), then following S (1989), the two generalformula: categories of structural change that determine them
can be identified as changes in technical coefficientsKóZMñ1 (9)and changes in final demand. Thus,
where Z was defined above and M is the diagonal *Xó(Ztò1ñZt)ytò1òZt(ytò1ñyt) (13)matrix whose elements Mjj equal the sum of the jthcolumn of Z. where �X is the difference in total outputs; and Zt and
Ztò1, and yt and ytò1 are the inverse matrices and theThe elements of each column of the Leontief inverseare normalized by their respective column sums, as the final demands, respectively, in two different years.12
In the first term on the right-hand side of equationtransition matrices must have column sums equal to 1.This process standardizes for changes in magnitudes of (13), the difference in the inverse matrices of input
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608 Claudia Ciobanu et al.
coefficients weighted with the tò1 level of final vegetables, fruits, livestock). In the aggregation process,some information is lost.demand, results in the gross production change between
Employment and income data for each sector (ast and tò1 that is attributed exclusively to changingdefined in the regional I–O tables) were obtained fromcoefficients given period tò1’s final demand. In thethe National Statistical Service of Greece (Labor Forcesecond term, the difference of final demand weightedSurvey Department), the Ministry of Agriculturewith the inverse input coefficients of the year t results(Department of Agricultural Statistics) and the Nationalin the gross production change between t and tò1Accounts of Greece (Division of Primary Sector andsolely attributable to changes in final demand.Industrial Survey Department). Also, output and inputstructure data for the agricultural subsectors were
Data obtained directly from the Ministry of Agriculture(Department of Agricultural Statistics), while the I–OThe estimates presented in this paper were derivedstructure of food and beverages as well as of tobaccofrom the regional I–O tables for EMT correspondingproducts was obtained by information acquired throughto 1980 and 1997. The 1980 I–O regional tables werebusiness surveys.computed from the 187-sector Greek table published
by M (1989). The basis of the 1997 regionalI–O tables was the latest available 1994 I–O table
RESULTSfor the Greek economy (N SS G, 1998), which records 59 sectors. This section gives a quantitative description of theSince the data contained therein relate to 1994, this economic structure of the region EMT for 1980–97,table was updated to 1997 (M et al., 1984). focusing on the 17 economic sectors. Results of output
The original tables were aggregated to 17 sectors multipliers for 1980 and 1997 are shown in the firstfollowing the Greek Standard Industrial Classification. and second columns, respectively, of Table 2. SectorsThe identity of the leading regional sectors was pre- that exhibit relatively high output multipliers in 1980served and the relatively unimportant manufacturing include food and beverages, livestock, construction,sectors were aggregated. For example, food and bever- tobacco products, and electricity and water. The outputages and tobacco products retained separate sectors multiplier for food and beverages indicates that anreflecting their relative importance, while other manu- increase of 1 million drachmas in the final demand offacturing sectors merged with other industries in view this sector calls forth an increase of the total regionalof their insignificant contribution. Taking into consid- output by 1.958 million drachmas. For 1997, fooderation that 40% of the working population of the and beverages, tobacco products, construction, otherregion is engaged in agriculture, it was decided to industries and vegetables are the sectors with the largest
output multipliers. The output multipliers for cerealsdisaggregate this sector into four subsectors (cereals,
Table 2. Sectoral multipliers for 1980 and 1997
Output Income Employment
Sectors 1980 1997 1980 1997 1980 1997
Cereals 1.045 (14) 1.303 (11) 1.033 (14) 1.286 (11) 1.034 (14) 1.246 (10)Vegetables 1.061 (13) 1.442 (5) 1.048 (13) 1.277 (12) 1.049 (13) 1.532 (5)Fruits 1.038 (15) 1.399 (7) 1.012 (16) 1.317 (9) 1.014 (16) 1.272 (9)Livestock 1.579 (2) 1.372 (8) 2.581 (3) 1.469 (7) 2.658 (2) 1.298 (8)Forestry 1.002 (17) 1.033 (16) 1.002 (17) 1.536 (6) 1.003 (17) 1.011 (17)Fishing 1.247 (9) 1.304 (10) 1.144 (10) 1.297 (10) 1.181 (9) 1.207 (12)Mining 1.026 (16) 1.023 (17) 1.025 (15) 1.145 (15) 1.023 (15) 1.231 (11)Food and beverages 1.958 (1) 1.998 (1) 3.155 (2) 2.856 (1) 3.324 (1) 2.494 (1)Tobacco products 1.352 (4) 1.598 (2) 3.380 (1) 2.347 (2) 2.235 (3) 2.210 (2)Other industries 1.259 (7) 1.478 (4) 1.326 (5) 2.298 (3) 1.373 (4) 1.128 (15)Electricity and water 1.292 (5) 1.235 (13) 1.331 (4) 1.196 (13) 1.324 (5) 1.298 (7))Construction 1.391 (3) 1.586 (3) 1.322 (6) 1.566 (4) 1.298 (6) 1.665 (4)Trade and hotels 1.275 (6) 1.436 (6) 1.289 (7) 1.149 (14) 1.264 (7) 1.195 (13)Transport and communication 1.189 (10) 1.317 (9) 1.166 (9) 1.375 (8) 1.163 (10) 1.375 (6)Finance 1.133 (11) 1.219 (14) 1.236 (8) 1.539 (5) 1.229 (8) 1.761 (3)Public administration and defence 1.094 (12) 1.300 (12) 1.076 (12) 1.058 (17) 1.075 (12) 1.073 (16)Other services 1.253 (8) 1.205 (15) 1.091 (11) 1.115 (16) 1.083 (11) 1.134 (14)
Notes: 1. Figures are input–output multipliers and indicate the impact of changes in final demand on output, income and employmentthroughout the economy.2. Corresponding sectoral rankings are in parentheses.
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Table 3. Sectoral elasticities for 1980 and 1997
Output Income Employment
Sectors 1980 1997 1980 1997 1980 1997
Cereals 0.0842 (6) 0.0141 (10) 0.0832 (7) 0.0150 (12) 0.0832 (7) 0.0136 (12)Vegetables 0.0075 (15) 0.0006 (15) 0.0078 (17) 0.0003 (16) 0.0079 (17) 0.0005 (17)Fruits 0.0089 (14) 0.0003 (17) 0.0094 (16) 0.0003 (17) 0.0096 (16) 0.0007 (16)Livestock 0.0994 (5) 0.0020 (13) 0.1928 (3) 0.0032 (14) 0.2003 (3) 0.0020 (13)Forestry 0.0105 (13) 0.0011 (14) 0.0107 (15) 0.0434 (9) 0.0107 (15) 0.0018 (14)Fishing 0.0066 (16) 0.0022 (12) 0.0144 (13) 0.7177 (1) 0.0166 (12) 0.0145 (11)Mining 0.0109 (12) 0.0712 (4) 0.0125 (14) 0.0821 (6) 0.0124 (14) 0.0864 (5)Food and beverages 0.1313 (3) 0.1573 (2) 0.5753 (2) 0.2122 (3) 0.6762 (1) 0.4507 (1)Tobacco products 0.0381 (9) 0.0039 (11) 0.6068 (1) 0.0074 (13) 0.5916 (2) 0.0273 (8)Other industries 0.1359 (2) 0.4347 (1) 0.1341 (5) 0.4047 (2) 0.1369 (5) 0.4228 (2)Electricity and water 0.0045 (17) 0.0005 (16) 0.0151 (12) 0.0005 (15) 0.0147 (13) 0.0009 (15)Construction 0.1774 (1) 0.1172 (3) 0.1624 (4) 0.1587 (4) 0.1588 (4) 0.1517 (3)Trade and hotels 0.1255 (4) 0.0241 (8) 0.1141 (6) 0.0199 (10) 0.1126 (6) 0.0198 (9)Transport and communication 0.0402 (8) 0.0344 (7) 0.0408 (9) 0.0476 (8) 0.0406 (9) 0.0450 (7)Finance 0.0274 (11) 0.0690 (5) 0.0364 (10) 0.0843 (5) 0.0358 (10) 0.0926 (4)Public administration and defence 0.0629 (7) 0.0172 (9) 0.0647 (8) 0.0181 (11) 0.0645 (8) 0.0181 (10)Other services 0.0287 (10) 0.0627 (6) 0.0271 (11) 0.0603 (7) 0.0269 (11) 0.0603 (6)
Notes: 1. Figures are input–output elasticities and reveal the percentage change in total output, income or employment of the economy dueto percentage changes in final demand of any sector.2. Corresponding sectoral rankings are in parentheses.
increased from 1.045 in 1980 to 1.303 in 1997, for Food and beverages and tobacco products industriesare ranked at the top of all considered sectors invegetables from 1.061 in 1980 to 1.442, and for fruits
from 1.038 to 1.399 over the same period. The live- terms of output, income and employment multipliers.Nonetheless, the I–O multipliers of other regionalstock sector reduced its relative influence on the econ-
omy, recording a multiplier of 1.372 in 1997 compared sectors increase or decrease over the period mentionedabove, thus implying an expansion or contraction of awith 1.579 in 1980. A general increase of the size of
output multipliers is also recorded for manufacturing particular sector’s economic activity as a result ofstructural change in intersectoral relationships.sectors. The service sectors have a similar tendency,
with the exception of other services, which recorded Further, the output, income and employmentelasticities computed for the 17 sectors of the regionala decrease on their relative impact on the economy.
The third and the forth columns of Table 2 show economy are reported in Table 3. For 1980, the con-struction sector seems to have the highest potential tothe sectoral income multipliers for 1980 and 1997,
respectively. For 1980, sectors with the highest income generate output in the regional economy. A 10%increase in the final demand of this sector leads to amultipliers include tobacco products, food and bever-
ages, livestock, electricity and water, and other indus- 1.774% increase in the output of the regional economy.In addition, the livestock and cereals sectors have atries, while for 1997 sectors with comparatively high
income multipliers include food and beverages, tobacco high output potential. Other sectors with large outputelasticities include other industries, food and beverages,products, other industries, construction, and finance.
The reason behind high-income multipliers in the and trade and hotels. Sectors ranked high based onoutput elasticities are also ranked high based on incomesectors of food and beverages and tobacco products is
not only their low direct income coefficients, but and employment elasticities, respectively. Although theranking changes slightly, all sectors ranked in the topalso their significant intersectoral linkages. With the
exception of livestock, agricultural sectors again five based on output elasticities follow the same patternin the case of income and employment generationrecorded an increase of income multipliers during
1980–97. (except for trade and hotels).For 1997, rankings based on output elasticitiesIn Table 2, the fifth and sixth columns show the
employment multipliers. Food and beverages and identify other industries, food and beverages, construc-tion, mining, and finance as the top five sectors fortobacco products are again the sectors with relatively
high values. The high indirect output requirements by output generation. As for income and employmentpotentials, these rankings recorded a rather smallthese sectors, combined with their low labour-output
ratios, contribute to these high employment multipliers. difference.During the period under study, the agriculturalAs regards the agricultural sectors, estimated multipliers
are relatively low, mostly due to the high direct employ- sectors became less relevant for the regional economyin terms of generating output, income and employmentment linkages of these sectors rather than to the indirect
employment effects, which are rather low. exhibiting reduced level of elasticities. On the other
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610 Claudia Ciobanu et al.
hand, as an evidence of the structural changes that Table 4. Sectoral structural changes for 1980–97 based onleft causative matrix resultshave occurred in the regional economy, the remaining
sectors of the local economy increased or decreased1997 1980
their relative importance with regard to their potential compared with 1980 compared with 1997to generate output, income or employment.
Diagonal Row Diagonal RowTo determine whether there is any pattern in the sizeSectors elements sums elements sumsof multipliers and elasticities, correlation coefficients
representing relationships among the various indicators Cereals 1.0390 1.1788 0.9625 0.8010Vegetables 1.1455 1.1021 0.8740 0.9132were estimated. These coefficients were computedFruits 1.0901 0.9131 0.9194 1.0885using sectoral rankings based on those indicators.Livestock 0.7061 0.8042 1.4154 1.2347Results suggest that the correlation among the regionalForestry 1.0304 1.0256 0.9704 0.9716
indices during 1980–97 is relatively weak or moderate. Fishing 1.0947 1.0903 0.9134 0.9164Hence, a weak correlation exists between income Mining 0.9963 0.9518 1.0038 1.0588
Food andelasticity and output, income and employment multi-beverages 0.9951 0.8844 1.0106 1.1009pliers (coefficients of 0.194, 0.172, and 0.243, respec-
Tobacco products 1.0846 1.0846 0.9220 0.9220tively) and between income elasticities (0.238). Also,Other industries 1.0530 0.7828 0.9513 1.7830
output elasticities demonstrate a fairly low correlation Electricity andwith output and employment multipliers (coefficients water 1.0010 0.8746 1.0001 1.0266
Construction 1.1397 1.1609 0.8778 0.8598of 0.294 and 0.069, respectively). A moderate correla-Trade and hotels 1.1100 1.0812 0.8997 0.7856tion is also observed between output multipliersTransport and(0.618), income multipliers (0.515), output elasticities
communication 1.0019 1.0999 0.8550 0.7751(0.689) and employment elasticities (0.588). In addi- Finance 1.0541 1.0022 0.9485 0.8764tion, income multipliers are moderately correlated with Public
administrationoutput and employment multipliers (coefficients ofand defence 1.1882 1.0610 0.8416 0.79740.596 and 0.525, respectively) as well as with income
Other services 0.9659 0.9026 1.0343 1.0891and output elasticities (0.500).The weak and moderate correlation among the
rank measurements based on multipliers and elasticitiesindicates that significant differences exist between the fruits, livestock, mining, food and beverages, other
industries, electricity and water, and other services.estimates used to assess the importance of sectors asstimulators of output, income and employment changes Thus, these sectors are more competitive in supplying
the requirements of sectors. This implies they have ain the economy of the region during 1980–97.Table 4 presents the results of the left causative greater contribution to output multipliers than do other
sectors. Finally, other sectors recorded row sums lessmatrix for each of the 17 sectors, both for 1997compared with 1980 and for 1980 compared with than unity, indicating that they have become less impor-
tant suppliers to sectors of the regional economy.1997, based on the deviations of their dominant diago-nal elements and sums of their respective row elements Table 5 shows the sources of changes in output and
employment between 1980 and 1997. The secondfrom unity.13 Briefly, results for 1997 compared with1980 show that the row sums exceed unity for cereals, column presents the percentage change in sectoral
gross output, solely attributed to changes in technicalvegetables, forestry, fishing, tobacco products, construc-tion, trade and hotels, transport and communication, coefficients. In more than half of the regional produc-
tion sectors, changing coefficients result in increases infinance and public administration, and defence, imply-ing their increasing role as suppliers. The absolute their output requirements. The largest positive impact
(over 20%) of changing technical relationships on grosschange in the total output multipliers of the specifiedsectors (Table 2) reveals that final demand in these output requirements is observed in livestock, mining,
electricity and water, and finance. The largest reduc-sectors generates strengthened total output impacts. Atthe same time, the row sums of the causative matrix tions (over 20%) in output requirements because of
technological change is recorded in cereals, fruits andelements corresponding to these sectors, suggest thatfinal demand in other sectors is overall generating other industries. The third column shows the percent-
age change in gross production that is exclusively dueincreased output impacts. Referring to cereals andvegetables, their diagonal elements exceed unity. to changes in final demand. The largest percentage
increase occurs in transport and communications andHence, their final demand impacts, relative to that ofother sectors, are increasingly internalized within the the lowest one in tobacco products. Comparing the
percentage changes due to final demand with thosesectors. Fruits and livestock recorded an opposite situa-tion: relative to the impacts on other sectors, the final attributed to changes in technical coefficients (ignoring
the signs), it is observed that in all sectors the impactdemand of these sectors creates a reduced output impacton their own sectors. The results for 1980 compared of final demand on gross production was larger than
that of changing coefficients.with 1997 indicate that the row sums exceed unity for
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Structural Changes in Less Developed Areas: An Input–Output Framework 611
Table 5. Decomposition of forces determining output and employment change between 1980 and 1997
Per cent change in gross output Per cent change in employment
Due to Due to Due totechnical Due to final technical employment
coefficients demand coefficients coefficientsSectors Total change change Total change change
Cereals 56.20 ñ39.01 95.21 ñ118.53 38.86 ñ157.39Vegetables 117.34 12.46 104.88 ñ137.01 ñ47.60 ñ89.41Fruits 74.86 ñ44.09 118.95 ñ83.88 ñ27.80 ñ56.08Livestock 152.74 20.08 132.66 ñ155.16 ñ90.15 ñ65.01Forestry 102.53 16.83 85.70 ñ56.67 8.79 ñ65.46Fishing 84.99 ñ7.24 92.23 ñ91.91 34.61 ñ126.52Mining 155.93 66.32 89.61 ñ101.31 ñ45.76 ñ55.55Food and beverages 94.97 ñ2.24 97.21 69.31 15.76 53.55Tobacco products 81.40 10.36 71.04 40.34 8.85 31.49Other industries 46.80 ñ25.17 71.97 47.20 ñ9.47 56.67Electricity and water 172.95 82.92 90.03 ñ17.40 64.16 ñ81.56Construction 123.44 ñ11.46 134.90 77.42 ñ12.43 89.85Trade and hotels 128.42 ñ9.10 137.52 ñ106.04 30.30 ñ136.34Transport and communication 197.74 ñ16.66 214.40 ñ7.33 69.74 ñ77.07Finance 207.17 74.29 132.88 80.00 39.69 40.31Public administration and defence 133.78 6.46 127.32 ñ45.81 20.77 ñ66.58Other services 155.16 8.69 146.47 ñ1.41 75.90 ñ77.31
The fifth column of Table 5 presents the changes in 1980 and 1997. In addition, the use of I–O elasticitiesdistinguished important economic sectors according toemployment that are solely due to changes in the
technical coefficients. Note that in more than half of their relative size. Moreover, the use of a causativematrix approach provided information on the structuralthe regional sectors, changes in technical coefficients
resulted in an increase of labour requirements. In the changes in the web of I–O relationships over time,while the structural decomposition distinguishedcase of cereals, fishing, electricity and water, trade and
hotels, transport and communication, finance, public between technical and final demand change in theregional economy.administration, defence, and other services, increases
in labour requirements exceeded 20%, while for the Estimated I–O sectoral multipliers strongly supportthe contention that both food and beverages andremaining sectors that recorded increases in labour
requirements, the changes were less than 20%. The tobacco products form an important part of the localeconomy in terms of output, income and employment.rest of the sectors recorded a reduction in labour
requirements, which is exclusively attributed to changes The examination of changes in the regional economyin technical coefficients. The changes in employment during 1980 and 1997 based on elasticities revealed acoefficients, shown in the sixth column, resulted in shift of the local economic activity from agriculturalconsiderable labour savings, with the exception of food sectors towards manufacturing sectors and services. Thisand beverages, tobacco products, other industries, and is an indication that their relative size, in terms of totalconstruction and finance. Generally, the effect of the sales to final demand, decreases according to theirchanged employment coefficients (ignoring the signs) contribution to a region’s growth potential. The weakon labour requirements was, with the exception of the and moderate correlation among the most of the rankedlivestock sector, higher than the effect of technical measurements used to assess output, income andchange. employment potentials is an indication that the eco-
nomic structure of production of the region hasrecorded significant dissimilarities during the period
CONCLUSIONS under study. The estimated causative matrices revealedthat during 1980–97 most of the sectors are character-This paper has analysed and quantified the changes inized by a greater endogenization of their own finalthe economic structure of the EMT region, a primarilydemand impacts and increased output impacts causedagricultural area in Greece, which (similar to otherfrom other sectors. The structural decomposition anal-parts of the country) has faced serious structural prob-ysis showed that the effects of final demand on grosslems since the accession of Greece into the EU.production were more important than those thatSectoral multipliers were used as quantitative mea-
sures of dynamics in the regional economy between occurred due to changes in the regional technical
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612 Claudia Ciobanu et al.
clearly the relative size of the purchasing sector may alsocoefficients. Regarding the employment requirements,be of crucial importance in determining the extent ofit was found that significant labour saving occurred asregional imports. The use of the Cross Industrial Loca-a result of combined effect of technical changes andtion Quotient, proposed by J and L (1987),changes in employment coefficients, with the last factorhelps to overcome this problem by taking into accountbeing the dominant one.the relative local importance of the purchasing sector asThe results suggest that all of the regional sectorswell as of the selling sector. Thus, it compares thehave experienced rather major structural changes dur-proportion of regional employment in selling sector i in
ing the period under study. In addition, findings indi- the nation to that of purchasing sector j.cate that the economy of EMT is still dependent on 4. The following notation is used throughout:agricultural activities, which are also strongly linked Aó{aij}óxij/xj is the direct requirements coefficientwith the rest of the regional economy. Thus, it is matrix, which is also called the technical coefficientsevident that the EU Common Agricultural Policy matrix, where xij is sector j’s direct input from sector i,reforms of the late 1980s and early 1990s have increased and xj is the total output of sector j; X is the gross
output; y is the final demand; and Zó{zij}ó(IñA)ñ1the need for modernization and better management inis the total requirements matrix, which is often referredthe agricultural sector of the region, but so far theseto as the Leontief inverse matrix. These satisfy the usualchanges seem to occur rather slowly. However, theI–O equation: Xó(IñA)ñ1y .forthcoming reform of the Common Agricultural Pol-
5. For additional details on I–O multipliers, see Micy that will also take into account EU enlargement(1965), R (1972) and P andand the expected competition from the new EU Mem-H (1982).ber States (especially in the case of cereals, vegetables
6. Gross output is defined as Xó&nió1xi, whereand fruits) clearly point to the need for necessary
xió&njó1zijyj is the output of sector i and zij is the
actions for the much needed improvement in agricul- element of the Leontief inverse, while yj is the finaltural productivity and competitiveness that will eventu- demand for sector j. The change in the total output ofally create growth and employment in the region, and sector i due to a unit change in the final demand ofgenerally contribute to the policy target of economic sector j is dxi/dyjózij. Summing over all sectors yieldscohesion of EMT with the rest of the EU. &n
ió1(dxi/dyj)ó&nió1zijób.j, where b.j is known as back-
On the other hand, the analysis of structural changes ward linkage used to assess output potentials of economicsectors (A and H, 1971; H andrevealed that the manufacturing and service sectorsS, 1985). However, the index b.j can misleadhave increased in importance in the regional economy.policy-makers about the importance of a sector becauseThese sectors are expected to play a significant role init does not take into account the relative size of thatregional economic development in the future. Hence,sector. Since elasticities take into account the relativethe formulation of a realistic developing plan to sustainsizes of economic sectors, they provide a more reliableeconomic growth in the region can be achieved byidentification of key sectors in an economy.combining the expansion of sectors with high I–O
7. J et al. (1990) focus upon changes in themultipliers and elasticities.Leontief inverse matrix to understand changes in inputintensities and output multipliers. The diagonally domi-nant Leontief inverse facilitates the interpretation of theAcknowledgements – The authors gratefully acknowledgecausative matrix elements.the helpful comments and suggestions of the Editors and two
8. A reverse comparison is possible, tò1 on t, insteadanonymous referees.of t on tò1. KtóCKtò1, where C is the causative matrixfor the reverse analysis, is denoted as follows:
NOTES CóKtKñ1tò1óCñ1.
9. J et al. (1990) suggested a right causative matrix,1. Influential work by B and S-I-MR, defined for sales coefficients and forward linkages:(1992, 1995) and S-I-M (1996a, b) on theKtò1óRKt, RóKñ1
t Ktò1. The left causative matrixconvergence properties of regions within variousdefined for sales coefficients makes less sense because itcountries (including regions within several Europeanfocuses on the inflow perspective, where sectors competecountries) has shown that poorer regions within a coun-with each other to supply a given sector. Therefore, ittry tend to grow faster than richer regions. The speed ofis appropriate for the study of changes in backwardconvergence, however, is not rapid (about 2% per year).linkages.2. For more detailed exposure on this technique, see J
10. The causative matrix can be defined as follows:and L (1987), T and M (1995)CóZtò1Mñ1
tò1MtZñ1t . It is therefore related to the ratioand L et al. (2000).
of output multipliers (Mñ1tò1Mt). If the output multipliers3. The GRIT technique, as originally developed by
do not change, this term will be identity. C then dependsJ et al. (1979), used the Simple Location Quotiententirely on the difference between Kt and Ktñ1.as a method of coefficient reduction. The quotient itself
11. J et al. (1990) have proposed the double causa-compares the relative importance of a sector regionallytive model where both effects described in the right andto its relative importance in the nation. A potentialleft matrices are explicit in the same model. However,drawback of the quotient approach is that only the size
of the selling sector is taken into account, although as they noted: ‘The use of a doubly causative model
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Structural Changes in Less Developed Areas: An Input–Output Framework 613
in equation (13) the inverse matrices and final demands arewould require both additional data and additional consid-eration of appropriate techniques’. An alternative was replaced by I–O matrices and employment coefficients,
respectively, in two different years (S , 1980).proposed by D M (2000): the bicausativemodel. De Mesnard proved that the model is deceptive 13. The two computations are conducted to provide a more
consistent check of sorts. That is, if the results arebecause the diagonal matrices are unidentified and theinterpretation of results is unclear. asymmetric in interpretation, that would throw doubt
upon the credibility of the approach.12. For decomposition of forces determining employment,
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