Sectoral linkages and industrial efficiency: a dilemma or a requisition in identifying development...

Ann Reg Sci (2010) 45:207–233DOI 10.1007/s00168-008-0280-5

ORIGINAL PAPER

Sectoral linkages and industrial efficiency: a dilemmaor a requisition in identifying development priorities?

Giannis Karagiannis · Vangelis Tzouvelekas

Received: 4 September 2007 / Accepted: 19 November 2008 / Published online: 10 December 2008© Springer-Verlag 2008

Abstract This paper attempts to provide an empirical evaluation of the potentialrelationship between sectoral linkages (backward and forward) and technical efficiencyusing the 1995 input–output tables for 14 EU countries. Sectoral technical efficiencyis obtained by the econometric estimation of a “multilateral” stochastic input distancefunction, while sectoral backward and forward linkage coefficients were computedusing the noncomplete hypothetical extraction method suggested by Dietzenbacherand Van der Linden (J Reg Sci 37:235–257, 1997). The empirical results suggest thatthe relationship between industrial technical efficiency and sectoral interdependenceis ambiguous. Although the majority of the countries, in the sample exhibit a negativerelationship, for some countries, the opposite is revealed. This implies that policy mak-ers should not be blindly based on sectoral interdependence in forming developmentplans, and they should take into consideration the efficiency of resource utilization ofindividual sectors. The combination of the information provided by both indices willassist in devising effective policy plans in stimulating internal economic growth.

JEL Classification O21 · O52 · D24 · D57

1 Introduction

In forming development policies, the assessment of both sectoral economic perfor-mance and production interdependence are very important issues. Even though sec-toral interdependence is one of the most important sources of economic expansion

G. KaragiannisDepartment of Economics, University of Macedonia, Thessaloníki, Greece

V. Tzouvelekas (B)Department of Economics, Faculty of Social Sciences, University of Crete,University Campus, 741 00 Rethymno, Crete, Greecee-mail: [email protected]

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208 G. Karagiannis, V. Tzouvelekas

in a competitive economy, efficiency is the most significant parameter of interestfor assessing the utilization of inputs. As it has been shown by several authors, thecontribution of a sector to national economy growth is directly related to its produc-tivity growth and hence efficiency improvement, relative size and intermediate inputsintensity (Domar 1961; Hulten 1978; Jorgenson et al. 2005). Rapidly growing, largeror more intermediate inputs intensive sectors tend to have ceteris paribus a greaterimpact. A sector that operates efficiently (i.e., producing as much output as the inputsat its disposal permit under the current state of technology) could stimulate long-rungrowth through its increased competitiveness. The higher the degree of efficiency, thelower will be the unit cost of production, and as a result, industries will be able tosupply their products at lower prices. Consequently, more efficient industries wouldhave better chances of surviving and prospering in the future than less efficient ones.On the other hand, the relative size of a sector, measured in terms of its share in GDP,is however related to its forward linkage coefficient, while intermediate inputs’ inten-sity is related to its backward linkage coefficient. These interrelationships are ratherclear as forward linkages are inversely related to the amount of sectoral output goingto final demand and backward linkages are related to the amount by which the outputof a sector depends on intermediate inputs. Thus, sectors with strong backward and/orweak forward linkages tend to have a greater impact on national economy growth.

The aim of this paper is to go beyond the analysis of sectoral contribution to nationaleconomy growth and to examine the relation between sectoral performance on the onehand and forward and backward linkages on the other hand. Technical efficiency is usedas a metric of sectoral performance, while forward and backward linkage coefficientsare computed using the noncomplete hypothetical extraction method (Dietzenbacherand Van der Linden 1997). The impact of backward and forward linkages on sectoralperformance is analyzed with the technical inefficiency effect model (Battese andCoelli 1995) using a “multilateral” input distance function for 14 EU countries. Ourstudy, though case-specific, can assist policy makers in two directions: first, by identi-fying correctly key economic sectors with important potentials to stimulate economicgrowth domestically and second, to reveal if a clear pattern emerges between the twoindices of sectoral performance.

Weak backward linkages imply low intermediate inputs intensity and thus valueadded is not much less than output produced. This means that the interdependencefrom the other sectors is relatively low and thus any ongoing changes in the perfor-mance of the sectors supplying the intermediate inputs are going to be of limitedimportance for buying sector’s efficiency. In this case, a statistically insignificant rela-tionship is expected between technical efficiency and the backward linkage coefficient.On the other hand, strong backward linkages, which imply that intermediate inputsare a significant part of output produced, means increased interdependence with thesuppliers of intermediate inputs, and thus any changes in these sectors are likely to betransmitted to the performance of the buying sector. This implies a statistically signif-icant relationship between technical efficiency and the backward linkage coefficient.However, if intermediate purchases are coming from sectors with growing efficiencylevels, the impact will be positive as the supplying inputs become more efficient orare of higher quality. The opposite would be true if intermediate purchases are comingfrom sectors with declining efficiency levels.

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Sectoral linkages and industrial efficiency 209

Strong forward linkages, on the other hand, imply that the supplying sector is rela-tively more important in the sense that it supports through its sales a large number ofother sectors in the economy. Because of this, the derived demand for supplying sec-tor’s output would be relatively inelastic, which, in turn, put less pressure to becomemore efficient. This pressure is more intense in cases that the intermediate sales of thesupplying sector are more essential for the buying sectors with respect to the remainingintermediate purchases within the economy. However, if the more important buyingsectors do not depend solely on one supplying sector for their production but on manyothers (i.e., ease of substitution), then the internal competition between selling sectorswill improve their own efficiency levels. Thus, strong forward linkages are expected tohave either a positive or a negative effect on sectoral technical efficiency depending onthe relative essentiality of intermediate sales compared with the remaining purchasesof the buying sectors.

The rest of this paper is organized as follows: the methodological framework forthe computation of backward and forward linkage coefficients using Dietzenbacherand Van der Linden (1997) noncomplete hypothetical extraction method is presentedin the next section. The use of input distance function in stochastic frontier modelingand the associated measurement of input technical efficiency is discussed in the thirdsection. A discussion of empirical findings and a comparison of sectoral technicalefficiency and linkage coefficients are given in the fifth section. Concluding remarksfollow in the last section.

2 Theoretical model

2.1 Noncomplete hypothetical extraction method and computation of backwardand forward linkage indicators

Since the pioneering work of Rasmussen (1956), Chenery and Watanabe (1958) andHirschman (1958), a number of studies employing input–output techniques have reliedon linkage analysis to describe the interdependence between economic sectors and toassist in the formulation of development strategies.1 Extending this part of the liter-ature and using Strassert (1968) original ideas on the hypothetical extraction of onesector from the economy, Dietzenbacher and Van der Linden (1997) provide a morerealistic approach for measuring both backward and forward linkage indicators.

The basic idea of Strassert (1968) original approach hinges on the hypotheticalextraction 2 of one sector from the economy and then the examination of the impactof this hypothetical extraction on other sectors of the economy. Under the usual

1 Through years, the methodological framework has been improved and expanded in several ways, andmany different analytical methods have been proposed for the measurement of interindustry linkage coeffi-cients (Jones 1976; Cella 1984; Heimler 1991; Sonis et al. 1995; Dietzenbacher and Van der Linden 1997;Oosterhaven and Stelder 2002). Miller and Blair (1985) present a detailed review of the developments inlinkage indicators since the original contributions, while Midmore et al. (2006) and Dietzenbacher (2005)provide a critical review on the alternative methods of evaluating interindustry linkages.2 It should be noted that before Strassert (1968) the notion of hypothetical extraction has been first usedby Miller (1966) in his empirical measurement of interregional interdependence.

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assumption of the input–output system, the reduced output vector obtained fromsolving the conventional demand-driven model y(− j) = [I(− j) − A(− j)]−1f(− j) isless than the original output vector y, where A(− j) is the (n − 1xn − 1) input coeffi-cient matrix, the elements of which measure the delivery of sector i to sector j per unitof buyer’s output, and f(− j) = { f1, . . . fn−1} is the (n − 1x1) vector of sectoral finaldemand. Then, the sum of the differential between the output vector excluding thej th element and the reduced output

∑n−1i=1 (yi − yi ), in the case that j = n, provide a

measure of the total (both backward and forward) linkage effect of the extracted sectorj on the economy.3 As noted by several authors, the Strassert’s hypothetical extractionmethod has two important shortcomings: first, it fails to distinguish between backwardand forward linkages, and second, the complete extraction of an entire sector from thesystem seems to be rather excessive (Meller and Marfán 1981; Cella 1984; Clements1990).

Recognizing the above shortcomings, Dietzenbacher and Van der Linden (1997)improved the methodological framework suggesting a noncomplete extractionmethod.4 Their approach is based on the assumption that backward linkages shouldreflect a sector’s interdependence on inputs that are produced within the economy.Therefore, only these intermediate inputs should be hypothetically eliminated in orderto measure the backward linkages.

Let us assume that the economy is divided into two separate blocks of industries,primary sectors (p) and manufacturing (m), and that we want to calculate the back-ward linkage for primary sectors. Primary sectors buy no intermediate inputs from theother production sectors within the economy; they have their origin outside of the sys-tem (i.e., imports). Therefore, the corresponding elements of the technical coefficientmatrix, A, are set equal to zero. Then the demand-driven system can be expressed as

[yp

ym

]=

[0 Apm

0 Amm

] [yp

ym

]+

[fp

fm

](1)

where yp is the vector of output for primary sectors, ym is the vector of output ofmanufacturing sectors, Apm is the matrix of technical coefficients for the demandof the products of primary sectors by manufacturing, Amm is the matrix of technicalcoefficients for self-consumption of manufacturing sectors, fp and fm are the finaldemand vectors of the primary and manufacturing sectors, respectively. Solving theabove system for the sectoral output we obtain

[yp

ym

]=

[I Apm (I − Amm)−1

0 (I − Amm)−1

] [fp

fm

](2)

3 In cases when a sector j purchases no inputs from any other sector in the economy, the total absolutebackward linkage is zero and the actual situation is exactly the same as the hypothesized one.4 Promising methodological improvements of the Strassert extraction method were also suggested by Cella(1984) and Sonis et al. (1995). However, the proposed backward and forward linkages are not symmetrical,and thus, are not comparable to the corresponding traditional linkage indicators of Chenery and Watanabe(1958) and Rasmussen (1956).

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The absolute backward linkage coefficient for the primary sectors is defined as thedifference between actual total output of the economy and that after the extractionof the primary sectors. The latter is less than the former due to the fact that primarysectors no longer depend on manufacturing with regard to their input requirements.This output decrease reflects the dependence of the primary sectors on manufacturing,as well as on themselves.

ABLDLp = c′

[yp − yp

ym − ym

](3)

or

ABLDLp = [

(� − I) + c′�mmAmp�]

fp

+ [(� − I) Apm�mm + c′�mmAmp�Apm�mm

]fm (4)

where �mm = (I − Amm)−1,� = (I − App − Apm�mmAmp)−1 and c is a (n × 1)

vector of ones. The magnitude of the above absolute backward linkage is determinedby two factors: first, the relative size of the sector, and second, its dependence perunit of output (i.e., its output multiplier). Since the primal concern is the sectoralinterdependence, Dietzenbacher and Van der Linden (1997) suggest normalizing theaforestated value by dividing the absolute figures by the value of sectoral output. Thisresults in the backward dependence of primary sectors on manufacturing as

BLDLp = ABLDL

p

yp(5)

In a similar manner, forward linkage indicators can be obtained using thesupply-driven Leontief input–output system (Dietzenbacher and Van der Linden 1997,pp. 247–252).5 These backward (forward) linkages actually examine the extent of theimpact derived from the hypothetical extraction of a sector on total output of the econ-omy. This includes the effect of all other sectors on total output through the feedbackin connection with the inputs (or sales) of the hypothetical extracted sector (Andre-osso-O’Callaghan and Yue 2000).

2.2 The input distance function and the measurement of sectoral technical efficiency

Quantitative measures of input technical efficiency can be obtained by econometricallyestimating an input distance function. This is feasible with only input and output quan-tity data, and the use of a single-equation estimation procedure as the input distance

5 The use of the supply-driven input–output system for the computation of forward linkages dispenses withthe restrictive assumption of the Hirschmanian forward linkages according to which the demand of everyother industry increases simultaneously (Augustinovics 1970). However, since the inverted matrix representsthe sectoral sales shares and not the technology of production, forward linkages computed using the supply-driven system are mutually inconsistent with backward linkages computed using the demand-driven system(Cella 1984; Heimler 1991). Dietzenbacher (1997) showed the plausibility of the supply-driven model andgave a sound interpretation in terms of linkages.

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function is agnostic with respect to the economic motivation of the decision makers(Grosskopf et al. 1995). Since industrial sectors exhibit different technological condi-tions, we extend Jorgenson and Nishimizu (1978) “bilateral” production structure toa “multilateral” context within a Cobb–Douglas input distance function (Karagianniset al. 2006), i.e.,

ln DIik

(y, x, DS

)= β0 + βyi ln yik +

L∑l=1

βli ln xlik (6)

where i = 1, . . . , N are the industrial sectors, k = 1, . . . , K are the countries inthe sample, l = 1, . . . , L are the inputs used by industrial sectors, βyi = βy DS

i andβli = βl DS

i with DS being a dummy variable indicating the N industrial sectors, i.e.,DS

i = 1 for sector i and DSi = 0 for every other sector j �= i. The above specifica-

tion considers the data on input and sectoral gross output for each one of the indus-trial sectors as a separate set of observations, which are assumed to be generated by“multilateral” models of production. Hence, the presence of DS as an argument in theinput distance function allows for different production technologies to be assigned tothe different industrial sectors. In other words, we assume that the same sectors acrossEU countries are utilizing the same production technology. This assumption is not toorestrictive given that all EU countries in the sample share the same market conditionswith free factor mobility across national borders. Thus, besides possible differences intheir factor endowments, the production technology is rather common across sectorsin the EU.6

The required regularity conditions include homogeneity of degree one in inputs.These imply the following restriction on the parameters of (6):

∑l βli = 1, ∀i. The

homogeneity restriction may also be imposed by dividing the left-hand side and allinput quantities in the right-hand side of (6) by the quantity of any input used asnumeraire. Given the imposition of linear homogeneity, to obtain an estimable formof the input distance function, rewrite (6) as − ln xhik = f (ln y, ln(xik/xhik)) −ln DI

ik(y, x, DS), where h is the numeraire input and f (·) is the right-hand side of(6). Since ln DI

ik(y, x, DS) is unobservable and given that ln DIik(y, x, DS) ≤ 0, we

can make the following replacement: ln DIik(y, x, DS) = −uik, where uik is a one-

side, nonnegative, error term representing the stochastic shortfall of the i th industrialsector’s output from its own production frontier. Then, a stochastic version of the“multilateral” input distance function may be written as

− ln xhik = f (ln y, ln (xik/xhik)) − uik + wik (7)

where wik depicts a symmetric and normally distributed error term, wik ∼ N (0, σ 2w)

(i.e., statistical noise), which represents those factors that cannot be controlled byproducers, left-out explanatory variables and measurement errors in the dependent

6 A striking example is agriculture, where the application of common agricultural policy in the last 40 yearshave resulted in a homogeneous production structure across EU member states (Fennel 1997).

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variable. It is also assumed that vik and uik are independently distributed from eachother.

Since our main purpose is to explore the possible relationship between industriallinkages and input technical efficiency, we assume that the mean (i.e., μ) of the pre-truncated distribution of uik depends on forward (FLDL) and backward (BLDL) linkageindicators. Furthermore, to provide a possible explanation of efficiency differentialsacross EU countries, we have also included in the mean of uik the following variables:7

(a) Barro and Lee (1993) education index as a proxy of human capital differencesacross EU countries (EDU); (b) a Gini coefficient as a measure of income inequalityin each country in the sample (INQ); (c) the share of high-tech exports to total exportsof each sector in every country (HTC); (d) the total R&D expenditures in each sectormeasured as the purchasing power standard per capita at constant 1995 prices (RD);(e) the share of public expenditures for education per pupil (IPE). This assumptionresults in Battese and Coelli (1995) formulation of the technical inefficiency effectsmodel. Further, to account for intercountry differences in the relationship betweensectoral linkages and input technical inefficiency, we have extended the multilateralnotion explained previously to the inefficiency effects model, i.e.,8

μik = δo + δBLkBLDLik + δFLkFLDL

ik + δEDUEDUk

+δINQINQk + δHTCHTCik + δRDRDik + δIPEIPEk + ωik (8)

where δ0, δEDU, δINQ, δHTC, δRD, δIPE, δBLk = δBL DCk and δFLk = δFL DC

k are theparameters to be estimated, DC is a dummy variable indicating the K countries in thesample, i.e., DC

k = 1 for country k and DCk = 0 for every other country, and ωik is

independently and identically distributed with N (0, σ 2u ) random variable truncated at

−μik from below. The latter implies that uik ∼ N (μik, σ2u ) is truncated at zero from

below. The specification stated before implies that the means of the uik are differentbut the variances, σ 2

u , are assumed to be the same. Hence, the presence of DC in thetechnical inefficiency effects model allows for different relationship between sectorallinkages and technical inefficiency among countries in the sample. In other words,it is assumed that different sectors in the same countries have identical relationshipbetween linkage coefficients and efficiency levels.9

The specification stated before has the advantage of allowing a direct statisticaltest of the potential relationship between sectoral linkages and input technical ineffi-ciency. Specifically, if δBLk = δFLk = 0 ∀k, then both linkage indicators do not haveany effect on sectoral input technical efficiency. If, however, δBLk = 0 or δFLk = 0

7 We have tried to include these variables into the deterministic kernel of (7) to extend our model to anonneutral stochastic frontier framework (Huang and Liu 1994). However, statistical testing rejected thatparticular specification of the frontier model.8 Since sectoral dummies have been included in (7), it is worthless to do the same in the inefficiency effectsmodel, as input technical efficiency is measured relative to the sectoral specific technology. Instead, it isimportant to find out any spatial differences that may reflect the peculiarities of any one of the 14 EUeconomies considered.9 Although this assumption may been seen as too restrictive we have tried to fit a Cobb–Douglas inputdistance function allowing for intersectoral differences in the relationship between linkage indicators andtechnical efficiency, which unfortunately did not converge.

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∀k, backward or forward linkage indicators, respectively, does not affect industrialefficiency. Further, if δBLk = δBL ∀k or δFLk = δFL ∀k, then there is a commonpattern among countries concerning the relationship between technical inefficiencyand industrial interdependence. Finally, if δEDU = δINQ = δHTC = δRD = δIPE, thenthe country- and sectoral-specific variables included in (7) do not have an effect ontechnical efficiency differentials across countries and sectors in the sample.10

3 Data and empirical results

For the empirical assessment of the relationship between industrial efficiency andsectoral linkages, we have used the 1995 input–output tables for 14 EU countries pub-lished by Eurostat.11 They have been constructed from the internationally harmonizedinput–output tables and include the following countries: Austria, Belgium, Germany,Denmark, Spain, Finland, France, Ireland, Italy, Luxemburg, Netherlands, Portugal,Sweden and UK. The 14 national tables report domestic transactions in producers’prices and imports in ex-custom prices, and include 24 sectors (Table 1).

The “multilateral” stochastic input distance function in (7) includes one output andfour inputs. The output is the total domestic sectoral output measured as the sumof production sold to other sectors within the economy as intermediate goods, tofinal consumption, to fixed capital formation, and to exports. The input measures areas follows: (a) the total number of employees in each sector, obtained from Eurostat,augmented by the Barro and Lee (1993) education index to account for inter-country differences in human capital;12 (b) the total sectoral intermediate consump-tion purchased domestically, obtained from the national input–output tables; (c) thetotal value of sectoral capital stock, also published by Eurostat; (d) the total sectoralimports obtained from the national input–output tables.13 In the technical inefficiencyeffects model in (8), we have included the corresponding backward and forward link-age indicators computed by applying the Dietzenbacher and Van der Linden (1997)noncomplete hypothetical extraction method as well as the country- and sectoral-specific characteristics presented in the previous section. Summary statistics of allvariables used are presented in Table 2.

Both backward and forward linkage indicators are presented in Tables 3a and bfor all 14 EU countries. To be comparable across EU countries in the sample they

10 These tests can be conducted using the generalized likelihood-ratio test statistic, i.e., λ = −2{ln L(H0)−ln L(H1)}, where L(H0) and L(H1) denote the values of the likelihood function under the null (H0) andthe alternative (H1) hypothesis, respectively.11 The input–output accounts are based on an update of the 1990 benchmark accounts, and they reflect therecent comprehensive revision of the national income and product accounts.12 Following Griliches (1963), both the quantity and the quality dimensions of labor can be combined intoone variable before estimating the model. The product of the total number of employees and the educationindex gives the relevant variable. In turn, this is equivalent to including the education index multiplicativelywith the total number of employees in (7), which implies that the coefficient of the education index shouldbe equal to the coefficient of the total number of employees.13 Imports were treated as an input in the production process, since they are often used as raw materi-als or intermediate goods, while even in the case that refers to finished products they have value addeddomestically through transportation and retailing costs (Kohli 1991).

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Table 1 Sectoral classification of input–output tables in the 14 EU countries

Sectoral classification List of countries

01 Agriculture, forestry and fishery products Austria (AU)

06 Fuel and power products Belgium (BE)

13 Ferrous and nonferrous ores and metals Germany (DE)

15 Nonmetallic mineral products Denmark (DK)

17 Chemical products Spain (SP)

19 Metal products except machinery Finland (FI)

21 Agricultural and industrial machinery France (FR)

23 Office and data-processing machines Ireland (IR)

25 Electrical goods Italy (IT)

28 Transport equipment Luxemburg (LU)

36 Food, beverages, tobacco Netherlands (NL)

42 Textiles and clothing, leather and footwear Portugal (PO)

47 Paper and printing products Sweden (SW)

49 Rubber and plastic products United Kingdom (UK)

51 Other manufacturing products

53 Building and construction

56 Recovery, repair services, wholesale, retail

59 Lodging and catering services

61 Inland transport services

63 Maritime and air transport services

65 Auxiliary transport services

67 Communication services

69 Services of credit and insurance institutions

74&86 Other market and nonmarket services

have been normalized around the mean linkage coefficient in each country.14 Con-cerning first backward linkages, the variation of “key” economic sectors across EUcountries reflects the different national economic structure. An important feature is thefact that average backward linkage indicators, despite of the significant intercountrydifferences across sectors, are very close to each other. Specifically, the average back-ward linkage indicator for all 14 EU countries is 0.935 ranging from a minimum of0.730 in Luxemburg to a maximum of 1.054 in Netherlands. The standard deviationof these figures is only 0.094, which can be explained by the fact that the linkagesare mostly determined by the technological conditions in each country rather than bycountry-specific factors that are outside the economic system.

Concerning the key sectors in each of the 14 EU countries, there is a relative dis-persion across them. The only notable exception is the food, beverage and tobaccoindustry (36), which is ranked high in all countries except of Portugal.15 Specifically,

14 This point has been raised by one of the referees.15 Our empirical findings on food manufacturing are in accordance with those reported by Dietzenbacherand Van der Linden (1997) in their sample of eight EU countries.

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Table 2 Summary statistics of the variables

Countries Outputa Capitala Laborb Importsa Inter. inputsa

AU 549,480 817,799 3,408 73,168 307,417

BE 719,565 664,078 3,700 141,103 374,241

DE 5,972,200 9,073,655 28,482 426,505 3,711,288

DK 400,698 626,153 2,510 40,243 227,980

SP 1,265,889 1,747,749 12,759 100,537 738,161

FI 298,767 536,558 1,945 27,402 175,115

FR 3,419,728 4,389,252 22,326 249,709 1,995,229

IR 172,689 156,521 1,258 30,734 94,203

IT 2,524,378 3,485,571 22,165 195,820 1,498,732

LU 49,581 46,754 213 11,344 25,637

NL 1,041,434 1,194,474 5,434 139,812 602,508

PO 271,973 293,689 4,407 29,868 165,269

SW 563,582 682,747 4,101 58,771 326,237

UK 2,860,484 3,302,880 25,634 252,976 1,773,227

Education indexc Inequality indexd High-tech exportse R&D expensesf IPEg

AU 8.75 28.9 11.88 305.9 0.305

BE 8.37 26.6 7.85 315.4 0.275

DE 8.48 32.2 14.19 414.6 0.287

DK 9.13 33.2 13.88 352.6 0.405

SP 7.15 25.9 5.94 106.8 0.216

FI 9.80 26.1 20.69 358.2 0.284

FR 8.50 34.9 23.96 389.3 0.221

IR 12.57 34.6 39.40 190.4 0.176

IT 7.94 32.7 7.51 172.9 0.195

LU 8.14 24.1 15.07 687.5 0.235

NL 8.11 28.1 21.86 357.4 0.330

PO 5.72 36.7 4.37 59.5 0.198

SW 9.85 32.5 17.8 603.2 0.397

UK 10.21 32.3 27.34 317.7 0.251a In millions of eurosb In thousands of personsc Barro and Lee (1993) education indexd Gini coefficiente Share of high-tech exports to total exportsf Purchasing power standard per capita at constant 1995 pricesg Public expenditures on education per pupil

if the food-processing sector is hypothetically removed, the total national output willfall by 157.6% of this sector’s actual output in Austria, 186.4% in Denmark, 151.0%in Spain and by 182.2% in Ireland. In all these countries, the food industry is ranked

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Table 3 Normalized backward and forward linkage coefficients for the 14 EU countries

Sector Austria Belgium Germany Denmark Spain Finland France

BL FL BL FL BL FL BL FL BL FL BL FL BL FL

(a)

01 1.133 1.315 2.031 2.470 1.208 1.347 1.130 1.459 1.489 1.797 1.166 1.537 1.032 1.232

06 0.878 1.774 0.436 1.101 0.734 1.363 0.867 1.030 0.404 0.985 0.964 2.062 0.743 1.093

13 0.889 0.959 1.138 1.658 1.502 1.324 1.026 0.796 1.315 2.145 1.514 1.924 1.044 1.905

15 1.126 1.683 1.151 1.955 0.988 1.511 0.891 1.437 1.077 1.661 1.060 1.175 1.053 1.567

17 0.954 0.896 0.700 0.380 1.060 1.536 0.830 0.741 1.013 0.725 0.920 1.023 1.170 1.398

19 1.003 0.821 1.128 1.329 0.840 0.786 0.792 1.089 1.169 0.905 0.955 1.123 0.998 1.205

21 1.004 0.666 1.166 0.251 1.122 1.053 0.837 0.734 1.117 0.869 0.811 0.829 1.262 0.598

23 0.924 0.337 0.977 0.386 1.042 0.516 0.754 0.294 0.932 0.651 0.666 0.385 1.575 0.709

25 0.794 0.617 1.002 1.253 1.007 0.931 0.767 0.920 0.926 0.392 0.580 0.269 1.213 0.922

28 0.941 0.306 0.531 0.237 1.211 1.148 1.089 0.450 1.365 0.868 0.824 0.580 1.464 1.388

36 1.576 1.895 1.678 1.942 1.246 1.436 1.864 1.647 1.510 1.546 1.607 1.427 1.382 1.128

42 0.806 0.500 0.843 0.608 1.095 1.407 0.934 0.892 0.943 0.920 0.690 0.558 0.981 1.089

47 1.041 1.672 0.784 1.537 0.820 1.159 0.942 1.941 1.076 1.820 1.215 1.691 0.938 1.651

49 0.763 0.582 0.939 0.607 1.085 1.061 0.699 0.746 0.960 0.857 0.606 0.699 1.142 0.977

51 1.219 1.196 1.034 0.840 1.096 0.907 0.939 0.821 1.098 1.030 1.300 1.137 0.997 0.832

53 1.115 1.305 1.389 0.361 1.116 0.427 1.419 0.740 1.039 0.310 1.214 0.438 1.149 0.122

56 0.833 0.677 0.593 0.746 0.737 0.498 0.863 0.695 0.534 0.438 0.852 0.763 0.535 0.413

59 1.400 0.308 1.108 0.102 1.251 0.451 1.568 0.215 1.174 0.173 1.602 0.193 0.592 0.200

61 0.997 0.823 1.021 1.200 0.775 0.848 0.643 0.978 1.133 0.817 0.659 1.044 0.801 0.738

63 1.214 1.569 1.278 0.180 0.489 0.879 0.603 0.480 0.933 0.322 0.963 0.600 1.424 0.741

65 1.177 0.389 0.136 0.455 1.570 0.988 1.350 2.199 0.720 0.822 0.774 0.264 0.523 1.019

67 0.300 1.021 0.249 1.770 0.239 0.631 1.031 1.520 0.218 1.003 0.599 0.949 0.331 0.724

69 1.090 1.359 2.210 1.952 1.186 0.752 1.363 1.191 0.710 1.551 1.690 2.379 1.275 1.788

74&86 0.822 1.331 0.479 0.678 0.580 1.040 0.799 0.985 1.144 1.395 0.768 0.953 0.375 0.560

first in terms of its backward interdependence, while in the remaining countries therelative ranking varies between the seventh and the second position. Inspecting fur-ther this finding, the hypothetical extraction of food manufacture from the economicsystem induces the largest reduction in the output produced of agriculture, trade andagricultural and industrial machinery sectors. This is rather uniform across countrieswith the only exception being again Portugal.

In general, services sectors—including lodging and catering services; inland, mar-itime, air and auxiliary transport services; communication services and other marketand nonmarket services—are ranked low in all EU countries indicating weak backwardinterdependence. This is partly explained by the fact that services sectors are ratherlabor intensive, and hence they exhibit low intermediate purchases from the other sec-tors in the economy. The only exceptions are lodging and catering services as well as

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Table 3 continued

Sector Ireland Italy Luxemburg Netherlands Portugal Sweden UK

BL FL BL FL BL FL BL FL BL FL BL FL BL FL

(b)

01 1.421 2.844 0.848 1.321 1.233 2.209 1.360 1.630 0.860 1.142 1.043 1.340 1.033 1.356

06 0.833 1.716 0.334 0.801 1.130 1.519 0.558 0.865 0.685 1.573 0.484 0.866 0.722 1.240

13 1.057 0.784 1.072 1.701 1.061 0.286 0.927 0.687 1.125 1.436 1.332 1.211 1.112 1.690

15 0.776 1.140 1.181 1.719 1.001 1.728 1.009 1.306 0.946 1.143 1.132 1.525 1.193 1.110

17 0.580 0.272 1.052 1.771 0.918 1.468 1.005 1.044 0.972 1.390 0.893 1.232 0.999 0.864

19 0.879 0.509 1.305 1.218 1.176 0.678 0.985 1.334 1.143 0.794 0.952 1.658 1.089 0.900

21 0.845 0.795 1.297 0.563 1.571 0.907 1.085 0.657 1.022 0.801 0.915 0.542 1.129 1.026

23 0.422 0.075 1.038 0.142 0.501 0.975 0.942 0.646 0.775 0.796 1.034 0.014 0.826 0.671

25 0.462 0.062 1.118 0.763 1.223 0.872 0.807 0.465 1.016 0.716 0.703 0.523 1.094 0.603

28 0.473 0.406 1.304 0.710 0.381 0.353 1.190 0.700 0.846 0.752 0.891 0.468 1.098 0.499

36 1.822 2.426 1.203 1.282 1.735 1.950 1.441 1.444 0.887 0.956 1.442 1.968 1.108 0.909

42 0.802 0.099 0.964 1.338 0.544 1.084 0.862 0.677 0.999 1.232 1.776 0.749 0.794 0.593

47 0.783 1.135 1.007 1.993 0.980 1.810 0.843 1.427 1.110 1.580 1.184 1.643 0.840 1.342

49 0.876 0.386 1.138 0.909 0.201 0.058 0.889 0.716 1.112 0.725 0.684 0.835 0.889 0.973

51 1.063 1.254 1.066 1.446 1.700 0.101 0.952 0.776 0.655 0.622 0.898 0.680 0.998 1.017

53 1.187 1.089 1.158 0.450 0.809 0.509 1.330 2.069 1.043 0.144 1.121 0.664 1.169 1.779

56 1.164 0.911 0.685 0.610 0.297 0.699 1.321 1.948 0.843 0.736 0.829 0.634 1.049 0.576

59 1.502 0.236 1.203 0.180 1.458 0.511 1.249 0.552 0.811 0.288 1.085 0.823 1.006 0.171

61 1.148 0.971 1.044 0.763 0.640 1.127 0.949 1.028 0.961 0.757 1.016 1.217 0.983 0.948

63 1.319 0.302 1.132 0.142 2.135 0.000 0.656 0.338 1.742 0.923 0.902 0.979 1.295 0.475

65 1.085 0.229 0.899 1.109 0.933 0.290 0.953 1.076 1.159 1.114 0.967 0.949 0.850 1.537

67 1.225 1.809 0.495 0.731 0.325 2.073 0.626 0.992 0.505 1.399 0.814 1.307 0.662 1.133

69 1.729 3.583 1.097 1.744 1.765 1.945 1.544 0.818 1.154 1.423 1.428 1.365 1.488 1.688

74&86 0.547 0.967 0.361 0.594 0.282 0.847 0.520 0.802 1.628 1.557 0.473 0.808 0.575 0.899

financial services by credit and insurance institutions.16 On the other hand, cateringservices show a strong dependence on trade services resulting in a high backwardlinkage value. Finally, in some countries (i.e., Belgium, France, Ireland, Portugal),maritime and air transport services are ranked high indicating the relative significanceof these activities for the national economies.

Computing the average backward linkage indicators for each one of the 24 sectorsacross all EU countries in the sample, again food processing, beverages and tobaccoproducts is the sector with the highest reduction in national output ranked first in terms

16 Looking at the input–output tables, it turns out that financial services depend to a large extent on them-selves for their intermediate purchases. According to Eurostat, from which the input–output tables areobtained, the imputed output of bank services is recorded in the statistics as a delivery of financial servicesto themselves.

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of its backward linkage indicator (Table 8). If this sector is hypothetically removedfrom the EU economies, the total output of these countries will fall by 146.4% ofthis sector average output produced. Financial services is the second highest sectorwith strong backward interdependence, followed by lodging and catering services;agriculture, forestry and fishery products; building and construction; and ferrous andnonferrous ores and metals. On the other hand, the sectors with the weakest averagebackward linkages are communication services; other market and nonmarket services;fuel and power products; trade services; and rubber and plastic products.

Regarding forward linkage indicators, which are also presented in Tables 3a andb, the variation across EU countries is more intense than for backward linkages. Theaverage value across countries is 0.804 ranging from a minimum of 0.460 in Irelandto a maximum of 1.135 in Portugal. The standard deviation is higher than that of thebackward linkage coefficients, 0.171. Further, there is a greater dispersion as far as theidentification of the key sectors is concerned. The only common pattern that emergesis for agriculture, forestry and fishery products and food processing, beverages andtobacco products. Both sectors are ranked in a relatively high position in terms oftheir forward linkage indicators. Agricultural sectors are ranked first in Belgium andLuxemburg and second in Ireland, while in Austria are in the eighth position andin Portugal in the tenth position. On the other hand, food processing, beverages andtobacco products are ranked first in Austria and Sweden, whereas the lowest rankingis observed in UK (fourteenth postion).

These results are rather expected given the high horizontal dispersion of agriculturaloutput throughout the rest of the economy, as an important part of sector’s total outputis directed to other sectors as intermediate sales. On the other hand, the high forwardlinkage value of food processing is due to the high share of own purchases of thissector. This is also confirmed by the high forward linkage value of financial servicesby credit and insurance institutions observed in the majority of countries in the sample.Looking at the average forward linkage values in Table 8, financial services are rankedfirst, followed by agriculture, forestry and fishing products; paper and printing prod-ucts; food processing, beverages and tobacco products; nonmetallic mineral products;ferrous and nonferrous ores and minerals; and fuel and power products. For example,if financial services are hypothetically removed, then the average output of the 14EU countries will fall by 168.1% of this sector average total production. Contrarily,if lodging and catering services are removed from the economic system, then theaverage output will fall only by 31.5% of this sector average total output. Officeand data-processing machines, maritime and air transport services and transportequipment are having the weakest average forward interdependence for the 14 EUcountries.

The relationship between forward and backward linkage indicators is rather weakfor the majority of countries. Specifically, Spearman correlation coefficients presentedin Table 4 indicate that, only in Belgium, Finland and Ireland, there is a statisticallysignificant correspondence between the two indicators in identifying key economicsectors (see the diagonal of the relevant table). However, still for some countries, thereis a moderated correspondence. Luxemburg, Italy, UK and Portugal exhibit almost neg-ligible correlation among the two indices of interdependence. Obviously the buyer’sintermediate purchases from a particular seller are part of this seller’s intermediate

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Table 4 Spearman correlation coefficients of backward (above italic values) and forward (below italicvalues) linkage indicators among the 14 EU countries

AU BE DE DK SP FI FR IR IT LU NL PO SW UK

AU 0.27 0.52 0.50 0.55 0.59 0.67∗∗ 0.33 0.49 0.50 0.68∗ 0.54 0.16 0.35 0.49

BE 0.55 0.44∗∗ 0.30 0.32 0.48 0.62∗∗ 0.54 0.57 0.47 0.59 0.65∗∗ 0.12 0.49 0.72∗

DE 0.36 0.33 0.23 0.51 0.50 0.42 0.33 0.12 0.51 0.20 0.56 −0.08 0.45 0.33

DK 0.42 0.61 0.35 0.29 0.23 0.69∗ −0.02 0.65∗∗ 0.16 0.29 0.64∗ −0.23 0.46 0.22

SP 0.50 0.75∗ 0.59 0.55 0.32 0.37 0.45 0.04 0.58 0.24 0.37 0.22 0.31 0.29

FI 0.66∗ 0.70∗ 0.41 0.35 0.77∗ 0.52∗∗ 0.15 0.66∗ 0.27 0.62∗∗ 0.58 −0.01 0.48 0.51

FR 0.27 0.56 0.66∗ 0.40 0.75∗ 0.67∗ 0.30 −0.10 0.69∗ 0.37 0.33 0.11 0.31 0.59

IR 0.63∗∗ 0.74∗ 0.11 0.43 0.56 0.75∗ 0.30 0.64∗ 0.08 0.51 0.55 −0.04 0.41 0.42

IT 0.37 0.60 0.63∗∗ 0.57 0.74∗ 0.64∗∗ 0.83∗ 0.37 0.11 0.39 0.50 0.09 0.45 0.69∗

LU 0.49 0.72∗ 0.32 0.52 0.49 0.54 0.35 0.68∗ 0.42 0.04 0.25 0.19 0.24 0.62∗∗

NL 0.33 0.34 −0.08 0.40 0.15 0.16 −0.08 0.36 0.23 0.34 0.47 −0.22 0.50 0.65∗

PO 0.42 0.45 0.58 0.50 0.67∗ 0.64∗∗ 0.64∗∗ 0.34 0.55 0.53 −0.11 0.15 −0.04 0.20

SW 0.61 0.71* 0.41 0.65∗∗ 0.59 0.58 0.52 0.52 0.64∗∗ 0.55 0.37 0.41 0.33 0.38

UK 0.41 0.47 0.14 0.56 0.56 0.57 0.39 0.53 0.50 0.26 0.40 0.34 0.35 0.12

The correlation coefficients between backward and forward linkage indicators for each one of the 14 countries are italicizeddiagonally∗(∗∗) Indicate significance at the 1(5)% level

sales. So, it may be possible if the seller is extremely small to have a concordancebetween the two indices of sectoral dependence. However, this relationship does nothold in general as backward linkages are measured from the buyer’s point of viewand forward linkages from the seller’s point of view. Therefore, a large backwarddependence of buyer j on sector’s i output does not need to imply that the forwarddependence of sector i on sector j is also large. This depends on the relative size ofthe sector (Dietzenbacher and Van der Linden 1997).

Concerning the correlation of backward linkages among the 14 EU countries, theSpearman correlation coefficients reported above the diagonal in Table 4 indicate amoderate spatial relationship. Finish, Irish, Dutch and British economies exhibit astatistically significant correlation of their backward linkages with some other EUcountries. Instead the spatial correlation with respect to forward linkage coefficientis more in evidence (see the lower triangle of Table 4). Although for some sectorspresented previously there exist a concordance in the relative rankings, this is notuniform for all sectors in the national economies for which a great dispersion isobserved.

Turning now to the sectoral efficiency measurement and its potential relation-ship with linkage indicators, the maximum likelihood estimates of the Cobb–Douglas“multilateral” stochastic input distance function along with corresponding standarderrors are presented in Table 5, whereas those of the technical inefficiency effectsmodel are presented in Table 6. The Cobb–Douglas input distance function is foundto be nonincreasing in sectoral output and nondecreasing in inputs indicating the con-cavity and convexity of the underlying production technology with respect to inputs

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Table 5 Parameter estimates of the multilateral stochastic Cobb–Douglas input distance function

Sector Parameters

βy βL βI βC βM

01 −0.347 (0.111)∗ 0.204 (0.114)∗∗ 0.089 (0.027)∗ 0.421 (0.142)∗ 0.286

06 −0.408 (0.217)∗∗ 0.062 (0.054) 0.092 (0.031)∗ 0.552 (0.193)∗ 0.294

13 −0.139 (0.047)∗ 0.247 (0.154)∗∗ 0.124 (0.055)∗ 0.424 (0.215)∗∗ 0.205

15 −0.428 (0.074)∗ 0.217 (0.146)∗∗ 0.121 (0.066)∗∗ 0.466 (0.185)∗ 0.196

17 −0.117 (0.081) 0.123 (0.064)∗∗ 0.074 (0.048) 0.588 (0.201)∗ 0.215

19 −0.108 (0.084) 0.186 (0.047)∗ 0.103 (0.032)∗ 0.544 (0.251)∗∗ 0.167

21 −0.257 (0.142)∗∗ 0.174 (0.053)∗ 0.112 (0.055)∗∗ 0.502 (0.214)∗ 0.212

23 −0.431 (0.208)∗∗ 0.152 (0.038)∗ 0.112 (0.064)∗∗ 0.442 (0.359) 0.294

25 −0.244 (0.085)∗ 0.123 (0.051)∗ 0.135 (0.071)∗∗ 0.527 (0.298) 0.215

28 −0.102 (0.071) 0.211 (0.117)∗∗ 0.082 (0.068) 0.495 (0.164)∗ 0.212

36 −0.418 (0.107)∗ 0.142 (0.084)∗∗ 0.105 (0.052)∗∗ 0.617 (0.242)∗ 0.136

42 −0.341 (0.196)∗∗ 0.384 (0.148)∗ 0.114 (0.061)∗∗ 0.323 (0.285) 0.179

47 −0.312 (0.162)∗∗ 0.145 (0.054)∗ 0.152 (0.061)∗ 0.574 (0.223)∗∗ 0.129

49 −0.172 (0.094)∗∗ 0.212 (0.108)∗ 0.105 (0.036)∗ 0.503 (0.163)∗ 0.180

51 −0.417 (0.128)∗ 0.232 (0.122)∗∗ 0.094 (0.061) 0.574 (0.211)∗ 0.100

53 −0.366 (0.109)∗ 0.174 (0.163) 0.072 (0.056) 0.535 (0.171)∗ 0.219

56 −0.342 (0.181)∗∗ 0.241 (0.086)∗ 0.128 (0.041)∗ 0.512 (0.253)∗∗ 0.119

59 −0.282 (0.091)∗ 0.247 (0.091)∗ 0.124 (0.046)∗ 0.500 (0.252)∗∗ 0.129

61 −0.335 (0.210)∗∗ 0.241 (0.075)∗ 0.142 (0.082)∗∗ 0.493 (0.211)∗ 0.124

63 −0.102 (0.072) 0.275 (0.083)∗ 0.062 (0.054) 0.544 (0.225)∗ 0.119

65 −0.262 (0.124)∗∗ 0.236 (0.092)∗ 0.082 (0.039)∗∗ 0.517 (0.244)∗∗ 0.165

67 −0.205 (0.086)∗ 0.192 (0.100)∗∗ 0.091 (0.039)∗ 0.536 (0.208)∗ 0.181

69 −0.195 (0.103)∗∗ 0.167 (0.066)∗ 0.101 (0.055)∗∗ 0.552 (0.192)∗ 0.180

74&86 −0.068 (0.073) 0.211 (0.086)∗ 0.111 (0.062)∗∗ 0.541 (0.247)∗∗ 0.137

β0 0.924 (0.042)∗γ 0.921 (0.217)∗

σ 2 0.365 (0.052)∗LnL −105.428

The corresponding asymptotic standard errors are given in parantheses. The corresponding parameterestimates for imports were obtained from the homogeneity restrictionsy sectoral output, L labor, I intermediate inputs, C capital, M imports∗(∗∗) Indicate significance at the 1(5)% level

and outputs, respectively. The logarithm of the likelihood function indicates a satisfac-tory fit for the particular functional specification (Table 5). In addition, the computedpseudo-R2 (Greene 1993, p. 651) is 0.623, indicating that the proposed stochasticCobb–Douglas “multilateral” input distance function model is a good representationof the data-generation process.

Prior to the analysis of the relationship between industrial efficiency and sec-toral interdependence, we have statistically examined the “multilateral” structure of

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Table 6 Parameter estimates of the technical inefficiency effects model

Parameter Estimate SE Parameter Estimate SE

δ0 2.174 (0.685)∗ δHTC 0.104 (0.095)

δEDU 0.587 (0.104)∗ δRD 0.086 (0.044)∗∗δINQ −0.074 (0.034)∗∗ δIPE 0.045 (0.013)∗

δAUBL 0.011 (0.017) δAU

FL −0.317 (0.101)∗

δBEBL −0.214 (0.092)∗∗ δBE

FL −0.242 (0.123)∗∗

δDEBL 0.144 (0.077)∗∗ δDE

FL 0.131 (0.065)∗∗

δDKBL −0.325 (0.127)∗ δDK

FL −0.122 (0.063)∗∗

δSPBL −0.317 (0.121)∗ δSP

FL 0.072 (0.082)

δFIBL −0.082 (0.041)∗∗ δFI

FL 0.071 (0.080)

δFRBL −0.047 (0.051) δFR

FL 0.036 (0.042)

δIRBL 0.517 (0.098)∗ δIR

FL 0.089 (0.040)∗∗

δITBL −0.086 (0.044)∗∗ δIT

FL 0.132 (0.122)

δLUBL −0.161 (0.065)∗ δLU

FL 0.085 (0.067)

δNLBL −0.218 (0.091)∗∗ δNL

FL 0.211 (0.173)

δPOBL 0.066 (0.035)∗∗ δPO

FL 0.032 (0.074)

δSWBL −0.103 (0.065)∗∗ δSW

FL 0.121 (0.077)

δUKBL 0.382 (0.117)∗ δUK

FL 0.141 (0.071)

The corresponding asymptotic standard errors are given in paranthesesBL backward linkage coefficients, FL forward linkage coefficients∗(∗∗) Indicate significance at the 1(5)% level

production technology. Using the traditional likelihood ratio test statistic,17 we statis-tically tested the hypothesis that βyi = βy and βli = βl ∀i and it was rejected at the5% level of significance. This is also true when the two hypotheses were examinedseparately, i.e., βyi = βy or βli = βl ∀i. Thus, the formulation of “multilateral” sto-chastic input distance function was validated indicating that sectors included in theinput–output tables have different technologies.18

17 The corresponding likelihood ratio tests are not presented herein but are available upon request. TheLR-test statistic follows a chi-squared distribution with df, the number of restrictions except in the case thatinvolves the hypothesis that γ = 0. In that case, it follows approximately a chi-squared distribution, thecritical values of which are provided by Kodde and Palm (1986).18 We have tried to fit a stochastic “multilateral” input distance function also taking into account the differ-ences across countries in the sample, but statistical testing rejected that specification. Probably the fact thatour sample consists of only EU countries, we may assume that sectoral technological conditions are moreor less uniform across them resulting thus in the rejection of the relevant functional specification. Severalother hypotheses concerning model representation were examined using the likelihood ratio test. First, theaverage Cobb–Douglas input distance function does not adequately represent the structure of technologyfor the 14 EU countries considered. The null hypotheses that γ = 0 and γ = δ0 = δBLk = δFLk = 0∀k are rejected at the 5% level of significance, indicating that the technical inefficiency effects are in factstochastic. This is also depicted by the statistical significance of the γ -parameter presented in Table 3.

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In addition, statistical testing also validates the relation between industrial efficiencyand backward and forward linkages. Specifically, the hypothesis that δBLk = δFLk = 0∀k is rejected at the 5% level of significance indicating that both linkage indicatorsaffect individual sectors’ technical efficiency levels. Examining separately the impactof backward and forward linkages, the same finding is confirmed. In particular, bothhypotheses that δBLk = 0 ∀k and δFLk = 0 ∀k are also rejected using the likeli-hood ratio test. Hence, both linkage indicators are related with resource utilization.The magnitude of this relation, however, differs across countries as the hypothesesthat δBLk = δBL ∀k and δFLk = δFL ∀k are rejected at the 5% level of significance.The same result arises when the both linkage indicators are examined separately, i.e.,δBLk = δBL or δFLk = δFL∀k.

Finally, the joint hypothesis that all country- and sectoral-specific variables includedin the inefficiency effects model are zero (i.e., δEDU = δINQ = δHTC = δRD = δIPE = 0)

is also rejected at the 5% level of significance. Individual parameter estimates appear-ing in Table 6 indicate that only the share of high-tech exports to total exports doesnot influence inefficiency differentials. All other variables seems to have an effect oninput technical efficiency at a different magnitude and direction though. Specifically,the highest positive effect comes from education (0.587) followed by R&D expenses(0.086) and public expenditures per pupil (0.045). On the other hand, income inequal-ity affect negatively the technical efficiency, as the corresponding parameter estimateturned to a negative statistically significant value, −0.074. It seems, therefore, thathuman capital has a major influence on individual technical efficiency scores amongcountries in the sample. That is a rather expected result, given that education improvesknow-how and allows for better management of available resources. The same istrue for R&D expenses as more investments on new and more efficient technologiescombined with improved human capital in each country have a positive impact onresource utilization. Finally, income inequality seems to deteriorate efficiency, becauseit reduces motivation for better management and resource utilization.

Sectoral input technical efficiency for each country in the sample is shown in Table 7.The mean value over countries and sectors is 73.5%, indicating that on average a 26.5%decrease in total cost of production could be achieved for the 14 EU countries, with-out altering the volume of sectoral output, production technology and input usage aslong as producers’ know-how is improved. In other words, it is possible for the EUeconomies to become more efficient and to maintain the same level of sectoral outputby reducing labor, intermediate consumption, capital equipment and sectoral importsby almost 27%. The variation of this mean value is 6.3% ranging from a minimum of63.2% in Finland to a maximum of 81.4% in Germany (Table 8).

There are only three countries achieving technical efficiency ratings above 80%,namely Germany (80.6%), Netherlands (80.7%) and France (81.4%). The majority ofcountries (seven out of fourteen) have technical efficiency ratings between 60 and 70%.

Footnote 18 continuedMoreover, Schmidt and Lin (1984) test for the skewness of the composed error term also confirms the

existence of technical inefficiency (The test-statistic computed as√

b1 = m3/m3/22 with m3 and m2 being

the third and second moments of the residuals eik and b1 the coefficient of skewness is 3.014, which is wellabove the corresponding critical value of 0.298 at the 5% level of significance).

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Table 7 Sectoral input technical efficiency for the 14 EU countries

Sector AU BE DE DK SP FI FR IR IT LU NL PO SW UK

01 56.7 56.8 92.1 81.3 58.4 69.9 86.6 62.6 83.2 52.1 82.1 92.1 80.2 75.4

06 69.9 74.2 77.5 89.6 75.7 52.1 93.3 65.5 96.3 56.5 72.1 44.4 65.9 63.5

13 51.2 62.5 80.2 62.1 60.2 40.2 85.5 55.2 85.5 72.5 96.6 52.4 78.5 84.6

15 68.5 79.6 89.0 89.6 79.6 67.0 65.4 62.5 72.5 42.1 79.9 63.5 75.5 87.6

17 78.5 82.1 75.5 82.1 68.6 75.5 72.5 50.2 93.7 62.1 68.5 66.6 77.4 65.9

19 81.3 52.1 86.6 82.1 70.2 68.5 90.2 55.7 62.6 70.1 92.6 75.5 76.5 84.6

21 82.5 69.9 82.1 63.2 77.4 70.2 85.4 75.4 63.3 52.1 58.7 85.4 72.7 79.5

23 79.9 82.0 62.5 79.9 79.5 62.3 68.6 52.1 80.3 63.3 74.4 70.2 69.9 86.6

25 80.3 65.1 79.5 92.1 82.1 65.9 89.7 44.2 72.5 60.5 92.7 62.6 55.9 72.5

28 96.6 95.5 85.7 72.1 62.1 52.3 62.1 52.4 63.3 77.5 79.9 65.4 57.0 65.9

36 76.5 52.1 92.4 62.4 69.9 41.2 70.2 70.3 73.7 50.3 68.6 72.3 59.9 80.2

42 96.6 69.9 75.4 72.1 79.9 55.9 86.6 60.2 65.9 90.2 92.6 80.2 68.6 75.5

47 79.9 85.2 92.1 82.5 89.6 68.6 77.0 65.8 82.6 74.6 96.6 85.3 55.7 79.4

49 65.5 83.3 82.1 72.5 62.5 93.3 85.6 55.7 65.9 72.7 79.9 72.5 79.9 62.1

51 62.3 80.3 92.2 82.1 74.6 58.7 86.6 75.5 93.7 54.7 92.6 59.7 85.5 85.7

53 52.1 89.5 82.1 62.6 71.3 53.7 90.2 76.3 72.5 66.6 68.5 56.5 85.8 88.5

56 76.5 96.3 50.2 83.2 96.3 56.5 86.6 72.2 82.5 62.5 79.5 62.5 82.5 70.2

59 69.9 61.3 92.1 80.3 59.9 64.2 77.6 65.9 80.2 65.2 62.6 58.9 59.8 74.5

61 84.1 70.2 82.5 82.2 66.5 82.1 79.9 72.1 82.1 60.3 92.5 48.8 87.4 85.7

63 59.9 72.1 69.6 92.1 95.7 51.2 76.0 63.5 63.3 51.0 92.3 40.2 87.6 82.5

65 82.5 82.3 92.6 57.0 90.3 68.6 85.7 66.6 86.2 72.2 83.2 62.5 75.5 75.5

67 66.2 79.7 51.2 62.1 72.3 80.2 75.2 72.5 80.2 95.9 92.7 63.2 62.1 65.9

69 68.7 56.2 96.6 67.0 93.4 50.7 82.5 72.1 72.3 58.7 62.2 78.6 72.1 85.7

74&86 49.9 80.3 72.5 82.1 58.4 80.2 95.4 53.3 92.5 92.1 74.5 88.5 86.9 69.9

Mean 72.3 74.1 80.6 76.4 74.8 63.6 81.4 63.2 77.9 65.9 80.7 67.0 73.3 77.0

These are Italy (77.9%), Denmark (76.4%), UK (77.0%), Spain (74.8%), Belgium(74.1%), Sweden (73.3%) and Austria (72.3%). Finally, there are four countries withmore severe technical inefficiency problems: Portugal (67.0%), Luxemburg (65.9%),Ireland (63.2%) and Finland (63.6%). However, even in Germany, Netherlands andFrance, the countries with the highest input technical efficiency scores, there is asignificant part of industrial sectors that are faced with significant inefficiency prob-lems: 25, 29 and 28% of the industrial sectors exhibit technical efficiency below60%, respectively. Regarding the intracountry levels of these technical efficiencyratings, we observe a great variation across sectors. Luxemburg, Finland and Aus-tria exhibit the greatest variability of sectoral technical efficiency, whereas France,Ireland and Italy the lowest. Nonmetallic mineral products in Luxemburg have thelowest technical efficiency score of 42.1%, while wholesales and retailing trade inAustria and Services of Credit and Insurance Institutions in Germany the highest valueof 96.6%.

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Table 8 Summary statistics of sectoral input technical efficiency and linkage indicators

Sector TE BL FL

Agriculture, forestry and fishery products 73.5 1.213 (4) 1.643 (2)

Fuel and power products 71.2 0.698 (22) 1.285 (7)

Ferrous and nonferrous ores and metals 69.1 1.151 (6) 1.322 (6)

Nonmetallic mineral products 73.0 1.042 (10) 1.476 (5)

Chemical products 72.8 0.933 (15) 1.053 (9)

Metal products except machinery 74.9 1.030 (11) 1.025 (10)

Agricultural and industrial machinery 72.7 1.085 (8) 0.735 (18)

Office and data-processing machines 72.2 0.886 (19) 0.471 (23)

Electrical goods 72.5 0.908 (18) 0.665 (20)

Transport equipment 70.5 0.972 (12) 0.633 (21)

Food, beverages, tobacco 67.0 1.464 (1) 1.568 (4)

Textiles and clothing, leather and footwear 76.4 0.931 (16) 0.839 (15)

Paper and printing products 79.6 0.969 (13) 1.600 (3)

Rubber and plastic products 73.8 0.856 (20) 0.724 (19)

Other manufacturing products 77.4 1.073 (9) 0.904 (13)

Building and construction 72.6 1.161 (5) 0.743 (16)

Recovery, repair services, wholesale, retail 75.6 0.795 (21) 0.739 (17)

Lodging and catering services 69.4 1.215 (3) 0.315 (24)

Inland transport services 76.9 0.912 (17) 0.947 (12)

Maritime and air transport services 71.2 1.149 (7) 0.566 (22)

Auxiliary transport services 77.2 0.936 (14) 0.889 (14)

Communication services 72.8 0.544 (24) 1.219 (8)

Services of credit and insurance institutions 72.6 1.409 (2) 1.681 (1)

Other market and nonmarket services 77.7 0.668 (23) 0.958 (11)

Total 73.5

The terms in parentheses indicate the ranking of the sector

Concerning the intercountry variation, as it is shown from Table 7, there is nota common pattern of sectoral technical efficiency across countries. In each country,the sectoral efficiency levels depend on domestic conditions and not on any kind ofmultinational standards. For instance, technical efficiency of agriculture, forestry andfishery products ranges from a minimum of 52.1% in Luxemburg to a maximum of92.1% in Portugal and Germany, while food, beverages and tobacco manufacturing,a sector with high backward linkage indicator, ranges from 41.2% in Finland to amaximum of 92.4% in Germany. The sectors with the highest variability across coun-tries are maritime and air transport services with a standard deviation of 17.4%, othermarket and nonmarket services with 15.6%, ferrous and nonferrous ores and mineralswith 16.5% and transport equipment with 14.6% (Table 8).

The intercountry differences in technical efficiency levels are further confirmedfrom the Spearman correlation coefficients reported in Table 9. As it is shown fromthis table, the only statistically significant negative correlation in efficiency rankings

123


Table 9 Spearman correlation coefficients of input technical efficiency ratings among the 14 EU countries


AU – 0.09 0.03 −0.01 0.25 0.02 −0.35 −0.15 −0.36∗∗ 0.05 0.05 0.12 −0.51 −0.15

BE −0.43∗ 0.04 0.18 0.21 −0.18 0.02 0.17 0.27 0.04 −0.16 0.12 −0.31

DE −0.11 −0.18 −0.13 −0.01 0.09 −0.09 −0.36∗∗ −0.15 0.21 −0.13 0.33

DK 0.12 0.15 0.01 −0.35∗∗ 0.16 −0.31∗∗ 0.23 −0.19 0.08 −0.06

SP −0.25 −0.03 0.28 −0.16 −0.24 0.10 −0.14 −0.04 0.22

FI 0.12 −0.15 0.10 0.29 0.08 0.29 0.18 −0.35

FR 0.10 0.25 0.21 0.08 0.10 0.37 −0.08

IR 0.03 −0.24 −0.21 −0.12 0.21 0.32

IT 0.10 −0.01 −0.11 0.18 −0.19

LU 0.28 0.25 −0.13 −0.41

NL −0.19 0.06 0.09

PO −0.16 −0.07

SW 0.30

UK –

∗(∗∗) Indicate significance at the 1(5)% level

that was found is between Belgium and Germany, and between Denmark and Ireland,Austria and Italy, Germany and Luxemburg, and Denmark and Luxemburg. For all therest of the country-pairs, Spearman correlation coefficients were statistically insig-nificant. It is obvious therefore that common market and free factor mobility has notresulted in common utilization of production technology among member states. Thereare still differences related mainly to domestic factors that affect the productivity ofsectors across national economies. As provided by the estimates of the inefficiencyeffects model, human capital and income inequality differences as well as with invest-ments in R&D affect significantly the technical efficiency in EU countries.

Concerning the relationship among backward and forward linkage indicators andinput technical efficiency, the corresponding parameter estimates in the inefficiencyeffects model indicate that no clear pattern emerges. Concerning backward linkagesfirst, the econometric estimates presented in Table 6 reveal a negative relationship forsome countries, in some others a positive one, while for two countries in the sam-ple no relationship was statistically established. Specifically, in Belgium, Denmark,Spain, Finland, Italy, Luxemburg, Netherlands and Sweden, high sectoral backwardlinkages are associated with low input technical efficiency levels. On the other hand,in Germany, Ireland, Portugal and UK, high industrial efficiency is related positivelywith high backward linkage coefficient value.

Unlikely, the relationship between forward linkage indicators and input technicalefficiency is less clear. In only two countries (Ireland and Germany), we find a positiverelationship, in three countries a negative one (Austria, Belgium and Denmark), whilein the rest nine countries there is no statistical significant relationship. From bothindices, it is evident from the results that there is no clear pattern that relates sec-toral interdependence with resource utilization. Strengthening further the aforestatedfinding, Table 10 presents the first ten sectors with the highest backward and forward

123


Table 10 Technical efficiency of the ten sectors with the highest backward and forward linkage coefficientfor the 14 EU countries


High backward interdependence

76.5 56.2 92.6 62.4 69.9 50.7 68.6 70.3 62.6 51.0 62.2 40.2 68.6 85.7

69.9 56.8 80.2 80.3 58.4 39.9 62.1 72.1 63.3 58.7 68.6 88.5 59.9 82.5

62.3 52.1 92.1 62.6 62.1 64.2 76 65.9 63.3 50.3 82.1 62.5 72.1 87.6

59.9 89.5 92.4 67.0 60.2 40.2 70.2 62.6 73.7 54.7 68.5 78.6 78.5 88.5

82.5 72.1 85.7 57.0 59.9 58.7 82.5 63.5 80.2 52.1 79.5 75.5 55.7 79.5

56.7 69.9 92.1 81.3 70.2 68.6 85.4 72.5 72.5 65.2 62.6 52.4 75.5 84.6

68.5 79.6 96.3 72.1 58.4 53.7 89.7 76.3 72.5 52.1 79.9 72.5 85.8 80.2

52.1 62.5 82.1 62.1 66.5 69.9 72.5 72.2 65.9 60.5 58.7 85.3 59.8 65.9

68.7 52.1 82.1 62.1 77.4 67.0 90.2 72.1 63.3 70.1 79.9 56.5 80.2 72.5

79.9 61.3 92.2 82.5 74.6 52.1 85.6 66.6 72.5 56.5 68.5 85.4 69.9 84.6

−0.04a −0.63∗ 0.63∗ −0.58∗ −0.53∗ −0.60∗∗ −0.43 0.66∗ −0.67∗ −0.66∗ −0.48∗∗ 0.08 −0.16 0.50∗∗

High forward interdependence

76.5 56.8 75.5 57.0 60.2 50.7 85.5 72.1 82.6 52.1 68.5 85.3 59.9 88.5

69.9 79.6 89.0 82.5 89.6 52.1 82.5 62.6 93.7 95.9 79.5 44.4 76.5 84.6

68.5 56.2 92.4 62.4 58.4 40.2 77.0 70.3 72.3 50.3 82.1 88.5 55.7 85.7

79.9 52.1 75.4 62.1 79.6 68.6 65.4 72.5 72.5 58.7 68.6 52.4 75.5 75.5

59.9 79.7 77.5 81.3 93.4 69.9 72.5 65.5 85.5 74.6 96.6 78.6 72.1 75.4

68.7 62.5 92.1 89.6 69.9 39.9 62.1 75.5 93.7 42.1 92.6 63.2 80.2 79.4

49.9 85.2 80.2 67.0 58.4 67.0 86.6 62.5 65.9 56.5 79.9 66.6 62.1 63.5

56.7 52.1 92.1 82.1 74.6 58.7 90.2 65.8 83.2 62.1 83.2 80.2 77.4 65.9

52.1 65.1 85.7 89.6 72.3 68.5 70.2 76.3 73.7 60.3 68.5 63.5 87.4 87.6

62.3 70.2 82.1 82.1 75.7 82.1 93.3 72.1 62.6 90.2 92.5 92.1 78.5 79.5

−0.48∗∗,a −0.49∗∗ 0.33 −0.27 −0.19 −0.28 −0.26 0.49∗∗ 0.18 −0.10 −0.08 0.25 −0.07 0.30

∗(∗∗) Indicate significance at the 1(5)% levela In the relevant rows the Spearman correlation coefficients between input technical efficiency and linkage indicators arereported

linkage coefficients value along with their corresponding estimates of input technicalefficiency scores. As is clearly perceived from this table, this ambiguous relationshipis confirmed. Despite some notable exceptions, the general tendency is in accordancewith the estimates presented in Table 4. This is confirmed from Spearman correlationcoefficients between sectoral technical efficiency and linkage indicators (also reportedin Table 10).

As stated at the outset, this result is explained by the nature of backward andforward relationship within each European economy. Concerning backward interde-pendence first, the reliance on sectors with high technical efficiency levels will resultin higher performance for the buying sectors as any changes in supplying sectors aretransmitted to them. On the other hand, if intermediate sales are subject to intensecompetition between sectors within national economies put more pressure to sell-ing sectors, to improve their competitiveness domestically. To support our argument,Table 11 presents two different indicative sectors in each EU country with low and

123


Tabl

e11

Key

cont

ribu

tors

and

the

rela

tions

hip

betw

een

linka

gein

dica

tors

and

inpu

ttec

hnic

alef

ficie

ncy

Sect

orB

LFL

TE

Mos

tim

port

ants

uppl

ying

sect

ors

Mos

tim

port

antb

uyin

gse

ctor

s

Sect

orT

EB

uysa

Sect

orT

EB

uysa

Sect

orT

EB

uysa

Sect

orSa

lesb

Buy

scSe

ctor

Sale

sbB

uysc

Aus

tria

651.

177

0.38

982

.561

84.1

16.5

2896

.612

.359

69.9

21.9

0616

.61.

863

12.8

12.5

531.

115

1.30

552

.153

52.1

22.2

1568

.514

.374

–86

49.9

11.7

5325

.922

.274

–86

37.8

23.4

Bel

gium

012.

031

2.47

056

.836

52.1

42.5

0156

.813

.006

74.2

6.5

3685

.350

.501

10.2

12.9

531.

305

0.36

189

.515

79.6

25.6

74–8

680

.316

.256

96.3

12.1

568.

44.

374

–86

15.6

18.0

Ger

man

y

231.

042

0.51

662

.574

72.5

30.5

5650

.215

.123

62.5

12.6

74–7

658

.221

.523

10.4

15.7

361.

246

1.43

692

.436

92.4

22.6

0192

.122

.174

–86

72.5

21.3

3651

.712

.559

23.1

24.2

Den

mar

k

250.

767

0.92

092

.125

92.1

31.2

74–8

682

.114

.119

82.1

12.3

2523

.421

.221

15.9

8.3

651.

350

2.19

957

.065

57.0

69.3

6182

.28.

953

62.6

4.2

6550

.469

.456

19.7

8.8

Spai

n

471.

076

1.82

089

.647

89.6

31.7

5696

.318

.206

75.7

8.5

74–8

624

.72.

147

21.9

31.7

011.

489

1.79

758

.436

69.9

31.5

0158

.420

.406

75.7

12.1

3670

.551

.401

17.3

20.4

Fin

land

490.

606

0.69

993

.317

75.5

23.4

74–8

680

.220

.149

93.3

9.1

3620

.21.

553

13.8

2.1

131.

514

1.92

440

.213

40.2

24.4

1968

.517

.006

52.1

16.8

1934

.723

.513

32.3

24.4

Fra

nce

060.

743

1.09

393

.306

93.3

43.4

74–8

695

.430

.853

90.2

6.6

0629

.413

.574

–86

13.9

3.9

281.

464

1.38

862

.128

62.1

22.5

2589

.77.

113

85.5

6.2

2856

.522

.474

–86

28.4

13.5

123


Tabl

e11

cont

inue

d

Sect

orB

LFL

TE

Mos

tim

port

ants

uppl

ying

sect

ors

Mos

tim

port

antb

uyin

gse

ctor

s

Sect

orT

EB

uysa

Sect

orT

EB

uysa

Sect

orT

EB

uysa

Sect

orSa

lesb

Buy

scSe

ctor

Sale

sbB

uysc

Irel

and

531.

187

1.08

976

.315

62.5

23.8

5672

.222

.153

76.3

9.5

74–8

631

.616

.756

10.1

10.2

190.

879

0.50

955

.774

–86

53.3

19.8

1355

.218

.606

65.5

8.2

5330

.725

.317

11.7

25.6

Ital

y

060.

334

0.80

196

.306

96.3

57.5

5372

.56.

561

82.1

6.3

5612

.810

.674

–86

10.1

7.6

281.

304

0.71

063

.319

62.6

16.8

2863

.313

.549

65.9

8.3

5626

.815

.328

32.6

14.7

Lux

embu

rg

420.

544

1.81

090

.242

90.2

70.1

5662

.511

.067

95.9

7.3

5142

.114

.542

30.9

10.9

011.

233

2.20

952

.136

50.3

27.1

0152

.126

.869

58.7

18.2

3674

.539

.501

14.5

26.1

Net

herl

ands

630.

656

0.33

892

.365

83.2

26.2

6392

.314

.806

72.1

10.8

5653

.21.

063

25.1

14.7

691.

544

0.81

862

.269

62.2

75.8

74–8

674

.510

.453

68.5

3.5

6981

.275

.774

–86

6.2

2.9

Port

ugal

74–8

61.

628

1.55

788

.569

78.6

47.1

74–8

688

.517

.947

85.3

8.5

6938

.77.

374

–86

17.8

14.0

610.

961

0.75

748

.806

44.4

21.7

5662

.521

.528

65.4

6.2

5631

.216

.865

11.4

21.4

Swed

en

560.

829

0.63

482

.574

–86

86.9

29.4

6187

.415

.463

87.6

6.9

1912

.915

.174

–86

10.4

4.5

591.

085

0.82

359

.836

59.9

22.7

0665

.910

.156

82.5

7.5

5616

.821

.574

–86

31.4

15.2

UK

531.

169

1.77

988

.553

88.5

41.6

1587

.618

.774

–86

69.9

10.1

5361

.521

.674

–86

32.5

5.2

670.

662

1.13

365

.974

–86

69.9

34.7

6765

.915

.825

72.5

8.2

6943

.66.

574

–86

24.3

2.3

aPe

rcen

tage

ofto

tali

nter

med

iate

purc

hase

sco

min

gfr

omth

esu

pply

ing

sect

orb

Perc

enta

geof

tota

lint

erm

edia

tesa

les

toth

ebu

ying

sect

orc

Inte

rmed

iate

sale

sas

ape

rcen

tage

ofto

tali

nter

med

iate

purc

hase

sof

the

buyi

ngse

ctor

123


high input technical efficiency levels and examines the nature of their most importantintermediate purchases and sales within each national economy. In the left panel of thetable, the corresponding backward and forward linkage coefficients and input techni-cal efficiency values are reported. In the middle panel, the most important supplyingsectors from which intermediate purchases are arising together with their input techni-cal efficiency values are depicted. Finally, the right panel presents the most importantbuying sectors to which intermediate sales of the corresponding sectors are directed.

Concerning Austria first, auxiliary transport services exhibit a high input technicalefficiency value (82.5%) with a strong backward linkage indicator (1.177), the fifthhighest. Looking at its backward interdependence, this particular sector purchases the16.5% of its inputs from inland transport services with an input technical efficiencyvalue of 84.1%, the 12.3% from transport equipment with an efficiency value of 96.6%and the 21.9% from lodging and catering services with an efficiency value of 69.9%.Hence, its strong backward dependence and reliance on sectors with relatively highefficiency levels results also in a high efficiency value from this sector too. Lookingat its forward interdependence, 16.6% of this sector intermediate sales goes to fueland power products and 12.8% to maritime and transport services. However, thesesales correspond only to 1.8 and 12.5% of these two sectors intermediate purchasesindicating a high competition with other input suppliers within the economy. So, thehigh input technical efficiency value for this sector can be explained from the internalstructure of Austrian economy.

The same conclusion is derived by looking on the other sector of the Austrianeconomy, building and construction, which exhibits a low efficiency value (52.1%).As it is shown Table 11, the sector buys 22.2% of its inputs from itself, 14.3% fromnonmetallic mineral products and 11.7% from other market and nonmarket servicesthat exhibit an input technical efficiency level of 68.5 and 49.9%, respectively. Con-cerning its intermediate sales, those are mainly directed to the same sector (25.9%)and other market and nonmarket services (37.8%). For these two sectors, this sharecorresponds to the 22.2 and 23.4% of their total intermediate purchases indicatingtheir relative reliance on building and construction for their own production. Hence,the derived demand for building and construction is relatively inelastic, putting lesspressure to this particular sector to become more efficient. Although this finding issectoral-specific, it is observed in many other sectors within the Austrian economywith different direction of relationship resulting in a no clear pattern for the wholenational economy.

Looking at Italian economy, although fuel and power products exhibit a high inputtechnical efficiency level of 96.3%, they at the same time show weak forward andbackward interdependence with the remaining economic sectors. The low backwarddependence is due to the fact that this sector purchases 57.5% of its total inputs fromitself and only small portions from building and construction (6.5%) and inland trans-port services (6.3%). However, all these sectors exhibit high input technical efficiencylevels of 72.5 and 82.1%, explaining partly the high economic performance of thissector. This is further confirmed by its forward interdependence, as it sells small frac-tions of its total output to other sectors, which is rather low compared to other inputsuppliers for them. Specifically, 12.8% of its total output goes to trade services, form-ing, however, only 10.6% of this sector intermediate purchases and 10.1% to other

123


market and nonmarket services which is only the 7.6% of this sector intermediatepurchases. Hence, the competition through increased substitutability results also inincreased pressure to improve economic performance.

The above cases are only indicative, but it can be observed also to other sectors inother national economies, validating the hypothesis made herein about the relation-ship between backward and forward linkage coefficients and input technical efficiency.Obviously, there is no clear pattern that emerges from our findings, but certainly thereis sectoral dependence on the transmission of individual performance across sectors.Overall, there is no concordance for the national economies, but this is rather based onthe specificities of them and the nature of intermediate transactions in each nationaleconomic system.

4 Concluding remarks

In identifying sectoral development priorities, the assessment of both sectoral eco-nomic performance and production interdependence is an important issue. A sectorwith strong interindustry transactions may be seen as a good candidate to promoteoverall economic development in a national or regional economy. However, if thesame sector operates inefficiently, in the sense that it does not produce as much outputas its input usage allows, the anticipated benefits would not be as great as it wasexpected initially.

Along these lines, we attempted to present empirical evidences on the potentialrelationship between technical efficiency and sectoral linkages using recent meth-odological advances both in stochastic frontier modeling and in the measurementof interindustry linkages. Our analysis was focused on 14 EU countries using the1995 national input–output tables published by Eurostat. The empirical results reveala rather ambiguous relationship that depends on the peculiarities and the structuralcharacteristics of the national economies. Technically efficient sectors tend to exhibitboth low and high backward and forward linkage coefficients. This is confirmed fromcase-to-case examination as well as from the corresponding parameter estimates inthe inefficiency effects model and Spearman correlation coefficients.

As our analyses showed, this ambiguous finding is explained from the peculiaritiesand the nature of intermediate transactions in each country. Our hypothesis that strongbackward dependence results in the transmission of the relative perfomance of sellingsector to that of buying sector is confirmed in many sectors and in all countries. Onthe other hand, demand conditions and the potential substitutability of intermediatesales of any selling sector are affecting its own performance as competition increasesor declines.

Finally, our empirical analysis underlines the importance in investing in improvinghuman capital in national economies as means of improving know-how and bettermanagement of national resources. Further, income inequality also seems to play animportant role as it deteriorates motivation by individuals to achieve better perfor-mance. Also investing in research and development increases the potential of eachsector to increase its performance, and hence its competitiveness with adoption ofnew techniques may well improve sectoral efficiency.

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Acknowledgments The authors wish to thank the participants of the 4th ASSET Euroconference fortheir useful comments and suggestions that materially improved the paper. The usual caveats apply. Thisresearch has been partly supported by a Marie Curie Transfer of Knowledge Fellowship of the EuropeanCommunity’s Sixth Framework Programme under contract number MTKD-CT-014288.

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