Analysing technical and allocative efficiency in the French grocery retailing industry

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Analysing technical and allocativeefficiency in the French groceryretailing industryCarlos Pestana Barros a & Rozenn Perrigot ba Instituto Superior de Economia e Gestao, Technical University ofLisbon , Lisbon, Portugalb Graduate School of Business Administration (IGR-IAE), Universityof Rennes 1 , FrancePublished online: 22 Aug 2008.

To cite this article: Carlos Pestana Barros & Rozenn Perrigot (2008) Analysing technical andallocative efficiency in the French grocery retailing industry, The International Review of Retail,Distribution and Consumer Research, 18:4, 361-380

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Analysing technical and allocative efficiency in the French grocery retailing

industry

Carlos Pestana Barrosa* and Rozenn Perrigotb

aInstituto Superior de Economia e Gestao, Technical University of Lisbon, Lisbon, Portugal; bGraduateSchool of Business Administration (IGR-IAE), University of Rennes 1, France

(Received January 2008; final version received June 2008)

This paper focuses on the innovative two-stage procedure developed by Simar andWilson to estimate the determinants of French retailing efficiency. During the first stage,the technical and allocative efficiency of French retailers will be assessed using the DEA(Data Envelopment Analysis) methodology to identify the best companies, in order toserve as peers for improving the performance of weaker companies. The companiesanalysed have therefore been ranked according to their total productivity over theperiod 2000–2004. During the second stage, the Simar and Wilson model will be used tobootstrap DEA scores via a truncated regression. The economic and managerialimplications arising from this study will also be considered.

Keywords: Data Envelopment Analysis; retailing; France; efficiency; grocery sector

Introduction

The topic of efficiency has drawn attention from many researchers in retailing (see theSpecial Issue of the International Review of Retail, Distribution and Consumer Research,2005). This research has often focused on the UK retail sector (Pilat 2005; O’Mahony andVan Ark 2005; Reynolds et al. 2005). Economic efficiency relates to the concept of theproduction possibility frontier and comprises both technical efficiency and allocativeefficiency. Production functions are widely used to define the relationships between inputsand outputs by means of graphically depicting the maximum amount of outputsobtainable from a given amount of consumed inputs. In this manner, such functions reflectthe current status of available technology being applied within the industry. Sinceeconomic efficiency is a relative measure, with respect to the production function, itincludes within its definition a benchmark: the production frontier. The technical efficiencyof a company is a comparative assessment of how well it actually processes inputs in orderto achieve its outputs, in comparison with its maximum potential, as represented by thisproduction possibility frontier. The allocative efficiency of a retailer is a comparativemeasure of how well it sets prices according to marginal production productivity. A retailcompany can be technically inefficient if it operates beneath the production frontier.Moreover, it can be allocatively inefficient if it selects inputs at higher prices than theprevailing input prices, thereby producing a quantity of outputs at a non-optimal

*Corresponding author. Email: [email protected]

The International Review of Retail, Distribution and Consumer Research

Vol. 18, No. 4, September 2008, 361–380

ISSN 0959-3969 print/ISSN 1466-4402 online

� 2008 Taylor & Francis

DOI: 10.1080/09593960802299437

http://www.informaworld.com

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cost. Allocative efficiency thus relates to prices, whereas technical efficiency relates toquantities.

This paper will employ Data Envelopment Analysis (DEA) as an instrument forassessing the efficiency of French retail companies, with the aim of reducing waste in theiroperations. DEA was first introduced by Farrell (1957) and later consolidated by Charnes,Cooper and Rhodes (1978) as a non-parametric procedure for comparing a decision unitwith an efficient frontier using performance indicators. Efficiency studies in retailingfrequently use DEA methodology (Athanassopoulos 1995, 2003; Barros 2005, 2006;Barros and Alves 2003, 2004; Donthu and Yoo 1998; Thomas et al. 1998). In addition, theeconometric stochastic frontiers have been used on occasion (Barros 2005). DEA, a non-parametric method, is particularly appropriate when the researcher is interested ininvestigating efficiency in the case of multiple inputs and outputs and when the number ofobservations remain limited.

This paper extends previous research in retailing by dissociating economic efficiencyinto technical efficiency and allocative efficiency, and then by relating these efficiencies toregulatory variables according to a two-stage procedure. In the first stage, a DEA modelestimates efficiency scores and ranks companies according to these scores. During thesecond stage, a ‘bootstrap’ procedure is used to estimate a truncated model that relatesefficiency scores to their determinants (Simar and Wilson 1999, 2000, 2007).

The motivation behind the present paper stems from the following issues. First,grocery retailing is recognised as an oligopolistic market (Clarke et al. 2002). Oligopolisticmarkets are overseen by regulatory agencies, in recognition of their tendency to fix outputprices at higher than their marginal cost (Lafont and Tirole 1986, 1993, 2000). Regulatoryagencies have indeed issued warnings to certain hypermarket groups as a result of theirpricing policies. As an example, Leclerc has been asked to return 23.3 million euros to itssuppliers (Liberation, 18 November 2005). The hard discounter, Lidl, has also beenrequired to pay 500,000 euros due to its ‘collaboration fee’ (‘supplier rebate’)(L’Expansion, 30 November 2005). Second, economic efficiency enables companies toobtain income through the use of resources rather than from rents and therefore offers away to align regulatory principles with managerial procedures. Third, the French economyis highly regulated and the role of the State in administrating the national economy hasalways been substantial. Development of the retail industry has been constrained over theyears by a strict legal environment (Euromonitor 2006). Contemporary regulations placedon French retailers are mainly based on laws that restrict growth, i.e. the Royer law ofDecember 1973 and the Raffarin law of July 1996. Lastly, the aim of this paper is to applythe innovative procedure proposed by Simar and Wilson (2007) in analysing the efficiencyof French retailing companies.

The paper is organised as follows. The second section describes the institutional setting,while the third section provides a review of the pertinent literature existing on this topic. Thetheoretical framework for technical and allocative efficiency is explained in the fourth section.Data are introduced in the fifth section. In the sixth section, DEA results are presented anddiscussed, and the seventh section sets forth an analysis of the determinants of economicefficiency using a truncated model. Research contributions, limitations and perspectives arederived in the eighth section, before offering concluding remarks in the final section.

Institutional setting

Retail sales increased by just slightly less than 1% in France during 2005, amounting toe345 billion (Euromonitor 2006). Moreover, with a total of 2.2 million jobs, retailing

362 C.P. Barros and R. Perrigot

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accounted for 9.4% of the overall workforce (Euromonitor 2006). The French retailmarket is basically divided into two categories of outlets. The first comprises the largestores selling food and non-food products, i.e. hypermarkets, supermarkets and otherlarge-area specialist retailers, often located in the outskirts of cities. In response toconstraints placed on opening large stores at the urban periphery, the second retailcategory refers to stores located on the high streets of city centres. The first category isdominated by major supermarket and hypermarket chains (e.g. Carrefour, Casino) as wellas large-area specialist chains selling a variety of goods from clothing (e.g. Kiabi) to homeimprovement items (e.g. Castorama), leisure and cultural goods (e.g. Decathlon).

In the present paper, we will be focusing on this first category of outlets and morespecifically on the groups that manage hypermarkets, supermarkets and hard discountstores. Hypermarket and supermarket corporations earned e81.6 and e63 billion,respectively, in 2005, with growth in value equalling 70.4% and 71.1% from 2004 to2005 (Euromonitor 2006). Both types of retailers have been extremely successful in Francesince their market entry (the first supermarket was opened in 1957 and the firsthypermarket in 1963). Over time, they have gradually dominated almost all consumerproduct markets. Even though their growth has now been somewhat limited by the Royerlaw of December 1973 and the Raffarin law of July 1996, they still constitute the two mostprevalent forms within the French retail landscape.

Nevertheless, the supremacy of these two kinds of retail structures is being threatenedby a third form originating in Germany, namely the hard discount stores. Often centrallylocated, as opposed to supermarkets and hypermarkets set up at the outskirts, the harddiscount stores have deeply changed consumer habits, especially since the inception of theEuro currency in 2002, which engendered a noticeable rise in prices. These stores’ cheap,no-frills offers are proving very attractive to consumers looking for bargains. The hard-discounter segment accounted for just over 3% of total value sales in 2006, with e10 billion(Euromonitor 2006). Table 1 presents some 2006 statistical characteristics of these threekinds of retail forms.

Carrefour and Leclerc are competing for domination in the hypermarket segment.Their respective market shares in 2005 stood at 21.1% for Carrefour and 9.1% for Leclerc(Euromonitor 2006). Other companies present in the hypermarket segment are Auchan,Casino and Hyper U. With regard to the supermarket segment, ITM Entreprises is theleading chain with a network of some 2,000 supermarkets. The groups Carrefour, SystemeU and Casino are also active in this segment. As for the hard discount segment, adichotomy exists between the German chains, Lidl and Aldi, and the French players,Leader Price and Ed Discount. The Lidl brand share equalled 35.6%, which is muchhigher than that of the others (Ed Discount, 20.4%; Aldi, 17.3%).

Table 1. Characterisation of the three predominant retail formats in France (Panorama TradeDimensions 2007).

FormatNumberof stores

Totalcommercial

floorarea (m2)

Averagecommercial

floorarea (m2)

Totalnumber ofemployees

Averagenumber ofemployeesper store

Averagenumber ofemployeesper 1000 m2

Hypermarket 1435 8,205,567 5718 281,270 196 34.28Supermarket 5525 6,878,707 1245 162,630 29 23.64Hard discounter 4074 2,708,028 665 38,000 9 14.03

The International Review of Retail, Distribution and Consumer Research 363

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Literature review

Two competing scientific methods are available for analysing efficiency: the econometricfrontier, and the DEA. Several papers in the retailing literature use one or the othermethod. In Table 2, we have summarised these papers according to the models and inputs/outputs used.

It can be observed that most authors rely upon the DEA method and only a few papersmake use of econometric models. Sellers-Rubio and Mas-Ruiz (2007a) estimated aproduction function for Spanish retailers and then compared it with DEA models. Barros(2005) analysed the outlets of a Portuguese retailing company. Productivity at anaggregate level has been analysed in the USA by Ratchford (2003), using a cost functionwith the associated share equation, between 1959 and 1995, and the author concluded thatthe industry experienced modest growth in total productivity. Betancourt and Malanoski(1999) explored the growth in specific types of services offered by a sample of USsupermarkets with an econometrically simultaneous model and concluded that theevidence suggests constant returns to scale in either output or turnover, yet increasingreturns to scale with respect to the provision of distribution services. Ofer (1973) alsofound substantial economies of scale in the Israeli retail sector and Oi (1992) detected apositive association between store size and transaction size. The supply of broader andmore extensive product lines is the reason behind larger store size, in addition to providingthe source of economies of scale (Anderson and Betancourt 2002).

This brief literature review reveals the absence of published papers analysing theefficiency of grocery companies using a two-stage procedure and a ‘bootstrap’ procedure.Barros (2005) adopted a two-step procedure but did not bootstrap the estimatedparameters. Papers on Iberian retailers by Sellers-Rubio and Mas-Ruiz (2007b) estimateda Malmquist DEA model for Spanish retailers, and Sellers-Rubio and Mas-Ruiz (2006)have also estimated a DEA model for Spanish retailers.

Measuring productive efficiency in retailing

The DEA model used in the paper to evaluate retailer activities constitutes a non-parametric technique that allows the inclusion of multiple inputs and outputs within theproduction frontier. Following Farrell (1957), Charnes et al. (1978) first introduced theterm DEA (Data Envelopment Analysis) to describe a mathematical programmingapproach for constructing production frontiers and measuring efficiency with respect tothe constructed frontiers.

Four basic DEA models have been proposed: the CCR model by Charnes et al. (1978),the BCC model by Banker, Charnes and Coooper (1984), the additive model by Charneset al. (1985), and the multiplicative model by Charnes et al. (1982). The main differencesamong these four models stem from several factors, such as whether or not the existence ofeconomies of scale is taken into account, the geometric shape of the efficiency frontier, andthe way in which inefficient units are projected onto the frontier. Moreover, additionalDEA developments include the Cone-ratio DEA model by Charnes, Cooper and Huang(1990) and the Assurance Region DEA model by Thompson et al. (1986, 1990), both ofwhich allow for non-convex reference technologies (Petersen 1990). Last, the MalmquistIndex of total productivity (Malmquist 1953; Caves, Christensen and Diewert 1982) andthe allocative efficiency model by Coelli, Rao and Battese (1998) offer extensions to DEA.

In this paper, we are employing the CCR and BCC models. Since both are wellestablished and extensively applied in the literature, their discussion in this article will be


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Table

2.

Researchinto

retaileffi

ciency.

Papers

Method

Units

Inputs

Outputs

Athanassopoulos

(1995)

DEA

Restaurants

Adjustable

inputs:(1)bararea(ft2),and(2)

number

ofplace

settings.

Uncontrollable

inputs:(1)market

size

(potential

customers),(2)number

ofrestaurants

within

a1-m

ileradius,and(3)number

ofrestaurants

within

a3-m

ileradius

(1)Foodsales(invalue),and

(2)salesofbeverages

(in

value)

Athanassopoulos

(2003)

DEA

UK

grocery

retailers

(1)Number

ofoutlets,(2)fixed

assets,(3)capital

engaged,and(4)number

ofem

ployees

(1)Sales

Thomaset

al.

(1998)

DEA

500domesticretail

outletsofa

leading

specialistretailer

intheUSA

16inputs:(1)averagenumber

offull-tim

eem

ployeesper

square

footofsalesfloorarea

times

10,000;(2)ratiooftheaveragenumber

of

full-tim

eto

part-tim

eem

ployees;(3)total

annualsalaries

andwages

divided

bypayroll

hours;(4)averagehourlyem

ployee

tenure,in

years;(5)averagelength

ofstore

manager

tenure,in

years;(6)store

age,

inyears;(7)base

rentplusother

occupancy

expensesdivided

by

thetotalsquare

footageofthesalesfloorspace;

(8)dollars

ofannualoperatingexpensesper

store;(9)market

populationper

store;(10)

averageannualhousehold

incomewithin

a2-

mileradius;(11)number

ofhouseholdswithin

a2-m

ileradius;(12)distance

inmiles

tothe

nearest

alternativestore;(13)totalaverage

inventory

atcost,in

dollars;(14)averagedollar

amountoftransactions;(15)percentageof

annualturnover;andlastly

(16)dollar

shrinkagedivided

byinventory

dollars

(1)Salesand(2)profits

DonthuandYoo

(1998)

DEA

24outletsofa

fast-food

restaurantchain

(1)store

size,(2)manager

tenure,(3)store

location(insideashoppingmallvs.

free-standing),and(4)promotion/giveaway

expenses

(1)Sales(value)

and(2)

customer

satisfaction(a

5-pointscale)

(continued)


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Table

2.

(Continued

).

Papers

Method

Units

Inputs

Outputs

BarrosandAlves

(2003)

DEA–CCR

and

BCC

model

47outletsofa

Portuguese

hypermarket

retailcompany:

1999–2000

(1)full-tim

eem

ployees,(2)part-tim

eem

ployees,

(3)costoflabour,(4)absenteeism

,(5)floorarea

ofoutlets,(6)number

ofpoints

ofsale,(7)age

ofoutlet,(8)inventory

and(9)other

costs

(1)salesand(2)operating

earnings

Keh

andChu

(2003)

DEA

BCC

model

13USstores:

1988–1997

Labour[(1)floorstaffand(2)managem

entwages

andbenefits

forthenumber

ofhours

worked)],

andCapital[(3)occupancy,utilities,(4)

maintenance

andgeneralexpenditure

forthe

store

area]

(1)Accessibility(number

of

customersserved

divided

by

population),(2)assortment

(proxiedbythenumber

of

stock-keepingunits),(3)

product

deliveryassurance

(transportationandsecurity

expenses,aswellascard

fees

andbadchequelosses),(4)

availabilityofinform

ation

(number

ofweekly

advertisingfliers

distributed

toconsumers)

and(5)retail

ambience

(number

ofstore-

specificpromotions)

BarrosandAlves

(2004)

DEA–Malm

quist

index

47outletsofa

Portuguese

hypermarket

retailcompany:

1999–2000

(1)Number

offull-tim

eequivalentem

ployees,(2)

labourcosts,(3)number

ofcheckoutpoints,(4)

inventory

and(5)other

costs

(1)Salesand(2)operating

earnings

(continued)


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Table

2.

(Continued

).

Papers

Method

Units

Inputs

Outputs

Barros(2006)

Stochastic

Cobb–

Douglasmodel

47outletsofa

Portuguese

hypermarket

retailcompany:

1999–2000

(1)Logarithm

ofoperatingcosts,(2)logarithm

price

oflabour,(3)logarithm

ofprice

ofcapital

(1)Logarithm

ofsales,(2)

logarithm

ofearnings,(3)

logarithm

ofpopulation

livingwithin

5minutesofthe

outlet,(4)logarithm

ofthe

floorareaofcompetitors’

outletswithin

10minutesof

thestore,(5)logarithm

of

part-tim

eworkersasa

percentageoftotal

employment,(6)logarithm

ofaveragedaysofstaff

absenteeism

,and(7)

logarithm

ofpurchasing

power

inthearea

Barros(2006)

Twostage

procedure:(1)

DEA

model,

and(2)Tobit

model

22Portuguese

grocery

retailers.

1998–2003

(1)Labour,(2)capital.Tobitmodel

variables:(i)

Herfindhalindex,(ii)number

ofoutlets,(iii)

ownership,(iv)regulationand(v)location

(1)Sales,(2)operating

earnings,(3)valueadded

Sellers-R

ubio

andMas-

Ruiz

(2007)

Stochastic

production

functionand

DEA

models

491Spanish

retailers,2004

(1)Assets,(2)capitalengaged,(3)investm

ent,(4)

employmentand(5)salesfloorarea

(1)EBIT

and(2)net

income


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limited to a brief description. (For further details on model development, see Charnes et al.(1995), Coelli et al. (1998), Cooper, Seiford and Tone (2000), Fare, Grosskopf and Lovel(1994) and Thanassoulis (2001), plus Appendix A.)

Technical efficiency and allocative efficiency

The above discussion suggests a theoretical model in which retail companies pay aregulatory tax on the rent they earn within an oligopolistic market. The regulatory tax capis a regulatory mechanism analogous to the well-known price cap (Bos 1994). Let q0(l, k)be the retailer’s production function, where q is output, l labour and k capital. Let w and rdenote the unit price of labour and capital, respectively. Given q0[q(l0,k0)], the quantity tobe produced, a company subject to a subsidy cap must solve the following maximisationproblem:

minl;k

wlþ rk

s:t:

q0 � qðl; kÞwlþ rl� �pqðl; kÞ � tcap

ð1Þ

where tcap is the regulatory tax cap and �q the market price per unit of service item sold, asfixed by retail market competition. This assumption reflects the fact that the price of retailitems is fixed throughout the national market. From a regulatory perspective, thisassumption implies that the only available regulatory instrument is the rent that theregulator allows the company to collect, and not the price.

The Lagrangian of Problem (1) is given by:

minLl;k¼ wlþ rkþ # q0 � qðl; kÞ

� �þ l wlþ rk� �pqðl; kÞ � tcap½ � ð2Þ

from the Kuhn–Tucker conditions, since the production function is convex and theobjective function linear, j 4 0, hence q0 ¼ q(l, k), i.e. the market mechanism satisfies theeconomic efficiency property. The first conditions with respect to l and k are, respectively:

@L

@l¼ wþ lw� l�p

@q

@l� # @q

@l¼ 0

@L

@k¼ rþ lr� l�p

@q

@k� # @q

@k¼ 0

ð3Þ

which easily yields:

@q=@l

@q=@k¼ wð1þ lÞ

rð1þ lÞ ¼w

rð4Þ

Analogous to the price cap, the regulatory tax cap mechanism is compatible witheconomic efficiency properties (Leibenstein 1966). The final result, however, will dependboth on the extent to which the company is able to manipulate information sent to theregulator and on the level of control the regulator exerts on the retailer.


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In order for regulatory policy to increase resource allocation in the retail market, retailvalue must be correlated with economic efficiency of the regulated company. Thisprocedure accommodates the Crew and Kleindorfer (1996) suggestion whereby theregulatory tax mechanism should be based on a menu of contracts according to whichthe company is free to select the regulator factor (in this case, the economic productivitylevel) most appropriate to its specific characteristics.

The DEA model allows measuring economic efficiency and its decomposition intotechnical and allocative efficiency in the presence of price information and in the aim ofincorporating a behavioural objective, such as cost minimisation or revenue maximisation(Lovell 1993). Technical efficiency refers to the ability of a retail company to obtainmaximum output from a given set of inputs. In contrast, allocative efficiency refers to theability of a retailer to use both inputs and outputs in optimal proportions, given theirrespective price levels. These two measures combine to provide an overall measurement oftotal economic efficiency.

In input-oriented models, such as the one adopted in this paper, DEA seeks to identifytechnical inefficiency as a proportional reduction in input use. However, it is possible tomeasure an output-oriented model of technical inefficiency as a proportional increase inoutput produced. As far as retailing is concerned, cost-control seems to be the naturalchoice, due to the monopolistic position of companies within this particular market.

Figure 1 depicts the input-oriented technical and allocative efficiency for a unit withtwo inputs (l, k) and one output (y).

SS0 is the isoquant defined by the production function q ¼ q(l, k) and AA’ is the iso-cost function defined by C ¼ wl þ rk, which represents price information (Varian 1987,335) with C being the cost. If a given retailer uses quantities of inputs inefficiently, asdefined by point P, in order to produce a unit of output, the technical inefficiency of thatcompany could be represented by distance QP, which is the amount by which all inputscould be proportionally reduced without any reduction in output. The Q positioned in theisoquant defines the efficient use of inputs to produce the given output.

The technical efficiency (TE) of the unit is measured by means of the ratio TE ¼ OQ/OP, with Q being the point of intersection on the production possibility curve, equal to(1 – QP/OP). The value of this ratio lies between 0 and 1 and provides an indicator of thedegree of technical efficiency for the unit under analysis. A value of 1 indicates that theunit is at point Q, positioned within the efficient production possibility curve, and thusperforming efficiently. Q lies on the production possibility curve and serves as a technically

Figure 1. Efficiency curve.


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efficient point, but remains allocatively inefficient, due to lying off the iso-revenue line. Q0

is an efficient point both technically and allocatively.Allocative efficiency (AE) is measured by the ratio AE ¼ OR/OQ. The distance RQ

represents the potential increase in output if production were to occur at the allocativelyand technically efficient point Q0, instead of at the technically efficient yet allocativelyinefficient point Q.

Total economic efficiency is defined by the ratio: EE ¼ 0R/0P.

EE ¼ TE�AE ð5Þ

with all three measures bounded by 0 and 1.What causes a retail company to be technically inefficient while working below the

production function? The reasons are numerous, but DEA does not identify the factorsleading to inefficiency and only draws attention to those units where inefficiency exists.Moreover, since the production frontier is defined by the sample, all units in the samplecould possibly exhibit inefficiency, and therefore the main result of a benchmark analysis isto rank each company analysed relative to one another. This remains valid information,however, since the inputs and outputs contributing to this relative inefficiency are able tobe identified (Bessent and Bessent 1980).

Leading the list of managerial causes for technical efficiency are rigidities associatedwith the ownership pattern, capable of inducing the principal-agent relationship (Jensenand Meckling 1976). This relationship relates to the difficulty involved in controlling thoseempowered as managers to act on behalf of stakeholders. The second cause would berigidities associated with the labour market, which give rise to the collective actionproblem (Olson 1965), whereby workers can ‘free-ride’ on management efforts to improveperformance. Third, organisational factors associated with X-inefficiency (Leibenstein1966) also exist. X-inefficiency refers to an undefined kind of efficiency related to the factthat the production function is not completely specified or known, labour contracts areincomplete and all inputs are not being marketed on equal terms to all buyers. Thesecharacteristics arise from bounded rationality and a complex or uncertain environment. Inthis situation, management may be unable to achieve the efficiency frontier. As a fourthcause, unequal access to information on activities, due to asymmetric information betweenvarious companies, can be mentioned. Some companies enjoy more privileged access toinformation than others. This cause is inherent in the lack of market transparency(Williamson 1998). Such asymmetric information is particularly important within thecontext of retailing and may be characterised by asset-specificity based on the adoptedtechnology. In asset-specific contexts, a transaction can be ex-ante competitive, but ex-postcould transform into a bilateral monopoly, forcing the losing partner to accept inefficientmarket relationships (Williamson 1975). Under these conditions, some retail companiesmight be prevented from achieving efficiency by lacking the necessary information. Fifthand last, organisational factors associated with human capital, such as no incentive toimprove efficiency, time lags in acquiring new technology and the requisite commensurateskill upgrades due to inertia effects, can all be cited. As an example, rents collected by retailcompanies not linked to performance do not establish a differential incentive system andtherefore do not contribute to efficiency.

Contextual causes of technical inefficiency consist of size factors associated with scaleand scope, plus economies of scale within the activity. Allocative inefficiency arises whenretailers are not using their resources according to market price. Reasons for such


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inefficiency include labour market rules imposed by unions, which distort labour resourceflexibility and increase labour price on the one hand and the level of governmentregulation on the other. While the causes of technical inefficiency are usually internal tothe management of the analysed unit and thus pertain to management responsibility, thecauses of allocative inefficiency are usually related to the market and thus stem fromgovernment responsibility. Inefficiency relative to insufficient control exercised by theindustry regulator is due to ineffectual monitoring.

Regression analysis of efficiency determinants

We will next briefly outline an application of regression analysis for studying thedependency between efficiency scores and hypothesised explanatory variables, accordingto the approach developed by Simar and Wilson (2007). We will assume and test thefollowing specification:

TEj ¼ aþ Zjdþ ej; j ¼ 1; . . . ; n ð6Þ

which can be interpreted as the first-order approximation of the unknown truerelationship. In Equation (6), a is the constant term, ej the statistical noise and Zj a(row) vector of observation-specific variables for DMU, with j expected to relate to theDMU efficiency score, TEj, through the vector of parameters d (common to all j) requiringour estimation.

One common practice in the DEA literature for estimating model (7) had been toemploy the Tobit estimator, until Simar and Wilson (2007) showed that such an approachwas inappropriate. These authors justified an alternative approach based on a truncated-regression with a bootstrap and illustrated (in Monte Carlo experiments) its satisfactoryperformance. We have adopted their approach in this paper. More specifically, in notingthat the distribution of ej is restricted by the condition ej � 1 7 a 7 Zjd (since both sidesof Equation (7) are bounded by unity), we utilise the Simar and Wilson (2007)developments and assume that this distribution is truncated normal with a zero mean(before truncation), unknown variance and a (left) truncation point determined by thisvery condition. Furthermore, we have replaced the true but unobserved regressand inEquation (7), TEj, by its DEA estimate TEj. As a formal expression, our econometricmodel is given by:

TEj � aþ Zjdþ ej; j ¼ 1; . . . ; n; ð7Þ

where:

ej � Nð0; s2e Þ such that ej � 1� a� Zjd; j ¼ 1; . . . ; n; ð8Þ

which we estimate by maximising the corresponding likelihood function with respect to(d; s2e ), given our data. By relying on asymptotic theory, normal tables can be used toconstruct confidence intervals, though greater precision can be gained using the bootstrap,in particular because our regressand is composed not of true variables, but their estimates,which are likely to be dependent (see Simar and Wilson (2007) for details). We thereforeemploy the parametric bootstrap for the regression, as this incorporates information onthe parametric structure and distributional assumption in order to construct the bootstrap


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confidence intervals for parameter estimates (d; s2e ). For the sake of brevity, we referreaders to Simar and Wilson (2007) for details on this estimation algorithm.

Data

Table 3 presents characteristics of the variables. In order to estimate the productionfrontier, we have used panel data on French grocery retailers for the years 2000through 2004 (5 years 6 11 units ¼ 55 observations). The companies considered in theanalysis are listed in Table 4. Data selection consisted of a two-step process. Webegan by examining the annual sectorial rankings in the Grand Atlas desEntreprises, published by the journal Enjeux. We then focused on the retailing ranking,which features 20–30 companies, depending on the year. Given the data limitationsherein, we sought further information on the ranked companies in the Kompassdatabase. A total of 11 companies was found to be sufficiently well-described forintegration into our sample.

We measured outputs through turnover and profit, while inputs were measured bylabour (which in turn was measured by the number of equivalent full-time workers) andcapital (measured by the value of company assets).

Input prices were not directly observed and therefore had to be constructed from theavailable information, i.e. by dividing expenditure flows by stock. The labour price equalswages and fringe benefits divided by the number of employees. Adding fringe benefits towages allows taking the overall salary structure into account. The price of physical capitalequals expenditures on equipment and premises divided by the book value of physicalassets.

Using price estimates is common in efficiency studies because of data constraints. Wedo recognise, however, that the use of input proxies constitutes a limitation in this study.All variables in value terms (turnover, profit, asset value) were deflated in order to obtainimplicit quantities, by dividing the reported value by the GDP deflator. These observationsand variables used serve to satisfy the DEA convention that the minimum number ofDMUs is greater than 3 times the number of inputs plus output (55 � 3(2 þ 2)) (Raaband Lichty 2002).

The other variables presented in Table 3 have been used in the truncated regressionmodel.

DEA results

DEA is a non-parametric methodology. The assumptions on underlying technologydetermine whether the efficient frontier is forced to pass through the origin, thus implyinga constant return to scale (CRS), or prevented from passing through the origin, implyingvariable returns to scale (VRS). The CRS assumption is appropriate only when productionis optimal, i.e. when it corresponds to the flat portion of the long-run average cost curve. Anumber of factors induce suboptimal production as the means for regulating imperfectcompetition. The use of a CRS specification when production is not at its optimal level willresult in measurements of technical efficiency distorted by scale efficiencies (Ali and Seiford1993). Use of the VRS specification enables calculating technical efficiencies devoid ofthese scale-efficiency effects. While CRS is a required condition for competitive markets(Varian 1987), it is recognised in retailing that the market is not competitive and thereforeVRS describes its context more accurately. Consequently, CRS is typically calculated forcomparative purposes.


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Table

3.

Inputandoutputcharacteristics,2000–2004.

Variable

Units

Minim

um

Maxim

um

Mean

Square

ofdeviation

Outputs

Turnover

Value,

inmillionsofeuros

120,750,000

16,026,000,000

40,432,26,451

5,406,472,810

Profit

Value,

inmillionsofeuros

1,000,000

7,630,204,094

91,083,436

159,296,615

Inputs

Labour

Number

ofem

ployees

200

451,983

70,641

125,833

Capital

Value,

inmillionsofeuros

40,016,666

7,070,000,000

1,716,564,761

2,068,133,546

Inputprice

Price

oflabour

Wages

andbenefits

divided

bynumber

ofequivalent

employees

382.978

65,000

16,600

12,991

Price

ofcapital

Expendituresonequipmentandpremises

divided

bythebook

valueofphysicalassets

0.298

15.860

4.549

3.287

Truncatedregressionmodel

variables

Trend

Yearlytrendto

accountfortimeeff

ects

intheeffi

ciency

scores

15

31.427

Square

trend

Square

valueofthetrendto

accountfortimeeff

ects

inthe

efficiency

scores

125

11

8.728

Quoted

Dummyvariable,equalto

1forlisted

grocery

retailersand0

otherwise

01

0.363

0.485

M&A

Dummyvariables,equalto

1forgrocery

retailersinvolved

inmergersandacquisitions

01

0.352

0.328

Group


1forcompaniesbelongingto

nationalgroupsofseveralretailform

s0

10.545

0.502

Regulation


1forgrocery

companiesthathave

beenthetarget

ofdirectqueriesbytheregulatorrelativeto

inform

ation,price

restrictionsandrestrictionto

open

new

shops

01

0.156

0.217

International


1forgrocery

companiesengaged

ininternationalactivityand0otherwise

01

0.368

0.512


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Table 4 shows the technical efficiency, allocative efficiency and economic efficiency withboth CRS (constant returns to scale) and VRS (variable returns to scale) for the period2000–2004. The efficiency scores, ranging between 0 and 1, define an average ranking of

Table 4. Technical efficiency and allocative efficiency of French retailers.

French retailers Technical efficiency Allocative efficiency Economic efficiency

CRS average values: 2000–2004Le Mutant 0.210 0.984 0.207Mazagran 0.392 0.845 0.331Systeme Ouest 0.432 0.711 0.307Cadi 0.855 0.836 0.715Auchan 0.365 0.675 0.246Carrefour 1.000 1.000 1.000Casino 1.000 1.000 1.000Hyparlo 0.384 0.976 0.375Match 0.377 0.805 0.304Monoprix 0.385 0.646 0.249System U Centrale Nationale 1.000 0.553 0.553Mean 0.581 0.821 0.480Median 0.392 0.836 0.331Std Dev. 0.310 0.158 0.296

VRS average values: 2000–2004Le Mutant 0.74 0.961 0.712Mazagran 0.916 0.969 0.887Systeme Ouest 1.000 0.807 0.807Cadi 0.882 0.931 0.820Auchan 0.382 0.988 0.378Carrefour 1.000 1.000 1.000Casino 1.000 1.000 1.000Hyparlo 0.403 0.942 0.380Match 1.000 1.000 1.000Monoprix 0.549 0.621 0.341System U Centrale Nationale 1.000 1.000 1.000Mean 0.779 0.923 0.731Median 0.916 0.969 0.820Std Dev. 0.248 0.116 0.268

Table 5. Truncated bootstrapped second-stage regression (dependent variable: CCR index).

Variable Model 1 Model 2 Model 3 Tobit

Constant 71.16*** 71.10*** 71.16*** 1.32***Trend 0.11*** 70.09** 0.19*** 0.43***Square trend 0.03 0.07** 0.07 0.12*Quoted 0.03 0.00 – 0.21M&A 0.03 0.00 0.04 0.43Group 0.16*** 0.13*** 0.15*** 0.35Regulation 0.04*** 0.04*** 0.03*** 0.08***International 0.01 – –Variance 0.06*** 0.07*** 0.06*** 1.34Total number of observations 1000 1000 1000 1000

***, ** statistically significant parameters at the 1% and 5% levels.

Note: The Tobit model variance is sigma.


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French retailers. A value of 1 corresponds to the most efficient company and 0 cor-responds to the least efficient.

We can observe that average economic efficiency is higher with VRS technology thanwith CRS, signifying that scale constitutes the main cause of efficiency in this market. Wewould therefore expect to find that larger retailers are more efficient than smaller ones.Moreover, we have observed that the average scale in VRS is 0.779, yet with a median of0.916, meaning that a higher proportion of retailers display efficiency scores above themean. The same situation is noted for the allocative and economic efficiencies. In addition,four companies are found to be efficient, i.e. displaying a score equal to one, whichindicates that they are the most efficient for the given period and sample analysed. Lastly,we have determined that scores are higher in the case of allocative efficiency than technicalefficiency, suggesting that allocative efficiency is more critical than technical efficiency.

Determinants of efficiency

In order to examine the hypothesis that the efficiency of French grocery retailers isdetermined by different contextual variables, we followed the two-step approach, as setforth by Coelli et al. (1998), to estimate the regression shown below. In the DEA literature,it is recognised that the efficiency scores obtained during the first stage are correlated withthe explanatory variables used over the second term and that second-stage estimates will,as a result, be inconsistent and biased. A bootstrap procedure is thus needed to overcomethis problem (Efron and Tibshirani 1993).

The estimated specification is as follows:

yi;t ¼ b0 þ b1 Trendi;t þ b2 Trend2i;t þ b3 Quotedi;t þ b4M & Ai;t

þ b5Groupi;t þ b6Regulationit þ b7Internationali;t þ ei;tð9Þ

where y represents the efficient score (TFP score). Trend is a yearly trend. Square trend isthe squared value of the trend. Quoted is a dummy variable that equals 1 for stock market-listed grocery retailers and 0 otherwise. It seeks to capture the efficiency relative to thescrutiny inherent in stock exchange exposure. M&A is a dummy variable equal to 1 forcompanies involved in mergers and acquisitions and 0 otherwise. Its purpose is to capturegrowth-orientation strategies inherent to some grocery retailers. Group is a dummyvariable equal to 1 for grocery retailers belonging to an economic group and 0 otherwise.This variable seeks to capture economies of scope within the activity. Regulation is adummy variable equal to 1 for grocery companies that have been targeted by the regulator,either through simple investigation, financial penalties or restriction on opening newshops. Such action affects some of bigger retailers during the study period. Lastly,International is a dummy equal to 1 for grocery retailers promoting an internationalexpansion strategy. This variable seeks to capture international growth-orientationstrategies (Berger and Humphrey 1997) (see Table 5).

The truncated-regression with a bootstrap model appears to fit the data quite well, withpositive t-statistics that prove significant for all parameters with the exception of trend.The estimations generally conform to a priori expectations. The efficiency scores arepositively related and statistically significant with respect to all variables, with theexception of the constant and square trend. We have observed that efficiency increasesover the observation period, as a function of trend, but at decreasing rates vs. the squareterm. One rationale for this is based on the growth limitations of internal markets.


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Quotation contributes to efficiency, which means that the stock exchange discipline andinherent public scrutiny contribute to the efficiency of grocery retailers. Being involved inmergers and acquisitions also contributes to efficiency, as would be expected sincecompanies involved in M&As are more likely to be aware of their market environment andmust also be aware of their efficiency. Belonging to a group is beneficial to efficiency sinceit induces internal benchmarking, which in turn enhances efficiency. Finally, beinginvolved in an international expansion strategy also increases efficiency. One rationale forthis is based on the awareness that internationalisation necessitates and this naturallytranslates into higher efficiency.

Discussion

Technical efficiency is broken down into pure technical efficiency and scale efficiency andtherefore reflects both managerial and dimensional effects, while allocative efficiencyreflects just price effects. The findings of our empirical study therefore show that Frenchretailers display higher allocative efficiency than technical efficiency, indicating that pricesare less distorted than managerial procedures in this market. Moreover, Carrefour andCasino are the most efficient retailers in technical efficiency and allocative efficiency, bydisplaying a remarkable level of efficiency that should be used by other units as abenchmark. The French grocery retailing sector offers a number of efficiency drivers,which reflect the fact of being listed on the stock exchange, adopting M&A strategies,belonging to a group and being involved in an international expansion strategy. It isimportant to take these elements into consideration when designing a new strategy aimedat improving company efficiency. Being listed on the stock market increases scrutiny ofretailer performance and thus requires the company to adopt a performance-orientedstrategy. Adopting a M&A strategy signifies that the company is growing by acquisition ina market where internal growth is restricted and where survival dictates rapid relativegrowth. Regulation has a positive effect on efficiency, meaning that grocery companiesadapt their efficiency to regulatory changes. Lastly, being part of a group reflects theadvantages conglomerates enjoy in contemporary markets.

What, then, is the policy implication of this research? In France, as in most Europeancountries, retailing regulation restricts the number of outlets and merger and acquisitionactivity, in the aim of protecting small shops. Based on these results, the main policyimplication herein is that legal regulation does not restrict the most competitive retailers insucceeding to increase their technical and allocative efficiencies. In addition, while therestrictions on shop openings have not been taken into account, we have analysed mergerand acquisition activity and shown that it definitely contributes to efficiency. Despite therestrictions on mergers and acquisitions, for those retailers that have decided to adoptthem, this policy actually contributes to efficiency. The restrictive retailing policyshould be abandoned and instead a policy orientation towards consumers adopted.Furthermore, regulation should focus on efficiency improvement as a means of restrictingretail price increases. Such a policy would represent a sound, consumer-oriented retailingstrategy.

As far as the managerial implications of the present research are concerned, Frenchretailers should adopt a benchmark exercise in order to succeed in the marketplace, incopying the managerial and price policies of the most efficient retailers, which over thestudy period and sample were Carrefour and Casino. It is essential for the companies toidentify the best practices of competitors and apply them while obviously maintainingsome elements of differentiation. This situation could change for different samples and


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periods. Since this research is exploratory, the intent is not to obtain definitive results fordirect use by French retailers.

This paper has two main limitations: one related to the data set, and the other relatedto the DEA method. With reference to the data set, the homogeneity of retail companiesused in the analysis is questionable, since we have compared companies with different sizesand production characteristics. They may face different restrictions and, as a result, mightnot be considered directly comparable. We can always claim, however, that the unitsare not comparable and that a ratio analysis therefore could not be carried out. Moreover,the data set is small, making the conclusions limited. In order for the conclusions to begeneralised, we would require a larger panel data set. Reducing the number ofobservations on DEA variables increases the likelihood that a given observation will beconsidered relatively efficient (Banker 1993).

The DEA model limitations are as follows. The DEA does not impose any functionalform on the data and does not adopt distributional assumptions for the inefficiency term.In addition, it does not draw a preliminary distinction between the relative importance ofany combination of inputs and outputs. None the less, these limitations actually serve asthe most distinctive and attractive characteristics of DEA. This efficiency measurementassumes that the production function of the fully efficient unit is known. In practice, suchis not the case and the efficient isoquant must be estimated from sample data. Under theseconditions, the frontier is relative to the sample considered in the analysis. The leastattractive characteristic of DEA is that without any statistical distribution hypotheses, themodel does not allow for random errors in the data and hence assumes away measurementerror and chance as factors affecting outcomes (Seiford and Thrall 1990).

Several paths for future research can be recommended. First of all, in this analysis, theDEA model has allowed for complete weighting flexibility. In situations where somemeasures are likely to be more important than others, DEA enables restricting factorweights through linear constraints, with these constraints representing ranges for relativepreferences among factors based on managerial input. Such analyses enable effectiveincorporation of managerial input into the DEA evaluations. Second, the input andoutput dimensions considered are context-specific. More comprehensive input and outputmeasurements, namely in allowing for non-discretionary factors, such as environmental,socio-economic and quality inputs and outputs, to be taken into consideration. Theinfluence of non-discretionary variables, excluded from our analysis, amounts to anassumption that these factors are constant across the sample. Third, non-parametric, free-disposal hull analysis or, alternatively, parametric analysis can be implemented to assessefficiency scores. Previous research has shown however that DEA scores lie beloweconometric scores, yet the ranking remains the same (Bauer et al. 1998).

Conclusion

This article has proposed a simple framework for evaluating French retail companies andrationalising their operational activities. The analysis was based on a DEA model thatallowed for the incorporation of multiple inputs and outputs in determining relativeefficiencies. Benchmarks were provided in order to improve operations of the more poorlyperforming companies. The determinants of economic efficiency were investigated, andseveral interesting and useful managerial insights and implications from the study werediscussed.

The general conclusion is that inefficient French retailers should adopt a more efficientprocedure for driving their business. They should seek to identify the best practices of their


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more efficient peers identified from the benchmark procedure applied in this paper. Such apolicy change is critical for them to identify best practices while maintaining someelements of differentiation.

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Appendix A: Data Envelopment Analysis

DEA is a non-parametric, multi-factor productivity analysis model that evaluates the relativeefficiency of a homogenous set of decision-making units in the presence of multiple input and outputfactors. The model is run n times, where n represents the number of decision-making units, todetermine the efficiency scores of all units. Each unit selects optimal weights to maximise itsefficiency, which is assessed as the weighted output-to-weighted input.

LP model:

Minl;xiw0ixi

st

� yi þ Yl � 0

xi � xl � 0

N10l ¼ 1

l � 0

where i is the unit being evaluated, wi a vector of input prices for the i unit, xi the cost-minimisingvector of input quantities for the i unit (calculated by LP), given input prices wi and output level yi. lare the weights assigned to output and input, respectively.

The total efficiency is CE ¼ wi’xi/wi’xi*, where xi* is the observed vector of inputs. AE ¼ CE/TE, with TE calculated using a standard DEA model. [See Charnes et al. (1978) and Thanassoulis(2001).]


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Documents

Analysing technical and allocative efficiency in the French grocery retailing industry