Visser Tavara Villaran Gamarra Cluster Growing but Not Developing KNAG Netherland 2015

GROWING BUT NOT DEVELOPING: LONG-TERMEFFECTS OF CLUSTERING IN THE PERUVIANCLOTHING INDUSTRY

EVERT-JAN VISSER*1, JOSÉ I. TÁVARA** & FERNANDO VILLARAN***

*Ministry of Economic Affairs, Bezuidenhoutseweg 73, The Hague, the Netherlands. E-mail:[email protected]**Pontificia Universidad Catolica del Perú, Departamento de Economia, Av. Universitaria 1801, SanMiguel, Lima-32, Peru. E-mail: [email protected]***Dean Antonio Ruiz de Montoya University-UARM, Engineering and Management Faculty, Av. Pasode los Andes 970, Pueblo Libre, Lima 21, Peru. E-mail: [email protected]

Received April 2013; accepted November 2013

ABSTRACTThis paper analyses how a cluster of clothing firms in Peru fared over a 15 year period. Thequestion is how and why this cluster has changed. We collected data for 1993 and 2007, comparingclustered and dispersed firms. The cluster grew significantly in terms of the number of firms andemployment, due to the attraction of trade activities towards the cluster. The productivity ofclustered producers fell somewhat, although they maintain an advantage over dispersed firms.This is due to static advantages falling into a producer’s lap once located in the area anddeveloping at the level of transacting inputs and output. Clustered producers do not use profits toupgrade businesses but rather invest in real estate. On the whole, they are struggling. Enhancingthe quality of cluster governance is critical to prevent a further decline of the production part ofthe cluster.

Key words: Clusters, cluster dynamics, SMEs, emerging economies, Peru

INTRODUCTION

Cluster theory aims to understand the underly-ing factors as well as the necessary and sufficientconditions for a sustained positive impact ofclustering on firm-level performance (Porter1998; Brenner 2004; Visser 2009). A number ofstudies revealed a positive influence of cluster-ing on firm-level performance (Saxenian 1994;Visser 1999; Baptista & Swann 1998). However,these are not ever-lasting effects; clusters growbut may also decline faster than firms operatingoutside clusters (Menzel & Fornahl 2009). Inthis paper, we follow propositions to analyseclusters over longer time periods (Lorenzen2005; Menzel & Fornahl 2009). The need forlongitudinal cluster analyses is evident. Clusters

are based on spatial concentration processesoccurring through time, and may involvechanging interactions between actors and/orfactors, with cluster advantages (knowledgeheterogeneity, local competition and localisedlearning) turning into disadvantages (myopicbehaviour and several forms of lock-in) thatproduce stagnation and decline in stead of con-tinued and relatively fast growth of clusters.

In this paper, we observe and analyse howa cluster in the Peruvian clothing industry,located in the capital city of Lima, fared over a15 year period. The question is whether, howand why this cluster changed in terms of size,activities, strength and nature of clusteringadvantages, and development stage of thecluster (growth, stagnation or decline). To

Tijdschrift voor Economische en Sociale Geografie – 2015, DOI:10.1111/tesg.12083, Vol. 106, No. 1, pp. 78–93.© 2014 Royal Dutch Geographical Society KNAG

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answer this question, we collected data for 1993and 2007, comparing clustered firms with pro-ducers elsewhere in Lima.

The paper is organised as follows. The follow-ing section examines critical propositions inthe literature on the evolution of clusters. Thethird section describes key features and trendsin the spatial and functional boundaries of thecluster under review. The fourth section pres-ents the results of a cross-section analysis ofclustered and dispersed producers based onsurvey data for 1993 and 2007. The final sectiondraws conclusions and derives policy implica-tions, while keeping in mind that the worldwideclothing industry is constantly changing.

THEORETICAL FRAMEWORK

Over the past decades, researchers have beenpaying attention to the concept of clusters offirms specialised in a set of related economicactivities and to the effects of clustering onproductivity and innovation (Becattini 1990;Porter 1990, 1998, 2000; Asheim 1996, Baptista& Swann 1998, Cooke 2001, Asheim et al.2006). In addition, a network approach to pro-ductivity enhancement, learning and innova-tion emerged, which focuses on strategic,preferential and repetitive knowledge interac-tions between firms and other organisations(DeBresson & Amesse 1991; Uzzi 1996;Hotz-Hart 2000; Rutten 2002; Nooteboom2004; Powell and Grodall 2005; Porter et al.2007). This gave rise to the analysis of the struc-ture of networks within clusters to explain whysome clusters perform better than others(Giuliani 2007) and to highlight the impor-tance of non-local ties for cluster development(Bathelt et al. 2004).

It is important to distinguish between the con-cepts of clusters and networks, because of theirdifferent effects for learning and for the abilityto co-ordinate joint actions and collective invest-ments (Visser 2009). Brenner’s (2004) work onthe interactions and (necessary and sufficient)conditions that make clusters tick has alsobeen useful to identify the various ‘local self-augmenting processes’ that induce the spatialconcentration of industrial development.Other authors focused on classifying clusters,aiming at identifying different types of pro-cesses leading to spatial concentration and dif-

ferent effects for the firms involved (Newlands2003; Visser & Atzema 2008). This work does notfully explain, however, why clusters grow fasterthan competitors elsewhere, why they may alsofail and enter a period of decline, and whatshould and can be done to avoid such an expe-rience. Hence, recent work focuses on the analy-sis of the long-term evolution of clusters.

Gilsing and Hospers (2000) distinguish fivestages of cluster development: formation,expansion, saturation, decline and revival.During the expansion phase, the number offirms increases due to the attraction of outsidefirms towards the cluster, a relatively high rateof start-ups and spin-offs, and the growth ofincumbent firms. During the saturation stage, adominant design emerges, firm strategies shiftfrom innovation to cost efficiency, competitionintensifies, and agglomeration diseconomies(congestion, pollution, and price increases forland, real estate and specialist labour) stimulateexit while enhancing the average size of firms inthe cluster. Institutional rigidities may acceler-ate this process and even push the cluster into astage of decline, during which ‘cut-throat’ costcompetition, protectionist practices and rentseeking behaviour lead to a reduction in thenumber of firms. Sometimes, however, depend-ing on the quality of cluster governance, firmsin a cluster are capable of seeking a way-out ofthe crisis through joint actions and collectiveinvestments. If effective their number may sta-bilise, albeit at a lower level than during thecluster’s hay-days.

Menzel and Fornahl (2009) argue that clus-tering matters during two stages of develop-ment: the growth and decline of clusters. Putsimply, clusters grow but may also decline fasterthan industries elsewhere. Faster growth is aresult of superior knowledge and technologybeing developed in the cluster, which in turn isdue to initially high levels of cluster-internalknowledge heterogeneity on the one hand, andeffective localised learning and local spilloversproducing technological convergence withinthe cluster on the other hand. Such sustains acluster’s dominance, albeit temporarily, in theglobal market space. However, technologicalconvergence decreases the level of knowledgeheterogeneity within the cluster – once a sourceof spillovers and cluster growth. Technologicalconvergence and local path dependence may

CLUSTERING IN THE PERUVIAN CLOTHING INDUSTRY 79

© 2014 Royal Dutch Geographical Society KNAG

lead to a situation of sustaining clusters that arein equilibrium, and where the number of firmsand employment do not change much, apartfrom smaller cyclical fluctuations. But it mayalso produce technological and other forms oflock-in, thus initiating the decline of clusters,with the number of firms and employmentdiminishing faster than elsewhere due toreduced firm entry, increasing firm exits,mergers and rationalisations (Menzel &Fornahl 2009). This process may go on until thecluster is entirely gone, or it may evoke clus-tered firms to respond to the crisis, throughtimely adaptation to changes in the businessenvironment, more radical technological andproduct renewal or even a complete transfor-mation of the cluster by moving towardsentirely new economic activities (Menzel &Fornahl 2009). So, clusters may stop decline, asthey may have a governance advantage whileseeking a way-out of the crisis through jointactions and collective investments (Visser2009). Such also depends, however, on thewider socio-institutional context of a cluster.

Based on previous studies of the clusterunder review that point at problems of decreas-ing knowledge heterogeneity, lock-in, lack ofnon-local ties and wider socio-institutional con-straints (Visser 1996, 2000; Cornejo Manrique1999; Tello & Távara 2010) and considering themounting international pressure on the Peru-vian clothing industry, we expect the clusterunder review below to have entered the stage ofdecline and to struggle to adapt and renewitself, let alone induce a transformation towardsnew activities.

CASE DESCRIPTION

The cluster under review is locally known as theGamarra cluster. It is located in La Victoria, oneof the 43 municipal districts of Lima, the capitalcity of Peru (Figure 1) and located close to thecity centre (Figure 2). The La Victoria district isknown as an industrious, dynamic and eco-nomically dense part of Lima.

The Gamarra cluster took various decades todevelop (for an overview and analysis of drivingfactors , see Ponce 1994; Távara &Visser 1995,Visser 1996). The first real-estate investmentswere made in the early 1970s. In August 1993,the cluster consisted of about 35 housing blocks

(Ponce 1994). At that time, it comprised morethan 150 so-called ‘galerias’: buildings con-structed by private investors using profits theymade with garment-making. These buildingscomprise a few tens up to several hundreds ofsmall workshops and stores. In August 1993,6,800 firms were active in the cluster: 2,000clothing firms; 4,100 traders of cloth fabricsand accessories, 150 sellers of equipment andcomponents and about 300 small restaurants(Ponce 1994). These figures excluded streetsellers and other informal businesses operatingsomewhere in the area, but without a formaladdress. The Superintendence of Tax Admin-istration (SUNAT) estimated that in 1993, thetotal number of firms in the cluster was 8,000 –a number that was likely to come closer toreality, as the tax office was at that time active inraising taxes and shutting down businesses notcomplying with tax obligations. For the sake ofcomparison, we assume that the total numberof firms in 1993 did not exceed 10,000.

In 2007, the Gamarra cluster comprised20,393 firms involved in clothing-related activi-ties (Municipality of La Victoria census data for2007). This boils down to an increase in thenumber of firms of about 100 per cent over 15years, implying an annual growth rate of 5 percent of the cluster. The number of clothingproducers more than doubled (4,336 in 2007),but the bulk of the cluster’s growth took placein trade activities. In 2007, 14,630 traders ofcloth fabric, accessories, machinery and com-ponents were active in the cluster, with another1,120 warehouses and 307 restaurants operat-ing in the area. In 2007, the share of producersin the total number of clustered firms was about21 per cent, while traders had a share of 72 percent. As these figures for 1993 were 29 and 60per cent respectively, it seems that the cluster ismoving towards trade activities. Skyrocketingreal estate prices and rents appear to havedriven this process, inducing less profitableactivities to relocate towards cheaper sites inthe city of Lima (Chion 2001). So, the share ofthe production part of the cluster is in decline,with trade activities flourishing in both absoluteand relative terms.

The cluster’s recent expansion and shifttowards trade took place in a context of fastmacroeconomic growth in Peru, with annualgrowth rates of GDP in constant prices

80 EVERT-JAN VISSER ET AL.


amounting to 5 per cent between 1993 and2007. Higher incomes enhance demand forclothing, thus fuelling trade activities, also inthe cluster. Peruvian exports of textile and

clothing products increased 160 per centduring 2001–2007 (Gonzales de Olarte 2008),but clustered producers hardly participated inthis boom (Tello & Távara 2010). This suggests

Figure 1. Map of Peru.



that clustered producers are not prepared wellenough to export. They enjoy advantages thathelp them to withstand (foreign) competitionin good times, but these are insufficient to par-ticipate in export markets and maintain sales, atthe cost of competitors, in bad times.

A final comment is that the cluster underreview is a good case of a significant process ofspatial concentration of firms specialised ina certain core activity (garment-making) thatattracts related trade and service activitiestowards the cluster. Next, this process takesplace in a developing country context, withlimited institutional strength and very scarce ifnot absent services from public and semi-publicinstitutes.

TRACING CLUSTERING EFFECTSTHROUGH TIME

This section describes the results of a cross-section survey of small and medium enterprises(SMEs) in the Peruvian apparel industry, com-paring clustered producers with dispersed

firms operating elsewhere in the city of Lima,and contrasting the data gathered for the year2007 with the results of identical surveys for twoprevious years: 1993 and 1994 (see Appendixfor information on sampling methods). Surveyquestions focus on the performance of firmsduring the previous year, the importance ofseveral location factors, the perceived advan-tages of clustering, the external organisation ofbusiness processes, and the geography of differ-ent linkages with other firms and markets. Byasking identical questions after a period of 15years, it is possible to trace trends in the perfor-mance and behaviour of firms, the type of clus-tering process underway in Gamarra, and theadvantages and disadvantages of this processfor the firms involved.

Tracing the performance gap between clus-tered and dispersed firms2 – Regarding employ-ment size, the sample average for 2007 is aboutsix workers, among other factors due to thelimited importance of scale economies ingarment making (Visser 1996). There is no

Figure 2. Map of Lima with the Gamarra cluster shown.



difference across the two sub-samples regard-ing the average employment size of firms in2007. However, as the distributions of the twosub-samples differ, it makes sense to analyse thedata for different size classes:3 micro enter-prises (1–4 workers), small enterprises (twosubcategories: 5–9 and 10–19 workers; seeVisser 1999 for an explanation) and medium-sized enterprises (20–99 workers). Table 1summarises the outcomes.

In 1993, clustered firms most often pertainedto the small-scale subcategory of 5–9 firms,whereas dispersed firms most often belongedto the micro-enterprise class. In 2007, thisdifference has gone. This is partly due to anoverall trend of micro enterprises increasing inimportance in the sample as a whole, from 38 to60 per cent. This is most pronounced amongdispersed producers, however, where 71 percent of firms are micro-sized in 2007, against 33per cent in 1993. In the cluster, 50 per cent ofthe subsample falls into the micro category in2007, against 25 per cent in 1993.

So, are clustered producers cutting employ-ment? The answer is not necessarily affirmative.An alternative explanation is that the universefrom which the sub-samples were taken in 1993was biased toward small enterprises, since taxreform was just underway and the informalsector was larger then. The 2007 register ismore comprehensive and includes a (much)larger fraction of micro firms that entered themarket in a context of enhanced demand forclothing products. Next, clusters grow anddecline due to firm entry, exit and relocation offirms. High start-up rates may decrease the

average size of firms. In Gamarra, relocation offast-growing firms is moreover fuelled by thelimited size of workspace available in the cluster(Távara & Visser 1995). This also constrains therelocation of firms towards the cluster. So, start-ups and relocation may also be responsible forthe finding that micro enterprises have becomemore important. Among dispersed producers,however, employment growth has been laggingand in some cases even turned negative, sug-gesting that downsizing indeed contributed todecreasing employment in this group.

The second performance indicator is annualemployment growth. Most firms expand em-ployment, adding on average 0.64 workplacesper year. There are marked differences acrossclustered and dispersed producers, however.Only some clustered firms fail to grow, whereasa relatively high number of dispersed produc-ers have been cutting employment. As a result,the mean for the clustered group is 0.88 work-places a year over the average lifetime of firms,against 0.4 workplaces in the group of dis-persed producers (t-test 2.57, two-tailed signifi-cance at 0.01).

Considering the monthly average of grosssales per worker, an important finding for1993 was that clustered firms sold significantlymore than firms elsewhere in Lima: low-income areas, high-income areas and firmsin the immediate surroundings of the cluster(see Appendix). Subsequent data collectionfocused on the comparison between clusteredfirms with producers in high-income areas, fortwo reasons: the good and seemingly improvingsales performance in this control group, and

Table 1. Employment size of firms by location (in per cent of row totals).

Year/location 1–4 workers 5–9 workers 10–19 workers 20–99 workers

2007a

Clustered 50b 31 14 5Dispersed 71 13 6 10Sample 60 23 10 7

1993Clustered 25 54 13 8Dispersed 33 23 27 17Sample 38 37 16 9

Notes : a Figures printed in bold indicate the modal firm–size category in each group.b Pearson’s χ2 is significant (2–sided), with P = 0,007.

Source : Survey: July/August 2008 (2007 data), Visser (1999, 1996 (1993 data).



the relatively dynamic production and market-ing behaviour of some firms in this group(Visser 1996). So, the 2007 survey also com-pares clustered producers with the most chal-lenging group outside the cluster.

Against this background, it may not be sur-prising that the sales difference between thetwo groups in 2007 is not significant. This onlyholds, however, comparing all the firms in thetwo sub-samples. Outlier analysis reveals a veryskewed distribution of the sales performance offirms, especially in the control group, suggest-ing that some firms in high-income areas dovery well. Their performance obscures the sightof what is happening to most firms in thisgroup. Excluding outliers (one in the clusterand seven in the control group), the sales dif-ference between clustered and dispersed pro-ducers is large and significant (Table 2).

Visser (1996) argued that clustered firmshave a better sales record due to relatively highlabour productivity in volume terms and thuslower unit production costs, longer workingdays, and a relatively good market-fit of gar-ments ‘made in Gamarra’ and sold to wholesal-ers in Lima, other Peruvian regions and

neighbouring countries. Relatively high labourproductivity is a well-known Marshallian effectof clustering. Other relevant advantages arethe relatively good trade connections betweenthe cluster and various Peruvian and Andeanconsumer markets and the favourable reputa-tion of the cluster in consumer markets. Thisenhances firm entry and relocation towards thecluster, which in turn intensifies competition,stimulates the use and circulation of locallyavailable product and market informationand also fosters specialisation of producersin certain product lines or producer services.Internal economies do not appear to be cata-lysts in all this, whereas external factors doappear to play a decisive role (Visser 1996). In2007, all of the above factors were still at workand effective, yielding clustering advantagesthat are persistent through time. At the sametime, one should consider the fact that themonthly average of gross sales per worker in thecluster was US$1,148 in 1993, and US$983 in2007. So, the sales performance of clusteredfirms actually deteriorated over time, while thefall is steeper when correcting for inflation andexchange rate fluctuations.

Table 2. Average monthly gross sales per worker in 2007, 1994 and 1993 by location.

Location Observations(number)

Mean(US dollars)b

SD(US dollars)

Sign.a

2007c

Clustered 85 983 747 0.000Dispersed (high-income) 63 557 368

1994d

Clustered 19 837 428 0.03Dispersed (high-income) 17 529 406

1993Clustered 23 1,148 852 0.001Dispersed (high-income) 28 510 354Idem (low-income) 31 660 777Idem (elsewhere La Victoria) 17 380 346

Notes : a Kruskal–Wallis χ2 for the 1993 and 1994 data; t–test for equality of means for the 2007 data.b As the purpose is to compare clustered and dispersed producers, the figures are in current dollars.c The analysis for 2007 excludes one extreme value from the cluster sub–sample and seven extreme

values from the control group.d In 1994, there was also no significant (P = 0.63) difference between clustered and dispersed firms

when taking into account all firms in the two sub–samples. The above result refers to firms employingless than 20 workers in both sub-samples, and excludes the effect of one outlier in the control group,which was left outside the comparison.

Source : Survey July/August 2008 (2007 data), Visser (1996 (1993 and 1994 data)).



A next performance indicator relates to(producer estimates of the) the monthlyaverage of pay per worker. In 1993, the scorefor clustered firms was 30 per cent higher thanelsewhere in the city of Lima. High standarddeviations, the influence of firm-size on work-er’s pay, and the possibility of longer workingdays required caution in interpreting the 1993finding, however (Visser 1999). In 2007, wefind again that workers in the cluster earn morethan their homologues elsewhere in Lima,the difference being US$70 per month. Thistime, the difference is statistically significant(t-test 3.02, sign. level of 0.,003). So, clusteringmatters for worker’s pay, although the relationmay run through the positive impact of cluster-ing on sales per worker, and thus may incorpo-rate longer working days.

A final indicator is the use of family labour.In 1993, family labour was least important inthe cluster and high-income areas, comparedwith low-income areas and firms operating else-where in the La Victoria district. In high-income areas, family labour was relatively oftenunpaid, however, whereas unpaid family labourwas a rare phenomenon in the cluster.4 So,clustered producers depended least on rela-tives, and if they did, these were paid for theirservices. For 2007, we do not find a significantdifference between clustered and dispersedproducers any more. In both sub-samples, firmsmake use of family labour. Once relatives workin the firm, they get paid, in both sub-samples.

It may be interesting to see whether the posi-tive impact of clustering on performance holdsfor all size categories, just one, or but a few.Considering the monthly average of gross salesper worker in 1993, clustered firms obtainedthe highest scores in each size category,although high standard deviations then hin-dered the observance of statistically significantdifferences (Visser 1999). For 2007, the pictureis clearer. This is not the case considering thedata for all firms, including a few relatively suc-cessful firms in the cluster and the group ofdispersed producers, as the correspondinganalysis yields insignificant differences betweenthe size classes across locations. After deletingthe extreme values from the two sub-sampleshowever (see footnote c in Table 2 and foot-note b in Table 3), it appears that micro firmsperform better in the cluster than elsewhere

(t-value 3.46; significance 0.001). This size cat-egory contains the bulk of firms in both groups,so one may safely say that clustering especiallyyields positive effects for micro-sized firms.Comparison of the means of the other sizeclasses does not yield significant results, due tolower numbers of observations (Table 3). Yet,the sales figures of clustered producers in thesmall and medium-sized categories also tend tobe higher than in the control group.

One may also interpret Table 3 in anothermanner, considering the importance of firmsize for sales in the two sub-samples. In 1993, wefound that in a setting of clustering, firm sizedoes not (positively) influence sales perfor-mance and that clustering enabled some microand small firms to considerably enhance sales(Visser 1996). On the other hand, we foundthat ‘big is better’ in control groups, particu-larly in low-income areas and the La Victoriadistrict. The high-income control group wasa special case, as the correlation coefficientbetween sales and size was as low and insignifi-cant as in the cluster, due to the poor perfor-mance of medium-sized enterprises in high-income areas that were restructuring at thetime (Visser 1999). In 2007, size still does notmatter for clustered producers, and also not inthe control group, where the insignificantsales/size correlation coefficient is even slightlynegative. So, it seems that size is even lessimportant in 2007 than it was in 1993. In asetting of clustering, location effects continueto overrule the importance of size-related vari-ables (Visser 1996). In the 2007 control group,size matters less than entrepreneurship andcreativity.

To sum up, the performance gap betweenclustered and dispersed firms has remainedintact over time. Clustering advantages that areexternal to individual firms appear to drive thedifference. Especially the bulk of producers inthe micro enterprise size category appearsto profit from the advantages of clustering.The persistence of these advantages sheds alight on previous concerns (Visser 1996) aboutthe static (cost-related) and passive (windfall)advantages of clustering for transformation,transacting and strategic decision-making pro-cesses. Moreover, local spillovers often refer tooutdated information, which is insufficientlydiverse and publicly available, and thus hardly



differentiating one producer from another. So,the task ahead is to determine whether newtypes of clustering advantages have cropped up,for example, regarding the production (trans-formation) behaviour of firms or other busi-ness processes.

Tracing transformation behaviour of clusteredand dispersed firms – Vertical specialisation,inter-firm division of labour and enhanced sub-contracting may yield efficiency and innovationgains (Scott 1988, Maskell 2001). In 1993 and1994, inter-firm division of labour in Gamarrawas limited, compared with dispersed produc-ers but also in the light of the neo-Marshalliancluster literature (Newlands 2003). The modalnumber of activities that clustered producerssubcontract in 1993 was one, usually a finish-ing operation. Subcontracting of pre-assemblyactivities was absent, due to risks of counterfeit-ing, robbery and poor quality. Outside thecluster, some firms were very active in outsourc-ing work to other firms (Visser 1996). In 2007,the situation had changed somewhat. Clusteredproducers subcontract on average three activi-

ties. However, this largely refers to outsourcinga higher number of finishing activities. Yet, in2007 nearly all firms in the Gamarra clustersubcontracted such activities to other firms,whereas one third of dispersed firms com-pletely abstains from outsourcing. In thecluster, the modal number of outsourced activi-ties is two, against zero in the control group.

This evidence on subcontracting mirrorsthe data concerning the number of operationsa firm is unable to realise. In 1993, most pro-ducers in the cluster mentioned one opera-tion, usually a finishing operation, which theythus outsourced. In 2007, clustered producersmention two operations on average that theyare unable to perform themselves. Overall, itseems that in the cluster, subcontracting hasbecome a bit more important than fifteenyears before. But again, we emphasise that thislargely refers to finishing activities that theycan not perform in-house (due to space ortechnological constraints): printing, computer-ised embroidery, colouring, buttonholing andfixing, ironing and packing. This demand forspecialised services within the cluster adds to

Table 3. Average monthly gross sales per worker in 1993, by size and location

Location Size (no. of workers)

1–4 5–9 10–19 20–99 t/χ2 Sign. sales/sizecorrelation

Sign.P value

2007a

Clustered 983 897 1,107 1,168 0.30 0.82 0.05 0.63(817)b (509) (969) (694)43 obs 26 obs 12 obs 4 obs

Dispersed 520 685 661 640 0.62 0.60 –0.02 0.86(394) (260) (314) (271)47 obs 7 obs 4 obs 5 obs

1993Clustered 949 1286 935 1,229 0.98 0.8 0.12 0.30

(930) (961) (473) (641)6 obs 12 obs 3 obs 2 obs

Dispersed 414 301 781 596 8.91 0.07 0.18 0.19(243) (171) (495) (322)9 obs 7 obs 7 obs 7 obs

Notes : a The analysis for 2007 excludes one extreme value from the cluster sub–sample and seven extremevalues from the control group. b Standard deviations are given between the brackets. Numbers of observations(obs) are below the standard deviations.

Source : Survey July/August 2008 (2007 data) and Visser (1996 (1993 data)).



the demand of producers elsewhere, especiallyenabling specialisation of producers in thecluster, and to a much lesser extent elsewhere.5

In 2007, the group of dispersed producers stilllodges a number of firms that apply outsourc-ing as an explicit business strategy, aimed atenjoying advantages of experience and thushigher labour productivity, costs (lower wagesand capital costs), flexibility (capacity, tech-nology) and perhaps also enhanced quality,problem-solving and learning. We have notobserved such frontrunners in the cluster.

Another difference is that clustered produc-ers relatively are often involved in so-calledcapacity contracting, in which contractorsinvest internally in labour and machinery justbelow the level of minimum expected demandand subcontract any demand above this level.In 1993, this practice was limited in relevancedue to over-investment of producers in inter-nal production capacity and despite thusensuing problems of excess capacity. Thiswas motivated by perceived risks of non-compliance (long lead times, poor quality)and high tariffs asked at times of peakdemand. In 2007, these risks are still relevant,considering that two-thirds of clustered firmsnever involved in this practice. The other one-third is involved in capacity contracting, albeitsometimes. This is significantly more than inthe group of dispersed firms (where only 1 in10 producers are involved in capacity contract-ing; t-test 3.5, sign. 0.001).

A third aspect is the possibility of inter-firmco-operation to pursue certain business goals,for example, related with production, logisticsor innovation. In 1993, co-operation was rela-tively rare in the cluster, although it laterbecame clear that clustered producers, oncethey perceive the need, more rapidly and effec-tively form networks,6 while the purpose ofco-operation in the cluster was more sophisti-cated than in control groups. In 2007, two-thirds of respondents in the cluster hold thatinter-firm co-operation has become moreimportant in Gamarra. Comparing the twogroups, clustered firms more often claim to beinvolved in joint actions, collective investmentsor knowledge exchange with competitors (13%in the cluster, 6% of the dispersed producers).This difference is not significant however(t-test, sign. 0.14). Only regarding linkages

with other sectors (such as logistics, softwaredevelopment, machinery, R&D institutes), thedifference between clustered and dispersedproducers is significant (t-test, sign. 0.04). Onlya few clustered producers co-operate with firmsin other sectors, however, whereas in the othergroup no one does.

On the whole, the conclusion is the same asbefore (Visser 1999): specialisation, subcon-tracting and inter-firm co-operation at the levelof the transformation process is still ratherlimited in the cluster. These practices do notyield enough gains to alter previous explana-tions of the performance gap between clus-tered and dispersed firms.

Tracing transacting behaviour of clustered anddispersed producers – Producers may alsoobtain information and even learn by interact-ing with traders: suppliers of inputs or buyersof output. Hence, this section focuses onupstream and downstream linkages. In 1993and 1994, we observed that clustered producersmainly obtained market and product informa-tion from two sources: the products of com-petitors (with producers walking around theneighbourhood, scanning fashion trends andthe success of new products, which they thenpurchase, disassemble, analyse and copy) andthe behaviour of competitors (direct observa-tion enables the diffusion of tacit knowledgeand work-in-progress). New ideas thus quicklybecome public locally and can be obtained atlow costs. So, clustered producers are not onlyable to come up with a new product at relativelyhigh speed but also at low costs (Visser1996,1999).

In 2007, clustered producers still rely to alarge extent on local public information todecide what to produce and how much. Askedabout their main source of market information,55 per cent of clustered producers mentiontheir own environment, against 1 per cent inthe control group (t-test, sign. 0.00). Next, clus-tered producers more easily obtain marketinformation about rural and regional markets(sign. 0.003). A few dispersed producers on theother hand have access to and make use ofinformation sources from other parts of theworld (North America, Europe and Asia; t-test,sign. <0.03).



Another aspect is the type of information.Textile suppliers, suppliers of other inputs,competitors (direct observation), subcontrac-tors, and trade fairs are all relatively importantsources for clustered producers, but not somuch for dispersed producers (sign. <0.05).The latter group relatively more often readsfashion magazines (sign. 0.00). Both groupsrecur to clients (buyers), informal sources(including friends and relatives), and the Inter-net to obtain information. Next, market con-tacts are more important for both clustered anddispersed producers than diagonal linkageswith consultancy firms, NGOs, semi-publicand/or public institutes. Most interviewees inthe two sub-samples say that these sources are oflimited importance or no importance at all.

A final issue regarding access to markets andmarket information is that a typical firm in thecluster has two establishments (against 1.5 inthe group of dispersed producers, sign. 0.03):one workplace and one sales outlet, bothlocated in the cluster. Among dispersed firms itis not common for producers to have an ownsales point, let alone in the cluster. For clus-tered producers, a sales outlet in Gamarra iswhere they meet and talk with numerous clients(wholesalers, retailers and individuals) visitingthe cluster each day. So, while both clusteredand dispersed producers state that clients areimportant sources of market information, clus-tered producers have an advantage over dis-persed ones, in terms of the number and varietyof clients. Indeed, dispersed producers sellabout 80 per cent of their produce to individualcustomers, 10 per cent to retailers, 2 per cent towholesalers and 8 per cent to other clients (e.g.schools, companies, sport clubs or public insti-tutes). In the cluster, these figures are 19, 25, 38and 13 per cent respectively (the differencesare significant, sign. <0.05). More importantly,clustered producers sell a significantly higherportion of their produce to export agents.

In terms of the share of sales to differentgeographical markets, dispersed producersmore heavily rely on the capital city of Lima,where they sell 91 per cent of their produce,against 66 per cent in the cluster (sign. 0.00).Clustered producers sell more to the Peruviancountryside (24 against 3%, sign. 0.00), andalso to neighbouring countries (but these dif-ferences are not significant). Interestingly, a

few dispersed producers reach distant marketsin North America, Europe and Asia, while noneof the clustered producers goes there (the dif-ference being significant for North Americaonly, sign. 0.09). A similar pattern crops upwhen analysing production-on-orders of spe-cific clients. Again, dispersed producers gener-ally rely on individual customers in Lima,whereas clustered producers work far moreoften for retailers and wholesalers (including,albeit to a lesser extent, export agents) in Lima,the Peruvian countryside, neighbouring andother Latin-American countries. Yet, some dis-persed producers managed to link up with andintegrate themselves in the global value chainsof sophisticated buyers in distant markets else-where in the world. As far as clustered produc-ers are concerned, these are not integrated insuch chains.

With regard to the execution of upstreamtransactions with suppliers of inputs, in 1993clustered producers generally relied uponmarket linkages with suppliers of cloth fabric –a key input that largely determines the competi-tiveness of garment makers. These supplierswere usually wholesalers and retailers, and in afew cases manufacturers of cloth fabric. In2007, the situation changed somewhat. Whole-salers are still the most important source ofcloth inputs in the cluster, while retailers aremore important in the group of dispersed pro-ducers (both differences are significant at the5% level). What differs is that more than 25per cent of the clustered producers maintaina direct relation with textile manufacturers,some of whom (50%) even obtain all textileinputs by directly placing orders with a clothmanufacturer. This is more than in 1993 and1994, although there is no significant differ-ence with the control group, where 15 per centof the sub-sample (usually larger firms) directlydeals with cloth manufacturers. Yet, directcontacts with cloth manufacturers provide aplatform for co-operation regarding the designand quality of cloth input. In the cluster,the number of producers saying that theyco-operate with suppliers of cloth fabric is abit higher than in the control group (0.12against 0.04, sign. 0.10). So, enhanced verticalco-operation between clustered producers andcloth manufacturers may contribute to theircompetitive advantage over dispersed firms.



Clustering advantages through time – In 1993and 1994, much of the cluster’s competitive-ness stemmed from a series of cost advantagesalong the supply chain: low costs of search andmatching of inputs and final products forgarment producers and buyers respectively,and economies of scale and scope in the distri-bution of both types of products for wholesalersand garment producers respectively (Visser1996, 1999). Clustering induces fierce com-petition along the chain, so that these costsavings are passed on downstream to subse-quent clients. The cumulative cost savingstranslate into lower prices of final products,which is exactly what impoverished consumersin Peru and elsewhere in the Andes required inthe 1980s and 1990s. Yet, cost-related clusteringadvantages at the level of transacting activities,and not so much in transformation and/orinnovation activities, were responsible for thethen observed performance gap between clus-tered and dispersed producers.

In 2007, the market and competition contexthas changed but the same type of clusteringadvantages appear to sustain the gap in per-formance between clustered and dispersedproducers. When asked about the principaladvantage that they derive from operating afirm in Gamarra, no producer mentions noadvantage at all. Clustering still yields advan-tages, in the following order of importance:reduced costs of inputs (46 of 86 firms), attrac-tion of customers (20 firms), better product/market combinations (15 firms), higher labourproductivity (14 firms), reduced transport costs(14 firms), lower search costs (10 firms), higherquality of goods and services (8 firms), andlower transaction costs of subcontracting (4firms). These answers support the conclusionthat the clustering process at hand still mainlyyields static (cost-related) advantages thatare due to the co-location of firms, not co-operation among them. The gains are passivein nature (falling into a producer’s lap onceoperating in the cluster area), not active(created through strategic action). Finally,advantages at the level of transformation andinnovation processes are limited, while those atthe level of transacting inputs, services andoutput are still significant. So, not much haschanged in the cluster over the past 15 years.It remains vulnerable to competitive threats,

such as enhanced imports from China and/orlower macroeconomic growth, which puts atrisk both the incomes of clothing entrepre-neurs and their workers and the prospects oflocal economic development. We talked to theinterviewees about their continued reliance onstatic and passive advantages of clustering andthe associated risks. Their answers yield twopositives: one is that entrepreneurs appear tobe more aware than before of the shortcomingsof current clustering advantages for the com-petitiveness of garment-making operations,and that they should do something about it;two, there seems to have been some institu-tional responses to mitigate the shortcomings,with services provided from outside (govern-ment institutes, universities, NGOs) to thecluster. So, nowadays there may be moredemand for private-public interaction to stimu-late cluster development than before (seeVisser 2000). It is also clear, however, that somepast experiences ended up in disappointment;private-public co-operation is unlikely to haveplayed a positive role in the cluster’s develop-ment so far.

CONCLUDING REMARKS ANDPOLICY IMPLICATIONS

This paper set out to analyse how the Gamarracluster fared over a 15 year period, underconditions of macroeconomic growth, tradeliberalisation and economic globalisation. Themain question is whether, how and why thiscluster changed in terms of size, composition,magnitude and type of clustering advantages,and the development stage of the cluster(growth, stagnation or decline). To answer thisquestion, we collected data for 2007 and con-trasted the results with previous data for 1993and 1994, comparing clustered firms with dis-persed producers in these years. The hypoth-esis is that the cluster under review has entereda stage of decline and is struggling to adapt tothe mounting international pressures, to renewprocesses and products, let alone that it movesinto completely new directions. The empiricalevidence presented in this paper supportsthis hypothesis, notwithstanding the growth ofthe cluster in terms of the total number offirms and employment, which mainly reflectsthe growth of trade activities in the cluster,



including imported products. The productivityof clustered firms has fallen somewhat over 15years time, although they still maintain a com-petitive advantage over dispersed producers,especially in the smallest firm-size categories.We observe that the clustering process at handstill mainly yields static (cost-related) advan-tages that are due to the co-location of firms,which are passive in nature (falling into a pro-ducer’s lap once) and not so much active(created through strategic action), and whichmostly develop at the level of transacting inputsand output, not so much at the level of trans-formation and innovation processes. Next, clus-tered producers do not use profits to upgradetheir clothing business but to construct newbuildings and work places for other firms in thecluster, as rents have gone up due to surgingtrade activities in a context of increasingdemand for garments at times of fast macroeco-nomic growth in Peru over the past decades.Increasing rents and real estate prices decreasefirm entry and relocation of existing firmstowards the cluster, and may also stimulate firmexit. So, producers in the cluster are struggling,but this is obscured by a surge in trade activities.As the latter is deeply rooted in the mentalmodel of successful entrepreneurship in thecluster (Visser 2000), this shift towards tradeactivities should be regarded as a continuedand deepening exploitation of the existingmodel, not as change or development of thecluster.

Yet, the observed static, passive and trans-actions-related advantages of clustering havebeen important for clustered producers.Távara and Visser (1995) already stressed theincubator function of the Gamarra cluster: as aresult of the observed cluster advantages, newfirms have a higher chance to survive anddevelop into larger firms. These advantagesalso helped small firms in the cluster to survivetwo decades of international competition, fromChina and other (Asian) countries. While thiscompetition had very negative effects for theArgentinean and Chilean apparel industries,the impact has been smaller in the case of clus-tered producers in Lima, Peru. Favourablemacroeconomic conditions in Peru in theperiod from 1993 until 2007 have helped(clustered) producers to stay on their feet,however. Recent conditions have changed,

including worldwide economic stagnation,ongoing fierce competition from China andlower economic growth in Peru.

In 2007, awareness was emerging amongclustered firms that they should address short-comings in production, logistics and innova-tion processes by specializing and co-operatingwith other firms and institutes, includingpublic and semi-public organisations. Untilthen, experiences of private-public dialogueand co-operation have been exceptional, notonly in the cluster (Visser 1996, 2000) but indesigning and implementing productive devel-opment policies in Peru at large (Tello &Távara 2010). Programmes to support smalland micro enterprises have been dispersedamong state agencies, each with its own policiesand guidelines, most of them funded anddriven by external assistance from multilateraland bilateral donors. Results have not beensignificant, also due to the institutional frag-mentation and often conflicting relationshipsbetween different levels of government. Next, achange in the national government is followedby changes in the staff of supporting agencies,undermining the development of capabilitiesto solve collective action problems. Further-more, local authorities are mostly concernedwith urgent issues such as waste disposal andsecurity, and are unable to bring about theinstitutional reforms which are required tobuild an effective governance structure andfoster the development of the cluster (Tello &Távara 2010)

In the case of the Gamarra cluster, the qualityof cluster governance thus seems to be a criticalissue to prevent a further decline of the produc-tion part of the cluster and to reap the fullpotential of clustering advantages for produc-ers. Key challenges to increase value added arethe development of design capabilities, timelyaccess to information on fashion trends,market prices and technology, and improvingstrategic planning in a context of intensifyingcompetition. Individual firms are too small toaddress these challenges on their own, andmarket incentives do not secure the effectiveprovision of required services, as these generatepositive externalities and have the attributes ofpublic goods. There are signs that firms in thecluster are willing to pay for these services,which might take the form of membership fees,



local taxes and other contributions. Unfortu-nately, however, free-riding undermined inter-nal solutions to collective action problems. Aworkable solution might involve an externalintervention along the lines of the Technologi-cal Innovation Centres (CITE) that have beenimplemented in Peru. The CITE programmehas been relatively successful in other clusters(Tello & Távara 2010) and may be replicatedin Gamarra. A critical condition is the politicalwill and commitment of local authorities andthe leaders of business associations.

Such would mean a break-away from a weakgovernment involvement in the developmentof the Gamarra cluster, at the local and nationallevel. Gamarra has been a ‘spontaneous’ expe-rience: an endogenous growth and spatial con-centration process. This feature of governmentand wider institutional ‘absence’ cannot con-tinue however, as, the weak organisationalstructure of the cluster has to strengthen aswell. Entrepreneurs need to organise and buildsolid and broad-based associations that can actas counterparts for government agencies (for arange of purposes and services), universities(for training purposes) and research centres(for technological change). The thus ensuingprivate-public co-operation should subse-quently aim at (i) fostering trust and co-operation between firms, mainly vertically butalso horizontally, (ii) changing the internalmarket orientation of clustered firms to exportsand international markets (taking advantage ofthe recent Free Trade Agreements with theUSA, Europe and China), and therewith (iii)reinforcing the core function of the cluster:production, without which related activities,including trade and real state, will not prosper.This agenda is relevant in good times (theperiod of worldwide economic growth thatlasted until 2007/09), but all the more so in badtimes (crisis, enhanced competition and slug-gish growth).

Notes

1. Any views and opinions presented in this articleare solely those of the author and do not repre-sent those of the Ministry of Economic Affairs.

2. To measure performance differences betweenclustered and dispersed producers, the followingindicators were used in 1993 and 1994: employ-ment size, employment growth, the monthly

average of gross sales per worker, the averagemonthly pay per worker, and the use of (unpaid)family labour. The gross-sales-per-worker indica-tor is especially important because it is a relativemeasure of business performance. Price effectsmake it a second-best indicator of productivity,but it is the best one can get under the circum-stances. Some underestimation of sales values mayhave occurred, but we assume this problem to beequally relevant in both sub-samples. The oppo-site may have happened with the average-monthly-wage-per-worker indicator (overestimation), asproducers do not want to look too bad, but againthis problem is expected to be equally relevant inboth groups. Moreover, the interviewers weretrained to compare gross-sales and wage dataafter asking the respective questions and to recon-sider the answers in case of anomalies or otherproblems.

3. For reasons of comparison with the 1993 and 1994data, a Peruvian size classification scheme is usedthat was prevalent in the 1990s, instead of thecurrent and common definitions of micro enter-prises (1 to 10 workers) and small firms (11 to 50workers).

4. This difference between the cluster and thecontrol groups was significant (Mann-Whitney,P = 0.05).

5. This is due to scale and scope economies reducingprices and enhancing variety of services (Visser1996).

6. Screening, selection and monitoring is facilitatedthrough family and ethnic ties, proximity andlocal reputations.

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APPENDIX: SAMPLING METHODS

For a description of the survey methodologyand the questionnaire used in 1993, we refer toVisser (1996). For a summary of this informa-tion, see Visser (1999). The 2007 questionnaireis available upon request.

In 2007, a two-stage sampling method wasused, to serve the purpose of comparing clus-tered producers with dispersed firms operatingelsewhere in Lima. For the cluster sub-sample,

we used a register of the municipal district ofLa Victoria, which contains data on all firmslocated in the Gamarra cluster, whether thesecomply with (tax and other legal) regulations,or not. This register was compiled in 2007,based on comprehensive census data coveringtwo areas in the cluster, labelled Gamarra A andB. Gamarra A refers to the original area wherethe clustering process took place, whereasGamarra B refers to a neighbouring area that isincreasingly part of the clustering process.Excluding firms that were closed at the time ofthe census, Gamarra A comprises 17,918 firms,of which 3,621 firms are garment producers.From this last sub-group, a random sample of125 firms was drawn, of which 86 firmsanswered the questions in our questionnaire(response rate 69%). For the control group, aregister of the Peruvian Ministry of Production(PRODUCE) was used. This register comprisesall registered firms operating in differentindustries within the province of Lima. Afterseparating garment firms operating in themunicipal districts of Magdalena, San Isidroand Miraflores, a random sample of 100 firmswas drawn taking into account the size of eachof these three districts. The number of respon-dents was 70 (response rate 70%).



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