Cepal r.2172ictinla - Gutierrez

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ICT in Latin America. A microdata analysis

I. ICT and labor productivity inColombian manufacturing industry

Luis H. Gutirrez 1

1. Introduction

The information and communication technologies (ICT) are constituted by theinvestments in hardware, software and communication media. The fast technologicaldevelopments in the production of microprocessors and their increasingly low priceshave strongly reduced the price of computers and other computing and communicationequipments. This has allowed the business sector to make important disbursementsfor the purchase of such equipments. Researchers on the economic growth theoryand the technological innovation area have been studying the relationship betweeninvestment in ICT and productivity for some decades. Due to the data availability,the rst studies were focused on the existing relationship at country or industry level.Nevertheless, these studies did not show the expected positive relationship betweenICT and productivity (Brynjolfsson, 1993).

The present study analyzes the relationship between ICT investments and productivityin the Colombian manufacturing industry. It uses information from the second Surveyof Development and Technological Innovation (Encuesta de Desarrollo e InnovacinTecnolgica - EDIT) performed in 2005. The EDIT survey asks about investmentdecisions on innovation and technological development, amounts invested, reasonsfor investing, occupation level and quality of employed personnel, financingmechanisms and loan commitments, effect of innovation, among others. Due tothe fact that the EDIT did not pose questions on firm performance variables, weuse complementary data from the Annual Manufacturing Survey (Encuesta AnualManufacturera - EAM) of 2004.

1 I thank Magaly Herrera for data processing and her excellent assistance and comments and suggestions of Roxana Barrantes and Sebastin Rovira. I also thank Jess Otero for his assistance on econometric matters. Ithank the Departmento Nacional de Planeacin (DNP) and the Colombian Institute for the Development of Scienceand Technology Francisco Jos de Caldas (COLCIENCIAS) and the Departamento Administrativo Nacional deEstadstica (DANE) for providing the Encuesta de Innovacin y Desarrollo Tecnolgico (EDIT) and Encuesta AnualManufacturera (EAM) 2004. I specially thank DANEs staff member Gilma B. Ferreira for collaborating in clarifyingdoubts concerning the data. Usual disclaimers apply.


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The contribution of this work is manifold. First, it is the rst Colombian study that developsan econometric approach to investigate the relationship between ICT investmentsand productivity in the manufacturing industry. Second, by including variables relatedto investments, efforts in organizational changes and human capital and innovationoutcomes, the study explores the hypothesis of complementarity and the impactof innovation. Finally, the data sample used covers a great number of small rmswhich will allow us to test the differentiating effects, by rm size, of ICT investments,organizational changes and human capital, on labor productivity. The main outcomescon rm some of the ndings of the literature. First, investment in ICT and the effortin quali cation of human resources affect positively the labor productivity. Second,efforts in organizational change or management apparently do not produce short-termproductivity gains. Third, innovating efforts have a positive impact on productivity.Finally, the complementarity between organizational changes, human capital and ICT

investments is not con rmed.

The study is organized as follows. Section 2 resumes the academic literature related toICT investments, human capital, organizational changes and productivity. Then, Section 3describes the EDIT database and Section 4 establishes the hypothesis and the empiricalapproach. Finally, Section 5 discusses the econometric results and Section 6 concludes.

2. Literature review

Since the mid eighties, the discussion dealt mainly with the productivity paradox.

Indeed, Robert Solow stated that, one can see the computer age everywhere exceptin the productivity statistics .2 Indeed, during the nineties it was dif cult to measure theeffects of ICT and the productivity paradox became an important subject of research. 3 However, recent studies have demonstrated that the productivity paradox was the resultof several factors. First, most of the analysis was implemented at an aggregated level,either macroeconomic or at industry level. 4 Second, maybe it was a consequence of case study analysis. Third, that the analysis was based on data constructed by usinginappropriate de ators or non-representative samples. Finally, that the expected positiverelationship between investment in ICT and productivity is more complex and/or long-term (Brynjolfsson, 1993). The following literature review focuses on published studiesconcerning ICT investments and its effects on productivity at rm level.

2 Quoted in Stiroh (2008, p. 360).3 We shall use indistinctly the terms information system (IS), information technology (IT) and information and com-

munication technologies (ICT).4 For example, see Jorgenson and Vu (2007), OMahony and Vecchi (2005), Stiroh (2008), Stiroh and Botsch (2007),

Basu and Fernald (2007), Inklaar and Timmer (2007), Inklaar et al . (2007) and Eicher and Roehn (2007).


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What are the ICT and how could they affect productivity in the rm? Brynjolfsson andYang (1996) stated that ICT includes of ce, calculation and accounting equipments whichconsists mainly of computers and it also includes communication equipment and relatedsoftware and services. This de nition considers the ICT as general purpose technologiesbecause they are used by all economic activities and have dynamic technologicaldevelopments (Bresnahan and Trajtenberg, 1995). 5 Due to the fact that these technologiesenable the redesign of industrial processes and improve the coordination of work groups,they contribute to the technical change in rms. Generally, their adoption is based onthree elements: a) economic incentives for the managers or owners of the rm to adoptthese technologies, b) risks resulting from not knowing the potential performances of these technologies and problems of adjustment, management and establishment of ICTwithin the productive and organization processes, and c) dissemination level in the marketwhere the rm competes, costs and complementary services of the ICT expenditures.

Once a rm manager or owner invests in ICT he should expect to see cost reductions or productivity gains, however, these may not be immediate.

In the rst half of the nineties several studies addressed the issue of ICT and productivity.Brynjolfsson and Hitt (1996) developed one of the rst researches dealing with the effectof ICT investment on productivity at US rm level, using data from ve annual surveysapplied to large companies from 1987 to 1991. Their two hypotheses were that the impactof computer capital and ICT personnel is positive, and that contribution is positive evenif you deduce the ICT capital and labor cost depreciation. An important feature of their work consisted in disaggregating the rms capital in computer capital and capital of other investment goods; and to disaggregate personnel into ICT personnel and others. Theauthors used a Cobb-Douglas speci cation in which the product was measured by sales.

The results suggested a positive effect of computer investment on sales, and that theperformance of employed ICT personnel was relevant, which may be due to the relativelyhigh education level of these workers. Additionally, the authors showed that the return for investments in computers is higher than the return for other type of investments. 6

Theoretically, Milgrom and Roberts (1990, 1995) developed several models to understandthe changes occurred in North American manufacturing sector with the introduction of

5 In an evolutionary perspective, Perez (1985) presented the idea of techno-economic paradigms, which are dominatedby a key factor with the following features: (1) low and decreasing costs, (2) almost unlimited offer, (3) evident potentialto in uence all production spheres, (4) capacity to reduce costs and change the quality of capital, work and productequipments due to its interconnection with technical and organizational innovations. The techno-economic paradigmsinclude, among others: new ef ciency concepts for the organization of production at plant level, new models of businessorganization and management, new innovation patterns, less work by product unit.

6 The study discusses three relevant factors that must be considered in the analysis of ICT investment and productivity:errors in ICT equipment rating, errors in the de ators used, and understatement or wrong classi cation of what isconsidered investment in information systems.


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information technologies. 7 They argued that, given an exogenous reduction in theprices of technologies, that is, CAD (Computer-aided Design) and CAM (Computer-aided Manufacturing), this reduction implied direct and indirect effects on theapplicability of those technologies in the productive process. A price reduction directeffect promotes its use, develops the design capacity and tends to improve theproduction levels by reducing the marginal costs. Nevertheless, the indirect effects areeven more important. These indirect effects tend, basically, to strengthen the directeffects, because the corresponding relationships are complements. Here, we use theterm complements not only in the traditional sense of a relationship between input pairs, but also in a wider sense as a relationship between activity groups. The de ningfeature of these complement groups is that if the level of any of the subset of activitiesincreases, then the marginal output of increases in any of the other activities alsoincreases (p. 514). Although they studied a case related to CAD or CAM, it should be

possible to infer that the equipments or computing systems may reduce the productionprocesses and inventories, increase data communication and the product redesigningbecome complements.

Brynjolfsson and Hitt (2000) took the complementarity analysis to argue that a signi cantfactor of the information technology value is its capacity to enable complementaryorganizational investments such as rm processes and labor practices and that thosecomplementary investments produce in turn productivity increases, by reducing costsand, more important, by enabling the rm to increase the quality of the products in theform of new products or to improve intangible aspects of the existing products, such asconvenience, immediate delivery, quality and variety (p. 24-25). This argument renewedthe research concerning the relationship between ICT investment and productivity, but

extended to include complementarities. For example, Black and Lynch (2001) partiallytested this argument by using several databases of the US Census Of ce. They estimatedan extended Cobb-Douglas production function with variables of labor practices, humancapital and information technology dissemination. Although the results were mixed andthey did not explicitly include a measure of investment in computers, it is worthwhilestressing the nding concerning the importance of human capital, which was positivelyassociated to productivity. 8 In a related study, Caroli and Van Reenen (2001) useddata of British and French companies in the eighties to investigate the determinantsof organizational change and productivity. Interestingly, the results for the French case

7 There are several studies that analyze the impact of ICT on productivity using data at establishment level in theUnited States and Canada. For the United States, see Nguyen and Atrostic (2006), Nguyen and Atrostic (2005) andAtrostic and Nguyen (2002). For Canada, see Sharp (2006).

8 Greenan et al . (2001) carried out a similar analysis with a sample of French medium and large rms for the period 1986-1994. The authors used as ICT variable what they called investment in computerization, which is the proportion of thegross value of the of ce assets and hardware in relation to the gross value of total physical assets. They employed four measures of human capital, emphasizing the number of ICT employees and employees in R&D activities. The resultsdepend on the adopted models. While cross-section estimations shows signi cant positive correlations between ITinvestment, human capital and productivity, panel data estimations reveal no relevant correlations.


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showed that new labor practices increase productivity only if the rm have implementedother activities or have invested in complementarity assets, a result in the line of Blackand Lynch (2001).

In the context of US rms, Bresnahan et al. (2002) developed research by explicitlyconsidering complementarities between ICT investments, organizational changesand human capital in the production function. The correlation analysis shows thatorganizational changes are strongly correlated with human capital and informationsystems, and that investment in information systems are correlated with human capital.The rst estimations suggest that both organizational change and human capital predictthe demand for information systems correctly, although they are weak explanatoryfactors of the demand for other investment goods. In a second set of estimations, resultsreveal that the different information system investments are determinant factors of the

human capital level. Finally, the authors analyzed the joint effect of the three inputson productivity. The evidence suggests the existence of complementarities betweeninvestment in information systems, human capital and organizational changes.

Brynjolfsson and Hitt (2003) investigated the relationship between investment in computersand productivity for a sample of very large US companies. They stated that long-termbene ts of computerization alone exceed the short-term contribution (p. 793). This wouldexplain why some previous studies at rm level found a low or null relationship. With adatabase for eight years for a sample of four thousand rms, the authors discovered thatinvestment in computers is positively correlated to productivity growth. Moreover, theyfond that the estimated coef cient increases monotonically and substantially as youmove from one year differences speci cations to seven years differences speci cations

(p. 798).9

Bertschek and Kaiser (2004) analyzed the relationship between ICTinvestments, productivity and organizational changes, controlling by investment in other capital assets. Their main argument is that labor productivity may be affected by ICT andnon-ICT investments and organizational changes, but the relationship is bidirectional.The hypothesis is that labor reorganizations, such as hierarchy reduction within a rmand team work strengthening, act as a change parameter of the production function.The results show a positive relationship between productivity and ICT investments. Theevidence also suggests that workplace reorganizations induce an increase in productivitythat is attributable to complementarities with input factors. 10

9 Zwick (2003) also studied the dynamic effects of ICT investments. For this purpose, he used panel data in the USfor the period 1997-2001. By using OLS, the author found a small effect of ICT investments on productivity. Theauthor argues that this result may be due to unobserved heterogeneity and endogeneity. A new round of estimations,controlling for both problems, shows a positive and signi cant impact of ICT investments on productivity.

10 Hempell (2005) used a sample of German rms for the period 1994-1999 to study how different econometricapproaches produced potentially different results. The evidence obtained is indeed ambiguous and not robust todifferent speci cations. However, the results also suggest that quali ed human capital is a prerequisite for the ICT tohave a positive effect on productivity.


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More recently, several studies evaluate the relationship between IT and productivity,controlling by complementary factors. For instance, Arvanitis (2005) develops the same workof Bresnahan et al. (2002) for a sample of Swiss rms. The author studies the relationshipbetween productivity and ICT investment but resulting from organizational changes and humancapital variables. One of the new aspects of the survey is that it uses different measures of ICTinputs. In particular, the author used the proportion of employees connected to the Internet andIntranet. The estimations show that all variables except the variable that measures intensity of ICT investment are positively correlated to productivity. The organizational variables presentsimilar results, but its relation with productivity is less robust than for ICT variables. Likewise,the results concerning human capital are the expected ones; its impact is higher than that of theorganizational changes, but lower than the ICT inputs. Additionally, the author nds evidencefor the complementarity hypothesis only between ICT inputs and human capital.

Gargallo-Castel and Galve-Grriz (2007a) investigates the situation in Spanish rms andadopt three main hypotheses: ICT investment per se does not generate any productivitygain; its effect will be positive only if it is combined with a high quali cation level of theemployees; and it will be higher in rms where management has proactive attitudestowards ICT investments. Indeed, they propose that rms that combine ICT with processinnovation will obtain higher productivity. The econometric results reveal that: i) the effectof the ICT investment is lower than investment in other goods, ii) the interactions of theICT variable with human capital variable and management attitude variables are alwayspositive and relevant and iii) the interaction between ICT and the variable of investmentin process innovation is not signi cant. Also considering Spanish rms, Badescu andGarcs-Ayerbe (2009) estimate the impact of ICT investment on labor productivity for apanel of 341 medium and large rms during 1994-1998. The results show an elasticity of

0.9%, implying that IT investment is a determinant factor of productivity growth in Spain.11

Giuri et al. (2008) study the relationship between ICT adoption, human capital,organizational changes and productivity in Italy for the period 1995-2003. Due to thepanel data availability, the authors use least squares and xed effect estimations. Theevidence using least squares show a positive relationship between ICT capital stockand productivity, but negative relationships in the inter-relations of ICT investment andhuman capital and organizational change. The results are worse considering the dynamiceffects, and only the interaction between ICT and human capital is positively associatedto productivity. The interaction between IT, human capital and organizational change isnegatively associated to productivity. Given that the sample of rms is basically composedby small and medium size enterprises (SMEs), this leads the authors to declare that

11 In developing countries, the empirical evidence is scarce. One exception is Chowdhury (2006), who examined thesituation in both Kenya and Tanzania. Surprisingly, the productivity outcomes show that it relates negatively to ICTinvestments. Chowdhury argues that this result may be explained as an excessive ICT investment or disarrangementin the relationship technology-human capital. Also, this result could be due to t he fact that ICT investments take timeto materialize its effect on productivity.


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in the SMEs there is a limited scope for obtaining productivity gains associated to thesimultaneous adoption of different complementary strategies .

Koellinger (2008), using data for 25 European countries, analyze the determining factors for thenew technologies and innovative activities to succeed. The results were the expected ones: alltypes of innovations are positively associated to performance variables (employment, sales andrate of return). Nevertheless, there are differences concerning product and process innovations.The former are associated positively to the rate of return and sales, while the latter do not relatepositively to productivity and they relate negatively to the decreasing employment. The author explains this by arguing that process innovations are defensive strategies to maintain marketshares, while product innovations are offensive strategies to capture new markets.

Overall, the literature on productivity and investment in ICT allows us to conclude some

key facts. First, most of the studies show a positive, sizeable and signi cant impact of ICTinvestments on productivity. However, it is important to mention that this literature refers mainlyto medium and large rms. Second, the literature also includes other factors that may boostthe impact of ICT investments or that may be complementary. Indeed, the empirical studiesshow that, although it is possible to consider a positive impact of ICT investment, this impactholds even controlling by complementarities between human capital, organizational changesand innovations. Finally, the ICT impact on productivity is positive and relevant for most of thecross-section studies, but the outcome is less robust for panel data analysis. Although thetheoretical perception and Brynjolfsson and Hitt (2003) show that lagged impacts of ICT mustbe positive, the few studies which used static and dynamic panel data have mixed results(Hempell, 2005; Giuri et al ., 2008). In this perspective, it is essential to generate much morepanel databases to test whether ICT investments and its potential complementarities with

human capital and organizational change affect productivity in a dynamic pattern.

3. The Development and TechnologicalInnovation Survey 2005

The National Planning Department (Departamento Nacional de Planeacin - DNP), andthe Colombian Institute for the Development of Science and Technology Francisco Josde Caldas (COLCIENCIAS), carried out the rst Survey on Technological Development(EDT) in the Colombian manufacturing sector. The survey was implemented in 1996 to885 industrial establishments with more than 20 employees, and it was representative of

the universe of 4,501 industrial establishments. 12 One key characteristic is that the mostfrequent innovation activity undertaken by industrial rms was the acquisition of capital-

12 The DANE reports that in 1995, the number of industrial establishments was 7,909. A rm may have one or more establishments.


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embodied technologies, followed by product and process designs. In fact, investment incapital-embodied technologies was more than 3 times higher than investment in other type of innovations and 25 times higher than the R&D investments.

Nine years later, the DNP, COLCIENCIAS and the National Statistical Administrative Department(Departamento Administrativo Nacional de Estadstica - DANE) implemented a second Surveyon Development and Technological Innovation (EDIT)which is the primary database of thisstudy. The survey was answered by approximately six thousand rms in 2005. 13 A crucialdifference between EDIT and EDT is that the former considers the universe of Colombianmanufacturing rms and the latter considers only a representative sample. DANE provided anEDIT database composed by 6,106 rms. Table VI.1 displays the size distribution of rms. Asit may be noticed, 64% of the rms have less than 50 employees. The participation of mediumand large rms represents the remaining 36%. This sampling distribution is relevant because,

as we discussed in the previous section, most of the literature refers to medium and large rms.

The EDIT 2005 is composed by seven chapters. The rst chapter asks if the rms investedor not in 75 development and technological innovation activities and their respectivemonetary amount during 2003 and 2004. This chapter also asks for investmentdecisions (answer yes or no) between product, process, organization and marketing, andthe rating given to each activity. Table VI.2 displays the outcomes of investing decisionsrelated to ICT and innovation activities in 2004. 14 Clearly, the larger the rm size, thehigher seems to be the involvement in all the development and technological innovationactivities (henceforth DTIA). For example, while 23% of small rms invested in ICTequipments, 41.1% of medium rms up to 100 employees invested in ICT equipment,52.2% of medium rms up to 200 employees, 55.6% of large rms up to 500 employees

and 65.2% of the very large ones. Additionally, the relevance of each activity is different.Indeed, the proportion of rms that invested in R&D is the lowest, regardless of rm size.

Table VI.1Firm distribution by size(Number and percentages)

Number of employees Number of rms %

Until 50 3 903 63.9%

51-100 959 15.7%

101-200 604 9.9%

201-500 426 7.0%

More than 500 214 3.5%

Total 6 106 100.0%

Source: Authors elaboration based on EDIT 2005.

13 The DANE informs that the number of rms vary between 5,950 and 6,106 according to the EDITs chapters. Thetotal number of rms informed by the EAM (Annual Manufacturing Survey) was 6,847 in 2004.

14 It must be considered that these are aggregates which take the value yes, if the rm answered yes in at least one of the items.


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Table VI.2Proportion of rms investing in ICT and innovation-related activities by size, 2004

(Percentages and number )Type/Size (employees) Until 50 51-100 101-200 201-500 More than 500

ICT equipment 23.0 41.1 52.5 55.6 65.2

Capital equipment - hardware 43.1 68.3 78.1 85.1 90.4

Management 34.1 51.1 63.1 71.3 77.0

Cross-cutting technologies 24.5 63.9 66.6 55.1 77.5

R&D 4.4 6.7 9.7 12.3 22.5

Training 41.7 61.5 73.7 80.0 83.7

Total investment 69.0 86.2 92.4 95.4 96.6

Number of rms 3 897 919 566 390 178


Table VI.3 displays the distribution of the invested amounts by type of activity and by rmsize. In some cases, data are surprising. The most striking one shows that small rmsinvest a slightly higher percentage in R&D than those which have between 51 and 100employees. This is probably a problem of the number of the reported employees or anoverestimation of the total amount invested in that activity by those rms. The data of investment in training also attracts attention. Of the total amount invested in DTIA, smallrms invested proportionally more in training than any other rms in 2004. On the other hand,it is not surprising that the item of investment in capital equipment and hardware appearsas the most important for technological embodiment, regardless of the rm size. Likewise,monetary investments in R&D activities are minimal in all rm sizes, which corroborate thescarce effort in R&D activities. A similar result was obtained in the EDT of 1996.

Chapter II of the EDIT asks about the characteristics of the rm personnel in 2004 andalso for the average labor cost according to different areas or departments. 15 Table VI.4shows how the personnel were distributed according to education levels and rm size.Interestingly, the proportion of personnel for small rms with a doctorate is higher thanthe proportion for medium and large rms. In general, data shows that the personnel aredistributed in a more or less homogenous way along the different rm sizes. The highestnumber of employees, regardless of rm size, has an education level which does notexceed the secondary education.

15 The EDIT requested information on the personnel according to the type of contract with the rm (permanent,temporary and owner) and by area (administration, sales, design, engineering, R&D, production, quality and trials,environment, occupational health, informatics and systems and others). Chapter II also requested a depiction of personnel by educational levels: doctor, master, specialization, professional, professional trainee, technologist,technician, SENA apprentice, secondary and primary education.


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Table VI.3ICT and innovation-related investments by rm size, 2003

(Percentages)

Type/Size (employees) Until 50 51-100 101-200 201-500 More than 500

ICT equipment 7.98 9.02 9.33 8.32 6.81

Capital equipment - hardware 35.52 45.07 50.15 51.39 54.40

Management 24.15 19.49 19.25 19.84 16.15

ICT 11.21 10.36 8.82 6.66 7.95

Cross-cutting technologies 26.25 24.07 19.33 18.34 19.30

R&D 1.26 1.05 1.29 0.94 1.50

Training 12.84 10.33 10.00 9.49 8.66


Table VI.4Firm personnel by education level and rm size, 2004

(Percentages and number)

Education/ Size(empl oyees) Until 50 51-100 101-200 201-500 More than 500

Doctor (PhD) 0.07 0.19 0.04 0.04 0.05

Master 0.22 0.15 0.20 0.22 0.26

Specialization 1.03 1.45 1.52 1.94 2.19

Professional 12.68 10.92 11.66 11.63 12.92

Professional trainee 0.39 0.40 0.34 0.40 0.44

Technologist 6.02 5.99 5.21 6.15 5.58

Technician 9.30 7.65 8.43 8.77 8.93

SENA apprentice 1.38 1.96 2.19 2.00 2.70

Secondary education 45.81 50.36 51.07 51.32 53.04

Primary education 21.93 18.58 18.35 16.23 12.38

Other 1.18 0.95 1.00 1.30 1.53

Number of rms 3 903 924 566 391 178


Table VI.5 displays information concerning objectives and innovation results. 16 It isclear that rm size is a relevant factor of innovation activities ( e.g. Cohen and Levin,1989). In fact, 69% of the very large Colombian manufacturing rms implementedinnovations in products and processes against only 41% of the small rms. Interestingly,the highest percentage of rms that did not develop any innovation corresponds to the

16 The calculation methodology of the innovation measures is explained in the empirical approach.


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medium rms up to 200 employees. Finally, data suggest that product innovations weremore intensively implemented than process innovation, regardless of rm size. This isinteresting considering the rst one as an offensive strategy for capturing new markets,and the second one as a defensive strategy to maintain market shares (Kamien andSchwartz 1982 and Koellinger 2008).

Chapter IV of EDIT asked about expenditures of DTIA investments and nancial sources.This information is shown in Table V.6. It is clear that, regardless of the rm size, ownfunds is the basic source for nancing DTIA. The second source is commercial banking,while public sector nancing is important only for large companies. Finally, Table VI.7presents two productivity measures by rm size, total sales per employee and valueadded per employee. As expected, productivity increased monotonically with the rmsize, regardless of the productivity measure.

Table VI.5Innovation activities by size, 2004

(Percentages )

Innovat ions/Size (employees) Unti l 50 51-100 101-200 201-500 More than 500

0 - No innovations 30.0 20.8 18.3 11.0 15.4

1 - Process innovations 11.3 11.4 10.7 10.5 4.6

2 - Product innovations 17.8 18.5 19.6 18.6 10.9

3 - Product and process innovations 40.9 49.3 51.4 59.8 69.1

Number of rms 3 108 843 541 381 175


Table VI.6Financing sources by rm size, 2003

(Percentages and number )

Financing source/Size (employees) Until 50 51-100 101-200 201-500 More than 500

Public sector (co- nancing) 1.44 3.80 3.00 4.62 6.18

Public sector (credit) 2.82 8.26 8.83 10.77 18.54

Commercial banking (credit) 12.98 20.98 25.80 29.23 42.70

External sector (credit) 0.36 0.87 1.06 0.77 2.81

Other sources 0.49 0.76 0.35 1.03 3.37

Businesses sources (other sources) 51.21 65.98 71.20 78.72 73.60

Number of rms 3 898 920 566 390 178



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Table VI.7Labor productivity by rm size, 2004(Sales and value added per employee)

Productivi ty /Size (employees) Unt il 50 51-100 101-200 201-500 More than 500

Sales per worker 15 057 67 976 158 875 404 677 1 587 539

Value added per worker 5 351 26 447 58 857 173 807 742 094

Number of rms 3 903 960 602 427 214

Source: Authors elaboration based on EDIT 2005 and EAM 2004.

4. Empirical approach

As we mentioned before, the main database used for this study is the Survey of Development and Technological Innovation (EDIT) 2005, which is complemented withdata from the Annual Manufacturing Survey (EAM) 2004. Although the EDIT comprises atotal of 6,106 companies, representing the universe of small, medium and large Colombianmanufacturing rms, not all EDITs chapters contain data for the total number of rms.After checking inconsistencies and other factors concerning information availability, theestimation sample is composed by 5,900 rms. 17

Considering the main hypotheses tested in the literature and the data availability for theColombian Manufacturing sector, we postulate four hypotheses to test: i) ICT investmentis positively related to labor productivity ; ii) Investments on human capital affects labor

productivity positively ; iii) Investments on organizational technologies are positivelyrelated to labor productivity and iv) The differentiated effect of ICT on labor productivityshall be higher for those rms that innovate in processes and/or products . Following theliterature, we use an extended Cobb-Douglas production function:

Ln (Y/L) = 1 LnStock NO-ICT,i + 2 LnStock ICT ,i + 3 LnL i + 4 LnHumanCapital i + 5 D_Management i + 6 Innovation i + 7 Z i + i (1)

We consider two productivity measures at rm level as dependent variable: i) value addedper worker and ii) sales per worker. LnStock NO-ICT corresponds to the value of the non-ICTcapital stock and LnStock ICT is the ICT equipment capital stock. We calculate these two

17 Several factors reduced the rm sample. First, some rm managers did not answer certain modules of the survey.Second, not all rms reported data on t he number of employees. Third, due to the fact that the EDIT did not consider any performance variable, it was necessary to ask DANE for EAM data for 2004 and 2005. However, DANE keepscon dentiality agreements even with random codes. Likewise, some rms may have not reported information tothe EAM, but rather to the EDIT. Finally, with the information of the EAM-2004, additional rms were eliminatedaccording to two criteria: rms that that did not report data on value added or sales and rms that declared to haveno value in the assets account.


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inputs in relation to the total number of employees. In the econometric estimations, we alsouse the variable LnI no-ICT which corresponds to the investment in machinery and equipmentsother than ICT equipments and LnI ICT that is the investment in ICT. In accordance withBecchetti et al. (2003), we also introduce the investment in software reported by each rm.The items include software for goods and services production, administration software,marketing technologies, web sites and access to the Internet. 18 The labor input is measuredby number of employees ( LnLi ). Following the literature, we construct two proxy variables of human capital: proportion of employees with university education level and percentage of employees performing activities other than production ( LnHumanCapital i ). The hypothesisis that new computing technologies allow a higher ef ciency in task performance, but thishigher ef ciency is possible only if employees have appropriate skills.

In relation to organizational changes, the EDIT asked about rm investments in 17 different

management technologies. Among these are: quality control, permanent improvement,total quality management (TQM), just-in-time (JIT), changes in the structure of services andproduction lines, project reengineering, benchmarking, vertical and horizontal disintegrationand exible manufacturing cells, among others. For estimation purposes, we use a proxy variable of organizational changes: a dummy that takes the value 1 if the rm implementedorganizational innovations in any of the 17 management technologies and zero otherwise(D_Management ). With respect to technological innovations and following Romo and Hill(2006), we create a categorical variable ( Innovation ) which takes the value zero if the rmdid not implement any innovation at all, 1 if the rm implemented process innovation, 2 if itdeveloped product innovation and 3 if it developed both types of innovations. 19 A justi cationfor proceeding this way is the analysis of Kamien and Schwartz (1982), who point out thatproduct innovations are associated to a new production function, while process innovations

are related to a shifting of the actual production function.

Additionally, the econometric estimations control for several other rm characteristics(vector of variables Z i): rm size,

20 age and dummy variables related to ownership (foreign/

18 As a complementary measure, we use aggregate data of investment in ICT equipments in addition to the investmentin ICT software ( I ICT_SOFT .).

19 The EDIT did not explicitly ask if the rm developed product, process innovation, or both. However, Chapter III,Objectives, Results and Idea Sources for the Technological Innovation, introduces a section asking about the stateof progress with regard to the technological activity. This section asks for a total of 7 items and 44 objectives of innovation and technological development. Of these 7 items, 3 refer to the introduction of new or improved products for the domestic or international market and 2 refer to new processes . The manager had to answer each of the 7items and 44 objectives with either: obtained, dropped, in process and does not apply . From the 44 objectives, wewill restrict ourselves to the rst 7, which are Associated to the market and products. We created dummy variablesrelated to product innovation with the value of 1 each time the answer was obtained on the 3 items of the state of progress for the 7 objectives, and otherwise zero. Then we added the 21 possible results and created a dummy of product innovation for those rms that reported obtained in the state of progress in at least one of the objectives.In a similar way, we construct the dummy process innovation , but taking the answer obtained for the two questionson process innovation in the state of progress section.

20 We introduced 4 different size dummies: Small (less than 50 employees), Med (51-100), Med-Lge (101-200), Large (201-500) and Very Large (more than 500 employees).


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national), 21 exports, industrial sectors (22 dummies by ISIC Rev.3) and presence of R&DCenter. 22 Finally, we include in equation (1) interaction terms between ICT investments,human capital and organizational changes in order to test for complementarities amongthese input factors.

Table A.1 in the Appendix displays basic statistics of the estimation variables by rm size. Thedata shows high heterogeneity by rm size. Additionally, Table A.2 displays the correlationmatrix of the estimations variables. It shows relevant correlations between most variablesand, especially, between labor productivity and ICT and non-ICT capital stocks. Additionally,it shows that the correlation between human capital and productivity is signi cant at 5% andno signi cant correlation between productivity and organizational management. Finally,age, R&D center and innovation are positively correlated to productivity.

5. Econometric results

We develop two cross-sectional econometric estimations using ordinary least squarescorrecting by heteroskedasticity. In the rst estimation we analyze the effect of investmentin ICT, human capital and organizational change on labor productivity. In the secondestimation, we present evidence concerning the hypothesis of complementarity, followingGiuri et al. (2008). Tables VI.8 and VI.9 present the estimation results. Each columncorresponds to subsequent estimations that include new explanatory variables.

The most important result is the positive and highly signi cant impact of ICT investment on

labor productivity. This is consistent across the different econometric speci cations (seeTable VI.8). Additionally, there is positive correlation between human capital and labor productivity (column 2). This result veri es the ndings of Arvanitis (2005), Bresnahanet al. (2002), Black and Lynch (2001), among others. Additionally, the impact of humancapital is robust to the inclusion of other covariates (see columns 3 to 6). The coef cientassociated to the organizational change variable is not signi cant in all estimations. Thus,the hypothesis that organizational changes affect labor productivity is not veri ed. We mayconsider that these changes take time to have a relevant impact on productivity. A secondaspect which may obscure the real relationship among these variables is the proxiesused. 23 Finally, as can be seen in column 5 and 6, there is a positive relationship between

21 This dummy variable is equal to 1 if more than 50% of the capital is national and zero otherwise.22 This dummy variable is equal to 1 if the rm has employees working in R&D activities and zero otherwise.23 The measures used in the literature are multiple. For instance, Arvanitis (2005) used delegation of responsibilities

of managers to employees, capacity of employees to solve problems (directly) in the production area, capacityof employees to contact customers directly, reduction of number of administrative levels; and Black and Lynch(2001) used reengineering, benchmarking, number of administrative levels, proportion of employees in self-directedequipments, among others.


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innovation and productivity in the presence of investment in ICT, quali ed human capitaland organizational changes. This relationship is robust to the inclusion of other covariates.

Other interesting implications can be inferred from the control variables in the estimations.For instance, it seems that there is no relevant relationship between the age of the rm andlabor productivity. Additionally, the dummy variable of export condition is highly relevantand has a positive sign. The variable that control for the presence of R&D employees isnon-statistically signi cant across all estimations. This may re ect that the sole presenceof personnel involved in R&D activities is not enough to increase productivity. Finally,the negative coef cient of the variable D_National shows that national rms obtain lower productivities in relation to foreign rms.

Table VI.8

OLS estimations 1Variable 1 2 3 4 5 6

LnStock ICT 0.1475*** 0.0998*** 0.0998*** 0.0929*** 0.0917*** 0.0855***

LnStock NO-ICT 0.1305*** 0.1262*** 0.1261*** 0.1225*** 0.1228*** 0.1168***

LnPersonnel 0.0601** 0.1032*** 0.1031*** 0.0527* 0.0408 0.0334

LnHuman Capital 1.5974*** 1.5972*** 1.5906*** 1.5640*** 1.4947***

D_Management -0.0062 -0.021 -0.0189 -0.0171

D_Export 0.3476*** 0.3447*** 0.3283***

D_R&D -0.0382 -0.0422 -0.0443

LnAge 0.0082 0.0107 0.0156

Innovation 0.0342*** 0.0374***

D_National -0.3355***Small (20-50) -0.0918 0.0081 0.0077 0.0052 -0.0106 0.0333

Medium (50-100) 0.064 0.1499 0.1498 0.1363 0.1216 0.1625Medium-large (101-200) 0.0847 0.1476 0.1473 0.1392 0.1292 0.1574

Large (201-500) 0.2085** 0.2507** 0.2505** 0.2616*** 0.2509** 0.2708***

Constant 7.5907*** 7.4822*** 6.5739*** 7.7282*** 6.5621*** 8.1256***

N 5 045 5 045 5 045 4 878 4, 890 4 878

R2adj 0.268 0.3013 0.3011 0.3288 0.3304 0.3356

F 54.2135 59.6083 60.3888 63.1316 60.002 60.6993

Source: Authors own elaboration.Note: *, **, *** denotes statistical relevance at 10%, 5% and 1% level; heteroskedasticity-robust standard errors.Estimations include-but not reported- dummy variables at sectoral (ISIC Rev. 3) and geographical levels.

Milgrom and Roberts (1990, 1995) developed theoretical models associated to the moderntheory of the rm where the complementarity of ICT investment with organizationalchanges and personnel quali cation plays a key role. Following this argument, we test afth hypothesis: there exist complementarities between ICT investment, human capital


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and organizational change that affect labor productivity positively . Following Giuri et al. (2008), we include interaction terms between ICT investments with the proxy variablesof human capital and organizational change. The purpose is to analyze which type of complementarities affect labor productivity. The rst four regressions include alternativelythe interaction terms between ICT investment and human capital, human capital andmanagement, ICT investment and management, and the interaction between these threeproductive factors (see Table VI.9). The last two columns divide the total sample in twogroups: the rst group which did not undertake organizational changes and the secondone which did take organizational changes.

The results are mixed. Human capital and ICT investments maintain the statistical sign,magnitude and signi cance. The coef cient of the management variable is positive, but notrelevant. Nevertheless, the interaction coef cients have all negative signs and they are almostall non-signi cant. This indicates that there is no complementarity between ICT investmentand human capital and neither between management and human capital and between ICTinvestment and organizational changes. This outcome, which coincides with the ndings of Giuri et al. (2008), could be explained by the fact that the data may not capture, in a singleyear, the effect of input combinations on labor productivity. It is possible to assume that, at rst,organizational changes generate con ict situations that affect productivity in an even negativeway. Moreover, we can expect a learning process by the employees on the use of ICT andunder organizational changes. In order to make a more robust test of the complementaritybetween ICT and human capital, the last two columns present the regressions by dividing thesample. The results con rm the non-existence of complementarities. Indeed, for both rmgroups the interaction between ICT and human capital is not signi cant. Again, the cross-section characteristic of the data may explain this result.

In order to analyze the robustness of the empirical results, we perform several additionalestimations. First, we use sales per employee as a dependent variable. Second, weattributed ICT investment values to the rms that did not report data, following Hempell(2005). Third, a total of nearly ten different explanatory variables were used in theestimations. For instance, we estimated the model by using another proxy variable for human capital percentage of employees performing activities other than productionand we used the Pavitt taxonomy for creating the sectoral dummy variables. 24 Basically,the main results of the estimations remain the same and it allows us to conclude thatthe relationship between ICT investment, human capital and organizational change, andlabor productivity, is robust. 25 The implications of the positive impact of ICT investmentand human capital and innovation on productivity are magni ed when we observe that

nearly 65% of the 5 thousand rms of the sample are small rms or have less than

24 We use the sectoral classi cation proposed by Pavitt (1984) and adapted by Gutti (2008) according to severalpatterns of technical change: natural resource intensive; dominated by suppliers; intensive in economies of scale;specialized suppliers and dominated by science.

25 Estimations are available upon request to the author.


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50 employees. First, it is bene cial for manufacturing rms to invest in information andcommunication technologies, regardless of complementary investments. Second, havingbetter trained personnel is an additional engine for obtaining gains in productivity. Finally,the innovating efforts, in whatever form, allow for productive improvements.

Nevertheless, it is necessary to clarify that some cross-section studies have reporteda positive and signi cant relationship between ICT investments and productivity, but anon-relevant relationship and even a negative one when relying on panel data (Hempell,2005). Indeed, cross-section studies do not capture the dynamic of entry and exit of rmsand this may affect the relation investment-productivity. Finally, it is important to mentionthat endogeneity problems may be relevant in this type of estimations. It is necessaryto expand the analysis to longer periods of time and to be able to nd valid instrumentsthat control for endogeneity. This would help in understanding the dynamic nature of the

relationship between ICT, human capital and productivity.

Table VI.9OLS estimations 2

Variables 1 2 3 4No

organiza-tional change

Organiza-tional

change

LnStock ICT 0.0922*** 0.1563*** 0.0916*** 0.1060*** 0.10028*** 0.0890***

LnStock NO-ICT 0.1228*** 0.1255*** 0.1227*** 0.1229*** 0.1229*** 0.1206***

LnPersonnel 0.0411 -0.0107 0.0405 0.0403 0.0258 0.0513

LnHuman Capital 1.5894* 1.8795*** 2.1136* 2.038434** 1.2375

D_Export 0.3444*** 0.3662*** 0.3443*** 0.3442*** 0.3888*** 0.3077***

D_R&D -0.0416 0.0344 -0.0443 -0.0444 -0.0278 -0.0505

LnAge 0.0106 0.0156 0.0109 0.0108 0.0429** 0.0153**D_Innovation 0.0343*** 0.0448*** 0.0339*** 0.0340*** 0.0284* 0.0378**

D_Management 0.2213 0.059 0.222

LnStock ICT*LnHuman Capital 0.003 -0.037 -0.0266 0.01594

LnStock ICT*D_Management -0.036* -0.025

LnHuman Capital * D_Management -0.549* -0.955

LnStock ICT * LnHuman Capital* D_Management 0.064

Small (0-50) -0.0089 -0.0948 -0.0181 -0.017 -0.0136 -0.0106

Medium-Large (50-100) 0.1225 0.0508 0.1123 0.1144 0.1524 0.0879

Medium (101-200) 0.1305 0.0734 0.1217 0.1225 0.1447 0.1091

Large (201-500) 0.2516** 0.2133** 0.2443** 0.2456** 0.2969** 0.2133*

Constant 7.7110*** 7.7088*** 7.6924*** 7.5967*** 7.5830 7.829***

N 4 878 4 878 4 878 4 878 2 149 2 729R2adj 0.3303 0.2989 0.3315 0.3312 0.3802 0.3076

F 62.195 56.006 61.776 58.2204 37.5200 .

Source: Authors own elaboration.Note: *, **, *** denotes statistical relevance at 10%, 5% and 1% level; heteroskedasticity-robust standard errors.Estimations include-but not reported- dummy variables at sectoral level, regional level.


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6. Concluding remarks

The main result of this study is that there is a positive and signi cant effect of ICTinvestments on labor productivity in Colombian manufacturing. The productivity gains arealso reinforced by the investments in intangibles, such as human capital and innovation.Additionally, the exporting rms obtain a productivity prime in relation to the non-exportingrms. These results may suggest that it is necessary to support rms in order to performthis type of activities permanently.

The ICT investments, mostly through the purchase of imported hardware and software,have been encouraged by the appreciation of the Colombian peso in the last 8 years.However, the purchase of hardware and software requires nancing, and although priceshave also dropped in the last years due to the strong reduction of the interest rates, it is notalways easily available for the small and medium rms. These two factors, appreciation of the peso and reduction of the interest rates, are important, but they can be reverted if theinternational nancial situation changes. A more direct action to support ICT investmentsis to create special credit lines for the small and medium rms. A mechanism whichshould be consolidated is the program MiPyme Digital which the Ministry of Informationand Communication Technologies launched on October 2008. This program seeks toovercome the lag of the micro, small and medium rms with regard to the ICT use,and aims at promoting the implementation of technological solutions that support their operation processes by integrating hardware, software, Internet connectivity and training.This mechanism could also be complemented by exceptions in customs duties and taxesfor the purchase of hardware (imported and domestic). Nevertheless, if we want this typeof investments to have a higher impact, we must also consider the importance of trainingthe human resources.

A direct implication of the study is to spread among the managers the need to makeinvestments on ICT goods, to improve continually the training of the employeesand to develop innovative processes. Indeed, the national government and thebusiness associations could organize regional workshops, where companies withinvestments in innovative processes, ICT and human capital could share their experiences so as to motivate the rest of the manufacturing rms to undertakeinvestments in this eld. In a certain way, the program MiPyme Digital seems to aimat these activities. Another implication is to implement surveys on innovation andtechnological development on a more regular basis in Colombia. The availability of regular data may allow researchers to determine the long-run relationship between

ICT investments, organizational intangibles and innovation, and labor productivity.However, concerning the Colombian survey, it is also necessary to re ne and reducethe number of questions considered in such a survey, with the purpose of obtainingmore accurate answers from the managers.


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Finally, it is necessary to develop more research on the subject using microdata not only inthe manufacturing but also in the service sector, including nancial activities. Since 2006,the EAM includes an ICT module which contains data on computers and percentage of computer use, Internet and usage activities of Internet. This new information will de nitelyallow for a better evaluation of the ICT impact on the performance of the Colombianmanufacturing rms.

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8. Appendix

Table A.1Variables description

(In logs)

Variables Obs. Mean Std. Dev. Min Max Median

Medium and large rms

Productivity 2 072 10.29 1.03 4.18 14.33 10.27

LnStock NO-ICT 2 049 10.57 2.37 3.71 21.98 10.26

LnStock ICT 1 993 7.00 1.31 0.69 12.59 7.04

Non-ICT investment per employee 1 367 6.96 2.09 0 14.66 6.92

ICT investment per employee 1 008 4.93 1.62 0 14.17 4.76

LnPersonnel 2 074 4.93 0.83 3.93 8.84 4.71LnHuman Capital 2 058 7.30 0.87 5.71 11.50 7.10

D_Management 2 074 0.56 0.50 0 1 1

D_Export 2 069 0.42 0.49 0 1 0

LnAge 2 043 2.84 0.95 0 4.57 3.04

D_R&D 2 074 0.20 0.40 0 1 0

D_Innovation 2 074 1.96 1.22 0 3 3

Small

Productivity 3 857 9.73 0.98 2.08 17.39 9.76

LnStock NO-ICT 3 760 9.61 1.29 2.94 15.05 9.69

LnStock ICT 3 061 6.52 1.23 0 11.71 6.54

Non-ICT investment per employee 1 299 4.98 1.68 0 10.48 5.05

ICT investment per employee 896 3.29 1.06 0 6.65 3.16LnPersonnel 3 903 2.86 0.67 0 3.91 2.89

LnHuman Capital 3 902 5.19 0.71 0.69 6.50 5.24

D_Management 3 903 0.31 0.46 0 1 0

D_Export 3 852 0.13 0.34 0 1 0

LnAge 3 661 2.43 0.99 0 4.63 2.71

D_R&D 3 903 0.04 0.18 0 1 0

D_Innovation 3 903 1.35 1.33 0 3 1



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Table A.2Spearman correlation matrix

Variables 1 2 3 4 5 6 7 8 9 10

1. Productivity 1

2. LnStock NO-ICT 0.414* 1

3. LnStock ICT 0.407* 0.503* 1

4. Non-ICT invest-mentper employee

0.301* 0.204* 0.237* 1

5. ICTinvestmentper employee

0.326* 0.374* 0.239* -0.014 1

6. LnPersonnel -0.012 -0.002 -0.021 -0.006 -0.004 1

7. LnHumanCapital 0.262* 0.153* 0.135* 0.380* 0.063* 0.005 1

8. D _Management 0.178* 0.1998* 0.2703* 0.2389* 0.0936* 0.004 0.117* 1

9. D_Export 0.223* 0.2051* 0.181* 0.296* 0.177* -0.019 0.1984* 0.1185* 1

10. LnAge 0.169* 0.1173* 0.1203* 0.273* 0.114* -0.027* 0.132* 0.055* 0.137* 1


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