Innovation and information technology in services

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1 Innovaton and nformaton technology n servces G E O R G L I C H T and D I E T M A R M O C H Zentrum für Europäsche Wrtschaftsforschung, Mannhem (ZEW) (Centre for European Economc Research) 1. Introducton Research on the mpacts of IT usually starts wth the basc assumpton that 1 computers enhance productvty. The avalable evdence s mxed. Brynjolfsson and Htt (1996), Segel and Grlches (1992), Greenan and Maresse (1996) and Lchtenberg (1995) present evdence supportng a postve productvty mpact of IT. Landauer (1995) argues the evdence n favour of productvty effects s weak and that exstng studes suffer from severe measurement problems. Yet, nformaton technology has become an mportant feld of corporate nvestment n all sectors. For example, the German IT market was valued at about 37 bllon ECU n 1994 or about 4.5 percent of Germany s GDP. Most of these goods and servces are demanded by frms. The fact that companes contnue to nvest n IT suggests that there must be postve mpacts. In Germany, nvestment n IT s especally mportant n the servce sector, as t s n other countres too. Ths observaton s supported by the sector-specfc absolute amounts of money nvested as well as the shares of IT-nvestment relatve to other nvestment. In the most dynamc servce ndustres, nvestment n IT s sad to be larger than n manufacturng. Lookng at the total IT-nvestment n the economy, servce ndustres are responsble for the largest and ever growng share of total expendture on IT n the economy. Yet, productvty growth seems to be slower n these servce ndustres. Landauer (1995, p. x) states that for the jobs most people do n servce enterprses, most computer applcatons make work only a lttle more effcent. Some scholars such as Gordon (1996) argue that the structural shft towards servce ndustres s at least partly responsble for our nablty to measure postve productvty effects of IT. Qualty aspects of techncal change are hard to assess, especally n servces. Based on new data for Germany we try to show that ths s a sgnfcant problem for emprcal research on the effects of IT. We use new data to assess the mportance of certan problems concernng the measurement of IT productvty effects. Ths s the frst study on ths topc usng German data. Our data set has the usual defcences assocated wth output Canadan Journal of Economcs Revue canadenne d Economque, Vol. 32, No. 2 Aprl / avrl Prnted n Canada Imprmé au Canada / 99 / $1.50 Canadan Economcs Assocaton

2 364 Georg Lcht and Detmar Moch msmeasurement, nput msmeasurement of IT, and the endogenety of computer captal and other nputs. Nevertheless we are able to offer some nterestng stylzed facts. Also, we demonstrate that t s mportant to dstngush between dfferent types of IT nvestment. Consderng the enormous varety of computer equpment rangng from personal computers to manframe computers, one should not be surprsed to fnd that aggregaton of computer captal matters. We proceed as follows. In secton 2 we brefly descrbe the theoretcal background of productvty analyss n the servce sector. In secton 3 we assess the mpact of IT on nnovaton n servces usng data from the Mannhem Innovaton Panel for the Servce Sector (MIP-S). Secton 4 analyzes the mpact of the structure of IT nvestment on labour productvty usng data from Internatonal Data Corporaton Deutschland (IDC). We present regresson results of a Cobb-Douglas model that allows for the IT-structure. Secton 5 summarzes the emprcal results from both data sets and draws conclusons. 2. A short survey of the recent emprcal lterature The problem of measurng productvty n the servce sector has receved consderable attenton recently (see, for nstance, Grlches 1992). Therefore we only provde a bref summary of related lterature. One possble way to analyze these effects s to separate computer-captal from non-computer captal as Brynjolfsson and Htt (1996) dd. They found that computer captal shows a hgher rate of return. Constructng a captal stock n computers as they dd, however, assumes that the utlty derved from an nvestment only depends on the nomnal value. We would expect that there are dfferences n the productvty mpact of varous types of IT equpment. There have been other approaches to tryng to explan the productvty paradox that have focused on the mportance of correct prce ndex seres for the nputs and outputs. One possblty s that the productvty paradox s due to the use of deflators that do not truly capture qualty changes. Durng the last decades, the capacty and performance of computer hardware and software have dramatcally mproved. At the same tme, nomnal prces have ether decreased slghtly or just mantaned ther level. Recent studes on the declne of qualtycontrolled prces for personal computers (PCs) (e.g. Gordon 1990, Berndt and Grlches 1993, Nelson, Tanguay, and Patterson 1994, and Berndt, Grlches and Rappaport 1995) have found qualty-adjusted prces for PCs declned on average by percent per year n recent years. The growth of nomnal nvestment n the presence of a decrease n qualty-adjusted prces allowed the acquston of large IT related captal-stocks n many frms. We should expect to fnd stronger postve mpacts of ths recent IT nvestment on productvty n the present, snce users now can get the same utlty at consderably lower prces. Moreover, modern IT equpment can be more easly combned wth other assets of a frm snce computer sklls have become more wdespread. Ths has

3 Innovaton and nformaton made modern IT-equpment more of a general purpose technology. If ths s mportant, more recent data should show stronger IT-effects than analyses that use data of the egthes. However, IT-effects of ths sort would also reflect complementary assets. Moreover, the compatblty of software and hardware has been ncreasng snce personal computers became the leadng IT-technology. Ths s confrmed by a recent study of Harhoff and Moch (1996). Ths too suggests that there should be larger IT-productvty gans today than n the past. 3. Impacts of IT n servces - some assessments usng data on nnovaton actvtes n servces 3.1 Data on nnovaton n the servce sector The frst part of our emprcal analyss draws on the Mannhem Innovaton Panel for the Servce Sector (MIP-S). These data stem from a mal survey on the 3 nnovaton behavour n the servce sector carred out n The frst author was responsble for the desgn and mplementaton of the survey. Summary statstcs for the MIP-S data set are gven n appendx 1. The MIP-S extends the concept of nnovaton surveys n manufacturng as summarzed n OECD s Oslo-Manual (1997) to the servce sector. Topcs covered n the questonnare nclude (1) general nformaton about the frm (sze, ndustry, sales, number of employees, labour costs, exports, strategc management objectves, customers and product characterstcs), (2) the human captal of the frm, and (3) nvestment n new physcal assets and n nformaton technologes. Specal attenton was pad to the nnovatve actvtes of frms wth questons on R&D personnel, the acquston of knowledge, co-operatve nnovaton actvtes, lnkages to publc R&D nsttutons, the mportance of varous new technologes and the mpacts of nnovaton. Frm nvestments n IT are seen as a by-product of nnovaton and not a man focus of ths survey. The survey covers all servce sector frms wth more than four employees contaned n the records of CREDITREFORM, Germany s largest credt-ratng 4 agency. Ths source provdes data on the number of employees, ndustry and the addresses of frms. Ths nformaton was used to stratfy the sample by sector and frm sze as well as former West-Germany and East-Germany. The MIP-S s restrcted to marketed servces and, therefore, only ncludes frms from wholesale and retal trade, transport, traffc, bankng, nsurance, software, techncal consultancy, marketng and other busness servces. About 2,800 frms partcpated n ths voluntary survey. The overall response rate was about 26%, whch s n lne wth the response rates for other voluntary mal surveys n Germany. The response rate was above average for the frms n the bankng sector and for the frms n technologcal servces and software frms (around 30%), and t was below average for the frms n wholesale and retal 5 trade (around 24%). Partcpaton slghtly ncreased wth frm sze. Before consderng the role of IT n servce nnovaton we examned some

4 366 Georg Lcht and Detmar Moch basc results on nnovaton actvtes n the servce sector. In partcular, we 6 examned the weghted shares of frms wth fve or more employees that reported product, process or organzatonal nnovatons. Product nnovatons are defned as new or mproved servces whch are offered to customers. Process nnovaton refers to mprovements n the producton process appled nternally n the frm. Organzatonal nnovaton encompasses sgnfcant changes n the organzatonal structure (for example, changes n the number of herarchcal layers) or n basc organzatonal patterns (such as n the ntroducton of total qualty management procedures). Product nnovatons were found to domnate. However, n many cases nnovatons cannot be clearly categorzed as product or process. Ths problem s llustrated by the ntroducton of 24 hour-cash-dspensers. The lengthenng of access to bankng servces can be regarded as a product nnovaton, although the actual bankng product beng dspensed through the machne, the transfer of money, remans unaltered. On the other hand, snce ths servce s not rendered through personal contact at the counter but rather by customer self servce, ths could also be vewed as a process nnovaton. We refran from usng the selfassessed dfferentatons for product and process nnovatons. Further problems arse from the defnton and nterpretaton of research and development (R&D). What ths s for servce sector frms s not always compatble wth the defnton of R&D n offcal R&D statstcs. Nevertheless, we use R&D expendture as an ndcator of a knowledge-ntensve nput. 3.2 Impact of nnovaton n servces Our nablty to measure productvty changes n servces owng to the use of new technologes n servce sectors may be due to the fact that, unlke the manufacturng case, nnovaton n servces often s nether represented by new servces nor by process mprovements whch ncrease output or decrease nputs. Innovaton n servce s often more closely connected to the way products are delvered. For example, techncal change s often assocated wth the number of hours durng whch a servce can be delvered or wth mprovements n the spatal 7 dmenson of the servces (e.g. home bankng). Measurng nnovaton outputs by sales shares for new products, as s often done n manufacturng, s not adequate to capture product nnovaton n servces. As a frst step to evaluate the mpact of techncal change n servces, we use a qualtatve, multdmensonal approach. Frms were asked to rate dfferent dmensons of the mpact of nnovatons on a 5-pont scale rangng from not at all mportant (=1) to very mportant (=5). These dmensons are gven n table 1, whch also shows the shares of frms by sector and frm sze whch evaluate these dfferent dmensons as mportant (=4) or very mportant (=5). Overall, most frms vew ncreasng productvty as mportant or very mportant. So, we should expect techncal change to be reflected n the tradtonal (labour) productvty measurement. Other dmensons such as ncreasng

5 TABLE 1 Dmensons of the mpact of technologcal change n servces Part I: by ndustry Wholesale Retal Bankng Fnancal Overall Trade Trade Transport Insurance Servces Software Consultng Others Flexblty n adjustng customer needs User frendlness Relablty Temporal avalablty Spatal avalablty Delvery speed Fulfllng standards, regulatons Fulfllng health, ecologcal, safety regulatons Output level of customers Well-beng of customers Productvty of customers Mantenance or recyclng propertes Motvaton of employees Productvty

6 368 Georg Lcht and Detmar Moch TABLE 1(contnued) Dmensons of the mpact of technologcal change n servces Part II: by frm sze >=250 employees employees employees employees Flexblty n adjustng product to customer needs User frendlness Relablty Temporal avalablty Spatal avalablty Delvery speed Fulfllng standards, regulatons Fulfllng health care, ecologcal, safety regulatons Output level of customers Well-beng of customers Productvty of customers Mantenance, recyclng propertes Motvaton of employees Productvty flexblty, ncreasng avalablty n the tme dmenson, ncreasng avalablty n the space dmenson as well as ncreasng delvery speed are found to be nearly as mportant n the judgement of the respondents. Items reflectng customer benefts such as enhancng productvty of customers or well-beng of customers are mportant for about 40% of the frms. Only a mnorty vew regulaton aspects (concernng health care, safety, ecology, etc.) and ncreasng product lfe-tme (e.g. mprovng mantenance propertes) as mportant mpacts of ther nnovatve actvtes. Moreover, t can be seen from table 1 that nter-ndustry dfferences seem to be more mportant than frm sze dfferences. For nstance, the majorty of frms n the software ndustry expect ther nnovatve actvtes to have huge productvty mpacts on ther customers whch wll also strengthen the customers ablty to ncrease ther output. Ths knd of effect s not evdent from the frm sze dstrbuton. Moreover, f ths s the case, the productvty effect of nnovatons n the software ndustry should be expected not wthn the software ndustry but rather n ther downstream ndustres. We consder four dmensons of nnovaton based on a factor analyss of the qualtatve assessments reported n table 2. These are: (1) nnovatons that enhance the qualty and the scope of servce sector products by ncreasng delvery speed or the temporal or spatal avalablty of a servce, (2) nnovatons that ncrease the productvty of the customers of servce frms, (3) techncal changes that nduce productvty changes wthn servce frms, and (4) nnovatons that are made to fulfll regulatons or standards.

7 Innovaton and nformaton 369 TABLE 2 Factor analyss of varous dmensons of mpacts of techncal change n servces Factor loadngs (varmax-rotaton) Std. Factor Factor Factor Factor Varable Mean Dev Flexblty n adjustng product to customer needs User frendlness Relablty Temporal avalablty Spatal avalablty Delvery speed Fulfllng standards, regulatons Fulfllng health, ecologcal or safety regulatons Output level of customers Well-beng of customers Productvty of customers Mantenance, recyclng propertes Motvaton of employees Productvty Egenvalue Scale Relablty: Cronbach s Alpha Number of frms: 1869 NOTE: The largest factor loadng of each row s bold and talcs. Each of the four components covers a dfferent aspect of servce nnovaton and entals dfferent measurement problems wth respect to tradtonal approaches to measurng productvty enhancng techncal change n servces. For nstance, f IT-use enables a frm (and the frm s compettors) to run a 24-hour-servce, the volume of sales and ts cost of producton mght be unchanged but customer choce would be expended. Tradtonal productvty measurement would not reflect these sorts of mpacts. Measurement problems may also arse wth the elements of dmenson (2) whch reflect ncreases n servce that enhance the productvty or output levels of customers. Increased competton n the software market and the steep declne of software prces, whch s hardly reflected n prce ndces, wll cause measurement problems for ths dmenson too. Only dmenson (3) s easy to assess. Increased productvty of the servce generaton process and motvaton of employees wll mmedately affect tradtonal productvty measurements. Dmenson (4) s harder to evaluate snce fulfllng new legal standards or work place safety regulatons wll usually not be reflected n ncreases n sales or lower factor nput requrements. These arguments mply that relatng labour (or total) productvty numbers to factor nputs n servces s not lkely to fully reflect the effect of technologcal change n servces. Rather, productvty effects of physcal captal nvestment

8 370 Georg Lcht and Detmar Moch and/or knowledge nput uncovered by ths knd of analyss wll represent a lower bound to the total effects of technologcal change n servces. 3.3 Assessng the mpact of nnovaton nputs on nnovaton outputs As shown by MIP-S, IT technologes represent the most mportant technologes for the overwhelmng majorty of frms. Nearly all frms that ntroduced product, process or organzatonal mprovements vew IT as a key nstrument for nnovatve actvtes (see table 3). So, we should expect that the mpacts of nnovatve actvtes are related to the nvestment of frms n IT. However, based on the above assessment, t seems reasonable to argue that the mssng productvty effect of nformaton technology can be partly explaned by hdden output dmensons (see e.g. Grlches 1994). To assess the hypothess of unmeasured output generated by IT, we use the qualtatve assessment of the mpact of nnovatve actvtes and relate these output ndcators to nput factors. Gven the way the queston s posed n the questonnare we have nterpreted these qualtatve assessments as an ndcator of a change n output characterstcs rather than n the level of output. We relate ths change n output to the change n nputs whch s gven by the nvestment n physcal captal, nvestment n nformaton technologes, R&D expendture, and also nvestment n the capabltes of employees ( tranng ). We normalze these dfferent types of nput by the number of employees and use addtonal controls 8 for the level of human captal, ndustry, frm sze, and regon. Therefore, our emprcal model s gven by D 3 7 = I + β IT + δ R & D + γ E + λ j, S + j= 1 j = 1 α κ Z + µ G + ε where the symbols are defned by: D -th dmenson of output of nnovaton actvtes n I nvestment n non-it physcal captal per employee 1994 IT nvestment n nformaton technologes per employee n 1994 R&D share of R&D employees n total employment n 1994 E 1994, per employee tranng and professonal educaton expendtures S frm sze dummes 9 Z ndustry dummes 10 G East-German frm. If most of the output of nformaton technology for servce ndustres s unmeasurable, then we mght expect to fnd only a weak correlaton, or no correlaton, between the dmenson of nnovaton output whch refers to the productvty of a frm and IT-nvestment. j,

9 TABLE 3 Importance of new technologes n servces, by ndustry (percentage shares) Wholesale Retal Bankng Fnancal Techncal Trade Trade Transport Insurance Servces Software Consultancy Software Computer hardware, etc Hgh-speed communcaton networks (ISDN) Meda-, publshng and prntng technologes Transport technologes, logstcs Measurement, control technologes Medcal technologes Botechnology Envronmental technologes New materals NOTE: The table gves the percentage shares of nnovatve frms whch state that these technologes are mportant for ther nnovatve actvtes. The lst of these technologes was gven n the questonnare.

10 372 Georg Lcht and Detmar Moch TABLE 4 Relatng factor nputs to qualtatve assessments of nnovaton mpacts n servces: ordered probt regressons* Investment IT- Tranng No. of (excl. IT) Investment expenses frms/ per per R&D- per ch2(16) employee employee Intensty employee -test Flexblty n adjustng to customer needs (5.35) (2.05) (0.73) (1.55) 71.2 User frendlness (2.50) (3.00) (1.48) (0.84) Relablty (2.08) (-0.04) (3.96) (0.87) Temporal avalablty (2.09) (2.93) (1.61) (3.89) Spatal avalablty (6.01) (1.67) (0.03) (3.37) Delvery speed (3.62) (3.34) (1.41) (1.89) Fulfllng standards, regulatons (7.02) (1.60) (3.01) (1.55) Fulfllng health, ecologcal, safety regulatons (6.71) (0.04) (2.48) (2.97) Output level of customers (6.02) (0.25) (3.30) (3.49) Well-beng of customers (6.64) (-0.85) (0.00) (3.75) Productvty of customers (6.29) (0.46) (3.74) (3.30) Mantenance, recyclng propertes (2.69) (-2.13) (1.78) (1.68) Motvaton of employees (2.92) (1.35) (3.61) (3.50) Productvty (2.06) (1.38) (3.10) (2.42) * t-values are gven n parentheses below the regresson coeffcents. NOTE: The model also ncludes controls for human captal level, ndustry afflaton, frm sze, and regon. Results are not reported but are avalable upon request. Gven the qualtatve nature of the output dmenson (5-pont Lkert-scale), we use ordered probt models. The results are reported n table 4 where each row 11 represents an ordered probt regresson. The results of our exercse are qute robust wth respect to the ncluson of addtonal varables. In partcular, we ncluded varables for export shares, ownershp and more dsaggregated ndustry classfcatons. Addng these varables left our basc conclusons unchanged.

11 Innovaton and nformaton 373 In summary, we found sgnfcant effects of IT-nvestment on flexblty n adjustng products to customer needs, user-frendlness of the products, and the temporal avalablty of servces and delvery speed, but only a weak mpact of IT-nvestments per employee on the productvty of the frm. We even found a negatve correlaton between the mantenance or recyclng propertes and the ntensty of IT-nvestment. Thus, we conclude that the effects of IT-nvestment on nnovaton n servces are reflected manly n product qualty mprovements. On the other hand, non-it physcal nvestment (calculated as total nvestment mnus IT-nvestment) bears a sgnfcantly postve sgn when compared to the dmensons of nnovaton output. Strong assocatons are found for enhancng the flexblty n adjustng products to customer needs, fulfllng legal standards and regulaton, and wth respect to the productvty of customers. Moreover, R&D efforts are relevant to several features of servce 12 nnovatons. R&D enhances the productvty of frms as well as the customers. R&D s assocated wth unmeasureable components lke relablty and the ablty to fulfll regulatory requrements. Lkewse, expendtures of frms on human captal enhance productvty and the qualty of servces. We further test the valdty of these results by consderng potental complementartes between the factor nputs and by takng nto account unmeasured nput dmensons whch could lead to correlatons between the error terms n the regresson models for dfferent output dmensons. We revew these results below. If there are complementartes between IT-nvestment on the one hand and non- IT-nvestment, R&D ntensty or human captal ntensty on the other hand, our model specfcaton wll fal to uncover these effects. To account for ths possblty, we also ran regressons wth addtonal nteracton terms for ITnvestment and the other forms of nvestment. Indeed, we fnd especally strong assocatons between one of these nteracton terms (IT-nvestment * non-itphyscal nvestment) and the productvty mpact of servce nnovaton. There are also assocatons, though weaker, wth respect to customer s productvty, and to the temporal and spatal avalablty of servce nnovatons. However, we faled to uncover complementartes between nvestment n human captal (measured 13 by expendtures on tranng) and IT-nvestment. These results hghlght another measurement problem: our ablty to measure the productvty effects of IT s also affected by the degree of complementarty between physcal non-itnvestment and IT-nvestment. Hence some productvty effects of IT are probably reflected n the measured effects of non-it-nvestment. To summarze the results so far, we should keep n mnd that IT-nvestment seems to often be assocated wth qualty aspects of servce nnovatons. Therefore, there s the danger that labour productvty or total factor productvty wll not adequately reflect the true mpact of IT. Ths suggests that the productvty paradox wth respect to IT nvestments n the servce sector can at least partly be attrbuted to measurement problems.

12 374 Georg Lcht and Detmar Moch 4. Productvty effects of nformaton technology 4.1 Data on nformaton technology The second part of our emprcal analyss s based on the German Informaton Technology Survey. Ths data set contans nformaton on the dfferent knds of IT captal used by frms. We use only a part of the nformaton that has been 14 provded by Internatonal Data Corporaton Germany (IDC). About 3800 IT managers or managng drectors of establshments from all sectors voluntarly partcpated n the telephone ntervews n January and February We use data from the 317 establshments from the manufacturng sector and the 474 establshments from the servce sector. We wll make extensve use of varables representng the varous knds of computer hardware used. The data dstngush among four dfferent types of computer termnals. There are termnals 16 that are ether connected to a manframe (e.g. IBM 3090) or to a mdrange computer (e.g. IBM AS/400). There are also stand-alone UNIX workstatons and personal computers that are ether IBM-compatble or Apple compatble. We use the number of IT workplaces per employee that exsted by the end of 1995 as a proxy for the IT-ntensty of a frm. The number of termnals s a better proxy for 17 the real use of IT than aggregatons of memory or MIPS nstalled. The data set also contans the number of employees and sales fgures. These were used to calculate labour productvty. However the orgnal dataset dd not contan nformaton on captal stock and the materals used. We constructed frm specfc values of these varables usng the dsaggregated natonal accounts data on sector specfc ratos of captal stock and materals per employee for Snce the IT-Survey data were collected n telephone ntervews, we chose estmaton technques that are robust aganst outlers. 4.2 Estmatng the productvty mpact of IT Our attempt to estmate the productvty mpacts of IT starts from a Cobb- Douglas producton functon lnkng sales (Y) wth labour (L), captal (K) and 19 materals (M) : á â ã Y = A * K * L * M. We mpose constant returns to scale by settng (á + â + ã = 1). If the 20 aggregaton of IT captal does not dstngush between dfferent types, the IT captal varable may not reflect the true value of IT. Our model allows for the possblty of dfferent productvty mpacts for dfferent types of IT. In prncple, IT nvestments affect the margnal productvty of all factor nputs. Moreover, expendtures related to IT are hdden n all factor nputs. The book values of IT are ncluded n K, the captal stock varable. The IT expenses on software and servces and the leasng rates are ncluded n M. Labour nput comprses workers who are and are not able to use computers. Recent emprcal work by Autor et al. (1996) ponts to hgher wages for workers who can use

13 Innovaton and nformaton 375 computers as well as shfts n the skll structure of labour towards more computer sklls. Despte these consderatons, data lmtatons force us to follow the common practce of dstngushng IT captal and non-it captal and neglectng IT shares n the other factors. Ths may lead to an overestmaton of the margnal productvty of IT captal, snce all dfferences between IT users and non-users show up n our emprcal model va the captal stock. There may also be other problems wth the captal stock. Frst of all, t s dffcult to assess the IT used by companes. If we take the book value of computers owned, we face a bas arsng from dfferent ownershp concepts. A company may buy or rent a computer. Ths s especally a problem wth manframe computers. In the frst case we would fnd the value of the computer n the busness records and would count ths n the captal varable, whereas n the second case the leasng rates would add to the materals varable as ntermedate nputs. Second, we expect the choce of IT-products and IT-brands to have a strong nfluence on the prce and thus on the captal stock fgures whereas we expect the productvty of an employee to be ndependent from the brand chosen. Thrd, techncal change s very fast n the IT ndustry. Therefore the constructon of a computer captal fracton of the captal stock requres qualty adjusted prce ndexes whch are hard to obtan for dfferent types of IT equpment. Fourth, t s - at least n Germany - not allowed to carry software and 21 IT servces as assets, makng t hard to track IT captal over tme. The most mportant problem, however, s that f t s the specfc technology used that matters, a dsaggregaton of producton functons by IT-nputs s needed. To deal wth some of the problems wth the captal stock varable, we also ncluded a varable for IT-equpment: the number of workplaces equpped wth access to computng servces. We dstngush three types of computng devces. Frst, there s centralzed computng wth termnals that usually provde a character based dsplay and that are ether connected to a manframe or to a mdrange system. The processng s done centrally n a computng center. Second, there s localzed UNIX computng wth stand-alone UNIX workstatons. These are hgh powered devces that have propretary hardware and requre admnstratve servces. Thrd, there are personal computers that are ether IBMcompatble or Apple systems that provde local computng power and usually a graphcal user nterface as well. A wde varety of nexpensve packaged software s avalable for PCs. Our emprcal model s gven by LP ' A%áK%ãM%ë 1 TERM%ë 2 UNIX%ë 3 PC%' v j, S%' k j, Z%g where the symbols are: LP labour productvty 23 K log of physcal captal per employee M log of materals per employee j'1 j'1

14 376 Georg Lcht and Detmar Moch TERM log of termnals per employee UNIX log of standalone UNIX workstatons per employee PC log of PCs per employee S frm sze dummes Z ndustry dummes. 24 In addton to the varables for whch coeffcent estmates are shown n table 5, all regresson models nclude ndustry dummes and frm sze dummes. The regresson models for all frms nclude some zero-expendture values. In place of the undefned log-values for those cases, we have substtuted the lowest postve expendture values n our sample and an addtonal dummy varable has been added that takes the value of one n those cases. TABLE 5 25 Labour productvty and IT-equpment: LAD regresson estmates All Sectors Servce Sector log (Labour productvty) Coef. t-value Coef. t-value log(captal)* log(materal)* log(termnals)* log(unix workstatons)* log(pcs and MACs)* Test Statstcs Sze dummes F(3,773) = 3.68 F(3,459) = 2.97 Industry dummes F(7,773) = 36.5 F(4,459) = F-test (whole regresson) F(17,773) = F(14,459) = * Per employee The proper nterpretaton of our regresson results depends on whether there exsts double-countng for IT or not. We wll consder two extreme cases: a pessmstc one and an optmstc one. In the pessmstc case there does not exst any double countng. In ths case, there s no IT n the captal stock varable K, and the estmated coeffcents ë must be sgnfcantly greater than á to exhbt above average productvty gans for IT aganst other types of captal. In the optmstc case, there s full double-countng of IT. In ths case the ë represent the addtonal productvty of IT compared to the average captal, and t wll be suffcent for ë to be sgnfcantly greater than zero for there to be above average productvty gans for IT. Strkngly, n both regressons PC equpment shows very large mpacts on labour productvty whereas the effect of other IT-equpment s low or zero. Lookng only at servce sector results, we do not fnd any sgnfcant mpacts of termnals and UNIX workstatons. So, from the optmstc pont of vew, we can confrm postve mpacts n the servce sector for only the PC technology. To

15 Innovaton and nformaton 377 explore the pessmstc case, for each type of IT we start wth the hypothess that ts mpact on productvty s the same as the average mpact of captal. Ths hypothess can be tested for each type of IT by an F-test. For manframes, F(1,459)=1.36 s nsgnfcant. We fnd, moreover, that the productvty mpacts of manframes are lower than for other captal. For PCs the hypothess s also rejected, wth F(1,459)=2.15. The results for the all sectors case pont n the same drecton wth nsgnfcant F(1,773) values of 1.72, 2.08 and 0.83 for PCs, UNIX and termnals, respectvely. One mght argue that PC nvestment may be so large because frms do not recognze the real cost of a PC. Usually, the establshments do not take account of the opportunty costs of tme spent by ther workforce mantanng ther PCs. If ths nterpretaton s true, our estmaton results capture not only a PC effect but also the effect of the complementary know-how of employees. Manframe envronments are complex systems that requre bg staff and longterm projects. Hence, they exhbt a very conservatve nfluence on the development of organzatons. In a world where markets and product cycles are speedng up, they do not allow ther users to quckly adapt to dfferent condtons. In contrast PC-users can react very fast to nformaton needs. For example spreadsheets and databases allow user defned data aggregatons whch, n a manframe envronment, would requre ether tedous programmng or the prntng and manual processng of long lsts. Another mportant explanaton of why the nfluence of PCs s so large s 26 provded by the theory of general purpose technologes (GPT). Accordng to ths approach, the knowledge emboded n the product enables productvty gans. The PC s thought to represent more of a GPT than the other types of IT equpment do. We fnd hgher correlaton of PCs wth labour productvty than wth any other type of IT equpment. Fnally, network effects are a very mportant factor n computer related productvty. Standardzaton processes lead to network externaltes and thus to productvty effects. Ths hypothess s also supported by Harhoff and Moch (1996) who fnd sgnfcant prce prema for packaged PC database software that are postvely correlated wth the degree of compatblty. 5. Summary and conclusons Ths study presents some evdence about the use of IT n the servce sector and about the effects of IT on the qualty of servce sector output as well as on productvty. The MIP-S data ndcate that nformaton technology has strong mpacts on the qualty aspect of servce nnovatons. We fnd plausble correlatons between our qualtatve output ndcators on the one hand and captal nvestment, R&D, and human captal on the other hand. Contrary to these results, IT seems to affect some qualty aspects of servce sector products but not productvty. Although a hgh percentage of nnovatng frms clam to have

16 378 Georg Lcht and Detmar Moch realzed productvty gans, managers of servce frms seem to be less convnced about the productvty benefts of IT nvestments. In ths respect, our results are consstent wth pessmstc vews on the productvty effects of IT. It could be, however, that we do not fnd sgnfcant productvty effects usng the MIP-S data because we do not consder the type of equpment. Maybe t s more mportant what type of IT companes use than how much they nvest. Ths argument s supported by the IT-survey data that exhbts large dfferences n the correlatons between each type of IT and labour productvty. Surely, automaton s expected to brng productvty gans. However, we expect much more utlty from oblteratng busness functons. Frms just started restructurng ther IT. Most frms move along tradtonal lnes and contnue revampng tradtonal busness functons wth IT. Ths may prohbt the ndustry from reapng the benefts, or mght even have negatve effects. As Paul Davd (1991) argues, the real benefts of electrc power became vsble after we had learned to replace central power dstrbuted wth transmsson belts by locally powered devces. Therefore, we should expect that the real mpacts of IT on productvty and product qualty are stll to come. Notes Ths paper was prepared for the conference on Servce Sector Productvty that was organzed by the Centre for the Study of Lvng Standards n Ottawa, Aprl 12, We would lke to thank the partcpants for stmulatng dscussons. We are also very grateful to Alce Nakamura for numerous suggestons that sgnfcantly mproved our paper. Of course we are responsble for all remanng errors. 1 See Landauer (1995) for a comprehensve survey of studes of the productvty paradox and a collecton of arguments suggestng that IT mproves productvty. 2 Blechnger and Pfeffer (1996) show that computer use n the workplace has ncreased sharply n the last two decades. In 1979, about 6% of employees n Germany used computers. The share of computer users amounted to 17% n 1985 and to 32% n The recent ncrease s manly due to the dffuson of PCs. Black and Lynch (1996) draw smlar conclusons. 3 The MIP-S was commssoned by the federal mnstry of scence and technology (BMBF). Other contrbutors to ths project are the Fraunhofer Insttut für System- und Innovatonsforschung (ISI) and nfas. The concept of the MIP-S and further results are presented n Lcht et al. (1997). 4 Ths seems to be the best avalable data source. No offcal regster s avalable for servce sector enterprses. The most recent data from offcal sources on frm sze or ndustry dstrbuton of servce sector frms s for Another 1,000 frms partcpated n a short telephone survey of randomly drawn nonrespondents whch was conducted to reveal potental non-random selecton effects dependng on nnovaton actvtes. Comparng the results from the mal and the telephone survey we conclude that there seems to be only a small response bas n favour of nnovatve companes. There seems to be a small over-representaton of product nnovatve frms. Frms wth process nnovatons are slghtly underrepresented (cf. Lcht et al. 1997).

17 Innovaton and nformaton Throughout the paper we use as weghts the product of the nverse of the ncluson probablty and the nverse of the response rate. 7 Recent studes on servce sector productvty develop physcal output measures (e.g. tons of klometers for transport servces) for specfc servce sector ndustres (see van Ark 1996 for further references). Mnd that meanngful physcal nput measurement can only be derved for homogeneous output categores. Gven the large heterogenety of servces, the development of physcal output ndcators s only possble for narrowly defned ndustres. Developng physcal output measures for the dfferent sectors was beyond the scope of the MIP-S. The use of the qualtatve output assessment n ths study should be vewed as a frst step towards more refned quanttatve measurement of nnovaton output n servces. 8 Addtonal controls for export status and the ntroducton of a full set of nteracton terms were added as well n prelmnary work but were dropped subsequently. The basc conclusons are unaffected by ths. 9 We use four dfferent sze classes: 1-19, 20-49, , 250 or more employees. 10 We dstngush 8 dfferent ndustres n the servce sector: wholesale trade, retal trade, transport, bankng and nsurance, fnancal servces, software, and consultng. 11 To save space we only report the coeffcents and t-values for the varables whch are used to test our hypothess. In addton to the results reported, we fnd sgnfcant nterndustry and nter-regonal dfferences. Sze class effects play a mnor role. 12 Remember that the nterpretaton of R&D by the respondng frms s only partly compatble wth the standardzed R&D defntons as gven n the FRASCATI manual. R&D should be best nterpreted here as efforts on near-market product development. 13 When asked for medum term employment expectatons by skll level, the respondents reply that they expect larger skll shfts f they nvest more n IT. So, complementartes between IT and human captal nvestment are present n the long-run and wth respect to general human captal. Tranng expendtures that are used here reflect frm-specfc human captal nvestment. 14 We thank the Internatonal Data Corporaton (IDC) for provdng us wth the data and helpful nformaton. IDC s conductng market research, analyss and consultng for the nformaton technology ndustry. 15 Ths data set provdes detaled nformaton about the IT-equpment n use and the IT related expendtures. Unfortunately only about 30% of the companes provde nformaton on ther sales. We drop all observatons from other sectors and all nonproft organzatons. Appendx 2 shows some descrptve statstcs on the servce sector frms n the IT-survey. 16 Only termnals connected to a manframe n the establshment were consdered. 17 Mllon Instructons Per Second (MIPS) s a measure for computng power. 18 Usng captal stock of the year 1992 ensures that there s less double countng of ITcaptal n the captal stock snce the IT-equpment s usually replaced after 3-4 years. 19 Brynjolffson also uses a Cobb-Douglas model to estmate the productvty mpacts of IT. 20 Note that the coeffcents used here are dfferent from those n the prevous secton. 21 Due to the prudence concept n German law, t s not allowed to show ntangble goods n the balance sheet, f they are self-provded. 22 Please note that the data taken from natonal accounts (.e. materal and captal ntensty by sector) correspond to the year Labour productvty s defned as log (sales per employee). In bankng we use the

18 380 Georg Lcht and Detmar Moch balance sheet total as output and for nsurance companes we use total prema ncome. 24 Because we have less observatons than n the SIAS, we combne the SIAS groups of wholesale trade and resale trade, transport and fnancal servces, software engneerng and of consultng. The regresson on all sectors ncludes 3 addtonal dummes for non-servce sector ndustres. 25 LAD stands for Least Absolute Devaton regresson. 26 See Helpman and Trajtenberg (1996). References Ark van, Bart (1996) Issues n measurement and nternatonal comparson ssues of productvty - an overvew, n Industry Productvty. Internatonal Comparson and Measurement Issues, ed. OECD, OECD Proceedngs (Pars) Autor, Davd, and Lawrence F. Katz, and A. B. Krueger (1996) Computng nequalty: have computers changed the labor market? NBER, Workng Paper 5956 (Cambrdge, MA) Berndt, R. Ernst, and Cathrne J. Morrson (1995) Hgh-tech captal formaton and economc performance n U.S. manufacturng ndustres: an exploratory analyss, Journal of Econometrcs 65, 9-43 Berndt, R. Ernst, and Cathrne J. Morrson, and Larry S. Rosenblum (1992) Hghtech captal formaton and labor composton n U.S. manufacturng ndustres: an exploratory analyss, NBER, Workng Paper 4010 (Cambrdge, MA) Berndt, R. Ernst, and Zv Grlches (1993) Prce ndexes for mcrocomputers: an exploratory study, n Prce Measurements and ther uses. ed. M. Foss, et al. (Chcago: The Unversty of Chcago Press) Berndt, R Ernst, and Zv Grlches and Neal Rappaport (1995) Econometrc estmates of prce ndexes for personal computers n the 1990 s, Journal of Econometrcs 68, Black, E. Sandra, and Lsa M. Lynch (1996) How to compete: the mpact of workplace practces and nformaton technology on productvty, The Amercan Economc Revew LXXXVI, Blechnger, Dors, and Fredhelm Pfeffer (1996) Technologcal change and skll obsolescence: the case of German apprentceshp tranng, ZEW Dscusson Paper (Mannhem) Brynjolfsson, Erk, and Lorn M. Htt (1996) Computers and productvty growth: frm-level evdence, mmeo Computer Scence and Telecommuncatons Board (1994) Informaton technology n the servce socety: a twenty-frst century lever, Natonal Academy Press (Washngton, DC) Davd, Paul A. (1991) Computer and dynamo: the modern productvty paradox n a not-too-dstant mrror n technology and productvty: the challenge for economc polcy, OECD (Pars) Gault, Fred D. (1996) Servce ndustres n the nnovaton survey, background paper for the revson of the OSLO-manual, OECD-Document STI/EAS/STP/NESTI (96) 4 (Pars) Gordon, Robert J. (1990) The Measurement of Durable Goods Prces (Chcago: Unversty of Chcago Press)

19 Innovaton and nformaton 381 )))) (1996) Problems n the measurement and performance of servce-sector productvty n the Unted States, NBER Workng Paper 5519 (Cambrdge, MA) Greenan, Nathale, and Jacques Maresse (1996) Computers and productvty n France: some evdence, NBER Workng Paper 5836 (Cambrdge, MA) Grlches, Zv, ed. (1992) Output Measurement n the Servce Sectors (Chcago: The Unversty of Chcago Press) )))) (1994) Productvty, R&D, and the data constrant, Amercan Economc Revew 84, 1-23 )))) (1995) Comments on measurement ssues n: relatng IT expendtures to productvty growth, Economc Innovaton New Technologes 3, Grlches, Zv, and Jacques Maresse (1995) Computers and productvty n France: some evdence, NBER Workng Paper 5836 (Cambrdge, MA) Harhoff, Detmar, and Detmar Moch (1996) Prce ndexes for PC database software and the value of code compatblty, Research Polcy 26, (Amsterdam: North-Holland) Harhoff, Detmar,Georg Lcht Harald Stahl, Maran Bese, Johannes Felder and Erc Nerlnger (1996) Innovatonsaktvtäten klener und mttlerer Unternehmen. Ergebnsse des Mannhemer Innovatonspanels, n ZEW Schrftenrehe Bd. 8 (Baden-Baden: Nomos) Helpman, Elhanan, and Manuel Trajtenberg (1996) Dffuson of general purpose technologes, NBER Workng Paper 5773 (Cambrdge, MA) Höflch-Häberlen, Lsa, and Hubertus Häbler (1989) Dffuson neuer Technologen und hre Auswrkungen m prvaten Denstlestungssektor, n Technologscher Wandel und Beschäftgung (Arbetsmarktwrkungen moderner Technologen, 1), eds. R. Schettkat and M. Wagner (Berln: Gruyter) Landau, Ralph (1989) Technology and captal formaton, n Technology and Captal Formaton, eds. W. Dale Jorgenson and Ralph Landau (MIT Press) Landauer, Thomas K. (1995) The trouble wth computers - usefulness, usablty and productvty. (Cambrdge, MA: MIT Press) Lehr, Wllam and Frank R. Lchtenberg (1996) Computer use and productvty growth n federal government agences 1987 to 1992, NBER Workng Paper 5616 (Cambrdge, MA) Lcht, Georg, Chrstane Hpp, Martn Kukuk, and Gunnar Münt (1997) Innovatonen m denstlestungssektor. emprscher befund und wrtschaftspoltsche konsequenzen, n ZEW Schrftenrehe Bd. 24 (Baden-Baden: Nomos) Lchtenberg, Frank R. (1995) The output contrbutons of computer equpment and personnel: a frm-level analyss, Economc Innovaton New Technologes 3, Mles, Ian and M. Matthews (1992) Informaton technology and the nformaton economy, n Understandng Informaton Technology ed. K. Robns (London) Nelson, A. Randy, Tm L. Tanguay, and Chrstopher D. Patterson (1994) A qualty adjusted prce-ndex for personal computers, Journal of the Amercan Statstcal Assocaton 12, OECD (1996) Industry Productvty: Internatonal Comparson and Measurement Issues (Pars)

20 382 Georg Lcht and Detmar Moch )))) (1997) Gudelnes for Collectng and Interpretng Innovaton Survey Data - The OSLO Manual, second edton (Pars) Olner, D Stephen, and Danel E. Schel (1994) Computers and output growth revsted: how bg s the puzzle? Brookngs Papers on Economc Actvty 2 Segel, Donald, and Zv Grlches (1992) Purchased servces, outsourcng, computers, and productvty n manufacturng, n Output Measurement n the Servce Sector, ed. Z. Grlches (Chcago: The Unversty of Chcago Press) Appendx 1 Summary statstcs for the MIP-S data set Raw data Weghted data* Varable Mean Std. Dev. Mean Std. Dev. Labour productvty** Investment (excludng IT) per employee** IT-nvestment per employee** R&D employment per employee [%] Expendtures on professonal educaton per employee** Share of hgh-qualfed employees East-Germany (Base: Retal trade) Wholesale trade Transport Bankng / Insurance Other fnancal servces Software Techncal consultancy Other busness servces (Base category: less than 20 employees) employees employees and more employees * Weghts are the product of the nverse of a frm s ncluson probablty and the nverse of the response rate. ** In 1000s of DM.

21 Appendx 2 Mean values for IT-survey data varables: servce sector Wholesale Retal Bankng, Fnancal Overall Trade Trade Transport Insurance Servces Software Consultng Others log(labour productvty)* log(captal)* log(materal)* log(manframe termnals)* log(mdrange termnals)* log(unix workstatons)* log (PC and MACs)* sze class 1: employees sze class 2: employees sze class 3: employees sze class 4: >= employees Wholesale/Trade Retal/Trade Transport Bankng/Insurance Fnancal Servces Software /Consultng Others N = 474