1997 barua - productivity paradox

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The International Journal of Flexible Manufacturing Systems KL451-03-Barua May 20, 1997 17:16

The International Journal of Flexible Manufacturing Systems, 9 (1997): 145–166c© 1997 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.

The Information Technology Productivity ParadoxRevisited: A Theoretical and Empirical Investigation inthe Manufacturing Sector

ANITESH BARUACenter for Information Systems Management, Department of Management Science and Information Systems,Graduate School of Business, The University of Texas at Austin, Austin, TX 78712

BYUNGTAE LEEDepartment of Management Information Systems, Karl Eller Graduate School of Management, The University ofArizona, Tucson, AZ 85721

Abstract. The lack of empirical support for the positive economic impact of information technology (IT) hasbeen called theIT productivity paradox. Even though output measurement problems have often been held respon-sible for the paradox, we conjecture that modeling limitations in production-economics-based studies and inputmeasurement also might have contributed to the paucity of systematic evidence regarding the impact of IT. We takethe position that output measurement is slightly less problematic in manufacturing than in the service sector andthat there is sound a priori rationale to expect substantial productivity gains from IT investments in manufacturingand production management. We revisit the IT productivity paradox to highlight some potential limitations ofearlier research and obtain empirical support for these conjectures. We apply a theoretical framework involvingexplicit modeling of a strategic business unit’s (SBU)1 input choices to a secondary data set in the manufacturingsector. A widely cited study by Loveman (1994) with the same dataset showed that the marginal contribution of ITto productivity was negative. However, our analysis reveals a significant positive impact of IT investment on SBUoutput. We show that Loveman’s negative results can be attributed to the deflator used for the IT capital. Further,modeling issues such as a firm’s choice of inputs like IT, non-IT, and labor lead to major differences in the ITproductivity estimates. The question as to whether firms actually achieved economic benefits from IT investmentsin the past decade has been raised in the literature, and our results provide evidence of sizable productivity gainsby large successful corporations in the manufacturing sector during the same time period.

Key Words: IT productivity paradox, production economics, input choices, marginal revenue product, manu-facturing sector, input deflator

1. Introduction

Even though worldwide investments in information technology (IT) have reached staggeringproportions, empirical evidence regarding the bottom-line benefits from such investmentsremains tenuous at best. As suggested by aBusiness Weekarticle (“The Technology Payoff,”1993), in the 1980s, U.S. businesses alone invested $1 trillion in IT; the investment figurefor 1992 was nearly $160 billion (in constant 1987 dollars). Commonsense reasoning andday-to-day observations suggest that IT has a tremendous potential to make organizationsmore efficient, improve the quality of products and services, and spawn new businesses.Indeed, IT has the fastest growing share of capital inputs. For example, thereal investmentin information processing equipment as a share ofrealfixed business investment2 rose from

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146 ANITESH BARUA AND BYUNGTAE LEE

around 9% in 1974 to 18.5% in 1984, to 37% in 1994. The WEFA group report (1994)projects the share of IT to rise to 46.8% in 2003 and 55.6% by 2018. The real investmentin IT as a share of durable equipment rose from around 14% in 1974 to 30% in 1984, to47.5% in 1994. The corresponding ratios of IT to industrial equipment were 42.5%, 125%,and 210%, respectively.

Despite its intuitive appeal, investment in IT requires economic justification of benefits,and studies investigating the productivity and business impact of IT have been unable to val-idate a consistent relationship between IT investments and firm performance. This dilemmafacing senior MIS managers and researchers was recognized by Roach (1987) as the “ITproductivity paradox” and was most aptly summarized by Solow (1987): “You can see thecomputer age everywhere but in the productivity statistics.” Of course, computers are onlyone component of IT. For example, the operational definition of IT capital for collectingthe data used in this study corresponds to the category Office, Computing, and AccountingMachinery of the U.S. Bureau of Economic Analysis (BEA).3 According to this definition,IT consists of computers, communications equipment, instruments, photocopiers, and re-lated equipment (Bureau of Labor Statistics, 1983; CITIBASE, 1992). Software and relatedservices are considered separately.

Measurement problems largely have been held responsible for the seemingly lacklusterreturns from IT. For example, Gordon (1989) and Baily and Gordon (1988) point out poten-tial problems with output metrics that do not capture quality impact of IT. The WEFA groupreport (1994) also emphasizes the problem of coming up with suitable output measures:

The development of the personal computer and subsequent quality improvements havefueled this explosion in information processing equipment. The mystery is why thismassive computer investment has not resulted in measured productivity gains in theservice sector. It is very difficult to measure output in many service sectors and inputsare used as a proxy for output, which by definition, will constrain productivity gains.

While measures such as output volume or its value cannot capture the potentially largeimpact of IT on product and service quality, we believe that simple efficiency gains canstill be assessed through conventional output measures. Intuition would suggest that themanufacturing sector has achieved significant productivity improvements through basicIT applications in inventory management, scheduling, capacity planning, purchasing rawmaterials, process monitoring, and quality control. For example, the benefits of maturesystems such as materials requirement planning are well documented:

1. Turnover increase, lead-time reduction, reduced material waste (Cerveny and Scott,1989; Schroeder, Anderson, Tupy, and White, 1981; Yeo, Ong, and Wong, 1988).

2. Coordinated purchasing, inventory management, and production planning, which re-duces delays and improves the ability to meet deadlines and delivery schedules (Duchessi,Schaninger, Hobbs, and Pentak, 1988).

3. Reduction in costly emergency orders (Schroeder et al., 1981).4. Fewer out-of-stock conditions, which would adversely affect scheduled production. Out-

of-stock situations may further require expediters and lead to inefficient rescheduling ofproduction as well as split orders (Schroeder et al., 1981).

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THE INFORMATION TECHNOLOGY PRODUCTIVITY PARADOX 147

5. Better management of financial and personnel resources (Duchessi et al., 1988; Senn,1990).

Each of these benefits of materials requirement planning systems can be translated into in-creased output, everything else remaining constant. For example, an out-of-stock conditionfor raw materials implies that the machines are idle and extra output could have been pro-duced during this idle period. Output measures in the manufacturing sector are slightly lessproblematic than those in the service sector (although we certainly do not imply that quality-adjusted output is easy to derive even in the manufacturing sector). Although more recentIT applications in the areas of flexible manufacturing, just-in-time inventory management,and CAD/CAM create the potential for more spectacular gains, the lack of positive resultsinvolving the manufacturing sector appears to be an artifact of the productivity assessmenttechnique.

We address two additional issues (other than output measurement) that might have addedto the productivity estimation problem. The first involves the modeling approach usually em-ployed in MIS research on productivity measurement. We take the position that production-function-based MIS studies have not exploited the fundamental theoretical foundation ofproduction economics involving profit maximization or cost minimization. Second, inputmeasurement issues dealing with the very definition of IT might have led to disappointingresults in a widely cited and influential study by Loveman (1994). As we have stated pre-viously, IT consists of much more than just computers and using a deflator correspondingto computer capital will overdeflate the IT input because of dramatic improvements in theprice-performance ratio for computers and peripherals. These theoretical and measurementissues provide the motivation for this study.

Based on a production theory model of profit maximization, we analyze the productivitygains from IT investments in the manufacturing sector using different functional specifica-tions. We use the same data set deployed by Loveman and show that IT had a very significantpositive impact on the productivity of strategic business units in the sample. According toour results, the average marginal contribution of IT capital to revenue product was 74%.The significant positive impact of IT is consistent across model specifications (e.g., Cobb-Douglas and translog production functions). The contribution of this paper is twofold. First,it raises a concern for a theoretical modeling issue in IT productivity studies and providesempirical evidence in support of the concern. Second, it shows that input measurementproblems can be attributed to the disappointing IT productivity estimates in an importantprior study.

The balance of the paper is organized as follows. Modeling issues and some production-economics-based studies of IT productivity are reviewed in the next section. Section 3provides the theoretical model involving input choices facing a firm. Two basic hypothesesalso are formulated in this section. The data set and measurement issues are discussed inSection 4. Model estimation and results are presented in Section 5. Limitations and futureresearch are outlined in Section 6. We conclude in Section 7.

2. Relevant literature and motivation

Studies focusing on the economic impact of IT investments generally can be classifiedinto two categories: production function based and business value modeling. The latter

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involves tracing and measuring IT impacts on various performance metrics through a webof relationships, which often include organizational variables (e.g., see Barua, Kriebel, andMukhopadhyay, 1995; Dos Santos, Peffers, and Mauer, 1992; Kauffman and Kriebel, 1988;Weill, 1992, for various business-value-oriented models). In this paper, however, we focuson a set of studies using the production economics approach.

Production-theory-based studies use parametric specifications for the technology thatconverts inputs to outputs. With two recent exceptions involving a common dataset, how-ever, most other studies have failed to provide evidence of significant productivity gainsfrom IT investments. For example, Morrison and Berndt (1990) find that a $1 investment inIT contributed $.80 of additional value. Roach (1987) reports disappointing results involv-ing the productivity of information workers. Along similar lines, Baily and Chakrabarti(1988) suggest the absence of significant productivity gains from IT investments. One ofthe most important and influential studies of IT productivity at the SBU level is that ofLoveman (1994). Loveman investigated IT productivity in the manufacturing sector forthe time period 1978–1984 and concluded that the marginal dollar spent on IT would havebeen better spent on non-IT inputs to production. However, using the same data set witha business value approach, Barua et al. (1995) showed that IT was positively related tointermediate level performance measures such as capacity utilization, inventory turnover,quality, relative price, and new product introduction; also, these intermediate variables werepredictors of higher level measures such as return on assets and market share.

Recently, two studies (using a common data set) have found significant productivitygains from investments in computer capital. Using data collected by the International DataGroup, Brynjolfsson and Hitt (1993) and Lichtenberg (1993) find large positive returnsfrom computer capital. This is encouraging news for MIS academics and professionals, buton the balance, the evidence of IT productivity still is mixed at best. Note that computercapital is only a subset of the broad IT category. Are the returns from IT comparable tothose from computers? Further, Brynjolfsson and Hitt (1993) remark: “Because the modelswe applied were essentially the same as those that have been previously used to assess thecontribution of IT and other factors of production, we attribute the different results to therecency and larger size of our dataset.” By focusing on the “recency” aspects, implicitlyBrynjolfsson and Hitt (1993) are questioning whether firms actually obtained economicbenefits from their IT investments in the earlier phases of computing. We use Loveman’sdata set to analyze whether the real paucity of IT productivity gains or measurement issuesled to the negative results.

Measuring inputs and outputs are two key limitations of the production economics ap-proach. In the service sector, the definition of output itself can pose significant problems.For example, Gordon (1989), Baily and Gordon (1988), and Brynjolfsson (1993) discussthe limitations of the approaches used by the Bureau of Economic Analysis, which, theysuggest, underestimate productivity. Fortunately, defining output is a little easier in themanufacturing sector. Of course, conventional production economic measures of outputcannot easily account for improvements in product quality or the creation of new productsand also are likely to underestimate IT productivity.

Having stated the limitations of the production approach and its attendant measurementissues, it is equally important to state its positive features. It provides a normative framework

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to understand how firms behave with respect to input and output markets and the technologyof transforming inputs and outputs. From an operational standpoint, sophisticated econo-metric techniques can address issues that arise in conjunction with the estimation of thetheoretical models.

We believe that most MIS studies on IT productivity have not taken the theoreticalroute. Unless the behavior of the firms in setting inputs, outputs, and prices (where appli-cable) is explicitly modeled, we are not utilizing the theoretical premise of the productioneconomics framework. In other words, apart from some assumptions about possible sub-stitutions between the various inputs, no “theory” stands behind the estimation of a singleproduction function.

In this research, we seek to establish (apart from other things) that estimating produc-tion functions without modeling input and output choices can lead to misleading pro-ductivity figures. Second, we investigate the deflators for IT capital. How different arethey from the deflators used for computer capital? How does the choice of the deflatoraffect (if at all) the IT productivity estimates? By addressing these modeling and estima-tion issues, we expect to get deeper insight into the very nature of the IT productivityparadox.

3. Production-economic-based assessment of IT impact

IT productivity assessment studies using production economics generally involve the esti-mation of a Cobb-Douglas or translog production function (e.g., Loveman, 1994; Brynjolf-sson and Hitt, 1993). However, microeconomic production theory is based on the premiseof profit maximization or cost minimization. A production function represents the under-lying production technology, a specification of how inputs can be combined to produceoutput; it does not involve input and output prices or how a firm or SBU should chooseits input and output levels. Much of production economics is concerned with the optimalchoice of inputs (and outputs, where applicable). That is, the estimation of a productionfunction without modeling how firms choose their inputs or outputs is not consistent withthe theoretical foundation of production economics.

Suppose there areN inputs to a firm’s production process. Given a competitive marketoutput price,p, and aN-dimensional input price vector,w = (w1, w2, . . . , wN), a firmcan maximize its profits by choosing the optimal output quantity,q, and input quantitiesx = (x1, x2, . . . , xN). When the production capacity is inherently limited (e.g., in theelectric power supply business), firms minimize production costs for a given output quantity.In this paper, we restrict our focus to the profit maximization perspective. If the quantity (q)of output produced is given by a production function,f (x), then the profit maximizationproblem is given by

maxx=(x1,x2,...,xN )≥0

p f (x)−N∑

i=1

wi xi . (1)

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3.1. Specific functional forms

We start the analysis with a Cobb-Douglas specification for the production functionf (x)with a disembodied technological change rate,λ,

q = AeλtN∏

i=1

xαii ,

which, after a log transformation yields a linear form,

ln q = α0+ λt +N∑

i=1

αi ln xi + ε, (2)

whereε is a random error term. Because a firm can choose its input mix (based on the inputand output prices), instead of directly estimating the production function, we obtain a setof N equations (one for each input) as first-order conditions for profit maximization. Thefirst-order condition fori th input(i = 1, 2, . . . , N) is

wi = pAeλtαi xαi−1i

N∏j=1

xα j

j . (3)

After a log transformation, we obtain

ln(wi /p) = ln q + lnαi − ln xi .

This first-order condition suggests that the unit price of thei th input(i = 1, 2, . . . , N)mustequal the value of the marginal product for thei th input. Thus, the profit maximizationframework results in a system onN+1 equations, one for the production function, andN fordescribing the behavior of the firms in choosingN inputs. The system of equations impliesthat, except under very restrictive conditions, estimation techniques such as ordinary leastsquares cannot be used to estimate the parameters of interest. More significant, it has animportant implication for the underlying nature of the inputs. This is discussed next.

3.2. Endogeneity of inputs to production

Using a profit maximization (or cost minimization) framework suggests that the inputquantities areendogenouslydetermined within the model, and the output and input pricesare theexogenousvariables. This is in direct contrast with the single equation methodusually employed in IT productivity assessment. For example, in Loveman’s (1994) andBrynjolfsson and Hitt’s (1993) models, expenditures on inputs such as IT and labor areexogenous variables.

When a firm is technically efficient and achieves scale efficiency (i.e., produces theoptimal quantity of goods), it can achieve profit maximization only when it chooses the bestinput mix, which gives the lowest production cost. The best mix of inputs is the point whereits production frontier and iso-cost curve meet (this choice is represented by the first-order

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conditions for various inputs). Thus, production theory provides a clear basis for assuminga priori that the inputs areendogenous. Accordingly we state our first hypothesis.

H1: The inputs to production are endogenous.The significance of this hypothesis isthat, if empirically supported, it would require IT productivity to be measured as a systemof equations (using some technique that provides consistent estimates) as opposed to thesingle equation generally employed in the MIS literature. As we have emphasized earlier,this endogeneity assumption is the very basis of production theory, but surprisingly hasreceived little attention in MIS studies. A well-known result in econometrics (e.g., seeChristensen and Greene, 1976; Schmidt and Knox Lovell, 1979; Kumbhakar, 1987), isthat estimating the production function without modeling the firm’s input choices providesconsistent estimates of the parameters only when the inputs truly are exogenous. In otherwords, unless the firms have no choice over how much of each input they use in production,the preceding estimation will lead to inconsistent results. Because data often are collectedat the SBU or firm level, they represent micro-level observations. Whereas exogeneity ofinputs may be assumed during the model specification phase for economywide data, it isdifficult to provide a theoretical rationale for such an assumption in the case of micro-levelobservations.

3.3. Translog functional form

Because the Cobb-Douglas production function has some restrictions, like perfect substitu-tion among inputs, more general forms, such as the translog and quadratic functions, havebeen suggested as alternatives. We choose a translog production function because it imposesminimal a priori restrictions on the underlying production technology and approximates awide variety of functional forms. The translog function is given by

ln y = α0+k∑

i=1

αi ln xi + αt t

+k∑

i=1

βt i t ln xi + 1/2

{k∑

i=1

k∑j=1

βi j ln xi ln xj + βt t t2

}+ ε, (3)

whereε is the random error term.Berndt and Wood (1975) provide the revenue share equation of thei th input (after a log

transformation):

ln xi − ln q + ln(wi /p) = 1/2 ln

(αi +

N∑j=1

βi j ln xj + βt i t

)2

. (4)

The production function (3) and the revenue share equations (4) can be estimated by thefull information maximum likelihood (FIML) method. Also, as in the case of the Cobb-Douglas formulation with profit maximization,q andx are endogenous variables, butpandw are exogenously specified.4

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3.4. Contributions of IT investment

3.4.1. Productivity measures.The production function estimation technique is based onthe total factor or multifactor productivity, which is defined as output divided by somecombination of input variables. Mathematically it is represented asy/g(x), whereg(x)specifies how the inputsx are combined (Bodea, 1994; Grosskopf, 1993). We are moreinterested in knowing the productivity contribution by a single factor such as IT. This isgiven by the elasticity of total production with respect to thei th input: Ri = ∂ f (x)/∂xi .This elasticity,Ri , is positive for aproductiveinput.

3.4.2. Marginal revenue product. Rather than calculate the contribution of an input towardthe output quantity, it is more interesting to determine the revenue contribution that theadditional output can make (if the firm can sell the additional output at the given price).The marginal revenue product (MRP) with respect to an input is the value of the additionaloutput that would be produced from increasing the input by one unit. Also, instead of usingthe physical input quantity, we ask the question, if we invest an additional dollar in IT, byhow much will it increase the revenue product? From Eq. (2), the MRP contribution of theinvestment in thei th input is given by

∂(pq)

∂(wi xi )

∣∣∣∣q,xi ,p,wi

= α̂i pq

wi xi= Ri p/wi . (5)

3.4.3. Marginal revenue product and profitability. It is important to note that a positiveproductivity contribution from an input (measured in this paper by Eq. (5)) does not nec-essarily imply increased profitability or better financial performance. According to Doumaand Schereuder (1992), firms try to maximize profit or minimize costs under given marketconditions. However, in highly competitive markets, a firm may be forced to pass on theIT-generated benefits to the consumers (say, in the form of lower prices) due to competi-tion. Such analysis is beyond the scope of the traditional production economics framework.

Even when an additional investment in IT (or any other input) does not lead to increasedprofits, it is still in the best interest of the firm, as long as it increases the MRP. The firm mustseek the best mix of inputs to make the production process efficient, because not doing somay carry a heavy opportunity cost. This relates to the notion of IT as a strategic necessity(Clemons and Kimbrough, 1987; Barua et al., 1991), which suggests that, although IT maynot be a source of competitive advantage (i.e., not lead to consistent above-normal returns),firms have to invest in IT to remain competitive.

3.4.4. Marginal revenue product of IT. This productivity measure has the limitation thatit does not consider quality improvements brought about by an input such as IT. In theintroduction, we alluded to this problem. However, even when an output measure doesnot reflect changes in quality, we still would expect to see significant gains in the SBUoutput from IT investments. Let us consider some examples that would fit the computingapplications during the time period covered by the study (1978–1984).

A major fraction of IT investments typically would involve transaction processing andshop-floor automation systems. For example, if an investment is made in numerically

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controlled machines, we would expect a given amount of labor to produce more output.By investing in scheduling systems, we can reduce slack time, which can translate intoincreased output, ceteris paribus. Electronic monitoring of labor productivity also will leadto increased output. Shop floor automation projects will help supervisors locate faultymachines or parts faster, leading to increased output, ceteris paribus. For example, shop-floor control systems, although less sophisticated than computer-aided manufacturing, pro-vide on-line real-time control and monitoring of machines (Martin, Dehaoyes, Hoffer, andPerkins, 1991). IT for quality and process control increases the output quantity withoutdefects. These scenarios may appear naive compared to the complex quality, demand, andpricing issues (and their strategic implications) associated with modern applications of IT,but they indicate that we should find significant productivity gains from IT investments inthe manufacturing sector.

Anecdotal evidence of IT productivity in the manufacturing sector abound the MISliterature. For example, through IT applications Deere and Company was able to reduceits break even point by 50% in 10 years and also reduce space and machine investments(Martin et al., 1991). By adopting group technology, Deere was able to significantly reducesetup time and lead time for parts production (Vonderembse and White, 1988). In 1983,Omark Industries, Inc., developed the zero inventory and production system for inventorymanagement. Apart from annual savings of $7 million on inventory holding costs, Omarkachieved remarkable improvements in material movement, work-in-process inventory, andlead time (Vonderembse and White, 1988). All of these impacts translate into increasedoutput. These observations provide the rationale for the following hypothesis.

H2A: The contribution of IT investment to the marginal revenue product is positive.Whatcan we expect regarding the relative contributions of IT and other inputs such as labor andnon-IT capital? For nearly 100 years, over 60% of the annual increase in productivity inthe United States was attributed to management, while labor and equipment contributedapproximately 20% each (Heizer and Render, 1988). Management involved the use oftechnology and knowledge, and early advances in IT created the potential for managingproduction processes and inventories in an efficient manner. Even manufacturing processesstarted becoming more information intensive (processes in the service sector naturally wereinformation intensive), while blue-collar labor intensity got reduced. Building on the sametheme, we suggest that, on an average, the amount of computing that we can buy for $1should contribute more to output, ceteris paribus, than the amount of labor that can beobtained for $1. Based on this discussion, we state our hypothesis regarding the relativecontribution of IT, labor, and non-IT capital.

H2B: IT contributes more to the marginal revenue product than non-IT capital and labor.

4. Data and measurement issues

4.1. Data description

The data used in this study was provided by the Strategic Planning Institute (SPI), Boston. Itis referred to as the management productivity and information technology (MPIT) database

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and contains data on corporate balance-sheet items and organizational variables on about60 SBUs5 from 1978 to 1984. The SBUs belong to large corporations involving variousmanufacturing sectors such as consumer products, components, and raw or semi finishedmaterials. Excluding missing variable cases, we have 47 SBUs with a total of 231 obser-vations. Further, the busineses units are observed for some consecutive years, starting andending in different years.

The MPIT database reports IT capital and purchased IT services separated from othercapital investments, which enables us to investigate the impact of IT investments. ITequipment consists of communications, computers and peripheral equipment, word pro-cessing reprographics, facsimile, and science and engineering instruments. Purchased ITservices include information services, databases, software, communications services, andreprographics services. The data set also contains a summary of balance-sheet and incomestatements. It reports labor input, land and plant capital stock, non-IT equipment capitalstock, and IT capital stock as well as inventory and expenditure for non-IT and IT service.

The data were self-reported by the participating SBUs and checked for consistency bythe SPI staff. Time series and other statistical analysis were also performed by SPI. Theparticipating companies received “specific reports comparing a business (or group of busi-ness) to these average findings as well as to its own unique benchmarks.” Additional detailsof data collection procedures and data quality issues for the MPIT database are discussedin Loveman (1994).

Although the MPIT data set dates back to the late 1970s and early 1980s, re-examinationof the data can provide valuable insight into the nature of the productivity paradox. Forexample, are Loveman’s results indicative of the absence of positive productivity impactin Fortune 500 manufacturing organizations or can they by attributed to some aspect(s) ofmeasurement and analysis?

The scope of computing in organizations has been enhanced dramatically over the lastdecade. Fueled by the explosion in local and wide-area networking, creative applicationsof IT to enhance quality, customer service, and intra- and interfirm coordination havewidened the potential for very high impact from IT investments. Ironically, however, thecomplexity of the IT architecture and applications also has made it equally difficult to as-sess their economic impact in empirical studies. On the output side, the MIS researcherhas to come up with economic measures that capture the quality changes brought aboutby IT. The proliferation of networking and the shift in the computing paradigm from cen-tralized mainframes to client-server architecture make it extremely difficult to accuratelymeasure the input side of the productivity equation. A large fraction of the IT capital inputtoday would consist of network-related investments, without which the end-user com-puters will not accomplish any productive task. So, although some recent studies on ITproductivity have focused on the return from computer capital, we argue that investmentsin networking are complementary to investments in end-user computers such as PCs andworkstations and that we even may obtain misleading results by ignoring the network-ing factor. In other words, measuring IT productivity today may be more complex thanwith the MPIT data, because the computing scenario then was dominated primarily bymainframes.

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Table 1. Derived input prices.

Inputs Mean Standard deviation

Structure 1.50 0.73

Non-IT 1.32 0.59

IT capital 1.66 0.65

Inventory 1.82 0.93

4.2. Input quantities and unit prices

The total labor cost and quantity (converted to the number of full-time employees) wereavailable in the MPIT database.

The next step in our productivity analysis involves the derivation of quantities and pricesfor capital inputs.Capital inputis defined as services from physical assets (Bureau of LaborStatistics, 1983). The capital stock of a depreciable asset (such as IT) is proportional tothe services from that asset. The total current services from an asset are proportional tothe productive stock, which is the amount of new investment required to provide the sameservices actually produced by existing assets. We get an approximation of the productivecapital stock from the wealth stock, which represents the present value of all future servicesembodied in existing assets and reflects the current market value of new and used capitalgoods.

We apply economic depreciation rates instead of the reported (accounting) depreciationrates to derive the price of capital services according to the methods developed by Chris-tensen and Jorgenson (1969). We use the “perpetual inventory method” to derive the stocksof IT, non-IT equipment, structure, and inventory. Further, because all data in the MPITdata set are end-of-the-year figures, we apply the “half-year convention” for depreciationand the calculation of the productive stock. In deriving input prices, we follow the Bureauof Labor Statistics (BLS) method (1983), which itself is based on the work of Christensenand Jorgenson (1969). The formulas used in the calculation of input prices (values ofwi )are provided in the Appendix to this paper. Outlier analysis revealed two SBUs with ex-treme values. The average input prices and their standard deviations are shown in Table 1.Because the MPIT data set reports purchased IT services separately, we follow the BLSconvention of multiplying the purchases by the average life expectancy (see Table 6 in theAppendix) and adding the result to the IT capital stock. As an alternative to this approach,we also performed the analysis by assuming that all IT purchases should be treated like purecapital, whereby the purchases are directly added to the IT capital. However, the estimatedIT contributions using the two different capitalization methods are very close, showing thatthe results are robust with respect to the capitalization methods.

4.3. Output quantity and price

The MPIT data base contains the relative price (as a percentage of the weighted averageprice of three largest competitors, with price parity= 100). We match the MPIT industry

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definitions with those used by the U.S. Department of Labor and BLS. Industry outputprice indices are obtained from GNP industry-specific producer price indices in CITIBASE.The output prices for SBUs are derived from the average industry price index and the relativeprice. The output quantity is obtained as revenue less inventory changes, deflated by thederived output price.

5. Model estimation and results

We first present estimation results for the Cobb-Douglas function with exogenous inputs.We then compare the results with those obtained from the optimization model. Next, we testthe sensitivity of the results with a different functional specification. Finally, we compareour analysis and results with those of Loveman.

5.1. Nonoptimization versus optimization

Table 2 shows the estimation results of the Cobb-Douglas formulation under the assumptionof nonoptimization. That is, these estimates are based on a single equation specifyingthe production technology. We used ordinary least squares (OLS) to estimate the model.Note that IT shows a significant positive impact. However, other key inputs like non-IT,inventory, and labor do not contribute significantly to the output. This raises a concern aboutthe consistency of the estimates, because the data come from large successful organizations,where inputs like labor and non-IT capital would be expected to lead to more output. Toaddress this issue, we present the results of the optimization model with the Cobb-Douglasfunction in Table 3. Note that the input variables now are assumed to be endogenous (i.e.,their optimal levels are determined by the input and output prices) based on the first-orderconditions in Section 3.1 and the subsequent discussion of endogeneity in Section 3.2. Theoptimization model was estimated with the full information maximum likelihood method.

Table 2. Productivity model estimationwith exogenous inputs.

Parameter Estimate T-statistics

Constant −1.311 −3.36∗∗∗

Labor .171 1.96∗

Structure .130 2.34∗∗

Non-IT −.064 −.79

IT .683 6.40∗∗

Inventory .072 .32

Time trend .035 1.22

AdjustedR2 = .927.∗ p < .1.∗∗ p < .05.∗∗∗ p < .01.

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Table 3. Productivity model estimationwith endogenous inputs.

Parameter Estimate T-statistics

Constant −9.30 −29.99∗∗∗

Labor .291 22.21∗∗∗

Structure .086 10.34∗∗∗

Non-IT .372 11.40∗∗∗

IT .147 34.01∗∗∗

Inventory .101 3.07∗∗∗

Time trend −.028 −1.12

∗∗∗ p < .01.

The results are quite different than those from the nonoptimization model. In the systemof equations involving the input choices facing the SBUs, all non-IT inputs are highlysignificant, as normally would be expected in production function estimates.

5.2. Are the inputs endogenous?

The results for both optimization and nonoptimization models show significant positiveimpact capital investment in IT. The question is whether both sets of results are consistent.The nonoptimization framework attempts to fit the data into the model without consideringhow the firms choose the input mix based on input prices. In other words, that input quantityvariation among business units can be a rational decision based on differential input pricesfacing the business units is ignored in the estimation, and the estimates are based solely onhow well the input and output quantities fit the specified production function.

As we already have noted, the coefficients in the nonoptimization model indicate somepotential problems involving the nature of inputs. Of course, we can investigate the endo-geneity issue econometrically. We use Hausman’s (1978) specification error test to seekthe validation of hypothesis H1. This test involves two sets of estimators. The first setof estimators is efficient (or more efficient) and consistent under the null hypothesis (theinputs are exogenous) but is inconsistent under the alternative hypothesis. The second set ofestimators is consistent under both hypotheses but is not efficient under the null hypothesis.These conditions are exactly satisfied by the OLS estimates of the single production function(nonoptimization) model and the maximum likelihood estimates of the optimization model.The Hausman test statistic has aχ2 distribution with N degrees of freedom, whereN isthe number of parameters. The statistic obtained is 121.75***. Therefore, our hypothesisregarding the endogeneity of inputs is verified, implying that not considering the choice ofinputs would lead toinconsistentparameter estimates.

5.3. How much did IT contribute to output?

The testing of hypotheses H2A and H2B requires that we find the impact of unit investmentin IT and other inputs on the revenue product. In other words, we have to use input and

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Table 4. Contribution of labor, non-IT, and IT capital torevenue product.

Contribution (%) Labor Non-IT IT

Revenue 60.68∗∗∗ 58.92∗∗∗ 74.27∗∗∗

∗∗∗ p < .01.

output prices to obtain the MRPs with respect to IT capital and other inputs. The MRPsare shown in Table 4. The contribution of IT to the revenue product is very significantlypositive. Therefore, hypothesis H2A is verified. Note that revenue product is conceptuallydifferent from the actual revenue generated by the firm. The revenue product is the dollarequivalent of a quantity of output. For example, if increasing a particular input by $1 resultsin an increase in the output by 1% and the unit price of the output is $200, then the valueof the additional output is $2. However, whether or not the firm or SBU can increase itsrevenue by $2 by investing an additional $1 in an input depends on whether it can sellthe product in the market at that price. Because we did not endogenously model marketbehavior with respect to the output (we assumed the SBUs to be competitive), it would beunwarranted to claim the preceding figures as the contribution of IT and other inputs to aSBU’s revenue.

It is also important to note that we have reported theaverageMRPs for the sample.Further, the MRPs for each SBU were calculated at their current levels of IT and otherinput investments. In other words, their MRPs would have been even higher if they werecalculated at lower levels of input investment.

Did IT contribute more to revenue product than labor and non-IT capital? A comparisonof the contribution means witht-tests suggests that IT contributed significantly more thanlabor and non-IT capital (both differences significant atp < .01). Thus, hypothesis H2Balso is verified.

Having obtained empirical evidence supporting the two hypotheses involving input endo-geneity and MRP, we investigate whether the results are robust with respect to the functionalform used in the study.

5.4. Do the results change with model specification?

As noted earlier, the Cobb-Douglas function has some inherent restrictions. To test thesensitivity of the results obtained with the Cobb-Douglas functional form, we use atranslogproduction function with its own set of profit share equations. The coefficients in a translogproduction function are not meaningful, like the coefficients in a Cobb-Douglas function.What matters in a translog function is the implied elasticity of the output with respect toeach input. The implied output elasticity with respect to IT is calculated to be.177, which,along with the input and output prices, imply an IT contribution of 78.76% to revenueproduct. Note that this is close to the 74.27% contribution obtained with the Cobb-Douglasform.

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5.5. Why did Loveman’s study find a negative impact?

We started with a conjecture that the MPIT data, coming from large successful organizations,did have a significant positive contribution from IT and that measurement and modelingissues might have contributed to the negative results obtained by Loveman. We alreadyhave shown that modeling differences (e.g., assumptions of endogeneity and exogeneityof inputs) can lead to significantly different results. In this subsection, we investigate themeasurement issue. In replicating Loveman’s results, we discovered a striking differencebetween the IT deflators employed in our and Loveman’s studies. Loveman used the BEAquality-adjusted computer price index to deflate IT investment. This choice is appropriate ifIT consisted only of computers. However, the MPIT definition of IT corresponds well withthe BEA category Information Processing and Related Equipment. BEA’s classification ofProducers Durable Equipment (IPRE is one category within this group) is as follows:

• Information processing and related equipment

Computers and peripheral equipmentOffice, computing, and accountingOther office computing equipmentCommunication equipmentInstruments, science and engineeringPhotocopy and related equipment

• Industrial equipment• Transport and related equipment• Other.

Note that computers are included only in the subcategory Computers and PeripheralEquipment. As we would expect based on these subcategories, there are major differencesbetween the IPRE deflator we used in our study and the price index of computers. The IPREdeflators are shown in Table 5. As an illustration, in 1977, the BEA implicit price deflatorof IPRE was around .79, while the price index of computers (office and storage machines)

Table 5. Implicit price deflatorsfor IPRE.

Year IPRE

1977 0.79497

1978 0.83078

1979 0.86795

1980 0.92201

1981 0.99998

1982 1.04609

1983 1.06559

1984 1.06477

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was 3.27. In 1984, the corresponding figures were 1.06 and 1.60, respectively. Thus, usingthe computer price index under the assumption of IT as consisting only of computers resultsin too much deflation.

In its description of the MPIT database, the Strategic Planning Institute states that thedefinition used in collecting the IT data corresponds to the IPRE category. In addition to thedefinition of IT, indirect evidence suggests the IT investments reported in the MPIT data setinvolve more than computers. Computer capital constitutes about 1% of revenues in recenttimes (Brynjolfsson and Hitt, 1993). We found that IT constituted about 2.3% of revenuesin 1978 and increased to 2.92% in 1982. Thus, using Brynjolfsson and Hitt’s 1% figurefor investment in computers, noncomputer and peripheral categories of IT constituted 1.3to 1.92% of revenues. It implies that the manufacturing companies included in the samplespent more on noncomputer and peripheral equipment categories of IPRE than on com-puters and peripheral equipment. It is not surprising that categories like communicationsequipment and especially instruments for science and engineering would involve significantinvestments in the manufacturing sector. Given that the prices of these categories do notchange at the same rate as computers, deflating these investments with a computer priceindex in conceptually incorrect.

The preceding discussion does not imply in any way that the BEA deflators are accurate.Such deflators have their share of problems, including the inability to reflect importantchanges is quality and features of technologies. We take the position that, because theoperational definition of IT in the MPIT data set corresponds to the IPRE category, it wouldbe natural to choose the IPRE deflator (in spite of the well-documented limitations of anycapital input deflator) rather than a computer price index.

6. Limitations and future research

A study dealing with a complex problem, such as productivity analysis and its resultscannot be properly interpreted without a discussion of then methodological and data-relatedshortcomings.

6.1. Methodological issues

With its focus on input and output quantities, production economics does not directly addressthe issue of quality improvement. For example, if an input such as IT improves productquality instead of quantity, such a change would not be captured in the simple productiontheory framework. Further, we need richer models that explain the processes through whichIT creates its impact. The production function method takes a black-box orientation (Baruaet al., 1995). Business-value-oriented approaches have been adopted by many authors toaddress this limitation.

A second methodological limitation stems from the assumption of a competitive, exoge-nously specified output price. For example, the price may be dependent on the quantityproduced (Kreps, 1990) as well as on more complex factors such as product differentiation.In the case where the price depends on the quantity produced, the optimization problem in

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Eq. (1) becomes

maxx=(x1,x2,...,xN )≥0

p( f (x)) f (x)−N∑

i=1

wi xi

and the first-order condition for inputi is

wi = {p′( f (x)) f (x)+ p( f (x))}∂ f (x)

∂xi.

While relaxing the assumption of a competitive firm brings in additional complexity inestimation, sequel research should focus on the potentially endogenous nature of the outputprice.

Another useful area of research would involve empirical investigation of complementa-rity between IT and other inputs. In the economics literature, Milgrom and Roberts (1990)use complementarity to explain the simultaneous adoption of complementary strategies inmodern manufacturing. In the MIS literature, Barua, Lee, and Whinston (1995) recog-nized the complementarity between incentives, team and task characteristics, and systemdesign features. Barua, Lee, and Whinston (1996) use complementarity theory to developa foundation for assessing the value of business process reengineering. Empirical testingof complementarity between IT and non-IT factors can provide critical insights into theproductivity paradox.

6.2. Data related issues

Like most secondary data sets, the MPIT data do not provide information on the type ofcomputing environment (although it is likely to be dominated by “legacy systems”) or thenature of the application (e.g., inventory control versus payroll). Such finer partitioning ofthe data would have enabled a deeper understanding of the nature of the impact by IT.

We have some evidence of significant positive contribution of IT investments to produc-tivity, but the SBUs in the data set are by no means typical of the manufacturing sector atlarge. These SBUs belong to the elite Fortune 500 group and, therefore, by definition, arelarge successful entities. They naturally are expected to manage their resources better thana typical manufacturing SBU. However, earlier research indicated that even this specialgroup had failed to exploit their IT investments; out study finds support for the conjecturethat these SBUs are most likely to achieve large productivity gains from their IT (and other)investments.

Given the dramatic improvements in the price-performance ratio of desktop computersand the large-scale deployment of computing applications in the manufacturing sector, theanalysis of more recent data sets may show even more positive productivity contributionfrom IT investments. Whether such productivity improvements lead to competitive advan-tage is an open question, based on our discussion of strategic necessity in Subsection 3.4.3.

7. Conclusion

As organizations continue to increase the IT share of capital stock, the productivity fig-ures keep eluding MIS researchers. Although there are many approaches to assessing the

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economic benefits of IT, some of the often-quoted studies are based on the production func-tion framework. We address some theoretical issues in production economics, and suggesta potential limitation of the modeling technique used in MIS productivity studies. Further,we provide empirical evidence that the choice of the input deflator led to negative results inan important prior study. The absence of systematic evidence regarding IT productivity hasprompted some researchers to question the gains from IT investments in the earlier phasesof computing, but our results indicate that very significant productivity gains were realizedby large corporations in the manufacturing sector during the time period covered by thestudy. Despite several limitations to our study, we replicate the significantly positive contri-bution of IT using two different model specifications and estimation techniques. Our sequelresearch in this area will focus on assessing the efficiency impacts of IT in the same data set.

Appendix: Calculation of capital input prices

For producers’ durable equipment (IT and non-IT capital in this paper), the price of capitalservices is given by

wt = 1− ut zt − et

1− ut{qtrt + qtµt − (qt − qt−1)} + qt xt ,

where

ut is the corporate income tax rate,zt is the present value of $1 of tax depreciation allowances,et is the effective rate of the investment tax credit,rt is the nominal rate of return on capital,µt is the average rate of economic depreciation,qt is the deflator for new durable equipment capital goods (from BEA),xt is the rate of indirect taxes.

For structures held by a corporation, the price of capital services is

wt = 1− ut zt

1− ut{qtrt + qtµt − (qt − qt−1)} + qt xt ,

whereqt is the deflator for structures.For nonfarm inventories held by a corporation, the price of capital services is

wt = qtrt − (qt − qt−1)

1− ut+ qt xt ,

whereqt is the deflator for inventories. The detailed procedures for calculatingu, z, e, r, µ,andx are shown next.

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Table 6. Depreciation rates for capital inputs.

Type of asset Life (year) Depreciation rate

Non-IT capital 15 .133

IT capital 8 .250

Nonresidential structures 30 .0667

Economic depreciation rate(µt )

The BEA reports 47 types of assets and service life assumptions. The life and economicdepreciation rates (µ) are shown in Table 6. The depreciation rates are derived as 2/L,whereL is the life expectancy of an asset.

The corporate income tax rate(ut )

The traditional way of estimating this rate is to compute the ratio of total corporate profitstax liability to before-tax total profits. The corporate tax is reported in our data set and thebefore-tax total profit is calculated as total value-added less total costs.

The rate of indirect taxes(xt )

The effective rate of indirect taxes is assumed to be equal for all assets in all manufacturingsector, defined as total indirect taxes divided by the total wealth stock.

Present value of$1 of tax depreciation allowances(zt )

This is the proportion of investment expenses that can be recovered in capital consumptionallowances after discounting these allowances for nominal interest charges. For simplicity,all firms are assumed to select straight-line depreciation. Then, for a given discount rate,rt ,which is the average long-term bond rate, and lifetime allowable for tax purposes,L (whichwe chose in the preceding table), Christensen and Jorgenson’s (1969) formula is

zt = 1

rt L

{1−

(1

1+ rt

)L}.

Effective rate of the investment tax credit(et )

We use the nominal rate of the investment tax credit, 7%. The limitation of the nominalrate is discussed by Christensen and Jorgenson (1969).

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Nominal rate of return on capital(rt )

From the BEA definition,

rt = Yt − Ktqt xt − Kt (qtµt − qt + qt−1)(1− ut zt − et )/(1− ut )

Ktqt (1− ut zt − et )/(1− ut ),

whereYt is capital (property) income andKt is the capital stock. Capital income containsprofits, net interest, capital consumption allowances, transfers, indirect business taxes, andinventory valuation adjustments. Because MPIT reports value-added (total revenue lesspurchase) deduction of direct costs (labor) from this value added is an approximation ofcapital income.

Acknowledgments

We are grateful to the guest editor Professor Michael J. Shaw and anonymous reviewersfor many helpful comments and suggestions. We have also benefited from valuable insightsprovided by Professor Subal Kumbhakar, Department of Economics, The University ofTexas at Austin. We thank the Strategic Planning Institute, Boston, especially DonaldSwire, for providing access to the data used in this study. This research was supported inpart by grant No. IRI 9210398 from the National Science Foundation.

Notes

1. An SBU is defined as a unit of a firm “selling a distinct set of product(s) or service(s) to an identifiable setof customers in competition with a well-defined set of competitors” and constitutes the unit of analysis in ourstudy.

2. That is, thereal ratio of IT in 1987 constant dollars to that of fixed investments. Thenominalratio is calculatedbased on current dollar values.

3. After 1982, this became the definition of the category Information Processing and Related Equipment (IPRE).Since the change of definition involved only a regrouping of categories, throughout the paper we will use IPREas the definition of IT used in our study.

4. The Times Series Processor program allows the FIML estimation of a nonlinear simultaneous equations model.This procedure requires the specification of a list of endogenous variables (TSP International, 1992).

5. Barua et al. (1995) provide a theoretical rationale to support the SBU as an appropriate level of analysis inmeasuring IT impact.

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