A Price Index for Computer Software Using Scanner Data

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1 A Price Index for Computer Software Using Scanner Data Marc Prud homme and Kam Yu May 2002 Abstract In the last 20 years, the importance of pre-packaged software expenditures as a share of total software expenditures and as a share of total computing expenditures has been growing. Despite the fact that pre-packaged software can be considered a product subject to rapid technological change, the attention given to it in price research has been lagging compared to computers and other high-tech products. Applying hedonic regression techniques in order to control for temporal quality differences for pre-packages software is not always practical. In this study, matched model price indices are constructed using monthly scanner data on prices and unit values for various pre-packaged computer software titles and categories sold in Canada from January 1996 to June Quality differences are controlled for by applying the maximum overlap method. Results show that prices for business and government software declined during the studied period at an average annual rate of 4.4%. This rate of price decline compares well with that of the price index currently used by the System of National Accounts for deflating government and business investment expenditures for pre-packaged software, which declined at an average annual rate of slightly less than 4.0%. Among other findings, the paper shows that consumer software prices have been declining at a faster pace, 7.9%. JEL Classification: C43, E31, L86 Keywords: Price index, Matched model, Computer software, Quality, Scanner data Prices Division, Statistics Canada. Marc.Prud homme@statcan.ca Lakehead University, Canada. Kam.Yu@Lakeheadu.ca. The views expressed in this paper are those of the authors and do not represent those of Statistics Canada. The authors also wish to thanks Wally Lebreton for programming assistance. 1

2 1 Introduction This report is an initial investigation by Statistics Canada into the construction of a price index for computer software. More specifically, it examines the bahavior of pre-packaged software prices. 1 Research in this area is lagging compared to that of other high-tech products such as computers and semi-conductors, which has progressed rapidly. In fact, Jorgenson (2001) explicitely recognizes that important information gaps exist for software prices and that better information in this area is needed for improving the measurement of overall economic performance. This view is reinforced by various private studies on the economic benefits of investments for pre-pakaged software (PriceWaterhouseCoopers, 1999). 2 In many instances, the hedonic approach has proven successful for producing constant-quality price indexes for products subject to rapid technological change. In contrast, only a few studies that will be discussed later, have used hedonics for producing quality adjusted price indexes for software. A number of reasons are probably behind the absence of more research, here are three of them: 1. A reliable hedonic regression requires large amounts of good quality data with regards to a product s features and characteristics, such information for software has simply not been as readily available for researchers when compared, lets say, to computers; 3 1 For the remainder of the paper we will refer to pre-packaged software as simply software unless otherwise noted. 2 One of many studies on the subject states that large and growing body of evidence that attests to the productivity gains and returns on investment that can be achieved through the use of packaged software and related IT. Here are a few examples: 1) Research at leading universities shows that the wage rates of employees working with information systems is much higher than others, and that their productivity is even higher. The differential is as high as 300 percent for wages and 600 percent for productivity by some estimates; 2) Industry estimates suggest that sending an electronic mail ( ) message is faster, more reliable and up to 95 percent cheaper than sending a fax. Handling an electronic requisition is one tenth the cost of handling its paper equivalent; 3) Groupware is software that is used on PC-based networks to enable teams of users to plan projects and meetings, to structure work flow, and to view and work on projects and data concurrently. In a survey into the financial payback of one particular groupware product, IDC found that of 65 organizations surveyed, more than half had returns in excess of 100 percent, and more than a quarter had better than 200 percent. One extreme case even reported a 1666 percent payback; and 4) Some experts believe that small and medium-sized enterprises (SMEs) may be the biggest beneficiaries of IT and communications technologies, suggesting that SMEs could make average savings of 4 percent of their turnovers by adopting new communications services ( 3 At one time, we were thinking that maybe a reliable hedonic estimate could be obtained if the number of lines of codes in a given software program was used as an explanatory variable. The reasoning was that an increase in the number of lines of code from one period to the next of a given software title may be viewed as an improvement in quality over the previous version. For example, in 1990, Windows 3.1 had two and a half million lines of code. Today, Windows XP has 40 million 2

3 2. Characteristics associated with software are, in many instances, difficult to identify (Hollanders and Meijers, 2001). Oliner and Sichel, admit that hedonic models, because of their need for quantifiable characteristics for each product, may not be suited for complex and hard-to-describe products like software. Brynjolsson and Kemerer (1996) mention that estimating precise hedonic functions for software is likely to be difficult because stategic pricing and other factors beyond the hedonic functions may be significant. 4. Furthermore, certain important quality aspects such ease-of-learning and ease-of-operation are subjective and thus difficult to quantify (Varian, 1993); and 3. The absence of a solid research base and deeper understanding by economists about the software industry and more specifically the factors behind software pricing practices. Indeed, Varian (1993) states that the market for computer software is large and rapidly growing. Despite this, there has been little theoretical investigation of the unique economic features of the software market. Although Varian said this almost 10 years ago, we cannot say that this research field has exactly mushroomed since that time. 5 ACNielson has recently agreed to provide Statistics Canada with scanner data for pre-packaged computer software. The data are collected during the course of their market research activities. We believe that this is the first time that such information is made available to study software prices. Interest in scanner data for price index research is growing and with good reason. Hawkes and Piotrowski (2000) point out three benefits to price statisticians when using scanner data or other electronic point-of-sale data: 1) More data and, consequently, less variance; 2) Better data and consequently, less bias; and 3) Better methods. 6 Furthermore, the authors suggest that such data offer a way around many of the obstacles that prevent the use of hedonics, notably for items where product turnover is rapid, the number of lines of code. However, according to McGraw, a leading computer guru of sorts (See Festa, 2001), software is way more complicated than it used to be......and the best way to determine how many problems are going to be in a piece of software is to count how many lines of code it has. The simple metric goes like this: more lines, more bugs. 4 For example, in a web study comparing the virtues of WordPerfect 7 against MS Word 7, the number of features that were evaluated was over 150. This was for just two varieties of the same product. Clearly a hedonic price index for this category would be difficult to produce and near impossible if the exercise was extended to a larger number of products. By its very nature, the heterogeneity of the product itself precludes the possibility of producing hedonic price indexes. Prices move differently depending on the category, which means a large representative sample is required in order to attempt to reflect what is happening with software prices in general. Monitoring these changes would be very resource intensive. 5 A search on Econlit revealed just over 300 paper when using the keyword software in the title. A narrower search by adding the word quality produced 5 results. 6 These advantages to using scanner data are based on a study for food items. Clearly these same advantages would apply to other products. 3

4 relevant characteristics is large, and the current sample size is small. 7 Given some of the challenges associated with producing a hedonic price indexes for software, this paper proposes the use of scanner data with the maximum overlap method for dealing with the quality problem. This approach, a variant of the traditional matched model approach, seems well suited for producing price indexes when scanner data is available. Results show that software prices for business and government software declined during the studied period at an average annual rate of almost 4.4%. The current practice used by the Canadian System of National Accounts results in an average annual drop of slightly less than 4% for the price index used as a deflator for software investment expenditures by governments and businesses. Therefore are results are similar. The decline for consumer software prices fell faster, dropping, on average 7.9% annually. The paper proceeds as follows. Section 2 puts into context software expenditures in the Canadian national accounts and the consumer price index. Section 3 describes the data. This is followed by a brief discussion of previous research done on software price index in section 4. Section 5 introduces the maximum overlap approach used to compute the price indexes. Section 6 presents the results of the multi-period matching exercise and a number of indexes, followed by comparisons with other results in section 7. Section 8 summarizes the empirical results and provides suggestions for future research. 2 Software in the System of National Accounts and the Consumer Price Index This section will briefly describe the role of software expenditures in the System of National Accounts and the CPI Software in the System of National Accounts The Canadian System of National Accounts recognizes and tracks investment expenditures and prices for three broad software categories (Jackson, 2000). 9 They are custom software, own account software, and pre-packaged software. Pre-packaged software is sold or licensed in standardized form and is delivered in packages or electronic files downloaded from the Internet. Custom software is tailored to the specific 7 Among the growing body of studies using scanner data we note Lowe (1998), and Ioannides and Silver (1997). 8 For information purposes we add that according to US Business Reporter (2002), the major software categories include spreadsheets, word processing, data base, utilities, graphics, drawing, entertainment. 9 These categories are also the ones used by other countries. 4

5 application of the user and is delivered along with analysis, design, and programming services required for customization. Own-account software consists of software created for a specific application. 10 Furthermore, software investment expenditures in the National Accounts are divided into government purchases and business purchases, but given that both sectors probably buy the same software products, there is no need for a separate price index. From 1981 to 2001, total software investments increased almost 1400%. Growth in the business sector was almost twice as fast as the of the government sector (1548% versus 878%.) Investment for pre-packaged software expenditures was by far the largest contributor to the rise in overall software expenditures, increasing a phenomenal 4000% in both sectors of the economy; investments expenditure for custom software and own account software increased respectively 1800% and 500%. In contrast, investments in machinery and equipment, the broader investment category for which software is part of, increased 99% and 183% respectively for the government and business sectors. As a consequence of the rapid growth in the pre-packaged software sector, its share of total software expenditures increased but at the expense of own-account software. While in 1981, the share of the former represented 18% of total software investment, it grew to 45% in Own-account software saw its share decline from 60% to 25%. The share of custom software increased only slightly from 22% to 28%. The explosion in pre-packaged software investment in the last decade can be explained by the growing appreciation by firms and governments of the important role of this product for saving time and reducing costs. Moreover, demand for pre-packaged software has also grown as the latest software packages have reached levels of quality that make them equivalent to customized software because they can be parametrized to better meet the specific needs of the users. Clearly, the growing importance of pre-packaged software simply reinforces the need for accurate price indexes in this area. The share of software has also grown in terms of investments in machinery and equipment. While total software expenditures in 1981 accounted for less than 4% of spending in the machinery and equipment category, in 2001 the share was 20%. Accounting for less than 1% of machinery and equipment spending in 1981, prepackaged software expenditures represented 9.4% of spending in that category in Interestingly, the ratio of software and more precisely pre-packaged software investment spending to computer investment spending has also grown. 11 For instance, in 1986, the first year both software and hardware information are available simultaneously, the overall software to computer ratio was at 71%; in 2001, the ratio stood at 138%. Software expenditures surpassed hardware expenditures for the first 10 The definitions are borrowed from Parker and Grimm (2000). 11 By computer investment spending we mean spending on computer hardware. The category in the National Accounts is actually broader in scope because it includes spending on video units, printers, etc. The category is however dominated by computers. 5

6 Table 1: Consumer software expenditures: various years Year Avg. spending % Reporting Avg. spending Share of total per household per hsld reporting spending 1984 $13 3.2% $ % 1992 $17 5.9% $ % 1996 $ % $ % 1997 $ % $ % 1998 $ % $ % 1999 $ % $ % 2000 $ % $ % time in 1993, when the ratio reached 106%. The pre-packaged software to computer ratio has also grown, more than tripling over a 15 year period. In 1986, the ratio was at 19% while in 2001 the ratio was at 65%. 2.2 Software in the CPI A software price index would also have a role to play in improving the CPI. Currently, no software prices, imputed or otherwise, are collected for the Canadian CPI. The absence of a reliable quality adjustment methodology is part of the reason why no attempts have been made to collect software prices in the CPI. This is despite the fact that various family expenditure surveys have collected consumer expenditure data on software since In fact, according to the latest Canadian Survey of Household Spending (year 2000), 16.9% of households purchased software in 1999, which translates into an average expenditure per household of $36 or $211 per reporting household. As a share of total current consumption spending, software represented 0.09% in 2000, and in 1992, 1996, 1997, 1998, and 1999 the corresponding figures were 0.05%, 0.10%, 0.10%, 0.11%, and 0.11%, respectively. Table 1 summarizes some of the results from various family expenditure survey. Of particular interest is the slight turnaround in average household spending for software in year Unless there has been an increase in the public s propensity to pirate software, the decline in software expenditures can partly be the result of lower prices. Note also that average spending per household reporting has progressively shown a downward trend since Data Description The data obtained from ACNielsen is part of their Computer Product Index for the personal computer market. Samples are collected electronically and manually from twenty-eight major chain stores in addition to a smaller sample of independent outlets 6

7 Table 2: Major Chain Stores in the ACNielsen Data Business Depot/Staples Toys R Us Future Shop CompuSmart CompuCentre Computer City Compugen G.E. Capital Technology Inly Systems Intertec London Drugs The Notebook Store DataComm EDS Innovations Software Spectrum SoftChoice SoftOptions 3 Soft Corporate Software Telecom Computer Products Data Terminal Mart Radio Shack MicroAge Zellers MacWarehouse Inmac Wall-Mart Electronics Boutique across Canada. Among the sample we find some on-line retailers. 12 Table 2 presents the outlets in question. Data obtained through ACNielsen are monthly and cover the period from January 1996 through June Software products are categorized according to their function (e.g. games, word processing, etc.). A category typically includes the software title, its sales volume, total generated revenue, and its average price. Upon further inspection of the data, the average price field is derived by dividing total revenue by sales volume. In 1996, for example, ACNielsen surveyed 1,730 software titles that were in turn grouped into 34 categories. Each category thus averages 51 products. Each year new categories may be introduced to reflect new application fields and to further refine and adjust the existing classification. Additional information accompanying each title includes the language of the software and the operating system the particular software application is designed for. There is also an additional field that qualifies the title as a special edition version. For example, MICROSOFT WORD UPGRADE W95 stands for the upgraded version of Microsoft Word for 12 Information on how the samples are selected has not been shared by ACNielson. 13 There is also another source of scanner data that is available to us. International Data Corporation (IDC) has provided to Statistics Canada, as part of a previous agreement, a list prices for consumer software titles sold in Canada the January to September 2000 period. Price information for commercial software applications is however only available for the April to September 2000 period. Household software products are grouped into three categories: games, educational, and encyclopedia. Commercial software applications are grouped in much the same way (but not identical) as ACNielsen data. Overall, IDC data is similar to what is available from ACNielsen. There are some differences however. IDC provides product codes and version identifiers, which is computationally more advantageous. On the other hand and contrary to ACNielsen, IDC does not provide any volume and hence revenue information. 7

8 Windows 95. From the ACNielsen data it is however not possible to determine which version of the software is sampled. As will be shown later, this can have significant consequences for the construction of a price index. The date does provide the possibility to circumvent two weaknesses noted by Gandal (1994) in his study, namely the absence of unit values and the absence of transaction prices Previous Research A search of the literature reveals little in terms of research in the area of software prices. This contrasts sharply with computer hardware prices, for which there has been in the last 20 to 30 years a relative abundance of research by academics and practitioners alike. Furthermore, the studies that do deal with software price behavior have mostly limited themselves to a few applications, notably spreadsheets, databases, and word processors. Gandal (1994, 1995), in a empirical study that tests for network externalities in the US computer spreadsheet market, estimates price indexes for the 1986 to 1991 period. Given the large number of features that have either improved or been added to spreadsheets over the period, the author uses a hedonic regression approach with a log-linear specification to control for quality variations across time. Some of the characteristics used as explanatory variables are sorting, graph plotting, spreadsheet size, linking, and compatibility with Lotus 1-2-3, the most popular title at the time of the study. Gandal s 1994 study shows that the quality-adjusted price of spreadsheets declines at an average annual rate of 15% over the study period. The Gandal results are compatible with those obtained by Brynjolfsson and Chris Kemerer (1996) who, while studying the effects of network externalities and using a hedonic approach for the period, arrive at an annual decline of 16% for the spreadsheet market. These reported price declines by the previous authors would seem high. Oliner and Sichel (1994), because of the limitations mentioned previously in producing hedonic price indexes for software, use a matched-model approach for three popular categories of software applications: Word Processors, Spreadsheets, and Databases. They find that the matched-model indexes decline by an average of 2.7% a year for all three categories for the period. 15 Harhoff and Moch (1997) complete study of database prices for the 1986 to 1994 period and find that the hedonic price index declines by 7.41% a year while matched model indices at the version, product, and brand levels of aggregation fall on an annual basis by 9.25%, 4.36%, and 3.86% 14 The absence of transaction prices can have an important effect. Brynjolfsson and Chris Kemerer (1996) mention that the average discount can be as high as 30% and that the correlation between market price and list price was 0.88%. 15 In their study, spreadsheets prices fall by an annual rate of 4.5%, on average, for the period. 8

9 respectively. Moulton, Parker, and Seskin (1999) describe the sources of prices used to arrive at the deflators of their current-dollar estimates of business and government purchases of software. Price data for software are obtained from the following sources: BEA hedonic price indices for for business applications; matched model indices for selected types of software, including spreadsheets, databases, and wordprocessing; matched-model price indices for that were developed by Oliner and Sichel; and beginning in 1997, a BLS producer price index (PPI) for applications software that is also based on prices of matched models. A hedonic approach, however, is not feasible in our present study for at least three reasons. First, details of characteristics for each product are not available in our data set. Second, each hedonic study normally requires some product knowledge. Therefore, a certain amount of research effort is required to construct a hedonic index for any type of software. Given the number of categories available (34 in 1996), it is impractical to apply the hedonic technique to each individual category in order to come up with an aggregate price index for the software sector. Third, products in some categories such as games and education are very diversified in nature. They are grouped under the same categories not because of performing a commonly well defined task as in spreadsheet and word processing. Therefore it is difficult to find a common set of characteristics in these categories that would make a hedonic project feasible. Eurostat (1999) reports that none of the participating countries in Europe uses hedonic method to compute the price index for software. In what follows we experiment with a matched model and compare the result with some published software price indices. We find that our result is in line with other matched model indices for software. The methodology is described in the next section, followed by the empirical results. 5 Methodology In a typical matched model, the price of a product in the base period is compared with the price of the product with the identical attribute or characteristic in the comparison period. In this way the price difference is the pure price change not due to any quality improvement. In cases where an existing product disappears or is replaced by a new product with different characteristic, it is deleted from the sample. The new product is included in the sample to be matched in the next period. In effect we match all the products that are commonly available in two adjacent periods. This so-called maximum overlap method is used in the International Price Program (IPP) at the U.S. Bureau of Labor Statistics (BLS) for computing export and import price indices. 16 Alterman, Diewert, and Feenstra (1999) compares the IPP seasonal price 16 The Eurostat task force calls this a monthly chaining and resampling method and recommends it for computing software price index. This also describes our approach quite faithfully. Here, the 9

10 index with a more robust year-over-year index and find that the former performs very well. The method is employed by them because of a large number of commodities in the sample exhibit strong seasonal patterns and disappear in the market for several months of each year. Turvey (2001) suggests a method called replacement with multi-month overlap, which is in essence the same as the Eurostat monthly chaining and resampling method. According to Turvey, the approach is a possible solution for products that are subject to rapid technical progress leading to continually improving performance, where new models frequently appear in the market, and where models fall in price as they become older. Software in many respects falls into this category of products. In the context of quality adjustment, the matched model assumes that the average price ratio of the matched products is the same as that of the products not matched. In other words, we assume that when newly improved products come into the market, the prices of the existing old versions are bid down relative to the new one in equilibrium. Furthermore, we assume that the sellers do not incorporate pure price change when they introduce the new products, otherwise these pure price changes cannot be captured by the matched samples. Triplett (1997) argues that sellers do often include pure price change when they introduce new products. Also, when the market is slow to adjust to a new equilibrium, a matched model price index will be biased. The direction of the bias depends on whether the new products are priced upward or downward. For hi-tech products undergoing rapid quality change such as computers, manufacturers tend to decrease price when changing models. For example, a newly developed chip with higher speed may be cheaper to produce than the existing slower chips. In this case the matched model index will be biased upward. In fact many studies in computer price indices confirm that the matched model indices underestimate the price decline compared to the hedonic indices. Nevertheless, with high frequency sampling, it is possible to minimize the bias so that the resulting index from a matched model is a close approximation to a hedonic index. In the limiting case where products are identical in two periods with no quality change, a matched model index will in theory give the same result as in a hedonic index. 17 An initial count of the ACNielsen monthly data shows that the month-tomonth matches have an average of 80%. With this high percentage of matching we hope that the matched model will give a satisfactory quality adjusted price index for the pre-packaged software in Canada. After matching the products in each category in two adjacent months, we calcuprices of all varieties of a product that is on the market each month are observed (resampling). The price change is then computed from month to month as an average over those items that are in the sample in both months. Price changes between months that are further apart are calculated by multiplying the month-to-month links (chaining) (See Eurostat, 1999). 17 See Triplett (2000) and Diewert (2001). 10

11 late the Laspeyres price index, P L, and the Paasche price index, P P as follows: P L = i p2 i qi 1, (1) P P = i p1 i q1 i i p2 i q 2 i i p1 i q2 i, (2) where p t i and qi t are the price and quantity of product i sold in period t, t = 1, 2. The implicit Laspeyres quantity index is defined as Q L = 1 i p2 i qi 2 (3) P L i p1 i q1 i and similarly for the implicit Paasche quantity index Q P. 18 Definition (3) reflects a desirable property that the product of the price and quantity indices equals to the expenditure ratio of the two periods. It is well known in the theory of cost-of-living index (COLI) that if the consumers preferences are homothetic, the true COLI is bounded between Laspeyres and Paasche indices: 19 P P COLI P L (4) We approximate this COLI with the Fisher Ideal index, P F, a superlative index which is simply the geometric mean of P L and P P Empirical Results The Laspeyres, Paasche, and Fisher price indices for each category are initially computed by matching the product names using the full data set. The resulting monthly indices for the majority of categories are found to be quite erratic with the following common observations: The price index for a particular month is too high or too low. For example, the month-to-month Laspeyres and Paasche indices for the category Document Management are 6.54 and 9.83 respectively from January to February, The implicit Laspeyres (Paasche) quantity index can be shown to be exactly the Paasche (Laspeyres) quantity index. 19 A consumer with homothetic preferences has constant expenditure share for each commodity even when total expenditure varies. Technically it means the utility function is an increasing transform of a linearly homogeneous function. The same bounds apply to a producer price index if the production function is homothetic. 20 A superlative index is derived from an expenditure (cost) function which has a flexible functional form, i.e., it can approximate an arbitrary function to the second order. For this reason a superlative index mitigates substitution bias. See Diewert (1976). 11

12 Table 3: Outliers Count in 1997 No. of Observations Month Before After % Excluded Average The spread between the Laspeyres and Paasche indices is unreasonable large. For example, the month-to-month Laspeyres and Paasche indices for the category Mail are 1.47 and 0.51 respectively from March to April, The spread indicates that there are dramatic changes in price and/or quantity in some products within that category. The above aberrations are observed in about half of the monthly indices in a number of categories, namely, Anti-virus, Database, Mail, Network Administration, and Software Utility. Upon close examination of the data the abnormal behaviour of the price indices are caused by errors in the data. The most common forms of error are: Negative prices Sudden rise or fall in prices in one month and return to normal levels afterward. The price jumps can be as high as over ten times in a month. Fractional quantities with normal revenues, resulting in exceptional high prices. To exclude the outliers from the data the following screening test is imposed. In two adjacent months the price ratio p 2 /p 1 of a matched product is computed. The product is excluded from the sample if p 2 /p 1 is greater than 2 or less than 0.5. Table 3 illustrates the number of matched observations in all categories before and after the screening rule is applied to the 1997 data. On the average about 10% of the observations are discarded, which does not seemed a high price to pay in order to get a more representative price index. The price indices for each category are recalculated using the screened data. These indices are then aggregated to form a single index for the pre-packaged software sector. Table 4 shows the overall monthly bilateral and chained indices as well as the monthly percentage change computed with the three index formulae. As predicted 12

13 Table 4: Matched Model Software Price Indices in Canada Laspeyres Price Index Paasche Price Index Fisher Price Index Month Bilateral Chained % changed Bilateral Chained % changed Bilateral Chained % changed Average Monthly Index Average Annual Index

14 Table 5: Fisher Price Indices for Business Categories Category Average Average Revenue Monthly Annual Observations Matches Share (%) AGR (%) AGR (%) ACCOUNTING ANTI VIRUS CAD & CAM COMMUNICATION DATABASE DESKTOP PUBLISHING DOC MANAGEMENT ELECTRONIC FORMS EMULATORS GRAPHICS HARDWARE UTILITY INTEGRATED INTERNET MAIL NETWORK ADMIN NETWORK PEER NETWORKING OPERATING SYSTEMS PRESENTATION GRAPHICS PROGRAMMING PROJECT MANAGEMENT SOFTWARE UTILITY SPREADSHEET SUITE TAX WORD PROCESSING TOTAL BUSINESS by inequality (4) the Laspeyres index provides an upper bound to the true COLI, with average annual change of 18%. This is because the underlying utility function of the index is a Leontief form based on the first period purchase. 21 That is, the buyer will purchase the same first period basket of goods even the relative prices change in the second period. This is reflected in Equation (1) where the first period quantities qi 1 are used as the weight for the prices in both period. The buyers therefore does not react to any price change, resulting in an upward bias in the price index. On the other hand, the Paasche index assumes a Leontief utility function based on the second period purchase. Using a similar argument one can show that the Paasche index is downward biased. This is reflected in an average annual change of -24.9%. The Fisher index, as mentioned above, approximates the true COLI by accommodating the substitution effect. And from Table 4 we see that its average annual change is bounded between the other two indices at -5.9%. Tables 5 and 6 present the results for matched-model software Fisher price indices with market shares and price changes for Canada in the business and consumer categories respectively. From Table 5 we see that its average annual change for the overall business price index declines 4.4%. On an annual basis, the index registers its largest drop between 1999 and 2000, 9.3%; the smallest drop is between A Leontief utility function is defined by U(x) = min i {x i /a i a i > 0}. 14

15 Table 6: Fisher Price Indices for Consumer Categories Category Average Average Revenue Monthly Annual Observations Matches Share (%) AGR (%) AGR (%) ANTI VIRUS COMMUNICATION EDUCATION EDUTAINMENT EMULATORS ENCYCLOPEDIA GAMES HARDWARE UTILITY INTERNET MAIL OPERATING SYSTEMS PIM SOFTWARE UTILITY TAX TOTAL CONSUMER and 1998, 0.6%. The price changes are not uniform across all software applications. Although most exhibited price declines, some have seen their price increase. For instance, prices for electronic forms have fallen over 18% per year on average over the study period, the largest recorded drop among our business applications. In contrast, the profusion of computer networking in the last decade is certainly behind the average annual 18% increase in prices for those applications. Noteworthy among the price changes for business applications is the price behavior of what are considered the major categories. For instance, database applications, which recorded a price increase of 11% between 1996 and 1997, saw its average annual price fall by 1.6% as a result of consecutive price declines over the remainder of the study period. The price of speadsheets also dropped by almost 10%, on average, over the course of the study period. Word processors registered only a slight decline in their price, registering an slight average annual decline of 1.4%. From Table 6 we see that prices for consumer software titles also trend downward in most cases, although prices for the overall consumer software market fell almost 7.9%, almost twice as fast as prices for business applications. The most important category within the consumer classification is not surprisingly, games for which there has been a marked fall in their price since 1996, averaging an annual rate of decline of 11%. Other notable price decreases are also recorded for consumer software. For instance, antivirus, education, and tax softwares all declined in price on an average annual basis by 13.5%, 15.5%, and 9.3%, respectively. Not all classes of consumer titles fell in price over the study period however. Communication, mail, and personal information management (PIM) recorded annual average price increases of 22.9%, 9.1%, and 18.1%, respectively The reader will have certainly noticed that some classes of products appear in the business and consumer categories. This is expected because of the existence of some multi-purpose software titles such as antivirus software and operating systems. The ACNielsen data does not distinguish between 15

16 Although the business and consumer software markets both exhibit overall downward price trends during the study period, the rates of change are not identical. Consumer software prices declined at a much faster pace that those for business applications. Furthermore, software titles and applications within each broad category may exhibit very different price movements. 7 Comparison with Other Studies In 1998, the U.S. Bureau of Labor Statistics (BLS) started producing a subindex for computer software and accessories in the CPI based on a matched model approach including overlap and production cost approaches. Overall, the comparable average annual price changes are -5.8% and -8.1% for the U.S. and Canada, respectively. The difference can certainly be partly explained, on one hand by the use of a different index formula, and on the other hand, by differences in both markets. Table 7 compares the U.S. and the Canadian price indices from December 1997 to June 2000, during which data are available in both countries. The monthly price fluctuation in Canada seems to be slightly more volatile than the U.S. counterpart. This is perhaps because the U.S. subindex is broader in scope, including what they term other computer accessories, which may have a different price movement than software. Apart from the BLS, we mentioned in Section 4 that there are some published studies dealing with software price indices. Table 8 summarizes these studies according to the approach used to control for quality change, types of software covered in the studies, countries, the period of analysis, and the reported average annual price change. Almost all studies are for the period from mid-1980s to early 1990s. With the exception of Gandal (1995), and as is usually the case, hedonic price indices generally decline more rapidly than their matched-model counterparts. 23 The -4.4% average annual change in Harhoff and Moch s (1997) matched model index are based on matching the product name of the packages. When the packages are matched with the same versions in addition to names, they find that the index falls 9.25% annually, which is below their hedonic result of -7.41%. The wide diversity of the results in the hedonic studies is what led Oliner and Sichel (1994) to conjecture that the application of hedonic techniques to software may be problematic. software bought for home use and that bought for business use. When faced with multi-purpose software, we have therefore naively allocated half of the expenditures to the each category. When possible, we have also attempted to separate the products and allocate them to their appropriate class. For instance, we don t believe that many consumers are buying or have bought Windows NT or Windows 2000, therefore they are only found in the business category. 23 As previously mentioned, Gandal (1995) is examining the possibility of network externalities, and as a result his software price indexes play a secondary role in the study. Therefore, the paper does not present the price indices per se. Nevertheless, the numbers in Table 8 are derived from the estimated time dummies in Tables 3 and 4 of the paper. 16

17 Table 7: Comparison of U.S. and Canadian Prices in Computer Software, December 1997 to June 2000 U.S. Canada Month Price % changed Price % changed Average Monthly Index Average Annual Index

18 Table 8: Comparison of Price Index Studies in Computer Software Study Method Type Country Period AAC (%) Gandal (1994) Hedonic Spreadsheet U.S Gandal (1995) Hedonic Spreadsheet U.S Hedonic Database U.S Brynjolfsson and Kemerer (1996) Hedonic Spreadsheet U.S Harhoff and Moch (1997) Hedonic Database Germany Matched Database Germany Oliner and Sichel (1994) Matched Word Processing U.S Matched Spreadsheet U.S Matched Database U.S BLS Matched General U.S Overall, our price index for business software applications fall on an average annual basis by 4.4%. Notice that this result, because of the broader coverage of our index and the different time periods, is not strictly comparable with the previously mentioned studies. Nevertheless, it is interesting to note that drawing any inference that database, spreadsheet, and word processing software applications can be used as proxies (as suggested by some) for the overall pre-packaged software market can be misleading. For instance, as illustrated in Table 5, these applications, despite registering in one case the largest price decline (spreadsheets, -9.1%), accounted for respectively 3.1%, 1.2%, and 1.5% of the revenue share of the business software market. By contrast, office suites sales account for 30% of the market, yet no studies have examined the price behavior of this product. Moreover, other application categories within business also have a relatively important share of the market and exhibit differing price trends when compared to the overall category. For instance, programming, networking, and graphics applications with revenue shares of 2.5%, 6.3%, and 4.5%, respectively have either seen their prices decline by very little or even increase over the study period and therefore slowing the price decline of the overall business field. Currently the U.S. Bureau of Economic Analysis (BEA) uses in their National Income and Product Accounts a matched model price index supplied by BLS. 24 An arbitrary adjustment for bias of -6.3% per year is made to the index, which is one half of the difference between the Oliner-Sichel matched model index and a hedonic index produced by BEA from 1985 to 1993 for spreadsheets and word processors. 25 But since we see in Table 5 and 6 that price movement differ from category to category, imposing a bias adjustment based on two types of software with small value shares seems to be a risky conjecture. 24 See Moulton et al (1999),p The data on characteristics used in the regression from National Software Testing Laboratories Rating Reports are no longer available from 1994 onward. 18

19 8 Conclusions and Further Research We have obtained price and quantity data from ACNielsen for software products in Canada. Our initial investigation shows that given the data content and the complexity of the problem, it is impractical at this stage to carry out a full hedonic study in this sector. Despite the richness of ACNielsen data, they are still some drawbacks to using it. In some cases, there were negative prices and erroneously high or low values in prices, unit values, and revenue. By applying outlier detection procedure 10% of the observations were rejected. A small price to pay, for a database with still a large number of observations remaining. Matched-model results using a monthly chained Fisher index indicate that prices of pre-packaged software in Canada decline at an average annual rate of 5.9% overall and 4.4% for business and government software applications from January 1996 to June 2000; during the same period consumer software titles fell 7.9%. The results are comparable, at least with regards to the direction of the movements, to other matched-model indices, particularly the one produced by BLS for the CPI in the U.S. Published hedonic studies on spreadsheets and databases show high variability in the price indices. One study using German data shows that the direction of bias of the matched-model relative to hedonic approach depends on whether different versions of the same products are matched or not. Our data set includes limited information in this regard, therefore it is difficult to judge the direction and degree of the bias, if any, in our matched-model index. In fact, the results from using matched modeling for software may not be as far fetched as some believe, at least with regards to the later years. Most spreadsheets and word processors are quite compatible with each other these days. The new features that are added to the more recent versions of the more popular software applications seem to be marginal. For instance, Keizer (2001), in a review of Office XP, claims that newest edition of the world s leading productivity suite does not take us light-years ahead of Office 2000, its previous incarnation. Costa de Oliveira (1997), in a more scientific approach shares Keizer s view, whereby there is no value added to the software vendor to keep investing in easy-to-use enhancements in order to attract new users. Users are probably paying more today for their software but the marginal benefits of the additional features may apply only to the few (power users). This is certainly the case when one weights the benefits of the new features compared to the costs associated with not having the software. As for areas of future research, Triplett (2000) finds the monthly chaining and resampling (MCR) technique has some appeal, but more research should be done in order to compare results obtained with the hedonic approach against MCR. This is clearly not possible to do with software, but could be done using our computer hardware database. We plan on conducting this investigation in the near future. 19

20 References Alterman, William F., W. Erwin Diewert, and Robert C. Feenstra (1999) International Trade Price Indexes and Seasonal Commodities, Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics. Brynjolfsson, Erik and Chris F. Kemerer (1996) Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market, Management Science, Vol. 42, No. 12, Costa de Oliveira, Eduardo (1997) Growing Software: An Economic Analysis, / search?q=cache:mz0nvyl5oaec: RainForest/7362/ mmathf.pdf+eduardo+costa+software&hl=en Diewert, W.E. (1976) Exact and Superlative Index Numbers, Journal of Econometrics, Vol. 4, No. 2, Diewert, W.E. (2001) Hedonic Regressions: A Consumer Theory Approach, Discussion Paper No , Department of Economics, The University of British Columbia. Eurostat (1999) Report of the Task Force Volume Measures for Computers and Software, Eurostat B1/CN 408e, Luxembourg. Festa (2001) The Root of the Problem: Bad Software, html?legacy=cnet Gandal, Neil (1994) Hedonic price indexes for spreadsheets and an empirical test for network externalities, RAND Journal of Economics, Vol. 25, No. 1, Gandal, Neil (1995) Competing Compatibility Standards and Network Externalities in the PC Software Market, Review of Economics and Statistics, Vol. LXXVII, No. 4, Harhoff, Dietmar and Dietmar Moch (1997) Price Indexes for PC Database Software and the Value of Code Compatibility, Research Policy, Vol. 26, 4-5, Hawkes and Piotrowski (2000) Using Scanner Data to Improve the Quality of Measurement and the Measurement of Quality in the Consumer Price Index, AC- Nielsen Company. Unpublished manuscript, September 16,

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