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Deconstructing the Computer: Report of a Symposium
II
RESEARCH PAPER
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Deconstructing the Computer: Report of a Symposium
Performance Measures for Computers
Jack E. Triplett
The Brookings Institution
I. INTRODUCTION
The “Deconstructing the Computer” workshop has the purpose of gaining better understanding of computer performance, especially the contributions of computer components to computer performance. Two groups of professionals are interested in measuring the performance of computers, peripherals, and components. This paper provides a bridge between their interests.
Section II explains, primarily to computer professionals, why economists want to measure computer performance and what economists do with performance measures. Subsequent sections provide background on economists’ work on measuring computers and components. As this workshop is part of the STEP Board’s “New Economy” project, one of its objectives is obtaining better performance measures for economic uses.
A second audience consists of economists. It is clearly true, as Nordhaus (2002) remarked, that computer performance measures used by economists in recent years have, if anything, gone backward compared with the measures they used 15 or so years ago. We need to ask “why?” We also need to ask: “How much does it matter?”
I review in section III the performance measures used by economists in the earlier computer literature, which covers primarily the mainframe years. Sections
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IV and V review performance measures used by economists and by statistical agencies in more recent years, where studies have turned predominantly to personal computers (PCs).
II. WHAT ECONOMISTS DO WITH COMPUTER PERFORMANCE MEASURES
I begin by addressing technologists. Why do economists want to measure computer performance? And what do they do with performance measures? Technologists need to understand how economists use computer performance measures in order to converse with economists on this topic. The questions do not imply that the performance measures wanted by economists are the only performance measures that matter, but fortunately it turns out that what economists want is not that different from what technologists have developed. Indeed, historically, technologists and economists have proceeded in similar directions in measuring the performance of computers. But that gets ahead of the story.
Suppose, to create a simple illustrative example, one computer exists. Call it UNIVAC. Suppose three UNIVAC computers are made in 1952, and they cost $400K each.1 We are supposing that UNIVAC was the only computer produced in the economy, so total U.S. output of computers in 1952 was $1.2 million.
Now suppose a new computer is developed in 1955 (I call it “new computer”), and that it has higher performance than UNIVAC. Suppose “new computer” sells for $600,000 in 1955, and suppose further that it is the only computer available in 1955, the UNIVAC having disappeared. Ten computers of this new type are produced in 1955, so the economy’s output of computers is $6 million in 1955, a fivefold expansion since 1952 in what economists call “current-price” output. This is clear enough, but other aspects of the 1952–1955 comparison are less clear (see Table 1).
First, is there inflation in the computer market? “New computer” costs 50 percent more than UNIVAC, but the new computer also has higher performance. Part of its higher price is just a performance premium. Economists do not want to show an increase in computer performance as inflation. They want to measure computer inflation so that it is adjusted for changes in the performance of computers; in other words, computer inflation should be measured net of the performance premium. The example suggests that computer inflation was less than the 50 percent increase in selling price. How much less? To determine that, economists need a computer performance measure (or more precisely, the performance premium).
What about computer output? “New computer” has higher performance than UNIVAC, so each “new computer” is equivalent to more than one UNIVAC.
1
These numbers correspond to UNIVAC production in 1952. See Flamm (1988), Table 3-1 for the price and page 51 for the quantity.
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TABLE 1 UNIVAC and “New Computer,” Hypothetical Price and Output Calculations
UNIVAC (1953)
“New Computer” (1955) (Case One)
“New Computer” (1955) (Case Two)
Number produced
3
10
10
Price, each
$400 thousand
$600 thousand
$600 thousand
Current price output
$1.2 million
$6.0 million
$6.0 million
Performance index (M)
1.0
1.5
1.8
Computer inflation (with estimated performance premium = 1 + .7 (ΔM)
1.00
1.11
.96
Computer “constant price” output index
1.00
4.60
5.17
Computer output must have expanded by a factor greater than the threefold increase in units produced. How much greater? To answer that, economists also need a measure of computer performance.
Economists also want to calculate the productivity of making computers, just as they calculate productivity in other industries. One common form of productivity is labor productivity, defined as output per worker hour. Again, if “new computer” has higher performance than UNIVAC, economists want to calculate output per labor hour in producing computers with a “quality adjustment” that incorporates the improved performance of the new computer. For estimating productivity change, “new computer’s” higher performance must be factored into the output measure. Similar statements apply to other economic measures, particularly to computer investment and capital stock.
Thus, for measuring inflation, output growth, productivity growth, the volume of investment and capital stock, and for other economic measurements, economists need a measure of computer performance. It is well known that a bottom-end desktop computer today greatly outperforms anything available at the dawn of the commercial computer age, which was 50 years ago. Counting the number of computers produced will never tell us much about trends in computer output. The great expansion of computer output in the last 50 years is an expansion not only in numbers of computers but also in what might be thought of as “output per computer produced,” that is, performance per machine.
We need now to discuss the properties that economists want in their measures of computer performance. To carry this forward, suppose now that we all agree on a measure of computer performance. It should not be too surprising that, as I discuss in the following section, achieving measures of computer performance is not at all a straightforward task. But set that aside, for the moment.
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Suppose we have an agreed-on measure of computer performance that covers the UNIVAC and the 1955 “new computer.” Suppose that we standardize our performance measure so that the UNIVAC has 1.0 performance units, and the new computer has 1.5 performance units.2
Unfortunately, even when computer performance is a scalar measure, we cannot simply divide the value of UNIVAC or “new computer” production by the performance measure in order to compare 1952 and 1955 computer output. Economists need the value of the performance indicator. There are several reasons. An old computer relationship called Grosch’s Law indicates that the cost of a computer center does not rise linearly with its computing power. Similar arguments can be made on the demand side: The incremental value of improved performance to the user does not necessarily rise proportionately with an increase in performance. Thus if “new computer” has 1.5 times the performance of UNIVAC, we need some way to value this 1.5 performance improvement ratio. We must know the performance premium, a value measure, not just the increment in performance.
The valuation problem is truly daunting. Likely, UNIVAC and the replacement computer do not appear in the market at the same time. If they do, “new computer” should sell for more, and it is natural to take the ratio of the two machines’ prices as measuring the value of their relative performance. All kinds of problems exist with that, which I do not mean to minimize. For example, the high-end user might be willing to pay more than the actual price premium for “new computer” to get a high-end machine, but the low-end user might not be willing to pay the price difference; if so, the price differential only reflects the value of the performance difference to the user who is on the margin between buying the one or the other. But these are essentially aggregation problems (over users), which I set aside because they arise throughout economic statistics of this kind.
A more promising situation exists empirically if there are a large number of computer models, and we have data on their prices and their performance. One can then run a regression, such as:
(1)
In equation (1), P is a vector of prices of computers, where models are indexed by the letter i, M is the associated performance measure for each computer, and ei is the regression error term. Using equation (1), we estimate a1 and use a1 to put a value on the performance difference among machines: If UNIVAC has M = 1.0 and “new computer” has M = 1.5, then the quantity [a1(0.5)] gives a “quality adjustment” that can be used to value the difference between the two machines.
2
And we suppose, contrary to what is true, that computer performance can be represented as a simple scalar.
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Suppose that we estimate a1 to be 0.7. Computer inflation between 1952 and 1955 (“quality adjusted” for “new computer’s” performance premium) is then: $600K / {$400K ((1 + 0.7(0.5))} = 1.11, or 11 percent inflation. This number is clearly less than the 1.50 (equals 50 percent inflation) that the unadjusted data would show. If “new computer” has M = 1.8, then computer prices are falling: $600K / {$400K ((1 + 0.7 (0.8))} = 0.96, or 4 percent price decline.
Turning to computer output, the usual method for measuring output changes (in the national accounts, for example) is to “deflate” expenditures on a product by its price index (information on the U.S. national accounts is in Bureau of Economic Analysis, 2001). To form a deflated measure of computer output, we start from the change in “current price” output, which in our example was ($6.0 mil − $1.2 mil) / $1.2 mil, equal to a 400 percent increase. Deflating that by the price index of 1.11 gives for the “constant price” output change a 360 percent increase between 1952 and 1955. Deflated output grows less than current price output because in this example (M = 1.5) computer prices, performance adjusted, were rising.
When “new computer” has a larger performance differential over UNIVAC (in the second example, M = 1.8), the price index declines, to 0.96, or a 4 percent decline. Using this declining price index as a deflator results in a “constant price” output change that is larger than the “current price” change (400 × (1/.96) = 417). In national accounts, this “constant price” output measure is sometimes (rather inappropriately) known as “real output.”3
This simple example illustrates several principles that govern estimation of computer output and investment in the U.S. national accounts. The most important one is that it shows how strongly measures of computer performance influence economic measurement of computer price change, and through the deflation procedure, how strongly computer performance affects measures of computer output, investment and productivity.
Equation (1) is a relation that is known in economics as a “hedonic function,” although equation (1) is a very simple hedonic function. The “quality adjustment” outlined in the preceding paragraph is, in essence, the method applied by the Bureau of Labor Statistics (BLS) in estimating price indexes for computers, where quality adjustments for enhanced computer performance are derived from a hedonic function. This example is far too simple, however.
In general, computer performance is not a scalar; it is multidimensional (the implications of this are explored in the subsequent section). Thus, computer hedonic functions look, generally, like equation (2):
(2)
3
BEA also uses the somewhat cryptic term “chained dollars” to represent the same thing, and reports percentage changes in the form of index numbers, under the title “chained-type quantity index.”
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Each of the k variables in equation (2) is a “characteristic” of computer performance. The current BLS hedonic function for personal computers has more than a dozen characteristics. Each of the coefficients, ak in equation (2), is interpreted as the value of the corresponding computer performance characteristic. Because I have written equation (2) in a logarithmic form (the hedonic function often turns out to be logarithmic, but not always), these coefficients are not prices denominated in the usual dollars or euros, but dollar and euro prices can be extracted from the coefficients, if desired.
Hedonic price indexes have become the standard economic tool for measuring price change in computers. In principle, they measure the price of computing power.
Getting from the price indexes to the output investment numbers is relatively straightforward and follows the example already presented. Table 2 shows current dollar changes for computer investment in the national accounts, the computer deflator, and the resulting deflated investment numbers from the national accounts, for the years 1995–2002. In 1995 computer and peripheral “current price” investment in the United States equaled $64.6 billion. In 2000, the value of computer equipment investment equaled $93.3 billion. Thus, in current prices computer equipment investment increased by 44 percent.
The computer equipment price index declined by 71 percent over the same 1995–2000 interval (from 131 to 38, using 1996 as the base). The change in current price shipments divided by the change in the price index gives the “deflated” (also called “constant price” or “real”) value of the change in computer equipment investment over that interval: As Table 2 shows, this increased four-fold (the quantity index goes from 69 in 1995 to 348 in 2000). The source of the great increase in computer investment in the national accounts numbers is not only the increase in spending on computer equipment, but also the decline in performance-corrected prices for this equipment.
This same point is dramatically illustrated by the post-2000 experience. Actual spending on computer and peripheral equipment fell by 20 percent. But the
TABLE 2 Private Fixed Investment in Computers and Peripheral Equipment
1995
1996
1997
1998
1999
2000
2001
2002
Billions of current dollars
64.6
70.9
79.6
84.2
90.4
93.3
74.2
74.4
Billions of chained 1996 dollarsa
49.2
70.9
102.9
147.7
207.4
246.4
239.9
284.1
Chained price index
131.29
100
77.38
56.99
43.6
37.87
30.91
26.27
Quantity index
69.4
100
145.22
208.39
292.64
347.77
338.61
400.92
a“Chained dollars” is the Bureau of Economic Analysis name for a quantity index of computer equipment output (see text).
SOURCE: Bureau of Economic Analysis, NIPA Tables 5.4, 5.5, and 7.6.
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national accounts quantity index increased by 15 percent over the same 2000–2002 interval, because the price index fell by 30 percent (see Table 2).
Tables 3 and 4 show that these trends have been going on for a long time. The price of computer equipment (computers plus peripherals) has declined 17.5 percent per year over the whole period for which national accounts investment data are available. Moreover, over the whole of the historical period, prices of computers themselves have declined faster than prices of peripherals (this is evident from Tables 3-5). The price of computing power today approaches 1/1,000 of 1 percent of what it was at the introduction of the commercial computer 50 years ago (Table 4).4 Additionally, the prices of ancillary devices have also fallen, though their performance improvements are often overshadowed by the spectacular progress in computer hardware: PC World (March, 2003, page 91) reports that the cost of storage media (disks) has fallen from $16.25 per MB of data stored in 1981 to $0.0008 (8 percent of a penny) in 2003, an annual rate of decline of 36 percent, comparable to the rate for PC computers over the same interval.
Computer price indexes fall because the performance of computers is increasing very rapidly, where their actual selling prices are stable or falling. Accordingly, it is no surprise that computer output in the economy rises not so much because increasing numbers of computers are produced (though this is true) but because the capability of the computers that are produced has increased so much. The great increase in computer investment over the last 50 years as measured in the national accounts is in large part an estimate of the value of increased performance of computers over this interval.
Nordhaus (2002) takes the price of computing back another 50 years, using a different approach. Though the rates of decline in the last half century are greater than in the half century before that, Nordhaus’ results indicate that high demand for improvements in computational power has existed over a long time, as well as indicating the extraordinary fruits of innovative ability set to satisfy that demand.
Price indexes for computers transfer directly into economists’ measures of the output of computers, of “real” (an economist’s somewhat misleading jargon) computer investment and capital stock, and from these the rate of productivity improvement. As examples of the latter, Jorgenson, Ho, and Stiroh (2002) estimate that the contribution of ICT (information and communication technology) investment was responsible for a large proportion of the acceleration in U.S. labor productivity in years following 1995. Triplett and Bosworth (2002) reached comparable findings for the importance of ICT investment to the substantial gains
4
The mainframe index in Table 4 gives a beginning/end value of 3.91−05. But over the period for which mainframe and PC price indexes are available (1982 forward), PC prices have fallen at 21 percent per year in the government indexes, where mainframes have trailed, at 18 percent per year (Table 4). Moreover, studies suggest that the government PC price index records too little decline, certainly over the first part of this period—for example, Berndt and Rappaport (2001, Table 1) indicate that PC prices fell over 30 percent per year between 1983 and 1999. Hence, taking all this together, the round number 1/100,000 in the text.
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TABLE 3 Private Fixed Investment in Computers and Peripheral Equipment
Price Index (1996 = 100)
Quantity Index (1996 = 100)
1959
101372.4
0.000
1960
79593.2
0.00044
1961
58800.8
0.00077
1962
41710.3
0.00143
1963
27395.1
0.00396
1964
22916.4
0.00616
1965
18936.0
0.00979
1966
13272.8
0.02354
1967
10784.1
0.03091
1968
9202.6
0.03696
1969
8332.3
0.05038
1970
7484.4
0.0605
1971
5698.7
0.07458
1972
4592.4
0.11
1973
4354.0
0.12
1974
3554.9
0.15
1975
3288.5
0.15
1976
2746.5
0.23
1977
2390.1
0.34
1978
1616.8
0.66
1979
1339.7
1.07
1980
1045.6
1.69
1981
918.9
2.63
1982
822.3
3.24
1983
685.6
4.92
1984
554.6
8.04
1985
471.5
10.10
1986
406.2
11.61
1987
346.0
14.59
1988
321.4
16.67
1989
300.1
20.27
1990
272.3
20.03
1991
244.6
21.75
1992
209.2
29.40
1993
178.4
37.31
1994
157.3
46.00
1995
131.3
69.40
1996
100.0
100.00
1997
77.4
145.22
1998
57.0
208.39
1999
43.6
292.64
2000
37.9
347.77
2001
30.9
338.61
2002
26.3
400.92
SOURCE: Bureau of Economic Analysis, NIPA Table 7.6, and unpublished BEA data in possession of the author (with more precise quantity index for earlier years). In 1959, the quantity index (1972=1) equals 0 to three decimal places in the unpublished data.
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TABLE 4 Price Indexes for Domestic Mainframes and PCs (1996 = 100)
Mainframes
Personal Computers
1953
791125.1
1954
682645.1
1955
605330.6
1956
516628.7
1957
456095.6
1958
412943.3
1959
354208.3
1960
260717.8
1961
197440.1
1962
143811.5
1963
109881.7
1964
83549.6
1965
60060.6
1966
22761.6
1967
16110.6
1968
14560.0
1969
14513.8
1970
13967.4
1971
10847.4
1972
8871.7
1973
9453.9
1974
8041.6
1975
7771.7
1976
7106.5
1977
5582.2
1978
2812.2
1979
2306.9
1980
1591.9
1981
1311.4
1982
1106.3
1549.9
1983
1006.8
1086.5
1984
727.1
937.0
1985
537.1
877.3
1986
486.5
646.4
1987
419.1
582.9
1988
397.1
533.3
1989
346.5
496.1
1990
307.5
415.5
1991
297.6
350.4
1992
277.2
267.9
1993
234.2
207.3
1994
182.1
182.7
1995
144.0
145.3
1996
100.0
100.0
1997
68.6
67.1
1998
49.2
40.3
1999
38.6
25.7
2000
30.9
20.7
SOURCE: Triplett (1989) and unpublished Bureau of Economic Analysis data.
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Author
Data Sources
Dependent Variable
Explanatory Variables
Dummies for manufacturer and introduction year (gives price index, relative to earliest machines)
NB: Justification for forming indexes based on technical assumptions—e.g., number of tape drive substitutes for speed in achieving same results.
Cale, Gremillion, and McKenney (1979)
Datapro
Price at introduction for a “balanced” system (processor plus peripherals)
Memory size in bytes
Size (in megabytes) of online direct access storage
NB: Addition time and other unspecified speed measures insignificant, partly owing to multicollinearity
Fisher, McGowan and Greenwood (1983)
Government lease price lists
Lease prices to federal government
Memory size in thou sands of bits
Addition time (including access time)
Transfer rate (bytes per second)
Wallace (1985)
GML Corp.; International Data Corp; Phister (1979)
List prices of all machines
Linear combination of MIPS and KOPS
Memory size included or minimum memory size (units not given)
Dummy variables for computer size class
Dummy variables for manufacturers
Cartwright, Donohoe, and Parker (1985)
Auerbach Corp.; Datapro Corp.; and Computerworld
List prices, all machines available
Speed (memory cycle time, machine cycle time, or MIPS, depending on period)
Memory size (in megabytes)
Maximum number of channels
Levy and Welzer (1985)
Computerworld
Published (list) prices, all machines from major producers
MIPS
Average memory size
Dummy variables for manufacturer, and for newly introduced
Ein-Dor (1985)
Computerworld; other sources
List price, selection of 106 machines
MIPS (a number of other performance measures were related to MIPS and to “average computational cost”)
Flamm (1987)
Phister (1979)
List price, all machines in source
KOPS × 10−3
Memory size in megabytes
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Author
Data Sources
Dependent Variable
Explanatory Variables
Gordon (1989) 1954–1979 regressions
Phister (1979)
Prices of newly introduced machines
Memory cycle time (in microseconds)
Memory size (in megabytes)
IBM dummy
1977–1984 regressions
Computerworld
Prices of all machines
Machine cycle time (in nanoseconds
Memory size (in megabytes)
Minimum number of channels
Maximum number of channels
5. Cache buffer size (units not given)
Dulberger (this volume)
Datamation; Computerworld; IBM
List price, IBM and “plug-compatible” machines
MIPS
Memory size (in megabytes)—maximum and minimum
“Technology class” dummy variables
NB: Each machine entered twice in the data set, once with maximum memory size available, once with minimum memory size, with the appropriate price for each.
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ANNEX B1 Variables in Computer Hedonic Functions, Hardware Components Only
Cole et al. 1986
Berndt and Griliches 1993
Berndt et al. 1995 (desktops)
Berndt and Rappaport 2002a
Chwelos 2003 (laptops)
Processor (CPU)
Speed
MIPS
MHz
MHz
MHz
MHz * CPU or benchmark scores
Memory
MB (min and max)
KB
KB (installed and maximum)
MB
MB
Cache
no
no
no
no
no
Technology variables
Chip dummies
16- or 32-bit processor chip dummies
8-, 16- or 32-bit processor chip dummies
Processor type; processor type*MHz
Intel dummy
Disk (hard) drive
Capacity
MB
MB
MB
MB
MB
Speed
Sum of 3
no
no
no
no
Other
no
no
Dummy for no HD
no
no
Displays (terminals, monitors, and keyboards)
Screen size
Number of characters
no
no
no
Size
Resolution
Dpi
no
no
no
Pixels in maximum resolution
Color
Number
Dummy
no
no
Dummy
Other
Number of function keys
no
no
no
Active or passive matrix LCD dummies
Other hardware features (if yes, see Annex B2)
no
7
6
2
8
Software features (if yes, see Annex B2)
no
no
no
no
no
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Nelson et al. 1994
Pakes 2001
Moch 2001 (Germany)
Rao and Lynch 1993 (workstations)
Holdway 2001 (U.S.)
Bourot 1997 (INSEE)
MHz
MHz, MHz^2
Test score
MIPS
MHz
MHz
MB
MB
MB
KB
MB
MB3
no
no
KB
no
no
KB
Processor type
Maximum memory; Apple*speed
Architecture dummy
no
Celeron dummy
Chip dummies
MB
GB
MB
MB
MB
MB
no
no
no
no
no
no
no
no
no
no
no
Type dummies
no
no
Size
no
Size dummies
Size
no
no
no
no
Trinitron dummy
dpi
Dummy
no
Dummy
no
no
no
Monochrome monitor dummy
no
Monochrome monitor dummy
no
no
5
7
6
3
9
7
2
no
yes
no
3
yes
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Evans 2002
Barzyk 1999 (StatCan)
Dalen 1989 (Sweden)
Koshimaki and Vartia 2001
INSEE01
INSEE02
Processor (CPU)
Speed
MHz
b
Test score
MHz
MHz
Memory
MB
MB
MB
MB
MB
Cache
no
max
KB
no
no
Technology
Memory type; maximum memory
no
no
no
no
Disk (hard) drive
Capacity
GB
b
MB
MB
no
Speed
no
no
no
Access time
no
Other
no
no
Type dummies
no
no
Displays (terminals, monitors, and keyboards)
Screen size
no
no
no
no
no
Resolution
no
no
no
no
no
Color
no
no
no
no
no
Other
no
no
no
no
no
Other hardware features (if yes, see Annex B2)
7
4
6
no
no
Software features (if yes, see Annex B2)
no
no
no
no
no
aIncludes the same variables as Berndt and Rappaport (2001) plus microprocessor-type dummy variables and interactions between microprocessor type and clock speed.
bReplaced by external volume measure.
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Statistics Finland 2000
Okamoto and Sato 2001
Lim and McKenzie 2002
van Mulligen 2002
MHz
MHz
CPU score
MHz
MB
MB
MB
MB
no
no
KB
no
Type dummy
Processor type
no
Processor type
GB
MB
MB
GB
no
no
no
no
no
no
no
no
Size
Size
17″ dummy
no
no
no
no
no
no
no
no
no
no
No monitor dummy; LCD dummy
no
Dummy variable for presence
no
4
9
3
no
no
no
no
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ANNEX B2 Computer Hedonic Functions, Other Hardware and Software Features (for other variables and sources, see Annex B1)
Berndt and Griliches
Berndt et al.
Berndt and Rappaport
Chwelos
Nelson et al.
ZIP dummy
no
no
no
no
no
CDROM dummy
no
no
yes
no
no
CDROM speed
no
no
no
yes
no
CDRW dummy
no
no
no
no
no
DVD dummy
no
no
no
no
no
Sound card dummy
no
no
no
no
no
Video (MB)
no
no
no
no
no
Network card
no
no
no
no
no
Modem dummy
no
no
no
modem speed
no
Speakers dummy
no
no
no
no
no
Case type dummy
no
no
no
no
no
Warranty dummy
no
no
no
no
no
Seller dummies
yes
yes
major brand
major brand
yes
SCSI control
no
no
no
no
no
Operating system
no
no
no
no
yes
Other software
no
no
no
no
other software utilities
Other
number of floppy drives
slots available for expansion board
mobile dummy
discounted by vendor
age
extra hardware
two or more floppy drives dummy
size
weight
density
age
battery type
battery life index
density
discount price
weight
number of floppy drives
extended industry standard architecture bus
number of slots
number of ports
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Pakes
Moch
Rao and Lynch
Holdway
Bourot
no
no
no
yes
no
no
yes
no
no
yes
no
no
no
no
yes
yes
no
no
no
no
yes
no
no
yes
no
yes
no
no
no
yes
yes
yes
no
yes
yes
yes
no
no
yes
no
yes
no
no
yes
yes
no
no
no
yes
no
no
yes
no
no
yes
no
no
no
yes
no
Apple
no
yes
yes
no
no
no
yes
no
no
no
yes
no
yes
no
no
number of bundled applications
no
software office suite; MS Office
no
second floppy dummy bus width
number of graphics standards supported
business market
other cards
mouse dummy
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Evans
Barzyk
INSEE01
INSEE02
ZIP dummy
no
no
no
CDROM
no
no
yes
CDROM speed
yes
no
no
CDRW
yes
no
no
DVD dummy
no
no
no
Sound card dummy
yes
no
no
Video (MB)
no
yes
no
Network card
yes
no
yes
Modem dummy
yes
no
yes
Speakers dummy
no
no
no
Case-type dummy
yes
no
yes
Warranty dummy
no
yes
no
Seller dummies
no
yes
yes
SCSI control
no
no
yes
Operating system
no
no
no
Other software
no
no
no
Other
number of slots
network location
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Okamoto and Sato
Lim and McKenzie
van Mulligen
no
no
no
no
no
no
no
no
no
no
yes
no
no
no
no
no
no
no
no
yes
no
no
yes
no
yes
no
no
no
yes
no
no
yes
no
no
yes
no
Apple
yes
yes
no
yes
no
no
no
no
no
no
no
TV tuner
expandability
USB port
vintage
workstation dummies
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Representative terms from entire chapter:
price indexes
