THE EFFECT OF PRODUCT INNOVATION ON

EXISTING PRODUCTION GROWTH RATES IN DRAM INDUSTRYby:

Dillon KuiavaUniversity of Oregondkuiava@gmail.com

18 March 2015

ABSTRACT

This paper utilizes regression analysis to explore the relationship between industry growth rates, and the product lifecycle of Dynamic Access Memory Chips (DRAM). My results suggest that subsequent generations of DRAM as well as the age of a particular generation have a large negative impact on the growth rates of existing generations of DRAM. Changes in growth rates are motivated by a shift in Research and Development costs from an existing generation of DRAM chip, to a more sophisticated one. In a sense, the next generation’s determinant of entry is the previous generation’s determinant of exit.

Paper PreparedIn

Partial Fulfillment of the Requirements for an Economics Degree with Honors

Keywords: Dram, Growth, Product Life Cycles

JEL Codes: L93, L16

________

Acknowledgments to Wesley W. Wilson and Stephen Yeum.

Approved by

Wesley W Wilson

______________________

Professor of Economics

University of ORegon

INTRODUCTION

This study examines the effect of newly developed generations of Dynamic Random

Access Memory chips (DRAM) on growth rates of existing generations of DRAM at the market

level. As expected, the introduction of a new iteration of DRAM has a strong negative impact

on existing DRAM growth rates, signaling that new generations of DRAM have a strong

influence on product obsolescence. Further, this study intends to examine the product lifecycle

of a given generation of DRAM, as it follows the 5 stage theoretical product life cycle proposed

by Gort and Klepper (1982). Where this analysis differs from Klepper is the utilization of growth

rates to determine the trajectory of the product lifecycle, not the number of producers. Simply

put, the entrance of a new generation of DRAM plus time, equals the exit of an older

generation of DRAM. This empirical analysis provides evidence for that, as overall market

production of DRAM increases throughout time, across generations.

During the time period examined, the global DRAM market consisted of less than 50

firms internationally, with a small number of firms producing within the United States, Japan,

Korea, Germany, and England. Industry price is typically homogenous, with little product

differentiation between firms. The initial capital necessary to produce DRAM is high enough to

induce an oligopolistic market structure, with significant barriers to entry for new firms, and

rapidly diminishing marginal costs of production. Initial R&D costs are also a heavy barrier to

entry. The industry also benefits from learning-by-doing process improvements, and these

improvements tend to spill over to other firms, maintaining a consistent industry wide marginal

cost curve.

This study utilizes the intuition of Goran Eriksson (1984) and his growth theory scaled to market

level. Erikson shows the interrelationship between growth entry and exit of firms. In examining

the product lifecycle, The five stage product lifecycle proposed by Klepper and Gort (1982);

commercial introduction of new product, increase in number of firms producing, number of

firm entrants = number of firm exits, negative net entry of firms, destruction of a market.

Douglas A. Irwin (1998) provides an analysis of DRAM markets, and contextualization DRAM

production, including U.S. – Japan trade frictions.

In this paper, I develop and estimate a model that contains the essence of Klepper and Gort

(1984) on product lifecycles, and Eriksson (1984) on growth rates. I find that growth rates

follow the bell curve growth trajectory of Klepper and Gort, with diminishing growth rates over

time, spurred by the introduction of a new generation of DRAM. Generally, Macroeconomic

fluctuations have little influence on changes in DRAM production, but industry level shocks due.

There is also evidence that anti-competitive dumping of Japanese firms in the early 1980’s,

stagnated growth and may have contributed to a rapid rebound of earlier 4k iterations of

DRAM.

BACKGROUND INFORMATION

Below, I provide a brief contextualization of DRAM market history, which helps to

situate growth rates and product lifecycles of DRAM in a historical context. I also describe

DRAM market structure built upon Irwin (1998), which bolsters the historical contextualization

and provides a theoretical framework to view market level DRAM growth rates. To understand

the nature of growth rates, I review the theory proposed by Eriksson (1982) Then, I provide a

foundation for understanding product lifecycles, as proposed by Klepper and Gort (1982)

The rapid growth of DRAM production coincides with the growth of personal computer

systems. Random Access Memory, (RAM) is necessary in working computer functions, including

storing operating systems, executing files, and hosting process applications. The first DRAM

chip was patented in 1968 in the United States. Prior to 1975, the majority of DRAM chip

production was for strategic government purposes and academic pursuits. By 1975, the

introduction of the Altair personal computer increased demand for personal computers, and

subsequently the demand for 4k DRAM chip components necessary to their function. In the

early years of RAM production, DRAM outcompeted Static Random Access Memory chips

(SRAM), because of its relatively inexpensive production cost, though SRAM hardware is more

energy efficient and has a higher processing power than comparable DRAM products. DRAM 4k

chips were a mainstay of the industry, becoming obsolete 13 years after product introduction,

compared to an average of 10 years for other chips. The overall peak output of DRAM chips for

each iteration, increases over time. In 1983, the introduction of Apple II computer systems

made personal computers accessible to the general public, further increasing the demand for

DRAM chips, and in turn ramping up production for 16k and 64K chips. The increasing

popularity of videogame systems in the early 1980’s also generated demand for DRAM chips.

By the early 1990’s, personal computer systems were widely used in office applications, as 1

megabyte and 4 megabyte chips were adopted by personal computer manufacturers. This

empirical study focuses on the time frame contextualized here from 1973-1993.

Market structure, industry level shocks, and international trade relations shape DRAM

growth rates, and fluctuations in theoretically grounded hypotheses projecting declining growth

over time. Irwin provides a contextualization of the above factors in his paper on the

semiconductor industry (which the DRAM industry is a subcategory of). The DRAM industry is

characterized by dynamic scale economies, and dynamic marginal cost. After significant R&D

investment in the introduction of a DRAM chip, the cost of producing an additional unit of

DRAM declines, as process improvements reduce marginal cost through learning by doing.

Learning by doing spillovers may also contribute to industry wide reductions of marginal cost.

Much of the literature on pricing assumes homogenous pricing, which is reasonable,

considering little product differentiation in DRAM chips, as computers need a one chip fits all

application. According to Irwin (1998), “the market for memory chips can be very competitive:

DRAMs are a standardized product and are almost perfectly interchangeable regardless of

which firm produces them” (p. 174). Additionally, a main contention of Irwin’s paper is that

Japanese dumping practices may have affected production of DRAM chips, in the early 1980’s, a

subject explored empirically in this paper. Tech industry market fluctuations have a significant

impact on DRAM production, as the demand for computer systems are interrelated with the

demand for DRAM chips. Despite all market fluctuations, this study finds that growth rates are

consistent across generations of DRAM. The theoretical intuition behind growth, entry and exit

of firms from the DRAM market is provided by Eriksson (1984), and scaled to industry level.

Main determinants of growth include product demand, the number of firms participating within

the industry, and research and development costs. Eriksson relates growth to market structure

stating “industry growth comes mainly from existing firms” (p. 52) which is reflected in the

empirical data with the long standing presence of firms such as AMD, Intel, and Toshiba.

Growth rates decrease over time in the DRAM industry, spurred by the introduction of a new

generation of product that captures market demand.

THEORETICAL MODEL

Most products, particularly those subject to rapid technological change, follow a

product life cycle. This product lifecycle is evident in figure 1 below, which examines total

industry output of both 4k and 16k DRAM chips over time.

A typical product lifecycle includes, but is not limited to the following stages; commercial

introduction of new product, increase in number of firms producing, number of firm entrants =

number of firm exits, negative net entry of firms, destruction of a market. The commercial

introduction of chips is characterized by small output, by few firms, which is evident for 4k

chips between 1974 and 1975. This stage is followed by a rapid increase in the number of firms,

evident between 1975 and 1997, and the expanded output of 4k DRAM during this time. The

point of interest in the timeframe of DRAM production below is when 4k chip production

reaches its maximum. This corresponds to the increase in firms, and output for the 16k DRAM

market. Thus it is evident that market entry and exit equilibrium of firms in the 4k DRAM

market, is related to the growth in 16k DRAM production. Thereafter, production of 4k DRAM

chips declines until product obsolescence. From the above example, and building on Klepper

and Gort (1982) I propose that obsolescence spurred by the demand for new products

negatively impacts production of existing products.

Entry determinants of DRAM are largely influenced by the ability to overcome barriers

to market entry. Early in the product lifecycle, the returns are large for R&D. As time goes on,

and knowledge spillovers entice other entrants, Innovation in a particular product is less

profitable, and a shift in R&D resources to the next generation of product becomes a better use

of capital resources. Growth is spurred by rapid technological innovation, and knowledge

spillovers between industries. Growth rates stabilize as more efficient, and larger firms come to

dominate and stabilize their market share. As firm exits overtake firm entrants within the

industry, there is a stasis in production levels. This is often where shift’s in R&D costs change to

new product innovations. Existing product demand in the market makes uptake of production

of newer improved generations of DRAM slow initially. However, in time the new product over

takes the old product, and seals the existing products obsolescence.

EMPIRICAL MODEL

Below is the econometric model used to test the theoretical underpinning of

diminishing growth rates, and their relationship to new product entries.

Log(Qgenkt/Qgenkt-1) = β1 + βkPresneceGenk + β3 TimeExistk + β4 MacroShox + β5IndShx + β6 Quarter +

β7 JapanDumping + ui

The dependent variable Log(Qgenkt/Qgenkt-1) is the percent change in growth with respect to the

entrance of a new generation of DRAM. β1 is the growth rate of DRAM generation k where k =

{4k, 16k, 64k, 256K; 1m, 4m, 16m} respectively. Βk is the coefficient explaining the change in

growth determined by the presence of a new generation of DRAM. Β3 considers

macroeconomic shocks from recessions. Β4 is a dummy variable that accounts for non-

recessionary industry shocks as determined by the San Francisco Fed Pulse index. Β5 explains

the change in the growth of production determined by the age of a generation of DRAM by

yearly quarter. The Β6 coefficient is a vector of quarterly adjusted time variables. β7 considers

Japanese dumping practices. There is also a small sub regression, used to determine negative

growth rates relating the log of industry production after peak industry production.

DATA SECTION

The data set was obtained from DATAQUEST, utilizing sample surveys from DRAM producers.

The data utilizes information from 34 firm level producers of DRAM, from nations including

Japan, Korea, England, and the United States from 1973-1993. The information was collected at

quarterly intervals. For the purposes of this study, I aggregated firm level production into

industry level production for six of the seven generations observed in the study. The seventh

generation (16m DRAM) had too little information to be utilized in time-series analysis. The

sample size for all generations of DRAM range from 20 to 58 quarterly observations. Quarterly

dummy variables are implemented to check for seasonal variation in production. A low sample

size in some observations may have biased results, which will be considered for some variables.

Douglas Irwin also identified Japanese dumping practices in the late 1970’s through early 1980’s

which are taken into account for all regressions.

In order to examine the effect of macro-economic shocks, I utilized data from the St. Louis

Federal Reserve data base to match recessionary gaps to DRAM production for all 6

generations. Recessionary periods included in the analysis were from quarter 4 of 1973 to

quarter 2 of 1975, quarter 1 of 1980 to quarter 3 of 1980, q3 of 1981 to quarter 4 of 1982, and

quarter 3 of 1990 to quarter 2 of 1991.

Differentiate between technology industry shocks, and macroeconomic shocks I utilized the San

Francisco Federal Reserve Tech pulse index to mark tech-specific shocks, and their effect on

DRAM growth rates. The Tech Pulse Index is a coincidence index of activity in the U.S.

information technology sector. The index interpreted as the health of the tech sector. The

indicators used to compute the index include investment in IT goods, consumption of personal

computers and software, employment in the IT sector, industrial production of the technology

sector, and shipments by the technology sector. (San Francisco Federal Reserve, (2015). Periods

of variation in technological growth include quarter of 1974, quarter 3 of 1976, quarter 2 of

1974, and quarter 2 of 1988.

The dependent variable, the percentage change in quantity, is regressed on β1, which

explains the growth rate, plus the entrance of subsequent generations of DRAM chips, βk. It is

expected that growth rates will be positive, but the introduction of a new chip will have

downward pressure on those growth rates. The β3 coefficient measures the effect time has on

existing chip growth in production, which I expect to have a negative sign, as the longer a chip is

in the market, the more time there is for new product innovations. The β4 coefficient measures

the influence of macroeconomic shocks on growth rates, indicated by Federal Reserve

recessionary definitions. One would expect that recessions have a negative influence on output

of DRAM, but this is not necessarily the case in the data. The β5 coefficient measures industry

level negative shocks, shown by the Tech Pulse index. These dummy variables, are expected to

have a negative effect on DRAM production output. The β6 is a vector of dummy variables

measuring the effect of seasonal variation on DRAM production. It is expected that seasons

may explain a variation in output, as demand for electronics change throughout the year. The

β7 coefficient measures the effect of Japanese dumping on the production of DRAM chips. It is

expected that dumping will have a positive effect on growth rates for 4k DRAM.

EMPIRICAL RESULTS

The results provided in table 1 indicate that growth rates of DRAM diminish over time.

(1) (2) (3) (4) (5) (6)VARIABLES 4k 16k 64k 256k 1m 4m

presence16k -0.259*(0.132)

presence64k -0.164 0.176**(0.0973) (0.0740)

presence256k 0.117 -0.0244 -0.159(0.142) (0.0929) (0.196)

presence1m 0.0380 -0.357***(0.138) (0.121)

presence4m -0.202 -0.0223(0.127) (0.224)

presence16m -0.185(0.136)

timeadj4k -3.348**(1.642)

timeadj16k -10.93***(0.815)

timeadj64k 0.561(1.099)

timeadj256k -1.019(1.371)

timeadj1m -6.028***(1.200)

timeadj4m -2.152*(1.091)

tekshx1 0.137(0.156)

tekshx2 -0.0550 0.782***(0.112) (0.125)

tekshx3 0.0144 -0.152(0.0906) (0.114)

tekshx4 -0.160 0.00519 0.000671 -0.232(0.107) (0.133) (0.211) (0.200)

rec1 0.260(0.236)

rec2 -0.0574 0.0926 0.0565(0.103) (0.0686) (0.179)

rec3 -0.0409 0.0459 -0.332(0.147) (0.0962) (0.203)

rec4 0.184 -0.0891 -0.0378 -0.175(0.113) (0.0937) (0.149) (0.147)

jdump 0.260** -0.144** -0.280* -0.475**(0.103) (0.0673) (0.142) (0.216)

Constant 3.134** 9.468*** 0.196 1.955* 5.859*** 2.861***(1.217) (0.624) (0.907) (1.050) (1.021) (0.778)

Observations 47 37 58 43 32 20R-squared 0.798 0.959 0.722 0.835 0.704 0.914Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Industry Growth (Table 1)

The growth rate constant was positive and significant for all 6 generations of DRAM explored.

The presence variables of subsequent chips had a negative and significant effect on growth rates

for all generations as well, excluding the presence of 256k DRAM on growth rates of 4k DRAM.

Typically, the effect of subsequent iterations of DRAM on existing growth rates diminished after

2 generations. The age of a generation of DRAM also displayed downward pressure on growth

rates of DRAM for all generations, except 64k DRAM. The metric for time’s effect on a

generation of DRAM increased in weight over time. In testing for the significance of quarterly

dummy variables, an F-statistic yielded statistically insignificant results. Macroeconomic shocks

also showed indeterminate effects in almost all regressions, indicating that fluctuations in the

DRAM market may not change relative to larger overall macroeconomic effects. Another

explanation may be the small n values within the sample size, which do not pick up the effect of

recessions. Industry level shocks, did however show significance, showing that DRAM

production is more heavily influenced by the tech industry as a whole, rather than

macroeconomic shocks, though the two me be interrelated. Diminishing growth rates are

reflected after peak production periods, as shown in Figure 2.

(1) (2) (3) (4)VARIABLES lnq4k lnq16k lnq64k lnq256k

trend -0.00614 0.255*** 0.0409 0.223***(0.0335) (0.0297) (0.0406) (0.0396)

max4k -0.477(0.937)

max16k -3.612***(0.738)

max64k -0.198(1.419)

max256k -3.954***(1.049)

Constant 8.152*** 2.885*** 6.902*** -0.864(0.515) (0.767) (1.467) (1.970)

Observations 48 38 60 45R-squared 0.037 0.697 0.050 0.459Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Diminishing Growth (Table 2)

Finally, an F-statistic used to test for the joint significance of quarterly dummy variables was

used, and showed no statistical difference between the use of quarterly dummy variables, and

their absence, as provided in table 3.

The anomaly of increasing growth rates at the end of the product lifecycle shown below

in figure 4 between 1982 and 1983 was statistically significant, indicating that Japanese dumping

had a positive effect on growth of 4k chips during the early 1980’s.

The introduction of the Altair personal computer in 1975, spurred demand for 4k DRAM.

The general trend of 4k production exhibits downward growth rates until 1980, where growth

rates rebound heavily. This effect could have multiple explanations. First, the early recessionary

periods of the 1980’s may have prematurely decreased growth rates in DRAM, which rebounded

heavily after the recessionary end. Alternatively, Japanese dumping in the early 1980’s may have

decreased price enough, to entice a rapid increase in demand, reflected by highly positive growth

rates prior to obsolescence. All other growth rate patterns follow an exponentially diminishing

trajectory. The growth of 64K DRAM far exceeded other generations, as its introduction

coincided with the premier of the Apple II personal computer in 1983. During that time period,

there were 10 million personal computers in use in the United States, with maximum production

of 64K DRAM being nearly four times that of the previous generation 16K DRAM production.

The volume of production increases in this manner, for each subsequent generation of DRAM.

CONCLUSION

Growth rates of DRAM diminish over time. Macro-economic fluctuations seem to have

little effect on growth rates of DRAM, but industry level shocks do. A subject for further

research may be to explore the relationship between industry level shocks to DRAM, and

Macroeconomic shocks. Some metrics were indeterminate, which may be explained by the small

sample size of later generations. Japanese dumping effected the level of output of DRAM in the

late 1980’s, but that surge in production may also be related to recessionary rebounds, another

subject for further exploration.

The theoretical framework for product lifecycles is evident in the data across generations,

through the bell-shaped output curves. This study points to the importance of viewing product

lifecycles across generations in rapidly changing technological industries. It would also be

interesting to distinguish between the relative inflexibility of DRAM market shocks as a market

anomaly, or as a product of the rapid growth within the industy.

References

Almeida, P., & Kogut, B. (1997). The Exploration of Technological Diversity and the

Geographic Localization of Innovation. Small Business Economics, 9(1), 21-31.

Brown, C., & Linden, G. (2005). Offshoring In The Semiconductor Industry: A Historical

Perspective. Brookings Trade Forum, 279-322.

Bowen, J., & Leinbach, T. (2006). Competitive Advantage in Global Production Networks: Air

Freight Services and the Electronics Industry in Southeast Asia. Economic

Geography, 147-166.

Eriksson, G. (1984.). Growth, Entry and Exit of Firms. The Scandinavian Journal of

Economics,86(1), 52-67.

Cho, D., Kim, D., & Rhee, D. (1998). Latecomer Strategies: Evidence from the Semiconductor

Industry in Japan and Korea. Organization Science, 9(4), 489-505.h

Gort, M., & Klepper, S. (1982). Time Paths in the Diffusion of Product Innovations. The

Economic Journal, 92(367), 630-653.

Greenstein, S., & Wade, J. (1998). The Product Life Cycle in the Commercial Mainframe

Computer Market, 1968- 1982. The RAND Journal of Economics, 772-789.

Hatch, N., & Mowery, D. (1998). Process Innovation and Learning by Doing in Semiconductor

Manufacturing. Management Science, 44(11), 1461-1477.

Irwin, D. (1998). The Semiconductor Industry. Brookings Trade Forum, 173-200.

Irwin, D., & Klenow, P. (1994). Learning-by-Doing Spillovers in the Semiconductor

Industry.Journal of Political Economy, 1227-1227.

Klepper, S., Gort,. M. (Sep. 1982). “Time Paths in the Diffusion of Product Innovations”. Great

Britain. Royal Economic Society.

Sivakumar, K., & Cherian, J. (1995). Role Of Product Entry And Exit On The Attraction

Effect.Marketing Letters, 6(1), 45-51.

REGRESSIONS AND GRAPHS APPENDED

Testing For Joint Significance of Quarterly Dummies

Generation F-Statistic Result4k 0.15909 Fail to Reject - 1%16k 0.6619 Fail to Reject - 1%64k 2.09 Fail to Reject - 1%256k 0.567 Fail to Reject - 1%1m 2.0427 Fail to Reject - 1%

Table 3

Industry Growth 4k(1) (2) (3) (4) (5) (6)

VARIABLES 4k 4k 4k 4k 4k 4k

presence16k -0.464*** -0.304** -0.257* -0.300** -0.311** -0.298**(0.0841) (0.116) (0.129) (0.134) (0.140) (0.125)

presence64k -0.311*** -0.270*** -0.207** -0.218** -0.225** -0.217**(0.0748) (0.0754) (0.0898) (0.0940) (0.0980) (0.0882)

presence256k 0.173** 0.191*** 0.280** 0.173** 0.177** 0.0341(0.0694) (0.0678) (0.137) (0.0719) (0.0746) (0.0870)

timeadj4k -1.504* -2.690* -1.905* -1.960* -1.976**(0.770) (1.490) (1.011) (1.053) (0.949)

rec1 0.237(0.242)

rec2 -0.0627(0.109)

rec3 -0.0426(0.156)

rec4 = o, -

tekshx1 0.120 0.137 0.127(0.163) (0.171) (0.153)

tekshx2 -0.0828 -0.0712 -0.0814(0.115) (0.120) (0.108)

tekshx3 0.151 0.152 0.0354(0.137) (0.145) (0.137)

tekshx4 = o, - - -

q1 = o, -

q2 -0.0119(0.0777)

q3 0.0158(0.0793)

q4 -0.0368(0.0785)

jdump 0.256**(0.102)

Constant 0.570*** 1.820*** 2.648** 2.114** 2.162** 2.170***(0.0610) (0.642) (1.106) (0.809) (0.847) (0.759)

Observations 47 47 47 47 47 47R-squared 0.710 0.734 0.746 0.749 0.753 0.785Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

050

0010

000

1500

020

000

SC

AP

RA

1970 1975 1980 1985 1990year

CUMULATIVE INDUSTRY PRODUCTION BY YEARProduction of 4K DRAM

-.50

.51

grow

th4k

1970 1975 1980 1985 1990year

GROWTH RATES BY YEAR4K DRAM

020

000

4000

060

000

8000

0S

CA

PR

B

1970 1975 1980 1985 1990year

CUMULATIVE INDUSTRY PRODUCTION OF UNITS BY YEARProduction of 16K DRAM