learning to time capacity expansions: an empirical analysis of the worldwide petrochemical industry,...

Strategic Management JournalStrat. Mgmt. J., 24: 393–413 (2003)

Published online 26 February 2003 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/smj.309

LEARNING TO TIME CAPACITY EXPANSIONS: ANEMPIRICAL ANALYSIS OF THE WORLDWIDEPETROCHEMICAL INDUSTRY, 1975–95

JAMES HENDERSON1* and KAREL COOL2

1 Babson College, Babson Park, Massachusetts, U.S.A.2 INSEAD, Fontainebleau, France

This paper examines how firms may learn to better time their capacity expansion decisionsthrough their own and their rivals’ past experiences. A review of the literature shows thatthere may be several reasons for firms to bunch their capacity additions or ‘hop on aninvestment bandwagon.’ These reasons include coordinating through maintaining market shares,information effects, and decision-making biases. Given the substantial evidence of organizationallearning, firms may be expected to improve their timing skills of capacity additions throughtheir previous capacity expansion experience. Hypotheses are developed both for proprietarylearning and learning at the industry level, and for forgetting. These hypotheses are tested on adatabase consisting of 72 companies operating in the petrochemicals industry in the UnitedStates, Europe, and Japan from 1975 to 1995. The results indicate that learning in timingcapacity expansion decisions comes primarily from within firms through an accumulation of theirpoor outcomes. However, this timing skill is far more apparent in greenfield than incrementalexpansion decisions. Copyright 2003 John Wiley & Sons, Ltd.

Capacity expansion has long been recognized asan important and agonizing investment decision.To improve their competitive position, firms mayneed to expand capacity. At the same time, theyalso seek to avoid industry excess capacity aspoor industry conditions may erase the intendedvalue creation. This is a dilemma that is alltoo familiar in industries such as semiconduc-tors, shipping, petrochemicals, steel, hotels, and,more recently, telecommunications. There is quitea bit of evidence to suggest this dilemma is noteasily resolved. For example, between 1976 and1985, excess capacity in the European ethyleneindustry reached a high of 34 percent; the aver-age return on investment dropped to approximately

Key words: competitive decision making; learning; capac-ity expansion*Correspondence to: Professor James Henderson, Babson Col-lege, Babson Park, MA 02457, U.S.A.

1.5 percent, well below the cost of capital for thepetrochemical industry.

Many reasons have been given as to why firmsfind themselves in such situations. Firms expandhoping to preempt rivals, only to find that competi-tors also add capacity (Lieberman, 1987). Or firmsmay follow others’ leads believing that rivals haveaccess to privileged information (e.g., Scharfsteinand Stein, 1990; Banerjee, 1992). It has also beenargued that many managers use biased heuristicsin making investment decisions (Zajac and Bazer-man, 1991). All explanations have in common thatcompanies do not appear to learn very well frompast investment outcomes.

This conclusion is somewhat surprising giventhe evidence that managers and their organiza-tions try to learn. For example, the literature onthe learning or experience curve shows the impor-tance of learning for improving cost positions(see, for example, Yelle, 1979, for a review). The

Copyright 2003 John Wiley & Sons, Ltd. Received 6 October 1999Final revision received 13 September 2002

394 J. Henderson and K. Cool

literature on the returns to R&D (e.g., Mansfield,1991) has consistently found that the social returnis substantially higher than the private return toR&D, indicating that firms learn from each other.More recent research on R&D spillovers has doc-umented that some firms have a higher absorp-tive capacity or learning rate than others (Cohenand Levinthal, 1990). Extending this view, theresource-based view would argue that firms’ skillsin capacity expansion should improve with timeas they accumulate a capability or stock of experi-ence in making those decisions (e.g., Dierickx andCool, 1989).

This paper contributes to the organizationallearning literature by examining whether and howfirms learn to invest in capacity. The focus ison one aspect of these decisions: the timing ofwhen to bring capacity on stream. The setting forthe empirical analysis is the global petrochemi-cal industry during the period 1975–95. It is anindustry which frequently has to make big-capacityadditions and in which many firms apparentlystruggle to get the timing decision right (Lieber-man, 1987). It also is an industry where bettertiming yields superior returns. A study by Achiet al. (1996) on the U.S. petrochemical industryfound that the differences in returns in better tim-ing new capacity additions were greater than thedifferences in the returns between the lowest andhighest cost players. Thus, firms may be expectedto try to improve their capacity decision mak-ing and timing. The 21-year horizon allows usto study the timing decision over three economiccycles in Western Europe, the United States andJapan.

Contrary to the R&D literature (e.g., Mans-field, 1991; Pisano, 1996), this paper finds thatlearning in timing capacity expansions tends tobe proprietary; companies learn better from theirown mistakes than from the observation of theirrivals’ experience. Furthermore, this learning ismost apparent in greenfield rather than incrementalexpansions. The paper proceeds by briefly review-ing the major findings in the capacity expan-sion literature. Then, we derive four hypothesesdrawing on organizational learning research. Thesection thereafter describes the sample, the defini-tion of the variables, and the procedure to test thehypotheses. The final sections discuss the results,alternative explanations, and the limitations ofthe study.

LITERATURE REVIEW

A firm investing in capacity is faced with thefollowing dilemma: investing in large plantscan promise significant scale economies but toomany firms investing simultaneously can lead toovercapacity. The credible commitments literaturehas argued that a preemption strategy, in whicha firm builds enough capacity to supply allexpected demand, should discourage rivals frominvesting (Porter and Spence, 1982; Ghemawat,1984; Reynolds, 1986; Lieberman, 1987) and deterpotential competitors from entering (Wenders,1971; Spence, 1977; Salop, 1979; Eaton andLipsey, 1979; Spulber, 1981; Lyons, 1986).However, empirically, its success is rare (e.g.,Lieberman, 1987; Smiley, 1988). For example, intheir study of the U.S. chemical industry, Gilbertand Lieberman (1987) discovered that firmsappeared to ‘hop on an investment bandwagon.’Rather than deterring other firms from following,capacity expansion announcements tended toinduce more announcements.

Several explanations have been given as to whythis bandwagon behavior occurs. First, believingthey are unable to deter other firms from expand-ing, firms may be relying on some other mecha-nism to coordinate capacity expansions, for exam-ple, maintaining approximately constant marketshares (e.g., Gilbert and Lieberman, 1987). In thiscase, firms would decide to expand when their his-torical market share decreases.

Second, firms may invest because of informa-tion asymmetry in the industry (Banerjee, 1992).The mere announcement of one firm expand-ing capacity may send a signal to competingfirms on expected demand. Rather than rely solelyon their private information, firms may use theinformation contained in the decisions of oth-ers. For example, Gilbert and Lieberman (1987)observed that smaller rather than larger firms weremore likely to expand when their rivals invested,attributing this difference to the larger firms’ bet-ter ability in gathering and processing privateinformation.

Third, managers’ decision making may also beaffected by biases, which could lead to too manyfirms investing simultaneously. Managers may beoverconfident in their decisions to expand capac-ity, especially when they rely on confirmatoryevidence (Einhorn and Hogarth, 1978) such asconsistent demand growth forecasts, even though

Copyright 2003 John Wiley & Sons, Ltd. Strat. Mgmt. J., 24: 393–413 (2003)

Learning to Time Capacity Expansions 395

the accuracy of these forecasts may not haveimproved (Makridakis and Wheelwright, 1989).Furthermore, managers may be overly confidentthat they will be the first and only movers ina competitive situation (Farber, 1981) failing tosufficiently consider their rivals’ preemption at-tempts (Zajac and Bazerman, 1991). For exam-ple, in the commodity plastics industry in Europe,excess capacity reached a high of 40 percent by1978. As one industry expert stated:

It would seem that individual producers of LDPEhave planned their new capacities in a vacuum fortheir investment decisions must have been madeeither in spite of, or in ignorance of, the plans oftheir many competitors. (Hyde, 1977: 3)

In sum, there is plenty of evidence that capacityexpansions are often bunched, resulting in pro-longed industry overcapacity. However, not allfirms may join this investment bandwagon. First,we gave some evidence that small firms mayinvest differently than large firms due to infor-mation asymmetry. Second, larger firms may notbe as trigger-happy since the marginal revenuesthat they would expect from expansion wouldbe lower than those expected by smaller firms.Third, differences in size may also reflect dif-ferences in capacity expansion experience. Largerfirms may have made more capacity expansionsand have learned through previous moves and out-comes not to hop on the bandwagon. By audit-ing their previous expansions, these firms mayalso rely more on their own private information,not to fall victim to overconfidence biases, and/orto more fully consider rivals’ actions. If largefirms have more opportunities to learn, they mayalso be able to take advantage of less experi-enced rivals. In other words, if they were ableto discern when other firms would most likelyexpand, they could then be better positioned toavoid adding capacity during investment rushesand to initiate a capacity expansion in invest-ment troughs. While there is anecdotal evidenceto support this countercyclical investment behavior(Achi et al., 1996), there is no systematic empir-ical evidence. In the next section, we thereforeform hypotheses on why some firms may be bet-ter at learning to time capacity expansions thanothers.

HYPOTHESES ON LEARNING INCAPACITY EXPANSIONS

The view that managers learn and develop experi-ence in making important decisions is at the basisof the strategy literature. The incidence of learn-ing has certainly received an increasing amount ofattention in research on organizational design (e.g.,Duncan and Weiss, 1979; Senge, 1990), internalventures (e.g., Burgelman, 1988), and acquisitions(e.g., Hayward, 2002). Common in this researchis that decisions are assumed to arise from rou-tines (Cyert and March, 1963; Nelson and Winter,1982) where the logic of developing and followingroutines is one of appropriate rather than optimalbehavior (March, 1981). In this view, organiza-tions learn by ‘the encoding of inferences fromhistory into routines that guide behavior’ (Levittand March, 1988). Stated differently, organizationslearn when their knowledge of past experiencesupdates routines or develops new ones. Implicit inthis definition is that organization learning incor-porates not only a better understanding of makinggood decisions but also a change in behavior fromthat better understanding.

Firms are said to learn from several sources,the main ones being from the experience of doing(Yelle, 1979) and from the experience of the deci-sion outcomes (Cyert and March, 1963). How-ever, firms may also learn by observing the expe-riences of other firms (Huber, 1991; Miner andHaunschild, 1995). Hypotheses about these threepossible sources of learning are developed next.

Learning by doing

Organizations are said to learn by doing whenthe efficiency of a routine, such as planning ormanufacturing, improves (see, for example, Yelle,1979, for a review). In general, to become moreefficient, a routine requires repetition. However,as a routine is repeated, firms may also developcapabilities for effectiveness (Levitt and March,1988). For example, Cohen and Levinthal (1990)found that firms with larger stocks of researchand development experience developed a greaterabsorptive capacity or a capability to see the valueof external research and development information,to understand and deploy it commercially.

Learning by doing can be applied to capac-ity expansions. For example, in the petrochem-ical industry, 1–2 years after an expansion is



made, a postinvestment audit is typically con-ducted in order to improve the planning processand methods. Given the repetition of this postin-vestment audit exercise, general managers maylearn to develop more sophisticated demand fore-casts, and take more fully into account the con-tingent effects of rivals’ actions. As a result ofthese improvements, they may learn to time theirsubsequent expansion decisions more effectively.If this were the case, late entrants would be at adisadvantage since they would not have benefitedfrom as many previous expansion decisions. Thisleads to the following hypothesis:

Hypothesis 1: Firms with a larger stock of pre-vious expansion decisions will learn to time theirsubsequent expansion decisions more effectively.

Learning through poor outcomes

When actual outcomes fall short of intended out-comes there is a performance gap (Downs, 1966).If an outcome is deemed a failure or if there is alarge performance gap, firms will engage in localsearch for both the causes of and solutions to theproblem. This knowledge could subsequently beembodied in existing routines or lead to new rou-tines (Cyert and March, 1963). Learning, then,occurs by closing the gap between expectationsand outcomes and by incorporating the acquiredexperience into the routines.

When managers plan to expand capacity, theyoften estimate the effect of an addition on itsfuture capacity utilization. They may have antic-ipated some surplus capacity if their estimate ofexpected growth in market demand is less the sizeof their own capacity added. However, demandmay increase more than expected, absorbing notonly their expansion but also those of their rivals.In this case, even though a postinvestment auditmay be conducted, since the expansion would bedeemed a success, search for improving timingeffectiveness is likely to be reduced. Yet, demandmay not increase as expected and/or rivals’ expan-sions may not have been fully taken into con-sideration, potentially leading to far more excesscapacity than expected and, as a result, a muchlower performance than desired. In this case, bothauditors in the postinvestment analysis and, moreimportantly, managers in numerous board meet-ings would search for causes and solutions. Theknowledge gained from this search may then help

them time their subsequent expansions more effec-tively. This view is expressed with the follow-ing hypothesis:

Hypothesis 2: Firms with a larger stock of pooroutcomes from previous expansion decisions willlearn to time their subsequent capacity expan-sion decisions more effectively.

Learning through observation of rivals’ pooroutcomes

So far, we assumed there is no leak or decay inthe stock of experience. In other words, it simplyaccumulates over time through successive capac-ity expansion exercises. If this were the case,firms with a larger stock of capacity additionswould always be at an advantage over firms withfewer investments when studying the next expan-sion. However, this stock of proprietary experi-ence may leak due to spillovers; i.e., less expe-rienced firms may learn from more experiencedfirms (Levitt and March, 1988). It is indeed quiteplausible that learning may come from observ-ing the outcomes of other companies (Miner andHaunschild, 1995). In the R&D literature, forexample, Mansfield (1991) noted that the pri-vate returns from innovation were much lowerthan the social returns and argued that the differ-ence was due to intended or unintended spillovereffects.

Applied to capacity expansion decisions, firmsmay be able to collectively adjust the timing oftheir capacity decisions based on their observa-tion of their rivals’ poor outcomes. One way inwhich firms could better understand the conse-quences of bandwagon behavior is through indus-try associations and/or consulting firms. For exam-ple, in the petrochemical industry, presentationson this very issue are made in numerous indus-try conferences such as Chemsystems, Tecnon,and APME (Association of Plastics ManufacturersEurope) where the lessons behind poor industryperformance are often widely disseminated. Thistransfer of industry knowledge may therefore incitethe receiving firms to collectively develop newways to improve their timing and, consequently,coordination of capacity expansions. As a resultof these spillovers, rival firms may learn to bettertime their investments, resulting in lower industryexcess capacity and potentially higher collectiveperformance.



Hypothesis 3: Firms who observe the poor out-comes of their rivals’ expansion decisions willlearn to time their own capacity expansion deci-sions more effectively.

Forgetting

Learning tends to be myopic (Levinthal and March,1993). Based on the availability heuristic, actorsare apt to weigh recent events more heavily thanevents more distant in a long history of experiences(Tversky and Kahneman, 1986). Thus, if routinesare not repeated, memory or the stock of experi-ence may depreciate.

Capacity expansion decisions are infrequent.Any lessons from them may be forgotten by thenext capacity expansion exercise, especially if theexpansions are spaced apart over long periods oftime. For example, the manager responsible forthe decision may have been fired, left of his ownaccord, or moved to another position in the com-pany, taking his personal knowledge with him.Furthermore, while lessons may have been codifiedin previous postinvestment audits, the reports maybe gathering dust in the archives, leading to little ifany transfer of knowledge. As a result, firms maycontinue to hop on the investment bandwagon andpotentially suffer the consequences. The followinghypothesis reflects this limit to learning:

Hypothesis 4: The longer the time from theirlast capacity expansion decision, the more likelyfirms will time their subsequent capacity expan-sion less effectively.

In sum, two hypotheses have been proposed sug-gesting that firms with more proprietary experiencewill learn to take advantage of their less expe-rienced rivals by timing their capacity expansiondecisions more effectively. By better timing, firmswill be more cautious when their rivals are invest-ing and will more likely invest when their rivalsare not expanding. The stock of experience gainedby investing more frequently is subject to leaks,however. We raised two such leaks. First, firmsmay collectively learn from each other’s expan-sion experiences. If this were the case, investmentbandwagon behavior and excess capacity may beexpected to decline over time, all else equal.Second, since capacity expansion decisions areoften infrequent, the knowledge gained from theexpansion experience may be subject to significant

depreciation. From one expansion hboxdecision toanother, firms may simply forget the lessons of thepast and hence continue to follow others in subse-quent rounds. The following section describes howwe test these hypotheses.

RESEARCH DESIGN

Sample

After screening several industries, it was decidedto study the hypotheses with data on the approx-imate $400 billion petrochemical industry in theUnited States, Europe, and Japan during the period1975–95. While there are many other maturecommodity-like industries that behave in a simi-lar way (e.g., fertilizers, pulp and paper, aluminum,steel, hotels), the petrochemicals industry is partic-ularly interesting as it consists of numerous prod-ucts that can be studied while still maintaining ahomogeneous sample. The petrochemical industryspans many industrial classification codes includ-ing commodity chemicals, plastic resins, syntheticrubber, and fibers, which in turn comprise an esti-mated 14,000 different products (Chapman, 1991).Most of these products, however, can be traced toa relatively few number of base commodity chem-icals: the olefins and aromatics.

The following reasons motivated the choiceof this industry and time period. First, it is anindustry that is characterized by increasing scaleeconomies. In the 1940s, a minimum efficient scaleethylene plant could produce 40,000 tons. Thisnumber had increased by a factor of 8 by the mid-1970s and a factor of 15 by the mid-1990s. Theindustry also faced slow, cyclical demand growth,which made the absorption of new larger chunks ofcapacity increasingly complicated. This contrastswith the 1940s and 1950s, when it was grow-ing three to five times faster than GDP growth.Figure 1 shows industry capacity utilization andthe rate of return on capacity investment in theUnited States, Europe, and Japan for ethylene pro-duction. It illustrates the difficult return environ-ment firms faced in making investments in newcapacity. Since 1975, the industry experiencedthree cycles and was entering a fourth at the endof the 1975–95 period.



Figure 1. Comparison of European, U.S., and Japanese rates of return and capacity utilization in ethylene, 1976–96

Second, there are several consulting firms thatspecialize in the petrochemical industry and pub-lish, on a regular basis, market studies and multi-client reports. With data from the two major con-sulting firms, Tecnon and Chemsystems, a sampleof 4233 firm product-market year observationswas constructed for 72 firms out of a total of202 firms active in the European, American, andJapanese industries during 1975–95 (see Table 1).

The dataset tracks the following major commoditypetrochemicals: ethylene, vinyl chloride monomer(VCM), styrene, low-density polyethylene (LDPE),high-density polyethylene (HDPE), polypropylene(PP), polyvinyl chloride (PVC), and polystyrene(PS), which as a whole account for over 50percent of the industry’s total volume sales. Thesample consists, at the firm level, of product-market capacities, petrochemical division sales



Table 1. Countries, products included in the data sample

Productname

Country Number of firms Total capacity Number ofobservations

Number ofexpansions

1975 1995 1975 1995

Ethylene Europe 30 21 12,315 19,285 316 55HDPE Europe 17 13 1,722 4,251 229 59LDPE Europe 19 14 4,468 5,740 245 31PP Europe 10 15 959 5,782 223 66PS Europe 26 15 4,840 5,910 179 35PVC Europe 21 16 2,469 3,208 198 52Styrene Europe 15 8 2,835 4,360 197 21VCM Europe 21 12 5,065 6,160 183 15Ethylene U.S. 23 22 10,825 23,104 252 38HDPE U.S. 13 13 1,342 5,892 189 46LDPE U.S. 12 11 2,920 3,550 139 8PP U.S. 9 15 1,310 4,899 174 27PS U.S. 19 13 2,580 5,664 116 31PVC U.S. 16 14 1,897 2,837 162 31Styrene U.S. 9 10 2,461 5,365 116 9VCM U.S. 9 10 3,110 6,145 79 11Ethylene Japan 13 12 5,828 7,111 159 23HDPE Japan 12 7 830 1,203 124 20LDPE Japan 9 8 1,309 1,479 129 11PP Japan 8 13 777 2,509 169 33PS Japan 16 15 1,716 2,233 209 29PVC Japan 10 12 790 1,612 156 16Styrene Japan 12 8 1,421 2,690 117 19VCM Japan 15 12 1,890 2,588 173 7

Total 4233 693

and profitability, corporates sales, and corporateprofitability, and at the product-market level, pro-duction, consumption, imports, exports, prices, andproduct/market profitability. From Table 1, we seethat there has been a gentle consolidation overtime; thus, in 1995 there were a sufficient numberof firms in each product/market to make capacityexpansion timing a big issue. Given the slow con-solidation, firms typically remained in the industryfor a long period of time, the average durationbeing 18 years. Each firm produced on averagefive of the products in the sample, resulting in amean of 92 observations for each but ranging froma low of 10 to over 250.

Dependent variable

The study tries to understand whether and howfirms learn in timing their capacity expansions. Afirst variable to define is, thus, capacity expansion.Firms can expand capacity through three methods:adding a greenfield plant, de-bottlenecking anexisting plant, or adding an incremental unit toexisting plants. De-bottlenecking stems from im-

provements in a plant’s process flow. Since de-bottlenecking is carried out on an ongoing basisand does not represent a major, discrete investment,it was excluded from the total set of capacityexpansions. Limiting capacity expansion there-fore to discrete additions of capacity, we opera-tionalized the dependent variable, the incidence ofcapacity expansion, by a binary choice measure.For a given observation year, this was set equalto 1 for all observations where firm i expandseither by adding a new greenfield plant (represent-ing 25% of all major expansions) or by expandingone or more of its plant’s production capacity bymore than 10 percent1 or:

y =

1 if firm i expands by adding a greenfieldplant or by increasing anyone of itsexisting plants’ capacity > 10%

0 otherwise

1 Using the 10 percent cut-off, de-bottlenecking represented37 percent of all expansions. Using a 5 percent cut-off, de-bottlenecking represented 24 percent of all expansions. With the5 percent cut-off, there were no significant changes to the results.These estimations are available from the authors upon request.



Explanatory variables

The explanatory variables include an investmentbandwagon measure and the learning variables.As argued, we want to explore whether somefirms are better at timing capacity investments thantheir rivals. We test for one manifestation of this,namely that experienced firms try to avoid invest-ment bandwagons should they arise and investwhen their rivals are either unable or unwillingto do so. To explore this, we need measures of thedegree to which rivals invest simultaneously andindicators of experience.

Regarding the first variable, rivals’ expansion,two measures were calculated. The first, RIVAL1,was constructed as the percentage of rivals thatinvested simultaneously in a product-market dur-ing an observation year. To be able to compare ourresults with those of Gilbert and Lieberman (1987),a second indicator of rivals’ expansion was devel-oped, RIVAL2, which measures the percentageof total capacity that is added simultaneously byrivals. There are advantages and disadvantages toeither measure. The first allows us to test whetherfirms are more likely to invest when they see moreof their rivals’ investing, regardless of how muchcapacity they are adding. This measure has thedisadvantage that it does not focus on preemp-tion effects through the size of rivals’ investments.This is where the second variable has an advantagesince it directly measures the amount of capacityadded by rivals. Given the pros and cons of eithermeasure, both were used in the estimations. Fol-lowing the literature review, both are expected tohave a positive effect on a firm’s expansion prob-ability.

Regarding the second variable, experience, thehypothesis section suggested four effects. Thefirst hypothesis linked prior experience in capac-ity expansion to the ability to better time newinvestments. To analyze this relationship, a stockvariable was constructed, S DEC or the stock ofexpansion decisions, which accumulates the totalnumber of expansion decisions that a firm madeprior to the observation year. This cumulative mea-sure is consistent with the operationalizations inthe learning curve literature (e.g., Levy, 1965;Lieberman, 1984).

The second hypothesis related to the effect oflearning from poor capacity expansion outcomes.To operationalize this construct, we first calculated

the difference between the actual and expected out-come of each expansion decision. Since we did nothave expected financial outcomes for each capacityexpansion, we resorted to using expected capacityutilization. Expected capacity utilization was com-puted as the industry capacity utilization in theprevious year plus any decrease in capacity uti-lization that a naive firm would anticipate becauseof its own expansion, not taking into considera-tion those of its rivals. To get this measure, weneeded to estimate the firm’s expected growth ratein demand and to compare the size of the firm’sexpansion with its expected incremental increasein demand. We assumed the growth rate to bean extrapolation of the 4-year compound annualgrowth rate for the product-market. A poor out-come occurred when the actual industry capacityutilization in the industry during the observationyear was lower than the firm’s expected capac-ity utilization.2 Good outcomes (i.e., when theactual industry capacity utilization was greater thanthe expected industry capacity utilization) wereignored since it was assumed that search wouldbe reduced and therefore change in routines wouldnot occur. Poor outcomes occurred only when thefirm’s expected industry capacity utilization wasgreater than the actual industry capacity utiliza-tion. To obtain a measure of a firm’s track record(or stock) of poor capacity expansion outcomes,the firm’s poor outcomes were cumulated overtime. This stock, S PO, was called the stock ofpoor outcomes.

With the third hypothesis, we explore whether afirm’s capacity expansion decisions are related toits rivals’ aggregate track record of poor capacityexpansion outcomes. A measure was constructedthat aggregates the stock of poor outcomes foreach of the firm’s rivals in its product-market. Thisvariable, S RPO, or stock of rivals’ poor outcomes,includes the rivals’ poor outcomes both when thefirm expanded and when it did not expand.

2 Clearly, there exists a problem of excessive aggregation regard-ing the calculation of this stock variable. Furthermore, the mea-surement does not fully represent the planning behavior of petro-chemical companies. Many do indeed try to predict with varyingsuccess the cyclical swings rather than relying on extrapolationsof past demand growth rates. The Pearson correlation coefficientfor a firm’s poor outcomes cumulated over its product/marketsfor each observation year and its petrochemical division’s prof-itability is −0.15 and significant. This suggests that this measureis a useful proxy for a firm’s poor outcomes associated withcapacity expansion.



For the last hypothesis, which concerns the pos-sible declining effect of experience over time, ameasure of forgetting (FOR) was developed. Someempirical studies on the effect of R&D stocks oninnovation have estimated the decay either with aconstant depreciation rate (Henderson and Cock-burn, 1994) or by imposing a geometric lag struc-ture (Griliches, 1984). In these models, the annualflows into the stock are usually smooth, whichis not the case in this study, however. Capacityexpansion decisions are infrequent. We thereforeresorted to a simple measure that counts the num-ber of years from the previous capacity expansionin the product-market.

To test the hypotheses that experienced firmsmay improve the timing of their capacity addi-tions, we interacted the variables measuring rivalry(RIVAL1, RIVAL2) with the four learning vari-ables (S DEC, S PO, S RPO, FOR). We expectedthat S DEC and S PO would have a negativeinteraction effect with RIVAL on a firm’s expan-sion probability. Under the hypothesis that firmslearn from the mistakes of their rivals, we shouldalso find a negative interaction effect of S RPOwith RIVAL on a firm’s expansion probability.(Experienced firms would avoid investing whenmany rivals invest and when there is evidencethat many have experienced poor outcomes). Iffirms forget their experiences from previous expan-sions, then the FOR interaction term is expected tobe positive.

One issue concerning the stock variables re-quires comment: left censoring. Since the dataonly start in 1975, any experience that has beengained prior to this year is effectively ignored. Twopractical approaches were employed to deal withthis issue. First, we allowed the stocks to buildup for 10 years before proceeding with the esti-mations. Hence, instead of using 1975–95 data,the estimations employed the 1985–95 data. Sec-ondly, we employed the firm’s original relativemarket share (ORMS) in 1975 as an indicator ofits experience prior to 1975. However, the ori-gins of these experiences are unknown as theycould come from previous expansion decisionsor the poor outcomes of those decisions. Theresults show only those with original relative mar-ket share.3

3 The results using a 5- or 10-year stock build-up are similar andare available upon request from the authors.

Control variables

Four control variables—historical demand growth,lagged capacity utilization, supply lumpiness, andrelative market share—were included and prod-uct, firm, country, and time dummies were tested.First, since demand growth typically prompts firmsto expand capacity, it has been included in the esti-mations as a control variable. Furthermore, it hasbeen shown in previous studies to have a positiveeffect on the probability of expansion (Lieberman,1987; Gilbert and Lieberman, 1987). While firm-level growth in production may be more indicativeof the firms’ demand, such data are not available.Instead, a 4-year compound annual growth ratefor the product-market was employed. The growthmeasure is called GR.

Second, not only does higher demand growthtrigger capacity expansion decisions but also tightcapacity utilization rates. Capacity utilization hasbeen shown in previous studies to have a posi-tive and significant effect on the probability ofexpansion (Lieberman, 1987). Ideally, a capacityutilization measure should be calculated at the firmlevel. However, as with the other variables, suchdetailed data are not available. Thus, a product-market level measure of capacity utilization wasutilized, called CU. It was calculated as an averageof two measures of capacity utilization, one lagged1 year, the other lagged 2 years. This was done totake into consideration both incremental and green-field expansions, which have different lead timesbefore they become effective.

Third, while high demand growth and tightcapacity utilization rates may trigger expansion,decisions requiring the construction of large lumpsof capacity will be less forthcoming. Lumpinessin supply has been shown in previous studies tohave a strong negative effect on the probability ofexpansion (Lieberman, 1987). Lumpiness, in the-ory, should be measured as the fraction of themarket that a new minimum efficient scale plantwould cover. Instead, we resorted to the typicalproxy: average plant size divided by total product-market production. The measure of lumpiness iscalled LUMP.

Fourth, since larger firms (in market share) aremore likely to have more plants than smaller firms,they are also more likely to invest more. It hasbeen shown in previous studies that firms withhigher market shares are more likely to expand,all else equal (Lieberman, 1987). Relative market



share, RMS, was constructed as the firm’s shareof the total product-market capacity divided bythe average firm share of the total product-marketcapacity. For data reasons, we had to use capacitydata rather than output data.

Fifth, to control for any idiosyncratic fixedeffects, product, firm, country, and time dummieswere included in the estimations. They were keptin the estimations if they were jointly significant.

Estimation method

The relationship between a firm’s expansion prob-ability and its explanatory variables was analyzedthrough probit regression analysis. The generalform of the estimated equation is:

Prob(y = 1|X, ε) = �(α + β1GR + β2CU

+ β3LUMP + β4RMS + β5RIVAL

+4∑

j=1

λjLEARNj*RIVAL + ε)

where LEARNj stands for the four learning vari-ables, S DEC, S PO, S RPO, FOR, and ORMS tocontrol for previous experience. The equation wasfirst estimated with the intercept term α restrictedto be the same across firms, countries, products,and years. It was then tested for fixed effects, i.e.,a dummy variable for each of the possible inter-cept terms measuring firms, countries, products,and years.

EMPIRICAL RESULTS

Descriptive statistics

Table 2 shows summary statistics for the variablesthat are used in the estimations. All of the keyvariables show substantial variation. On average,the capacity added by firms amounted to approxi-mately 83 thousand tons per year, with the maxi-mum being 910 thousand tons per year, the size ofa new ethylene cracker. The average percentage ofrivals expanding in one year was approximately 16percent but reached a maximum of 71 percent (or11 out of 14 firms in that year). The 4-year demandgrowth rate was on average slightly above 2.9 per-cent per year but had a low of −11 percent anda high of 18 percent, reflecting demand volatil-ity in the industry. The demand cycles are alsoreflected in the capacity utilization rates, whichrange from a low of 47 percent to a high of 113percent.4

A number of observations can also be made withrespect to the stock variables. Firms in the samplemade approximately three capacity expansion deci-sions within each product-market, with the max-imum being 12 in 20 years.5 The average stockof poor outcomes (which accumulates the abso-lute difference between the firm’s actual capac-ity utilization during the observation year and the

4 Capacity utilization rates sometimes exceeded 100 percentwhen plants in the industry were run without planned stoppagesfor maintenance.5 Note that the stock of expansion decisions shows an average of1.28. This average includes when the stock was at zero (whichis 38% of the sample).

Table 2. Descriptive statistics, selected variables

Variable Number of observations = 4233

Mean S.D. Minimum Maximum

Decision to Add Capacity 0.16 0.37 0 1.00Total Capacity Added 96.46 123.36 10.00 910.00Historical Demand Growth 2.98 4.17 −11.00 17.79Capacity Utilization 82.04 11.29 47.59 113.12Supply Lumpiness 5.99 2.88 2.33 19.54Percentage of Rivals’ Expanding 16.00 14.19 0 71.42Percentage of Capacity Expanded by Rivals 4.54 5.00 0 30.50Stock of Previous Decisions 1.28 1.63 0 12.00Stock of Poor Outcomes 0.03 0.05 0 0.35Stock of Rivals’ Poor Outcomes 0.17 0.23 0 1.32Time Between Expansion Decisions 5.44 5.25 0 20.00Market Share 7.67 5.41 0 35.37Relative Market Share 1.17 0.78 0 6.71



firm’s expected capacity utilization for that year)is 2.6 percent, reflecting the relative infrequencyof capacity expansion decisions. Not surprisingly,however, the magnitude of the stock of poor out-comes for a firm’s competitors is large as this stockmeasure represents an accumulation of all rivals’poor outcomes.

Probit analysis

We postulated in the development of the hypothe-ses that more experienced firms would be lesslikely to expand when they see an increase in per-centage of their rivals expanding. Conversely, wesuggested that more experienced firms would bemore likely to expand when they see a decrease inthe percentage of rivals expanding. Thus, the coef-ficients of the RIVAL∗S DEC and RIVAL∗S POinteraction variables should be negative. We alsoargued that the coefficient of the RIVAL∗S RPOinteraction variable should be negative if moreexperienced firms learn from the poor investmentoutcomes of their rivals. Finally, the forgettinghypothesis should lead to a positive estimate of theRIVAL∗FOR interaction variable if firms indeedforget the experiences from the past.

Regarding the main effect variables and thecontrol variables, previous research and hypothe-ses lead to the following predictions for the esti-mated effect on expansion probability: a positivesign for both rivals’ expansion variables, RIVAL1and RIVAL2, for relative market share, RMS, fordemand growth, GR, and for capacity utilization,CU. We anticipate a negative sign for lumpiness,LUMP. Regarding the firm’s original market share(ORMS), which is a proxy for its expansion expe-rience prior to 1975, we expect a negative interac-tion effect with RIVAL1 and RIVAL2.

The probit regression results can be seen inTables 3 and 4. To determine the joint signifi-cance of all possible fixed effects (i.e., company,products, countries, and year), a likelihood ratiotest was performed on the restricted or pooledvs. unrestricted probit regressions (e.g., Judgeet al., 1982). Both the country dummies (χ 2(2) =1.79) and individual firm dummies (χ 2(67) =74.63) were insignificant. However, the χ 2 statis-tic was significant for the product (χ 2(7) = 20.12)and year dummies (χ 2(18) = 45.23).6 The productdummies were included in the estimations and are

6 The degrees of freedom were 18 rather than 20 because 2 yearswere dropped due to perfect collinearity.

Table 3. Probit analysis of expansion probability(yi,t = 1 if firm i is expanded by more than 10% in the observation year t)

Variable(mean values)

Model 1a Model 1b Model 2a Model 2b

Constant −0.48∗∗∗ −0.45∗∗∗ −0.50∗∗∗ −0.46∗∗∗

(1.00) (0.05) (0.05) (0.05) (0.05)GR 0.42∗∗∗ 0.44∗∗∗ 0.39∗∗ 0.44∗∗∗

(0.03) (0.16) (0.16) (0.15) (0.16)CU 0.13∗∗ 0.16∗∗∗ 0.12∗∗ 0.16∗∗∗

(0.82) (0.05) (0.05) (0.05) (0.05)LUMP −0.67∗∗ −1.23∗∗∗ −0.66∗∗ −1.21∗∗∗

(0.06) (0.28) (0.28) (0.29) (0.29)RIVAL 0.42∗∗∗ 0.60∗∗∗ 0.59∗∗∗ 0.93∗∗∗

(a = 0.16; b = 0.04) (0.04) (0.11) (0.06) (0.16)RMS 0.07∗∗∗ 0.07∗∗∗ 0.09∗∗∗ 0.08∗∗∗

(1.17) (0.01) (0.01) (0.01) (0.01)

InteractionsORMS −0.14∗∗∗ −0.30∗∗∗

(0.03) (0.11)

Number of observations 4,233 4,233 4,233 4,233Log likelihood −1,718.67 −1,753.72 −1,708.14 −1,749.80χ 2 for model 336.61∗∗∗ 266.50∗∗∗ 357.67∗∗∗ 274.34∗∗∗

χ 2 for products sign. sign. sign. sign.

Significant at the *0.10, **0.05, and ***0.01 level


404 J. Henderson and K. CoolTa

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reported at the bottom of each table. While theyear dummies were significant, the control vari-ables capturing cyclicality, i.e., GR and CU, turnedinsignificant. Since there was no loss in signif-icance in the variables of interest and that thecontrol variables adequately captured the circum-stances unique to particular points in time, the timedummies were not kept in the estimations.

Since there was a high degree of collinear-ity among the stock variables, each was enteredinto the estimations consecutively before theywere combined.7 The Appendix gives the Pear-son correlation coefficients. Tables 3 and 4 thusinclude several models. First, Table 3 has estimatesfor the control variables only (Model 1) and thecontrol variables together with the firm’s previousexpansion history, ORMS, interacted with rivals’expansion (Model 2). Table 4 has the estimates forthe control variables and the interaction variablesof rivals’ expansions with the learning indicators.These interaction effects are first entered separatelyin Models 3 to 5. Model 6 enters all interactioneffects with the control variables. Finally, also thepossible forgetting effects are estimated in Model7. The first column of Tables 3 and 4 lists the inde-pendent variables and their values at the samplemean. The following columns report the param-eter estimates and their t-statistics. The param-eter estimates are the partial derivatives of theprobability of expansion with respect to the inde-pendent variable, calculated at the sample mean.For each of the seven models, two columns are

7 We also checked for the presence of serial correlation on thefull model by examining the unstructured within-panel error cor-relation matrix for any patterns in the data. We could not find anydiscernible AR1 pattern, which would suggest autocorrelation isnot a problem.

We also tested for heteroskedasticity. We proceeded byassuming that the coefficients were biased and thus estimatedthe models using the sandwich or robust estimator of variance(i.e., when the source of the bias in unknown). Four estima-tions were conducted on the full model: the original model;robust standard errors across the whole sample; robust standarderrors within each country, company, product combination; andfinally, the most plausible, robust standard errors within eachcompany cluster of observations. Indeed, it is quite plausiblegiven the nature of petrochemicals (i.e., that they are manufac-tured in complexes) that the observations for each company arenot independent. Expansion may be due to balancing capacitieswithin the petrochemical complex. For example, increases inHDPE and LDPE capacity may be driven by increases in ethy-lene capacity. Increases in PVC capacity may be driven by VCMexpansion and PS expansion may be driven by styrene capac-ity. The changes, however, to the standard errors were minor,leaving the significance of the coefficients intact. Since no majorchanges were found, we proceeded with using the conventionalestimation techniques as shown in the paper.

reported reflecting the two measures for the vari-able, rivals’ expansion. The first ‘a’ column givesthe results for the interactions with rivals’ expan-sion defined in terms of the number of competi-tors who added capacity (RIVAL1). The second‘b’ column shows the figures for rivals’ expansiondefined in terms of capacity added by the firm’scompetitors (RIVAL2).

Focusing first on Models 1a and 1b, we notethat the control variables are significant and in theexpected direction. The coefficients for GR (0.42),CU (0.13), LUMP (−0.67), and RMS (0.07) indi-cate, as reported in previous studies, that largerfirms are more likely to expand than smaller firms;growth and capacity utilization are primary thresh-olds for expansion, and higher lumpiness lowersthe probability of expanding. What may be strikingis the size and significance of the rivals’ expan-sion term for both measures (RIVAL1 = 0.42,RIVAL2 = 0.60). To illustrate their effect, con-sider the following example from Model 1a: a1 percent increase in the percentage of rivals’expanding (i.e., from 0.16 to 0.17) would lead toa 0.49 percent increase above the average likeli-hood (16%) of a firm expanding. In other words,the estimates indeed suggest there are very strongherd effects in expanding capacity.

Second, in Models 2a and 2b, the firm’soriginal market share is added as an interactionterm with rivals’ expansion.8 The coefficientsfor RIVAL1∗ORMS (−0.14) and RIVAL2∗ORMS

8 We also tested for the alternative procedure to correct for leftcensoring: the build-up of 5 and 10 years’ worth of experience.There were no significant changes to the results presented inTables 3 and 4. Furthermore, we tested whether the results weresensitive to the time periods we used. We proceeded with foursensitivities to cover all possible cases:

Case 1. Starting with Low and Ending with High IndustryProfitability: Original Set: 1975–95 (the stock of decisions,poor outcomes, and rivals’ poor outcomes built up from1975 onwards).Case 2. Starting with Low and Ending with Low IndustryProfitability: 1975–93 (the stock of decisions, poor outcomes,and rivals’ poor outcomes built up from 1975 onwards).Case 3. Starting with High and Ending with High IndustryProfitability: 1979–95 (the stock of decisions, poor outcomes,and rivals’ poor outcomes built up from 1979 onwards. We startwith 1979, a higher profitability point for the industry than 1975,to determine if that has an effect on the results.)Case 4. Starting with High and Ending with Low IndustryProfitability: 1979–93 (the stock of decisions, poor outcomesand rivals’ poor outcomes built up from 1979 onwards).

The differences in the coefficient values are negligible acrossthe four cases. The results are available from the authors uponrequest.



(−0.30) are negative and significant, as expected.Thus, firms with greater experience in capacityexpansion (as measured by their historicalrelative market share) are less likely thantheir less experienced rivals to hop on anexpansion bandwagon.

Third, in Models 3a and 3b in Table 4, the stockof previous expansion decisions is added. Thecoefficients for the interactions RIVAL1∗S DEC(−0.003) and RIVAL2∗S DEC (−0.01) are nega-tive but insignificantly different from zero. There-fore, it seems that a greater history of expandingdoes not necessarily prevent firms from jumpingon an investment bandwagon. While postinvest-ment audits may be repeated over time to improvethe planning process and outcomes, a capabilityfor timing expansions through more sophisticatedforecasting methods or through a greater awarenessof rivals’ decisions does not seem to emerge.

Fourth, while the experience of expanding isnot necessarily a good teacher for improving thetiming of future expansions, an accumulation ofthe outcomes from poor decisions is. The coeffi-cients for the stock of poor outcome experienceinteracted with rivals’ expansion RIVAL1∗S PO(−1.03) and RIVAL2∗S PO (−2.97) in Models4a and 4b are significant and in the hypothesizeddirection. In other words, firms are less likely tohop on an investment bandwagon when they haveexperienced particularly poor outcomes in previ-ous rounds. Thus, firms burned in the past willless likely be burned again.

Fifth, Models 5a and 5b test for theeffect of industry learning. The coefficient forRIVAL1∗S RPO (−0.08) and RIVAL2∗S RPO(−0.18) are in the hypothesized direction butinsignificant. This finding provides little supportfor industry-level learning. Thus, unlike in theR&D field where there are significant spilloversin learning, firms seem to learn little from thecapacity expansion experiences of others in theirproduct/market.

The full learning model is reported in Mod-els 6a and 6b. When the learning variables areincluded together, the coefficients for the stockof previous decisions RIVAL1∗S DEC (0.02) andRIVAL2∗S DEC (0.06) turn out to be positive andsignificantly different from zero. While collinear-ity may play a role, two other explanations maybe given for this positive result. The first expla-nation may stem from the actual decision-making

process when planning for expansion. If, for exam-ple, the decision-making process were deemed asuccess, the decision-makers may be encouragedto repeat the exercise, irrespective of the outcome(March, Sproull, and Tamuz, 1991). In his study ofFinnish firms investing overseas, Bjorkman (1990)found that prior to receiving information on theresults of their first investments overseas, simplyas a result of their experience in making the firstdecisions, they increased their propensity to makemore investments. Thus, these managers were seento invest further because they knew how to do it.That could contribute to the result found here. Sec-ond, even if an expansion were deemed a successin that no poor outcome was created, the source ofthis success may not be known or the decision-makers may have attributed the success to thewrong reasons. The firm may have been lucky but,nonetheless, continued to invest because it thoughtit had made a timely investment. We cannot beconclusive on any interpretation, however, as longas we have no further clinical company data.

At the same time, the coefficients for the stockof poor outcomes interactions RIVAL1∗S PO(−1.35) and RIVAL2∗S PO (−4.21) remainnegative and significant. However, the coeffi-cients for the interaction variables with rivals’poor outcomes, RIVAL1∗S RPO (−0.01) andRIVAL2∗S RPO (−0.04), are insignificantly dif-ferent than zero. Thus, contrary to findings in theR&D literature, firms seem to learn very littlefrom seeing others make mistakes. That is, learningseems to come primarily from the poor outcomesassociated with a firm’s own decisions rather thanfrom either the repetition of those decisions orfrom observing rivals’ poor outcomes. Unfortu-nately, if the results of Models 4 and 6 are rep-resentative, aggregating across all types of expan-sions, firms seem to learn at a very slow pace.Assuming the firm was an average-sized player in1975 (i.e., relative market share = 1.0), a stock ofexperience close to the maximum reported in thesample would be required to offset the positivedirect effect of rivals’ expansion on the proba-bility of a firm adding capacity. The incentive tohop on the investment bandwagon, in spite of theexperience of poor outcomes in the past, is stillimmensely strong.

Finally, the variable forgetting is included inthe estimations and can be found in Models 7aand 7b. The coefficients for forgetting interactedwith rivals’ expansion RIVAL1∗S FOR (0.01) and



RIVAL2∗S FOR (0.04) are significant and in thehypothesized direction. In Model 7a, 10 yearsbetween capacity expansion decisions would in-crease, ceteris paribus, the effect of rivals’ expan-sion on a firm’s expansion probability from 0.5to 0.6. Thus, the already slow learning is furtherretarded by the forgetting of past decisions.

ALTERNATIVE EXPLANATORY ANDDEPENDENT VARIABLES

Alternative explanatory variables

It is possible that firm and industry asymmetriesother than learning and experience may be drivingdifferences in investment behavior. As a result, weexamined a number of firm-level variables includ-ing firm size, vertical integration, diversification,and a number of industry-level variables includingindustry concentration, and industry maturation, to

test the robustness of the learning variables. Whilesome of these additional explanatory variablesproved significant in the full model, the signifi-cance of learning variables remained unchanged.Table 5 reports the results; each column showingthe coefficient values represents the results for eachalternative explanation.9

Experience variables as main effects

It is quite plausible that the variables represent-ing expansion experience when interacted withRIVAL1 and RIVAL2 alone may be proxying fortheir main effects. We therefore proceeded to testfor this possibility. However, from the secondcolumn in Table 5, we find that while the maineffect variables are not significant, the interactions

9 Only those results for RIVAL1 or percentage of rivals expand-ing were shown in Table 6. Results for RIVAL2 (percentage ofcapacity expanded by rivals) are available upon request.

Table 5. Alternative explanations

Originalmodel

Maineffects

Firmsize

Verticalintegration

Diversification Industrystructure

Industrymaturation

Constant −0.50∗∗∗ −0.47∗∗∗ −0.52∗∗∗ −0.47∗∗∗ −0.48∗∗∗ −0.51∗∗∗ −0.50∗∗∗

GR 0.39∗∗ 0.37∗∗ 0.39∗∗ 0.38∗∗ 0.38∗∗ 0.39∗∗ 0.41∗∗

CU 0.12∗∗ 0.13∗∗ 0.10∗∗ 0.11∗∗ 0.11∗∗ 0.09∗ 0.11∗∗

LUMP −0.68∗∗ −0.75∗∗ −0.62∗∗ −0.70∗∗ −0.68∗∗ −0.81∗∗ −0.67∗∗

RIVAL 0.60∗∗∗ 0.41∗∗∗ 0.69∗∗∗ 0.37∗∗∗ 0.46∗∗∗ 0.57∗∗∗ 0.52∗∗∗

RMS 0.09∗∗∗ 0.09∗∗∗ 0.12∗∗∗ 0.09∗∗∗ 0.10∗∗∗ 0.09∗∗∗ 0.09∗∗∗

S−DEC −0.002S−PO 0.05S−RPO −0.05FOR −0.003VI −0.02COMM −0.02HERF 0.05

InteractionsORMS −0.15∗∗∗ −0.14∗∗∗ −0.15∗∗∗ −0.15∗∗∗ −0.15∗∗∗ −0.15∗∗∗

RMS −0.28∗∗∗

VI 0.18∗

COMM 0.06HERF −0.01GR −0.15S−DEC 0.03∗∗ 0.04∗ 0.04∗∗ 0.03∗∗ 0.03∗∗ 0.03∗∗ 0.03∗∗

S−PO −1.35∗∗ −1.52∗∗ −1.16∗∗ −1.16∗∗ −1.37∗∗∗ −1.37∗∗∗ −1.35∗∗∗

S−RPO −0.04 0.12 −0.09 −0.07 −0.03 −0.04 −0.04FOR 0.01∗∗ 0.02∗∗ 0.01∗ 0.01∗∗ 0.01∗∗ 0.01∗∗ 0.01∗∗

Number of obs. 4,233 4,233 4,233 4,233 4,233 4,233 4,233Log likelihood −1,701.11 −1,699.45 −1649.58 −1,689.88 −1,700.09 −1,700.49 −1,701.10χ 2 for model 371.72∗∗∗ 375.06∗∗∗ 384.78∗∗∗ 376.19∗∗∗ 373.77∗∗∗ 372.96∗∗∗ 371.75∗∗∗

χ 2 for products sign. sign. sign. sign. sign. sign. sign.




Table 6. Tobit analysis, rate of expansion(yi,t = amount added/total firm capacity in the productmarket if firm i is expanded by more than 10% in theobservation year t)

Using both measures forRIVAL

Constant 133.11∗∗∗ −119.54∗∗∗

(14.41) (14.03)GR 123.152∗∗∗ 114.09∗∗∗

(44.64) (44.46)LUMP −193.92∗∗ −352.86∗∗∗

(85.52) (83.14)CU 23.95∗ 34.10∗∗

(14.60) (14.44)RIVAL 161.35∗∗∗ 343.91∗∗∗

(19.45) (55.12)RMS 24.82∗∗∗ 21.89∗∗∗

(2.54) (2.33)

InteractionsORMS −51.35∗∗∗ −158.92∗∗∗

(8.89) (30.72)S−DEC 2.70 9.90

(4.39) (15.52)S−PO −231.09∗ −874.48∗∗

(135.56) (442.21)S−RPO 4.70 −6.56

(27.87) (92.78)FOR 4.01∗∗∗ 12.15∗∗

(1.47) (5.00)

Number ofobservations

4,188 4,188

Log likelihood −5351.28 −5381.64χ 2 for model 336.75 276.03χ 2 for products sign. sign.


remain significant. We can therefore conclude thatthe experience variables are indeed moderatingthe relationship between rivals’ expansion and theprobability of the focal firm expanding.

Firm size

It is plausible that the original relative market shareand stock of experience variables in the probitanalyses are capturing not just experience but alsothe tendency for larger firms to not be as trig-ger happy as smaller firms. For larger firms, themarginal revenues from expansion would indeedbe lower than for smaller firms since they wouldbe bearing a larger portion of the excess capacity.Given these two possible explanations, we sub-stituted original relative market share for relativemarket share to determine whether the coefficients

of the stock variables remained the same. From thethird column in Table 5 we see that both the mar-ket share variable representing firm size and thesame learning variables, S DEC, S PO and FORfrom the original model, are significant.

Vertical integration

It can be argued that vertically integrated firmswould be less affected by rivals’ investmentsbecause they are insulated from these supplyuncertainties. Only when they would be ‘long’in their upstream activity would they more likelyinvest in their downstream products regardless ofwhen their rivals are expanding. Thus, we couldplausibly expect vertical integration to have a neg-ative interaction effect. We constructed a dummyvariable to represent those firms backward inte-grated into the preceding production activity foreach of the products in the sample. Thus, if thefirm were active in PVC and VCM production, itwould be counted as being vertically integrated.As one can see from Table 5, column 4, whilethe vertical integration interaction term does havea weakly significant positive effect on the invest-ment behavior of firms, the learning variables stillremain significant.

Diversification

It is plausible that larger, more diversified firmsmay have more stable cash flows than smallerpure-play firms and therefore have better accessto the capital markets during troughs in the cycle.These corporations would therefore be better ableto invest countercyclically or against the band-wagon. To test this relationship, we included ameasure, relative commitment (COMM), as thepercentage of revenues of the firm in the petro-chemical industry (or its divisional revenues) asa percentage of its total revenues relative to itspeers lagged by 1 year. As one can observe fromTable 5, column 5, commitment does not have asignificant effect on bandwagon behavior; yet, thelearning variables other than S RPO remain sig-nificant as in the original model.

Industry concentration

We also tested for the effect of industryconcentration on investment behavior. Differencesin investment behavior could indeed be attributedto a consolidating industry (easier coordination



of capacity expansion decisions) rather than toexperience. Thus, while dummy variables for eachproduct were already included in the estimations,we also added a Herfindahl index of producerconcentration to determine its effect. From thesixth column in Table 5, we see that Herfindahlindex variable was not significant, while thelearning variables remained unchanged.10

10 Interestingly, the coefficients for HERF as a main effect andinteraction term under the second model (i.e., using percentage ofnew capacity) were highly significant. In other words, a firm in amore concentrated industry would be less likely to expand shouldthe capacity expanded by its rivals (rather than the numberof rivals) be particularly large. Therefore, we can concludethat when rivals make incremental plant expansions, the focalfirm is not dissuaded from investing simultaneously. The effectof simultaneous expansions on excess capacity in concentratedindustries is smaller because there are fewer players. However,when rivals make significant-sized expansions, then the focalfirm will more likely be dissuaded from investing at the same

Industry maturation

It is also plausible that a decrease in bandwagonbehavior is simply reflecting the maturing of theindustry over time. In order to test for this effect,the growth variable was also included as an inter-action term with rivals’ expansion. As we can seein Table 7, column 7, the results show no signif-icant differences in bandwagon behavior due toindustry maturation. Furthermore, the coefficientsof the learning variables remained unchanged fromthe original model.

Alternative dependent variables

Does learning affect the rate or type rather thanthe incidence of expansion? In other words, do

time. This suggests that concentrated product-markets result inbetter coordination of capacity expansions.

Table 7. Multinomial logit analysis, greenfield vs. incremental expansions

Variable(mean values)

RIVAL1 RIVAL2

Greenfield Incremental Greenfield Incremental

Constant −4.75∗∗∗ −4.32∗∗∗ −3.94∗∗∗ −3.94∗∗∗

(1.00) (0.80) (0.44) (0.79) (0.44)GR −3.36 4.48∗∗∗ −2.09 4.36∗∗∗

(0.03) (2.60) (1.43) (2.66) (1.42)CU 0.91 1.14∗∗ 0.91 1.46∗∗∗

(0.82) (0.85) (0.46) (0.84) (0.46)LUMP −36.54∗∗∗ −1.11 −0.66∗∗ −5.23∗∗

(0.06) (0.07) (2.72) (0.29) (2.62)RIVAL 5.39∗∗∗ 3.86∗∗∗ 5.88∗ 5.63∗∗∗

(a = 0.16; b =0.04)

(1.00) (0.59) (3.16) (1.64)

RMS 0.91∗∗∗ 0.71∗∗∗ 0.69∗∗∗ 0.61∗∗∗

(1.17) (0.12) (0.08) (0.11) (0.07)

InteractionsORMS −2.21∗∗∗ −0.98∗∗∗ −6.01∗∗∗ −1.83∗∗∗

(0.48) (0.26) (1.88) (0.84)S−DEC 0.40∗∗ 0.21∗ 1.33∗ 0.62∗

(0.20) (0.13) (0.72) (0.37)S−PO −26.48∗∗∗ −6.70∗ −69.23∗∗∗ −21.66∗

(7.44) (4.05) (26.87) (12.72)S−RPO −0.19 −0.57 2.45 −1.25

(1.22) (0.89) (4.14) (2.90)FOR −0.06 0.12∗∗∗ −0.09 0.34∗∗

(0.09) (0.04) (0.38) (0.14)

Number ofobservations

4,233 4,233

Log likelihood −2,067.52 −2113.17χ 2 for model 489.85∗∗∗ 398.54∗∗∗

χ 2 for products sign. sign.




firms learn to temper the size of their capacityadditions through experience of previous capac-ity decisions? Do firms rely on experience of pastcapacity decisions more for greenfield than incre-mental investments? The answer to these questionsrequires different dependent variables and estima-tion methodologies.

Rate of expansion and tobit regression analysis

The relationship between a firm’s expansion rateand the same explanatory variables was analyzedthrough a tobit regression analysis (since all of theobservations were greater than or equal to zero).A firm’s rate of expansion was calculated as theamounted added by the firm divided by its totalcapacity in the previous year. Refer to Table 6 forthe results. As one can observe from these tests, themain learning variables, which were significant inthe probit analyses, are significant in these modelsas well.

Type of expansion and multinomial logit analysis

The relationship between the type of expansion,incremental or greenfield, and the same explana-tory variables was studied through a multinomiallogit analysis to determine the likelihood of one orthe other with respect to not expanding at all. Wethus divided the sample into greenfield (i.e., anyevent when a greenfield investment occurred11) andincremental investments. Of all capacity expan-sions (at the plant level) about 25 percent weregreenfield, which represent about 4 percent of thetotal number of observations. Refer to Table 7for the results. Several observations can be made.First, greenfield, unlike incremental investments,are made regardless of the growth and capacity uti-lization; both GR (−3.36) and CU (0.91) are notsignificant in the second column. Secondly, despitethe differences in the traditional drivers of capacityexpansion, there are still strong bandwagon effectsin both models. Thirdly, while experience plays

11 The sample is not at the plant level but at the firm levelof analysis. Therefore, there may be several expansions duringthe same year, which are counted as an expansion at the firmlevel in the probit analysis. Three capacity expansion cases, infact, can be identified: incremental only, greenfield only, andincremental and greenfield at the same time. For those caseswhere an incremental and greenfield occurred, we categorized itas greenfield.

a role in both models through the stock of deci-sions and the stock of poor outcomes, the magni-tude is different. In particular, previous experienceof poor outcomes (S PO = −26.48) plays a verystrong role in dissuading companies from invest-ing in greenfield plants compared to incrementalinvestments (S PO = −6.70). For example, for amedium-sized player in 1975 (i.e., ORMS = 1),a stock of poor outcomes of only 12 percentcould reverse the positive effect of RIVAL1 onthe likelihood of greenfield expansion.12 In con-trast, for incremental expansion, the stock of pooroutcomes would have to be greater than the max-imum reported in the sample to offset the positivebandwagon effect. Finally, the memory of pre-vious investment decisions for greenfield invest-ments does not appear to decay through forgetting(FOR = −0.06) as it does for incremental invest-ments (FOR = 0.12). While we can only speculate,companies must resort more heavily on their auditsof previous poor expansion decisions to ensurethat their greenfield investments are timed better.Clearly more in-depth clinical research on thesedecisions is warranted to substantiate these claims.

DISCUSSION AND CONCLUSIONS

This paper examined whether experience fromprevious capacity expansion decisions is a goodteacher using a sample on the global petrochemicalindustry during the years 1975–95. It was assumedthat firms that accumulate stocks of experiencerelated to capacity expansion would better timefuture capacity expansions. Experience, we argued,could come from either previous expansion deci-sions or from poor outcomes of those decisions.Thus, experienced firms would try to avoid invest-ment bandwagons should they arise and initiatecapacity expansion decisions when their rivals areeither unable or unwilling to invest. Following theR&D literature, we suggested that learning maynot be fully proprietary and that firms may learnthrough the observation of each other’s outcomes.Thus, it was hypothesized that a firm’s observationof its rivals’ poor outcomes should have a negativeeffect on it hopping on an investment bandwagon.

12 Note that for the second model the experience of a mid-sizedplayer (ORMS = 1) in 1975 is already enough to offset thepositive effect of RIVAL2 on the likelihood of expansion.



Whether these spillover effects outweighed pro-prietary learning was left as an empirical question.Stocks of proprietary experience are also subject todecay over time such that actors may forget pastmoves and repeat the same mistakes. Thus, it wasalso hypothesized that as the time between capacityexpansion decisions increases, firms will be morelikely to invest when their rivals are expanding.

The results provide varying support for thehypotheses. First, firms with poor expansion out-comes in previous rounds are less likely to hopon an investment bandwagon in future rounds. Inothers words, firms that were burned in the pastare less likely to be burned again. However, theincentive to jump on an investment bandwagonespecially for incremental expansions is still verystrong, suggesting that learning, on average, isvery slow. Yet, firms tend to rely more on pastexperiences for their larger greenfield expansions.Second, while firms learn to be more cautiousfrom their previous history of poor outcomes, theycan easily slip again. For example, firms, espe-cially for incremental investments, seem to forgetpast moves. That is, firms will be more likely tohop on an investment bandwagon as the numberof years between expansion decisions increases.Both of these findings suggest that the type ofexpansion, greenfield or incremental, has an impacton whether the lessons of the past are used ornot. Third, contrary to the R&D literature wherefirms have been observed to learn from each other,petrochemical firms appear to learn little fromeach others’ poor experiences in capacity expan-sion despite the existence of numerous seminarson the topic. Thus, most of the learning in bet-ter timing capacity expansion decisions seems tobe occurring from within the firm’s own experi-ences.

The results of this study can be linked to theresource-based view in strategy research (see, forexample, Rumelt et al., 1994, for a review). Acapability of timing capacity expansions does seemto emerge through the accumulation of previouspoor expansion outcomes but seems to be useddifferently for greenfield vs. incremental invest-ments. This finding is important since, in manycommodity industries, the difference in returnsin better timing new investments can indeed begreater than the differences in the returns of thelowest and highest cost players. However, no firmwould want to experience the pain of past out-comes in order to be better in the future. Based

on the estimation results, the costs in buildingthe capability especially for incremental invest-ments may indeed be greater than the returns fromthe capability. If, however, a firm were able tolearn more quickly than its rivals through similarexpansion experiences, its returns from better tim-ing capacity expansion decisions could indeed besignificant.

Several limitations of the study can be high-lighted. First, while this study establishes the factthat experienced-based learning matters in capac-ity expansion timing, it implicitly assumes that therate of learning from experience and its decay arethe same across each firm, industry, and country.However, previous studies have shown that ratesof learning (and decay) may vary across differentorganizational contexts, different industry struc-tures, or different national environments (Argoteet al., 1990). Firms may learn more from eachother if they have more information available, areallowed to discuss these issues with each other,are few in numbers, and/or have similar incentivestructures. Second, while the measure for poor out-comes is correlated with divisional performance, itdoes implicitly assume that all firms are affectedin the same way. However, excess capacity mayhave a smaller effect on one company than onanother since each may have different capacity uti-lization break-even levels. This difference couldaffect the incentive to learn from previous out-comes. Third, we are capturing learning as changedbehavior rather than learning as a cognitive under-standing. For example, for incremental expansions,as the results show, firms seem to clearly under-stand the dynamics of capacity expansions fromprevious outcomes, but still continue to hop on theinvestment bandwagon. Fourth, given the natureof our data, we cannot make any inferences aboutthe full intent of a company’s capacity expansion.For example, while we tried to limit the sampleto products that have not incurred significant pro-cess improvements, we cannot be fully certain thatthe expansion bandwagons reflect the dissemina-tion of new process technology rather than themisperception of rival behavior. Finally, beforethe results can be considered generalizable, furtherresearch would need to be done on other industries.We hope, however, that in spite of the limita-tions, new light has been shed on the importantissue of learning in key strategic decision-makingsituations.



ACKNOWLEDGEMENTS

The authors gratefully acknowledge the data col-lection support from Dr John Wyatt of TECNONand Dr David Glass of Chemsystems. The authorswould also like to thank two anonymous review-ers and the following people for their constructivecomments on earlier versions of the paper: Pro-fessors Subramanian Rangan, Peter Zemsky, andIngemar Dierickx.

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APPENDIX 1

Pearson Correlation Coefficients of the Independent Variables

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Demand Growth 1.00Capacity Utilization 0.15∗ 1.00Lumpiness −0.10∗ −0.03 1.00Rivals’ Expansion

(1st Definition)0.27∗ 0.15∗ −0.23∗ 1.00

Rivals’ Expansion(2nd Definition)

0.33∗ 0.16∗ 0.01 0.54∗ 1.00

Stock of PreviousDecisions

0.03∗ 0.20∗ −0.34∗ 0.11∗ −0.03∗ 1.00

Stock of PoorOutcomes

0.05∗ 0.11∗ −0.19∗ 0.08∗ 0.01 0.61∗ 1.00

Stock of Rivals’ PoorOutcomes

0.01 0.12∗ −0.32∗ 0.09∗ −0.02 0.46∗ 0.46∗ 1.00

Forgetting −0.08∗ 0.08 0.15∗ −0.13∗ −0.10∗ −0.46∗ −0.30∗ −0.14∗ 1.00Relative Market

Share−0.03∗ −0.07∗ 0.10∗ −0.02 −0.06 0.26∗ 0.22∗ 0.02∗ −0.26∗ 1.00

∗Significant at the 5% level.


learning to time capacity expansions: an empirical analysis of the worldwide petrochemical industry,...

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