Excess capacity and the probability of entry An application to the US titanium industry

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UTTERWORTH E IN E M A

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Resources Poli¢T. Vol. 21, No. I. pp. 43 51. 1995 Copyright ~-~ 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0301-4207/95 $10.00 + 0.00

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Excess capacity and the probability of entry An application to the US titanium industry Janet Koscianski and Stephen Mathis Department of Economics, Shippensburg University, 1871 Old Main Drive, Shippensburg, PA 17257. USA

Excess capacity has been a characteristic of the metals industries for the past twenty years. This p h e n o m e n o n may either be inherent to the industries or the result of direct actions taken by firms operating within oligopolistic industries. The purpose of this paper is to construct and empirically test a model designed to determine the impact, if any, of expected excess capacity on the probability of firm entry into the US titanium metal industry. Using vector autoregressive estimation techniques along with Iogit models, results are generated which indicate that expected levels of excess capacity served as an effective barrier to entry.

Throughout the 1970s and 1980s, excess production capacity has been a pervasive phenomenon among the various metals industries worldwide. The existence of excess capacity in these industries has largely been attributed to three interrelated factors: (I) increasing capital intensity in metals production; (2) aggressive, heavily debt burdened firms comprising the metals industries; and (3) the inability of firms to accurately forecast future demand for their output (Cordes and Tomes, i 989, p I). Further, a major source of the difficulty inherent in predicting future metal consumption, and hence the potential for surplus capacity, is that of cyclical instability in these markets. Specifically, metals use is primarily concentrated in the manufacture of transport equipment, consumer durables, and plant and equipment; all sectors of the economy which are closely tied to the business cycle. To further complicate this problem of cyclical instability is the relative insensitivity of metals producers to changes in the price of their output. As a result, producers in these industries often delay the decision to invest in new production capacity until they see evidence o f long-term strength in the macroeconomy. Unfortunately, however, due to time lags, this new productive capacity often becomes operational after the peak of the Resources Poli~3, Volume 21 N u m b e r 1 March 1995

business cycle, thereby contributing to the problem of excess capacity (Eggert, 1991, pp 93-95). Alternatively, the existence of excess capacity can be the direct result of actions taken by incumbent firms operating within the industry. This involves the building of suboptimally high levels of plant and equipment thus enabling established firms to expand production quickly when deemed desirable. In particular, by maintaining excess capacity, the incumbent firms can block entry into their market via the ability to increase production and possibly engage in price warfare thus making it unprofitable for new entrants. More importantly, it may be argued that the mere presence of excess capacity, whether intentional or inherent to the industry, may serve as a powerful barrier to entry. This situation can produce several adverse results from society's perspective, including higher prices, restricted output, unnecessarily high production costs, and generally a distribution of resources to uses other than where they are most highly valued. In the light of this theoretical ability of firms to use excess capacity to deter entry into an industry, coupled with the existence of surplus capacity in the metals industries, it is of interest to determine the effectiveness of excess capacity to preempt entry into a particular metal industry. The decision to enter an industry is largely 43

The US titanium industry: J Koscianski and S Mathis

based on the perceived profitability for the potential entrant. Since the decision to enter is made some time prior to actual entry, this perceived profitability is logically, in part, affected by the projected levels of excess capacity for the incumbent firms. This is due to the possibility that these projected levels of excess capacity may provide the incumbent firms with the latitude to react and expand output should entry occur. It is the primary purpose of this paper to construct and estimate a model designed to generate projected levels of excess capacity for incumbent firms and employ these projections to measure the impact, if any, on the probability of firm entry. The model will control for projected levels of demand, in order to isolate the impact on entry resulting from projected levels of excess capacity. However, it should be noted that the model does not make an attempt to distinguish the intent to deter entry from business behavior that generates this same result. The question of intent is often subjective, and is difficult to establish given data constraints. The issue addressed in this paper, the effectiveness of excess capacity as an entry deterrent, is still a very important one, however, in that it circumscribes those situations which should be subject to further study. In addition, the question of price endogeny is also explored. This is deemed necessary since the issues of excess capacity and price manipulation are often treated as interrelated phenomena. The analysis is conducted within the context of a single metal industry. Given its suitability as a homogeneous product oligopoly, the industry chosen for analysis is that of titanium sponge, an intermediate form of titanium metal. Literature

review

Several studies have provided the theoretical basis for the use of excess capacity as a successful barrier to entry, perhaps the most prominent being those by Dixit (1980) and Spence (1977). In particular, Spence contends that firms carrying excess capacity have no need to suboptimally set price, because they can adjust price and expand output to capacity should entry occur. By so doing, these incumbent firms, with inherent cost advantages over new entrants, eg technical expertise, location etc, are able to create an unprofitable situation for a new entrant. There are additional arguments supporting the effectiveness o f excess capacity as a barrier to entry. Hilke (1984) reasons that investment in excess capacity serves to convert fixed costs into sunk costs for new entrants, because it reduces the market for used equipment. Traditionally, fixed costs can be eliminated as a firm ceases production and sells off its plant and equipment. Sunk costs, on the other hand, are not recoverable, and exist, in part, becaues of the difficulty associated with disposing of used equipment. As a result, these sunk 44

costs constitute a significant factor to be considered by a potential entrant. Cowling (1983) maintains that excess capacity tends to facilitate collusive behavior, and hence serve as a barrier to entry, by making the threat of price cutting more credible. Scherer (1990) contends that, within cartel arrangements, there is often an incentive for firms to expand excess capacity because the allocation of production within the cartel may be on the basis of capacity. Although the theoretical positions regarding excess capacity as a barrier to entry are generally logical, most of the associated empirical studies have failed to provide strong supporting evidence. Hilke (1984) regressed combined market shares of domestic and foreign entrants on measures of growth, profits, excess capacity and other barriers. He used pooled cross-section and time series data for 16 manufacturing industries for the years 1950~6. His findings were that growth and traditional barriers such as scale economies served as effective barriers to entry, but that excess capacity was not significant at even the 10% level. Hannan and Torries (1989) used factor analysis to test the relationship between excess production capacity and industry structure. They employed time series data for the period 1970--85 from four metal industries: aluminum, copper, lead and zinc. Their results indicated a positive relationship between excess capacity and the degree of monopoly power in an industry. However, it must be noted that Hannan and Torries do not explicitly analyze the ability of those metals firms maintaining excess capacity to preempt entry during the time period under analysis. Masson and Shaanan (1985) tested the proposition that firms may use limit pricing (suboptimally low pricing) and excess capacity together to deter entry. They used pooled cross-section, time series data pertaining to 26 manufacturing industries for the years 1954-58. Designating the market share of an entrant(s) as the dependent variable, they determined that while limit pricing was a significant deterrent, excess capacity was not effective as a barrier to entry. Lieberman (1987) employed a logit model to regress entry on excess capacity and other variables such as capacity utilization and industry growth. Analyzing data for 38 domestic chemical product industries, he concluded that while excess capacity had no significant impact on entry, both capacity utilization and industry growth affected it in a positive manner. Finally, Ghemawat (1984), using data for the years 1955-70, analyzed Dupont's alleged attempt to use excess capacity as a barrier to entry in the titanium dioxide industry. He regressed measures of cost on output, capacity and capacity utilization. When the demand for titanium dioxide declined significantly, Dupont's use of excess capacity as a barrier to entry became unprofitable and therefore unsuccessful. Unfortunately because of Resources Policy Volume 21 Number 1 March 1995

The US titanium industry: J Kosciansla" and S Mathis

these changing market circumstances, it is difficult to draw hard conclusions from Ghemawat's results. The failure of many of these studies to provide evidence supporting the effectiveness of excess capacity as a barrier to entry may very well be inherent in the testability of the situation, rather than due to any shortcomings associated with the underlying theory. Some of these common problems are the unavailability of pertinent micro data, the problems associated with using cross-section and time series data, and the problems of using data from collections of often heterogeneous industries. The model developed in the next section embodies an attempt to circumvent some of these shortcomings.

The model Industry description and data requirements The primary objective of this paper is to construct and empirically test a model designed to determine the effectiveness of excess capacity as a barrier to entry within the confines of a single manufacturing industry. By targeting the analysis to a single industry many of the problems inherent in cross-section and pooled crosssection/time series models will be avoided. In particular, one of the problems associated with examining a crosssection of industries over time is that the effects of specific environmental or economic policies can often have differing impacts on each industry within the crosssection. In addition, when pooled cross-section time series data are used over an extended time period it would be incorrect to assume that all industries in the sample react similarly to the various stages of the macroeconomic business cycle. These difficulties may be manifested through the problem of heteroskedasticity, leading to the generation of inefficient least squares estimates in a statistical analysis. The use of time series data for a single industry provides an approach which circumvents this dilemma. The industry chosen, that of titanium metal, is ideally suited to test the theory of excess capacity being used as a barrier to entry. Throughout its 40-year commercial history, the titanium industry has exhibited the classic characteristics associated with an oligopolistic market structure: a small number of relatively large firms, homogeneous output, common output price, and significant barriers to entry. Titanium sponge is produced in several countries, the UK, Japan, the former Soviet republics, the People's Republic of China and the USA. Within this context, the focus of this paper is on the US titanium industry. Between 1962 and 1991, the number of firms operating in the US titanium metal industry ranged from two to six. Despite significant entry and Resources Poli~T Volume 21 Number 1 March 1995

exit by firms, the industry has remained an oligopoly over this thirty year period. Currently, the industry is a duopoly. The primary use of titanium metal has been in the manufacture of civilian and military aircraft. Approximately 75% of the titanium sponge consumed in the USA is used in jet engines, airframes, and space and missile applications. This is becuase in its metallic form titanium is light in weight and extremely strong. To date, no close substitutes have been developed for titanium metal in its aerospace applications. Owing to the cyclical nature of the civilian aerospace industry and the unpredictability of government orders for aircraft, demand for titanium metal has been characterized by extreme fluctuations. As a result, excess capacity, which has ranged from 12% of productive capacity in 1988 to nearly 60% in 1983, is in part inherent in the industry. Historically, US titanium producers have primarily sold their output domestically and have been largely capable of fulfilling this demand. As a result, net import reliance as a percentage of US consumption of titanium metal has typically been negligible. Very few empirical studies have been conducted using data pertaining to a single oligopoly, undoubtedly stemming from the difficulties in obtaining the necessary data. Since there have been so few firms operating in the titanium industry, individual firms are reluctant to disclose proprietary data to the public. Furthermore, when the number of firms operating in the industry falls below three, the US Bureau of Mines will withhold aggregated industry data from public documents. The data utilized in this study were largely obtained from government sources published by the US Bureau of Mines. For those years when the data were withheld from public documents, written requests were made to titanium firms to obtain permission to use the data for research purposes. Annual titanium industry data were collected for the years 1962 through 1991. The data set employed in the empirical analysis is comprised of annual observations of titanium metal production and capacity, from which industry excess capacity is computed, real output price and total titanium consumption. This total consumption variable consists of reported consumption, which incorporates net exports and changes in industry stocks, plus changes in government stocks. Real US macroeconomic data pertaining to gross national product and short-term interest rates on government bonds are also included to assess, respectively, the stage of the business cycle and the opportunity cost of excess capacity. In addition, information regarding real new orders for civilian and military aviation vehicles is contained in the data set as a leading indicator of future titanium metal consumption. Using many of the data series discussed above, a vector autoregressive model is developed to 45

The US titanium #zdusoy: J Koscianski and S Mathis

Table I Variable acronyms, definitions and data sources Acronym

Definition

Source

CAP

Annual titanium metal industry production capacity (short tons) Annual titanium metal industry production (short tons)

US Bureau of Mines, Mineral Commodity Summaries

PROD EXCA P GNP IRA TE TIP TNA V TOTCONSU

FIRMS FIRMENT

Annual titanium metal industry excess capacity measured as capacity less production (short tons) Real annual US gross national product in billions of 1982 dollars Real annual short-term interest rate on US government bonds (%) Real average annual price of titanium metal ( 1982 US$/lb) Real annual total new orders for civilian and military aviation vehicles (US$ million) Annual total titanium metal consumption measured as consumption + change in government stocks + change in industry stocks + net exports (short tons) Number of firms operating in the titanium industry in year t Dummy variable equal to 1 if entry occurred in year t otherwise equal to zero

predict future titanium metal consumption and industrywide excess capacity. Finally, data concerning the number of firms operating in the titanium metal industry are used to construct a dummy variable indicating years for which entry into the industry occurred. The information embodied in the dummy variable is later used in a Iogit model along with the aforementioned predicted values of titanium metal consumption and excess capacity, to estimate the probability of firm entry into the industry. The variable acronyms used in this paper, their definitions, and the sources of these data are provided in

Table 1. Estimating procedures and results of prelimina~ tests While there are numerous advantages associated with conducting the analysis for a single oligopolistic industry, there are two major problems which must be addressed. First, although a significant amount of microeconomic data were obtained for this study, the unavailability of cost information virtually precluded the implementation of a true structural model. However, the relevant data that were procured for such important variables as capacity, production, consumption, price, and new orders for civilian and military aircraft, along with macroeconomic data for interest rates and gross national product, can be utilized in a vector autoregressive model to generate some useful results. Before this model can be specified, however, there is a second potential problem which must be addressed. Given that the titanium industry has consistently remained oligopolistic, containing two to six firms at various points in time, a time series analysis must be used to obtain sufficient observations for analysis. This involves collecting data on the 46

US Bureau of Mines, Mineral Commodity Summaries and selected firms' proprietary data US Bureau of Mines, Mineral Commodity Summaries and selected firms' proprietary data. US Bureau of Economic Analysis, Economic Report of the President. International Monetary Fund, International Financial Statistics US Bureau of Mines, Non[errous Metals Prices in the United States Through 1988 and Minerals Yearbook US Bureau of the Census, Business Statistics US Bureau of Mines, Mineral Commodity Summaries

US Bureau of Mines, Mineral Commodity Summaries US Bureau of Mines, Mineral Commodity Summaries

variables discussed above for the years 1962-91, and as with any time series, there are potential problems of non-stationarity. Specifically, the mean, variance and covariance of these variables may change over time, invalidating traditional hypothesis testing techniques. Accordingly, it was necessary to apply augmented DickeyFuller (ADF) tests to each of the single series and EngleGranger cointegration tests to the various combinations of these series (Dickey and Fuller, 1979). The ADF test involves estimating the regression equation: 9 Yt = B0 + B1Yt-I + E B i + 2 A Y t - i - I i=0

+Vt

(I)

where Y represents each of the variables to be tested (separately), and p is the number of lagged differences in Y. The lagged differences are included to correct for the presence of serial correlation. The hypothesis to be tested is that B 1 = 1, or in some versions where 6 = 1 Bi, that 6 = 0. Failure to reject the hypothesis that 5 = 0 or B 1 = 1 implies non-stationarity of the series. Depending on the level of significance chosen, only the variable total consumption (TOTCONSU), was interpreted as having a stationary series. This interpretation, however, only holds at the 5% level of significance. At the 1% level, none of the variables appeared to have a stationary series, and therefore were not integrated of order zero. These results are not surprising, since many time series are not stationary. However in many cases, the first differences of the series may be stationary, thereby implying that the original series may be integrated of order one. Testing for stationarity of the first differences involves estimating the regression: Resources Policy Volume 21 Number 1 March 1995

The US titanium industry: J Koscianski and S Mathis

Table 2 Cointegration results for combinations of time series data a Dependent variable Independent variable PROD CAP, TOTCONSU, TNA V, TIP, IRATE, GNP CAP PROD, TOTCONSU, TNA V, TIP, IRATE, GNP TOTCONSU CAP, PROD, TNA V, TIP, IRA TE, GNP TIP CAP, PROD, TOTCONSU, TNA V, IRA TE, GNP

Dickey-Fuller statistic

Durbin-Watson statistic

~6.83 b -5.25 b -8.30 b -2.56

2.14 2.06 2.03 1.80

aMacKinnan critical values for 5% and 10% levels of significance are -5.35 and -4.92 respectively. blndicates significance at the 10% level.

P

A Y t : C O +C, AYt_ , + ~ C i + 2 A ( A Y t _ i _ , ) + E ,

(2)

i=0

The As represent the first differences of the variables to be tested. The relevant test is for the hypothesis that C 1 = 1 or some 8 = 1 - Cj = 0. The results of this test indicated that the first differences for all o f the variables were stationary, or that the original series for each variable was integrated o f order one. In order to test relationships between variables, however, it will be necessary to estimate regression equations containing several o f the time series. Since the variables, taken individually, have been determined to be integrated o f the same order (1), there may exist linear combinations which are integrated o f order zero or are stationary. This latter determination is essential for the results o f any subsequent statistical analysis to be meaningful, because it is important to establish the fact that two or more time series do not drift apart over time. The Engle-Granger cointegration test provides a method for determining the stationarity, or order o f integration, for combinations o f time series data. Since the first differences o f each individual series were stationary, the cointegrating procedure can be specified as: tl

Yt = °~o + Y~ o~i Xi., + e,

(3)

i=1

where Yt represents an arbitrarily designated dependent variable from the group, and Xi. t includes each o f the n remaining variables. The relevant linear combination to be tested is represented by the error term e r Thus the ADF test can be applied to the regression: P

e, = ale,_ I + E ai+2 A e , - i - !

(4)

i=o

The latter term, P

Z ai+2 Aet-i-I

(5)

i=0

is included to correct, if necessary, for serial correlation among the error terms, and the relevant hypothesis to be tested is that aj = 1 or alternatively 8 = I - a I = O. Since a vector autoregressive model is to be developed to predict values for excess capacity (capacity - production) and total consumption, there are four relevant linear Resources Policy Volume 21 Number 1 March 1995

combinations to be tested for cointegration. The designated dependent variables in each combination are production, capacity, total consumption and price. The results pertaining to these cointegration tests are shown in Table 2. Since the Durbin-Watson statistics are close to 2.0 for all cases, corrections for serial correlation were not deemed necessary. Based on a 10% level o f significance, the results indicate that the first three relevant combinations of data series are cointegrated, thereby indicating that the series do not drift apart over time. This implies that subsequent statistical analysis using these series can provide meaningful results. However, the data series pertaining to the fourth combination, using price as the dependent variable, does not appear to be cointegrated. The implication o f this result will be further analyzed in the next section. Vector autoregressive model and preliminary estimates

Logically the decision to enter an industry is based on the expected profitability for the potential entrant. In part, this expected profitability is affected by the perceived ability o f incumbent firms to react in a manner that will adversely affect the new entrant's return. The primary goal o f this paper is to determine whether the existence of excess capacity in the US titanium industry has adversely affected entry. To test this hypothesis it is necessary to accurately model the decision process for firms considering entry into this industry. In order to do so, a means must be established for accurately estimating projected excess capacity. In addition, historical data indicate that excess capacity has, in turn, been influenced by the extreme volatility o f titanium metal demand. As a result, predicted estimates will be developed for both excess capacity and total consumption o f titanium as it is logical to assume that both o f these factors influence entry decisions. In order to construct the best model for generating predicted estimates for these variables, given data constraints, the question o f titanium price endogeny must be analyzed. Initially, we would expect such a price for the domestic market, as a whole, to be endogenous. If this were the case, the model should be constructed in a manner that incorporates the determination o f price, along with excess capacity and consumption. 47

The US titanium indusoy: J Koscianskiand S Mathis Table 3 Equation for predicted change in excess capacity with titanium price as exogenous: dependent variable is A E X C A P a Independent variable Coefficient t-statistic ,5 EXCAP( I) -O.14 0.55 `SEXCAP(-2) 0.57 2.2 I ATOTCONSU(.-I ) 0.24 1.19 ATOTCONSU(-2) 0.08 0.44 ATIP 1271.88 1.24 `STIP(-I ) ~069.80 i.65 `STNAV -0.08 -0.83 `STNA V(-I ) -~).21 1.97 AGNP -10.37 -O.60 AGNP(-1 ) -9.44 -0.53 `5IRA TE 861.84 0.99 AIRA TE( 1) 953.65 1.18

Table 4 Equation for predicted change in total consumption with titanium price as exogenous; dependent variable is A T O T C O N S U a Independent variable Coefficient t-statistic AEXCAP(1) 0.32 1.01 AEXCAP(~) 0.21 0.68 ATOTCONSU(-1 ) -0.62 ~.59 ATOTCONSU(-2) -0.20 -0.88 AT1P 1404.90 1.18 ATIP( 1) 2728.32 1.87 ATNA V 0.06 0.52 ATNAV(-I) 0.23 1.80 AGNP 8.12 0.40 AGNP(-1 ) 33.24 1.61 `51RATE 500.29 0.49 `5IRATE(-1 ) ~ 18.29 -0.76

aR2 0.69; Durbin Watson statistic 2.31; F- statistic 2.58; probability (F-statistic) 0.04.

aR2 0.71; Durbin-Watson statistic 1.90; F-statistic 2.83; probability (F-statistic) 0.03.

The issue of price endogeny, however, is less clear from a practical perspective. Since excess capacity is often prevalent in oligopolistic markets, marginal cost and ultimately price, are not particularly sensitive to changes in demand-side variables. This is because firms maintaining excess capacity may be able to expand production without the necessity of a price increase. Given this situation, price is more likely to be affected by cost considerations, and therefore should be treated as exogenous in a model based on demand-side variables. In order to construct the most appropriate model, given the data considerations, this issue of price endogeny must be empirically resolved before any meaningful estimates of excess capacity can be obtained and utilized in subsequent model pertaining to entry. Accordingly, two alternative models will be specified and tested, before proceeding further. The first will treat price as an endogenous variable. It should be noted that some suspicion about this specification has already been raised by the earlier cointegration tests indicating a nonstationarity among the data series when specifying price as a dependent variable. The econometric method chosen to generate projections of the changes in titanium metal consumption and excess capacity is that of vector autoregression (VAR). Unlike other econometric estimation methods, VAR imposes minimal theoretical restrictions on the structure of the model, which given the lack of information regarding investment decisions in the titanium industry, makes the VAR approach an appropriate means of predicting excess capacity. In addition, as a result of the interrelatedness of titanium consumption and excess capacity in the industry, it is apparent that the predictions derived for these variables can be estimated on the basis of similar determinants. The VAR procedure is designed to estimate systems of equations using lagged values of the model's endogenous variables and current and lagged values of exogenous variables.

The first VAR model estimated for the present analysis contained three endogenous variables: annual industry excess capacity (EXCAP), total annual consumption of titanium metal (TOTCONSU), and the real price of titanium (TIP). Three more variables were utilized to generate exogenous variables: total real new orders for civilian and military aviation vehicles (TNAV), real short-term interest rates on US government bonds (IRATE), and real US gross national product (GNP). The data used in the VAR model are measured as first differences to induce stationarity, since previously discussed Dickey-Fuller tests indicated the variables were all integrated of order one. The diagnostic statistics associated with the estimated VAR model indicate that collectively the variables contained in each equation did explain much of the variation in two of the model's endogenous variables, AEXCAP and ATOTCONSU. Specifically, the reported F-statistic for each respective estimating equation was statistically significant at the 5% level. However, the F-statistic pertaining to the equation containing price as the dependent variable was not statistically significant. This result indicated that the explanatory variables, as a group, could not be relied upon to obtain estimates of the change in titanium price, thereby casting doubt on the reliability of this version of the VAR model to generate useful estimates. These results, coupled with the earlier cointegration tests, suggested that price should not be treated as an endogenous variable when used with the other variables in this model. Accordingly, a second VAR model was estimated, containing only two endogenous variables: excess capacity (EXCAP) and total consumption (TOTCONSU). Titanium price (TIP) was treated as exogenous along with total new orders for aviation vehicles (TNAV), short-term interest rates (IRATE) and US gross national product (GNP). As before, all variables are measured in real terms, and expressed in first difference form. The

48

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The US titanium industry: J Koscianski and S Mathis

results from this model are presented in Tables 3 and 4. The reported F-statistic for each equation is statistically significant at the 5% level, indicating that, collectively, the variables contained in each equation explain much of the variation in the two endogenous variables, AEXCAP and ATOTCONSU. In addition, the coefficient of determination for each equation indicates that approximately 70% of the variation in each endogenous variable is explained by the model. Furthermore, there is no indication of serial correlation of the error terms as evidenced by the value of the Durbin-Watson statistic reported for each equation. Overall, this model seems to provide a reasonable basis for generating projected values for changes in excess capacity and total consumption, designated as AEXCAP and ATOTCONSU respectively. Moreover, the results should prove to be particularly reliable, since included variables have been proven to be cointegrated. These projected changes, A E X C A P and ATOTCONSU, can be used to obtain within period forecasts for the projected levels of excess capacity and total consumption defined as EXCAP and TOTCONSU respectively, which would logically be taken into consideration by potential entrants as predictors of their own future profitability. The relevant forecasts are logically established when the entry decision is made. It would be useful to generate these forecasts as far into the future as possible, but this is constrained by the necessity to keep the forecasts reasonably accurate. Thus, the forecasts in this model will be kept within the period of the data, and will extend over a projected four-year average from the year of entry decision. Ultimately, in subsequent analysis, the actual year of entry shall be lagged one period, so that the forecast will embody not only the year of entry, but one year beyond the year of entry as well. An additional issue which must be addressed is the length of time between the year of the entry decision and the year of actual entry. This lag exists, of course, since it takes time to construct a plant and make it operational. Previous research in minerals industry investment has suggested at least a three year time lag between capital spending decisions and production (Adams, 1989, p 57). However, this lag structure is considered more applicable to the mining stage, not the refining stage of titanium metal production under analysis in the present study. Moreover, since the production of titanium sponge is essentially an electrochemical process, the use of a two-year lag is consistent with the observed behavior of firms operating in the chemicals industry. On average, it was found there exists a two-year lag between the time the decision to build a new chemical plant is made and the date at which it becomes operational (Lieberman, 1987, p 613). Accordingly, it is assumed that the relevant forecasts facing prospective entrants will be those made Resources Policy Volume 21 Number 1 March 1995

during the entry decision year, which is lagged three years back from the present time period, but two years prior to the year of actual entry. The forecast values for excess capacity (EXCAP) of incumbent firms can be obtained from the following relationships: EXCAPt-3 = EXCAPt_ 4 + A EXCAPt-3 EXCAPt-2 = EXCAPt-3 + A EXCAPt-2 EXCAPt-I = EXCAPt-2 + A EXCAPt-I EXCAPt = EXCAPt-I + A EXCAPt

(6)

The year of the entry decision is t - 3, so the last year for which excess capacity is known is t - 4, since measurements are recorded at the end of the year. The relationships in Equation (6) can be used to compute a four-year forecast average pertaining to excess capacity (EXCAPAV), which includes the decision year t - 3, and the subsequent years, t - 2, t - !, and t. Thus: 4

EXCAPAVt = ~ EXCAPt-i+, / 4 i=1

(7)

As an example, suppose entry occurs in the year 1980, which would be designated year t - 1. The year of entry decision would be two years prior, 1978 ( t - 3). The forecast average excess capacity then pertains to the years 1978, 1979, 1980 and 1981. The last known level of excess capacity would be for 1977 (t - 4), one year prior to the decision year. Thus, this level is incorporated as the base of the forecast, to which the projected changes in excess capacity are added. Ultimately, in this example, the forecasts start at the year of the entry decision, 1978, and extend one year past the year of actual entry, 1980, to the subsequent year, 1981. Analogous forecasts can be generated for projected levels of total consumption ( TOTCONSU): TOTCONSUt-3 = TOTCONSUt_ 4 + A TOTCONSUt-3 TOTCONSUt-2 = TOTCONSUt-3 + ATOTCONSUt_2 TOTCONSUt-I = TOTCONSUt-2 + ATOTCONSUt_I TOTCONSUt = TOTCONSUt_I + ATOTCONSUt

(8) The forecast four-year average for total consumption (TOTCONAV), including the year of the decision t - 3, and the three years beyond, t - 2, t - 1, and t is: 4

TOTCONA Vt = ~ TOTCONSUt-i+I / 4

(9)

i=1

49

The US titanium industry: J Koscianski and S Mathis

These forecast averages can be employed in a logit model designed to estimate the probability of firm entry into the US titanium industry. Logit model

The decision by a firm to enter an industry is obviously a dichotomous one. Therefore, such a decision must be represented in an empirical analysis by a binary dummy variable which assumes the value of one if the event occurs or zero if the event does not occur. In the present analysis firm entry into the titanium metal industry is used as the basis for constructing the relevant dependent variable. As a result, it is necessary to employ a logit procedure when modeling the impact of projected excess capacity and projected consumption of titanium metal on firm entry into the industry. In general, a logit model may be expressed as follows: L t = In ( l_P~tp~) : 130 + ~, X,, + [32X2t +...

+ f~.,xo,+w,

(1o)

where the X It ..... Xnt represent either discrete or continuous explanatory variables, and the dependent variable, L t or logit, is measured as the natural log of the probability of an event occurring (Pt) divided by the probability of an event not occurring (1 - Pt). The term, P/(I - Pt), known as the odds ratio, measures the odds in favor of the event occurring (Gujarati, 1992, p 423). Specifically, the following logit model was estimated: ENTR Yt_ 1 = 81EXCAPA Vt + Sz TOTCONA Vt + FIRMSt-3 + vt

(11)

The variable ENTRYt_ d represents the natural log of the probability of firm entry in year t - 1 divided by the probability of no entry in year t - 1. It is hypothesized that the probability of entry in year t - 1 is affected by the projected averages for excess capacity and total consumption made in period t - 3, the year of entry decision. The variable F I R M S t 3, is included to control for the number of firms in the decision year. In addition, it should be noted that the forecasts for both excess capacity and total consumption include the year t. Thus these forecasts include the actual year of entry, t - 1, but also one year beyond entry, t, as well as some prior years. The forecasts for total consumption (TOTCONA lit) are included in order to control for projected demand, and as a result, to isolate the effect of projected excess capacity on the probability of firm entry. The empirical results obtained from the logit model, along with the salient conclusions which may be drawn from this analysis, are detailed in the following section. 50

Table 5 Logit results Indicating impacts on the probability of entryt_ I

Independent variable

Coefficient

EXCAPA VI TOTCONA VI FIRMSt. 3

~.00035 0.00015 ~.97056

t-statistic --2.13 a 1.22 1.14

~Indicates significance at the 5% level.

Final results and conclusions The results obtained from estimating the logit model provide some interesting insights regarding the effectiveness of excess capacity as a barrier to entry. The estimated coefficients can be interpreted as representing the potential effects of each independent variable on the natural log of the odds ratio, P/(1 - P t ) , which is the ratio of the probability of entry to no entry in any given year in the survey. Logically, the decision to enter an industry is based on predicted profitability, which is in large part determined by predicted demand for the product and the predicted possibility of preemptive measures on the part of incumbent firms. It is a hypothesis of this paper that these predicted preemptive measures are at least partly embedded in projected values of excess capacity (EXCAPAV). The results generated by the estimation procedure are presented in Table 5. The coefficient pertaining to EXCAPA V is negative and significant at the 5% level, indicating that increases in the projected four-year average for levels of excess capacity do appear to adversely affect the probability of firm entry. This result is observed after controlling for the projected four-year average of total consumption, as well as for the number of firms, during the entry decision year. Further, the estimated coefficient pertaining to TOTCONAV, though positive as expected, has less than half the magnitude of the coefficient on EXCAPAV, and more importantly, is not statistically significant. Thus it would seem that after controlling for projected changes in demand, projected excess capacity does play a significant role in affecting the probability of firm entry. When entry did occur, it coincided with low projected levels of excess capacity. The implications of these logit results can be further demonstrated by computing the associated probability of entry for each year in the study. These results are illustrated by the graph in Figure 1 which shows projected excess capacity and the probability of firm entry largely mirroring each other over time. Moreover, it was found that on average, the probability of entry as computed by the logit model was 0.59 for the years that entry actually occurred, but only 0.10 for the years that it did not occur. There is an important qualification which needs to be noted. The results of this paper tend to show that the existence of projected levels of excess capacity on the part of incumbent firms does appear to lower the probability Resources Policy Volume 21 Number 1 March 1995

The US titanium industry: J Koscianski a n d S Mathis

35000

1.00

30 000 /i

0.75

25000

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20 000

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15000

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-~

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1970

1980

1990

Figure 1 Projected excess capacity and probability of firm entry

of firm entry, and thus serves as an effective barrier to entry for the US titanium industry. However, these results do not establish proof of intent to preempt, in the sense that incumbents may deviate from profit maximizing behavior in order to create a barrier to entry. In order to investigate this possibility, it would be necessary to obtain a substantial amount of cost information, which was not available for this industry. Another problem encountered was the loss of some degrees of freedom as first differences were used and forecasts were kept within period. It is expected that the results obtained could be strengthened as additional years are added to the time series. However, the results of cointegration tests indicated that only those combinations of variables which could be made stationary by first differencing were to be used in the analysis. Further, within period forecasts tend to be considerably more accurate than those which extrapolate beyond the data series. Overall, these efforts have been made to ensure the integrity of the generated results. Although proof of intent may not be established by the present analysis, the results of this study still remain significant. Specifically, they demonstrate that projected excess capacity did serve as an effective barrier to entry, and by so doing, provide a basis for designating those industries which may require further examination.

References Adams, Robin G (1989) 'Metals industry forecasting: information, expectations and the question of capacity' in Cordes, J A and Torries, T F (eds) Surplus Capacity in the International Metals Industries Society of Mining Engineers, Littleton, CO Cordes, John A and Torries, Thomas (1989) 'Metals industry capacity: an overview and readers' guide' in Cordes, J A and Torries, T F (eds) Surplus Capacity in the International Metals Industries Society of Mining Engineers, Littleton, CO

Resources Policy Volume 21 Number 1 March 1995

Cowling, Keith (1983) 'Excess capacity and the degree of collusion: oligopoly behaviour in the slump' The Manchester School o f Economic andSocial Studies 51 (2) 341 359 Dickey, David A and Fuller, Wayne A (1979) 'Distribution of the estimators for autoregressive time-series with a unit root' Journal of the American Statistical Association 74 (366) 427~.31 Dixit, Avinash (1980) 'The role of investment in entry-deterrence' The Economic Journal 90 (3) 95-106 Eggert, Roderick G (1991) "Living with cyclical instability: an empirical and conceptual introduction' Resources Policy 17 (2) 91-99 Ghemawat, Pankaj (1984) "Capacity expansion in the titanium dioxide industry' The Journal of Industrial Economics 33 (2) 145-163 Gujarati, Damodar (1992) Essentials of Econometrics McGraw-Hill, New York Hannan, Michael J and Torries, Thomas F (1989) "Industry structure and capacity' in Cordes, J A and Torries, T F (eds) Surplus Capacity in the International Metals Industries Society of Mining Engineers, Littleton, CO Hilke, John C (1984) 'Excess capacity and entry: some empirical evidence' The Journal of Industrial Economic:~ 33 (2) 233-240 Humpage, Owen F (1992) 'An introduction to the international implications of US fiscal policy' Federal Reserve Bank of'Cleveland Economic Review 28 (3) 27 39 International Monetary Fund (1990) International Financial Statistics Yearbook International Monetary Fund, Washington, DC Lieberman, Marvin B (1987) 'Excess capacity as a barrier to entry: an empirical appraisal' The Journal o f Industrial Economics 35 (4) 607~27 Masson, Robert T and Shaanan, Joseph (1986) "Excess capacity and limit pricing: an empirical test' Economica 53 (8) 365-378 Pindyck, Robert S and Rubinfield, Daniel L (1991) Econometric Models and Economic Forecasts 3rd edn, McGraw-Hill, New York Roskill Information Services (1991 ) The Economics of Titanium 7th edn, Roskill Information Services, London Scherer, F M and Ross, David Ross (1990) Industrial Market Structure and Economic Per/brmance 3rd edn, Houghton Mifflin, Boston, MA Spence, Michael A (1977) 'Entry, capacity, investment and oligopolistic pricing' The Bell Journal o]'Economics 8 (2) 534-544 US Department of Commerce (1989) Business Statistics 1961 1988 Bureau of the Census, Washington, DC US Department of Commerce (1992) Economic Report of the President Bureau of Economic Analysis, Washington, DC US Department of the Interior (various years) Mineral Commodity Summaries Bureau of Mines, Washington, DC US Department of the Interior (various years) Minerals Yearbook Bureau of Mines, Washington, DC US Department of the Interior (1989) Nonferrous Metal Prices in the United States Through 1988 Bureau of Mines, Washington, DC

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