Strategy, Scale or Policy? Exit in the Australian Car Industry *

Transcription

1 THE ECONOMIC RECORD, VOL. 77, NO. 239, DECEMBER, 2001, 351^360 Strategy, Scale or Policy? Exit in the Australian Car Industry * MATTHEW P. FLEISCHMANN RatioConsultants DAVID PRENTICE LaTrobeUniversity This paper estimates the importance of strategy, scale and policy in determining the pattern of exit in the Australian car industry. Previous studies found only a weak role for strategy in exit from declining industries. Using a new dataset and improvements on the speci cation used in earlier studies, we nd evidence that strategy in uences the pattern of exit where economies of scale are less important. Protection is also found to negatively in uence the likelihood of exit. I Introduction Since 1959, the number of assembly plants in the Australian car industry has shrunk from twenty-four to currently just four. This dramatic reorganization occurred while the government actively and extensively intervened to support the industry. Two popularly cited causes are competition e ectively driving out small high-cost plants (due to the failure to achieve economies of scale) and cuts in protection from imports. Popular discussions of rm strategy have focused on plant closures to achieve economies of scale at fewer larger plants. However, a theoretical literature on strategic exit suggests, when an industry * This is a revised version of the honours thesis of Matthew Fleischmann. Revisions were partially funded by a Faculty Grant from La Trobe University. We would like to thank Karen Wade and Tony Salvage for their research assistance. We would also like to thank Harry Bloch, Bruce Chapman, Phillip Leslie, participants at seminars at RSSS, La Trobe University, the Labor Econometrics Workshop 2000, the Industry Economics Conference 2000, the University of Western Australia and two anonymous referees for their helpful comments. Some work was done while D. Prentice was visiting the Economics Program at RSSS. He would like to thank them for their hospitality. All errors remain our own responsibility. is in decline, size has strategic disadvantages that may o set cost advantages (Ghemawat and Nalebu 1985, 1990; Dierickx et al. 1991). The importance of rm strategies, relative to government policy and competition in determining exit in the car industry has not previously been analysed. Furthermore, as speculation continues on the possibility of another domestic car manufacturer closing, such a study is timely. This paper analyses the determinants of exit in the Australian car industry. Speci cally, we estimate an exit model using a panel of data for nearly all plants that operated between 1920 and To more extensively analyse the role of rm strategy as a determinant, we extend the model used in previous papers to permit di erent relationships between size and exit at di erent plant sizes. Estimating this new model reveals that strategic behaviour, as well as economies of scale, determine the pattern of exit. Furthermore, we perform the rst direct test of the prediction from the theoretical literature that cost structure determines rm exit strategies, but nd no support for it. As well as nding roles for competition and rm strategy, we also con rm a negative relationship between protection and the probability of exit. Finally, the dataset used for this paper is new and is more extensive than that used in earlier empirical studies of the Australian car industry. 351 # The Economic Society of Australia. ISSN 0013^0249.

2 352 ECONOMIC RECORD DECEMBER The results of our study are signi cant in several respects. First, we provide the rst econometric evidence of strategic e ects on the pattern of exit highlighted in economic theory. This result, though, highlights the tension between the strategic behaviour induced by the small numbers of rms and the economies of scale that produced the oligopoly. Beyond relatively small sizes, it appears that achieving economies of scale is required to survive in the car industry rather than more elaborate strategies. Second, we nd the government's policy of protection did reduce the likelihood of plant closure. Though the model does not enable structural forecasting of the e ects of policy changes on exit, the ndings are suggestive. While all currently operating plants feature ^ by Australian standards ^ high capacities, further tari cuts will increase the likelihood of exit and the smallest plant, of Mitsubishi, is the most likely to close. 1 In section II, the theoretical framework and previous empirical work are surveyed. This is followed in section III by background on the industry in which we argue that it is a particularly good case in which to examine these issues. Section IV provides an econometric speci cation for testing, and introduces the data. Estimation is performed in section V and the results discussed in section VI. Finally, some conclusions are drawn in section VII. 1 See Clarke et al. (1998) for more details on the future of the industry. II A Review of the Theory and Evidence on Exit To motivate the empirical work, we follow Gibson and Harris (1996) in presenting a set of hypotheses (summarized in Table 1) suggested by the theoretical and empirical literature on rm exit. The empirical evidence on these hypotheses, where available, is also reviewed. The rst hypothesis ^ scale ^ is suggested by the textbook analysis of economies of scale. If demand declines, competition forces the small high cost plants to exit. The striking result of Ghemawat and Nalebu (1985), in comparison with the competitive case provides the second hypothesis: Strategy (strong). Ghemawat and Nalebu show that for a duopoly of a large rm and a small rm facing declining demand, the subgame perfect equilibrium of the exit game features the large rm closing rst. 2 The intuition for this result is as follows. The exit game begins when it is no longer pro table for both rms to continue operating. There are two Nash equilibria, each featuring one rm exiting immediately and the other remaining until operating is no longer pro table. Each equilibrium is based on a threat to outlast the other rm. The equilibrium in which the large rm exits rst, though, is the only subgame perfect equilibrium. This is because the small rm is not committed to producing large outputs so its threat is credible as it can recover any losses as a monopoly. Additional capacity places the large rm at a strategic disadvantage. If the Ghemawat^Nalebu model is extended so that rms can continuously shrink capacity, rather than just make an all-or-nothing choice, the largest rms still close capacity rst (Ghemawat and Nalebu 1990). This is because, when demand declines, excess capacity has no strategic value. This result yields the third hypothesis: Strategy (weak). However satisfying this hypothesis provides only weak support for strategy as a determinant of exit. This is because multi-plant rms may close plants rst because it is cheaper for them than single-plant rms, e.g. lower redundancy costs as workers are employed at other plants (Harrigan 1980; Baden-Fuller 1989). Econometric analyses of exit under declining demand in the US steel industry (Deily 1991), chemicals (Lieberman 1990) and the New Zealand manufacturing industry (Gibson and Harris 1996) support the Scale hypothesis and (for the latter two) the Strategy (weak) hypothesis. There is no econometric evidence in support of the Strategy (strong) hypothesis. The fourth hypothesis ^ Cost type ^ is suggested by a further extension of this literature in which the model features two rms, with di erent cost structures, and two ways in which demand declines (Dierickx et al. 1991). The rst type of declining demand occurs if the demand curve shifts horizontally downwards ^ this is referred 2 Note, size is built into the model by assuming marginal revenue exceeds marginal cost at all outputs below capacity. In addition, with su cient economies of scale the strategic disadvantage is outweighed and the result is reversed (Ghemawat and Nalebu 1985).

3 2001 STRATEGY, SCALE OR POLICY? EXIT IN THE AUSTRALIAN CAR INDUSTRY 353 Scale Strategy (strong) Strategy (weak) Table " List of Hypotheses There is a negative relationship between size and the likelihood of exit. There is a positive relationship between size and the likelihood of exit (Ghemawat and Nalebu 1985). There is a positive relationship between the number of plants a rm has and the likelihood of exit (Ghemawat and Nalebu 1990; Baden-Fuller 1989). Cost type If the willingness to pay declines, there is a greater likelihood of exit for plants with low xed costs and high variable costs. If the customer base declines, there is a greater likelihood of exit for plants with high xed costs and low variable costs (Dierickx et al. 1991). Age There is a negative relationship between age and the likelihood of exit (Jovanovic 1982; Baden-Fuller 1989). Old age There is a positive relationship between age and the likelihood of exit (Deily 1988). to as a reduction in consumer willingness to pay for a product. The rm with relatively low xed costs and high variable costs exits rst as price no longer covers average cost. The second type of declining demand occurs if the demand curve pivots inwards around the vertical intercept ^ this is referred to as shrinkage in the customer base. In this case, the high- xed-cost rm closes rst because insu cient revenue is earned to cover the xed costs. The low- xed-cost rm can still cover its costs from the remaining customers willing to pay a high price for its product. 3 This hypothesis has not been directly tested empirically. The fth hypothesis ^ Age ^ is suggested by an entry model where entrants quickly exit again on discovering they have unsuccessfully innovated (Jovanovic 1982). The negative relationship between age and exit also results if newer capital equipment has a higher resale value (Baden-Fuller 1989). This hypothesis (and the Scale hypothesis) is supported by empirical papers on industry dynamics ^ see Sutton (1997) and Caves (1998) for recent surveys ^ demonstrating entry and exit rates are highly correlated, with exit rates highest among recent, small, entrants. 4 The sixth hypothesis ^ Old age ^ is suggested by Deily (1988) who argues plants with older capital are more likely to exit because of higher production and maintenance costs. She supports this hypothesis with evidence from the US steel 3 Ghemawat (1997) surveys other extensions and nds the central ndings hold in most cases. 4 Exit is unrelated to age if innovation (and potential failure) is ongoing (Ericson and Pakes 1995). industry. Failure to reinvest suggests gradual exit is taking place. In summary, while economic theory suggests any of competition, strategy or failed innovation could drive exit, the empirical evidence to date provides only weak support for strategy as a determinant. Instead, there is a consistent pattern of support for rst, the textbook hypothesis that competition drives out small high-cost plants and, second, that relatively new plants are more likely to close. III The Australian Car Industry 5 In this section, we describe the Australian car industry. First, we argue that it is a suitable case in which to test the hypotheses introduced in section II. Second, we provide a more detailed analysis of the dynamics of the industry. Third, we outline the role government policy has played in shaping the industry. Finally, we compare this case with those examined in previous empirical work. For the Australian car industry to be a suitable case for testing the hypotheses raised in section II, it must be an oligopoly, there must have been exit, and there must have been declining demand for locally produced cars. The rst two conditions are easily satis ed. The industry has been highly concentrated throughout its history, as demonstrated by the high concentration ratios reported in the rst row of Table 2. 5 This section draws on the following sources for historical background: Birney (1985), Conlon and Perkins (1995, 1997), Darwin (1983, 1986), Davidson and Stewardson (1974), Davis, P. (1987), Davis, T. (1987), Gregory (1988), Maxcy (1963), Stubbs (1972), Swan (1972) and Wright (1998).

4 354 ECONOMIC RECORD DECEMBER Table á Descriptive Statistics on the Australian Car Industry Period 1920^ ^ ^ ^ ^98 Four (eight) rm Concentration ratio* 69.2 (78.5) 81.4 (95.8) 78.1 (94.0) 74.3 (95.3) 69.1 (90.1) Entrants Exits New plants Average annual growth in registrations 8.9% 14.6%** 4% 0.14% 3% Industry capacity (end of period) Average plant capacity (end of period) Number of plants (end of period) * The concentration ratios are calculated using shares of total registrations (including imports) at the midpoint of each period, except for period one, when cumulative registrations, as reported in the 1948 census, are used. ** 1950^63 ^ to avoid distortions in the immediate postwar period. 6 We do not include plants that solely manufacture components. Second, there has been substantial exit, as demonstrated in row three of Table 2, which reports the number of manufacturing and assembly plants that exit in each period. 6 A manufacturing plant makes the bodies, the engine (usually) and other components and then assembles the car. Assembly plants receive packs of parts from either a manufacturing plant of the same company, or from another manufacturer. These packs are then assembled, with additional domestic parts. Assembly plants feature low xed costs but higher variable costs; manufacturing plants feature higher xed costs and lower variable costs. Note, though, that because cars are di erentiated products, exit is no longer identical to plant closure. A change in ownership may not change the engineering characteristics of a plant but could have competitive consequences. Hence, a substantial change in ownership is treated as an exit and entry. For example, when Mitsubishi purchased the Chrysler plants, Chrysler is counted as having exited, and Mitsubishi as having entered. Finally, we argue the car industry experienced declining demand after the third period, as presented in Table 2. First, note row 5 of Table 2 shows average annual growth rates of total registrations slowed considerably after 1963, though was on average negative only in 1975^83, when quotas were in place. Furthermore, examining exponential growth rates by period for each group of cars by nationality (English, European, Japanese and US) reveals falling registrations for all groups assembling in Australia after 1974 except for the Japanese. Furthermore, registrations for European cars started declining in 1964 and for English cars, from the early 1950s. Third, the number of entrants declines after period two, and only four assembly new plants were built after 1963, two of which were built in the 1950s as component manufacturing plants. For GMH and Chrysler (Wright 1998, Chrysler annual reports) there seems to have been a switch from optimism to pessimism, though a more defensive trade policy seems to have begun in the mid-late 1960s (Gregory 1988; Stubbs 1972). But, as capacity continued to expand in the 1960s, it appears the period of de nite decline for the Australian industry started around Finally, although total registrations are growing in the nal period, for Ford and GMH registrations continued to decline (at a slower rate) and for Mitsubishi, Toyota and Nissan, registrations were virtually constant, while protection was falling. We now discuss the historical pattern of exit in more detail. For the rst two periods, as in many industries considered in the empirical industry dynamics literature, entry and exit are correlated. In the later periods though, there is more exit than entry. While total capacity peaks in the third period, the average capacity of assembly plants rises over the period. This suggests that, over time, competition eliminates the smaller plants unable to achieve economies of scale. However,

5 2001 STRATEGY, SCALE OR POLICY? EXIT IN THE AUSTRALIAN CAR INDUSTRY 355 closer inspection of the pattern of exit reveals exit is not strictly in order of size. In particular, several relatively large manufacturers (Volkswagen, British Leyland, Chrysler) exit before the smaller assembly plants of British Leyland, Ford, GMH and Renault. Much of the entry and exit in the third and fourth periods was concentrated in the small car segment where Japanese models (perceived to be of higher quality) replaced the English and European models. Hence, strategy may have played some role in determining exit. The car industry has been regulated in ve periods as summarized in Table 3. Each period is distinctive in terms of the mix of instruments used and the aims of the protection. Particularly clear accounts of the degree of protection are provided by Stubbs (1972), Gregory (1988) and the Industry Commission (1997). Finally, we compare the Australian car industry as a case study with those industries used in earlier work. It is distinctive in two respects. First, unlike steel and chemicals, cars are di erentiated rather than homogeneous products. Di erentiation reduces the importance of the strategic disadvantage of size but also the importance of economies of scale. Hence, there is the possibility that strategy plays a greater role in determining exit in the car industry than in steel and chemicals. Second, the car industry provides an opportunity to test the cost type hypothesis of Dierickx et al. (1991). Car assembly plants and manufacturing plants have di erent costs similar to those considered in Dierickx et al.'s model. If the type of demand decline is known, then the importance of cost type can be tested. Anecdotal evidence suggests a falling willingness to pay for the incumbent producer's cars. Hence, Dierickx et al.'s model suggests small car assembly plants will close before manufacturing plants. IV The Econometric Models In this section, the set of econometric models estimated in section VI is presented. First, we discuss the speci cation used in earlier papers and how we adjust it to estimate it with panel data rather than the cross-sections used in earlier work (yielding the Basic speci cation). Then we introduce two sets of extensions (the Extended and Full speci cations). Most related empirical work has begun with a cross-section of plants in one period of declining demand. To test the Scale, Strategy (strong), Strategy (weak) and, sometimes, Age or Old age hypotheses, a discrete choice model of the exit decision is estimated. The dependent variable, y it is de ned as follows. For the ith plant that operating in period t: y it ˆ 0 if operating at the end of the period 1 if closed at the end of the period 1 For explanatory variables, measures of size (Size), the number of plants (Multiplant) and age (Age) and other controls, depending on the industry Table â Protection in the Industry Period Mix of instruments Distinctive features 1920^ ^ ^ ^ ^2000 Tari s on components (not cars). Duty paid depended on components. Tari s on components and (from 1957) cars. Manufacturers exempted from exchange controls and component duties. Motor Vehicle Plan ^ tari s; exemptions from tari s for manufacturers and assemblers. Motor Vehicle Plan continued, without small volume plan. Export facilitation introduced (1982). Button plan: tari s falling after 1988; tari - quota system (until 1988), export facilitation expanded. Aimed at protecting local assemblers and component manufacturers. Aimed at encouraging manufacturing, protecting assemblers and component manufacturers. Encouraged manufacturing and also small volume assembly. Tari raised to deter imports. Tari s increased and quotas to restrict imports to 20% of market. Aim to create industry that will survive at low rates of protection. Encourage exports.

6 356 ECONOMIC RECORD DECEMBER and environment, are used. This is referred to as the Basic speci cation. Because our dataset is an unbalanced panel of plants through multiple periods of growing demand and declining demand rather than a crosssection, we make two adjustments for our Basic speci cation. The Strategy (strong) and Strategy (weak) hypotheses suggest the sign on Size and Multiplant may di er across di erent periods with demand. So, rst, we create two dummy variables for periods of increasing demand D and decreasing demand D so to create four new explanatory variables (Size, Size Multiplant, Multiplant ) as follows: Size ˆ Size*D Size ˆ Size*D Multiplant ˆ Multiplant*D Multiplant ˆ Multiplant*D Second, because the increase or decline in demand may vary across the periods, we need to control for this. Hence, we include a measure of the growth of demand, D mkt. Because the degree of government support for the industry also varies over time, we also include as a control the level of protection, Tari : y it ˆ f Size ; Size ; Age; Multiplant ; Multiplant ; D mkt ; Tari 2 If the coe cients on all size variables are negative then the Scale hypothesis is supported and competition determines the pattern of exit. If the coe cient on Multiplant is positive then the Strategy (weak) hypothesis is satis ed, providing weak support for a role for strategy in the pattern of exit. If the coe cient on Size is positive then the Strategy (strong) hypothesis is satis ed, providing unambiguous support for strategy as determining exit. If the coe cient on Age is negative then the Age hypothesis is satis ed; otherwise the Old age hypothesis is satis ed. The coe cient on D mkt is expected to be positive. If the coe cient on Tari is negative, then protection reduces the likelihood of exit. For the Extended speci cation, we adjust the Basic speci cation to allow for di erent relationships between size and the likelihood of exit depending on the size of the plant. In particular, for large plants, economies of scale should dominate strategic factors or even product di erentiation, yielding a negative relationship, whereas for a group of medium-sized plants strategic considerations may suggest that a larger plant should close. So we create a new explanatory variable of the square of size (Size 2 ), to allow for a quadratic relationship between size and exit. We also continue to allow for di erent e ects of size in periods of di erent demand, yielding the Extended speci cation: y it ˆ f Size ; Size 2 ; Size ; Size 2 ; Age; Multiplant ; Multiplant ; D mkt ; Tari 3 If Size and Size 2 have di erent signs, this allows for competition and strategy to play di erent roles in determining exit, depending on the size of the plant. Otherwise, the interpretation of the coe cients is as before. In our Full speci cation, we test the role of two factors that have been highlighted in policy discussions and the academic literature. First, to test the Cost type hypothesis of Dierickx et al. (1991), we introduce the dummy variable (PT ) de ned as follows: PT it ˆ 0 if a low fixed cost and high variable cost plant 1 if a high fixed cost and low variable cost plant PT ;it ˆ PT itd Second, because it was argued in policy discussions that Australian plants were high-cost plants because they assembled too many models and could not take advantage of economies of scale, we introduce a measure of the number of models assembled at the plant (Models). The Full speci cation is: y it ˆ f Size ; Size 2 ; Size ; Size 2 ; Age; Multiplant ; Multiplant ; D mkt ; Tari ; Models; PT 4 The coe cient on Models is expected to be positive as a plant of a given size is more likely to exit the greater the number of models assembled. As we have argued that the negative growth re ected a decline in the willingness to pay, if the coe cient on PT is negative this supports the Cost type hypothesis. Otherwise, the hypothesis is rejected or the decline in demand is best characterized as a decline in customer base. 7 7 In addition, we also perform three sets of sensitivity tests. First, we divide the makes sold into four groups by origin, and allow group speci c values for D and D. Second, we divide the sample by type of cars (small or large). Third, we allow for some di erent coe cients during the fth period of the Button plan. These tests are hindered by small sample sizes, but do not alter the main results. A complete reporting is available from the authors.

7 2001 STRATEGY, SCALE OR POLICY? EXIT IN THE AUSTRALIAN CAR INDUSTRY 357 Table ã List of Explanatory Variables Explanatory variable Source De nition Tari Industry Commission and Average Tari rate predecessors D mkt Stubbs (1972), ABS Average annual growth rates of car registrations Size Various sources* Maximum potential number of cars assembled per year divided by the size of the largest plant for the period Age Various sources* ˆ Year closed ^ Year opened if closed in period; ˆ Year end of period ^ Year opened otherwise Models Various sources* Typical number of models assembled PT Various sources* ˆ 1 if manufacturing plant; ˆ 0 otherwise Multiplant Various sources* Number of assembly and manufacturing plants in company when plant closed or end of period D, D D ˆ 1 for periods 1^3, 0 otherwise D ˆ 1 for periods 4^5, 0 otherwise Notes: * ABS, Industry group and government reports, newspaper accounts, annual reports Complete details on the construction of the dataset are available from the authors. 8 Data are missing for two small assembly plants in the 1950s and the small independent assembly plants in the rst period. These plants mainly assembled British and small American brand cars. V Highlights of the Data In this section, the de nitions and nature of the data used are presented. A complete list of sources and further details on construction are presented in a data appendix available from the authors. The sample includes all manufacturing plants and nearly all assembly plants that operated in the Australian car industry from 1920 to For each plant, for each of the ve periods as de ned in Table 3 in which it operates, the dependant variable, as de ned in (1) is calculated and a matching set of explanatory variables compiled. As discussed earlier, a plant is stated to have exited if it ceases assembling cars or if the main make assembled changes. The de nitions of the explanatory variables required for each of the models are summarized in Table 4. Two items of data require further explanation. Relative size is used rather than absolute size to capture the competitive pressure. A cars per year plant is large in 1938 but small in Second, estimates of plant capacities are not obtainable from one source so we use various sources to obtain estimates. Engineering estimates are used where possible. If these are unavailable, then maximum registrations for each period are used as a proxy. VI Results This section presents the results of estimating the broad speci cations outlined in section IV. To improve the analysis, we vary the speci- cations from those proposed in section IV in two ways. First, Size 2 is omitted from the Extended and Full models as it is highly correlated with Size resulting in the coe cients on both being insigni cant from zero. Second, Age 2 replaces Age in all speci cations except the Basic. This is because experimentation with a nonlinear relationship revealed Age 2 is a signi cant determinant of exit whereas Age is not. Age is retained in the Basic speci cation, consistent with earlier work, for comparison. The results of estimating the Basic, Extended and Full speci cations are presented in Table 5. Estimates of the Basic speci cation are presented in the second and third columns of Table 5. They are similar to the results in other studies. The Strategy (strong) hypothesis is rejected, the Strategy (weak) hypothesis is supported, as is the Scale hypothesis. A Wald test that the di erence in the coe cients on Multiplant and Multiplant is signi cant at the 10% level. The coe cient on Multiplant is only signi cantly positive at the 10% level of signi cance. At best, this only pro-

8 358 ECONOMIC RECORD DECEMBER Variable Coe cient (2) Constant (2.395)* Size ( 2.903)* Size ( 2.214)* Size 2 Tari ( 2.308)* D mkt ( 0.989) Multiplant (0.412) Multiplant (1.889)# Age Age ( 1.055) Table ä The Results Probit Basic model Extended model Full model Marginal e ect (3) ( 2.90)* (2.21)* ( 2.31)* ( 0.99) (0.41) (1.89)# ( 1.06) Coe cient (4) (2.919)* ( 2.905)* (2.160)* ( 2.312)* ( 2.953)* ( 1.158) (1.459) (0.562) (1.705)# Marginal e ect (5) ( 2.90)* (2.16)* ( 2.31)* ( 2.95)* ( 1.16) (1.46) (0.56) (1.71)# Coe cient (6) (2.973)* ( 3.009)* (2.027)* ( 1.958)* ( 3.059)* ( 1.157) (1.237) (0.848) ( 1.888)# PT ( 0.251) Models (1.308) Log-likelihood statistic LR test of model signi cance LR tests of models 21.21* 32.04* 34.04* (Models being compared): Value of test statistic: (Extended vs. Basic) * Notes: t-statistics are included in parentheses in rows 4^15. * Signi cantly di erent from zero at the 5% level; # Signi cantly di erent from zero at the 10% level. Marginal e ect (7) ( 3.01)* (2.03)* ( 1.96)* ( 3.06)* ( 1.16) (1.24) (0.85) ( 1.89)# ( 0.25) (1.31) (Full vs. Extended) vides weak evidence in support of the Strategy (weak) hypothesis. Tari has a signi cantly negative e ect on the likelihood of exit. Altogether, the results suggest competition and trade policy drive the pattern of exit with non-conclusive evidence in support of strategy. Estimates of the Extended speci cation are presented in the fourth and fth columns. First, note an LR test suggests the Extended speci- cation improves signi cantly on the Basic speci- cation. 9 Age 2 is now signi cantly (at the 10% level) negative, supporting the Age hypothesis. 9 The test is performed comparing the basic speci- cation with an encompassing speci cation of the two models. The encompassing speci cation (with a log likelihood statistic of ) is signi cantly better. The extended and encompassing speci cation are not signi- cantly di erent.

9 2001 STRATEGY, SCALE OR POLICY? EXIT IN THE AUSTRALIAN CAR INDUSTRY 359 More strikingly, the di erent signs on Size and Size 2 suggest a hill-shaped relationship between size and exit during the period of decline. In particular, for plants with capacity below and vehicles per year, for periods 4 and 5 respectively, there is a positive relationship between size and exit, suggesting strategy in uences the pattern of exit. These include most of the assembly plants, though none of the manufacturing plants. For all larger plants, competition, and as before, protection determine exit. Estimates of the Full speci cation are presented in columns six and seven. An LR test suggests the Extended and Full models are not signi cantly di erent. The coe cients on Models and PT are not signi cantly di erent from zero, providing no support for the Cost type hypothesis. In summary, these estimates provide the rst econometric evidence supporting the Strategy (strong) hypothesis, over relatively small plant sizes. This, though, highlights a tension in the application of game theoretic models to oligopolies. Oligopolies exist when economies of scale combined with barriers to entry result in a relatively small number of rms. And it is in an oligopoly where there is the most scope for strategic competition. But, if oligopolists are heterogeneous, economies of scale provide a substantial advantage to the large oligopolist and limit the opportunities for strategic competition. While game theory suggests much is possible, the empirical signi cance could well be limited. VII Conclusion In this paper, we analyse the importance of economies of scale, rm strategies and government policy in determining the pattern of exit in the Australian car industry from 1920 to The rst set of major ndings of this paper con rms what have been the most commonly cited causes of the substantial numbers of exits since the late 1950s, namely falling protection and competition driving out high cost plants. The likelihood of exit increases as protection falls; and, for large plants, the larger the plant, the lower the likelihood of exit. The second set of major ndings though add an additional dimension to understanding the pattern of exit over this period. For smaller plants, we nd the larger the plant, the greater the likelihood of exit. This is consistent with a theoretical literature highlighting size as a strategic disadvantage (Ghemawat and Nalebu 1985). Our study is the rst to nd econometric evidence in support of this hypothesis. In a further contribution, we perform the rst direct test of cost structure as an exit determinant ^ as suggested by Dierickx et al. (1991) ^ but fail to nd any evidence supporting this hypothesis. Other factors such as macroeconomic conditions and managerial factors (including the fortunes of parent companies) no doubt in uence, in a complex way, the precise timing of plant closures. However, by considering exit over relatively long periods, we hope to abstract from these potential problems. A short lived boom or optimistic manager will only delay a closure for a while. A further limitation is that the model cannot be used for forecasting but is suggestive that further tari cuts increase the likelihood of further exit, with the smallest producer, Mitsubishi, being at most risk of closure. REFERENCES Baden-Fuller, Charles W.F. (1989), `Exit from Declining Industries and the Case of Steel Casting', Economic Journal 99, 949^61. Birney, S. (1985), Australia's Own: The History of Holden, Golden Press, Sydney, NSW. Caves, R.E. (1998), `Industrial Organization and New Findings on the Turnover and Mobility of Firms', Journal of Economic Literature 36(4), 1947^82. Chrysler Australia (various), Annual Report/Chrysler Australia Ltd, Chrysler Australia, Adelaide, SA. Clarke, H., McCormack, D. and Sunderland, J. (1998), `The Future of Australian Motor Vehicle Assembly', Mimeo, La Trobe University, April. Conlon, R.M. and Perkins, J.A. (1995), `Automotive Industry Policy in Australia: Origins, Impact and Prospects', Economic Papers 14(3), 49^68. öö and öö (1997), `Political Economy of Assistance to the Automobile Industry: Have We Seen It All Before?', Economic Papers 16(2), 76^91. Darwin, N. (1983), The History of Holden Since 1917, E.L. Ford Publications, Newstead, VIC. öö (1986), The History of Ford in Australia, E.L. Ford Publications, Newstead, VIC. Davidson, F.G. and Stewardson, B.R. (1974), Economics and Australian Industry, Longman, Melbourne. Davis, P. (1987), Wheels Across Australia, Marque Publishing Co., Hurtsville, NSW. Davis, T. (1987), The Valiant Book, Marque Publishing Co., Hurtsville, NSW. Deily, M. (1988), `Investment Activity and the Investment Decision', Review of Economics and Statistics 70(4), 565^602. öö (1991), `Exit Strategies and Plant Closing Decisions: The Case of Steel', RAND Journal of Economics 22(2), 250^63.

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