Firm Survival and Chain Growth in a Privatized Retail Liquor Store Industry. Andrew Eckert and Douglas S. West*

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1 Firm Survival and Chain Growth in a Privatized Retail Liquor Store Industry by Andrew Eckert and Douglas S. West* Department of Economics University of Alberta Edmonton, Alberta T6G 2H4 aeckert@ualberta.ca Douglas.west@ualberta.ca February 2007 Abstract This paper uses data on liquor retailing in Alberta since privatization to provide a description of the evolution of a privatized retail industry from the date of its creation. As well, this paper considers which factors contribute to the survival of stores and firms, and the growth and creation of retail liquor store chains. Location, and in particular whether a liquor store is located in a shopping center or near a store of a major supermarket chain, is associated with the survival of a liquor store. JEL Classification Numbers: L11, L81 *The authors would like to thank Nathan Yang and Ben Atkinson for research assistance. The financial support of the Social Sciences and Humanities Research Council is gratefully acknowledged. 1

2 1 Introduction Liquor retailing in Alberta was privatized in September Under the privatization model adopted by the Government of Alberta, liquor products are to be sold through licensed liquor stores that have 90 percent of their sales in beverage alcohol. By December 1995 there were 605 liquor stores operating in the province, up from 205 stores owned and operated by the Government of Alberta in Over the next ten years, there was a rather substantial increase in the store count. By December 2005, there were 1026 liquor stores in Alberta, an increase of 69 percent since Accompanying the growth in store counts has been a change in the proportions of chain store and independent liquor retailers. While in some cases, the growth of particular retail chains has been dramatic, many liquor store chains remain small. Although there may be incentives for chain formation in the privatized retail liquor store industry, they apparently do not affect all firms in the same way and to the same extent. The purpose of this paper is to use data on liquor retailing in Alberta since privatization to provide a description of the evolution of a privatized retail industry from the date of its creation, and to determine which factors contribute to whether a new liquor retailing firm or store survives or ultimately exits the market, either by shutting down stores or by being acquired by another retailer. Potential explanations for survival and growth of both stores and firms will be drawn from the economic literature on firm and industry dynamics, and from the business literature on the survival and growth of franchised chains. Explanations to be considered include a first mover advantage to retailers that entered immediately after privatization, locational 1 See West (2003). Prior to privatization, the Government of Alberta had licensed a combined total of 53 wine stores and cold beer stores. West (2003) describes the retail liquor store industry in Alberta prior to privatization as well as the privatization process. He also examines some of the economic impacts of privatization, including impacts on the store count, product selection, price, government revenues, employment and wages. 2

3 advantages, initial size, and the age of stores and firms. The economic literature on entry and exit has focused almost exclusively on manufacturing, largely ignoring retailing. As well, those studies that have been able to consider entry and exit in retailing have not been able to observe the process from the initiation of a new industry, and have not been able to describe the evolution of a new retail industry from a known starting date. Finally, while differences between retail liquor chains and independents have been studied elsewhere (Dinlersoz, 2004), the analysis was static, and for California, which has a different regulatory structure and allows the sale of liquor within other businesses such as supermarkets. In the next section, the economic literature on entry and exit will be surveyed, the focus being on those papers that generate testable predictions that would be relevant for a retail industry. Hypotheses arising from the literature that can be tested with the liquor store data will be stated and discussed. Section 3 contains a description of the liquor store data used in our analysis, along with figures on annual store counts, the size distribution of chains, and entry, exit, moves, and store conversions for both independents and liquor store chains. Section 4 reports the results of our examination of the testable predictions set out in Section 2, while Section 5 contains a summary and our concluding remarks. 2 Entry, Exit, Survival and Growth 2.1 Theory Economic theory on the dynamics of firms and the evolution of markets and industries primarily has considered these issues at a general level, and has not focused specifically on retailing. Jovanovic (1982) has constructed a model in a widely cited paper that assumes that at 3

4 the time of entry, a firm s potential profitability is unknown to the entrepreneur. Potential profitability is distributed randomly across firms, so that some firms will ultimately be profitable while others will not. Each period that a firm operates, it learns about its potential profitability. Because yearly profits are random, a firm cannot learn completely about its profitability in a single year, but updates its beliefs about potential profitability every year. Eventually, if a firm s updated beliefs suggest that it is sufficiently unlikely that the firm will be profitable over time, the firm will exit. Alternatively, firms that learn that they are profitable will grow. 2 Jovanovic s model has been interpreted as generating the following predictions: 1. The size distribution of surviving firms increases over time. 2. The hazard rate (the probability that a firm will shut down this year given that it was in operation last year) is nonincreasing in the current size of the firm, conditional on age. 3. The hazard rate of a firm is nonincreasing in its age. That is, as firms get older, it is less likely (or just as likely) that they will shut down. 4. As the age of firms increases, the variance in growth rates is nonincreasing. 3 A well known alternative model is presented by Pakes and Ericson (1998). They outline a theory of active exploration, in which every period a firm can make investments in its business (e.g., through research and development) which have random contributions to the profitability of the firm. Firms which get better than expected results from these investments survive and grow, 2 Jovanovic (1982) assumes homogeneous products. Differentiated products are considered in Jovanovic and Rob (1987). 3 See Pakes and Ericson (1998). Pakes and Ericson argue that predictions 3 and 4 do not actually follow from a general model of passive learning. It may take time for a firm to come to the realization that it is not profitable, so that the hazard rate in the second or third year may be higher than the hazard rate in the first year. Predictions 1 and 2 stand on firmer ground. Unfortunately, testing these predictions requires data on firm size, which may be lacking. Pakes and Ericson (1998) develop another prediction of the passive learning model: as time passes, the original size of the firm should remain a determinant of its current size. 4

5 while firms that get worse than expected results dwindle and exit. 4 Much of the remaining theory on entry and exit focuses on explaining a particular stylized fact: the observation of an S-shaped curve describing the number of firms in a new industry over time, along which the number of competitors in a new industry increases slowly at first, speeds up, and then converges to an equilibrium level (possibly then declining at the end of the industry s life cycle). Evidence in support of such a time path is given, for example, in Gort and Klepper (1982). Theoretical explanations behind the S-curve are offered in (1) Cabral (1993), where a coordination problem among potential entrants can result in either initial over entry or under entry; (2) Rob (1991) and Vettas (1997), where initial under-entry can occur from uncertainty by new entrants over the market demand function; and (3) Jovanovic and Lach (2001), in which firms may choose to delay entry so that they may learn from the experiences of the initial entrants. None of these explanations for the possible observation of an S-curve in the privatized retail liquor store industry is particularly compelling. 2.2 Empirical Work While there is an enormous empirical literature that examines the entry, exit, and growth rates of firms, this literature has been focused almost entirely on manufacturing. 5 Geroski (1995), 4 While this sort of theory may be less applicable to liquor retailing, one can think of a liquor store s investment in its product selection as making a random contribution to the profitability of the firm, given the widely different preferences of consumers. Members of chain stores could be attractive to consumers if they have predictable and attractive product selections, thus enhancing their growth and survival prospects. 5 While we are not aware of a privatized retail industry (other than liquor stores in Alberta) whose evolution postprivatization has been studied, two papers look at the effects of introducing or removing regulations that increase the difficulty of starting new retail businesses. Carree and Nijkamp (2001) examine the effect of reductions in institutional barriers to entry into Dutch retailing, and conclude that equilibrium firm counts have increased, as has the speed with which the firm count adjusts to equilibrium. Bertrand and Kramarz (2002) examine the effect of the 5

6 who surveys a large number of empirical studies of manufacturing, identifies many stylized facts and results. These include that entry and exit rates tend to be highly and positively correlated, that most entrants have low survival rates and may take a decade to achieve the size of incumbents, that entry rates (and the types of entrants) vary over the life of a market, that entry reacts slowly to high profits, and that the size and age of a firm are correlated with its survival and growth. Audretsch et al. (1999) examine whether certain of these stylized facts and results are maintained for retailing, using data on Dutch firms from 1985 to The authors find that, as in manufacturing, entry is common, is typically accompanied with exit, and survival rates among entrants are low. However, the authors (p. 250) report that the relationships between firm survival, growth, age and size, which have emerged so consistently in manufacturing as to constitute a Stylized Result (Geroski, 1995) or Statistical Regularities (Sutton, 1997), do not exist in the services for all but the smallest firms. The post-entry dynamics appear to be strikingly different in services than in manufacturing. Likewise, Pakes and Ericson (1998), using firm level data for Wisconsin from 1978 to 1986, find differences between the manufacturing and retailing sectors. Similar conclusions are reached by Petrunia (2003) using data for retailing and manufacturing firms in Canada. 6 The survival and growth of franchised chains is examined empirically by Kosova and Lafontaine (2005), using a panel data set of approximately 1000 franchised chains over more introduction of a new and restrictive zoning approval process for the startup of new retail businesses in France. The authors conclude that these zoning regulations negatively affected employment and increased concentration. 6 As well, entry and exit in retailing have been studied using data aggregated to the level of the store type. See Carree and Thurik (1996). 6

7 than 20 years. 7 A number of other papers attempt to examine how the survival rates of independents differ from the survival rates of franchisees, and consider factors determining the survival rates of franchisees and franchisors. 8 The results from this literature show considerable variation, perhaps due to differences in the data sets and variable definitions chosen. In a related article, Holmberg and Morgan (2003) argue that it is an oversimplification to focus on whether a franchisee survives or fails. The authors identify five different events in the life of a franchisee that might represent its failure. Our study of liquor store survival will also have to give due consideration to the various ways (e.g., acquisition, shutdown, a move) that a store s status might change over time. 2.3 Hypotheses The central questions of this paper regard the factors that determine whether a new liquor store firm, and a new liquor store, will survive in the market and which firms and stores will disappear. The literature cited above, as well as some papers cited below, lead to the following hypotheses regarding the determinants of survival: Hypothesis 1a: Firms that entered the retail liquor store industry in the years immediately following privatization will have a higher survival rate than firms that entered in subsequent years. Hypothesis 1b: The probability that a new independent liquor store will survive is decreasing in the year of its entry. Likewise, the probability that a new chain liquor store will survive is decreasing in the year of its entry. The premise underlying Hypotheses 1a and 1b is that early entrants to a market enjoy a 7 In a related paper, Jarmin, Klimek, and Miranda (2004) construct a data set of retail firms over 26 years using data from the U.S. Census Bureau. The authors find that while many general patterns from manufacturing data are also observed in the retailing data, entry and exit rates tend to be larger in retailing. 8 For recent studies in this literature, see Holmberg and Morgan (2003), Bates (1998), Lafontaine and Shaw (1998), Litz and Stewart (1998), and Michael (2003). 7

8 first-mover advantage. Michael (2003) discusses two possible sources of a first mover advantage: the ability to pre-empt certain resources (such as real estate) and the ability to shape consumer preferences. Michael (2003) then finds evidence in support of the existence of a first mover advantage in the restaurant industry. 9 The alternative hypothesis, that early entrants will have a lower survival rate than firms that enter later in the sample, might find support in the data if late entrants are able to learn from their predecessors (as suggested by Jovanovic and Lach, 2001). Again, this is not obviously the case in liquor retailing. Another hypothesis dealing with timing of entry and survival is as follows: Hypothesis 2: As firms get older they are more likely to survive. Likewise, as stores get older they are more likely to survive. Hypothesis 2 is adapted from prediction three, given in Section 2.1, for the Jovanovic (1982) model of industry evolution. In the manufacturing industries, this hypothesis is typically tested at the level of the firm. However, the basic reasoning also applies at the store level. The intuition is that information that reveals that a firm or a store is ultimately unprofitable will be realized sooner rather than later; if a store has been operating for a long time, then it will have received a series of positive signals, and is unlikely to receive a signal so negative as to prompt it to shut down. Note that in considering the survival rates of individual stores, Hypothesis 1 controls for whether a store is an independent or part of a chain. If existing chains have access to capital that allows them to outbid new entrants for better locations, and have reputations that make them more attractive tenants than new firms, then new stores that are part of chains may be expected to 9 Another analysis of the role of the first mover advantage in industry evolution is given in Agarwal and Gort (2001). 8

9 have higher survival rates than new independent stores. 10 Likewise, to the extent that a retail chain enjoys certain scale economies, or is better able to quickly achieve name brand recognition, one should expect firms that enter the market as chains to be more successful. As well, previously existing chains will have greater business and industry experience than new entrants, and will have better insight into selecting profitable locations. Note that this hypothesis is essentially the second hypothesis adapted from Jovanovic (1982), that conditional on age the probability that a firm will shut down is nonincreasing in firm size. Alternatively, independents might be prepared to remain in the market with lower returns than would be acceptable to a retail chain. Some independent liquor stores might be operated as family businesses, and underestimate the opportunity costs of staying in the market. There may well be evidence of independents continuing in business even as their trade areas are squeezed by new entry in their neighborhoods. follows: Based on the preceding discussion, Hypotheses 3a and 3b can be formally stated as Hypothesis 3a: Liquor stores that enter the industry as the sole store of a new independent will have a lower survival rate than new stores that are part of a retail chain. Hypothesis 3b: Firms that enter the market as chains will have higher success rates than firms that enter the market as independents. Hypotheses 1 and 3 are based on the assumption that retail chains, and firms that choose store locations early, will acquire the better locations. This leads us to the following general hypothesis: Hypothesis 4: The survival of a store will depend on its location, holding everything else constant. This hypothesis is related to the first mover advantage. If certain locations are more 10 This same explanation has been given for the prevalence of retail chains over independents in planned regional shopping centers. See Kinnard and Messner (1972). 9

10 profitable than others, and the early entrants acquire them, one would expect to find that survival depends upon location. Likewise, if chains are better able to acquire the more profitable locations, then we should expect to see the chains choose different types of locations compared to those of the independents. However, it is an empirical question as to what constitutes a good location. The observation that for some people, a liquor purchase is part of a multipurpose shopping trip would suggest that liquor store locations near complementary retailers will be attractive. In particular, we expect locations near major supermarkets to be most attractive because of the demand externalities that liquor stores and supermarkets confer on each other. In addition, locations in planned shopping centers should be attractive for two reasons. First, planned center developers will choose tenants and their locations within the shopping center to take advantage of demand externalities among retail stores. Second, in unplanned retail locations, high liquor store profits can reasonably be expected to attract new entry, leading to reduced liquor store profits. A planned shopping center, on the other hand, is controlled by a single owner/developer, and entry by competing liquor stores into the center can be prevented. 3 Data and Description of an Evolving Retail Liquor Store Industry The privatization of Alberta s liquor stores began in September Between September 4, 1993, and March 5, 1994, all government-owned Alberta Liquor Control Board (ALCB) stores were closed. In Calgary, all 24 ALCB stores were converted to private liquor stores, while 20 out of 23 ALCB stores in Edmonton were converted to private liquor stores. Apart from minimal licensing requirements and municipal zoning restrictions, there are no government imposed restrictions on entry into the retail liquor store industry in Alberta. There are, however, two important government-imposed restrictions that affect liquor retailers: (1) 10

11 liquor products are to be sold in liquor stores that have 90 percent of their sales in beverage alcohol; the liquor store must be a physically separate business, with no cross marketing or joint advertising with any other commonly owned or affiliated business, (2) uniform wholesale prices: all liquor stores in Alberta must purchase their liquor products from the government wholesaler at prices published on a wholesale price list that is permitted to change every two weeks. While retailers cannot negotiate discounts with product suppliers, they are allowed to set their own retail prices. The data set consists of annual observations on the names and locations of all liquor stores operating in Calgary and Edmonton, Alberta from December 1995 to December In addition, data are also available on the locations of all ALCB liquor stores and private cold beer and wine stores in operation just prior to privatization in September A key focus of this paper is the development of retail chains. Hence, we must determine whether a liquor store is part of a chain, and track the evolution of that chain over time. The data provide two ways to identify chains. First, in any year, two stores will be considered to be part of the same chain if they share the same name. However, it is possible that two stores will be part of the same chain, but bear different names. For that reason, we also consider the license holder information provided in the data. If two stores have different names but the same license holder, then these two stores are also considered to be part of the same chain. Our exploration of the characteristics of the privatized retail liquor store industry in Calgary and Edmonton begins in Table 1, which presents the store counts and chain store counts for each year for both cities. Table 1 shows that the percentage of stores accounted for by chain stores increased from 24 percent to 39 percent in Edmonton over the sample period, and from 25 percent to 52 percent in Calgary. Note that for Edmonton, the fraction of stores accounted for by 11

12 chains has been growing slowly over time. It increased from 24 percent to 36 percent between December 1995 and December 1998, and increased by 3 percent (to 39 percent) over the last seven years of the sample. Likewise, in Calgary, the percentage of stores accounted for by chains has been growing throughout the sample period, with much of that increase occurring in the first half of the sample period. While there is entry and exit in any given year of the sample, the net store count has continued to rise over time. The average annual increase in the store count is approximately 9 stores in Edmonton and 15 stores in Calgary, giving Calgary a larger end of sample store count of 266 compared to Edmonton s 187. Figure 1 shows that the store counts in Calgary and Edmonton are not well-described by an S-curve. The increase in the store counts was quite rapid after privatization. Store counts then increase by almost the same number per year after December The populations of Calgary and Edmonton were 727,719 and 626,999 in 1993, respectively, while they were 956,078 and 712,391 in 2005, respectively. 11 Figure 2 shows that the populations per store in Calgary and Edmonton were different at the time of privatization in 1993, but have since remarkably converged to virtually the same average. Of interest is the decline in the average population per store from more than 6000 in December 1995 to less than 4000 in December The increase in population in both cities supports the increase in the store counts, but the rate of increase in the store count clearly exceeds that of the population. Table 2 gives a breakdown of the number and sizes of chains in December 1995 and December 2005 for Edmonton and Calgary combined. From December 1995 to December 2005, the number of chains present in Edmonton or Calgary increased by 128 percent, from 18 to Historical population figures for Calgary and Edmonton were obtained from the websites for these cities. See and last accessed on November 9,

13 The average number of stores in each chain in Edmonton and Calgary increased from 2.9 to 5.1, an increase of 76 percent. 12 Table 2 also illustrates that most chains are small. In December 1995, 83 percent of chains had four or fewer stores in Edmonton and Calgary; in December 2005, this percentage is 75. With respect to the origins of the 41 chains present in December 2005, only 6 were present as chains in December 1995, while another 12 were present as independents. Next, basic entry and exit patterns at the level of the store are examined. Table 3 provides the number of store entries and exits in each year of the sample, divided into stores that entered the sample as independents and those that entered as chain stores. Here a store represents a physical location, and is considered to enter the market the first year that a liquor store is found at that address. A store exits the market when it is found in the market in one year but not in the next. Table 3 also reports the number of stores that changed locations in a given year, and the number of stores each year that converted from independents to chain stores, and that converted from chain stores to independents. As Table 3 illustrates, throughout the sample period there has been considerable entry and exit. Each year, total entry has ranged between 6 and 18 percent of the number of stores in Edmonton and Calgary, while each year between 2 and 4 percent of stores have exited. On average, each year there have been almost four times as many entries as exits. No obvious trend is demonstrated by Table 3, as the numbers of entries and exits per year seem reasonably stable over time. There is, however, a decline in the entry rate over time, while no obvious trend emerges for exits. Regarding the relative evolutions of independents and chains, according to Table 3, 12 Table 2 counts only stores in Edmonton and Calgary. A chain present in these cities may also have stores elsewhere in the province that are not counted. As a consequence, a chain may have only one store in Edmonton/Calgary, but be counted as a chain because it has additional stores elsewhere in the province. 13

14 independents accounted for 57 percent of all entry, and 66 percent of all exit. There are no obvious strong trends in the number of independents that enter each year or in the number of chains that enter each year. The fraction of new entry each year accounted for by independents falls over the sample, from 63 percent in the first year of the sample to 40 percent in the last year of the sample. Finally, Table 3 provides some insight into the question of whether the number of chain stores grew through conversion of independents or through building new stores. Over the sample, 88 stores converted from independents to chains, while 137 new stores entered the sample as chain stores. Hence conversions accounted for 39 percent of new additions to the chain store category. We now turn to an empirical assessment of Hypotheses 1 to 4. 4 Examination of Hypotheses The hypotheses under consideration all focus on the survival of firms and stores that entered the market after privatization. To address survival at the level of the store, Table 4 presents, for the period from December 1995 to December 2000, the number of new stores in each year (for December 1995, we report all stores operating at that time), and the fates of those stores after five years. 13 So for example, in December 1995 there were 161 independent stores. By December 2000 (five years later), 17 had shut down (not including moved stores), 8 had moved to new locations, 111 were still independents, and 25 had become chain stores. Likewise, there were 24 new independents between December 1995 and December 1996, 3 of which shut down or moved by December Similar results obtain if a three year horizon is considered. 14

15 4.1 Hypotheses 1a and 1b: The First Mover Advantage Under Hypothesis 1b, the survival probability of an independent store should be higher for those stores that entered the market early than for those that entered later. The same should be true of chain stores. However, no such pattern emerges from Table 4. Of the original 161 independent stores, 11 percent had shut down five years later (by December 2000). In comparison, 9 percent of independent stores that entered between December 1995 and December 2000 had shut down after 5 years. Similarly, the five year shut down rate was 21 percent for chain stores that were in the sample as of December 1995, compared to 10 percent for chain stores that entered between December 1995 and December Hence, for independent stores, the five year survival rate for the initial independents is roughly the same as for subsequent entrants; for chain stores, the shutdown rate is twice as high for the initial stores as for subsequent stores. Hypothesis 1a states that the survival rates of the initial firms to enter the market should be greater than the survival rates of subsequent entrant firms. Using our method of tracking firms discussed in Section 2, our results for firms are consistent with our findings for stores. Of the independent firms observed in the sample as of December 1995, 25 percent had exited the sample by December (Note that not all of these involved a store closing its doors; in many cases, an independent firm can exit while the store it operated is acquired by a chain or 14 One issue is that stores observed in the sample in December 1995 may have entered in 1995 or in previous years, meaning that the data in Table 4 do not indicate the number of the stores existing in 1995 to survive their first five years of life. Assuming that for stores entering before December 1995 the license date given represents the date of entry, and assuming that the chain / independent status of a store as of December 1995 represents its status at entry, one can demonstrate that this issue does not impact results. By these assumptions, the five year shutdown rate of stores entering in 1995 is nine percent for independents and eight percent for chains, consistent with subsequent years as described in Table 4. For stores entering in 1994, the five year shutdown rate conditional on surviving the first year is 11 percent for independents and 24 percent for chains; for stores entering in 1993, this conditional shutdown rate is 10 percent for independents and 8 percent for chains. Since these conditional rates must be the same or lower than the unconditional five year survival rates, these results indicate that, contrary to Hypothesis 1a, it is not the case that stores that entered at the beginning of the sample had a higher rate of survival than those that entered later. 15

16 independent.) In comparison, for new independent entrants over the period from December 1995 to December 2000, the five year exit rate was 25 percent. Thus, there appears to be no difference between the survival rates of independent firms that entered by December 1995, and those that entered from December 1995 to December Likewise, the chain survival data are not consistent with Hypothesis 1a. For chains, the five year exit rate of the original 18 chains is 17 percent. Over the subsequent five years, only nine new firms entered the data set as chains. Of these nine firms, none exited over the first five years of life. 4.2 Hypothesis 2: Exit Rate and Age Hypothesis 2 states that exit rates of stores and firms should decrease with the age of the store or firm. The evidence, however, is mixed. While the five year exit rate of the initial independent stores was 11 percent, the rate of exit of the surviving stores over the next five years from 2000 to 2005 was 13 percent. For the initial chain stores, the expected result is observed; the chain store exit rate fell from 21 percent over the first five years to 2 percent for the surviving chain stores over the subsequent five years. Hence, the data for the initial 1995 stores show an exit rate that is roughly constant over age for independents, but decreasing with age for chain stores. 15 Different results are obtained for stores that enter after For stores that enter the data between December 1995 and December 1999, the exit rate after three years is 9 percent for both independent and chain stores. However, conditional on surviving the first three years, the exit rate over the second three years falls to four percent for chains; none of the 73 independent stores 15 Similar conclusions were reached using a three-year interval instead of a five-year interval. Of the stores existing in December 1995, 15 percent of chain stores shut down in the first three years, compared to eight percent of independent stores. Conditional on surviving the first three years, seven percent of the remaining chain stores and eight percent of the remaining independent stores survive the next three year period. 16

17 that survived their first three years after entry shut down during their second three year period. Results are similarly weak when the data are considered at the level of the firm. For independents, the five year exit rate of the initial independents is 25 percent, compared to an exit rate of the surviving firms over the next five years of 30 percent (the numbers are 15 percent and 30 percent using a three year interval). For independent entrant firms over the period from December 1995 to December 1999, the exit rate after the first three years is 15 percent, compared to a rate of 12 percent for the second five years. For chain firms, of the original 18 chains, 17 percent exited in the first five years, while 25 percent of the survivors exited over the next five years. As noted earlier, none of the chains that entered between December 1995 and December 1999 shut down after six years. 4.3 Hypotheses 3a and 3b: Chains versus Independents Hypotheses 3a and 3b state that stores that enter as part of a chain and firms that enter as chains should have higher survival rates than stores and firms that enter as independents. As illustrated in Table 4, we find no evidence to support such a relationship at the level of the store. As discussed above, the fraction of the initial independent stores that shut down within five years was approximately one-half the fraction of chain stores that shut down within five years. For stores entering between December 1995 and December 2000, the shutdown rates for independent and chain stores were approximately equal. Hence, the data suggest that, at least initially, chain stores had a lower survival rate than independents, which would support the alternative hypothesis that independents are willing to remain open in circumstances and for profits not 17

18 considered acceptable for a chain. 16 A similar result obtains at the level of the firm. Of the initial independent firms, 27 percent had disappeared from the data by December 2000, and 47 percent by December 2005, compared to exit rates of 17 percent and 50 percent for the 18 retail chains observed in the data in December Hypothesis 4: Location Matters Hypothesis 4 is that the locations of stores should be related to their survival. This follows since locations are expected to differ in terms of local demand. As well, stores that adopt certain locations may enjoy barriers to entry that prevent other stores from entering nearby. In this paper, three distinct locational features are considered: whether the liquor store is located in a planned shopping center, whether the liquor store is located near a major chain supermarket, and whether the liquor store has located at the site of an original pre-privatization liquor store (either government operated or an original private beer or wine store). Beyond considering how these characteristics affect the survival of a liquor store, how the evolution of the market towards a higher presence of chain stores was related to these characteristics will be examined. Did chain stores initially choose different types of locations than independents? Did retail chains evolve towards different characteristics than independents? Regarding shopping centers, a first consideration is that shopping centers are typically thought of as existing in a hierarchy. The shopping center hierarchy that has been analyzed in the past contains at least four levels of centers: neighborhood centers, community centers, regional centers, and the central retail district. The hierarchy of shopping centers has two important 16 Note however that, using a Pearson test of association (see Winkler and Hays, 1975, ), the null hypothesis of independence between whether a 1995 store was a chain or independent and whether it shut down after five years cannot be rejected in favor of the alternative of an association at standard significance levels. 18

19 characteristics: (1) a large number of small centers and a small number of large centers, and (2) centers at a given level of the hierarchy contain all of the store types that locate in lower level centers, plus additional store types as well. 17 A shopping center s position in the hierarchy will be determined by its store count as reported in the city directories for Calgary or Edmonton. Shopping centers will be categorized as neighborhood centers if they contain between 7 and 16 retail stores, community centers if they contain between 17 and 49 stores, and regional centers if they contain 50 or more stores. Shopping centers will also be designated as planned if they are under the control of a single owner/developer. There are two expected advantages to a liquor store locating in a planned shopping center. The first is that, to the extent that a shopping center facilitates multipurpose shopping, a liquor store will benefit from the demand externality created by other retailers in the center that would be part of a multipurpose trip. Secondly, because entry into the shopping center is controlled by a single owner/developer, the liquor store may be able to sign a contract that prohibits additional liquor store entry, especially given that such entry will likely be viewed by the shopping center owner/developer as creating excess liquor store capacity. It seems most likely that the demand externality from multipurpose shopping will be greatest for a liquor store in a community or neighborhood center, as these types of centers are expected to contain a higher proportion of stores selling goods and services that would be purchased during the same multipurpose trip as liquor. 18 Therefore, in most of what follows we will consider whether a liquor store is in a planned neighborhood or community shopping center. Liquor stores in planned centers with six or fewer retailers are treated the same way as 17 On the characteristics of the shopping center hierarchy in Edmonton, see West, Von Hohenbalken, and Kroner (1985). 18 As well, smaller centers are more likely to offer external access to a liquor store than large shopping malls, facilitating the consumer s purchase of liquor products. 19

20 liquor stores in unplanned centers. 19 Table 5 gives a breakdown of independent and chain liquor stores in operation in December 1995, and that entered from 1996 to 2000, according to this categorization. Table 6 gives a breakdown, for both chain liquor stores and independents in 1995 and 2005, of whether the store was near a major supermarket chain store. Locations of major supermarket chain stores were obtained from telephone directories. A liquor store is considered to be near a supermarket if it is on the same block, or on the next block on either side of the supermarket. 20 The categorizations used in Tables 5 and 6 were necessary in order to create Table 7, which presents the five and ten year survival rates of liquor stores that were operating by December 1995, according to different locational characteristics. In Table 7, a store that moves is not considered to have shut down. The following observations emerge from Table 7. First, there is essentially no association between whether a liquor store is located in one of the original government-operated ALCB stores and whether it shuts down within five or ten years. Such an association may have been expected, since the original government operated stores would have been the first available to initial entrants, and snapped up by those firms hoping to be among the first to enter the market. Likewise, Table 7 provides little evidence of a relationship between shutdown and whether a liquor store was operating pre-privatization as a private beer or wine store. This is a bit surprising given that these stores had the advantage of being established successful businesses prior to privatization, and one might expect them to have a higher survival rate. Many of the pre- 19 Planned centers with six or fewer retailers do not usually have the characteristics of a neighborhood shopping center, but rather they are usually clusters of stores sharing the same address, and may, for example, be located on the main floor of an office building. 20 Small grocery stores not associated with a major supermarket chain were not included because of difficulties in identifying them, and because they are expected to confer less benefit to a nearby liquor store than would a store with a major supermarket chain. 20

21 privatization beer stores were also operated in conjunction with a motel or hotel, conferring a positive demand externality on the liquor store. 21 Table 7 does suggest associations between shutdown and whether a liquor store locates near major chain supermarkets, or in planned shopping centers at the neighborhood and community levels. We find that only four percent (two stores) of all liquor stores in 1995 that were near major chain supermarkets shut down after five years, compared to 17 percent of liquor stores not near supermarkets. After 10 years, these numbers increase to 12 and 24 percent, respectively. The association between shutdown and location in planned neighborhood and community centers seems even stronger. After five years (10 years), only 5 (7) percent of stores in these neighborhood and community centers had shut down, compared to 18 (31) percent of stores not in planned neighborhood or community centers. 22 Interestingly, the association between shutdown and whether a store is located near a supermarket or in a planned center seems to be related to whether the store entered the market originally as a chain or as an independent retailer. 23 Table 8 gives five and ten year shutdown rates, for independents and chains, according to whether a store was near a supermarket or in a planned neighborhood or community center. As Table 8 shows, while for independents the survival rates of stores near a supermarket or in a planned center are not greatly different from 21 Note that here and elsewhere in the paper, our ability to identify associations between characteristics and shutdown is hampered by the low rate of shutdown and the small numbers of liquor stores in certain categories. 22 Pearson tests of association reject the null hypothesis of independence between whether a store is near a supermarket and whether it shuts down by December 2000, and between whether a store is in a planned neighborhood or community center and whether it shuts down by December 2000, at the five percent level. For a 10 year survival rate, independence is only rejected at the five percent level for whether a store is in a neighborhood or community center. 23 The data on new entrants over the period from December 1995 to December 2000 was also examined for an association between exit and whether the store was near a major supermarket chain store or in a planned center of the neighborhood or community sizes. Results regarding new entrants are weaker. Five percent (two stores) of new entrants near supermarkets shut down after five years, compared to 12 percent of new entrants not near supermarkets. Likewise, five percent (two stores) of new entrants in neighborhood or community planned centers shut down after five years, compared to 12 percent of other new entrants. Pearson tests of association could not reject the null of independence at reasonable significance levels. 21

22 survival rates for other independents, the same cannot be said for chain stores. While only one of the original 32 chain stores that were near supermarkets or in planned neighborhood/community centers shut down after five years, approximately one-half of all other chain stores operating in 1995 had shut down after five years. 24 Thus, Table 8 suggests that the difference in survival rates identified in Table 7 is primarily driven by chain stores. The observation that chain stores were primarily shutting down stores that were not in planned neighborhood/community centers or near supermarkets raises the question of whether the distributions of chains and independents across locations near supermarkets or in planned centers changed over the sample. Table 6 presents the numbers of chain stores and independents near and not near supermarkets in 1995 and While in 1995, 40 percent of liquor stores near supermarkets were chain stores, by 2005 this fraction had increased to 78 percent. Likewise, the percentage of stores not near supermarkets that are chain stores has increased from 19 to 37 percent. Hence, Table 6 suggests that by the end of 2005, while most liquor stores near supermarkets were chain stores, chains continued to operate stores that were not located near supermarkets. The 78 chain stores near supermarkets in 2005 can be further broken down. Thirty-nine of these are liquor stores owned by a supermarket chain or its parent. Of the remaining 41, 25 are of the Liquor Depot chain; 52 percent of all Liquor Depot stores are located near supermarkets, compared with only 14 percent of all other chain liquor stores (ignoring those affiliated with supermarket chains), and only 9 percent of independent stores. Hence, the dramatic growth in the percentage of stores near supermarkets that are chains is being driven by the emergence of 24 An additional note is that of the nine stores near major supermarkets or in neighborhood of community shopping centers that shut down, two were replaced within the same shopping center (but at a different store number within the shopping center) by a store of the Liquor Depot chain. Hence these shopping centers were never without liquor stores in our data. 22

23 supermarket retailers into liquor retailing, and by the expansion of Liquor Depot. A similar evolution is not observed in the percentage of stores in planned centers that are chains. Because city directories were not available for recent years, to consider the fraction of liquor stores in planned centers in 2005 we approximated whether a store was in a planned center by considering whether its address listed a separate store number along with its street address. As well, all Real Canadian Liquor Stores, Calgary Co-op Liquor Stores and Safeway Liquor Stores are known to be in planned centers. This method was applied to construct an approximate planned center variable for both 1995 and According to this data, the fraction of liquor stores in planned centers that are chains increased from 45 to 60 percent from 1995 to 2005; however, much of this is likely attributed to the emergence of supermarkets into liquor retailing. A final issue to be considered in this section is that, while most stores that shut down are not in planned neighborhood or community shopping centers or near a major chain supermarket, it is unclear what determines which stores not in these types of shopping centers or near major supermarkets shut down. A first consideration is that exit may have occurred in areas where there was initial over entry, while little or no exit occurred where there was initial under entry. To consider this possibility, we divided the two cities into a total of 15 broad regions based on natural water boundaries and breaks in the spatial distribution of stores. We then computed the population per store of each region, based on 1995 store counts and 1996 census data. The tenyear shutdown rate of the original 1995 stores in the 8 regions with the highest population per store was 25 percent, compared to a 10 year shutdown rate of 21 percent of the 7 regions with the lowest population per store. Similar computations also failed to identify a strong relationship between population per store and shutdown. 25 Our approximate variable for 1995 matches up with our variable built from city directories for 83 percent of stores. 23

24 Likewise, the 8 regions with the highest population growth rates had a 10 year shutdown rate of 22 percent, compared to a rate of 23 percent for the 7 regions with the lowest population growth rates. Similarly, no important difference in shutdown rates could be identified between the outskirts of the cities, where population growth is expected to be highest, and the interior cores of the two cities. A second consideration is the possibility that exit may be triggered by local new entry. To consider this, we identified, for each year of the data from December 1995 to January 2004, each store that faced entry by at least one store within a 0.5 km radius over the next two years. The shutdown rates of these stores facing new competition were then compared to the shutdown rates of stores not facing new competition. On average, 91 percent of stores that faced new entry in a given year were still operating two years later, compared to 94 percent of stores in a given year that did not face entry. Hence there is little evidence to support local entry as an important driver of exit. 26 Overall, therefore, it does not appear that local competition, entry, or local population offer a strong explanation for most exit decisions. Indeed, exit may be the result of a long list of factors, including changes to the road network, the acquisition of one chain by another, who then closes some stores, and poor management. The main conclusions of this section regarding the fates of the original 1995 stores are confirmed by basic probit estimation. Econometric probit models, in which the probability that an original 1995 store survived a fixed number of years is a function of locational characteristics and whether the store is a chain store, were estimated. Locational characteristics controlled for were whether the store was near a major chain supermarket, whether it was in a planned neighborhood or community center, whether it was an original ALCB store or beer/wine store, whether it was in the outskirts of the city, which city it was in, and the population per store of the 26 Of course, these comparisons do not control for existing local competition, or local population counts. 24