The economic impact of a mega-multi-mall: Estimation issues in the case of West Edmonton Mall

I~ U T T E R W O R T H I N E M A N N

0261-5177(95)00040-2

Tourism Management, Vol. 16, No. 5, pp. 36%373, 1995 Copyright © 1995 Elsevier Science Ltd

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The economic impact of a mega-multi-mall

Estimation issues in the case of West Edmonton Mall

Adam Finn Canadian Institute of Retailing and Services Studies, Department of Marketing and Economic Analysis, Faculty of Business, University of Alberta, Edmonton, Alta, Canada T6G 2R6, tel: +1 403 492-5369

Tulin Erdem Haas School of Business, University of California, Berkeley, USA

Casual observation suggests that a mega-multi-mail , such as West Edmonton Mall (WEM) and the Mall of America, which combines a very large shopping centre with a theme park, can play an important role in generating urban tourism. In the course of a campaign directed at obtaining government subsidies, the developers of WEM claimed it 'brings nine million tourists per year to Edmonton' and that 'Tourists attracted to WEM and Canada Fantasyland spent over $700 million in Edmonton alone last year'. Thus it is important for governments to be able to assess the validity of such claims, and to determine whether such malls really generate substantial externalities. But mega-multi-malls simultaneously service a number of consumer markets, making an assessment more complicated than for most other tourist attractions. Here the approach WEM used to support its claims is described, and then the various methodological issues which arise when trying to obtain valid estimates of the economic impact of an institution such as WEM are discussed. Following this assessment useful supplementary data are identified, the data used by WEM are adjusted to account for clear methodological weaknesses, and the supplementary and revised data are then combined to produce a more reasonable estimate of the true economic impact of WEM.

This article examines the problem of assessing the economic impact of a new urban tourism destina- tion, the mega-multi-mall (MMM). Finn and Rigby, who noted the synergy between the theme park and shopping facilities at West Edmonton Mall (WEM), predicted there could be eight or 10 such MMMs in North America by the year 2000.1 The apparent success of the Mall of America in Bloomington, Minnesota may support their prediction. 2 MMM developers have promoted the economic benefits of attracting large numbers of tourists to their centres. In a 1987 advertising campaign, Triple Five, the developer of WEM, claimed that it 'brings nine million tourists per year to Edmonton ' and that 'tourists attracted to WEM and Canada Fantasyland

spent over $700 million in Edmonton alone last year'.

It is important to be able to evaluate such claims because they have been used to try to obtain regional and local government subsidies or tax concessions for MMMs in Edmonton and in several European cities. 3 The Mall of America reportedly received $160 million in concessions from city and state governments, as well as $80 million in taxes set aside for road building. New York State was reported to have offered about $400 million in incentives to have Triple Five build a $1.2 billion MMM at Niagara Falls, rather than in Southern Ontario. 4

Isolating the economic impact of an MMM is difficult, because local residents must be disting-

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The economic impact of an MMM: A Finn and T Erdem

uished from tourists. Unlike a single-purpose attrac- tion such as a theme park, there is no park admission process to simplify identifying the universe of visitors attracted to the park, nor can ticket sales accurately identify the daily flow of visitors to the park. ~ This restricts the use of probability sampling, needed to obtain unbiased estimates of where visitors are from and what they have spent.

Basis for the Triple Five claims The WEM claims were supported by traffic counts, designed to determine the annual total number of peop le visiting the mall, and intercept surveys, designed to determine the proport ion of tourists amongst the visitors and how much they spent in Edmonton. *

In August 1986 a visual count was conducted of all pedestrians and vehicle occupants entering the WEM site at all l 1 access points to the parking area on two days. These visual counts were used to calibrate mechanical counts of vehicles entering and exiting the mall parking lots during a week in August. A similar visual count was conducted on a Saturday in October, and for four access points on Saturdays in December and in March 1987. Total attendance estimates were obtained by assuming the number of visitors using an access point consistently made up a fixed proportion of total arrivals. They appear to have been 62 000 for Wednesday and Thursday in August, and for Saturdays, 55 000 in October, 70 500 in December, and 45 000 in March 1987.

Mall intercept surveys were conducted monthly from August 1986 to January 1987. These surveys identified the monthly proportion of tourists as 0.63 in August, and then 0.44, 0.44, 0.51, 0.33 and finally 0.36 for January. These surveys also provided estimates of total Edmonton area and WEM expenditures by these tourists, While providing some useful information, this research suffered from a number of major conceptual shortcomings.

Conceptual issues A number of interpretations are used when discus- sing the economic impact of tourist attractions. However, when assessing a case for subsidies, only incremental effects need be considered. As shown in Figure 1, it is only the effects of area expenditures which would not occur if WEM did not exist which are important. Those travellers who are attracted to Edmonton by WEM are most important. However, not even all their spending is relevant when deciding whether an MMM deserves a subsidy. This is be-

* Based on data in a series of reports provided to West Edmonton Mall by R.W. Urban Consultants, beginning with West Edmon- ton Mall Shopper Intercept Survey, 1985.

Edmonton area expenditures

Motivation for visiting Edmonton

Visit All other WEM (eg. Business, VFR)

Stay Not extended extended

M L N

At WEM

Elsewhere in the area

NEW

NEO

[] Incremental expenditures attributable to the existence of WEM already captured by WEM

[ ~ ncremental expenditures attributable to the existence of WEM but not already captured by WEM

Figure 1 Breakdown of travellers' expenditures in the Edmonton area

cause an MMM already captures the immediate benefits from expenditures at the MMM, through the terms of its leasing contracts with mall tenants. Only the effects of their non-mall expenditures are relevant when considering the merits of subsidies. Therefore (i) mall visits made by local residents must be distinguished from visits made by travellers, (ii) travellers actually attracted to Edmonton by the mall must be distinguished from those who would be in Edmonton anyhow, and (iii) categories of spending must be distinguished, to allow for their differential impact on area income and employment.

Thus, economic impact estimates are sensitive to the methods used to (i) estimate the total numbers of person arrivals, (ii) sample person arrivals, (iii) collect travel purpose, (iv) collect expenditure data, and (v) select multipliers for the expenditures. In the discussions below we begin by presenting what we know about how to approach each of these estimates. Then, we evaluate the data which are available, and how they were employed by WEM. Final- ly, we provide our own estimates and an assessment of the implications for the future of MMMs such as WEM.

Determining the total number of mall arrivals Accurately determining the total number of arrivals at an MMM is difficult. It is uneconomic simply to count arrivals over any extended period of time. Locating observers at all 47 WEM mall entrances would cost at least $3000 a day. Acquiring automatic sensing devices for every entrance would cost up- wards of $100 000. An economic estimate of the

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annual attendance at an MMM must be obtained by generalizing from a sample of daily attendance data.

Model of daily MMM attendance. The daily attendance at an MMM is expected to vary due to a combination of trend (eg business cycle), periodic (eg seasonal and day of the week), and random effects (eg weather conditions). Thus the total mall attendance for a type of visitor for a day of interest might be modelled as function of (i) their daily mean attendance, (ii) a trend-cycle factor, (iii) a relative monthly seasonal factor (iv) a relative day of the week factor, and (v) a relative random factor, where the factors are expressed in percentages around their averages of 100% .6

If a large number of daily attendance observations were available, then the attendance factors could all be estimated and a summation over days used to estimate the annual attendance. Cost considerations suggest the monthly and day of the week parameters be estimated from similarly behaved series, and combined with a small number of actual daily attendance observations to estimate the daily grand mean and total annual attendance.

Sampling days. The days on which attendance is observed should be selected using probabil i ty methods. Stratified sampling is preferable, because the variance in daily attendance at an MMM is likely to vary by such strata as weekdays, Saturdays (or public holidays) and Sundays, and the four seasons. High variance strata should be more heavily sampled. Over-sampling should also be used to compen- sate for any important differences in the cost of the observation by strata. 7

Sampling entrances and periods during the day. Given that a day has been selected, total estimation costs can be reduced by counting only for a random- ly selected sample of entrances and periods during the day, rather than at all entrances all day. The use of entrances would be expected to be quite highly skewed and could be dependent on the distribution of available parking spaces at different times during the day. Thus, the optimal approach for selecting entrances and times is again a form of stratified sampling, with the strata identified based on the best available data for the relative levels of use of the entrances at different times of the day.

Determining the proportion of travellers and their travel motivation The proportion of travellers can be determined only by interviewing a representative sample of mall arrivals. It is also necessary to identify what proportion of the travellers came to Edmonton to visit WEM and how many of the others were induced to stay longer and/or spend more just to visit WEM.

Mall arrival interviews would be preferred for


collecting visit motivation information. As many MMM traveller arrivals are in family groups a deci- sion must be made whether to sample individuals or arrival groups, and whether to collect the motivation and expenditure data for individuals or for groups, without introducing any group size bias. 8 The preferred solution is to sample individuals, but to have them report both for themselves and for anyone sharing their trip, with the same reason for being in Edmonton. This means group size must be considered when estimating the expenditure per person.

The sample size needed to achieve a desired relative precision for a proportion is a function of the unknown proportion itself. A relative precision of plus or minus 10% with 95% confidence requires a sample size of only 400 if the proportion is 0.5 (confidence interval of 0.45-0.55) but increases to 3200 if the proportion is 0.1 (confidence interval of 0.09 to 0.11). Estimating the proportion of travellers who came to Edmonton to visit WEM requires a larger sample than is needed to estimate the proportion of travellers with the same accuracy.

Restricting interviewing to a random sample of days would result in excessive interviewing costs. Cost considerations favour treating the proportion of travellers as a function of seasonal and day-of-the- week factors, and estimating the required propor- tions over a longer period such as a week. However, it would be unwise to assume the proportion of arrivals who are travellers was the same for each day during the week and at the weekend, particularly for off-season months such as February or October.

The daily traveller attendance can be used in estimating the annual number of traveller arrivals. These arrivals are not necessarily all different peo- ple. Some travellers may visit Edmonton and WEM more than once during a year. Fortunately, this does not introduce problems. Second, some travellers may visit WEM more than once during a trip to the city. This must be taken into account when estimating the Edmonton area expenditure per person for the travellers. The two options available for obtaining the sample to est imate the propor t ion of travellers and their motivations are mall arrival (or exit) interviews and mall intercept interviews.

Mall arrival (or exit) interviews. Sudman describes an optimal procedure for sampling arrivals at a shopping centre. 9 It requires reasonably accurate estimates of the ratio of persons who will use each entrance during each time period, enabling selection of time and entrance clusters with probabilities proportional to the predicted traffic counts. Less frequently used entrances are sampled fewer times than more heavily used entrances. Arrivals are then sampled at a rate inversely proportional to the predicted traffic counts. The result is that roughly the same numbers of interviews are conducted at each selected entrance and time period combination.

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Mall intercept interviews. It is difficult to ensure representativeness if mall intercepts are used, because the probability of being approached is related to the duration of and nature of a visit. Tourists are likely to differ from local residents by staying longer and spending a higher proportion of their time looking around in public areas. Thus intercept interviews will over-represent the proportion of tourists, even if multiple sampling points are used. A correction for this bias could be attempted, by having respondents estimate how much time they expect to spend in the areas of the mall where sampling could occur. A somewhat less satisfactory correction can be made using the respondent's estimate of the expected duration of their visit to the mall. In either case, Sudman identifies the correction as weighting respondents inversely with these times or durations. 9

Non-response bias

All interview procedures are subject to the problem of non-response. Reported mall intercept response rates have ranged from as high as 73% for Bush and Hair 1° to just 16% for Wiseman et al. 1| Bias would be introduced by any differential response rates. If tourists looking around WEM have a higher response rate than area residents, many of whom might be heading for particular destinations within the mall, their numbers will be overstated. The interception of known tourists and known locals could be used to determine whether such differential response rates were a problem.

Determining the total and incremental expenditures o f travellers The Edmonton area expenditure by segments of mall arrivals can be broken down into spending (i) at WEM or elsewhere, and (ii) by type (eg accommodation, meals, local transportation, entertainment and consumer goods). Expenditure data would be most accurate if interviews were conducted when visitors were leaving Edmonton but this presents a very difficult sampling problem. Mall exit interviews would enable optimal estimates of expenditures during a just-completed mall visit, and could be used to collect prior and expected further expenditures outside the mall while in Edmonton. However, the latter estimates would not be as valid as estimates made at the conclusion of their visit.12

An optimal compromise may be to rely on the travellers identified from the carefully selected sample of visitors arriving at WEM. Those interviewed could be asked to take away a mail return expenditure survey to be completed when they are leaving Edmonton. At the same time they could report on whether they changed the duration of their stay in the city and whether they returned to WEM again after receiving the survey. Intercept interview and mail-back surveys have been used successfully with city centre visitors. 13

Determining the impact o f incremental tourist spending The economic impact of travellers' expenditures depends in part on the characteristics of the spending. Spending on services, such as transportation, lodging and entertainment, will have a greater impact than expenditure on consumer goods, such as clothes or consumer electronic items, where only a small proportion of the value is added in the Edmon- ton area. These differences are the subject of economic impact analysis.

Economic impact analysis An economic impact analysis requires two compo- nents, namely an estimate of the direct impact and a model of the regional economy that will produce estimates of the indirect effects. 14 The subsequent rounds of spending generated by the initial direct expenditures are quantified by multipliers. For the WEM problem, the expenditures by travellers are the exogenous stimuli, while claims such as the numbers of jobs produced are the economic impacts.

An input-output model of a regional economy provides the best multipliers to use in an economic impact analysis. Because an input-output model represents transactions between producers as well as between producers and consumers, it provides considerably more detail regarding the economy than alternative models.IS Since the claims made by the developers of WEM refer to immediate effects of WEM, short-run multipliers can be used to assess the economic impact of WEM.

Available supplementary data A number of useful supplementary data sources were located and assessed as potentially useful for either determining an optimal sampling plan or for estimating the periodic parameters in the attendance model.

• Use o f entrances by time o f day: Finn and Woolley-Fisher found that the use of WEM entrances by area residents was quite skewed. 16 Three entrances were used by 20% of arrivals, while the 12 least used entrances accounted for just 5% of arrivals. Moreover, 17% arrived before 11 am, 39% between 11 am and 2 pro, 26% between 2 pm and 6 pro, and 18% arrived after 6 pro.

• Monthly retail sales: Statistics Canada provides provincial data for total retail sales and for 29 types of retailers, and Edmonton area data for department stores' sales, w After adjusting for changes in the Edmonton area price index, the latter series probably provides the best indicator of the seasonality of area resident trips to WEM. For 1986, the monthly seasonality values were, beginning in January, 83.7, 78.6, 95.6, 98.2, 110.5, 96.8, 93.5, 95.6, 104.9, 102.4, 127.7, and

370 Tourism Managemenl 1995 Volume 16 Number 5

181.4 for the peak month of December. • Day of the week: Surprisingly we could not locate

any regularly published government or trade association data for department store-type mer- chandise sales or shopping centre traffic levels by day of the week. Two other data sources appeared somewhat relevant. A syndicated research service produced weights for the day of the week when their respondents usually do their major food shopping, of 50% for Monday, increasing through 51%, 75%, 102% and 173% up to 200% on Saturday, before dropping to 48% on Sunday. Finn and Rigby found that 62% of most recent visits to WEM amongst Edmonton area residents were from Monday to Friday, with 26% on Saturday and 12% on Sunday. 1~ Combining these figures suggested daily traffic factor values of 48% for Monday, increasing through 49%, 72%, 98% and 166% to 185% for Saturday, and then dropping to 82% for Sunday.

• Seasonality in tourist traffic: There is no source of seasonality data which includes all forms of travel and is specific to Edmonton. The 1988 Canadian Travel Survey reported that travel with Alberta destinations exhibited quarterly seasonality levels of 76%, 96%, 172% and 60%. Statistics Canada collects monthly data for the number of international arrivals in Alberta, with 1986 levels of 43.5 in January, followed by 42.2, 55.5, 49.8, 84.9, 167.2, and 285.9 for the peak month of July, then declining to 226.0, 108.1, 52.9, 34.4 and 52.2 for December. These data were used for our estimates. No data could be found for tourist travel by day of the week.

• Economic multipliers: Because no multipliers were available for Edmonton, Alberta multipliers were used as a substitute. Alberta Bureau of Statistics provides absolute and ratio input, output and employment multipliers with and without leakages for 38 sectors of the Alberta economy, including 'wholesale and retail trade', 'transportation and storage' and 'accommodation and food services', w Since Edmonton and Alberta are small, open economies, multipliers with leakages were the most appropriate ones to use.

Adjusted economic impact estimates Mall attendance counts Without access to full details of the visual and mechanical counts conducted for Triple Five, there was insufficient information to adjust the WEM daily attendance data. Of particular concern was the use of judgment samples when choosing days, hours and access points for the counts. It was necessary to accept the unrealistic assumption that the proportion of traveller arrivals in the evenings would be the same as during the day.


Intercept surveys There were also problems with the intercept surveys. Data were collected on a judgmental sample of days, and mall intercept rather than exit interviews were used. Moreover interviewers were allowed latitude in selecting respondents and were paid on the basis of completed interviews. This incentive to select those most highly motivated to respond could bias them towards selecting tourist groups. The high reported completion rate of over 75% was likely a consequence of some selection bias. Therefore, the reported proportion of travellers was adjusted for (i) differences in the expected length of stay at WEM, and (ii) an assumed conservative response rate dif- ference of 0.9 for travellers and 0.7 for residents. This reduced the reported proportion of travellers to 0.52 for August, 0.36 for October, 0.22 for Decem- ber, and 0.27 for January.

Traveller motivations The proportion of travellers reporting that they visited Edmonton to come to WEM was 0.43 in August, 0.29 in September, 0.30 in October, 0.29 in November, 0.30 in December and 0.29 in January. To obtain an annual proportion, July was assumed to be the same as August and all other months were assumed to be 0.29, the mean outside the peak summer period.

Expenditures Average area expenditures by travellers were available for four categories of spending, namely accommodation, food, shopping and other goods. Expend- itures within these categories were fairly stable over the six months of observations, apart from the higher amounts spent on shopping in November and December. Using the number of travellers per month to weight the means from the six monthly surveys, the surveyed travellers reported mean Edmonton expenditures of $215 on shopping, $168 on accommodation and food, and $51 on other goods. These were treated as the expenditures for groups, and then adjusted for group size which averaged 3.61 on a similar weighted basis.

Traveller attendance Combining the estimated total attendance figures and proportion of travellers for August, October, December and January with the best estimates of the daily and monthly seasonality factors produced an average daily traveller attendance estimate of 14 810 and an annual traveller attendance estimate of about 5 400 000. Using the motivation data given earlier this suggests that 1 885 000 travellers a year visited Edmonton to go to WEM.

Estimate of the economic impact For 1986, these 1 885 000 travellers are estimated to have spent $112 million on shopping, $88 million on



food and accommodation, and $26 million on other goods, for a total of $227 million while in Edmonton. Applying Alberta multipliers (absolute form, with leakages) to these expenditures produces estimates for the effect of WEM on household income of $176 million, on GDP at factor cost of $290 million, on area employment of 13 800 jobs, and on gross output of $650 million. These figures ignored the impact of the remaining 3 515 000 travellers who also visited WEM because there was no way to estimate their incremental expenditures.

Discussion

Assessing the economic impact of WEM is important because it provides a precedent for those proposing to develop similar centres in other cities. Those proposing to develop MMMs in other areas have consistently asked for substantial subsidies, because of their claimed economic impact and positive externalities. Evaluating these claims is no easy task. A large number of parameters must be estimated accurately. The cost of collecting detailed data clearly precludes ever producing a very precise answer.

While the general approach employed by Triple Five was acceptable, the estimates it advertised considerably overstated the true economic impact of WEM. Biases were introduced through the substitu- tion of judgment sampling for true probability sampling, the failure to control for differential time spent and intercept response rates, and the reporting of total rather than just incremental effects.

This article has discussed a number of such design problems and suggested how they might be mini- mized. In some instances we were also able to make corrections which could help to reduce the bias in a re-estimate of the economic impact. This process reduced the Triple Five claim of 9 million annual traveller visits in 1986 to 5.4 million. Of these visits, only an estimated 1.9 million were by travellers who came to Edmonton to see WEM. The $227 million these visitors spent in Edmonton generated $176 million in household income and 13 800 jobs. Moreover, less than half of the $227 million were spent outside WEM, suggesting an even smaller base for potential positive externalities. Other visitors to WEM spent an additional $166 million. However, most of this money would have been spent elsewhere in Edmonton if WEM did not exist. These figures obviously fall substantially short of the less clearly defined economic impact claims made by Triple Five, of $700 million in spending and 40 000 jobs. Our estimates are more consistent with other secon- dary Edmonton data. For example, the 9 million tourists implied summer hotel/motel occupancy rates far in excess of the available number of area rooms at the time.

An independently conducted August 1987 mall

exit survey of WEM visitors estimated that 54"/,, of respondents came from outside Edmonton. 2° As an upper limit for the proportion of visitors travelling more than 80 km to WEM, this was reasonably consistent with 0.47 obtained here from the WEM data for August 1986. Interestingly, high-cost resident and non-resident travel surveys were conducted sometime after the novelty of WEM had begun to wane. 21"22 Together these studies suggest that WEM drew a total of 3.3 million visitors in 1990. Further custom analysis has since estimated that they generated incremental Edmonton area spending of $142 million, producing $179 million in income and 4220 jobs. 23

Some methodological problems could not be dealt with in this article. No access could be obtained to the consultants' raw data, nor to the detailed calcula- tions, nor to a full explanation of how the various numbers presented in the consultants' reports were derived. We simply employed our best judgment in interpreting the available data. In addition no allow- ance was made for any effects on spending by area residents. Some residents may have foregone spending during trips away from Edmonton because WEM enabled them to satisfy their need within Edmonton. Although likely to be small, such Edmonton expenditures should conceptually be included when estimating the economic impact.

Conclusion

It is important to be able to assess the validity of the claimed economic impact of MMMs and festival market-places, which serve local residents as well as tourists. Public subsidies are only justified if an MMM really generates substantial externalities. This article has examined the key issues which arise when trying to obtain valid estimates of the economic impact of institutions such as WEM. These include distinguishing the incremental expenditures from total expenditures, employing a probability sampling of days, entrances and time periods when estimating the attendance, using exit rather than intercept interviews or adjusting for differential visit durations and adjusting for differential non-response rates when estimating the proportion of tourists attracted by the mall itself, and using input-output multipliers specific to the types of expenditures.

We are able to suggest how these things should best be done in the future. We were also able to implement corrections designed to minimize the bias in the existing data, and so obtain somewhat more realistic estimates of WEM's economic impact. WEM was estimated to have attracted about 5 million tourist visits in 1986, not the 9 million claimed by Triple Five. Less than 2 million of these were by travellers who came to Edmonton to see WEM. The $227 million spent by visitors attracted by WEM produced an increase of about $176 million

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in household income and about 13 800 jobs in the Edmonton area. These impact estimates are far more realistic than the Triple Five claims of $700 million and 40 000 jobs.

R e f e r e n c e s

~Finn, A and Rigby, J 'West Edmonton Mall: consumer combined-purpose trips and the birth of the mega-multi-mall' Canadian J Administrative Sciences 1992 9 (2), 134-145 2Spellmeyer, A W 'Mall of America: confounding the skeptics" Urban Land 1993 54 (4) 4.%46, 81-83 3Ingrain, M 'Malling the world' Alberta Report 13 February 1989, 20-25 aEdmonton Journal, 'N.Y. Mall Incentives Total $400M' 23 September 1986, A10 ~Boggs, P and Wall, G 'The economic impact of Canada's wonderland on Toronto' Recreation Research Review 1985 11 (3 and 4) 35-43 "Bar-On, R Travel and Tourism Data Oryx Press, Phoenix, AZ (1989) 7Sudman, S Applied Sampling Academic Press, New York (1976) 116 ~Latham, J 'Bias due to group size in visitor surveys' J Travel Research 1991 (Spring) 32-35 '~Sudman, S 'Improving the quality of shopping centre sampling' J Marketing Research 1980 17 (4) 423-431 ~°Bush A J and Hair, Jr, J F 'An assessment of the mall intercept interview as a data collection method" J Marketing Research 1985 22 (2) 158-167


~Wiseman, F, Schafer, M and Schafer, R 'An experimental test of the effect of a monetary incentive on cooperation rates and data collection costs in central location interviewing' J Marketing Research 1983 20 (4) 439-442 ~2Howard, D R, Lankford, V and Havitz M E 'A method for authenticating pleasure travel expenditures' J Travel Research 1991 (Spring) 19-23 ~3Lorch, B J and Smith, M J 'Surveying downtown pedestrian movement: a comparative analysis of alternative methods' Oper- ational Geographer 1989 8 (1) 2-4 t4pleetcr, S Economic Impact Analysis: Methodology and Ap- plications Martinus Nijhoff (1980) ~Davis, H C Regional Economic Impact Analysis and Project Evaluation University of British Columbia Press (1990) 16Finn, A and Woolley-Fisher, P West Edmonton Mall and Shopping in the Edmonton Area Faculty of Business, University of Alberta (1988) WStatistics Canada Department Store Sales and Stocks 1987 Monthly series: 63-002 ~SFinn, A and Rigby, J Edmonton Area Residents" Views On and Use of West Edmonton Mall Faculty of Business, University of Alberta (1987) ~°Alberta Treasury, Bureau of Statistics Economic Multipliers for Alberta Industries and Commodities 1987 2°Archon International Marketing Systems Edmonton Local Awareness Study, Phase 1 - Research Study'Edmonton (1987) 170 2~Alberta Economic Development and Tourism The 1991 Alberta Resident Travel Survey (1993) 22Alberta Tourism The 1990 Alberta Non-Resident Travel Exit Survey (1992) 23Western Management Consultants The Economic Impact of West Edmonton Mall Alberta Economic Development and Tour- ism (1993)


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The economic impact of a mega-multi-mall: Estimation issues in the case of West Edmonton Mall