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Japanese manufacturing greenfields : the provincial location decision Brown, Iain A. 1993

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JAPANESE MANUFACTURING GREENFIELDS:The Provincial Location DecisionbyIAIN ANDREW BROWNB.Comm., The University of British Columbia, 1982A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCEIN BUSINESS ADMINISTRATIONinTHE FACULTY OF COMMERCE AND BUSINESS ADMINISTRATION(Department of International Business)We accept this thesis as confirmingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAApril 1993©Iain Andrew BrownIn presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.(Signature) Department of International BusinessThe University of British ColumbiaVancouver, CanadaDate April 21, 1993DE-6 (2/88)ABSTRACTThis paper examines why Japanese manufacturing greenfields locate in aparticular Canadian province. We find that the location preference is basedprimarily on the present distribution of Japanese and Canadian firms. Asecondary factor is market access, which includes the transportation costs ofexporting the Canadian manufactured product to Japan. Other important factorsare energy and labour costs. Having utilized quantitative methods to determinethat the presence of Japanese and Canadian firms are the main reasons why newgreenfields select the province they will locate in, we question the value of usingtax dollars to attract investments to locations lacking substantial industry activity.TABLE OF CONTENTSABSTRACT ^  iiTABLE OF CONTENTS ^  iiiLIST OF TABLES LIST OF FIGURES ^  viACKNOWLEDGEMENT  vii1. INTRODUCTION ^  12. JAPANESE MANUFACTURING INVESTMENT IN CANADA . . . 32.1. The Greenfield Component of Foreign Investment ^ 32.2. Japanese FDI ^  83. RELATED RESEARCH  104. COMPILING OUR DATA ^  154.1. Dependent Variable - Japanese Manufacturing Greenfields^154.2. Independent Variables - Provincial Characteristics ^ 164.3. Ship Rank, Energy and MetGDP ^  194.4. Industry Agglomeration as a measure of ManufacturingActivity ^  224.5. Data Collection Summary  245. OUR CONDITION LOGIT ECONOMETRIC MODEL ^ 266. RESULTS ^  307. CONCLUSION  367.1. Factors Influencing Japanese Manufacturing Greenfield'sLocation Decision ^  367.2. Limitations of Our Results  387.3. Policy Ramifications for Provinces Seeking FDI ^ 407.4. Further Study and Extensions ^  418. BIBLIOGRAPHY ^  43APPENDIX 1: FIFTY-FIVE JAPANESE MANUFACTURINGGREENFIELDS IN CANADA ^  46APPENDIX 2: PROVINCIAL CHARACTERISTICS ^ 49APPENDIX 3: NOTES TO JAPANESE MANUFACTURINGGREENFIELDS AND PROVINCIAL DATA ^ 52APPENDIX 4: JAPANESE MANUFACTURING GREENFIELDS -PROVINCIAL INDUSTRIAL LEVEL FOR 1987 (WAGES,FUEL, and REVENUE)   53APPENDIX 5: JAPANESE MANUFACTURING GREENFIELDS -NATIONAL INDUSTRIAL LEVEL FOR 1987 (WAGES, FUEL,and SHIPMENTS)   60APPENDIX 6: CONDITIONAL LOGIT REGRESSION RESULTS -VARIABLES & THEIR SOURCE ^  62LIST OF TABLESTABLE 4.1: Number of Japanese Manufacturing Greenfields in Canada ^ 16TABLE 4.2: 1989 (1980) Provincial Ranking ^  25TABLE 5.1: Energy Intensity ^  29TABLE 6.1: Final Results  30TABLE 6.2: Substituting Low Energy Price ^  31TABLE 6.3: Testing Unionization, Tax & Crime Rates ^ 32TABLE 6.4: Testing Factor Intensities ^  33TABLE 6.5: Replacing MetGDP with Quebec Dummy ^ 34TABLE 6.6: Adding a Quebec Dummy Variable  35LIST OF FIGURESFIGURE 2.1: Canada's Liabilities to Non-Residents ^ 5FIGURE 2.2: Acquisition and Greenfield FDI Inflows  7FIGURE 2.3: 1989 Worldwide Gross Outflow of FDI ^ 8FIGURE 4.1: Provincial Lowest Energy Costs  21FIGURE 4.2: Provincial Average Energy Costs ^  21FIGURE 6.1: MetGDP Variable ^  34ACKNOWLEDGEMENTI thank all those who have been inspirational during my work on this thesis. I amparticularly indebted to John Ries for his assistance throughout the preparationof this paper. My gratitude for John's contribution is only dwarfed by my respectfor his unselfish dedication to the project. Keith Head's assistance in thedevelopment of our model was most appreciated. I thank Jim Brander for helpingto polish and structure this paper for final presentation. Anna Kwan deservesrecognition for compiling this manuscript.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 11. INTRODUCTIONIn recent years Canada has sought new Japanese manufacturing investments. (Newinvestments are often referred to as greenfield investments). Public policy interest in greenfieldinvestment arises in part because of the political benefits of job creation that are associated withthem. Another reason that provinces are interested in attracting Japanese manufacturinggreenfields is technology transfer. However, job creation tends to be the host province's mainmotivation in soliciting Japanese manufacturing greenfields. Regardless of the reason(s) whyprovinces may wish to attract Japanese manufacturing greenfields, in order to have better successin attracting such investments, it would be useful for the provinces to know what attractsJapanese manufacturing greenfields to a particular province. This thesis focuses on this issue byasking the question what provincial characteristics influenced the greenfields to locate in theparticular province they selected?It turns out that between 1980 and 1991, 84% of Japanese manufacturinggreenfield investment locating in Canada established their new facilities in either Ontario (64 %)or British Columbia (20 %). Both these provinces attracted more greenfield investment than theirnational share of manufacturing. By using condition logit regression we discover that Japanesemanufacturing greenfields prefer to locate in provinces with a concentration of Canadian firmsin their industries, and in provinces with other Japanese manufacturing greenfields. Otherimportant provincial characteristics in determining location preference include wage and energylain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 2costs as well as geographic proximity to Japan. Past research on American state locationdecisions, which used similar statistical methodology as we employ, supports our findings in thatstate characteristics, similar to our provincial characteristics, are identified as reasons formanufacturing firms establishing new facilities in a particular location. However, our results maysuggest evidence of pure agglomeration effects. That is, if the combination of our variabledesigned to measure geographic proximity to Japan and our variable that counts the number ofalready existing Canadian firms in the establishing greenfield's industry, capture the endowmenteffect, then the significance of our variable measuring the size of existing Japanese investmentmay be evidence of pure agglomeration effects.Section 2 provides some background on Japanese investment. Section 3 reviewsrelated research. Section 4 explains how the data for our variables was compiled. Section 5describes our econometric model. Section 6 summarizes our results. Section 7 is our conclusionthat includes a discussion on the limitation of our results, policy implications and researchquestions that follow naturally from this study.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 32. JAPANESE MANUFACTURING INVESTMENT IN CANADAThe purpose of this section is to familiarize the reader with foreign investmentso that an appreciation is gained for what we mean by a Japanese manufacturing greenfield. Wepoint out some of the possible benefits that are associated with foreign direct investment.However, we are not saying that other forms of investment are any less interesting or beneficial,nor that our methodology would only work for Japanese investments. Our methodology couldhave been used to study, for example, the provincial location decision of German greenfields,but we elected to study Japanese greenfields and hence this why we provide an overview ofJapanese manufacturing investment in Canada.2.1. The Greenfield Component of Foreign InvestmentBefore addressing the issue of why we chose to measure the greenfield componentof Japanese foreign direct investment (as opposed to, say, American direct investment) let usfirst distinguish between foreign portfolio investment (FPI) and foreign direct investment (FDI).FDI is ownership (with control) of real domestic assets by a foreigner. StatisticsCanada considers foreign ownership to exist when a foreigner owns more that 10% of the equityof an investment'. Usually, FDI is undertaken by corporations to take advantage of the1^As Appendix 1 shows the lowest level of Japanese ownership was 17%, with over half the greenfieldinvestments being wholly owned subsidiaries.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 4comparative advantage that foreign production offers or to preserve access to markets in anenvironment where true global free trade does not exist (i.e., Japan exporting to Canada or theU.S., and for that matter, vice versa).FDI typically involves the transfer of capital as well as technology, marketing andorganizational skills. Management practises such as just-in-time (kanban) inventory controlprocedures lessen inventory carrying costs, quality circles (kaizen) reduce the number of defects,and consensus decision making (nemawashi) increase productivity, all originated in Japan.However, through technology transfer these management practises are no longer solely theproperty of Japanese transplants operating in Canada. They have also spread to Canadian ownedmanufacturing facilities, which now benefit from them. These examples of technology transfer,along with job creation, may motivate provinces to attract FDI, even though an increase inforeign ownership of this domestic industry will occur as ownership of these assets remainingwith the foreign firm.This ownership can take the form of acquisitions or the construction of newfacilities (greenfields). Other forms of FDI involve the establishment of a wholly-ownedsubsidiary or the formation of joint ventures and strategic alliances between firms. (Althoughstrategic alliances may not involve the transfer of capital they do tend to have the other benefitsthat are associated with FDI). The alternative type of business structure to be used in the hostcountry is important as the choice of organizational form often has significant implications forthe transfer of knowledge and other firm-specific skills.Canada's Liabilities to Non-res (Cdn BI)Corp. Bonds46.5Cdn. Gov. Bonds63.2STOCKS20.8Other.108.7Money Market Sec.25.4FDI126.6Prov. Gov. Bonds65Figure 2.1 (Source: Statistics Canada, 1990).lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 5Finally, as FDI involves the transfer relatively intangible resources, thecorporations involved typically operate with a fairly long time horizon, making flows of FDIstable in comparison to flows of FPI. However, as we see from Figure 2.1, FDI is about onequarter of the stock of foreign investment in Canada as of 1990. FDI is represented by the blackwedge in Figure 2.1, and theremaining pieces of the pie arecollectively known as foreignportfolio investment. FPI occurswhen foreigners own financialassets from another country.Figure 2.12 illustrates that in1990 foreigners owned $329.6billion worth of Canadianfinancial assets.FPI is undertaken mainly by individuals, institutional investors and governments(i.e. central banks). FPI involves transactions in the capital markets (e.g., stocks, bonds, loansor money market securities). Those involved in FPI have different objectives, risk preferences,and investment time horizons than those involved in FDI. The risk-adjusted real rates of return2 The 108.7 of Other Liabilities to Non-residents includes SDRs (Special Drawing Rights) and other officialgovernment flows. Also theses stocks are at historical costs and if the FDI was made some time ago itsmarket value may exceed its book value by a considerable margin. Thus, its is possible that if marketfigures were available that FDI would represent a bigger slice of the pie.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 6between assets in different countries is a major determinant of FPI flows. Thus, FPI flows aresensitive to changes in inflation, interest and exchange rates as well as portfolio diversificationrequirements. Accordingly, FPI tends to be in financial assets that are relatively liquid andmobile with fixed maturity dates.Just as FDI and FPI are less than perfect substitutes, acquisitions and greenfieldsare different types of FDI, if only for the reason that the former does not involve the samelocation decision. The location of a new plant is clearly a choice; whereas, it could be arguedthat an acquisition did not involve a location analysis because the site already exists. When acompany is acquired, it may be acquired despite of the location of its manufacturing facilities.That is, in an ideal situation the purchaser would prefer the manufacturing facilities to beelsewhere (perhaps in another province). Nevertheless, if the purchase takes place, because theoverall investment makes sense, then this non-ideal location was not actually chosen. However,in a greenfield investment, the ideal location would be selected. Thus, we have included onlyJapanese greenfields in our study.A secondary reason for only considering greenfields is unique to Canada: whatwe call "the FIRA (Foreign Investment Review Agency) effect." Prior to 1985 FIRA (agovernment organization designed to discourage foreign investment) may have inhibitedacquisitions. Greenfields apparently did not offset the reduced level of acquisitions during theFIRA years (1974-1985). Presumably a Canadian greenfield was not the foreign firm's secondchoice after a Canadian acquisition and the direct investment simply went elsewhere.Acquisition and Greenfield FDI InflowsCdn. Billions141210864201980 1981 1982 1983 1984 1985 1986 1987 198819891990• Greenfield* AcquisitionsInvestment CanadaFigure 2.2lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 7In 1985 FIRA was replaced by Investment Canada, which is a government bodywith a mandate to promote foreign investment. The mid 80's were also a period of heightenedmerger and acquisition activity. We suspect that a combination of these factors explains theincrease of acquisitions in1985 (refer to Figure 2.2).Regardless of the reason,acquisitions dramaticallyincreased in 1985. Thisimbalance of acquisitionactivity from the beginning ofthe decade to the end of thedecade is another reason wehave elected to study thegreenfield component of FDI.In summary, FDI and FPI cannot be treated as perfect or even close substitutesbecause they are motivated by different factors. We have elected to measure FDI rather thanFPI. We selected the greenfield component of FDI to avoid the FIRA effect as well as anycontroversy over an acquisition being a "non-location choice."• FranceA Germany■ JapanUnited KingdomUnited States^ Canada• Other DevelopedE Developing Countries•:::1989 Worldwide Gross Outflow of FDI (%)France Developing Countries9.9^4.5United Kingdom16.3United Nation, World Investment ReportUnited States13.5lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 82.2. Japanese FDIAnother reason that we are interested in Japanese greenfield FDI is Japan's recentdominance as an exporter of FDI. For example, Figure 2.3 shows Japan was the leadingexporter of FDI in 1989. With Japan being a prominent world exporter of FDI this is one reasonto study Japanese FDI. Secondly, although American FDI into Canada is also significant, infuture work we wish toobjectively measure the effectof the Canada/U.S. Free TradeAgreement on FDI into thistrade area. To do this we willneed to measure the locationdecision of an outside partyand by definition this excludesthe U.S.Figure 2.3For these reasons we have chosen to analyze Japanese greenfield FDI. We haveelected to look at manufacturing greenfields (as opposed to say distribution warehouses) forreasons such as the technology transfer and job creation benefits associated with theseinvestments. The time frame of our study is 1980 to 1991. This time frame was selected becauseit has really only been the last decade that Japanese FDI has become such a noticeable factor inindustrial host countries.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 9However, host countries are generally made up of regional political territories andin Canada these are called provinces. Often these provinces try to out bid each other (i.e., withtax holidays) for greenfield investments, possibly because of the job creation or technologytransfer associated with these new manufacturing facilities. Regardless of the reason(s) whyprovinces seek to host FDI, to gain insight into how host provincial governments may be moresuccessful in attracting Japanese manufacturing greenfields we will study the provincial locationdecision of these greenfields.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 103. RELATED RESEARCHBefore we describe how the data was collected for this study, we will first discussrelated research to this paper. These studies have aided us in selecting the variables that we willpresent in the next section and in designing the conditional logit econometric model that we willdescribe in Section 5. In this regard we note that logit regression was used by Bartik (1985),Carlton (1983), Coughlin, et. al. (1991), Head, Ries, and Swenson (1993), Luger and Shetty(1985), Schmenner, et. al. (1987), and Woodward (1992).Bartik (1985) looked at the influence of state characteristics on new manufacturingplant location decisions, emphasizing unionization and taxation. The location data included theFortune 500 companies' manufacturing plants from 1972-1978. He found a significantly largenegative effect of a state's unionized rate, while wages had only a marginal negativesignificance. The corporate tax rate also had a negative effect on plant location. Bartik estimateda powerful effect of existing manufacturing activity on new business location, i.e., "a state with10% greater existing manufacturing activity will have an 8% or 9% greater number of newplants" (Bartik, 1985).Schmenner, et. al. (1987) also examined new plant openings by Fortune 500 firmsbetween 1970-1980. Their innovations were to include plant-specific characteristics (data wasderived from surveying managers of the firms) in magnifying or tempering the state-specificlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 11effects. They argued that state characteristics such as wage rates would be more important toparticular plants (i.e., wages will matter more to labour intensive plants). By interacting stateand plant characteristics, Schmenner found that "Labor unionism, climate, population density,and building costs are prominent influences on location. . . Tax variables, on the other hand,make a weak showing" (Schmenner, et. al., 1987). Also the interaction terms were usuallysignificant.Carlton (1983) concentrated on a few industries that were selected to ensureequivalence (i.e., location choice is not hindered by local supply and demand factors). Hisresults included: [the] "wage effect cannot be measured very precisely; energy [prices] have alarge effect; taxes and state incentive programs do not seem to have major effects; [and] existingconcentrations of employment matter a great deal with the effect being stronger for industrieswith smaller average plant size" (Carlton, 1983).Insignificance of taxes was also evident in Luger & Shetty (1985), who measuredthe manufacturing activities of three specific industries. Their objective was to measure theelasticity of FDI in relation to a state's promotional programs, as well as the effect ofagglomeration and urbanization economies, and labour market conditions. They concluded "thatagglomeration economies and wage rates are the most important determinants of new plantlocation. . . [and] public policies do not have a uniform effect on industries" (Luger & Shetty,1985). To represent agglomeration attraction a measure of total manufacturing activity was used;thus they did not provide evidence on industry-level agglomeration. Moreover, unmeasured statelain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 12endowments (forests, research universities, or other omitted state characteristics) might bereflected in the coefficient on manufacturing activity.Like Luger & Shetty before them, Smith & Florida (1992) chose to explore aselect industry (automobile-related) and the industry-specific relationships associated with it.They concluded that Japanese auto-related parts suppliers tend to locate near their assembly plantand have a preference for areas with greater aggregate manufacturing activity. Taxes had amarginal showing and unionization was not a factor, while wages had a positive relationship.This positive relationship could be due to the need for technical skills that might be reflected inwages. For similar reasons, education level also had a significant positive effect.Coughlin's (1991) research data included all FDI transactions (acquisition, equityincrease, joint venture, merger, new plant or plant extension). His results indicated that FDIlocated in states with higher per capita incomes and higher levels of manufacturing activity.Coughlin also found that FDI is attracted to areas with lower wages and higher unemploymentrates, while higher taxes deterred FDI. Unionization had a marginal positive significance, whichcould be "because of the increased productive efficiency in manufacturing stemming fromunionization" (Coughlin, et. al., 1991).Woodward's (1992) study involved a measure of total manufacturing activity,rather than selecting certain industries. He analyzed Japanese-affiliated manufacturinginvestments in the U.S. between 1980-1989. The model worked on the assumption thatlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 13"Japanese firms, like all firms, seek branch locations with the highest expected profits"(Woodward, 1992). This relationship revealed a preference for strong markets and lowunionization rates. In addition, "Japanese manufacturing plants are most likely to select countiescharacterized by manufacturing agglomeration, low unemployment and poverty rates, andconcentrations of educated, productive workers" (Woodward, 1992).Head, Ries, and Swenson (1993) found that Japanese firms tend to locate nearboth other Japanese firms and U.S. firms that were in the same specific industry. They wereable to use industry-specific variables for both Japanese and American companies and found bothto be significant. They also found that the attractiveness of a state is increased by the level ofindustrial activity on bordering states. They showed that the positive relationship betweenindustry activity and location is partly due to agglomeration externalities, not simply anendowment effect.As not all the research measured the same variables, it is difficult to generalizethe findings of these studies. However, we are able to draw some conclusions from these papers.Intuitively, unionization, wages, and taxes should pay significant roles in the location decision.Bartik (1985), Schmenner (1987), and Woodward (1992) suggested unionization is negativelycorrelated with FDI, while Coughlin (1991) showed a marginal positive relationship. Luger &Shetty (1985) and Coughlin (1991) concluded a negative significance for wages, while Smith &Florida (1992) took the opposite view. The differences in these results may stem from industry-specific differences, in that capital intensive industry may be willing to "put up" with higherlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 14wages and unionization in exchange for better trained and more productive workers. Whilelabour intensive industries would place more importance on wages. Hence factor intensities couldbe the reason for the different findings.Taxes are generally found to be insignificant or inconclusive, with the exceptionof Bartik (1985) who found taxes had a negative effect. While never quite defined the same,when a variable is used to measure the level of manufacturing activity the results are usually that"existing manufacturing" attracts new manufacturing. In our next section we build on thisresearch to explain why Japanese manufacturing greenfields locate in the particular province thatthey do, once they have elected to enter the Canadian market.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 154. COMPILING OUR DATA4.1. Dependent Variable - Japanese Manufacturing GreenfieldsData collection for this study was itself a major task as this data is not readilyavailable. Furthermore, data available on Japanese-owned manufacturing facilities in Canada donot always clearly distinguish greenfield operations from acquisitions.To construct our detailed list we obtained much of our information from Japanesesources (Toyo, Jetro, Dodwell). At times these directories of Japanese companies in Canadaprovided conflicting information. Not one of them included all the companies that appear inAppendix 1. Furthermore, some information had to be verified by contacting the firms directly.This was often the case when Jetro claimed that a firm was a distribution manufacturing firm,but this firm was not listed in other manufacturing directories. It usually turned out that thesefirms were solely distribution companies. We found Toyo to be most reliable. We supplementthe data from these three directories with information on Japanese companies in Ontariocompiled by the Government of Ontario.We compiled a comprehensive list of fifty-five Japanese manufacturing greenfieldsthat had located in Canada since 1980 (see Appendix 1). These investments were spread oversix provinces, but were mainly concentrated in British Columbia (BC) and Ontario. Table 4.1lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 16portrays the distribution and growth of these firms in Canada during the period from 1980 to1990. For example, we note that British Columbia received one investment in 1980 and twomore Japanese greenfields located in the province in 1983; thus, from 1980 to 1983 threegreenfields had located in this province.TABLE 4.1NUMBER OF JAPANESE MANUFACTURING GREENFIELDS IN CANADA(Accumulated Totals Since 1980)Province 1980 1981 1982 1983 1984 I^1985 1986 1987 1988 1989 1990Alberta 0 0 0 0 0 0 0 0 0 0 1BC 1 1 1 3 3 3 3 3 5 8 11NB 1 1 1 1 1 1 1 1 1 2 2Ontario 4 5 6 7 10 13 20 27 30 33 35Quebec 0 0 0 0 0 0 2 2 2 4 4Sask. 0 0 0 0 0 0 0 1 2 2 2Canada 6 7 8 11^I 14^I 17 26 34 40 49 55BC = British Columbia^NB = New Brunswick^Sask. = Saskatchewan4.2. Independent Variables - Provincial CharacteristicsWith our dependent variable data collected we turned our attention to establishingand gathering factors (our independent variables) that may influence the selection of the provinceto locate in. To help determine what these factors may be we relied on recent work [Bartik(1985), Carlton (1983), Coughlin (1991), Head, Ries, and Swenson (1993), Luger and Shettylain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 17(1985), Schmenner (1987), Smith & Florida (1992), and Woodward (1992)] that has looked atsimilar location decisions in the United States. These papers focused on state choices and thuswere very applicable for our study on provincial location choices.The following list represents independent variables for which we collectedCanadian provincial data. A brief description of each is contained in this list. (For more detailand actual time series data see Appendix 2, with notes to this data in Appendix 3). With theexception of our Ship Rank, Average Energy and MetGDP variables (which are described infurther detail in the next section) and our Canadian Industries and Japanese Greenfieldsvariables (described in Section 4.4) the following variables are relatively self explanatory.1. Provincial Population (Cansim'). This is a measure designed to capture therelative size of the provincial economy.2. Percentage of population living in Metropolitan areas (Cansim). Although thepopulation of the province may be larger than another province, if it is veryspread out this tends to hamper economic activity. Thus, a measure ofconcentration of the province's population is useful. In Canada metropolitan areasinclude cities with over 100,000 people.3. Crime rate, a probabilistic variable is a measure of reported offenses per 100,000people (Cansim). This variable is included as a measure of quality of life, whichis increasingly receiving more attention. Thus, we thought that it was appropriateto see if the quality of life decision effected the provincial location process ofJapanese manufacturing greenfields.4. Unemployment rate, which has both positive and negative attributes associatedwith it. For instances, high unemployment is positive in terms of available workforce, but is a negative in terms purchasing power (Cansim). Nevertheless,Computerized on-line database of Statistics Canada.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 18despite the mixed effects of this variable, it is a general economic indicator; thus,its inclusion in our study.5. Unionization rate is for total provincial labour force; however, for the years wechecked, the manufacturing unionization rate did not significantly differ (StatisticsCanada, Advisory Services). Unionization is include in our model asmanufactures often are said to avoid it. However, unionization may also be ameasure of quality of the work force and as such capital intensive industries couldsee it as a desirable quality.6. Area in square kilometres not including fresh water (Canada Year Book). Weinclude this variable as a measure of room to grow and natural resources.7. All production managers tend to be concerned with the cost of averagemanufacturing Wages (Manufacturing Industries of Canada). However, highWages may also be associated with skilled productive workers and as such highWages in themselves may not be a bad thing.8. Provincial GDP (Cansim). This variable is included as a measure of market size.9. Highway Kilometres per square kilometre (Roads and Transportation Associationof Canada) is designed to measure the level of existing infrastructure, which isthought to be a factor in attracting new industry.10. Average Energy is a weighted average cost of fuel in the province (EnergyStatistics Hand Book). The weights are based on existing industrial use for 1992,of each fuel, in each province. The cost of energy is generally of concern tomanufacturers and as such is included in our study.11. Low Energy is a measure of the lowest priced form of energy in the provincesat the time the Japanese manufacturing greenfield was established (EnergyStatistics Hand Book). The cost of energy is important to manufacturers and assuch is included in our study.12. Airports with control towers (Transport Canada) are thought to represent ameasure of transportation infrastructure as well as the preferred choice ofexecutive transportation. Hence its inclusion.13. Provincial corporate Tax rate (Canadian Tax Reporter). As businesses are usuallyestablished to make profits, the tax rate that they will face is an important factorand possibly may determine the manufacturing facility's provincial location.However, high taxes in themselves are not necessarily bad. It depends on whatthe tax dollars are spent on, and how efficiently this process is carried out. Forlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 19example, if tax dollars are spent effectively on improving needed infrastructure,then this may be seen as a positive.14. Percentage of Provincial Labour Force in Manufacturing is a measure ofprovincial manufacturing activity (Cansim). It has been included in our study asa possible reason for plant location because Japanese manufacturing greenfieldsmay only be attract to the relatively more industrial provinces.15. The percentage of Canadian Manufacturing Labour Force that resides in theprovince is a measure of manufacturing activity (Cansim). It has been includedin our study as a possible reason for plant location because Japanesemanufacturing greenfields may only be attracted to the relatively more industrialprovinces.16. Agglomeration of specific Canadian Industries in each province is a measure ofindustry-specific manufacturing activity (Manufacturing Industries of Canada) ineach province. This variable is included as firms in the same industry may beattracted to each other.17. Agglomeration of Japanese Greenfields in each province is a measure ofJapanese manufacturing activity (Toyo, Jetro, Dodwell). We have included thedata in Table 4.1, as we wish to test if Japanese manufacturing greenfields areattracted to provinces that already have a base of such firms.18. Ship Rank is a measure of ease of water access to Japan and is important whenthe Japanese-owned firm is shipping the manufactured product back to Japan.This variable could also be of some important when parts are being shipped toCanada to be assembled (although closeness to the market may be moreimportant).4.3. Ship Rank, Energy and MetGDPOur Ship Rank variable is a combination of geographic location and port facilitiesand is a measures of ease of water access to Japan. This is important not only for shipping overparts to be assembled in Canada, but also to ship back finished goods to Japan, such as lumberfrom Japanese-owned sawmills in British Columbia.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 20British Columbia is ranked highest because it has deep water ports and the closestaccess to Japan. New Brunswick, with a deep water port, but requiring ships bound for Japanto utilize the Panama Canal, is second. Quebec, which requires a trip up the St. LawrenceSeaway, is third. Ontario fairs worse than Quebec, due to the canal that must be travelled toenter the Great Lakes from Montreal. The remaining two provinces hosting Japanesemanufacturing, are the only two land locked provinces in the country; thus, Alberta andSaskatchewan score worse in this category (Appendix 2 presents a Ship Rank for all provinces).Apart from water transportation, manufacturing goods are commonly moved byboth rail and truck. However, comparing the length of rail tracks in each province is lessfavourable than comparing the total paved highway kilometres, because of the history ofCanadian rail development. Rail development was more prominent in the east during the earlieryears of Canadian industrialization; whereas, road transportation developed later and as suchlacks the eastern bias associated with rail development. Thus, we have elected to concentrate onhighway, rather than rail kilometres.Along with our shipping variable we also devised our own method for measuringenergy costs. Our Low Energy variable is a measure of the lowest priced fuel (among crude oil,natural gas or electricity) that prevailed at the time the Japanese decided to establish thegreenfield in Canada. The second variable we constructed (which was also calculated byconverting fuel prices to dollars per million Btu's) was a weighted average cost variable. OurAverage Energy variable was calculated utilizing weights based on existing industrialProvincial Lowest Energy Costs76—5-B4—01980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990—Alberta^+British Columbia *New Brunswick+ Ontario Quebec^+ SaskatchewanStatistics Canada, Energy Statistics Hand BookProvincial Average Energy Costs102—0 ^1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990--Alberta^+ British Columbia *New Brunswick+ Ontario * Quebec^+ SaskatchewanStatistics Canada, Energy Statistics Hand BookFigure 4.1Figure 4.2lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 21consumption of fuel in eachprovince. These two variablesare displayed graphically inFigure 4.1 and Figure 4.2.Finally, let usexplain our MetGDP variable,which is designed to capturethe characteristics of theprovincial market, which isalso important to foreigninvestors. In general, a strongmarket is found where there isa concentration of people withpurchasing power. Thus, wewould expect the combinationof urban concentrations andgross domestic provincialproduct to be an influentialfactor. Urban concentration is represented by the percentage of the provincial population livingin a city with a population greater than 100,000 people. To arrive at our single measure ofmarket size (MetGDP) we multiplied GDP by Metropolitan.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 224.4. Industry Agglomeration as a measure of Manufacturing ActivityIndustry agglomeration in itself may attract additional industry. That is industryattracts industry. Krugman (1991) suggests that users and suppliers of intermediate inputs willlocate around one another in an effort to minimize transportation costs and encourage economiesof scale. Also, technological spillovers may cause firms in the same industry to locate inproximity to each other. Smith & Florida (1992) point out that Japanese auto part suppliers tendto locate near Japanese auto manufacturers. Bartik (1985) attributes "existing manufacturingactivity" as the reason for other manufactures locating there. Our study addresses this issue ofwhether the existence of manufacturing attracts new different kind of manufacturing to the area.If so, we would expect our variables measuring manufacturing intensity (i.e., variables 14 and15 in Section 4.2) to be reasons for Japanese greenfields locating in the province that they do.If however, the level of provincial overall manufacturing activity is not as gooda measure as industry-specific clusters are of provincial location choice, then we would expectour Canadian Industries and Japanese Greenfields variables to be more significant. Building onthe work of Head, Ries, and Swenson (1993), our Canadian Industries and Japanese Greenfieldsvariables are counts of establishments in specific industries, accumulated at the provincial level.To determine Canadian Industries we utilised SIC classifications (based on 4, or in some cases3, digits). That is, on a provincial basis we counted all Canadian industries that had the sameSIC classification as the entering Japanese greenfield. Appendix 4 contains the industry-specificcounts on a provincial basis for the greenfields, with Appendix 5 providing the national counts.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 23We also used the notion that Japanese firms are attracted to locate in proximityto other Japanese manufacturing facilities. Unfortunately, the industry-specific data (based onSIC classifications) that was collected for Japanese manufacturing facilities already in Canada,was of little use, because of the small number of industry-specific Japanese firms present in eachprovince at time of entry. Therefore, rather than use a Japanese count of existing firms in thesame industry we utilized our data presented in Table 4. I', which provided a count of Japanesemanufacturing greenfields that existed in the province prior to the new Japanese entrant.While there is a possibility that the general level of manufacturing will in itselfattract additional manufacturing facilities to locate there, we suspect that location choice isdriven by industry-specific establishment counts. However, as Head, Ries, and Swenson (1993)point out, the location of like industry, an agglomeration effect, should not be confused withendowment effects. For example, if a new pulp mill locates in a less industrial province (say,British Columbia rather than, say, Ontario) then it may have done so for either industry-specificreasons (most Canadian pulp mills are in B.C.) or endowment effects (most trees close to thePacific Ocean are in B.C.). In general, it is difficult to distinguish between agglomeration andendowment effects.4 For a true measure of Japanese greenfields we also included greenfields established prior to 1980.Accordingly, this increased the accumulated totals in Table 1. Alberta had one; British Columbia had two;Ontario had two; Quebec had four; and Saskatchewan and New Brunswick had no Japanese manufacturinggreenfields locate in the province prior to 1980.Lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision^page 244.5. Data Collection SummaryWe were able to collect Canadian data for all the variables listed in Section 4.2.We summarize our variables in Table 4.2. In this table each provincial variable is ranked forthe years 1989 and 1980. The 1980 ranking only appears in brackets if the ranking actuallychanged. We observe that approximately one third of the rankings do change. Generally, thesechanges are minor rearrangements. Only 3% of the rankings actually changed more than threeplaces and apart from the Ontario's unemployment rate becoming the lowest in Canada in 1989,and New Brunswick becoming more unionized in 1989, all other major changes in provincialrankings occurred in the area of provincial corporate tax rates.TABLE 4.21989 (1980) PROVINCIAL RANKINGProv Rank Pop Met Crime UI Union Area Wage GDP Hw Engy Air Tax P-m C-m ShipON 1 1 1 6 1(4) 3(5) 3 9(8) 1 6 5(4) 2 7(6) 1 1 6PQ 2 2 2 1(2) 6(7) 9(8) 1 7(6) 2 8 6 3 1(4) 2 2 5BC 3 3 5 10(8) 5 7(9) 2 10 3(4) 9 3 1 2(10) 5(3) 3 1AB 4 4 3 8(10) 2(1) 1(2) 4 8(9) 4(3) 5 1 4 4(2) 8 4 9MN 5 5 4 7 4(3) 8(7) 6 3(2) 5(6) 7 4(5) 7 10(8) 4 5 7NS 6 7 7 5 7(6) 2(4) 9 4 7 2 9(8) 9 6(5) 6 6 3SK 7 6 6 9 3(2) 5(1) 5 6(7) 6(5) 3 2 6 5(7) 10 8 10NB 8 8 9 3 8(9) 6(3) 8 5 8 4 8(7) 8 8(3) 3(5) 7 2PE 9 10 10 4 9(8) 4(6) 10 1 10 1 7(9) 10 3(1) 9 10 8NF 10 9 8 2(1) 10 10 7 2(3) 9 10 10 5 9 7 9 4Population 1 = Highest PROVINCE^TOTAL SCORES% of Population living in Metropolitan Area 1 = Highest 1. Ontario 53Crime Rate 1 = Lowest 2. Quebec^57Unemployment Rate 1 = Lowest (+ economic indicator) 3. British Columbia^69Unionization Rate 1 = Lowest 4. Alberta^69Area in Square Km 1 = Biggest 5. Manitoba 86Average Manufacturing Wages 1 = Lowest 6. Nova Scotia^89Provincial GDP 1 = Highest 7. Saskatchewan 90Highway Km per Square Km 1 = Highest 8. New Brunswick^95Average Energy 1 = Lowest 9. Prince Edward Island^106Airports with Control Towers 1 = Most 10. Newfoundland^111Provincial Corporate Tax Rate 1 = Lowest% of Provincial Labour Force in Manufacturing 1 = Highest NOTES:% of Cdn Manufact. Labour Force in Province 1 = Highest 31% of Rankings changed.Shipping - ease of water access to Japan 1 = Easiest 3% of Rankings changed 3 or more places.- 25 -lain Brown JAPANESE MANUFACTURING GREENFIELD& The Provincial Location Decision^page 265. OUR CONDITION LOGIT ECONOMETRIC MODELBuilding on related research (Section 3) our model also seeks to explain thelocation selection process, by determining which factors (from our list in Section 4.2) weresignificant. That is, why did a Japanese manufacturing greenfield select one province overanother, after having made the decision to locate in Canada? The structure of our model enablesus to analyze the fifty-five Japanese manufacturing greenfields that have located in Canadaduring 1980 to 1991.While in theory these firms could have selected any one of ten provinces and twoterritories, as we know from Table 4.1 and Appendix 1, these investments only involved a totalof six provinces. Recall that almost two thirds of the new investment went to Ontario (35investments), with British Columbia (11 investments) accounting for a fifth. This leaves Albertawith one investment, Saskatchewan with two investments, Quebec with four investments andNew Brunswick with two investments.Based on this it is not realistic to say that the whole of Canada represented achoice set, accordingly, we limited our choice set to only the provinces that had been picked inthe last decade. (However, for comparative purposes, Table 4.2 and Appendix 2 containprovincial data on all ten provinces). Thus, there are 330 (6 X 55) dichotomous choices thatstem from our 55 dependent variables. While there is no hard and fast rule on sample size, ourlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 27small sample size may be problematic; however, we have relied on McFadden (1974), whosuggests that logit regression is suitable for sample sizes over 50.We assume that a Japanese manufacturing greenfield will choose to invest in aparticular province if and only if it will maximize profit. Formally, the jth province is chosenby the i th firm, if and only iflIy = max {IL; m =^ (1)where Hy denotes the profit of the i th firm given that it locates in the jth province (for j =1,...,6). Following Carlton (1983) and Coughlin (1991), we assume thatIlw = c + Xift + E y^(2)where c is the constant term; X, is a vector of observable characteristics for the jth province; ftis a vector of unknown coefficients to be estimated; and E y is the random term. If the errorterm is independent and has a Weibull distribution, McFadden (1974) shows thatP = exp {X,J6} / E exp {X,fi}^(for k = 1,...,6)^(3)whereft is obtained by maximum likelihood estimation; and Pi denotes the probability a Japanesemanufacturing greenfield locating in province j. This decision depends on the level of thelain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 28province's characteristics that affect profits relative to the other provinces. In general, theexplanatory variables are provincial characteristics which are independent of the investment.Canadian Industries, Japanese Greenfields, Labor and Energy, however, containprovincial characteristics that are specific to each investment, and the variation is due to industrydifferences.The first two variables were introduced in Section 4. Labor and Energy are twoadditional variables we use to measure factor intensities. We personalize the data for eachJapanese establishment (based on work by Schmenner, 1987) by interacting industry labour(energy) intensity and wages (energy) costs in each province. Table 5.1 illustrate the calculationuse to arrive at our Energy variable. The Energy Intensity column for firm 10 is pulp industryfuel costs divided by pulp industry sales. To determine Energy, Energy Intensity is multipliedby the Average Energy price in each province prevailing at the time the greenfield is established.The calculation to arrive at our variable to measure labour intensity (Labor) uses the samemethodology. To calculate Labor, Wage Intensity is multiplied by Wages prevailing at time ofentry in each province.Table 5.1 also shows the variation in our data. For example, we see that energyprices vary among provinces (i.e. Alberta's Average Energy cost in 1990 was half of Quebec's1990 Average Energy cost). Variation also occurs over time (i.e. from 1986 to 1990 Quebec'sAverage Energy cost increased by $.61 per 1,000,000 Btu's, whereas the Average Energy costin Alberta decreased by $.26). In addition to these time and provincial variations that areoccurring throughout our model, we also experience industry variation amongst Canadianlain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 29Industries, Japanese Greenfields, Labor and Energy. In this regard Table 5.1 points out thenumber of pulp mills in British Columbia is greater than the number in Ontario. This is anexample of industry-specific variation.TABLE 5.1ENERGY INTENSITY(In Thousands)Firm#SIC#EnergyIntensity ProvinceAvg.Energy$/MBtuCdnEnergy Indust.CountAlberta 3.87 0.2362 210 2711 355/5817 B.C. 5.74 0.3503 16N.B. 8.61 0.5255 6Mitsubishi Pulp =0.06103 Ontario 6.81 0.4156 51990 Quebec 7.51 0.4583 8Sask. 5.05 0.3082 1Alberta 4.13 0.0106 028 3231 100/39093 B.C. 5.89 0.0151 3N.B. 8.04 0.0206 0Honda Auto =0.00256 Ontario 6.32 0.0162 151986 Quebec 6.90 0.0177 5Sask. 5.28 0.0135 1Finally Table 5.1 shows how our model works. Mitsubishi can chose from sixlocations, and while Honda will also chose from the same six provinces, between the twoinvestments there are a total of twelve alternatives. Honda and Mitsubishi face different locationchoices because of different investment times and the investments being in different industries.In our next section we reveal which provincial characteristics influenced the provincial locationdecision of the Japanese manufacturing greenfields.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 306. RESULTSThe following final results were obtained by estimating equation (3).TABLE 6.1FINAL RESULTSLOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICESParameter^Estimate-55.082155330Standard Error t-statisticManufacturing Wages -8.35985 5.35172 -1.56209Average Energy -7.11482 2. 83188 -2.51241 * 5Ship Rank -.350917 .204142 -1.71898Canadian Industries .657384 .281352 2.33652 *Japanese Greenfields 2.16394 .612262 3.53434 *Provincial MetGDP -.880592 .340797 -2.58392 *The log of each variable was taken, with exception of the Ship Rank variable.Testing the theoretical relationships presented in the previous section was severely constrainedby the limited sample size. The variables presented in Table 6.1 are those that had consistenteffects across various specifications. To narrow down our list of twenty independent variablesto the model above, the variable's significance was measured in terms of its coefficient and t-statistic (10 % significance level). For example, recall that as a measure of transportation serviceswe had collected provincial data on Highway Kilometres, Airports, and Ship Rank variable. Weselected Ship Rank because throughout our testing it remained generally significant, while theother two were not significant.5^Asterisked variables are statistically significant.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 31To a manager both the lowest available energy cost and the average energy costsare important. The choice between the two measures of energy cost was not intuitively obviousbecause both could be a guiding force for new plant location. To select the most appropriatemeasure of energy costs it was necessary to independently test both of our energy cost measures.As Table 6.2 illustrates, when Low Energy replaced Average Energy, the model was notimproved. Thus, average energy prices are more influential in a location decision than the priceof the lowest available energy source.TABLE 6.2SUBSTITUTING LOW ENERGY PRICELOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICESParameter^Estimate-55.991555330Standard Error t-statisticManufacturing Wages -3.58545 4.25787 -.842076Low Energy -3.14202 1.48912 -2.10999 *Ship Rank -.068488 .135675 -.504793Provincial MetGDP -.750461 .324385 -2.31349 *Canadian Industries .710716 .272957 2.60377 *Japanese Greenfields 1.92872 .592186 3.25695 *Recall Table 4.2 where we found that the variables that changed the most inprovincial ranking from 1980 to 1989 were Tax, Unionization, and Unemployment. We did notexamine the influence of unemployment, given the previously discussed ambiguity of highunemployment as a location factor (i.e. , being a drawing card for some, yet a deterrent toothers). However, we decided to test the provincial crime rate, as safety is considered to beimportant to the Japanese. Our Crime variable was offenses per 100,000 divided by provincialpopulation.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 32TABLE 6.3TESTING UNIONIZATION, TAX & CRIME RATESLOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICES-54.1813:^55330Parameter^Estimate Standard Error t-statisticManufacturing Wages -11.2525 6.69886 -1.67977Average Energy -8.59998 3.25604 -2.64124 *Ship Rank -.360553 .397184 -.907774Canadian Industries .651787 .289458 2.25175 *Japanese Greenfields 2.18708 .806161 2.71296 *Provincial MetGDP -1.27346 1.39549 -.912557Unionization .033940 .089459 .379387Tax .179514 .165387 1.08542Crime -14.3812 31.7353 -.453161Unlike Bartik (1985), who found tax and unionization to be significant in theUnited States, as Table 6.3 illustrates, we did not find this result. Nor did Crime have anyinfluence in the location decision of Japanese manufacturing greenfields. Thus, we could notsupport the stereotype that high rates of unionization, crime and taxes drive business away.However, our results show that Wages and Average Energy are negative and fairlysignificant throughout our testing. At first this may appear to contradict our findings in Table4.2. In Table 4.2 we see that Ontario and British Columbia have the highest wages and neitherhas the lowest energy prices, yet they account for 84% of new Japanese manufacturinginvestment in Canada in the last decade. This is not a contradiction, because while Japanesegreenfields prefer to avoid high wages and energy costs, they have an even stronger preferencefor like industries and presence of other Japanese greenfields. Thus, they are willing to pay thehigher price for inputs in order to be in areas of industry specific concentration.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 33Next we tested to see whether labour intensive industries were more concernedabout the level of wages, and energy intensive industries were more concerned about energycosts. As portrayed in Table 6.4 inconsistent results were found in that neither the Labor orEnergy variable were significant. In our sample, it appears that firms in labour or energyintensive industries tended to choose Ontario or British Columbia in spite of high wages andmid-priced energy costs.TABLE 6.4TESTING FACTOR INTENSITIESLOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICESParameter^Estimate-59.0074:^55330Standard Error t-statisticLabor 12.47260 12.5914 .990568Energy -3.207280 30.5082 -.105128Ship Rank .025374 .129654 .195703Provincial MetGDP -.500709 .288151 -1.73766Canadian Industries .690563 .270134 2.55637 *Japanese Greenfields 1.15395 .401528 2.87389 *In our section on independent variables we explained how we calculated MetGDP.We theorized that Japanese investors prefer large, concentrated markets, so we created thisvariable by multiplying the percentage of provincial population living in a city with provincialGDP. As Table 6.1 illustrated the coefficient for MetGDP is negative and this counter-intuitiverelationship also occurs for GDP (see Appendix 6).Re-examining the data suggested that Quebec might be unduly influencing themodel since it has the second highest GDP and Metropolitan population (see Table 4.2 and-A=*=/t=#1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 34Figure 6.1) yet has onlyhad four Japanesegreenfield investments inthe last decade. To explorethis, we created a Quebecdummy variable. Theresults in Table 6.5 wereinconclusive in that whenQuebec replaced theMetGDP variable it too Figure 6.1was negative and significant; however, when both the Quebec dummy variable and the MetGDPvariable were included in the model, as illustrated by Table 6.6, neither was significant.TABLE 6.5REPLACING MetGDP WITH QUEBEC DUMMYLOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICESParameter^Estimate-56.117355330Standard Error t-statisticAverage Energy -5.12543 2.6080 -1.96527Manufacturing Wages -10.5204 5.71940 -1.83942Canadian Industries .523389 .271664 1.92660Japanese Greenfields 1.10012 .424306 2.59274 *Ship Rank -.485113 .217339 -2.23206 *Quebec Dummy -1.42687 .677821 -2.10509 *Lain Brown JAPANESE MANUFACTURING GREEIVFIELDS• The Provincial Location Decision^page 35TABLE 6.6ADDING A QUEBEC DUMMY VARIABLELOG OF LIKELIHOOD FUNCTIONNUMBER OF CASESNUMBER OF CHOICESParameter^Estimate-55.072055330Standard Error t-statisticManufacturing Wages -8.71039 5.89270 -1.47817Average Energy -7.00878 2.92989 -2.39216 *Ship Rank -.366620 .232202 -1.57889Canadian Industries .648374 .28773 2.25341 *Japanese Greenfields 2.08530 .822973 2.53387 *Quebec Dummy -.157868 1.10961 -.142273Provincial MetGDP -.816484 .564654 -1.445990Intuitively the explanation is that the Japanese are merely following a Canadiantrend of avoiding Quebec due to government intervention into areas such as language legislation.Furthermore, one would suspect that a Japanese greenfield may be even more concerned aboutthe separation issue than a Canadian firm. This would be particularly true if the Japanese FDIis made in order to tariff jump. Locating in the "Country of Quebec" may not automaticallymean that the industry can benefit from existing free trade agreements within North America.While we can not confirm this, we can confirm that Japanese manufacturing greenfields havelocated in Ontario, which is close to Quebec's wealthy urban population, yet out of itsjurisdiction.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 367. CONCLUSIONIn this section we summarize our results and identify the main weaknesses of ouranalysis. Then we discuss policy ramifications and research questions that stem naturally fromour results.7.1. Factors Influencing Japanese Manufacturing Greenfield's Location DecisionWe found that new Japanese manufacturing facilities locate next to similarCanadian industry-specific firms and other Japanese firms. Ontario and British Columbia attracta greater proportion of Japanese manufacturing than their national share of manufacturing wouldsuggest. In this regard our data shows that Ontario has approximately half of Canadianmanufacturing, yet they have attracted 64% (35/55) of Japanese greenfields. The case in BritishColumbia is even more profound. The number of new Japanese manufacturing greenfields (11/55or 20%) is double British Columbia's share of national manufacturing. Thus, we conclude thatindustry-specific establishment counts are a more important determinant of a Japanesemanufacturing greenfield's location than the general level of provincial manufacturing.Industrial distribution of Canadian firms is not the only factor underlying thelocation choice. Our results show that Japanese firms prefer provinces where there are relativelymore Japanese manufacturing greenfields. That is to attract Japanese industry the presence oflain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 37similar Canadian industry (i.e., with the same SIC classification) is necessary and once Japaneseindustry is attracted then this in itself is a factor in attracting other industries from Japan.Furthermore, if two provinces share similar levels of industry-specific Canadian firms thengeographic proximity to Japan may be important. To summarize, Canadian Industries andJapanese Greenfields were always significant, with Ship Rank generally being significant. Apartfrom these three variables, Average Energy, Wages, and MetGDP were also part of the equation;however, their importance is minor in determining where Japanese manufacturing greenfieldswill locate in Canada.This brings us to the question of whether the positive coefficient on the CanadianIndustries variable reflects an agglomeration effect or an endowment effect. Greenfields inOntario tend to be in the automotive sector, which follows a traditional Canadian pattern.However, while both British Columbia and Ontario have high counts of forestry specificindustries the Japanese have elected to locate mostly in British Columbia presumably due to itseasier access to the Japanese market. In short, it is the only province with trees across the waterfrom Japan. This ease of transportation is captured by the significance of our Ship Rank variable,which measures the province's water access to Japan. Referring back to Table 4.2 we see thatBritish Columbia's geographic position gives it the comparative advantage over Ontario'sgeographic position. Head, Ries, and Swenson (1993) suggested that the concentration ofJapanese wood product firms in Washington State 6 is the result of the state's forests and6^Washington State is British Columbia's southern neighbour, and both are bounded by the Pacific Oceanon the west.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 38Washington's geographic proximity to Japan. Thus, endowments--British Columbia's forests andproximity to Japan--may explain the concentration of Japanese forestry investment in BritishColumbia.It is difficult to distinguish if clustering is due to agglomeration or endowment.That is, are Japanese pulp mills in British Columbia because it is the only province with treesacross the water from Japan (an endowment effect) or because the province of British Columbiahas more pulp mills than any other Canadian province (which could be due to agglomeration oran endowment effect)? While the answer is not intuitively obvious, our results may suggestevidence of pure agglomeration effects. That is, if all endowment effects are captured by thecombination of the Canadian Industries and Ship Rank variables, then the positive coefficienton the significant Japanese Greenfields variable may be evidence of pure agglomeration effects.7.2. Limitations of Our ResultsThe small number of Japanese manufacturing greenfields that have entered theCanadian economy in the last decade limits our ability to assess the determinants of investment.The many possible explanatory variables and little information to distinguish among them(especially in the presence of multicollinearity) adds further to the difficulties in determiningactual location factors that attract Japanese manufacturing greenfields.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 39The possibility of omitted explanatory variables exists. Utilizing past research weidentified and then tested the vast majority of factors that could influence the location decision.However, it is possible that our error term includes an independent variable that we were notable to identify and test for its significance. If we have omitted variables that are correlated withother independent variables, the estimated coefficients for those variables will be biased.Another possible problem with our model may be lack of variation amongst nonindustry-specific provincial characteristics. Table 4.2 illustrates that there is variation amongthese characteristics. However, the range of variation between these characteristics is not always,from a statistical point of view, as great as we would like it to be. This limited variation of nonindustry-specific provincial characteristics constrains our ability to distinguish the individualeffects of each variable.With the combination of Ontario (64 %) and British Columbia (20 %) receiving84 % of Japanese greenfield investment our distribution is skewed. This skewed distributioncompounds the problem of our limited variation. Furthermore, this distribution also gives riseto the possibility that a unique omitted feature of either Ontario or British Columbia could bedriving the results. Nevertheless, by measuring variation in both non industry-specific provincialcharacteristics and industry-specific characteristics, despite our small skewed sample size, wefeel that our study still provides results that provincial policy makers may find useful.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 407.3. Policy Ramifications for Provinces Seeking FDIRegardless of whether we think foreign investment is good or bad for Canada ithas become somewhat of a necessity given our desire to spend more than we earn. Accordingly,we find ourselves in the position of needing to attract foreign investment. Because of possiblejob creation and potential spillovers of management expertise, FDI has been highly sought afterin North America. In the U.S. large subsidies have been paid by the individual states trying toattract Japanese greenfield investments.However, our study suggests that unless Japanese manufacturing greenfields andindustry-specific Canadian firms are already in the province, and that the province providesmarket accessability, the province is unlikely to attract much Japanese investment. Hence,increasing schemes directed at attracting FDI will not have a significant effect if the industry-specific base of firms does not already exist.If the investment is to export products to Japan, then market access to Japan isdetermined by geography (a factor not easily modified). For example, although both Ontario andBritish Columbia have a forest industry, because the products being produced are for Japaneseconsumption, Japanese-owned plants tend to locate in British Columbia. If the market is Canada(or North America) then location is in Ontario with its central location and substantial industrialactivity.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 41Our results indicate that it is difficult for a province to try to attract Japanesegreenfield FDI without a market for the product, or without an existing industrial base. For aprovince to try to attract this type of FDI with neither, it is near impossible, unless governmentsintervene. However, even government intervention may do little to encourage locationpreferences. Our study suggests that corporate tax rates and unionization were not significant.Therefore, rather than offer subsidies or direct government resources to reducing unionization,perhaps tax dollars would be better spent on developing an industrial strategy to encourageclustering of industry.If tax dollars are efficiently spent on needed infrastructure that creates acompetitive advantage, then this may encourage clustering of firms that benefit from thisinfrastructure. Should Canadian industries cluster, then Japanese manufacturing greenfields inthe same industry are more likely to be attracted to this area. With a base of Japanesemanufacturing greenfields, more Japanese manufacturing greenfields are likely to be attracted.This possible Japanese agglomeration effect could then create industry diversification.7.4. Further Study and ExtensionsThis study questions government efforts to attract Japanese greenfield investmentsto areas that do not already have a base of firms in similar industries or Japanese manufacturinggreenfields. Our study also drew our attention to a possible free trade effect, in that we noticedthat 75% of greenfields locating in B.C. during the last decade did so after free trade. Contrarylain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 42to British Columbia, only 25% of Ontario's Japanese manufacturing greenfields located inOntario after free trade. While this may be a free trade effect, Ontario's relative fewerinvestments could reflect the change in business climate due to the more socialist governmentbeing elected in 1990. We feel it is also worth studying why the majority of Japanesemanufacturing greenfields locate in Ontario, which is close to Quebec's wealthy urbanpopulation, yet out of its jurisdiction. This too may be a free trade effect in that the Japanesemanufacturing greenfields wish to have access to North American free trade, which is somethingthat is not guaranteed if Quebec should separate from Canada. Alternatively, the possibleavoidance of Quebec could be a language preference.Future work will take into account non-free trade issues such as language orchanging political climates so that we may study whether the Canada/U.S. Free TradeAgreement has shifted FDI amongst the provinces and/or away from Canada to the U.S. Weplan to study this by comparing Japanese manufacturing greenfields locating in Canada to thoselocating in the U.S. We will seek to not only determine whether one country has attractedrelatively more Japanese manufacturing greenfields than the other, but also to determine ifindustry location is due to comparative advantage as FDI for tariff jumping is no longer requiredwithin the new North American trade zone.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 438. BIBLIOGRAPHYT. J. Bartik, "Business Location Decisions in the United States: Estimates of the Effects ofUnionization, Taxes and Other Characteristics of States," Journal of Business &Economic Statistics, vol. 3, no. 1, pp. 14-22. 1985.C.C.H. Canadian Limited, Canadian Tax Reporter, vol. 1, pp. 1602-1603. 1992.D. W. Carlton, "The Location and Employment Choices of New Firms: An Econometric ModelWith Discrete and Continuous Endogenous Variables," Review of Economics andStatistics, vol. 65 pp. 440-449. 1983.C. Coughlin, et. al., "State Characteristics and the Location of Foreign Direct Investment withthe United States," Review of Economics and Statistics, pp. 675-683. 1991.Dodwell Marketing Consultants, Auto Parts Industry of Japan (Tokyo: 1989).External Affairs Canada, Canada - U.S. Free Trade Agreement. 1988.N. Glickman & D. P. Woodward, "The Location of Foreign Direct Investment in the UnitedStates: Patterns and Determinants," International Regional Science Review, vol. 11, no.2, pp. 137-154. 1988.E. M. Graham & P. Krugman, Foreign Direct Investment in the United States (WashingtonD.C.: Institute for International Economics 1990).L. C. Hamilton, Regression with Graphics: A Second Course in Applied Statistics, (PacificGrove, California: Brooks/Cole Publishing 1992).K. Head, J. Ries, and D. Swenson, "Agglomeration Benefits and Location Choice: Evidencefrom Japanese Manufacturing Investments in the United States," University of BritishColumbia Working Paper. March 1993.Investment Canada, Annual Report. 1991.Investment Canada, International Investment's Canadian Developments in a Global Context.1991.Japan, Japanese Direct Investment Aboard in Fiscal 1990. Ministry of Finance. 1991.JETRO, Directory: Japanese-Affiliated Companies in USA & Canada. 1991-1992.lain Brown JAPANESE MANUFACTURING GREENFIELDS: The Provincial Location Decision ^page 44M. I. Luger & S. Shetty, "Determinants of Foreign Plant Start-ups in the United States: Lessonsfor Policymakers in the Southeast," Vanderbilt School of Transnational Law, vol. 18, pp.223-245. 1985.P. Krugman "Geography and Trade," Cambridge, MA: The MIT Press.D. McFadden, "Conditional Logit Analysis of Qualitative Choice Behaviour," in P. Zarembka,ed., Frontiers in Econometrics (New York: Academic Press 1974).D. McFadden, "Cost, Revenue, and Profit Functions," in D. McFadden and M. Fuss, eds.,Production Economics: A Dual Approach to Theory and Applications, vol. 1(Amsterdam: North-Holland 1978).R. J. Newman & D. H. Sullivan, "Econometric Analysis of Business Tax Impacts on IndustrialLocation: What Do We Know, and How Do We Know It?" Journal of Urban Economics,vol. 23, pp. 215-234. 1988.Ontario, Canada, Japanese Investment Profile. 1992.Roads and Transportation Association of Canada, Canada's Roadway Infrastructure SelectedFacts and Figures, (Ottawa, Canada 1990).A. M. Rugman, International Business in Canada Strategies for Management (Scarborough:Prentice-Hall 1989).R. W. Schmenner, J. C. Huber, and R. L. Cook, "Geographic Differences and the Location ofNew Manufacturing Facilities," Journal of Urban Economics, vol. 21, pp. 83-104. 1987.D. Smith & R. Florida, "Agglomeration and Industry Location: An Econometric Analysis ofJapanese-Affiliated Manufacturing Establishments in Automotive-Related Industries," H.John Heinz III School of Public Policy and Management (Carnegie Mellon University)Working Paper. 1992.Statistics Canada, "CALURA," Catalogue 71-201 Annual. 1991.Statistics Canada, "Canada Year Book," Annual. 1991.Statistics Canada, "Canada's International Investment Position." 1991.Statistics Canada, "Cansim Data Retrieval Program," (On-Line Database System, 1993).Statistics Canada, "Consumption of purchased fuel and electricity," Catalogue 57-208 Annual.1991.lain Brown JAPANESE MANUFACTURING GREE1VFIELDS: The Provincial Location Decision ^page 45Statistics Canada, "Energy Statistics Hand Book," Catalogue 57-601 Quarterly. September 1992.Statistics Canada, "Manufacturing industries of Canada: national and provincial areas,"Catalogue 31-203 Annual. 1991.Statistics Canada, Pacific Region Advisory Services, "Inquiry for Union Statistics," February9, 1993.Statistics Canada, "Quarterly report on energy supply-demand in Canada," Catalogue 57-003Quarterly. June 1992.Toyo Keizai Company, Japanese Overseas Investment (Tokyo: 1990).Transport Canada, "Airports Group - Airport Site Directory." 1993.United Nations, Foreign Direct Investment and the Service Sector and International Banking, theUnited Nations Centre for Transnational Corporations. 1991.United Nations, World Investment Report: the Triad in Foreign Direct Investment, the UnitedNations Centre for Transnational Corporations. 1991.D. P. Woodward, "Locational Determinants of Japanese Manufacturing Start-Ups in the UnitedStates," Southern Economic Journal. January, 1992.APPENDIX 1FIFTY-FIVE JAPANESE MANUFACTURING GREENFIELDS IN CANADA(Dependent Variable)PROV YEAR FIRM# CANADIAN COMPANY JAPANESE INVESTOR OWNER-SHIP SICCODE#CDNFIRMIN SICPRODUCT DESCRIPTIONAB 1990 1 Tomen Alberta Timber Industries Toyo Menka Kaisha 100.0 2512 1093 LumberBC 1989 2 Advanced Energy Technology NTT 45.0 3391 27 R&D rechargeable batteriesBC 1989 3 Atsugi Nylon Canada Inc Atsugi Nylon 24.0 1811 31 NylonBC 1991 4 Campbell River Fibre Ltd C Itoh and Co Ltd 90.0 2512 1093 WoodchipsBC 1983 5 Canadian Autoparts Toyota Inc Toyota Motor Corp 100.0 3255 47 Aluminum wheelsBC 1988 6 Canadian Chopstick Mfg Co Ltd Mitsubishi Corp 100.0 2599 272 ChopsticksBC 1980 7 Daiwa (Canada) Ltd Daiwa Seiko Inc 3931 209 Golf clubsBC 1983 8 Dominion Malting Ltd Sumitomo Corp 35.0 1131 48 Liquor maltBC 1991 9 I.S. Forest Products Inland Kogyo 17.0 2512 1093 Forest productBC 1990 10 M.C. Forest Investment Mitsubishi Corp 100.0 2711 39 PulpBC 1988 11 Primex Fibre Ltd Sanyang Pulp 50.0 2711 39 Pulp, chipsBC 1989 12 S.M. Cyclo of Canada Sumitomo Heavy Ind. 3199 772 Speed reducers & variators, motorsNB 1989 13 Ampal Pallets Inc Mitsui & Co Ltd 57.9 3099 463 Steel palletsNB 1980 14 NBIP Forest Products Inc Oji Paper Co/Mitsui & Co 33.0 2712 NewsprintON 1987 15 ABC Nishikawa Industries Nishikawa Kasei Co Ltd 49.0 3256 96 Plastic autoparts & armrestsON-OP 1988 16 Bellemar Parts Ind. Canada Honda Motor Co Ltd 100.0 3259 190 Seats for vehicles & tire assemblyON 1986 17 CAMI Automotive Inc Suzuki Motor Co Ltd 50.0 3231 27 Vehicles- 46 -ON-OP 1989 18 Canada Mold Technology Inc Nagase Ltd 100.0 3062 539 Prototype moldsON-OP 1990 19 Cangel Inc Nitta Gelatin Inc 100.0 1011 526 Gelatin & lardON 1987 20 Copar International Toyo Radiator Co Ltd 46.0 3251 50 Radiator & oil coolersON 1988 21 DDM Plastics Inc Daikyo/Suzuki/Mitsui 100.0 3256 96 Automotive plasticsON-OP 1985 22 Denon Canada Inc Nippon Columbia Co Ltd 95.0 3341 25 Car stereo, cassette & CD softwareON 1990 23 DNN Galvanizing Corp Nippon Kokan (NICK) 40.0 2912 26 Hot dip galvanizing steel sheetsON 1981 24 Epson Canada Ltd Seiko Epson Corp 19.0 3361 147 Computers, printers, software productsON 1980 25 Epson Manufacturing Ltd Seiko Epson Corp 3361 147 Printers, ribbons & technical productsON 1980 26 F&P Mfg Inc F. Tech Inc 55.0 3257 20 Auto part, pedal bracketON 1987 27 General Seating of Canada Ltd NHK Spring Co 65.0 3259 190 Seats for automobilesON 1986 28 Honda of Canada Mfg Inc Honda Motor Co Ltd 100.0 3231 27 AutomobilesON 1988 29 Inoac Canada Ltd Inoue MTP Co 50.0 3257 20 Automotive interior panels & armrestsON 1990 30 IDS Fitel Inc Furukawa Electric Co Ltd 50.0 3562 153 Passive fibre optic componentsON 1986 31 Kao-Didak Ltd Kao Corporation 93.0 3399 72 Floppy disksON 1984 32 Kuriyama Canada Ltd Kuriyama Corp 100.0 1621 77 Industrial plastic hose & plumbingON 1983 33 Mitsubishi Electronic Ind Mitsubishi Electric Corp 100.0 3341 25 Colour cathode ray tubesON 1987 34 Miura Boiler Co Miura Boiler Co 99.3 3011 43 High pressure steam boilerON 1980 35 Murata Erie North America Ltd Murata Mfg Co Ltd 100.0 351 90 Ceramic capacitorsON-OP 1987 36 Nichirin Inc Nichirin Co Ltd 100.0 3259 190 Hydraulic hoses for autos & motorcyclesON 1985 37 NKC of Canada Inc Nakanishi Metal Works 100.0 3192 533 Conveyor systemsON 1986 38 Quality Safety Systems Co Tokai Rika Co Ltd 40.0 3259 190 Seat belts & auto componentsON 1986 39 Rockwell Int'l Suspension Syst Mitsubishi Corp 40.0 3254 35 Coil springs & torsion barsON 1982 40 Sanyo Cdn Machine Works Inc Sanyo Machine Works Inc 100.0 3081 1464 Automatic assembly & welding machine- 47 -ON-OP 1985 41 SM Yttrium Canada Ltd Shin-Etsu Chemical Co 100.0 3731 94 SiliconON 1987 42 SMC Pneumatics Canada Inc SMC Corporations 3092 44 Cylinders & valvesON 1986 43 Toyota Motor Mfg Canada Inc Toyota Motor Corp 100.0 3231 27 AutomobilesON 1985 44 Trutech Canada Inc Nihon Parkerizing Co 3041 288 Paint finishing system, rolling oil concON 1989 45 UCAR Carbon Canada Inc Mitsubishi Corp 50.0 3399 72 Artificial graphic electrodesON 1980 46 UNIC International Corp 319 1601 Sandblasting equipmentON 1987 47 Vdo-Yazaki Ltd Yazaki Corporation 50.0 391 603 MetersON 1985 48 Woodbridge Inoac Inc Inoue MTP Co 50.0 3257 20 Automotive interior trimsON 1989 49 Yachiyo of Ontario Mfg Inc Yachiyo Industries 100.0 3259 190 Fuel tanksPQ 1986 50 Cree Yamaha Enterprise Ltd Yamaha Motors 40.0 3281 327 FRP boatsPQ 1986 51 H Aida Enterprise Inc Tokia 100.0 1021 414 Processing seafoodPQ 1989 52 Kobe Aluminium Canada Kobe Steel Ltd 100.0 2961 71 AluminiumPQ 1989 53 Miura Boiler Company Miura Company 3011 43 BoilersSK 1988 54 Hitachi Canadian Ind Ltd Hitachi Ltd 100.0 337 281 Electric power equipmentSK 1987 55 SK Turbine Ltd Marubeni Corporation 100.0 3194 116 TurbinesNOTE: "-OP" signifies that this is the year of operation, rather than year of establishmentAPPENDIX 2PROVINCIAL CHARACTERISTICS(Independent Variables)COLUMN INDEPENDENT VARIABLEPOP Population in 1000's% Metro Percentage of Population Living in Metropolitan AreasCrime Reported Offenses per 100,000 PeopleUI Unemployment RateUnion % Unionization RatesArea Land Area (excluding fresh water) Square KM (constant 1980-90)MAN$/CAP Average Annual Manufacturing Pay per WorkerGDP/CAP Provincial GDP per CapitaHighwayKM/SQ.ICMHighway Kilometres per Square Kilometre of Area (Constant 1980-90)AVG NRG$/MBtuWeighted Average Cost of Crude Oil, Natural Gas, and ElectricityPrices per Million Btu'sLow NRG$/MBtuLowest Cost of Either Crude Oil, Natural Gas, or Electricity Pricesper Million Btu'sAIRPORT Number of Airports with Control TowersTAX Provincial Corporate Tax Rate% PROV LAB in MFG Percentage of Work Force in Manufacturing (Manufacturing LabourForce/Total Labour Force)% CDN MFG LAB/PROV Percentage of Canadian Manufacturing Work Force in the Province(Provincial Manufacturing Labour Force/Total CanadianManufacturing Labour Force)Ship Rank Shipping1=BC (yearly deep water; no canal)2=NS, NB, NFL (yearly deep water)3 = Que (mostly open deep water)4= Ont (mostly open shallow water)5 =Man (partly open deep water)6 =PEI (deep water but no port facilities)7 = Ab, Sask (no water access)PROV YEAR POP %METRO CRIME UI UNION AREAAPPENDIX 2 CONT'DMAWCAP GDP/CAP HIGHWAY AVG NRG LOW NRG AIRPORT TAX %PROVLAB%CDN MFG SHIP000's RATE % % SQ.KM KM/SQ.KM $/MBtu $/MBtu^i % IN MFG LAB/PROV RANKBC 1980 2666.0 54.4 141.77 6.8 39.2 929730 22231 14343.21^0.071^3.17 1.64 26 15.0 12.30 8.65 1BC 1981 2744.2 54.0 150.63 6.7 40.0 929730 24388 16285.62^0.071^4.20 2.39^26 16.0 11.69 8.58 1BC 1982 2787.7 54.5 161.03 12.1 40.0 929730 27059 16542.31^0.071^5.32 3.29 26 16.0 10.25 8.24 1BC 1983 2813.8 54.8 156.34 13.8 40.4 929730 29242 17112.45^0.071^5.73 3.31^26 16.0 9.65 7.97 1BC 1984 2847.7 55.2 155.39 14.7 38.3 929730 29905 17950.98^0.071^6.14 3.49 26 16.0 9.45 8.00 1BC 1985 2870.1 55.5 154.99 14.1 36.9 929730 31389 18988.54^0.071^6.33 3.52^26 16.0 9.49 7.63 1BC 1986 2889.0 56.7 161.88 12.5 41.2 929730 31895 19848.39^0.071^5.89 3.31 26 16.0 9.19 7.38 1BC 1987 2925.0 57.2 165.63 11.9 37.5 929730 32375 21496.07^0.071^5.56 2.33^26 15.0 9.59 7.62 1BC 1988 2980.2 57.8 160.57 10.3 38.1 929730 33542 23325.28^0.071^5.44 2.36 26 14.0 10.06 7.82 1BC 1989 3048.3 58.3 166.60 9.1 36.4 929730 34841 25234.06^0.071^5.37 1.97^26 14.0 10.07 8.07 1BC 1990 3132.5 58.3 177.51 8.3 38.0 929730 35000 25764.09^0.071^5.74 2.28 26 14.0 10.08 8.10 1AB 1980 2140.6 60.7 153.14 3.7 22.0 644390 19003 20156.97^0.265^2.67 1.36^7 11.0 7.25 4.39 7AB 1981 2237.3 58.1 157.16 3.8 23.3 644390 21466 22318.87^0.265^3.48 1.92 7 11.0 7.21 4.66 7AB 1982 2314.5 56.4 149.12 7.7 22.7 644390 24248 22854.18^0.265^3.84 1.96^7 11.0 6.47 4.64 7AB 1983 2338.7 56.1 145.16 10.6 23.9 644390 25989 23682.39^0.265^4.31 2.16 7 11.0 5.86 4.33 7AB 1984 2338.5 55.9 129.87 11.1 23.6 644390 27241 25204.62^0.265^4.56 2.07^7 11.0 5.76 4.33 7AB 1985 2348.5 55.7 128.19 10.0 22.7 644390 27864 27826.70^0.265^4.61 2.13 7 11.0 5.98 4.23 7AB 1986 2375.1 61.4 134.52 9.8 26.4 644390 29027 24132.46^0.265^4.13 2.12^7 11.0 6.03 4.22 7AB 1987 2377.7 61.7 145.05 9.6 24.8 644390 29132 25051.52^0.265^4.24 2.14 7 15.0 6.17 4.20 7AB 1988 2388.7 62.1 148.41 8.0 26.1 644390 29453 26059.36^0.265^3.87 1.86^7 15.0 6.72 4.46 7AB 1989 2425.9 62.3 143.24 7.2 25.8 644390 30638 27059.24^0.265^3.79 1.40 7 15.0 7.02 4.66 7AB 1990 2473.1 62.6 145.49 7.0 26.6 644390 31000 28533.02^0.265^3.87 1.30^7 15.0 7.10 4.69 7SK 1980 959.4 32.5 142.96 4.4 18.8 570700 17526 12924.74^0.341^3.05 1.70^5 14.0 4.85 1.15 7SK 1981 968.3 32.8 152.39 4.6 28.9 570700 19690 14808.43^0.341^3.76 2.28 5 14.0 4.82 1.16 7SK 1982 977.0 33.3 139.13 6.1 28.3 570700 21926 15107.47^0.341^4.54 2.90^5 14.0 4.37 1.17 7SK 1983 989.3 33.7 135.83 7.3 28.2 570700 23495 15399.78^0.341^4.91 3.12 5 14.0 4.03 1.13 7SK 1984 1000.5 33.8 139.08 8.0 28.4 570700 25385 16381.81^0.341^5.26 3.17^5 16.0 3.98 1.14 7SK 1985 1008.4 34.2 144.83 8.1 27.1 570700 25272 17290.76^0.341^5.40 3.15 5 16.0 3.97 1.09 7SK 1986 1010.2 38.3 153.95 7.7 32.7 570700 25356 16971.89^0.341^5.28 3.15^5 17.0 3.95 1.07 7SK 1987 1015.8 38.6 155.71 7.4 30.5 570700 26141 16954.12^0.341^5.23 2.72 5 17.0 4.04 1.06 7SK 1988 1013.5 39.0 152.73 7.5 31.1 570700 27557 17945.73^0.341^4.54 2.52^5 17.0 4.19 1.05 7SK 1989 1006.7 39.2 148.37 7.4 32.2 570700 28379 19326.51^0.341^4.66 2.49 5 15.0 4.43 1.08 7SK 1990 997.1 39.7 145.92 7.0 32.3 570700 29000 20332.97^0.341^5.05 2.90^5 15.0 4.50 1.07 7MN 1980 1024.9 56.8 116.36 5.5 30.7 548360 15637 10916.19^0.154^4.22 2.25^4 15.0 12.00 3.14 5MN 1981 1026.2 57.2 124.75 5.9 30.2 548360 17546 12824.01^0.154^4.62 2.81 4 15.0 11.67 3.08 5MN 1982 1033.3 57.3 131.48 8.5 30.0 548360 19460 13562.37^0.154^5.03 3.48^4 15.0 10.78 3.13 5MN 1983 1045.6 57.4 133.19 9.4 26.6 548360 20393 14260.71^0.154^5.37 3.67 4 16.0 10.13 3.07 5MN 1984 1055.1 57.2 130.40 8.4 28.2 548360 21325 15657.28^0.154^5.65 3.69^4 16.0 10.00 3.11 5MN 1985 1064.0 57.5 132.60 8.2 28.7 548360 22157 16598.68^0.154^5.87 3.63 4 16.0 9.93 2.91 5MN 1986 1071.2 58.4 139.29 7.7 35.5 548360 23009 17196.60^0.154^5.49 3.40^4 17.0 10.05 2.92 5MN 1987 1079.0 58.6 143.43 7.4 35.4 548360 23775 18064.87^0.154^5.86 3.26 4 17.0 10.18 2.90 5MN 1988 1084.1 58.9 124.06 7.8 35.3 548360 24653 19895.77^0.154^5.88 2.94^4 17.0 10.70 2.94 5MN 1989 1086.3 59.0 115.93 7.5 36.7 548360 25926 21135.05^0.154^6.22 2.84 4 17.0 10.55 2.88 5MN 1990 1089.0 59.4 121.84 7.2 36.8 548360 27000 21769.51^0.154^6.85 2.76^4 17.0 10.40 2.91 5ON 1980 8569.7 64.8 116.24 6.8 29.7 891190 18083 13418.67^0.187^3.83 2.45^16 14.0 20.92 49.19 4ON 1981 8624.7 65.2 120.10 6.6 29.5 891190 20251 15285.29^0.187^4.61 3.13 16 14.0 20.44 49.20 4ON 1982 8702.5 65.4 116.39 9.7 30.2 891190 22375 15778.22^0.187^5.42 3.87^16 14.0 18.81 49.75 4ON 1983 8798.0 65.6 111.40 10.3 32.5 891190 24179 17270.40^0.187^5.92 4.33 16 15.0 18.39 50.20 4ON 1984 8901.7 65.8 109.71 9.0 32.1 891190 27996 19265.87^0.187^6.18 4.33^16 15.0 17.38 49.03 4ON 1985 9006.4 66.1 106.13 8.0 31.8 891190 27009 20381.17^0.187^6.46 4.31 16 15.0 19.16 51.60 4ON 1986 9113.0 69.4 111.70 7.0 31.6 891190 27870 22244.05^0.187^6.32 3.56^16 15.5 19.23 51.70 4ON 1987 9265.0 69.6 113.75 6.1 30.9 891190 28743 24162.12^0.187^6.15 3.68 16 15.5 19.18 51.36 4ON 1988 9431.1 69.7 113.42 5.0 31.1 891190 30303 26758.07^0.187^6.23 3.24^16 15.5 19.41 51.04 4ON 1989 9589.6 69.9 112.68 5.1 31.1 891190 31559 28380.85^0.187^6.54 3.51 16 15.5 19.53 51.69 4ON 1990 9749.6 70.2 116.12 6.3 31.7 891190 32500 28420.14^0.187^6.81 3.46^16 15.5 19.70 51.34 4- 50 -17.67 28.54 317.31 28.37 316.09 28.22 315.63 28.42 311.76 (3'29.3927.783315.86 27.84 316.00 27.92 316.28 27.69 315.68 26.60 316.00 26.83 311.51 1.74 211.02 1.70 210.11 1.67 29.87 1.70 29.84 1.73 29.82 1.67 210.39 1.76 210.36 1.74 210.85 1.77 211.12 1.83 211.30 1.86 210.94 2.11 210.81 2.09 29.57 2.02 29.30 2.05 29.10 2.12 29.13 1.99 29.10 1.97 29.43 2.02 29.75 2.04 29.73 2.04 29.70 2.07 25.62 0.16 65.74 0.16 65.47 0.17 65.57 0.18 65.29 0.18 65.84 0.19 65.79 0.19 65.88 0.19 66.06 0.19 65.94 0.19 65.90 0.19 68.54 0.95 28.75 0.98 28.10 0.99 27.47 0.95 27.30 0.96 27.36 0.92 27.82 0.96 28.35 1.00 28.30 0.99 27.83 0.95. 28.00 0.99 213.013.08.05.5Z.13.013.013.013.013.012.013.014.014.015.015.015.015.016.016.016.013.013.015.015.015.015.015.015.015.015.016.010.010.010.010.010.010.010.015.015.015.015.015.015.016.016.016.016.016.016.016.016.517.0PQ 1980 6386.1 61.5 77.80 9.8 35.9 1356790 16720 11308.94 0.080 4.91 2.69 15PQ 1981 6438.2 61.3 81.52 10.3 37.9 1356790 18720 12660.84 0.080 5.81 3.38 15PQ 1982 6462.2 61.4 78.66 13.8 36.5 1356790 20586 13343.44 0.080 6.73 4.41 15PQ 1983 6474.9 61.7 73.88 13.9 38.2^1356790 21859 14251.03 0.080 7.23 4.73 15PQ 1984 6492.0 61.8 74.29 12.8 38.7 1356790 23054 15556.22 0.080 7.23 5.06 15PQ 1985 6514.2 61.9 77.42 11.8 38.7 1356790 24040 16570.57 0.080 6.95 4.73 15PQ 1986 6540.2 63.4 78.23 11.0 38.5 1356790 24833 17964.74 0.080 6.90 3.60 15PQ 1987 6592.6 63.4 79.77 10.3 37.9 1356790 25830 19683.13 0.080 7.05 4.26 15PQ 1988 6640.8 63.6 78.93 9.4 37.8^1356790 27133 21372.73 0.080 7.09 3.28 15PQ 1989 6698.2 63.7 78.50 9.3 40.2^1356790 28810 22342.12 0.080 7.21 3.86 15PQ 1990 6768.2 63.9 85.18 10.1 40.0 1356790 30000 22763.22 0.080 7.51 4.11 15NB 1980 695.4 16.4 88.41 11.0 26.8^72090 16353 7217.43 0.284 6.18 3.21 4NB 1981 696.4 16.4 89.37 11.5 32.3^72090 18081 8548.25 0.284 7.39 4.82 4NB 1982 696.6 16.5 91.22 14.1 34.8^72090 19690 9369.8C 0.284 8.02 5.77 4NB 1983 703.2 16.4 89.14 14.8 37.5^72090 20878 10655.57 0.284 8.57 6.22 4NB 1984 707.9 16.3 85.79 14.8 31.8^72090 22128 11830.77 0.284 8.74 6.29 4NB 1985 709.9 16.5 87.10 15.1 29.3^72090 23494 12687.70 0.284 9.42 6.54 4NB 1986 710.4 17.1 90.53 14.3 30.0^72090 24026 14187.78 0.284 8.04 3.60 4NB 1987 712.3 17.1 90.59 13.1 34.6^72090 25856 15274.46 0.284 8.37 4.26 4NB 1988 714.3 17.2 89.99 12.0 34.1^72090 26997 16470.67 0.284 7.88 3.28 4NB 1989 717.8 17.1 89.00 12.5 35.3^72090 27517 17617.72 0.284 8.17 3.86 4NB 1990 722.4 17.2 96.51 12.1 36.9^72090 28000 18403.9 1 0.284 8.61 4.76 4NS 1980 845.1 32.4 115.07 9.7 27.0^52840 16069 7445.27 0.486 6.62 3.21 3NS 1981 847.4 32.7 119.89 10.1 29.5^52840 17829 8667.69 0.486 7.40 4.82 3NS 1982 849.5 32.9 123.32 13.1 30.4^52840 19437 9961.15 0.486 7.86 5.77 3NS 1983 857.0 32.9 105.88 13.2 29.5^52840 20747 11235.71 0.486 9.29 6.22 3NS 1984 864.4 33.1 99.58 13.0 28.0^52840 22170 12379.69 0.486 9.58 6.29 3NS 1985 871.0 33.4 100.24 13.6 28.3^52840 22039 13701.49 0.486 9.70 6.54 3NS 1986 873.2 33.9 103.41 13.1 27.8^52840 23206 14923.27 0.486 8.79 3.60 3NS 1987 878.0 34.2 103.97 12.3 30.9^52840 23981 15897.49 0.486 9.28 4.26 3NS 1988 881.9 34.2 103.86 10.2 30.5^52840 24688 17084.70 0.486 8.81 3.28 3NS 1989 888.3 34.5 106.62 10.3 30.8^52840 26413 18089.61 0.486 9.37 3.86 3NS 1990 895.1 34.9 116.69 10.5 30.4^52840 27100 19011.28 0.486 10.11 4.76 3PE 1980 122.8 0.0 92.39 10.6 30.0^5660 13003 6889.25 0.919 6.63 3.21 1PE 1981 122.5 0.0 93.51 11.2 30.1^5660 14074 8236.73 0.919 9.43 4.82 1PE 1982 122.4 0.0 97.04 12.9 26.4^5660 14964 8586.60 0.919 11.35 5.77 1PE 1983 123.7 0.0 98.42 12.2 31.2^5660 16226 9417.95 0.919 11.66 6.22 1PE 1984 125.1 0.0 101.78 12.8 22.9^5660 16766 10367.71 0.919 12.30 6.29 1PE 1985 126.0 0.0 100.87 13.3 21.6^5660 16966 10476.19 0.919 12.89 6.54 1PE 1986 126.6 0.0 96.47 13.4 23.0^5660 16975 11832.54 0.919 8.33 3.60 1PE 1987 127.3 0.0 88.66 13.2 29.0^5660 17195 12482.33 0.919 7.44 4.26 1PE 1988 128.5 0.0 100.71 13.0 29.5^5660 18855 13859.92 0.919 7.25 3.28 1PE 1989 129.9 0.0 104.26 14.1 31.3^5660 19375 14603.54 0.919 7.51 3.86 1PE 1990 130.7 0.0 107.74 14.9 33.7^5660 20000 15233.36 0.919 8.69 4.76 1NF 1980 565.6 27.1 71.96 13.2 43.7^371690 15645 7240.10 0.032 6.81 3.21 7NF 1981 567.7 27.1 79.74 13.8 49.2^371690 17631 8178.62 0.032 7.99 4.82 7NF 1982 566.2 27.6 80.13 17.3 49.4^371690 19707 8935.01 0.032 9.60 5.77 7NF 1983 571.4 27.7 78.19 19.2 51.9^371690 20605 9599.23 0.032 10.19 6.22 7NF 1984 572.4 28.0 77.98 20.6 47.5^371690 20308 10389.59 0.032 10.55 6.29 7NF 1985 571.5 28.1 74.60 20.9 40.3^371690 20125 11142.61 0.032 13.10 6.54 7NF 1986 568.3 28.5 76.99 18.7 45.6^371690 20723 11933.84 0.032 12.20 3.60 7NF 1987 568.1 28.6 77.37 16.8 48.8^371690 21336 12997.71 0.032 11.26 4.26 7NF 1988 568.8 28.5 75.74 15.2 51.8^371690 22835 13990.86 0.032 11.94 3.28 7NF 1989 571.1 28.6 82.30 15.4 52.0^371690 24252 14841.53 0.032 11.13 3.86 7NI' 1990 572.7 28.6 82.93 17.1 55.1^371690 25200 15343.11 0.032 11.02 4.76 7- 51 -APPENDIX 3NOTES TO JAPANESE MANUFACTURING GREENFIELDSAND PROVINCIAL DATA1. If the year of establishment was 1991 then for our purposes we classified thisestablishment as being in 1990, because data on provincial variables wasincomplete for 1991.2. If the year of establishment was actually the year of operation, then toapproximate the year of establishment we subtracted one year.3. Highway miles were assumed to remain constant over the eleven year period.Provincial figures for 1988 were used.4. Crude oil prices were not available from Statistics Canada for New Brunswickand were incomplete for Quebec. However, given the national energy policyand data that was available these missing figures were estimated. The Albertaamount plus $10.00 was substituted for Quebec and New Brunswick wasassumed to face the same oil prices as Quebec.APPENDIX 4JAPANESE MANUFACTURING GREENFIELDSPROVINCIAL INDUSTRIAL LEVEL FOR 1987WAGES, FUEL, and REVENUE (In Thousands)SIC Firm # Prov Estabs Wages Fuel Revenue2512 1 1 45 86281 12998 3573282 336 1158091 137610 53565533 66 58145 8953 2705204 185 202220 31180 8676725 338 365629 53416 17948286 10 15995 2303 669283391 2 1 02 43 04 16 45225 4347 3454775 5 7308 986 389246 01811 3 1 12 13 04 16 163922 33971 10045445 126 02512 4 1 45 86281 12998 3573282 336 1158091 137610 53565533 66 58145 8953 2705204 185 202220 31180 8676725 338 365629 53416 17948286 10 15995 2303 669283255 5 1 02 13 14 33 191871 14738 10060415 106 02599 6 1 14 3712 267 123422 9 22 34623 84 995 80 27802 2840 1232786 4 549 12 9552512 7 1 45 86281 12998 3573282 336 1158091 137610 53565533 66 58145 8953 2705204 185 202220 31180 8676725 338 365629 53416 17948286 10 15995 2303 66928- 53 -1131 8 123456783154324954450471704441944851166426803486146051420811991329412199231180198679285617862512 9 1 45 86281 12998 3573282 336 1158091 137610 53565533 66 58145 8953 2705204 185 202220 31180 8676725 338 365629 53416 17948286 10 15995 2303 669282711 10 1 22 16 322950 149825 26298043 6 125641 68704 8927084 5 125540 63380 9440655 8 94275 44371 7166616 12711 11 1 22 16 322950 149825 26298043 6 125641 68704 8927084 5 125540 63380 9440655 8 94275 44371 7166616 13199 12 1 26 7903 314 314862 62 30489 1057 1128473 7 3300 111 98124 450 526267 25434 26810065 191 207077 8114 7719076 73099 13 1 39 13182 837 557002 43 12532 832 457503 24 249 205147 19611 9020695 103 52915 3613 2175996 10 1254 133 63652712 14 1 02 4 262219 171838 17198183 34 9 348143 169559 18084505 21 727865 401159 36799686 03256 15 1 22 63 04 75 244844 19772 11190615 10 21581 2040 1183686 03259 16 123456312413723164411220550378269253300384906323231 17 1 02 33 04 15 1849987 89923 368347665 56 13062 18 1 4 1284 44 24222 14 2368 99 69073 04 427 271055 9612 6797735 86 27339 982 623246 01011 19 1 64 119395 9747 23404502 45 74661 5597 6256253 64 198 336150 28638 32372595 138 183860 25182 21989776 27 34637 3314 4772393251 20 1 02 43 14 37 594076 51832 34079275 5 544 53 21426 03256 21 1 22 63 04 75 244844 19772 11190615 10 21581 2040 1183686 03341 22 1 02 13 04 165 86 02912 23 1 32 43 04 125 56 23361 24 123456817086291771633102238360602267733960171370186707923913802762610133361 25 1 8 7716 77 186702 17 33102 339 792393 04 86 238360 6017 13802765 29 60226 1370 2610136 13257 26 1 3 499 17 18662 03 04 10 195106 3818 8730875 4 167 14 8586 23259 27 1 32 123 44 137 644112 37826 33003845 23 20550 925 906326 13231 28 1 02 33 04 15 1849987 89923 368347665 56 13257 29 1 3 499 17 18662 03 04 10 195106 3818 8730875 4 167 14 8586 23562 30 1 13 4372 325 240502 17 7451 316 274613 24 65 116417 12157 4645585 41 28854 1835 1505206 43399 31 1 22 33 04 43 83529 9900 3666855 21 37673 6340 1785116 11621 32 1234568933615371497794447592369110636166299256451757394743668653129823341 33 1 02 13 04 165 86 03011 34 1 4 6615 230 218982 33 24 24 111223 3336 4647615 6 27561 1029 1583766 2351 35 1 102 53 54 45 64697 20306 2898075 14 21030 7955 694946 33259 36 1 32 123 44 137 644112 37826 33003845 23 20550 925 906326 13192 37 1 117 127396 6455 5397442 68 57044 2224 2471753 7 7331 316 369164 222 384532 14476 21159725 88 73083 3165 3636396 13 5407 301 284643259 38 1 32 123 44 137 644112 37826 33003845 23 20550 925 906326 13254 39 1 12 5 1265 139 51643 04 235 6 4637 825 284656 03081 40 123456113180226523694533221616107846212299841731251914912556418907440735888748717166022358556718241621450033731 41 1 9 33247 11876 5245712 7 6225 685 712063 04 52 154734 44597 17233565 26 52056 18250 6421226 03092 42 1 112 4 2185 46 82123 14 22 26161 1062 1650165 56 03231 43 1 02 33 04 15 1849987 89923 368347665 56 13041 44 1 20 11069 1527 436822 24 7894 865 196993 14 178 172338 24917 7058465 50 22309 2964 691306 6 2190 287 59973399 45 1 22 33 04 43 83529 9900 3666855 21 37673 6340 1785116 1319 46 1 1632 1773 204 820 1134868 49033 56977375 345 345967 14774 13834316 22 11326 701 57585391 47 1 51 26408 516 946092 693 84 276 459214 15372 19856395 1396 193257 48 12345630010424991951061671738181418668730878583259 49 1 32 123 44 137 644112 37826 33003845 23 20550 925 906326 13281 50 1 3 717 51 20792 79 20594 841 903773 184 101 31106 1823 1495435 65 26920 1122 1411146 01021 51 1 12 48 100725 6435 10670233 73 54517 5384 4529114 195 40 38372 4005 2073216 12961 52 1 42 93 04 32 127082 19981 8785245 23 71529 11928 7070526 13011 53 1 4 6615 230 218982 33 24 24 111223 3336 4647615 6 27561 1029 1583766 2337 54 1 172 22 14877 481 565923 24 154 479350 17245 17527835 71 88879 3601 3995466 4 5267 934 230343194 55 1 32 10 8303 166 292363 4 1175 84 36984 63 139225 5218 4937245 33 36346 2311 1677586 1PROVINCE CODES1 = Alberta^2 = British Columbia^3 = New Brunswick4 = Ontario 5 = Quebec^ 6 = Saskatchewan- 59 -APPENDIX 5JAPANESE MANUFACTURING GREENFIELDSNATIONAL INDUSTRIAL LEVEL FOR 1987WAGES, FUEL and SHIPMENTS (In Millions)SIC Establishments Wages Fuel Shipments319 1601 1851 79 8638337 281 624 23 2390351 90 107 33 413391 603 591 18 24511011 526 851 82 98111021 414 530 43 41111131 48 502 42 25571621 77 89 11 8011811 31 211 43 11992512 1093 1919 252 88622599 272 63 5 2752711 39 757 355 58172712 43 1572 927 85372912 26 81 13 2042961 71 215 33 17013011 43 148 4 6523041 288 217 30 8503062 539 303 10 7533081 1464 436 19 11943092 44 71 2 2743099 463 290 25 12573192 533 666 27 33873194 116 187 7 7033199 772 802 36 37243231 27 2116 100 390933251 50 598 52 34303254 35 168 16 720- 60 -3255 47 209 16 10863256 96 270 22 12523257 20 196 3 8773259 190 672 39 34293281 327 93 5 4423341 25 83 1 8223361 147 367 8 20063391 27 66 6 4203399 72 122 16 5523562 153 161 14 6883731 94 246 75 29613931 209 144 8 788APPENDIX 6CONDITIONAL LOGIT REGRESSION RESULTSVARIABLES & THEIR SOURCE^LW =^log of manufacturing wages as listed in Appendix 2.^LE =^log of weighted average energy prices as listed in Appendix2.LE2 =^log of low energy prices as listed in Appendix 2.SHIPRANK =^as listed in Appendix 2.LMETGDP =^log of (metropolitan times GDP per Capita timespopulation, from Appendix 2).LCAN =^log of the number of Provincial firms in like industry aslisted in Appendix 4.LJPN =^log of the number of Japanese manufacturing greenfields inthe province as per Table 1.QUEBEC =^A dummy variable to measure the effect of this largeprovince with few Japanese greenfield investments.LABOR = Using data from Appendix 5 to estimate the labour. factorinput coefficient for each specific industry for the Cobb -Douglas Production Function.ENERGY =^Using data from Appendix 5 to estimate the energy factorinput coefficient for each specific industry for the Cobb -Douglas Production Function.UNION_P =^Unionization rate in the province as listed in Appendix 2.TAX =^Provincial Corporate Tax Rate as listed in Appendix 2.CRIME =^Provincial crime rate per 1000 divided by the provincialpopulation. Both of these numbers are in Appendix 2.55 Japanese Manufacturing GreenfieldsCHOICE FREQUENCY PERCENT1 (AB) 1 1.81822 (BC) 11 20.00003 (NB) 2 3.63644 (ON) 35 63.63645 (PQ) 4 7.27276 (SK) 23.6364Without a Measure of Market Size (i.e. GDP)LOG OF LIKELIHOOD FUNCTION^: -58.3579NUMBER OF CASES^: 55NUMBER OF CHOICES : 330StandardParameter^Estimate^Error^t-statisticLCAN .489813^.28027^1.74765LJPN^1.21409^.435358^2.78872SHIPRANK^-.324323^.196032^-1.65444LW^-5.31907^4.93843^-1.07708LV -4.91472^2.68017^-1.83373lw = log(avgmfgy), lv = log(avgengy)A better fit is achieved with GDPLOG OF LIKELIHOOD FUNCTION^: -55.1337NUMBER OF CASES^: 55NUMBER OF CHOICES : 330StandardParameter^Estimate^Error^t-statisticLCAN .677792^.285464^2.37435LJPN^2.26426^.641879^3.52755SHIPRANK^-.316939^.203115^-1.56039LW^-8.23081^5.30678^-1.5510LV -6.42729^2.72486^-2.35876LGDP^-1.29039^.501009^-2.57558

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