Electronic copy of this paper is available at: http://ssrn.com/abstract=968521

REVISITING THE INCOME AND GROWTH EFFECTS

OF PROFESSIONAL SPORT FRANCHISES: DOES SUCCESS MATTER?

Sam Richardson

Discussion Paper No. 02.02 – January 2002

DEPARTMENT OF APPLIED AND INTERNATIONAL ECONOMICS

Electronic copy of this paper is available at: http://ssrn.com/abstract=968521

This series contains work in progress at the Department of Applied and International Economics, Massey University. Comments and criticism are invited. Quotations may be made on explicit permission of the author(s). Further copies may be obtained from: The Secretary Department of Applied and International Economics Massey University Private Bag 11222 Palmerston North NEW ZEALAND Phone: 06 350 5799 Extn 2679 Fax: 06 350 5660

Discussion Paper 02.02

ISSN.1174-2542

Price: $10

REVISITING THE INCOME AND GROWTH EFFECTS OF PROFESSIONAL SPORT FRANCHISES: DOES SUCCESS

MATTER?*

Sam Richardson**

Department of Applied and International Economics College of Business Massey University.

ABSTRACT Coates and Humphreys (1999) examined the growth effects of professional sport franchises, stadia and arenas for three major professional sports in the United States (football, baseball and basketball). This paper re-examines the effects of the four major league sports in the US (as above with the addition of hockey) on their respective Standard Metropolitan Statistical Areas (SMSAs), utilising the model developed by Coates and Humphreys with a view towards answering the question: does the success of the local professional sports team(s) matter? Panel data for 57 SMSAs across a 28-year time-span (1970-1997) is utilised for this analysis. A fixed-effects pooled ordinary least squares regression model is estimated for both income and growth determination. Preliminary results do not support Coates and Humphreys' (1999) conclusions that the sports environment plays no role in determining local area economic growth. The addition of hockey-related variables yields some significant income determination results, however measures of franchise success are not found to be significant.

* An earlier version of this paper was presented at the Industry Economics Conference, University of Melbourne,

July 12-13, 2001. The author is extremely grateful for constructive comments that were received from conference participants.

** Correspondence address: Department of Applied and International Economics, College of Business, Massey University,

Palmerston North, New Zealand. Tel: +64 6 350 5799 ext. 4583; fax: +64 6 350 5660; e-mail: S.A.Richardson@massey.ac.nz.

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1. INTRODUCTION To succeed... You need to find something to hold on to, something to motivate you, something to inspire you.

—Tony Dorsett Sport is a multi-faceted activity in which participants are able to develop a variety of skills. These include performance, partnership, and leadership, to name a few. Over time, there has been considerable demand for, and commercial interest in, highly skilled competition, with the result that many sports have become professional, and as a result enjoy tremendous exposure to the public. In the United States, professional sport has become an important part of life, not only for players, coaches, and administrators but for the cities that the franchises call home as well. There has been healthy debate in the literature as to the extent of the relationship between professional sport franchises, their stadia, and the local economy. In this paper we ask whether the success of the sport franchise plays a part in explaining the importance of sport to the local economy. The mere existence of professional sports franchises would on the surface seem to be insufficient information to conduct a definitive assessment of the associated economic effects. Arguably the most important goal in a professional sports operation is to maximise the number of people that attend games. From game attendance, teams collect revenues to run the franchise.1 In order to maximise the potential attendance revenue, teams must (a) construct stadia (in which to play games) and (b) provide a product that fans will want to watch – i.e. a successful team. It thus stands to reason that a successful team (which in turn generates greater revenues) will be more likely to have a positive effect on the local economy than a less successful team. This paper seeks to examine and quantify the nature of this effect, using a variety of measures of success within an extension of the empirical framework established by Coates and Humphreys (1999). It is important to note that this study is not actively advocating nor is it seeking to jettison public expenditure on sports facilities and teams, as this is part of a much larger issue, that of appropriate and effective usage of public funds. As Coates and Humphreys (1999) note, the issue of the extent to which public expenditure “should” fund sports stadia and teams arises when team owners want the host city (and surrounding suburbs) to foot some of the bill. It is difficult to say with accuracy whether investment in a stadium or team is the “best” use of public funds, as compared to, say, investment in health, education, public facilities etc. A question that can be posed is, given the existence of a stadium and/or a team, are there benefits in terms of income and or growth in the local economy? Such a question can be quantified and examined using available data. Of course, there are many non-quantifiable effects associated with sports franchises, and these may be sizeable enough to warrant public subsidisation of franchises and stadiums. For the purpose of this study, only those measurable effects will be examined and commented on.

1 This is a generalisation, as revenue streams do not only flow from game attendance. Examples of other revenue streams

include merchandise sales, club membership fees etc. The majority of income received by professional sports teams, however, is from gate receipts and thus it is appropriate to generalise in this instance.

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Section 2 of the paper reviews the literature in this area, and Section 3 outlines the framework within which this analysis is conducted as well as details of data used for the analysis. Section 4 presents the results and implications of the model estimations, and the paper is concluded in Section 5. 2. LITERATURE REVIEW Winning isn’t everything, wanting to is.

—Anonymous 2.1 Professional Sport Franchises and Their Stadia – Effects on the Local Economy Numerous articles in the literature have addressed the issue of the economic effects of the construction of sports stadia and arenas on the local economy. Two views have emerged, not surprisingly one view being pro-stadia, the other anti-stadia. Proponents of stadia construction have argued that stadium investment spurs economic development. However, empirical examination in the literature shows results that indicate otherwise. Baade (1987) considered the economic rationale for stadia subsidisation, and noted that “stadium construction or renovation may exert a positive influence on SMSA income but the positive stadium effect is offset by the negative influence in city income induced by the presence of a professional baseball team” (p.16). Similar findings were found for football as well. While noting that spending on sports diverts money from other leisure activities, Baade’s results indicated economic ‘growth’ powered by sport franchises or stadiums was more of a realignment of spending than true growth, a conclusion also reached by Swindell and Rosentraub (1991) and Siegfried and Zimbalist (2000). Swindell and Rosentraub also pointed out that there was “ … no evidence that these facilities have significantly changed employment or residential patterns” (p.12). Bast (1998) noted a long list of reasons as to why there had been no quantifiable benefits from investments in sports stadia. Reasons included overlooking opportunity costs, a lack of “new money” attracted into the metropolitan area by the stadium, the flow of money out of the metropolitan area in the form of salaries to players who are not residents of the area, the low-paying and seasonal nature of sports jobs, and the lack of enticement of business by major league sport franchises. Irani (1997) noted that the stadium itself does not generate entertainment, it merely allows the entertainment to occur. In a criticism of previous analyses, Irani noted that studies that rejected the growth effects of a stadium on the local economy “ … have ignored or failed to measure the welfare gains of a stadium to the city” (p.250). Irani estimated net benefits that “ … ranged from minus $19.1 million to $32.8 million in the absence of any economic activity that may be induced by the stadium” (p.251).

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Advocates of stadia have emphasised the “intangible” benefits associated with stadium construction. The community may experience an image enhancement with a stadium and a sport franchise that is considered “major league”. The community may also develop a greater sense of pride and identity as a result of their major league status. Siegfried and Zimbalist (2000) refuted these benefits as questionable, as they were “ … at a minimum hard to measure, and there are even legitimate questions as to whether they are benefits at all” (p.99). Indeed, the methods used by proponents of stadium construction to generate economic impact studies have been the subject of heavy criticism from several economists. Hunter (1988) evaluated economic impact studies as misleading and unnecessary, being particularly critical of the use of multipliers, describing them as flawed. Crompton (1995) examined misapplications associated with impact studies and found that multiplier inaccuracies were one of the main culprits. However, Crompton disagreed with Hunter that multipliers were flawed concepts, noting that if the multiplier was specified correctly, economic impact studies could be useful. Spickard (1995) noted that economic impact studies were more political analyses than feasibility studies, such is the political nature of stadium construction and the funding of construction. Siegfried and Zimbalist (2000) noted that the reason many stadiums are constructed is the influence of such (flawed) studies on the public. The role of local government politics is another major area of debate. Shropshire (1995) noted that “[s]ome politicians have acknowledged that the true value is not merely monetary. If a city does not have a major sports franchise, it is not considered big-league and will be perceived in many ways as second-class.” (p.62). Current research, most recently from Siegfried and Zimbalist (2000), has cast doubt on this claim. Switching attention now towards the literature on the economic effects of sports franchises, we identify a similar trend in the research of economists refuting claims made by franchise proponents of local economy benefits. Baim (1990) noted that smaller cities would benefit more from the attraction of a professional sports franchise, as “the outside perception of the city will improve, and, with hotels more reasonably priced, an increase in the tourist trade might result if the team acquires a regional following” (p.14). Swindell and Rosentraub (1991) noted that arguments for attracting franchises included such spillover benefits as new job generation, revitalised downtown areas, improved land use patterns, as well as the entertainment provided to payers of admission fees. Newsome and Comer (2000) identified a statistically significant trend towards downtown sports venues for the four major league sports. Reasons for this trend included the existence of intra-metropolitan competition for teams and the desire to increase owner revenues, as well as evidence of a trend toward smaller markets. Newsome and Comer noted that the trend toward smaller markets was influenced by “… both the perceived impacts and the attractiveness of a downtown facility, as well as the need to create a focal point for a city’s big league status” (p.119). Spickard (1995) listed several classes of impacts that have been considered as beneficial to the local economy from playing host to a professional sport franchise. Spickard noted that "... for economic development purposes, sports stadiums and arenas are not particularly effective at creating jobs and income" (p.5). In terms of sports franchises as a stimulus for real estate development, Spickard noted that arenas that host basketball and hockey tended to

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be the most active facilities because "... arenas host not only their anchor tenants sports franchises but also house concerts, family shows and a wide variety of other sporting events" (p. 6). It was noted that these arenas tended to be used between 175 and 200 days per year. This can be contrasted with the larger stadiums for football and baseball being used primarily for their sports tenants only and being used for 12-16 days for football and around 80-90 days for baseball. Indoor arenas play an important role in the sports environment, and thus should be taken into account in an analysis such as this. Coates and Humphreys (1999) looked at the relationship between the professional sport franchises and their venues and the real per capita personal income in 37 metropolitan areas in the US over the period 1969-1994. They extended earlier research by considering the effects of franchise existence, franchise entry and exit, stadium construction, and stadium capacity on the local economy. It was found that, in general, “ … variation in the vector of sports-related variables … helps to explain the observed variation in the level of real per capita income, and that the overall impact of the sports variables reduces real per capita income” (p.614). Coates and Humphreys (1999) posed two questions from this conclusion: where do the revenue streams from sports teams go, if they don’t contribute positively to the economy; and how do professional sports franchises reduce local area real per capita income? In answer to the destinations of the revenue streams, it was suggested that sports teams are smaller businesses than other less prominent businesses, and player salaries are redirected out of the area. In answer to the observed income-reducing effect of professional sport franchises, a number of possible reasons were noted, including (i) that the negative effect was a “compensating differential” (Hamilton and Kahn, 1997);2 (ii) that substitution of public spending into sports teams diverted money away from other uses; (iii) that substitution away from goods with high multipliers to goods with low multipliers caused a negative effect; and (iv) sports was a distraction to workers and had a negative effect on productivity growth (pp.614-616). Coates and Humphreys (2001) used the framework developed in the 1999 paper to examine the economic consequences of strikes and lockouts in professional sport. The results from their study indicated that the three baseball strikes (1972, 1981, 1994) and the two football strikes (1982, 1987) within the sample period 1969-1996 had no effect on the income of local area economies with sports franchises. The evidence in the 2001 study supported their previous research that sports do not positively influence the economies of SMSA's with franchises. If the presence of a professional sports franchise does not have any quantifiable benefits, then why do people still insist that benefits exist?

2 See Coates and Humphreys (1999) for reference.

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Reeves (1996) noted that the intangible and emotional impacts of a professional sports team on a city were indisputable, and that "the team is closely identified with the city itself, and carries with it the name, support and spirit of the community." (p.6).3 Indeed, Mitrano (1999) analysed the psychological effects on sports fans of the relocation of the Hartford Whalers NHL hockey team to Greensboro in 1997, and found considerable effects on the fans in the former franchise city. Fans develop emotional attachments to teams, and these emotional attachments are difficult to quantify. Sports franchises have a strong link to the community through their logos and/or nicknames. In many instances, teams identify themselves with the characteristics of the city they reside in, for example, in Detroit there are the (basketball) Pistons, in Pittsburgh they have the (football) Steelers, Washington have the (hockey) Capitals, and the (baseball) Yankees and the Mets call New York home. Euchner (1993) noted that “industries that actually help to define the identities of their cities – use appeals similar to those of sports franchises [in negotiations with the local government]” (p.169). This implies that sports franchises often market themselves to local government as playing the “identity definer” or “identity reinforcer” role within the economy. If indeed sports franchises play this role, then we would expect franchises to have some quantifiable benefits to the local community. 2.2 Success and the Professional Sport Franchise Many studies have been undertaken examining the factors influencing the demand for professional sport, salary determination and team revenues, and a variety of factors have been identified as being important in these processes. These include roster turnover from year to year, customer preferences in terms of team composition, the level of violence, and franchise location characteristics, among others.4 Bruggink and Rose (1990) examined the impact of free agency in major league baseball, taking a close look at the determinants of team revenue. The team’s win percentage and the size of the local area were found to be significantly positively related, however, having two teams in the same metropolitan area had a significantly negative effect on team revenue. Richards and Guell (1998) noted that success for the professional sport franchise could be measured in three different ways: large and stable crowds, winning percentages, and winning championships. Obviously, definition of success depends on the motivating factor of the franchise owners – be it profit maximisation or championship maximisation. Richards and Guell noted that attendance success was “… likely to be affected by the enthusiasm for the local team, as well as community size” (p.293).

3 Reeves noted that Cleveland's experience with losing their football team the Browns to Baltimore was notable for the

substantial response of fans passionate about the team, and Cleveland won redress for the loss of the Browns. In contrast, Houston lost their team, the Oilers, fan opposition was minimal, and the move to Nashville went quietly. It is also interesting to note that Houston has a new expansion NFL team, the Houston Texans, who kick off in 2002.

4 See Jones and Walsh (1988), Burkedin and Idson (1991), Jones et al. (1996), Kahane and Shmanske (1997). Also see

Downward and Dawson (1999) for an excellent review of demand studies for professional sport.

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Depken III (2000) examined the effects of fan loyalty in major league baseball, and found that the longer a franchise resided in a city, the lower the level of attendance was. Another finding, that teams with older stadiums tended to have lower attendance – “…which is largely due to the fact that new stadiums tend to be larger” (p.132) – makes sense in that teams who relocate are often attracted by the lure of a new stadium. Both current and lagged win percentage were found to be significantly and positively related to attendance, reinforcing the earlier findings of Richards and Guell (1998).

There has been support within the literature for using championship success as an overall measure of success. Whitney (1988) noted that basketball and hockey fans “…occasionally lament that the regular season is ‘meaningless’ since such high proportions of teams qualify for the playoffs” (p.718). Indeed, teams qualifying for the playoffs in 2000 consisted of twelve out of thirty one football teams, sixteen out of twenty nine basketball teams, sixteen out of thirty hockey teams, and eight out of thirty baseball teams. This may explain to some degree the insignificant effects of sports franchises on their local economies – as many teams qualify for playoffs, the regular season becomes ‘meaningless’ and the small number of playoff appearances may not generate sufficient economic activity to positively affect the local economy. Such a theory is tested in this paper: is participation in the playoffs ‘meaningless’ in the context of the health of the local economy? 3. MODEL AND DATA

Think big, believe big, act big, and the results will be big. —Anonymous

In order to examine the economic effects of sports franchises and stadia on the local economy, and to analyse the impact of franchise success, an empirical model must be constructed in such a way as to accurately encompass the contributions of these factors in an income determination process. The empirical model used in this study is an extension of a model that has produced clear and easily interpreted results, the Coates and Humphreys' (1999) model. The model developed by Coates and Humphreys (1999) is a linear reduced-form empirical estimation that regresses a selection of local area- and sport franchise-related variables on (i) local area real per capita income, and (ii) local area income growth. The model in this study is specified as: ititititit szxy µλγβ +++= (1) where xit, zit and sit are matrices of local economy-, sports-, and success-related variables respectively. β , γ and λ are vectors of coefficients to be estimated. Note that in addition to local economy-related variables, xit also includes an SMSA-specific time trend to capture unobserved SMSA-specific effects across time. Coates and Humphreys noted that the addition of this time trend is possible due to the per-capita specification of real income, as the “…inevitable multicollinearity that would arise between population and the trend term…” would be avoided with its inclusion (p.607).

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Like Coates and Humphreys (1999), this study adds structure to the disturbance term, µit, by specifying the term in the following way: ittiit ev ++= µµ (2) where vi is an SMSA-specific disturbance which takes the same value across the sample period; tµ is a time-specific disturbance which takes the same value for all SMSA’s at a specific point in time; and eit is a random error term that is uncorrelated across SMSA’s and over time. The above specification is duplicated when models of local area economic growth are estimated, with the only difference being the substitution of growth for income as shown below: ititititit szxg µλγβ +++= (3) where git is local area economic growth in SMSA i at time period t, regressed on the same assortment of independent variables. The variables used in this analysis are primarily those used by Coates and Humphreys (1999). The summary statistics are reported in Table 1. We begin with a basic income determination model which specifies real per capita personal income as a function of a set of local-economy specific variables, xit. The variables included in this set are lagged real per capita personal income (in the previous period), change in population (measured as a percentage), and an SMSA-specific time trend, so as to capture the time-related income characteristics of the metropolitan area. The inclusion of a lagged dependent variable transforms the model into a dynamic panel data model, a point noted by Coates and Humphreys (p. 609). Ramanathan (1995) notes that the nature of the error term has important implications for the estimation of a dynamic model, and can lead to biases in parameter estimates. Coates and Humphreys cite evidence that the bias associated with a dynamic panel data model is both positive and limited to the parameter on the lagged dependent variable, and does not impact on the parameters of the exogenous variables (p.609). A set of sports-specific variables (zit) are then added, and these variables include stadium/arena capacity, capacity squared, and stadium construction variables (dummy variables that take the value of 1 for each of the ten years immediately preceding stadium construction). Variables are included for franchise existence variables as well as franchise entry and exit variables. Coates and Humphreys used two forms of franchise entry and exit variables: (i) single-entry and exit variables, which has the effect of placing an equal value on each entry or exit in a specific sport; and (ii) multiple entry and exit variables, which enables us to examine the effect of adding or subtracting an extra team from a metropolitan area that already has more than one franchise. Both forms of these entry and exit effects are represented in the data by dummy variables that take the value of 1 for each of the ten years preceding franchise entry or exit, and 0 otherwise.

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The inclusion of hockey-related variables transforms the Coates and Humphreys model into a genuine “major league” model by including all four established major league sports (football, baseball, basketball and hockey). An important reason for including hockey-related variables is that a potentially important omission from the Coates and Humphreys model is the construction of multipurpose indoor venues, which are frequently occupied by both basketball and hockey teams, the point noted by Spickard (1995). Such venues include Madison Square Garden (New York), Fleet Center (Boston), United Center (Chicago), Phillips Arena (Atlanta), MCI Center (Washington), America West Arena (Phoenix), Continental Airlines Arena (New Jersey) and First Union Center (Philadelphia). These arenas are also important in that they not only double as dual-sport arenas but also host numerous other indoor entertainment activities. Such facilities could play an important part in the local economy. Indeed, PricewaterhouseCoopers (2000), in a feasibility study for an NBA franchise in the Louisville area, estimated that average revenue potential for existing dual-purpose NBA and NHL facilities was approximately $38 million, in comparison to NBA-only facilities whose average revenue potential was approximately $7.3 million (p.31).5 As a direct result of the inclusion of hockey-related variables, the size of the SMSA sample was extended from 37 cities in Coates and Humphreys (1999) to 57 cities. This was necessary in order to include those cities who had, and those who gained and/or lost, an NHL franchise across the period of observation. 14 cities without sport franchises across the period are also included so as to create a more balanced SMSA sample, and thus the results can be treated as being more generally applicable to all cities rather than just “major league” cities. This is important as such cities as Columbus and Memphis (both of which are included in the sample) attracted NHL franchises in 2001, and had no previous experience with “major league” franchises. The 14 cities were chosen so that the 50 fastest growing SMSA’s in 2000, according to InformationPlease (www.infoplease.com), are represented in the sample. The SMSA’s, their average real per capita personal income, and professional sports franchises across the period 1970-1997 are reported in Tables 2A and 2B. In addition to the matrix of sports-related variables developed by Coates and Humphreys, we also include measures of success (sit) for each SMSA with sport franchise i in time period t, specifically: (i) teams reaching the playoffs; (ii) number of playoff series won; (iii) league champion in the current year; and (iv) league champion in the previous year. It is useful to think of the franchise existence variables mentioned earlier as having a team that plays in the regular season of a sport regardless of their performance record. If a team reaches the playoffs, it is a reflection of the superior nature of the season’s performance that year. It entitles the franchise to a minimum number of home playoff games for reaching the playoffs, thus increasing the possibility of benefiting the local community as a direct result. The initial number of games depends on the sport, and the subsequent number of home games depends on performance across the playoffs. The number of playoff series won should reflect the 5 Hockey may be a minor sport in the US in comparison to the three sports used in Coates and Humphreys (1999),

however, it is a well-established and stable professional sport in both Canada and the US. Riess (1991) noted that prior to 1966 “the [National Hockey League] NHL's stability reflected the limited regional appeal of the sport in the United States, the expenses entailed in building and maintaining arenas with the capacity for ice hockey rinks, and the high rate of profitability for established teams, which discouraged thoughts of change” (pp.234-235). Since 1967, the NHL has expanded from the Original Six teams (Boston, Chicago, Detroit, Montreal, New York and Toronto) to the present 30-team league. Such expansion and franchise movement within hockey is similar to the experiences of the other major leagues, and thus supports the inclusion of hockey variables in this analysis.

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progression from the first round of the playoffs to the final series, the League championship. A dummy variable for the league champion is included to capture the immediate effect on the local economy of the local franchise winning the League championship. A dummy variable for the previous year’s League champion is also included to capture possible spillover effects of the previous year’s success. In terms of expected signs for these success-related variables, the coefficients could be (a) all positive or, (b) all zero and/or negative, depending on the stance taken on the role of sports in economic development. Positive signs on the success variables would be consistent with the franchise and stadia proponents. Reaching the playoffs, advancing through the playoffs and eventually becoming League champion should have benefits for the local economy as it becomes associated with the 'elite' playoffs, renewing civic pride, and improving the city image. A zero or negative sign on these coefficients would be consistent with the anti-sports-as-a-growth-engine view, as measures of franchise success shouldn’t be expected to play any positive role in the local economy. The data set used for this analysis is a panel of 57 SMSA’s across a time period of 28 years (1970-1997), making a sample size of 1596 observations. For growth determination purposes, this sample is reduced to 1539 observations, consisting of 57 SMSA’s across a 27 year time period (1971-1997). In terms of data gathering, SMSA personal income and population data were obtained from the Bureau of Economic Analysis Regional Accounts Data from the US Department of Commerce (www.bea.doc.gov), franchise existence data were obtained from Information Please (infoplease.com), ballpark, arena capacity and construction data were obtained from Munsey and Suppes (ballparks.com). Sport-specific playoff success data were obtained from a number of sources, including ESPN.com (baseball), NFL.com (football), NBA.com (basketball) and the Hockey Database (hockey). Per-capita personal income was adjusted to represent real per capita personal income using the yearly Gross Domestic Product implicit price deflator data from Economagic.com. 4. RESULTS AND DISCUSSION "Winning is not a sometime thing; it’s an all time thing. You don’t win once in a while, you don’t do things right once in a while, you do them right all the time. Winning is habit. Unfortunately, so is losing."

—Vince Lombardi 4.1 Income Determination The income determination model, equation (1), is estimated as a fixed effects model, with separate intercept terms estimated for each SMSA. In order to avoid the singular matrix problem, the SMSA time trend for Arlington and the dummy variable for the year 1970 were omitted from the estimation. Two models were estimated, like Coates and Humphreys (1999), (a) the income determination model with single entry and exit effects, and (b) the

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model with multiple entry and exit effects. The full models estimated are presented below in algebraic form.6

RPCPI = f(RPCPI-1, DPOP, BKCAP, FBCAP, BSCAP, HYCAP, BKCAPSQ, FBCAPSQ, BBCAPSQ, HYCAPSQ, FBFR, BKFR, BSFR, HYFR, BKCO, FBCO, BSFBCO, BSCO, HYCO, BKHYCO, FBFE, BKFE, BSFE, HYFE, FBFD, BKFD, BSFD, HYFD, FBPO, FBPOSW, FBCH, FBCH-1, BKPO, BKPOSW, BKCH, BKCH-1, BSPO, BSPOSW, BSCH, BSCH-1, HYPO, HYPOSW, HYCH, HYCH-1)

(4a)

RPCPI = f(RPCPI-1, DPOP, BKCAP, FBCAP, BSCAP, HYCAP, BKCAPSQ, FBCAPSQ, BBCAPSQ, HYCAPSQ, FBFR, BKFR, BSFR, HYFR, BKCO, FBCO, BSFBCO, BSCO, HYCO, BKHYCO, FBFE1, FBFE2, BKFE1, BKFE2, BSFE1, BSFE2, HYFE1, HYFE2, HYFE3, FBFD1, FBFD2, BKFD1, BKFD2, BSFD1, BSFD2, HYFD1, FBPO, FBPOSW, FBCH, FBCH-1, BKPO, BKPOSW, BKCH, BKCH-1, BSPO, BSPOSW, BSCH, BSCH-1, HYPO, HYPOSW, HYCH, HYCH-1)

(4b) From the above estimations, a test of the joint significance of the insignificant variables was conducted for each variation of the model. Both of the insignificant variable sets were found to be statistically insignificant. A second group of models were then estimated for the purposes of this analysis. The results of these models are presented in Table 3. Table 3 shows that of the original 44 regressors in the single entry and exit effects model and the 52 regressors in the multiple entry and exit effects model, only 6 are statistically significantly related to real per capita personal income. These six variables account for approximately 99.6% of the total variation in per capita personal income. There are some striking results from both variations in the model. Looking firstly at the single entry and exit effects model, the only franchise existence variable that is found to be significantly related to income is that for baseball, and the effect is a per-capita benefit in excess of $519 per year.7 This could be due to the substantially larger number of games played at home by the local baseball franchise, as well as the prestige associated with being the United States’ “national game”. It is interesting to also note that both of the statistically significant construction variables, those for baseball-only stadiums and hockey-only arenas, are negatively related to income. Construction of a hockey-only arena cost the individual $171.58 per year for each of the ten years preceding construction. Construction of a baseball-only stadium incurred a per-capita cost of $132.17 per year for the ten years after construction. The only other significant sports-related variable was the entrance of a hockey franchise, and this resulted in a per capita benefit of $106.44 for each of the ten years preceding the beginning of the franchise beginning play in the city. 6 Note that these models also include SMSA-specific time trends and year-specific dummy variables. 7 Note that all dollar values discussed in this section are in United States dollar terms.

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If we combine these findings, we can see that the net effect of a city attracting a hockey franchise is a loss of approximately $65 per capita, with the costs of construction outweighing the benefits of franchise entry. These results fly in the face of proponents of stadiums as an engine of economic growth and support earlier findings of a negative impact of stadium construction. The lagged per capita personal income and change in population variables are also statistically significantly related to income. The yearly dummy variables and SMSA-specific time trends are not reported, however, of the yearly dummies, only one year (1971) was not statistically significantly different from zero. Of the SMSA-specific time trends, 20 of the 56 trends were not found to be statistically significantly different from zero.8 The individual city-specific intercepts for the fixed effects model are all found to be statistically different from zero at conventional significance levels. It is also worth noting that no success measures are found to be significantly related to income, nor do any stadium capacity or franchise exit variables. The multiple entry and exit effects model results are very similar to those of the single entry and exit effects model, with the exception of the baseball-only construction variable being insignificant. 4.2 Growth Determination The growth determination model, equation (3), was also estimated as a fixed effects model. The SMSA time trend for Arlington and the dummy variable for the year 1971 were omitted from the estimation in order to avoid the singular matrix problem. Two models were once again estimated, these being (a) the growth determination model with single entry and exit effects, and (b) the growth determination model with multiple entry and exit effects. The full models estimated are presented below in algebraic form. GROWTH = f(GROWTH-1, DPOP, BKCAP, FBCAP, BSCAP, HYCAP, BKCAPSQ,

FBCAPSQ, BBCAPSQ, HYCAPSQ, FBFR, BKFR, BSFR, HYFR, BKCO, FBCO, BSFBCO, BSCO, HYCO, BKHYCO, FBFE, BKFE, BSFE, HYFE, FBFD, BKFD, BSFD, HYFD, FBPO, FBPOSW, FBCH, FBCH-1, BKPO, BKPOSW, BKCH, BKCH-1, BSPO, BSPOSW, BSCH, BSCH-1, HYPO, HYPOSW, HYCH, HYCH-1)

(5a)

GROWTH = f(GROWTH-1, DPOP, BKCAP, FBCAP, BSCAP, HYCAP, BKCAPSQ, FBCAPSQ, BBCAPSQ, HYCAPSQ, FBFR, BKFR, BSFR, HYFR, BKCO, FBCO, BSFBCO, BSCO, HYCO, BKHYCO, FBFE1, FBFE2, BKFE1, BKFE2, BSFE1, BSFE2, HYFE1, HYFE2, HYFE3, FBFD1, FBFD2, BKFD1, BKFD2, BSFD1, BSFD2, HYFD1, FBPO, FBPOSW, FBCH, FBCH-1, BKPO, BKPOSW, BKCH, BKCH-1, BSPO, BSPOSW, BSCH, BSCH-1, HYPO, HYPOSW, HYCH, HYCH-1)

(5b)

8 Cities with insignificant time trends were Buffalo, Denver, Detroit, Jacksonville, Kansas City, Los Angeles, New

Orleans, Orlando, Phoenix, Portland, Sacramento, Salt Lake City, San Antonio, San Diego, Albuquerque, Las Vegas, Tucson, Tulsa, Virginia Beach and Wichita.

12

Like the previous analysis, from the above estimations, a test of the joint significance of the insignificant variables was conducted for each variation of the model. Both of the insignificant variable sets were found to be statistically insignificant. A second group of models were then estimated for the purposes of this analysis. The results of these models are presented in Table 4. Of the original 44 regressors in the single entry and exit effects model and the 52 regressors in the multiple entry and exit effects model, only 4 were found to be significantly related to growth in real per capita personal income. These four variables account for almost 60% of the total variation in growth in per capita personal income. Looking at the results, it is interesting to note that of the entire set of variables initially used to model the economic growth process, income growth in the previous period, change in population, baseball franchise existence and the departure of the first baseball franchise are the only variables found to be significantly related to growth. A one percent change in income growth in the previous period has a positive impact of 0.3% each year, and a one percent change in population results in a decrease of almost 0.2% per year. Of the sports-related variables, the baseball franchise existence variable takes a similar value across the two models, resulting in a boost to income growth by approximately 2.5% per year. This result is consistent with the earlier results on income determination. In the multiple entry and exit model, the departure of the first baseball franchise results in a fall of 1.82% in the growth rate of income in each of the ten years preceding the franchise exit from a city. Clearly, from these results, having a baseball franchise is better than losing it. No construction related variables or success measures are found to be significantly related to growth, nor do any stadium capacity or franchise entry variables. Coates and Humphreys (1999) noted in their analysis that the sports-related variables had no effect on the rate of growth of real per capita income. The results above indicate that there is evidence to suggest that the sports-related variables do have a statistically significant effect on the growth rate of real per capita income for both the single and multiple entry and exit effects models. 4.3 Comparisons with previous research If one is to believe the proponents of sports as an engine of economic growth and development, then we would expect that franchises and their associated activities (such as stadium capacity and construction, franchise existence, franchise entry and success) would have positive effects on the local economy. The only negative effect that would be expected would occur when a franchise departs it’s host city. Income determination findings were consistent with Coates and Humphreys (1999) as lagged income and change in population were found to be significant, the signficant construction variables were found to be negative, and existence and entrance variables were found to be positive. Also consistent was the effect that the construction variables outweighed the

13

franchise entry variables, so the net effect of gaining a franchise (at least over the initial ten years) was negative. This study found that success has no significant effect on per capita local area income, and thus one can say that previous research has not been deficient by ignoring success as a contributing factor from the franchise to the local economy. With the exception of the construction-related variables, the significant findings are intuitively consistent with the pro-sports as an engine of economic growth and development viewpoint. It must be noted, however, that only two sports are identified as having any influence on per capita income levels (baseball and hockey), and one sport with influence one sport with influence on local area growth (baseball). From this analysis, it would seem that the only sports that have positive economic effects on the local area are baseball and hockey. Proponents of attracting football and basketball franchises to cities would find it difficult to justify public expenditure on stadiums and franchises on local area economic grounds (per capita income and local area growth). At the conclusion of the 2001 season, baseball owners approved contraction of the number of franchises in Major League Baseball. From these results, cities that face the loss of their franchises should be concerned, as baseball franchise existence is consistently a beneficial factor to the local economy. Hockey has recently expanded to new markets in Minnesota and Nashville (2000) and Columbus and Memphis (2001), and indications from this analysis is that hockey may result in some monetary benefits to the host cities, but these are likely to be outweighed by negative construction effects. However, justification of public spending on local sports franchise(s) on non-economic grounds may be an entirely different story. 5. CONCLUSIONS The only way to overcome is to hang in. Even I’m starting to believe that.

—Dan O’Brien Throughout the course of this paper, several hypotheses have been examined and evaluated through empirical estimation of real per capita personal income determination and income growth for 57 SMSA’s, most of which play host to one or more sports teams in the four major league sports in the United States across the period 1970-1997. One of the significant findings of this paper that differs from those of previous research is that sports-related variables are found to have statistically significant effects on both real per capita personal income and growth in real per capita personal income. These results are important as they provide some justification for proponents of sports-led development, that sport does play an important role in the local economy. It is also found, however, that the overall effect of sports franchises is dependent upon the sport in question. We do find strong evidence to suggest that there are significant positive effects on the local economy caused by franchise existence for baseball. This lends support to those proponents of baseball franchises, in that the mere presence of a franchise does have positive impacts on both local area income and growth.

14

These results tend to support the arguments of those who have criticised the use of public funds for stadium construction. The direction of the coefficients on stadium construction variables is negative across the income determination models. It is difficult to make a definitive statement on the effect of possible intangible benefits of stadium construction in an income and growth determination process, however, it would seem that if these did exist they are not translated directly into increases in real per capita personal income. It seems clear from these results that stadium construction cannot be considered as significant spurs of economic development. We were also able to answer our primary question in this paper regarding the effect of franchise success on the local economy, and it was found that on-field success plays no role in either local area income determination or growth determination. If professional sports franchises do adversely affect the local economy, then it would seem from these results, at least, that franchise success (or futility) does not cause this effect. The addition of hockey-related variables can be considered successful, as several of these variables were found to be individually statistically significant. This does not translate into a positive outlook for proponents of hockey franchises. Cities successful in attracting a hockey franchise would be wary of the result that the negative effects of stadium construction outweigh the positive impact of the franchise entrance to the city. The finding that sports-related variables are jointly significant is at odds with previous research, which indicated that sports franchises did not play any significant role in the determination of growth in the local economy. Further work needs to be done in this area to identify the exact nature of the relationship between sports franchises and their local economies, such as the relationship between the level of public funding of sports stadiums and franchises and the local economy. Does a city that subsidizes their sport franchise benefit in comparison to a city who doesn’t provide any subsidization at all? The question we are left pondering is whether the oft-mentioned benefits of sports franchises such as civic pride and “major league status” among others can actually result in a direct effect on the local economy that is translated into dollars and cents per capita. According to the results of this study, this could quite possibly be the case.

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REFERENCES Baade, R.A. 1987. Is there an economic rationale for subsidizing sports stadiums? The

Heartland Institute, Policy Study, No. 13. Baim, D.V. 1990. Sports stadiums as “wise investments”: an evaluation. The Heartland

Institute, Policy Study, No. 32. Bast, J.L. 1998. Sports stadium madness: Why it started and how to stop it. The Heartland

Institute, Policy Study, No. 85. Bruggink, T.H. and Rose Jr., D.R 1990. Financial restraint in the free agent labor market for

major league baseball: Players look at strike three. Southern Economic Journal, No. 56, pp.1029-1043.

Burkedin, R.C.K. and Idson, T.L. 1991. Customer preferences, attendance and the racial

structure of professional basketball teams. Applied Economics, No. 23, pp.179-186. Coates, D. and Humphreys, B.R. 1999. The growth effects of sport franchises, stadia, and

arenas. Journal of Policy Analysis and Management, Vol. 18, No. 4, pp.601-624. Coates, D. and Humphreys, B.R. 2001. The economic consequences of professional sports

strikes and lockouts. Southern Economic Journal, No. 67, Vol. 3, pp.737-747. Cocco, A. and Jones, J.C.H. 1997. On going south: the economics of survival and relocation

of small market NHL franchises in Canada. Applied Economics, Vol. 29, pp.1537-1552.

Crompton, J.L. 1995. Economic impact analysis of sports facilities and events: Eleven

sources of misapplication. Journal of Sport Management, No. 9, pp.14-35. Depken II, C.A. 2000. Fan loyalty and stadium funding in professional baseball. Journal of

Sports Economics, Vol. 1, No. 2, pp.124-138. Downward, P. and Dawson., A. 1999. The demand for professional team sports: traditional

findings and new developments. Division of Economics, Staffordshire University Business School: Working Paper No. 99-7.

Euchner, C.C. 1993. Playing the Field – Why sports teams move and cities fight to keep them.

Baltimore: Johns Hopkins University Press. Hunter, W.J. 1988. Economic impact studies: Inaccurate, misleading and unnecessary. The

Heartland Institute, Policy Study No.21. Irani, D. 1997. Public subsidies to stadiums: do the costs outweigh the benefits? Public

Finance Review, Vol. 25, No. 2, pp.238-252.

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Jones, J.C.H., Stewart, K.G. and R. Sunderman, R. 1996. From the arena into the streets: Hockey violence, economic incentive and public policy. American Journal of Economics and Sociology, Vol. 55, No. 2, pp.231-243.

Jones, J.C.H. and Walsh, W.D. 1988. Salary determination in the National Hockey League:

The effects of skills, franchise characteristics, and discrimination. Industrial and Labor Relations Review, Vol. 41, No. 4, pp.592-603.

Kahane, L. and Shmanske, S. 1997. Team roster turnover and attendance in major league

baseball. Applied Economics, Vol. 29, pp.425-431. Mitrano, J.R. 1999. The “sudden death” of hockey in Hartford: sports fans and franchise

relocation. Sociology of Sport Journal, Vol. 16, pp.134-154. Newson, T.H. and Comer, J.C. 2000. Changing intra-urban location patterns of major league

sports facilities. Professional Geographer, Vol. 52, No. 1, pp.105-120. PricewaterhouseCoopers LLP. 2000. Feasibility study of a National Basketball Association

franchise in Louisville. (Study commissioned by Greater Louisville Inc.) Quantitative Micro Software. 2000. EViews 4.0 User’s Guide. California: Quantitative Micro

Software LLC. Ramanathan, R. 1995. Introductory Econometrics With Applications (Third Edition). Fort

Worth: The Dryden Press. Reeves, R.G. 1996. Franchise blackmail and the NFL: what a city can do to keep its home

team. Texas Entertainment and Sports Law Journal, Vol. 5, No. 3, pp.6-11. Richards, D.G. and Guell, R.C. 1998. Baseball success and the structure of salaries. Applied

Economics Letters, Vol. 5, pp.291-296. Riess, S.A. 1991. City Games: the evolution of American urban society and the rise of sports.

Urbana: University of Illinois Press. SHAZAM. 1997. SHAZAM Econometrics Computer Program, Users Reference Manual

Version 8.0. New York: McGraw-Hill. Shropshire, K.L. 1995. The Sports Franchise Game: Cities in search of sports franchises,

events, stadiums, and arenas. Philadelphia: University of Pennsylvania Press. Siegfried, J. and Zimbalist, A. 2000. The economics of sports facilities and their

communities. Journal of Economic Perspectives, Vol. 14, No. 3, pp.95-114.

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Spickard, S.E. 1995. Value of a major league sports franchise to a community. ERA Issue Paper. (presented to the Annual Conference of the Counselors of Real Estate, November 6, 1995, Atlanta, GA).

Swindell, D. and Rosentraub, M.S. 1998. Who benefits from the presence of professional

sports teams? The implications for public funding of stadiums and arenas. Public Administration Review, Vol. 58, No. 1, pp.11-20.

Whitney, J.D. 1988. Winning games versus winning championships: the economics of fan

interest and team performance. Economic Inquiry XXVI, pp.703-724.

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Table 1: Summary statistics

Variable Definition Mean Std. deviation

DPOP Percentage change in population 1.524 1.475 RPCPI Real per capita SMSA income (1996 dollars)9 20040.96 4531.06

GROWTH Growth in real per capita SMSA income (percent)

2.219 2.282

BKCAP Basketball stadia capacity, thousands 7.179 9.918 FBCAP Football stadia capacity, thousands 3.304 3.573 BSCAP Baseball stadia capacity, thousands 2.190 2.939 HYCAP Hockey stadia capacity, thousands 4.375 8.813

FBFR Football franchise present 0.487 0.549 BKFR Basketball franchise present 0.407 0.535 BSFR Baseball franchise present 0.427 0.561 HYFR Hockey franchise present 0.264 0.514 BKCO Basketball arena constructed (last 10 years) 0.108 0.319 FBCO Football stadium constructed (last 10 years) 0.062 0.241

BSFBCO Baseball/football stadium constructed (last 10 years)

0.065 0.246

BSCO Baseball stadium constructed (last 10 years) 0.046 0.209 HYCO Hockey arena constructed (last 10 years) 0.052 0.222

BKHYCO Basketball/hockey arena constructed (last 10 years)

0.058 0.234

FBFE Football franchise entered (last 10 years) 0.110 0.313 BKFE Basketball franchise entered (last 10 years) 0.141 0.348 BSFE Baseball franchise entered (last 10 years) 0.033 0.179 HYFE Hockey franchise entered (last 10 years) 0.080 0.272 FBFD Football franchise departed (last 10 years) 0.022 0.159 BKFD Basketball franchise departed (last 10 years) 0.053 0.223 BSFD Baseball franchise departed (last 10 years) 0.014 0.119 HYFD Hockey franchise departed (last 10 years) 0.041 0.199 FBFE1 First football franchise entered (last 10 years) 0.098 0.297 FBFE2 Second football franchise entered (last 10 years) 0.013 0.111 BKFE1 First basketball franchise entered (last 10 years) 0.128 0.334 BKFE2 Second basketball franchise entered (last 10

years) 0.013 0.111

BSFE1 First baseball franchise entered (last 10 years) 0.032 0.176 BSFE2 Second baseball franchise entered (last 10 years) 0.001 0.035 HYFE1 First hockey franchise entered (last 10 years) 0.068 0.251 HYFE2 Second hockey franchise entered (last 10 years) 0.006 0.079 HYFE3 Third hockey franchise entered (last 10 years) 0.006 0.079 FBFD1 First football franchise departed (last 10 years) 0.020 0.140

9 Measured in United States dollars.

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FBFD2 Second football franchise departed (last 10 years)

0.001 0.043

BKFD1 First basketball franchise departed (last 10 years)

0.057 0.232

BKFD2 Second basketball franchise departed (last 10 years)

0.001 0.025

BSFD1 First baseball franchise departed (last 10 years) 0.009 0.093 BSFD2 Second baseball franchise departed (last 10

years) 0.006 0.075

HYFD1 First hockey franchise departed (last 10 years) 0.041 0.199 FBPO Team(s) reached football playoffs 0.177 0.386

FBPOSW Football playoff series won 0.160 0.564 FBCH Football champions for that year 0.018 0.131

FBCH-1 Football champions in the previous year 0.017 0.129 BKPO Team(s) reached basketball playoffs 0.230 0.433

BKPOSW Basketball playoff series won 0.211 0.670 BKCH Basketball champions for that year 0.018 0.131

BKCH-1 Basketball champions in the previous year 0.017 0.129 BSPO Team(s) reached baseball playoffs 0.071 0.258

BSPOSW Baseball playoff series won 0.056 0.310 BSCH Baseball champions for that year 0.016 0.124

BSCH-1 Baseball champions in the previous year 0.015 0.122 HYPO Team(s) reached hockey playoffs 0.170 0.411

HYPOSW Hockey playoff series won 0.156 0.609 HYCH Hockey champions for that year 0.009 0.093

HYCH-1 Hockey champions in the previous year 0.008 0.090

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Table 2A: SMSA’s with sports franchises and average real per capita personal income

Sports franchises across the period 1970-1997 City (SMSA) Average

RPCPI Football (NFL)

Basketball (NBA)

Baseball (MLB)

Hockey (NHL)

Arlington 19227.34 0 0 1 0 Atlanta 20275.15 1 1 1 1 Baltimore 20732.07 1 1 1 0 Boston 22290.69 1 1 1 1 Buffalo 18680.28 1 1 0 1 Charlotte 18374.45 1 1 0 0 Chicago 22772.44 1 1 2 1 Cincinnati 19193.49 1 1 1 0 Cleveland 20775.87 1 1 1 1 Dallas 21928.40 1 1 0 1 Denver 22296.65 1 1 1 1 Detroit 21298.22 1 1 1 1 Green Bay 18525.90 1 0 0 0 Hartford 23337.02 0 0 0 1 Houston 21470.79 1 1 1 0 Indianapolis 19619.40 1 1 0 0 Jacksonville 18145.30 1 0 0 0 Kansas City 20144.17 1 1 1 1 Los Angeles 21463.21 2 2 1 1 Miami 18657.24 1 1 1 1 Milwaukee 20916.46 0 1 1 0 Minnesota 22111.58 1 1 1 1 Nashville 18753.17 0 0 0 1 New Orleans 17624.51 1 1 0 0 New York 24384.83 2 3 2 3 Oakland 23453.31 1 1 1 1 Orange County 23987.81 0 0 1 1 Orlando 17950.06 0 1 0 0 Philadelphia 21241.75 1 1 1 1 Phoenix 18639.91 1 1 0 1 Pittsburgh 19400.59 1 0 1 1 Portland 20033.66 0 1 0 0 Raleigh 18917.74 0 0 0 1 Sacramento 19831.04 0 1 0 0 Salt Lake City 16233.14 0 1 0 0 San Antonio 16360.16 0 1 0 0 San Diego 20079.26 1 2 1 0 San Francisco 29643.24 1 1 1 1 San Jose 25172.10 0 0 0 1 Seattle 23015.90 1 1 1 0 St Louis 20300.00 1 0 1 1 Tampa 18246.68 1 0 0 1 Washington 24829.68 1 1 1 1

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Table 2B: SMSA’s without sports franchises and average real per capita personal income

City (SMSA) Average RPCPI Albuquerque 17070.71

Austin 17825.63 Columbus 18903.61 El Paso 12310.12 Fresno 17054.18

Honolulu 21645.52 Las Vegas 19568.16 Memphis 17962.93 Oklahoma 18020.08

Omaha 19230.46 Tucson 16488.65 Tulsa 19184.98

Virginia Beach 17224.97 Wichita 19510.14

22

Table 3: Dependent variable: Real per capita personal income (RPCPI)

(a) Single entry and exit effects RPCPI = 0.828268*RPCPI-1 + 61.32462*DPOP + 519.0400*BSFR (0.0000) (0.0000) (0.0000) – 132.1721*BSCO – 171.5806*HYCO + 106.4374*HYFE (0.0143) (0.0014) (0.0235)

R-squared: 0.996090 Adjusted R-squared: 0.995699 F-statistic: 2547.515 Prob. (F-statistic): 0.000000 (b) Multiple entry and exit effects RPCPI = 0.829406*RPCPI-1 + 60.76653*DPOP + 525.0359*BSFR (0.0000) (0.0000) (0.0000) – 171.1809*HYCO + 105.4448*HYFE1 (0.0013) (0.0318)

R-squared: 0.996074 Adjusted R-squared: 0.995685 F-statistic: 2556.732 Prob. (F-statistic): 0.000000

Table 4: Dependent variable: Growth in real per capita personal income (GROWTH)

(a) Single entry and exit effects

GROWTH = 0.300298*GROWTH-1 – 0.185570*DPOP + 2.503504*BSFR (0.0000) (0.0011) (0.0001)

R-squared: 0.597049 Adjusted R-squared: 0.556379 F-statistic: 14.68031 Prob. (F-statistic): 0.000000 (b) Multiple entry and exit effects

GROWTH = 0.298828*GROWTH-1 – 0.197374*DPOP + 2.486379*BSFR (0.0000) (0.0011) (0.0001) – 1.820137*BSFD1 (0.0013)

R-squared: 0.598556 Adjusted R-squared: 0.557722 F-statistic: 14.65810 Prob. (F-statistic): 0.000000

23

LIST OF RECENT DISCUSSION PAPERS 01.01 J. Obben, H-J. Engelbrecht and V. W. Thompson, A Logit Model of the Incidence of

Long-Term Unemployment in New Zealand, March 2001. 01.02 H-J. Engelbrecht, The Role of Human Capital in Economic Growth: Some Empirical

Evidence on the ‘Lucas vs Nelson-Phelps’ Controversy, May 2001. 01.03 S. Chatterjee, A. N. Rae and S. Shakur, A Bold Millennium Round: Some Signposts

for Trade and Welfare Gains From Comprehensive Multisector Reforms, June 2001. 01.04 R. Gounder, Long-Term Growth in Fiji: Investment, Policy, Democracy and

Economic Freedom, July 2001. 01.05 S. M. Cassells, Deficiencies in New Zealand’s Approach to Recycling End-Of-Life

Vehicles, November 2001. 01.06 J. Ballingall, The Pacific Five Free Trade Area: Impacts on Agriculture in

New Zealand, November 2001. 01.07 N. Campbell, CFC Prohibition and the Porter Hypothesis, November 2001. 01.08 L. M. Smith, The Impact of the Third Wave – Mathematisation – on Samuelson’s

Economics, November 2001. 01.09 L. M. Smith, Samuelson’s Economics Through Fifty Years, November 2001. 01.10 H-J. Engelbrecht and N. McLellan, Economic Growth Revisited: Identifying a Class

of Growth Models for the New Zealand Economy, November 2001. 01.11 D. Pyne, Endogenous Trade Policy, Capital Ownership and Free Trade Agreements,

November 2001. 01.12 P. Magagula and J. Obben, Distinguishing Between Exporting and Non-Exporting

Small and Medium-Sized Enterprises in Swaziland, November 2001. 02.01 S. Chatterjee and N. Podder, States of distribution and social welfare: An application

of generalized Lorenz dominance to New Zealand incomes data 1984-98, January 2002.

02.02 S. Richardson, Revisiting the income and growth effects of professional sport

franchises: Does success matter?, January 2002.