The longitudinal analysis uses triangulated secondary,
company-specific data. Sources of the data include the company's
Uniform Franchise Offering Circular (UFOC), corporate 10K
Reports, Entrepreneur "Franchise 500" (1987-1994)
listings and direct contact with public franchisor shareholder
relations offices. One hundred forty companies were identified as
having their primary source of income in franchising, company
operations of a business-format franchise, and where ownership
was at least partially public. Of the 140 companies, data could
only be collected on 83 companies for which at least two
consistent sources of information existed. Time and resource
constraints precluded further data collection on the remaining
companies^{1}. For each public franchisor, monthly total
returns (dividends distributed and capital gains) were collected
for each month between January 1987 and December 1994 from the
CRSP tapes and Compustat. Similar returns variables were
collected for a market index, in this case the S&P500 Index.
These returns were used to estimate the systematic risk (beta) of
such firms as well as the abnormal monthly returns (Jensen's
Alpha). Independent variables captured include royalty rate,
franchise fee, contractually obligated advertising fee, number of
company-owned and franchised outlets, whether the company was
venture capital backed, years in business and years in
franchising and the rate of growth of outlets over the period
1987 through 1994.

Exhibit 1 plots the
performance of the Standard and Poors 500 Index versus an
equally-weighted index of 83 publicly listed franchisors between
January 1st 1987 and December 31st 1994. Both indices were set at
a base of 100 on January 1st 1987 to provide a comparable
starting point^{2}. It is interesting to note that the
franchisor index very much tracks the largest market index, with
two significant breaks, one in early 1992 and a second in late
1992. These discontinuities can be traced back to the addition to
the index of a number of franchisor firms with very strong early
post-IPO performance in the later part of 1992. In order to gauge
the return performance, it is essential to correct for the level
of risk inherent in these franchisor companies. The distribution
of systematic risk^{3} in the sample firms is presented
in Exhibit 2. The mean beta is
equal to 1.152 and is not statistically different from 1.00 at
the usual levels of confidence. The median beta is 1.008, with a
variance of 0.770.

Overall, franchisor companies seem to have offered returns very much in line with the market as a whole and with very similar average risk levels. They do not exhibit abnormal returns with respect to standard models of the risk-return process, as indicated in Exhibit 3 which graphs the distribution of Jensen's alpha, the stocks' average abnormal performance per month. This evidence does not support the view that franchisors constitute a very specific group in terms of the risk-performance relationship: quite to the contrary, they seem to offer the same general distribution of characteristics than the market as a whole.

System Growth, Franchise Format and Contractual Terms of Agreement

A basic hypothesis of this paper is that franchising offers a
very powerful system that helps deliver fast growth to companies
with promising concepts. The first hypothesis to be tested in
this respect is that growth can be accelerated by removing the
financing constraints inherent in corporate-based development and
instead calling upon a franchisee-owned growth. Similarly,
franchise contracts which tend to be more generous towards the
franchisees, i.e. those characterized by lower upfront franchise
fees, lower royalties, lower advertising fees and longer
contractual terms, should also facilitate the growth in number of
outlets. To test these relationships, regressions are run to
explain the rate of growth in total outlets over the period
1987-1994 (RGROWTH), using as explanatory variables the percent
of outlets which are company owned^{4} (PERCTOWN), the
total number of outlets in the system^{5} (TOTOUTS), the
franchise fees (FFEE), royalty payments (ROYALTY), advertising
fees (ADFEE) and contractual terms (TERM) of the franchise
agreement.

A first check is run in Exhibit 4 for the possibility of multicolinerarity in the explanatory variables, an issue that could hinder the interpretation of the regression coefficients. The Pearson correlation coefficients between pairs of variables remain acceptable in all instances.

The regression results are summarized in Exhibit 5. A number of interesting insights emerge. First of all, the percentage of outlets which are directly owned by the franchisor appears to be positively correlated with the rate of growth at the 5% level of confidence but the coefficient is positive, implying that growth in the number of outlets is actually positively correlated with company-financed outlets. Put differently, the fastest rates of outlet growth appear to be associated with franchisor-financed stores, thus not supporting the hypothesis that growth would be best served by calling upon external capital. Second, the coefficients on franchise fees and royalty are as expected (that is negative, meaning that higher fees tend to be associated with slower growth), as well as the coefficient on advertising fees (positive, to the effect that higher advertising fees, and hopefully expenses, are conducive to higher growth). Finally, the coefficient on total number of outlets is positively related to growth, something to be expected given that the variable is measured ex-post.

Beyond the basic franchisor rate of outlet growth, it is also important to gain a better understanding of the factors that ultimately determine value creation at the franchisor level, proxied here by the risk-adjusted rate of return on the franchisor stock. A number of relationships can be hypothesized. First of all, growth itself, and its associated investments in fixed assets and working capital, stresses the financial resources of a company, leading to relatively lower reported earnings. The financial constraints of growth should be particularly marked for these companies that elect to own most of the outlets in the system as opposed to those that tap the external capital markets by franchising a higher proportion of their outlets. Second, higher franchise fees, royalties, and advertising payments from franchisees should increase the returns to franchisor capital. The total number of outlets in the system can serve as a proxy for the franchisor's relative bargaining power vis-à-vis suppliers (positive effect anticipated on performance) and franchisees (negative effect anticipated). The combined effect of these changes in bargaining power with total franchise size is unknown. The terms of the franchise contract, including franchise fees, royalty payments, and advertising contributions are expected to positively affect franchisor performance by creating larger income and growth opportunities. The Pearson correlation coefficients for the explanatory variables are presented in Exhibit 6, while the model coefficients are detailed in Exhibit 7.

A number of interesting figures can be highlighted in Exhibit 7. First of all, the
explanatory power of the model as presented is extremely high
(R2=0.71 and R2adj=0.21), especially when considering the fact
that the dependent variable is risk-adjusted abnormal
performance, as measured by Jensen's Alpha, and not simply total
stock performance^{6} Second, even though none of the
variable coefficients are significantly different from zero at
the usual levels of confidence, they appear to be wearing the
expected signs. The percentage of outlets owned by the franchisor
seems to be positively correlated with performance, indicating
possibly inside information at the franchisor level (if the
prospects are really good, the incentives to shares the profits
with franchisees are limited). Royalty and advertising fees are
positively associated with franchisor stock returns, while the
franchisee fee is negatively related (larger franchise fees
associated with lower franchisor performance). Again, this
finding is consistent with the explanation above: franchisor with
the brightest prospects may signal their quality by contractually
favoring royalties and ad fees instead of the fixed franchise
fee. In other words, a high franchise fee creates a conflict of
interest between franchisor and franchisee, whereas a
performance-based payment system (especially the use of royalties
on sales) may realign the interest of both franchisor and
franchisees. The plot of the regression residuals indicates no
systematic bias.

An interesting question left to be analyzed is that of the factors contributing to franchisor risk, and in particular the systematic (or market-related) component of that risk. Beta risk is a combination of business and financial risk. With limited financial information at this stage, it is nevertheless interesting to investigate the effect of various components of business risk upon the resulting systematic risk. For example, high system growth can be hypothesized to create the need for further financing and thus of recourse to the market, with its rewards and dangers. This is particularly relevant when most of the growth in new outlets is financed by the franchisor itself and not franchisees. On the other hand, a large number of outlets, and their associated earning power, would dampen the dependence on the market. Franchise fees and royalties should similarly provide alternative sources of capital, reducing market sensitivity. These relationships are investigated in Exhibit 8.

Even though the overall explanatory power of the model is quite limited, and none of the regression coefficients are significant at the usual levels of confidence, it is interesting to note that high system growth in negatively correlated with beta. This finding may be linked to the previous observation that growth may be associated with internal financing, resulting in a lower degree of market exposure. As expected, high royalty payments seem to be associate with lower betas, but franchise and advertisement fees are positively associated with systematic risk. Again, this may be a reflection of the better alignment of franchise payments (expressed a percentage of sales), such as royalties and ad fees, with actual performance, than with the fixed franchise fee.

^{1}The study could clearly be enhanced by adding the
remaining 57 public franchisors to the sampel but no systematic
bias is anticipated from the current transaction.

^{2}As is common in this literature, monthly portfolio
rebalancing is assumed in both indices.

^{3}Systematic risks were measured with respect to the
same S&P500 market index used in Exhibit 2 using 60 monthly
returns. When less than 60 monthly returns were available but
more than 24, betas were calculated on the number of months at
hand. When less than 24 months of historical returns were
available for regression purposes, systematic risks were reported
as not available. This procedure is in line with standards
adopted by Value Line and Morningstar for reporting betas.

^{4}The rest of the franchised outlets are then
franchisee-owned.

^{5}As of December 31st. 1994.

^{6}Similar results have been obtained when utilizing
average monthly returns, unadjusted for risk, as the dependent

variable, even though the R2 were in general slightly smaller in
these cases.

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Last Updated 06/08/98