Corporate Social Responsibility in The Fast Food Industry in the United
States and its connection to financial performance
James Haidak
10824855
Economics and Business
Finance and Organization
Pepijn Trietsch
June 26, 2018
Abstract: This paper analyzes corporate social responsibility and its
potential connection to corporate financial performance in the fast
food industry in the United States. Using an event study, this paper will
attempt to establish whether or not a corporation’s corporate social
responsibility level effects its financial performance and whether or not
how healthy their menus are act as an adequate proxy for corporate
social responsibility. The results of the study show a possible positive
relationship between corporate social responsibility and financial
performance and hypothesizes that it is a result of its positive effect on
stakeholder relationships and branding. Additionally, the paper
concludes that how healthy a menu is indeed seems to function as an
adequate proxy for CSR.
Statement of Originality
I certify that, as far as I know, the content of this thesis is my own work.
This thesis has not been submitted for any degree or other purposes. I
certify that the intellectual content of this thesis is the product of my
own work and that all the assistance received in preparing this thesis
and sources have been acknowledged.
Table of Contents
1. Introduction ... 4
2. Related Literature ... 6
2.1.1 Defining CSR ... 7
2.1.2 Measuring CSR ... 8
2.2.1 CSR Studies in the Food and Beverage Industry ... 9
2.3.1 Studies Exploring the Link Between CSR and CFP ... 10
2.3.2 CSR and Branding ... 10
2.4.1 Measuring the Link Between CSR and CFP ... 11
2.4.2 Capital Asset Pricing Model ... 12
2.4.3 Fama & French 3 Factor Model ... 12
2.4.4 Carhart 4 Factor Model... 13
2.5.1 Goal of this study and hypothesis ... 13
3. Methods & Results ... 14
3.1.1 Event Study Methods and Data... 14
3.2.1 Checking for Potential Problems in the Models ... 15
3.2.2 Heteroscedasticity ... 15
3.2.3 Normality ... 15
3.2.4 Autocorrelation... 16
3.3.1 Determining the Accuracy of The Models ... 17
3.4.1 Results ... 18
4. Discussion & Concluding Remarks ... 22
4.1.1 Summary ... 22
4.1.2 Implications of the Results ... 22
4.1.3 Limitations & Further Research ... 23
Works Cited ... 25
Appendix A ... 29
Appendix B ... 30
1. Introduction
Corporate social responsibility (CSR) programs are becoming more important every year. According to the Governance & Accountability Institute, 85% of S&P 500 companies published CSR reports in 2017. That is a 3% increase over 2016. According to the EU Commission (2018), corporations are adopting these CSR programs to face environmental, public health, and other societal issues, while benefitting from an innovative edge, cost savings, and better risk
management.
CSR programs are not just for the benefit of the corporation. Governmental bodies,
investors, and the public at large are calling for corporations to develop these CSR programs as the world economy becomes more globalized. The Eu Commission (2018) explains that CSR programs’ contribution to a more sustainable economy while benefitting society as a whole is the reason they are pushing for their implementation.
Many papers, such as Chung et al. (2018), Moosa et al. (2014), Chau et al. (2014), and Barnett and Salomon (2014), investigate potential links between CSR and a firm’s value or financial performance. In the context of the food and beverage industry, CSR issues often concern public health. Papers such as Pozo and Schroeder (2016), and Almanza et al. (2013) examine the link between CSR and financial performance in the context of food recalls;
however, according to Banjeree et al. (2013), CSR outcomes are industry specific and little to no studies exist that examine the potential link between CSR and financial performance in the fast food industry. The central question of this paper is thus:
Is there a link between CSR and CFP in the fast food industry, and is how healthy a chain’s menu is a good proxy for CSR?
The United Nations is demanding more accountability from corporations operating in the food and beverage industry. Webster (2011) explains that members attending their summit on non-communicable diseases are pleading with corporations that sell processed foods to act
in a moral way. There is a 17% expected increase in deaths from these diseases, and rising rates of cancer, cardiovascular disease, diabetes, and chronic pulmonary disease will ultimately result in an estimated economic loss of US$7-trillion to countries worldwide between 2011 and 2025. Webster (2011) discusses the fact that global obesity levels have doubled since 1980, and with studies such as Cade, et al. (2012) finding a clear link between fast food availability and obesity levels, a study investigating possible connections between CSR and financial performance in the fast food industry is warranted.
In December 2016, a federal law took effect in the United States requiring restaurants to display calorie content information on their menus. If a connection between CSR and financial performance exists in the fast food industry, and if how healthy a chains’ menu is acts as a good proxy for CSR, then an event study analyzing any abnormal returns occurring after this law took effect could reveal it. Moosa et al. (2014) uses similar methodology to establish a connection between CSR and financial performance in the context of environmental friendliness as a measurement of CSR. Pozo and Schroeder (2016) and Almanza et al. (2013) also use event studies to examine a connection between CSR and financial performance in the food processing industry.
Using OLS regression and an event study, this paper will explore whether a connection might exist between CSR and financial performance in the U.S. fast food industry. The discovery of such a connection could have an impact on how managers in the fast food industry approach CSR program implementation. 30 publicly traded fast food restaurant chains are analyzed using the Capital Asset Pricing Model (CAPM), Fama and French 3-Factor (FFM), and the Carhart 4-Factor Models (CFM) to establish benchmark normal returns. The event study will then be used to determine whether or not this sample experienced significant cumulative average abnormal returns around the implementation of the federal law in December 2016.
This paper will begin by analyzing the spectrum of perspectives on CSR and then define it. It will then discuss the varying opinions on how CSR is measured and explain existing studies pertaining to the food and beverage industry. The paper will then explore previous literature surrounding the link between CSR and CFP and the potential avenues through which this link exists. An in-depth analysis of event studies and their use in previous studies capturing the link
between CSR and CFP will precede an explanation of the goals and hypothesis of the study. The results and implications of these results are then discussed followed by concluding remarks regarding limitations of this study and suggestions for further research.
2. Related Literature
Sources Defining CSR Shareholder
approach Stakeholder Approach Societal Approach Marrewijk (2003) Kim and Ramos (2018) CSR is a spectrum of perceived levels of corporate responsibility with three major approaches Most narrow approach. Focuses only on responsibility to shareholders Corporations are responsible to anyone they effect by conducting business activities Broadest approach. Corporations are responsible to society as a whole
Sources Measuring CSR Problems
measuring CSR CSR in fast food industry Crane et al. (2017) Capelle-Blancard et al. (2017 Bénabou and Tirole (2010) Aupperle and Wolfe (1991) Liang and Renneboog (2017) Various agencies exist which measure CSR of various companies according to their own systems CSR has many facets and is thus hard to capture with a single measurement Endogeneity caused by omitted variables, measurement error, and reverse causality
CSR encompasses a firm’s efforts to combat the
externalities it causes Fast food industry business operations contribute to public health issues, and thus their most obvious CSR initiatives should focus on mitigating their contributions to issues such as obesity, diabetes, and heart disease
Sources Link between
CSR and CFP Underlying mechanism behind CSR-CFP link Measuring link between CSR and CFP Chao et al. (2014) Chung et al. (2016) Moosa et al. (2014) Barnett and Salomon’s (2012) Keller and Lehmann (2003) Clark (2000) Battacharya et al. (2009)
The results from previous studies exploring CSR and its effect on CFP are mixed Results tend to either show a positive relationship or none at all CSR improves stakeholder relations and improves branding Better branding is shown to allow companies to charge a premium for their products, improves the elasticity of customers’ demand, and increase market share
Allows companies to communicate their goals and improve investor confidence
Event studies used to determine abnormal returns after a given event Regression models are used to establish benchmark normal returns CAPM, FFM, and CFM models 2.1.1 Defining CSR
Marrewijk (2003) explains that a general definition of CSR that could capture all of its aspects would be too broad to be useful in academia and that diversity in existing definitions has negatively affected research and debate. Marrewijk (2003) and Kim and Ramos (2018) describe three major perspectives in CSR and discuss their implications. The stakeholder, societal, and the shareholder approaches to CSR represent a spectrum of perceived levels of corporate responsibility, and each approach offers a unique outlook as to what constitutes CSR and thus ways to measure it.
The shareholder approach to CSR is the narrowest of the three perspectives. According to Friedman (1962), an organization’s only goal is to maximize profit. They must act solely in the interest of the shareholders. According to Marrewijk (2003), government is responsible for
social well-being and are thus also responsible for dealing with negative externalities that result from any organization’s activities. This approach only supports the implementation of CSR programs if these programs support the business and increase profits in some way.
The societal approach to CSR is the widest of the three perspectives and focuses on society as a whole. It states that a corporation must do everything in its power to mitigate any harmful externalities it may create by conducting business while contributing to society with positive externalities. This approach to CSR is based on morals and ethics as guiding principles. Marrewijk (2003) explains that because corporations operate by public consent and are a part of the society in which they operate, they have to take responsibility for how their activities affect these societies. According to Ferrell and Maignan (2001) however, this approach is ambiguous and too broad in scope to be practical.
The stakeholder approach, as defined by Freeman (1984), states that corporations are responsible to shareholders as well as anyone effected by an organization’s activities and is the most common approach to CSR. Donaldson and Preston (1995) explain that a company is a complex system of individuals that are involved either directly or indirectly in the business itself. Groups like political parties, customers, employees, governments, and more all interact with and are affected by decisions made by organizations. This approach is a middle ground between the shareholder and societal approaches and will be used in this paper as the definition of CSR.
2.1.2 Measuring CSR
CSR concerns multiple stakeholder groups and thus has many facets ranging from environmental friendliness to public health. This complication makes measuring CSR and its impact on corporate financial performance (CFP), as measured by abnormal returns, difficult. Crane et al. (2017) lists agencies such as Kinder, Lydenberg, Domini & Co., Vigeo, and Thomson Reuters Asset as sources for CSR rankings; however, they go on to list studies which find significant problems with them. Capelle-Blancard et al. (2017) criticize the aggregation of various CSR facets into a single CSR construct; furthermore, Bénabou and Tirole (2010) point out the larger issue of endogeneity. They explain that omitted variables, measurement error, and reverse causality can cause this problem, and according to Hamilton and Nickerson (2003), it leads to poorly estimated regression coefficients. These incorrect coefficients can thus lead to
inaccurate decisions regarding their significance and incorrect study results. Aupperle and Wolfe (1991) sum up the variety of opinions surrounding CSR measurement by stating that there is no single optimal way to measure CSR and that the exploration of new measurement techniques and more perspectives are needed to develop the field.
According to Liang and Renneboog (2017), CSR encompasses a firm’s efforts to combat the externalities it causes. In the fast food industry, obesity and health issues caused by high calorie food fall under this description. It is thus arguable that how healthy a menu is could be indicative of how strong a fast food chain’s CSR programs are. Kim and Ramos (2018) point out that in the fast food industry, their business operations contribute to public health issues, and thus their most obvious CSR initiatives should focus on mitigating their contributions to issues such as obesity, diabetes, and heart disease. Multiple studies such as Moosa et al. (2014) Hamilton (1995), White (1995), Klassen and McLaughlin (1996) already study environmental friendliness and other aspects of CSR; however, this paper will attempt to expand upon how it can be measured in the fast food industry.
2.2.1 CSR Studies in the Food and Beverage Industry
There is a growing body of literature regarding CSR in the food and beverage industry. Studies such as Pozo and Schroeder (2016) and Almanza et al. (2013) look at the public health aspect of CSR in the form of food recalls and how they affect corporate stock prices. They find food recalls, which can be considered a negative CSR shock, have a negative effect on a company’s stock returns.
Adams (2005) explains that one way in which fast food corporations often use CSR, is to respond to criticisms from the public regarding their products being unhealthy and improve branding. It is a way for these entities to earn public trust and improve their image. Many fast food chains such as Burger King, McDonalds, Starbucks, Subway, and Chipotle Mexican Grill have already launched large CSR programs covering charity, nutrition, and environmental issues.
Kim and Ramos (2018) analyze CSR in the fast food industry. Their study looks at how stakeholders perceive CSR attempts by fast food companies. They find a significant positive response by stakeholders to both general issue and public health related CSR programs. These studies suggest that effective CSR programs would lead to improved CFP; however, this paper
will attempt to verify these findings and establish how healthy menus are as an indicator of corporate CSR efforts in the fast food industry.
2.3.1 Studies Exploring the Link Between CSR and CFP
The results from previous studies exploring CSR and its effect on CFP are mixed. Chao et al. (2014) aggregates the results of 84 CSR studies from 2002-2011 that focus on its relationship with financial performance, and their results are inconclusive. A portion of the studies find a positive relationship between the CSR and CFP, and others find no relationship at all.
In the global banking sector, Chung et al. (2016) use three novel estimation methods to establish a link between CSR and strong financial performance. They establish that CSR banks outperform non-CSR banks in both return on assets and return on equity. The same kind of relationship can be seen in the Moosa et al. (2014) study in terms of environmental
friendliness. They look at several green policies implemented in the United State by the Obama Administration and how they effected the stock market. They find that the worst polluters suffered the highest negative returns with more socially responsible corporations being confronted with much smaller losses; however, not all studies find these significant links between CSR and CFP.
Barnett and Salomon’s (2012) study shows mixed results when it comes to the connection between CSR and financial performance. Crane et al. (2017) discuss how
endogeneity due to omitted variables, measurements error, and reverse causality may cause issues in the study of CSR and that once you correct for these issues the connection to financial performance becomes insignificant. Aside from analyzing studies that observe potential
relationships between CSR and CFP, it is also important to explore the underlying mechanisms which cause the connection
2.3.2 CSR and Branding
Keller and Lehmann (2003) explain that CSR programs are vital to a company’s branding and that branding is one of the strongest assets a company has. Effective CSR programs have positive long-term effects on customers and stakeholders, and Ragas and Roberts (2009) case study of Chipotle Mexican Grill supports this.
The study looks at Chipotle Mexican Grill’s extensive CSR programs regarding nutrition, and how the programs have contributed to their explosive growth as a company. These
programs strengthened brand performance, and according to Keller and Lehmann (2003), a strong brand allows companies to charge premiums for their products, improve the elasticity of customers’ demand, and increase market share. Such improvements are also vital indicators of expected growth to the investment community.
Corporations around the world use CSR programs as a way to maintain strong stakeholder and public relations. Clark (2000) performs a fundamental comparison between public relations and CSR and finds them to be quite similar. She goes on to explain that CSR programs which have effective communication methods contribute to enhanced stakeholder relations and thus brand image.
Battacharya et al. (2009) expands upon this view and posits that CSR itself is a communication tool that corporations can use to disseminate their views and goals. CSR programs demonstrate a commitment to combatting negative externalities caused by a corporation and thus builds stronger relations and trust with their various stakeholders. This trust is important for long term value creation and acts as a tool to strengthen brand image which is shown improve CFP.
2.4.1 Measuring the Link Between CSR and CFP
Some studies in the field of CSR utilize event studies in tandem with regression analysis to see if it is linked to financial performance. The event study was pioneered by Stephen J. Brown and Jerold B. Warner in order to analyze the impact of firm-specific events on share price. Event studies use models to determine benchmark normal returns which can then be compared to actual returns to determine if they are abnormal. These studies have become an integral part of financial analysis in various industries and can look at specific CSR events to determine whether they impact financial performance of a company.
This technique is already used in multiple studies to analyze the effect of environmental related CSR events. Moosa et al. (2014) Hamilton (1995), White (1995), Klassen and McLaughlin (1996) all use event studies to observe the effect of green policies on stock returns. Similar methodology is also applied in CSR studies in the food and beverage industry. Pozo and Schroeder (2016) and Almanza et al. (2013) use event studies to analyze CSR in the food production industry by looking at various food recalls and how it affects stock returns.
In order to conduct these event studies, however, a method for determining benchmark returns must also be used. Three such models often used in finance for this purpose are the Capital Asset Pricing Model (CAPM), the Fama & French Three-Factor Model (FFM), and the Carhart Four-Factor Model (CFM).
2.4.2 Capital Asset Pricing Model
The first and most basic of the three regression models used in this study is CAPM. Pioneered by Sharpe (1964) and Lintner (1965), CAPM is a model that prices securities while taking into account systematic risk in the market. This model can be used to find benchmark returns of a security while accounting for its sensitivity to non-diversifiable risk. De Jong (2007) describes CAPM as an improvement over other rudimentary estimation models such as mean-adjusted returns, and this paper defines CAPM using the following equation:
𝐸(𝑅𝑖) = 𝑅𝑓+ 𝛽𝑚𝑘𝑡(𝐸(𝑅𝑚𝑘𝑡) − 𝑅𝑓) + 𝜖𝑡
CAPM assumes that all investors: are rational, risk averse, price takers, can take loans at the risk-free rate, have the same expectations, have a diversified portfolio, maximize personal utility, and can trade without any market frictions such as transaction costs.
CAPM is not without problems. Since its creation many studies have found refuting evidence as to its efficacy. Black and Scholes (1973) were two of the first to present data showing that CAPM does not properly account for variation in stock returns. They show that stocks which have low betas tend to have higher returns than they should. Fama and French (1996) also show CAPM’s lack of efficacy by developing their own model which accounts for many of the “anomalies” that Sharpe (1964), and Lintner (1965) could not account for.
2.4.3 Fama & French 3 Factor Model
FFM is one of the most well-known responses to CAPM. This model introduces two additional control factors in order to create a better specified model of stock returns. This paper uses the following equation to define FFM:
𝐸(𝑅𝑖) = 𝑅𝑓+ 𝛽𝑚𝑘𝑡(𝐸(𝑅𝑚𝑘𝑡) − 𝑅𝑓) + 𝛽𝑆𝑀𝐵∗ 𝑆𝑀𝐵 + 𝛽𝐻𝑀𝐿∗ 𝐻𝑀𝐿 + 𝜖𝑡
Like CAPM, FFM takes into account market risk; however, FFM expands upon this idea by also accounting for the general outperformance of small versus big companies and high
2.4.4 Carhart 4 Factor Model
CFM was developed by Carhart (1997) as an extension of FFM. CFM expands upon FFM by introducing a momentum factor which captures a phenomenon discovered by Jegadeesh and Titman (1993). They find that if a portfolio manager purchases stocks which have been performing well in the past and sell those which have not, they will realize significant positive returns over a short (3-12 month) period. This phenomenon suggests that corporations which have been performing well or poorly will tend to continue to do so in the short run. This paper defines CFM using the following equation:
𝐸(𝑅𝑖) = 𝑅𝑓+ 𝛽𝑚𝑘𝑡(𝐸(𝑅𝑚𝑘𝑡) − 𝑅𝑓) + 𝛽𝑆𝑀𝐵∗ 𝑆𝑀𝐵 + 𝛽𝐻𝑀𝐿∗ 𝐻𝑀𝐿 + 𝛽𝑀 ∗ 𝑀 + 𝜖𝑡
2.5.1 Goal of this study and hypothesis
There are conflicting views on CSR literature, and the field still requires further exploration. There is debate not only as to how to define CSR but also regarding how to measure it. CSR is a broad term encompassing multiple topics, and the goal of this paper is an initial exploration into CSR in the fast food industry. Using an event study, this paper will
attempt to establish a link between CSR and financial performance. According to Banjeree et al. (2013) CSR outcomes are industry specific, and while CSR studies have been carried out in many industries, fast food is not one of them.
Kim and Ramos (2018) discover positive stakeholder reactions from CSR programs in the fast food industry, but this paper will expand upon this study by looking at the implementation of a federal law in the United States in December, 2016 which requires all restaurants to display calorie content on their menus. This law provides public awareness as to how healthy the food people are eating is that previously did not exist. If a connection between CSR and CFP exists in the fast food industry, one could posit that a law exposing the calorie content of all restaurant chains may produce abnormal stock returns. CSR, branding, stakeholder relations, and financial performance have all been connected in various studies, and this law provides an indication of how healthy these menus are in reality. Such information could affect customer and
stakeholder perception of these chains and thus financial performance.
This study will use ordinary least square (OLS) regression across three models to establish benchmark normal returns for thirty publicly traded fast food chains. It will then compare these to actual returns to establish abnormal returns. Once these abnormal returns are collected they
will be aggregated to find the cumulative abnormal return (CAR) across all thirty restaurant chains which can then be tested for statistical significance. If a potential connection between CSR and financial performance in the fast food industry exists, the following hypothesis could indicate it:
𝐻0: 𝐶𝐴𝑅 = 0, 𝐻1: 𝐶𝐴𝑅 ≠ 0
3. Methods & Results
3.1.1 Event Study Methods and Data
Various data is required to perform an event study. Daily closing stock prices are collected from Compustat via the Wharton Research Data Services (WRDS). Restaurant chains with missing daily returns are not included. 251 daily returns are used for the estimation window and 26 daily returns for the event window. According to Brown and Warner (1985), longer event windows are better specified when using daily returns for an event study. 251 daily returns are used as it represents one trading year and helps control for potential seasonal factors. A five-day transitory period between the estimation and event window is used to allow volatility to stabilize.
All of the data for the factors in the models is collected from Ken French’s website via WRDS. Using OLS regression with heteroskedastic robust standard errors through the statistical platform Gretl, the betas for the models for each of the thirty restaurant chains are estimated. These models are then used to establish normal returns.
The abnormal returns for the thirty restaurant chains are then aggregated to find daily cumulative abnormal return. These daily cumulative abnormal returns are then tested for statistical significance using a standard two-tailed t-test1. According to Brown and Warner
(1985), using a cross-section of securities in this manner causes the model to converge to normality. Samples of only five securities have proven to be well-specified, and this remains
1 t-statistics are calculated according to the following equation: 𝑡 =𝐶𝐴𝑅 𝜎 , 𝜎 = √
1
true when event days are clustered. De Jong (2007) also points out that using an average measure across multiple corporations helps to phase out movements in stock price caused by unrelated information.
3.2.1 Checking for Potential Problems in the Models
This study employs tests to check for issues involving heteroscedasticity, normality, and autocorrelation. White’s (1980) White test is used to check for heteroscedasticity in the
models; however, heteroskedastic-robust standard errors are used to account for potential problems. A standard chi-square test is used to check normality, and Box and Ljung’s (1978) Ljung-Box test for autocorrelation.
3.2.2 Heteroscedasticity
Heteroscedasticity is present in each of the models; however, it was not large enough to be an issue, and heteroskedastic-robust standard errors are used to address any potential problems. Heteroscedasticity is present in 23.3% of the CAPM regressions; however, this statistic drops to 16.7% for FFM and CFM indicating that these models are more accurate.
3.2.3 Normality
Normality proves to be a problem in all three models. According to De jong (2007) and Brown and Warner (1985), however, this is to be expected. This is especially true when using daily returns due to their volatile nature.
Figure a
Departures from normality are seen in Figure a when the data points deviate from the central line. This paper addresses the issue of non-normality by using 30 separate corporations and creating a cross-section of data.
3.2.4 Autocorrelation
Autocorrelation is present in all three models; however, it is within acceptable limits of natural error. It is interesting to note that autocorrelation increases by 3.4% when switching from CAPM to FFM. Autocorrelation is only present in 10% of cases in CFM.
Figure b
Autocorrelation issues can be seen when any of the vertical lines cross either of the two threshold lines.
3.3.1 Determining the Accuracy of The Models
This study determines the accuracy of the models by considering three factors. First, the mean-adjusted R-squared2 value is calculated to see how much variation in the expected return
each model accounts for. This value gives an indication of which model has the most
explanatory power across all 30 fast food chains. In addition, the median-adjusted R-squared value gives an indication as to the distribution of the Adj. R-Squared values.
Figure c
CAPM FFM CFM
Mean Adj. R Sq. 0.1526 0.1815 0.1918
Median Adj. R Sq. 0.1562 0.1793 0.1842
CFM performs the best in this regard, accounting for 19.18% of the variance of expected returns. FFM performs second best, accounting for 18.15% of the variance of expected returns.
2 Mean-adjusted R-squared values calculated according to the following equation: 𝑀𝑒𝑎𝑛 𝐴𝑑𝑗. 𝑅 𝑆𝑞. = 1
CAPM performs the worst, accounting for 15.25% of the variance of expected returns. The median distribution closely mirrors these results. The outcome suggested by these results is consistent with theory.
The third factor is the rate of significance for the coefficients across all three models and all thirty restaurant chains. 𝛽𝑚𝑘𝑡 was significant in 29 of the regressions for CAPM and FFM and
28 regressions for CFM. 𝛽𝑆𝑀𝐿 is significant most often in FFM with 18 regressions. 𝛽𝐻𝑀𝐿 is
significant most often in CFM with 11 regressions. 𝛽𝑀 is significant in 10 of the CFM regressions.
Figure d CAPM FFM CFM 𝜷𝒎𝒌𝒕 96.7% 96.7% 93.3% 𝜷𝑺𝑴𝑳 N/A 60% 46.7% 𝜷𝑯𝑴𝑳 N/A 30% 36.7% 𝜷𝑴 N/A N/A 33.3% 3.4.1 Results
Significant positive CARs were found in the cross-sectional data panel across all three models during the event window; however, these results are less pronounced for CAPM and FFM. According to CAPM there are significant positive CAR on seven out of the 26 trading days for a prevalence of 26.92%. FFM estimates the rate of significance to be much higher with 17 out of 26 days displaying significant positive CAR for a prevalence of 65.39%. CFM displays the most positive significant CAR with 24 out of 26 trading days for a prevalence of 92.31%.
Figure e
CFM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
0 0.03486 0.011777 2.959983** 0.001689 1 0.0261 0.012341 2.114903* 0.017724 2 0.02588 0.012938 2.00026* 0.023288 3 0.0278 0.013506 2.058412* 0.020304 4 0.03926 0.014064 2.791497** 0.00283 5 0.04501 0.014653 3.0718** 0.001184 6 0.04734 0.015116 3.131736*** 0.000974 21(*) 5% (**) 1% (***) .1% Event date Figure f FFM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
0 0.03251 0.011807 2.753503** 0.003168 1 0.02359 0.012369 1.907138* 0.028834 2 0.02274 0.012946 1.756521* 0.040122 3 0.02446 0.013506 1.811107* 0.035672 4 0.03565 0.014061 2.535457** 0.005925 5 0.04077 0.014618 2.788935** 0.002851 6 0.04353 0.015116 2.879689** 0.002166 21(*) 5% (**) 1% (***) .1% Event date
Figure g
CAPM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
0 0.01822 0.011836385 1.539321 0.0625 1 0.01014 0.012433825 0.815517 0.207779 2 0.01335 0.013015376 1.02571 0.153014 3 0.01722 0.013575714 1.268442 0.102915 4 0.02375 0.014127986 1.681061* 0.047005 5 0.03278 0.014656057 2.236618* 0.0131 6 0.03376 0.015169047 2.225585* 0.013471 21(*) 5% (**) 1% (***) .1% Event date
It is interesting to note that not only does the number of days with significant CAR increase as more explanatory variables are added to the model, but the level of significance does as well. In CAPM, five days display significant CAR at a 5% significance level, and two trading days at a 1% significance level. For FFM, seven trading days are at a 5% significance level and 10 days are at a 1% significance level. CFM has 13 days at a 5% significance level, 10 days at a 1% significance level, and one day at a .1% significance level. These improvements in
significance are indicative of the increase in predictive power of FFM and CFM over CAPM and are in line with theory. Figure h and Figure i show this massive increase in significance. Each time the CAR line crosses over the upper bound (UB) or lower bound (LB) it represents statistically significant CAR at a 5% significance level.
Figure h (top): CAPM, Figure i (Bottom): CFM
The mean CAR also increased across the three models. According to CAPM, mean CAR is 1.96%. When FFM is used, this amount increases to 3.06%. This increase in mean CAR occurs with CFM as well, although less pronounced, with mean CAR rising to 3.37%. This difference is likely due to CAPM’s lack of explanatory power. FFM and CFM’s addition of extra explanatory variables increases the models’ explanatory power and thus gives more accurate results. The data gleaned from the results suggests that these companies experienced significant positive abnormal returns as a result of the implementation of the federal law mandating the display of calorie content on menus, and this has several implications.
CAR CAR UB UB LB LB 0 X: time (days) Y: CAR
4. Discussion & Concluding Remarks
4.1.1 Summary
CSR is still a developing field and there are many different definitions as to what it is. Marrewijk (2003) and Kim and Ramos (2018) describe a spectrum of CSR approaches ranging on perceived levels of corporate responsibility. Measuring CSR is difficult because it has many facets, and according to Bénabou and Tirole (2010), the issue of endogeneity is the larger hurdle; however, Aupperle and Wolfe (1991) still push for the development of CSR measurement techniques as a way to overcome these issues.
Multiple studies look at CSR in the corporate world. Moosa et al. (2014), Hamilton (1995), White (1995), and Klassen and McLaughlin (1996) all look at CSR in terms of
environmental friendliness. Other studies like Pozo and Schroeder (2016) and Almanza et al. (2013) look at CSR in the food processing industry. All of these studies also seek to establish a connection between CSR and CFP through use of an event study. Chao et al. (2014) looks at 84 CSR studies from 2002-2011 and finds that the results are inconclusive. Studies either show a positive relationship or none at all. According to Keller and Lehmann (2003) and Kim and Ramos (2018), however, potential positive relationships between CSR and CFP happen due to
improved branding and stakeholder relations that result from strong CSR programs. According to Banjeree et al. (2013) CSR outcomes depend on the industry being considered. This paper attempts to contribute to CSR literature by observing whether a connection between CSR and CFP exists in the fast food industry and looks at whether or not how healthy a fast food chain’s menu is acts as an effective proxy for CSR. This paper will attempt to answer these questions through the use of an event study with benchmark returns established according to CAPM, FFM, and CFM. These benchmark returns will then be used to calculate abnormal returns around the event window and CAR will be calculated across 30 fast food chains and tested for significance according to a standard t-test.
4.1.2 Implications of the Results
The results of this study not only appear to support a relationship between CSR and CSP in the fast food industry but also suggests that how healthy a menu is could act as a potential measurement indication of CSR in the fast food industry. The significant positive CARs seen across the three models suggests that companies were preparing their menus to be healthier in
preparation for the implementation of the law. The fast food chains knew the law was coming, and according to Rosenbloom (2010), they even supported it as a way to bring a unified
national standard to food labeling. A previous study by Bleich et al. (2015) shows similar preparative behavior by restaurants. They find that fast food chains that were willingly displaying calories on their menu before the implementation of the December 2016 law had 136-139 less calories on average than companies that did not from 2012-2014. This
improvement of the health content of their menus and thus CSR appears to have improved CFP. Improved branding and stakeholder relations are both potential avenues through which CSR contributed to improved CFP. Restaurants supported this law and seem to be proactively moving towards the trend of increased CSR.
These results also have potential implications for how managers in the fast food industry should approach CSR. Ragas and Roberts (2009), Kim and Ramos (2018), and Adams (2005) all explore the benefits of CSR in the fast food industry and this analysis reveals avenues such as improved branding and stakeholder relations through which CSR can improve CFP. The results of this study appear to support this theory and demonstrates the importance of strong CSR programs in the fast food industry. Both laws and social trends are calling for increased responsibility for the products that these fast food chains are providing, and managers must adapt to these changes.
4.1.3 Limitations & Further Research
Some issues regarding data availability and methodology could call the results of this study into question. An initial issue is regarding the sample size. N=30 is the minimum
suggested sample size for a study like this; however, the potential sample pool is small as many fast food chains are privately owned. An additional study could potentially look at the food & beverage industry as a whole to have a larger sample size; however, the implications of such a study would be different as it would concern companies from broader industries such as food processing, alcohol, and beverage. These additions could introduce several more factors to consider as these industries face different laws and regulations.
Endogeneity Is another potential serious problem in this study. Bénabou and Tirole (2010) and Crane et al. (2017) explain that endogeneity plagues CSR-CFP research and occurs when correlation between the decision to implement CSR initiatives and the error term exist.
Unaccounted for factors which effect both the decision to implement CSR and the subsequent performance are thought to be the cause of this endogeneity. Omitted factors, measurement error, and reverse causalities are all additional potential causes of endogeneity and any of these issues can lead to biased results and false conclusions regarding significance. Further research into different factors that measure CSR and additional control variables could help alleviate this issue.
The choice of event window may also cause issues in this study. Bleich et al. (2015) mentions that some restaurants chose to begin displaying calories before the December, 2016 law was implemented. An attempt is made to exclude these companies from the study;
however, an exhaustive list regarding which fast food chains started displaying calories and when is not available. An additional study that could collect this information from different companies could then do additional event studies. This would help avoids bias introduced by the clustering of events in a study as well. This paper looks at only one event, and this puts the study at greater risk of bias. Using multiple events, like using a cross-panel of companies, helps phase out the effects of unrelated events that may be affecting returns.
This paper finds evidence of a potential link between CSR and CFP in the fast food industry; furthermore, it demonstrates that the health content of menus is a potentially good indicator of company CSR. This indicator, however, is only one of many potential measurement tools for capturing CSR. More research is needed to explore additional measurement factors and to see what constitutes CSR in the fast food industry.
Works Cited
Adams, R. (2005). Fast Food, Obesity, and Tort Reform: An Examination of Industry Responsibility for Public Health. Business and Society Review, 297-320.
Almanza, B., Behnke, C., Jang, S. S., Miao, L., & Seo, S. (2013). The impact of food safety events on the value of food-related firms: An event study approach. International Journal of
Hospitality Management, 153-165.
Aupperle, K., & Wolfe, R. (1991). Introduction to Corporate Social Performance: Methods for Evaluating an Elusive Construct. In D. o. College of Business Administration, Research in
Corporate Social Performance and Policy (pp. 265-268). Norman: University of
Oklahoma.
Banerjee, S. B., Iyer, E. S., & Kashyap, R. K. (2003). Corporate Environmentalism: Antecedents and Influence of Industry Type. Journal of Marketing, 106-122.
Barnett, M. L., & Salomon, R. M. (2012). Does it Pay to be Really Good? Addressinf the Shape of the Relationship Between Social and Financial Performance. Strat. Mgmt. J., 1304-1320. Bénabou, R., & Tirole, J. (2010). Individual and corporate social responsibility. Economica, 1-19. Bhattacharya, C., Korschun, D., & Sen, S. (2009). Strengthening Stakeholder–Company
Relationships Through Mutually Beneficial Corporate Social Responsibility Initiatives.
Journal of business Ethics, 257-272.
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of
Political Economy, 637-654.
Bleich, S. N., Block, J. P., Jarlenski, M. P., & Wolfson, J. A. (2015). Restaurants With Calories Displayed On Menus Had Lower Calorie Counts Compared To Restaurants Without Such Labels. Health Affairs.
Box, G. E., & Ljung, G. M. (1978). On a Measure of a Lack of Fit in Time Series Models.
Biometrika, 297-303.
Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal
Cade, J. E., Clarke, Edwards, K. L., Fraser, L. K., & P., G. (2012). Fast food and obesity: a spatial analysis in a large United Kingdom population of children aged 13–15. American Journal
of Preventative Medicine, e77-e85.
Capelle-Blancard, G., & Petit, A. (2017). The Weighting of CSR Dimensions: One Size Does Not Fit All. Business & Society, 919-943.
Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. Journal of Finance, 57-82. Chau, K., Lu, W., Pan, W., & Wang, H. (2014). A Decade's Debate on the Nexus Between
Corporate Social and Corporate Financial Performance: a Critical Review of Empirical Studies 2002-2011. Journal Of Cleaner Production, 195-206.
Chung, H. S., Hao, F., Meng, W. W., & Ting, H. C. (2016). To Engage or Not to Engage in Corporate Social Responsibility: Empirical Evidence From Global Banking Sector.
Economic Modeling, 207-225.
Clark, C. E. (2000). Differences between public relations and corporate social responsibility: an analysis. Public Relations Review, 363-380.
Corporate Social Responsibility (CSR). (2018, June 7). Retrieved from European Commission:
http://ec.europa.eu/growth/industry/corporate-social-responsibility_en
Crane, A., Henriques, I., Husted, B. W., & Matten, D. (2017). Measuring Corporate Social Responsibility and Impact: Enhancing Quantitative Research Design and Methods in Business and Society Research. Business & Society, 787-795.
de Jong, F. (2007). Event Studies Methodology. Tilburg University.
Donaldson, T., & Preston, L. (1995). The Stakeholder Theory of the Corporation: Concepts, Evidence, and Implications. The Academy of Management Review, 65.
Fama, E. F., & French, K. R. (1996). Multifactor Explanations of Asset Pricing Anomalies. Journal
of Finance, 55-84.
Ferrell, O. C., & Maignan, I. (2001). Corporate citizenship as a marketing instrument - Concepts, evidence and research directions. European Journal of Marketing, 457-484.
Freeman, R. E. (1984). Strategic management : a stakeholder approach. Boston: Pitman Cop. Friedman, M. (1962). Capitalism and Freedom. Chicago: University of Chicago Press.
Friedmn, M., & Selden, R. T. (1982). Capitalism and Freedom. Chicago: University of Chicago Press.
Governance & Accountability Institute, I. (2018). Flash Report: 85% of S&P 500 Index Companies
Publish Sustainability Reports in 2017. Governance & Accountability Institute, Inc.
Hamilton, B. H., & Nickerson, J. A. (2003). Correcting for Endogeneity in Strategic Management Research. Strategic Organization, 51-78.
Hamilton, J. T. (1995). Pollution as News: Media and Stock Market Reactions to the Toxics Release Inventory Data. Journal of Environmental Economics and Management, 98-113. Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications
for Stock Market Efficiency. Journal of Finance, 65-91.
Keller, K., & Lehmann, D. R. (2003). How do brands create value? Marketing Management, 26(6).
Kim, Y., & Zapata Ramos, M. L. (2018). Stakeholder responses toward fast food chains’ CSR: Public health-related vs generic social issue-related CSR initiatives. Corporate
Communications: An International Journal, 117-138.
Klassen, R. D., & Mclaughlin, C. P. (1996). The Impact of Environmental Management on Firm Performance. Management Science, 1199-1214.
Liang, H., & Renneboog, L. (2017). On the Foundations of Corporate Social Responsibility.
Journal of Finance, 853-910.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 13-37.
Moosa, I., Pichelli, J., & Ramiah, V. (2014). Environmental regulation, the Obama effect and the stock market: some empirical results. Applied Economics, 1-14.
Pozo, V. F., & Schroeder, T. C. (2016). Evaluating the costs of meat and poultry recalls to food firms using stock returns. Food Policy, 66-77.
Ragas, M. W., & Roberts, M. S. (2009). Communicating Corporate Social Responsibility and Brand Sincerity: A Case Study of Chipotle Mexican Grill's ‘Food with Integrity’ Program.
Rosenbloom, S. (2010, March 23). Calorie Data to Be Posted at Most Chains. Retrieved from The New York Times: https://www.nytimes.com/2010/03/24/business/24menu.html
Sharpe, W. F. (1964). Capital Asset Prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 425-442.
The Honor Roll. (2018). Retrieved from Governance & Accountability Institute, Inc.:
https://www.ga-institute.com/about-the-institute/the-honor-roll.html
van Marrewijk, M. (2003). Concepts and Definitions of CSR and Corporate Sustainability: Between Agency and Communion. Journal of Business Ethics, 95-105.
Webster, P. C. (2011). UN Summit Urges More Accountability Within Food and Beverage Industry. Canadian Medical Association Journal.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica: Journal of the Econometric Society, 817-838. White, M. (1995). Does it pay to be green? Corporate environmental responsibility and
Appendix A
Standard MethodologyEvent Window
The event window is the period of time chosen to analyze for abnormal returns. De Jong (2007) stresses that picking the event window is perhaps one of the most important aspects of an event study. If the wrong one is chosen, the true effect of an event will not be captured and false conclusions regarding potential significance can be drawn. This study uses daily returns from December 2016 as an event window.
Figure A1
Calculations
The following equation is used to determine the daily return from closing stock prices: 𝑅𝑖,𝑡+1 =𝑃𝑖,𝑡+1− 𝑃𝑖,𝑡
𝑃𝑖,𝑡
The following equation is used to determine abnormal returns (𝐴𝑅𝑖,𝑡) by comparing actual
return (𝑅𝑖,𝑡) to benchmark return (𝑁𝑅𝑖,𝑡):
𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡− 𝑁𝑅𝑖,𝑡
The following equation is used to determine cumulative abnormal returns (𝐶𝐴𝑅𝑡):
𝐶𝐴𝑅𝑡= ∑ 𝐴𝑅𝒊,𝒕 𝑁 𝑖=1 Event (t=0) Event Window Estimation Window
Appendix B
ResultsFigure B1
CFM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
-5 0.02226 0.00818 2.721117** 0.003486 -4 0.02592 0.008957 2.893787** 0.002075 -3 0.01721 0.009666 1.780482* 0.038118 -2 0.01771 0.010344 1.71209* 0.044073 -1 0.01959 0.01105 1.772869* 0.038746 0 0.03486 0.011777 2.959983** 0.001689 1 0.0261 0.012341 2.114903* 0.017724 2 0.02588 0.012938 2.00026* 0.023288 3 0.0278 0.013506 2.058412* 0.020304 4 0.03926 0.014064 2.791497** 0.00283 5 0.04501 0.014653 3.0718** 0.001184 6 0.04734 0.015116 3.131736*** 0.000974 7 0.04523 0.01555 2.908697** 0.001981 8 0.04218 0.015991 2.637796** 0.004439 9 0.04261 0.016407 2.597012** 0.004986 10 0.036 0.016891 2.131334* 0.017029 11 0.04802 0.017277 2.779393** 0.002934 12 0.04223 0.017723 2.382797** 0.008972 13 0.0383 0.018213 2.102936* 0.018246 14 0.03751 0.018639 2.012483* 0.022632 15 0.03465 0.019053 1.818653* 0.035093 16 0.03251 0.019419 1.674128* 0.047691 17 0.03383 0.019837 1.705413* 0.044692 18 0.03262 0.020211 1.613942 0.053914 19 0.03402 0.020584 1.652742* 0.049832 20 0.02653 0.020962 1.265631 0.103424 21(*) 5% (**) 1% (***) .1% Event date
Figure B2
FFM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
-5 0.0204 0.00819 2.490956** 0.0067 -4 0.02409 0.008977 2.683467** 0.00389 -3 0.0154 0.009695 1.588389 0.056741 -2 0.01588 0.010383 1.529471 0.063716 -1 0.01748 0.011082 1.577401 0.057994 0 0.03251 0.011807 2.753503** 0.003168 1 0.02359 0.012369 1.907138* 0.028834 2 0.02274 0.012946 1.756521* 0.040122 3 0.02446 0.013506 1.811107* 0.035672 4 0.03565 0.014061 2.535457** 0.005925 5 0.04077 0.014618 2.788935** 0.002851 6 0.04353 0.015116 2.879689** 0.002166 7 0.04187 0.015576 2.688175** 0.003838 8 0.03915 0.016037 2.44116** 0.007673 9 0.03996 0.016474 2.425608** 0.008002 10 0.03278 0.016938 1.935279* 0.027051 11 0.0455 0.017355 2.621706** 0.004647 12 0.03939 0.017793 2.21376* 0.013882 13 0.03457 0.018254 1.893856* 0.029709 14 0.03371 0.018682 1.804455* 0.036192 15 0.03098 0.019105 1.621567 0.053088 16 0.02901 0.019483 1.488965 0.068889 17 0.02994 0.01989 1.505302 0.066765 18 0.02906 0.020283 1.432727 0.076603 19 0.03048 0.020657 1.475549 0.070672 20 0.02323 0.021048 1.103691 0.135403 (*) 5% (**) 1% (***) .1% Event date
Figure B3
CAPM
t CAR 𝝈𝑪𝑨𝑹 T-statistic P-value
-5 0.02406 0.008295782 2.900269** 0.002032 -4 0.02762 0.009107689 3.032602** 0.001341 -3 0.01564 0.009847842 1.588165 0.056762 -2 0.01457 0.010549882 1.381058 0.084252 -1 0.01007 0.011207141 0.898534 0.184886 0 0.01822 0.011836385 1.539321 0.0625 1 0.01014 0.012433825 0.815517 0.207779 2 0.01335 0.013015376 1.02571 0.153014 3 0.01722 0.013575714 1.268442 0.102915 4 0.02375 0.014127986 1.681061* 0.047005 5 0.03278 0.014656057 2.236618* 0.0131 6 0.03376 0.015169047 2.225585* 0.013471 7 0.02812 0.015658863 1.795788* 0.036873 8 0.02323 0.016149303 1.438452 0.075783 9 0.02345 0.016607227 1.412036 0.079596 10 0.01714 0.017076299 1.00373 0.158243 11 0.03103 0.017524269 1.770687* 0.038921 12 0.02602 0.017972201 1.447792 0.074469 13 0.02158 0.018414668 1.171892 0.121183 14 0.01865 0.01883879 0.989979 0.161575 15 0.01215 0.019258764 0.630882 0.264349 16 0.01364 0.019674857 0.693271 0.244394 17 0.01511 0.020089798 0.752123 0.226345 18 0.01331 0.02048414 0.649771 0.258221 19 0.01595 0.020883007 0.763779 0.222863 20 0.008096 0.021274398 0.380551 0.351931 (*) 5% (**) 1% (***) .1% Event date
Appendix c
DataList of fast-food chains Bob Evans Farms Brinker International Inc. Flanigans Enterprises Inc.
Lubys Inc. McDonald’s Corp. Ruby Tuesday Inc. Nathan’s Famous Inc.
Jack In The Box Inc. Noodles & CO Potbelly Corp. Denny’s Corp El Pollo Holdings Inc. Habit Restaurants Inc.
Shake Shack Inc. Sonic Corp. Wingstop Inc. Panera Bread Co
Starbucks Corp. BJ’s Restaurants Inc. Famous Daves Of America Inc.
Yum Brands Inc Red Robin Gourmet Burgers
Buffalo Wild Wing Inc. Domino’s Pizza Inc. Texas Roadhouse Inc. Restaurant Brands Intl. Inc.
Chipotle Mexican Grill Inc. Bloomin’ Brands Inc. Dunkin’ Brands Group Inc.
Fogo De Chao Inc.
Reliability
All data is collected from WRDS in the time range of 2015-2016. The data is taken from a reliable database and any companies with missing data are removed from consideration.
