Marketing research Case Study Example | Topics and Well Written Essays - 2750 words

Marketing research - Case Study Example However, Alok (2009) is convinced that female shoppers are more likely to remain loyal to shops they have signed loyalty programs with. Nazia (2011) delved into the effect of income levels on shopping habits, supporting notions spread by Peter, Borle and Kadane (2003) that shoppers with higher income tend to be loyal to more shops than those with lower income. Yuping, Williams and Tam (2010) refuted this claim, noting that every individual signs up with a program based on how much they need the products offered by a store. Older buyers are shown in Rose (2013) as being less likely to make large purchases, due to their partial inability to earn at the same pace as the energetic younger cohorts. Seyhmus (2002), however, had a differing opinion, preceding Rose’s article with the assertion that age does not actually affect ability to shop (size of purchase) since there are many wealthy older persons. Based on these contradicting notes, this research poses the questions: The current research is based on a model depicting the consumer as more being more loyal based on their membership to loyalty programs. Therefore, the response (percentage of clothing budget spent at the store’s clothing category and amount of money spent at the store) are affected by the age, income, gender and membership to loyalty for the participants. The hypotheses developed in response to the research questions are: H4: There is no significant difference between amount spent at the clothing category and percentage of clothing budget spent on clothing at the store for participants signed to the loyalty program and those not signed. The sample comprised 202 participants who were all shoppers at the selected clothing store. 122(60.4%) were male while 80 (39.6%) were female shoppers. There were no shoppers below the age of 18 years. However, 184 (91.1%) were adults aged between 19 and 50 years while 18 (8.9%) were older persons aged above

What are the main issues which an organisation must consider when Essay

What are the main issues which an organisation must consider when implementing strategic change - Essay Example me main issues which organizations must consider in the strategic implementation process are focusing on the redesigning and restructuring of the organization. Another significant issue may be caused by the ambiguous flow of communication and the manner in which information is handled. Measuring competencies and planning resources is another challenge. Most firms have resorted to utilizing shared resources and competencies. This is especially true for mergers and acquisitions or even in situations when two departments or divisions may be merged. One more critical problem in the implementation stage may be created due to the inability to translate the aims developed in the strategic change stage. In order to take care of these issues, a maturity model has been developed which is associated with sustainability and have diverse elements of evaluation and resourcing, results, managing capability and capacity, strategy and policy and managing programs. The change management maturity matri x in this case defines various stages of strategic change implementation by classifying them as immature, early maturity period, defined, managed and optimization. The last state is the one which a firm needs to attain when it has optimum allocation of resources and garners optimum levels of revenue by implementing strategic change. Another core issue in handling strategic change is lack of effective leadership in the organization. It is important to have an effective leader who is able to deftly handle change management strategy and drive motivation and employee engagement. Organizations also face issues in strategic implementation while managing the employees. Major hurdles are faced because strategic change efforts are resisted by personnel. The role of non financial and financial...Scholars have focused on certain elements encompassing strategy. These are as follows: a) Depending on uncertain market conditions, strategies are revised and reformulated. b) Strategies demonstrate t he manner in which resources may be utilized to fulfill the desires of entrepreneurs c) They provide a direction to in developing the organization d) Strategies are aimed to make and develop long term prospects of success by gaining competitive edge over rival firms (Markiewicz, 2011). Implementation of strategic change is a very challenging task. Successful strategic change implementation leads to business success whereas failure in implementing strategic change may lead to catastrophic outcomes which may sometimes wipe out the business enterprise. Critical strategic change process happens when supervisors use symbolic resources and discourses in order to obliterate existing systems in meanings and find out new ones to try to set a direction to the strategy formulated and implemented (Buchanan & Dawson, 2007). In fact, a survey conducted in the year 2009, comprising of 190 staff and line managers confessed that strategic change implementation is critically significant in the strate gic implementation process. There are several hindrances in the strategic change implementation processes. Sometimes time management in formulating and implementing the new strategy may also lead to failure in the strategic change process.

Private International Law (Conflicts of Law) Problem Question Essay

Private International Law (Conflicts of Law) Problem Question - Essay Example It could be argued that because Chris has an office in London, the English jurisdiction applies in this case. This is because he is a domicile member. He rents the office not on temporary but permanent basis. In this case, English jurisdiction applies because Companies Act 2006 states that England can sue overseas companies if one of the parties in the case has offices located in England.1 The traditional rules govern foreign enterprises in this case. What is more, they are derived from Article 5(5). Companies Act 2006 also applies in this case; because Bratwurst GmbH and Havana entered into a contract, and was to sell some of their products in England; the England laws, therefore, govern the whole enterprise as well.2 The foreign company is this case has set shop in England as well, therefore the case can be determined by the English jurisdiction. Havana has the option of depending on the Companies Act. They have a right to do business in any place within the English Jurisdiction. Whether the company is legally registered in England or not, English courts have jurisdiction because Chris is involved and he has offices in London, England. An important law, CPR 6 states that whether a business takes a few days or the England is a market for the commodities produced; the English jurisdiction shall apply.3 In this case, the complainant, Havana, has lenient choices. The fact that in the original agreement the Spanish jurisdiction would apply is overridden. Naturally, English jurisdiction would have failed to apply because the complainant is not from England. However, he is allowed by law to request for an English jurisdiction. Despite the fact that both parties agreed to have a Spanish jurisdiction, the English jurisdiction still applies because the agreement was just mutual (oral). There was no written and legally binding agreement. Forum nonconvenience allows the claimant (Havana) the right to choose where he thinks it is convenient to have

Case study Essay Example | Topics and Well Written Essays - 2500 words - 11

Case study - Essay Example The fact that there is a personal relationship with a friend’s son affects the underlying professional relationship in connection to probationer. This is a major contributor to a state of conflict of interest. Changing of probation officers is in the best interest for each of the parties as they could face accusations of conflicts of interest and could be placed on leave or fined. In the case of the probationer, he could face accusations of bargaining with me, the officer, as there is proof of a mutual interest. All positive aspects occurring between the probationer and the officer could be diluted as a result of such conflict of interest. Future research should accurately conceptualise role conflict and not simply infer that officers experience role conflict on the basis of community correctional officers having differing role preferences (LearningExpress. 2007). One strategy that might be useful is the development of an occupationally specific questionnaire focusing on the t ensions between the welfare and enforcement aspects of the officers’ role. Such a questionnaire should differentiate between internal or personally based conflicts (intra role conflict) and external or organisationally based conflicts (inter-role conflict) (Mendicino, 2010). This approach appears valid in the present study, as both forms of conflict were related to emotional exhaustion. Such an approach would enable a more sophisticated understanding of the tensions faced by community correctional officers and clarification of the issues surrounding whether community correctional officers experience role conflict. If there is evidence of discriminatory against probationers in favour of a friend’s son, it is not appropriate to continue working on the case. There are other legal repercussions emerging against the probation agency. Probation authorities must adhere to lawful procedures, but must not adhere to any provisions with formal requirements of

Architecture and Environment Assignment Example | Topics and Well Written Essays - 250 words

Architecture and Environment - Assignment Example The architects scrutinize whether the site is legible for reducing waste hence minimizing the impact on the local ecology and the environment (Brebbia, & Broadbent, 2006). It also relates to the environment because architectural designs try to achieve thermal comfort for the people who will occupy the construction. They design buildings in a way that they control the internal environment factors such as air, temperature and humidity (Kembel et.al 2012: Smith, 2011). Architecture also relates to the environment in terms of study of behavior. They study the beliefs, behavior and attitudes of people regarding the environment (Carmona, & Tiesdell, 2007). They also evaluate the effectiveness of the environment in order to ensure that the process of construction meets the specific objectives (Marquardt, Bueter, & Motzek, 2014). They take a consideration of the human environment and behavioral systems such as planning and policies aimed at controlling the environment. Architecture is also interested in the study of the interrelation between human beings and their man-made and natural environment and the relation towards the field of environmental design (Mallory-Hill, Preiser, & Watson, 2012). However, a contrast exists between the two disciplines. While architecture deals with the management of any action that relays to the design and use of space and land, the environment, by contrast, is concerned with the management of the natural and built enviro nment (Thomas, 2002: Baker, & Steemers, 2000).   Kembel, S. W., Jones, E., Kline, J., Northcutt, D., Stenson, J., Womack, A. M., & Green, J. L. (2012). Architectural design influences the diversity and structure of the built environment microbiome.  The ISME Journal,  6(8), 1469-1479. Marquardt, G., Bueter, K., & Motzek, T. (2014). Impact of the design of the built environment on people with dementia: An evidence-based

The Organizational Ethics of Lockheed Martin Essay Example for Free

The Organizational Ethics of Lockheed Martin Essay Lockheed Martins Vision statement reads as follows; (Who We Are) be the global leader in supporting our customers to strengthen global security, deliver citizen services and advance scientific discovery. Lets break this statement down into separate components. First, supporting our customers to strengthen global security, is a phrase that is limited to a customer base but also inclusive to the entire globe. The ethical question with this part of their mission statement is; at what point does an organization like Lockheed impose their own ethical limits over their customers? This is a prime example of external factors influencing the companys ethical standards. Lockheed can find themselves at the mercy of their customers request and desires. This can lead to taking on a project that they may not be ethically inline with. They are in essence giving their customer complete control over what they believe is ethical in global security. This can obviously turn into a bias view of whats good for the world, when left up to a specific group of customers. The next part of the statement then puts the ethical decisions back into the hands of Lockheed, deliver citizen services. With this sentiment Lockheed gets to choose the services within a society that it feels will serve that citizens the best. Finally and maybe the most ethically sensitive area of Lockheeds mission, is advance scientific discovery. Science, especially in the area of discovery, can lead to many ethical questions depending on the nature of the research. The biggest questions arise when animal or human testing is involved. To specifically address their ethical philosophy Lockheed released this statement; ( ) We are committed to the highest standards of ethical  conduct in all that we do. We believe that honesty and integrity engender trust, which is the cornerstone of our business. We abide by the laws of the United States and other countries in which we do business, we strive to be good citizens and we take responsibility for our actions. The reality is Lockheed is in the business of making some of the worlds most advanced and destructive military weapon systems. Even though their products are used in combat to kill the enemy, there are still laws and rules governing the how destructive and lethal these weapons can be. Lockheed does make sure to operate within the guidelines set forth by Geneva Convention Treaties and Rules of Armed Conflict. These guidelines are examples of legal factors that give Lockheed guidance of where the ethical lines should be drawn. Lockheed addresses the fact that they not only have their own ethical standards to adhere to but that they are also a direct reflection of their suppliers. (Who We Are ), We want our suppliers to understand, foster, and mirror the ethical conduct we expect from our employees in all business challenges and transactions. This places them in the unique situation, not only to monitor their practices but also those of the organizations providing the materials they need to do business. Lockheed Martin expects their contractors to behave in a manner consistent with the principles of their code of ethics. One key element in regards to their suppliers is that Lockheed requires their suppliers to have a set and standardized Code of Ethics Programs within each organization. Lockheed also holds their employees to the same standards as their suppliers. In Lockheeds 2012 Employee Perspectives Survey, employees stated that they were more apt to report unethical behavior activity, while the percent of misconduct reported was at an all time low. Leo S. Mackay Jr., vice president of Ethics and Sustainability says, (FIve Lessons) â€Å"Even if you didn’t know anything about our Code of Ethics and Business Conduct, if you followed the value statements Do what’s right, Respect others, and Perform with excellence you could come pretty close to how we would want you to act in any situation that involved an ethical judgment.† One thing that makes Lockheeds ethic program work is the ethics officers, at the business level, are embedded directly with the employees. These ethics officers  attend business and planning meetings. This ensures that the officers understand the businesses they support, and by participating in the field they are able to bridge the gap between business and ethics. When employees have to deal with ethical issues, they have specific avenues to reach out to including; talking to their ethics officer, calling the Corporate Ethics HelpLine, or sending an email directly to the Lockheed Ethics Department. It is important to note that the aforementioned ethics officers are elected officials from within the company. Lockheed Martin must report certain types of misconduct to the government. This further signifies the importance the responsibility of all employees to report any ethical issues. Every Lockheed employee, even executives, must participate in ethics training once a year. References. Who We Are Ethics. (n.d.). Ethics  · Lockheed Martin. Retrieved July 28, 2014, from http://www.lockheedmartin.com/us/who-we-are/ethics.html Five Lessons for a Successful STEM Career. (n.d.). Polishing Our Ethics Performance  · Lockheed Martin. Retrieved July 28, 2014, from http://www.lockheedmartin.com/us/who-we-are/ethics/culture-ethics.html

Obesity and Fast Food Essay Example for Free

Obesity and Fast Food Essay January 2009 Abstract. We investigate the health consequences of changes in the supply of fast food using the exact geographical location of fast food restaurants. Specifically, we ask how the supply of fast food affects the obesity rates of 3 million school children and the weight gain of over 1 million pregnant women. We find that among 9th grade children, a fast food restaurant within a tenth of a mile of a school is associated with at least a 5. 2 percent increase in obesity rates. There is no discernable effect at . 25 miles and at . 5 miles. Among pregnant women, models with mother fixed effects indicate that a fast food restaurant within a half mile of her residence results in a 2. 5 percent increase in the probability of gaining over 20 kilos. The effect is larger, but less precisely estimated at . 1 miles. In contrast, the presence of non-fast food restaurants is uncorrelated with obesity and weight gain. Moreover, proximity to future fast food restaurants is uncorrelated with current obesity and weight gain, conditional on current proximity to fast food. The implied effects of fast-food on caloric intake are at least one order of magnitude smaller for mothers, which suggests that they are less constrained by travel costs than school children. Our results imply that policies restricting access to fast food near schools could have significant effects on obesity among school children, but similar policies restricting the availability of fast food in residential areas are unlikely to have large effects on adults. The authors thank John Cawley and participants in seminars at the NBER Summer Institute, the 2009 AEA Meetings, the ASSA 2009 Meetings, the Federal Reserve Banks of New York and Chicago, The New School, the Tinbergen Institute, the Rady School at UCSD, and Williams College for helpful comments. We thank Cecilia Machado, Emilia Simeonova, Johannes Schmeider, and Joshua Goodman for excellent research assistance. We thank Glenn Copeland of the Michigan Dept. of Community Health, Katherine Hempstead and Matthew Weinberg of the New Jersey Department of Health and Senior Services, Craig Edelman of the Pennsylvania Dept. of Health, Rachelle Moore of the Texas Dept. of State Health Services, and Gary Sammet and Joseph Shiveley of the Florida Department of Health for their help in accessing the data. The authors are solely responsible for the use that has been made of the data and for the contents of this article. 1 1. Introduction The prevalence of obesity and obesity related diseases has increased rapidly in the U. S. since the mid 1970s. At the same time, the number of fast food restaurants more than doubled over the same time period, while the number of other restaurants grew at a much slower pace according to the Census of Retail Trade (Chou, Grossman, and Saffer, 2004). In the public debate over obesity it is often assumed that the widespread availability of fast food restaurants is an important determinant of the dramatic increases in obesity rates. Policy makers in several cities have responded by restricting the availability or content of fast food, or by requiring posting of the caloric content of the meals (Mcbride, 2008; Mair et al. 2005). But the evidence linking fast food and obesity is not strong. Much of it is based on correlational studies in small data sets. In this paper we seek to identify the causal effect of increases in the supply of fast food restaurants on obesity rates. Specifically, using a detailed dataset on the exact geographical location restaurant establishments, we ask how proximity to fast food affects the obesity rates of 3 million school children and the weight gain of over 1 million pregnant women. For school children, we observe obesity rates for 9th graders in California over several years, and we are therefore able to estimate cross-sectional as well fixed effects models that control for characteristics of schools and neighborhoods. For mothers, we employ the information on weight gain during pregnancy reported in the Vital Statistics data for Michigan, New Jersey, and Texas covering fifteen years. 1 We focus on women who have at least two children so that we can follow a given woman across two pregnancies and estimate models that include mother fixed effects. The design employed in this study allows for a more precise identification of the effect of fast-food on obesity compared to the previous literature (summarized in Section 2). First, we observe information on weight for millions of individuals compared to at most tens of thousand in the standard data sets with weight information such as the NHANES and the BRFSS. This substantially increases the power of our estimates. Second, we exploit very detailed geographical location information, including distances The Vital Statistics data reports only the weight gain and not the weight at the beginning (or end) of the pregnancy. One advantage of focusing on a longitudinal measure of weight gain instead of a measure of weight in levels is that only the recent exposure to fast-food should matter. 1 2 of only one tenth of a mile. By comparing groups of individuals who are at only slightly different distances to a restaurant, we can arguably diminish the impact of unobservable differences in characteristics between the two groups. Third, we have a more precise idea of the timing of exposure than many previous studies: The 9th graders are exposed to fast food near their new school from September until the time of a spring fitness test, while weight gain during pregnancy pertains to the 9 months of pregnancy. While it is clear that fast food is generally unhealthy, it is not obvious a priori that changes in the availability of fast food should be expected to have an impact on health. On the one hand, it is possible that proximity to a fast food restaurant simply leads local consumers to substitute away from unhealthy food prepared at home or consumed in existing restaurants, without significant changes in the overall amount of unhealthy food consumed. On the other hand, proximity to a fast food restaurant could lower the monetary and non-monetary costs of accessing unhealthy food. In addition, proximity to fast food may increase consumption of unhealthy food even in the absence of any decrease in cost if individuals have self-control problems. Ultimately, the effect of changes in the supply of fast food on obesity is an empirical question. We find that among 9th grade children, the presence of a fast-food restaurant within a tenth of a mile of a school is associated with an increase of about 1. 7 percentage points in the fraction of students in a class who are obese relative to the presence at. 25 miles. This effect amounts to a 5. 2 percent increase in the incidence of obesity. Since grade 9 is the first year of high school and the fitness tests take place in the Spring, the period of fast-food exposure is approximately 30 weeks, implying an increased caloric intake of 30 to 100 calories per school-day. The effect is larger in models that include school fixed effects. Consistent with highly non–linear transportation costs, we find no discernable effect at . 25 miles and at . 5 miles. The effect is largest for Hispanic students and female students. Among pregnant women, we find that a fast food restaurant within a half mile of a residence results in 0. 19 percentage points higher probability of gaining over 20kg. This amounts to a 2. 5 percent increase in the probability of gaining over 20 kilos. The effect is larger at . 1 miles, but in contrast to the results for 9th graders, it is still discernable at . 25 miles and at . 5 miles. The increase in weight implies an increased caloric intake of 1 to 4 3 calories per day in the pregnancy period. The effect varies across races and educational levels. It is largest for African American mothers and for mothers with a high school education or less. It is zero for mothers with a college degree or an associate’s degree. Overall, our findings suggest that increases in the supply of fast food restaurants have a significant effect on obesity, at least in some groups. However, it is in principle possible that our estimates reflect unmeasured shifts in the demand for fast food. Fast food chains are likely to open new restaurants where they expect demand to be strong, and higher demand for unhealthy food is almost certainly correlated with higher risk of obesity. The presence of unobserved determinants of obesity that may be correlated with increases in the number of fast food restaurants would lead us to overestimate the role of fast food restaurants. We can not entirely rule out this possibility. However, three pieces of evidence lend some credibility to our interpretation. First, we find that observable characteristics of the schools are not associated with changes in the availability of a fast food in the immediate vicinity of a school. Furthermore, we show that within the geographical area under consideration, fast food restaurants are uniformly distributed over space. Specifically, fast food restaurants are equally likely to be located within . 1, . 25, and . 5 miles of a school. We also find that after conditioning on mother fixed effects, the observable characteristics of mothers that predict high weight gain are negatively (not positively) related to the presence of a fast-food chain, suggesting that any bias in our estimates may be downward, not upward. While these findings do not necessarily imply that changes in the supply of fast food restaurants are orthogonal to unobserved determinants of obesity, they are at least consistent with our identifying assumption. Second, while we find that proximity to a fast food restaurant is associated with increases in obesity rates and weight gains, proximity to non fast food restaurants has no discernible effect on obesity rates or weight gains. This suggests that our estimates are not just capturing increases in the local demand for restaurant establishments. Third, we find that while current proximity to a fast food restaurant affects current obesity rates, proximity to future fast food restaurants, controlling for current proximity, has no effect on current obesity rates and weight gains. Taken together, the weight of the 4 evidence is consistent with a causal effect of fast food restaurants on obesity rates among 9th graders and on weight gains among pregnant women. The results on the impact of fast-food on obesity are consistent with a model in which access to fast-foods increases obesity by lowering food prices or by tempting consumers with self-control problems. 2 Differences in travel costs between students and mothers could explain the different effects of proximity. Ninth graders have higher travel costs in the sense that they are constrained to stay near the school during the school day, and hence are more affected by fast-food restaurants that are very close to the school. For this group, proximity to fast-food has a quite sizeable effect on obesity. In contrast, for pregnant women, proximity to fast-food has a quantitatively small (albeit statistically significant) impact on weight gain. Our results suggest that a ban on fast-foods in the immediate proximity of schools could have a sizeable effect on obesity rates among affected students. However, a similar attempt to reduce access to fast food in residential neighborhoods would be unlikely to have much effect on adult consumers. The remainder of the paper is organized as follows. In Section 2 we review the existing literature. In Section 3 we describe our data sources. In Section 4, we present our econometric models and our empirical findings. Section 5 concludes. 2. Background While the main motivation for focusing on school children and pregnant women is the availability of geographically detailed data on weight measures for a very large sample, they are important groups to study in their own right. Among school aged children 6-19 rates of overweight have soared from about 5% in the early 1970s to 16% in 1999-2002 (Hedley et al. 2004). These rates are of particular concern given that children who are overweight are more likely to be overweight as adults, and are increasingly suffering from diseases associated with obesity while still in childhood (Krebs and Jacobson, 2003). At the same time, the fraction of women gaining over 60 2 Consumers with self-control problems are not as tempted by fatty foods if they first have to incur the transportation cost of walking to a fast-food restaurant. Only when a fast-food is right near the school, the temptation of the fast-food looms large. For an overview of the role of self-control in economic applications, see DellaVigna (2009). A model of cues in consumption (Laibson, 2001) has similar implications: a fast-food that is in immediate proximity from the school is more likely to trigger a cue that leads to over-consumption. 5 pounds during pregnancy doubled between 1989 and 2000 (Lin, forthcoming). Excessive weight gain during pregnancy is often associated with higher rates of hypertension, C-section, and large-for-gestational age infants, as well as with a higher incidence of later maternal obesity (Gunderson and Abrams, 2000; Rooney and Schauberger, 2002; Thorsdottir et al. , 2002; Wanjiku and Raynor, 2004). 3 Moreover, Figure 1 shows that the incidence of low APGAR scores (APGAR scores less than 8), an indicator of poor fetal health, increases sharply with weight gain above about 20 kilograms. Critics of the fast food industry point to several features that may make fast food less healthy than other types of restaurant food (Spurlock, 2004; Schlosser, 2002). These include low monetary and time costs, large portions, and high calorie density of signature menu items. Indeed, energy densities for individual food items are often so high that it would be difficult for individuals consuming them not to exceed their average recommended dietary intakes (Prentice and Jebb, 2003). Some consumers may be particularly vulnerable. In two randomized experimental trials involving 26 obese and 28 lean adolescents, Ebbeling et al. (2004) compared caloric intakes on â€Å"unlimited fast food days† and â€Å"no fast food days†. They found that obese adolescents had higher caloric intakes on the fast food days, but not on the no fast food days. The largest fast food chains are also characterized by aggressive marketing to children. One experimental study of young children 3 to 5 offered them identical pairs of foods and beverages, the only difference being that some of the foods were in McDonald’s packaging. Children were significantly more likely to choose items perceived to be from McDonald’s (Robinson et al.2007). Chou, Grossman, and Rashad (forthcoming) use data from the National Longitudinal Surveys (NLS) 1979 and 1997 cohorts to examine the effect of exposure to fast food advertising on overweight among children and adolescents. In ordinary least squares (OLS) models, they find significant effects in most specifications. 4 3 According to the Centers for Disease Control, obesity and excessive weight gain are independently associated with poor pregnancy outcomes. Recommended weight gain is lower for obese women than in others. (http://www. cdc.gov/pednss/how_to/read_a_data_table/prevalence_tables/birth_outcome. htm) 4 They also estimate instrumental variables (IV) models using the price of advertising as an instrument. However, while they find a significant â€Å"first stage†, they do not report the IV estimates because tests 6 Still, a recent review of the considerable epidemiological literature about the relationship between fast food and obesity (Rosenheck, 2008) concluded that â€Å"Findings from observational studies as yet are unable to demonstrate a causal link between fast food consumption and weight gain or obesity†. Most epidemiological studies have longitudinal designs in which large groups of participants are tracked over a period of time and changes in their body mass index (BMI) are correlated with baseline measures of fast food consumption. These studies typically find a positive link between obesity and fast food consumption. However, existing observational studies cannot rule out potential confounders such as lack of physical activity, consumption of sugary beverages, and so on. food. 5 There is also a rapidly growing economics literature on obesity, reviewed in Philipson and Posner (2008). Economic studies place varying amounts of emphasis on increased caloric consumption as a primary determinant of obesity (a trend that is consistent with the increased availability of fast food). Using data from the NLSY, Lakdawalla and Philipson (2002) conclude that about 40% of the increase in obesity from 1976 to 1994 is attributable to lower food prices (and increased consumption) while the remainder is due to reduced physical activity in market and home production. Bleich et al. (2007) examine data from several developed countries and conclude that increased caloric intake is the main contributor to obesity. Cutler et al. (2003) examine food diaries as well as time use data from the last few decades and conclude that rising obesity is linked to increased caloric intake and not to reduced energy expenditure. 6 7 Moreover, all of these studies rely on self-reported consumption of fast suggest that advertising exposure is not endogenous. They also estimate, but do not report individual fixed effects models, because these models have much larger standard errors than the ones reported. 5 A typical question is of the form â€Å"How often do you eat food from a place like McDonald’s, Kentucky Fried Chicken, Pizza Hut, Burger King or some other fast food restaurant? † 6 They suggest that the increased caloric intake is from greater frequency of snacking, and not from increased portion sizes at restaurants or fattening meals at fast food restaurants. They further suggest that technological change has lowered the time cost of food preparation which in turn has lead to more frequent consumption of food. Finally, they speculate that people with self control problems are over-consuming in response to the fall in the time cost of food preparation. Cawley (1999) discusses a similar behavioral theory of obesity as a consequence of addiction. 7 Courtemanche and Carden examine the impact on obesity of Wal-Mart and warehouse club retailers such as Sam’s club, Costco and BJ’s wholesale club which compete on price. They link store location data to individual data from the Behavioral Risk Factor Surveillance System (BRFSS. ) They find that non-grocery selling Wal-Mart stores reduce weight while non-grocery selling stores and warehouse clubs either reduce weight or have no effect. Their explanation is that reduced prices for everyday purchases expand real 7 A series of recent papers explicitly focus on fast food restaurants as potential contributors to obesity. Chou et al. (2004) estimate models combining state-level price data with individual demographic and weight data from the Behavioral Risk Factor Surveillance surveys and find a positive association between obesity and the per capita number of restaurants (fast food and others) in the state. Rashad, Grossman, and Chou (2005) present similar findings using data from the National Health and Nutrition Examination Surveys. Anderson and Butcher (2005) investigate the effect of school food policies on the BMI of adolescent students using data from the NLSY97. They assume that variation in financial pressure on schools across counties provides exogenous variation in availability of junk food in the schools. They find that a 10 percentage point increase in the probability of access to junk food at school can lead to about 1 percent increase in students BMI. Anderson, Butcher and Schanzenbach (2007) examine the elasticity of children’s BMI with respect to mother’s BMI and find that it has increased over time, suggesting an increased role for environmental factors in child obesity. Anderson, Butcher, and Levine (2003) find that maternal employment is related to childhood obesity, and speculate that employed mothers might spend more on fast food. Cawley and Liu (2007) use time use data and find that employed women spend less time cooking and are more likely to purchase prepared foods. The paper that is closest to ours is a recent study by Anderson and Matsa (2009) that focuses on the link between eating out and obesity using the presence of Interstate highways in rural areas as an instrument for restaurant density. Interstate highways increase restaurant density for communities adjacent to highways, reducing the travel costs of eating out for people in these communities. They find no evidence of a causal link between restaurants and obesity. Using data from the USDA, they argue that the lack of an effect is due to the presence of selection bias in restaurant patrons –people who eat out also consume more calories when they eat at homeand the fact that large portions at restaurants are offset by lower caloric intake at other times of the day. Our paper differs from Anderson and Matsa (2009) in four important dimensions, and these four differences are likely to explain the difference in our findings. incomes, enabling households to substitute away from cheap unhealthy foods to more expensive but healthier alternatives. 8 (i) First, our data allow us to distinguish between fast food restaurants and other restaurants. We can therefore estimate separately the impact of fast-foods and of other restaurants on obesity. In contrast, Anderson and Matsa do not have data on fast food restaurants and therefore focus on the effect of any restaurant on obesity. This difference turns out to be crucial, because when we estimate the effect of any restaurant on obesity using our data we also find no discernible effect on obesity. (ii) Second, we have a very large sample that allows us to identify even small effects, such as mean increases of 50 grams in the weight gain of mothers during pregnancy. Our estimates of weight gain for mothers are within the confidence interval of Anderson and Matsa’s two stage least squares estimates. Put differently, based on their sample size, our statistically significant estimates would have been considered statistically insignificant. (iii) Third, our data give us the exact location of each restaurant, school and mother. The spatial richness of our data allows us to examine the effect of fast food restaurants on obesity at a very detailed geographical level. For example, we can distinguish the effect at . 1 miles from the effect at . 25 miles. As it turns out, this feature is quite important, because the effects that we find are geographically extremely localized. For example, we find that fast food restaurant have an effect on 9th graders only for distances of . 1 miles or less. By contrast, Anderson and Matsa use a city as the level of geographical analysis. It is not surprising that at this level of aggregation the estimated effect is zero. (iv) Fourth, Anderson and Matsa’s identification strategy differs from ours, since we do not use an instrument for fast-food availability and focus instead on changes in the availability of fast-foods at very close distances. The populations under consideration are also different, and may react differently to proximity to a fast food restaurant. Anderson and Matsa focus on predominantly white rural communities, while we focus on primarily urban 9th graders and urban mothers. We document that the effects vary considerable depending on race, with blacks and Hispanics having the largest effect. Indeed, when Dunn (2008) uses an instrumental variables approach similar to the one used Anderson and Matsa based on proximity to freeways, he finds no effect for rural areas and for 9 whites in suburban areas, but strong effect for blacks and Hispanics. As we show below, we also find stronger effects for minorities. Taken together, these four differences lead us to conclude that the evidence in Anderson and Matsa is consistent with our evidence. 8 In summary, there is strong evidence of correlations between fast food consumption and obesity. It has been more difficult to demonstrate a causal role for fast food. In this paper we tap new data in an attempt to test the causal connection between fast food and obesity. 3. Data Sources and Summary Statistics Data for this project comes from three sources. (a) School Data. Data on children comes from the California public schools for the years 1999 and 2001 to 2007. The observations for 9th graders, which we focus on in this paper, represent 3. 06 million student-year observations. In the spring, California 9th graders are given a fitness assessment, the FITNESSGRAM ®. Data is reported at the class level in the form of the percentage of students who are obese, and who have acceptable levels of abdominal strength, aerobic capacity, flexibility, trunk strength, and upper body strength. Obesity is measured using actual body fat measures, which are considerably more accurate than the usual BMI measure (Cawley and Burkhauser, 2006). Data is also reported for sub-groups within the school (e. g. by race and gender) provided the cells have at least 10 students. Since grade 9 is the first year of high school and the fitness tests take place in the Spring, this impact corresponds to approximately 30 weeks of fast-food exposure. 9 This administrative data set is merged to information about schools (including the percent black, white, Hispanic, and Asian, percent immigrant, pupil/teacher ratios, fraction eligible for free lunch etc. ) from the National Center for Education Statistic’s Common Core of Data, as well as to the Start test scores for the 9th grade. The location of the school was also geocoded using ArcView. Finally, we merged in information. 8 9 See also Brennan and carpenter (2009). In very few cases, a high school is in the same location as a middle school, in which case the estimates reflect a longer-term impact of fast-food. 10 about the nearest Census block group of the school from the 2000 Census including the median earnings, percent high-school degree, percent unemployed, and percent urban. (b) Mothers Data. Data on mothers come from Vital Statistics Natality data from Michigan, New Jersey, and Texas. These data are from birth certificates, and cover all births in these states from 1989 to 2003 (from 1990 in Michigan). For these three states, we were able to gain access to confidential data including mothers names, birth dates, and addresses, which enabled us both to construct a panel data set linking births to the same mother over time, and to geocode her location (again using ArcView). The Natality data are very rich, and include information about the mother’s age, education, race and ethnicity; whether she smoked during pregnancy; the child’s gender, birth order, and gestation; whether it was a multiple birth; and maternal weight gain. We restrict the sample to singleton births and to mothers with at least two births in the sample, for a total of over 3. 5 million births. (c) Restaurant Data. Restaurant data with geo-coding information come from the National Establishment Time Series Database (Dun and Bradstreet). These data are used by all major banks, lending institutions, insurance and finance companies as the primary system for creditworthiness assessment of firms. As such, it is arguably more precise and comprehensive than yellow pages and business directories. 10 We obtained a panel of virtually all firms in Standard Industrial Classification 58 from 1990 to 2006, with names and addresses. Using this data, we constructed several different measures of â€Å"fast food† and â€Å"other restaurants,† as discussed further in Appendix 1. In this paper, the benchmark definition of fast-food restaurants includes only the top-10 fast-food chains, namely, Mc Donalds, Subway, Burger King, Taco Bell, Pizza Hut, Little Caesars, KFC, Wendy’s, Dominos Pizza, and Jack In The Box. We also show estimates using a broader definition that includes both chain restaurants and independent burger and pizza restaurants. Finally, we also measure the supply of non-fast food restaurants. The definition of â€Å"other restaurants† changes with the definition of fast food. Appendix Table 1 lists the top 10 fast food chains as well as examples of restaurants that we did not classify as fast food. The yellow pages are not intended to be a comprehensive listing of businesses they are a paid advertisement. Companies that do not pay are not listed. 10 11 Matching. Matching was performed using information on latitude and longitude of restaurant location. Specifically, we match the schools and mother’s residence to the closest restaurants using ArcView software. For the school data, we match the results on testing for the spring of year t with restaurant availability in year t-1. For the mother data, we match the data on weight gain during pregnancy with restaurant availability in the year that overlaps the most with the pregnancy. Summary Statistics. Using the data on restaurant, school, and mother’s locations, we constructed indicators for whether there are fast food or other restaurants within . 1, . 25, and . 5 miles of either the school or the mother’s residence. Table 1a shows summary characteristics of the schools data set by distance to a fast food restaurant. Here, as in most of the paper, we use the narrow definition of fast-food, including the top-10 fast-food chains. Relatively few schools are within . 1 miles of a fast food restaurant, and the characteristics of these schools are somewhat different than those of the average California school. Only 7% of schools have a fast food restaurant within . 1 miles, while 65% of all schools have a fast food restaurant within 1/2 of a mile. 11 Schools within . 1 miles of a fast food restaurant have more Hispanic students, a slightly higher fraction of students eligible for free lunch, and lower test scores. They are also located in poorer and more urban areas. The last row indicates that schools near a fast food restaurant have a higher incidence of obese students than the average California school. Table 1b shows a similar summary of the mother data. Again, mothers who live near fast food restaurants have different characteristics than the average mother. They are younger, less educated, more likely to be black or Hispanic, and less likely to be married. 4. Empirical Analysis We begin in Section 4. 1 by describing our econometric models and our identifying assumptions. In Section 4. 2 we show the correlation between restaurant location and student characteristics for the school sample, and the correlation between The average school in our sample had 4 fast foods within 1 mile and 24 other restaurants within the same radius. 11 12 restaurant location and mother characteristics for the mother sample. Our empirical estimates for students and mothers are in Section 4. 3 and 4. 4, respectively. 13 4. 1 Econometric Specifications Our empirical specification for schools is (1) Yst = ? F1st + ? F25st + ? F50st + ? ’ N1st + ? ’ N25st + ? ’ N50st + ? Xst + ? Zst + ds + est where Yst is the fraction of students in school s in a given grade who are obese in year t; F1st is an indicator equal to 1 if there is a fast food restaurant within . 1 mile from the school in year t; F25st is an indicator equal to 1 if there is a fast food restaurant within . 25 miles from the school in year t; F50st is an indicator equal to 1 if there is a fast food restaurant within . 5 mile from the school in year t; N1st, N25st and N50st are similar indicators for the presence of non-fast food restaurants within . 1, . 25 and . 5 miles from the school; ds is a fixed effect for the school. The vectors Xst and Zst include school and neighborhood time-varying characteristics that can potentially affect obesity rates. Specifically, Xst is a vector of school-grade specific characteristics including fraction blacks, fraction native Americans, fraction Hispanic, fraction immigrants, fraction female, fraction eligible for free lunch, whether the school is qualified for Title I funding, pupil/teacher ratio, and 9th grade tests scores, as well as school-district characteristics such as fraction immigrants, fraction of non-English speaking students (LEP/ELL), share of IEP students. Zst is a vector of characteristics of the Census block closest to the school including median income, median earnings, average household size, median rent, median housing value, percent white, percent black, percent Asian, percent.