Global Expansion Decision Making: An assessment of the impacting factors for restaurant franchises

by

Mahmood A. Khan, Ph.D.* Professor, Department of Hospitality & Tourism Management

Pamplin College of Business Virginia Tech, National Capital Region

7054 Haycock Road Falls Church, VA. 22043

mahmood@vt.edu

Maryam Khan, Ph.D. Director/Assistant Professor

Hospitality Management Program School of Business – Department of Management

Howard University 2600 6th Street, NW, Washington, DC 20059

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Abstract: Franchise businesses are rapidly expanding overseas. Since global expansion demands considerable capital investment careful decision making is essential. Considering the lack of available information this study explores factors that should be considered in decision making pertaining to expansion in foreign markets. Using data of restaurant franchises particularly McDonald's and KFC this study outlines some of the factors that are essential in decision making. From available data an analyses of the presence or absence of selected franchises in different countries were conducted to (a) assess the number of units in different countries; (b) correlate the number of restaurant units in different countries to the available indices measuring economical, social, and political indicators; (c) use any correlation with existing index or indices to identify potential factors for selecting foreign markets; (d) identify possible expansion within existing markets; and (e) identify potential foreign markets. Based on the results obtained a list of potential markets for restaurant franchising is presented and the factors that should be considered are outlined for each country or region.

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Introduction Global expansion of US-based chains is proceeding with an unprecedented pace. It was reported that more than ninety percent of the net new restaurants added by McDonald's and Burger King in the last completed fiscal years opened overseas. Evidently this underscores the importance of international expansion, particularly by matured chains (NRN, 2009). While there are plenty of opportunities available overseas, it is not easy to find a compatible match. Advances in technology, economic growth, social and political changes, are all providing market opportunities that were not foreseen in recent history. A major portion of revenue for the largest restaurant chains come from international expansion. About 55% of McDonald's revenue comes from Asia, Pacific Rim, Africa, Middle East and European divisions (Brodsky, 2008). People are becoming aware of different brands and are able to try, develop taste, and possibly adapt non-traditional foods. This trend my have a distinct impact on food habits, particularly among children. Among all the businesses, probably restaurants face the greatest challenge in venturing into a new global market, since they have to gain acceptance by individuals who are willing to compromise their social and cultural habits. Notable trends that favor the increase of international franchises in foreign markets include (a) increased educational status of the local population; (b) technological advancement facilitating travel, intercultural cooperation, and instant dissemination of information; (c) exposure to different foods, the willingness to try new products and unconventional types of foods; (d) rapid development of rural areas, construction of highways, improved transportation methods, and overall industrial development; (e) improved economies and increased disposable family income; (f) the increased numbers of women in the workforce and of two-income families; (g) the increased significance of convenience as a result of one or more factors mentioned above; and (h) the popularity of take-out or home-delivered meals (Khan, 1999). Considerable capital investment is needed to find suitable market and eventually a suitable business partner. Typically the business environment is very volatile and risky in international markets. Models that work in the USA may not work in other countries. One of the challenges faced by corporations is to the extent they can modify the current concept to align with the desired market. Global market segmentation demands that companies identify consumers in different countries who share similar needs and desires. McDonald's Corporation is a fast food legend with prominent international presence whose famous golden arches are said to be the second most recognized

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symbol in the world, behind the Olympic rings (Keegan & Green, 2008a). McDonald's global marketing strategy is based on a combination of global and local marketing mix elements. A vital element in McDonald's business model is a restaurant system that can be set up virtually everywhere in the world. It offers a core menu items – hamburgers, French fries, and soft drinks – in most countries, and it also customizes menu offerings in accordance with local customs (Keegan & Green, 2008). McDonald's executives’ credit three strategies for building sales in key European markets: up grading the customer and employee experience, building brand transparency, and preserving local relevance. In traffic-chocked cities from Manila to Montevideo, McDonald's deploys fleets of motor scooters to get food to customers fast. In Taipei, the company hires over 1,000 drivers. In Egypt, where the setup was pioneered in 1995, deliveries accounted for 27% to 80% of all McDonald's revenue (Arndt & Ghobrial, 2007). India, with a soaring economy that is helping to create a huge middle class, is emerging as the new China for American restaurant brands eager to expand in Asia. McDonald's also has benefited for the past several years from deriving strong earnings from its overseas markets, where it operates about half of its 31,000 locations, a scope that offsets weakness in some markets (Adamy, 2009). Half of the new outlets planned for China in 2008 had drive-through windows, as McDonald's seeks to further leverage China's status as the world's fastest-growing major auto market and give itself an edge against rival, KFC, which is far more popular in China (Fong, 2008). As evident from the earlier examples, McDonald's does not only enjoy the first mover advantage in many countries but is also involved in expansion. Their proven success in identifying profitable markets has led many businesses to closely observe and follow their patterns of market entry. Considering the lack of precise instruments to assess foreign markets, this study was undertaken with the following objectives: (a) assess the presence or absence of McDonald's restaurants in different parts of the world; (b) correlate the number of McDonald's in different countries with available indices measuring economical, social, political, cultural, or environmental parameters; (c) use any correlation with existing index or indices to identify potential factors for selecting foreign markets; (d) identify possible expansion within existing markets; and (e) identify potential foreign markets. Methodology Our first task was to collect the exact number of McDonald's in different countries. As easy as it may sound, this was the most difficult task since it was impossible to find this information in one publication. Efforts were made to collect this from McDonald's corporate publications and contacts. Although we were able to get regional or collective information, the country wide information

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was incomplete, outdated, or unavailable. For several countries, direct contacts were made to get as up to date information as possible. Data collected included the number of McDonald's restaurants in different countries as well as the list of countries where there are no existing restaurants. Next task involved exploring different available indices that might measure the business environmental conditions in different countries. Again the problem was with the lack of current information, incomplete available information about countries, and lack of data correlation capabilities. Analyses were conducted using Hofstede’s Cultural Dimension rankings. One of the drawbacks in using these rankings was that the current rankings were not available and many countries where McDonald's exist were not included in rankings. A review of the FSI (Failed States Index) published jointly by the Fund for Peace organization (2009) and the magazine Foreign Policy, revealed the existence of many of the usable parameters and reduced limitations of the earlier indices. The Fund for Peace is an independent, nonpartisan research and educational organization that works to prevent war and alleviate the conditions that cause conflict. FSI index uses 12 indicators of state cohesion and performance, compiled through a close examination of more than 30,000 publicly available sources in ranking 177 states in order from most to least at risk of failure. In this paper the words state (in italics) and countries are used interchangeable. FSI (2009) consist of 12 indicators as follows: Social Indicators: I-1. Mounting Demographic Pressures:

• Pressures deriving from high population density relative to food supply and other life-sustaining resources

• Pressures deriving from group settlement patterns that affect the freedom to participate in common forms of human and physical activity, including economic productivity, travel, social interaction, religious worship

• Pressures deriving from group settlement patterns and physical settings, including border disputes, ownership or occupancy of land, access to transportation outlets, control of religious or historical sites, and proximity to environmental hazards

• Pressures from skewed population distributions, such as a "youth or age bulge," or from divergent rates of population growth among competing communal groups

I-2. Massive Movement of Refugees or Internally Displaced Persons creating Complex Humanitarian Emergencies:

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• Forced uprooting of large communities as a result of random or targeted violence and/or repression, causing food shortages, disease, lack of clean water, land competition, and turmoil that can spiral into larger humanitarian and security problems, both within and between countries

I-3. Legacy of Vengeance-Seeking Group Grievance or Group Paranoia:

• History of aggrieved communal groups based on recent or past injustices, which could date back centuries

• Patterns of atrocities committed with impunity against communal groups • Specific groups singled out by state authorities, or by dominant groups, for

persecution or repression • Institutionalized political exclusion • Public scapegoating of groups believed to have acquired wealth, status or power

as evidenced in the emergence of "hate" radio, pamphleteering and stereotypical or nationalistic political rhetoric

I-4. Chronic and Sustained Human Flight:

• "Brain drain" of professionals, intellectuals and political dissidents fearing persecution or repression

• Voluntary emigration of "the middle class," particularly economically productive segments of the population, such as entrepreneurs, business people, artisans and traders, due to economic deterioration

• Growth of exile communities

Economic Indicators:

I-5. Uneven Economic Development along Group Lines:

• Group-based inequality, or perceived inequality, in education, jobs, and economic status

• Group-based impoverishment as measured by poverty levels, infant mortality rates, education levels

• Rise of communal nationalism based on real or perceived group inequalities

I-6. Sharp and/or Severe Economic Decline:

• A pattern of progressive economic decline of the society as a whole as measured by per capita income, GNP, debt, child mortality rates, poverty levels, business failures, and other economic measures

• Sudden drop in commodity prices, trade revenue, foreign investment or debt payments

• Collapse or devaluation of the national currency • Extreme social hardship imposed by economic austerity programs • Growth of hidden economies, including the drug trade, smuggling, and capital

flight

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• Increase in levels of corruption and illicit transactions among the general populace

• Failure of the state to pay salaries of government employees and armed forces or to meet other financial obligations to its citizens, such as pension payments

Political Indicators:

I-7. Criminalization and/or Delegitimization of the State:

• Massive and endemic corruption or profiteering by ruling elites • Resistance of ruling elites to transparency, accountability and political

representation • Widespread loss of popular confidence in state institutions and processes, e.g.,

widely boycotted or contested elections, mass public demonstrations, sustained civil disobedience, inability of the state to collect taxes, resistance to military conscription, rise of armed insurgencies

• Growth of crime syndicates linked to ruling elites

I-8. Progressive Deterioration of Public Services:

• Disappearance of basic state functions that serve the people, including failure to protect citizens from terrorism and violence and to provide essential services, such as health, education, sanitation, public transportation

• State apparatus narrows to those agencies that serve the ruling elites, such as the security forces, presidential staff, central bank, diplomatic service, customs and collection agencies

I-9. Suspension or Arbitrary Application of the Rule of Law and Widespread Violation of Human Rights:

• Emergence of authoritarian, dictatorial or military rule in which constitutional and democratic institutions and processes are suspended or manipulated

• Outbreak of politically inspired (as opposed to criminal) violence against innocent civilians

• Rising number of political prisoners or dissidents who are denied due process consistent with international norms and practices

• Widespread abuse of legal, political and social rights, including those of individuals, groups or cultural institutions (e.g., harassment of the press, politicization of the judiciary, internal use of military for political ends, public repression of political opponents, religious or cultural persecution)

I-10. Security Apparatus Operates as a “State Within a State”:

• Emergence of elite or praetorian guards that operate with impunity • Emergence of state-sponsored or state-supported private militias that terrorize

political opponents, suspected "enemies," or civilians seen to be sympathetic to the opposition

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• Emergence of an "army within an army" that serves the interests of the dominant military or political clique

• Emergence of rival militias, guerilla forces or private armies in an armed struggle or protracted violent campaigns against state security forces

I-11. Rise of Factionalized Elites:

• Fragmentation of ruling elites and state institutions along group lines • Use of nationalistic political rhetoric by ruling elites, often in terms of communal

irredentism, (e.g., a "greater Serbia") or of communal solidarity (e.g., "ethnic cleansing" or "defending the faith")

I-12 Intervention of Other States or External Political Actors:

• Military or Para-military engagement in the internal affairs of the state at risk by outside armies, states, identity groups or entities that affect the internal balance of power or resolution of the conflict

• Intervention by donors, especially if there is a tendency towards over-dependence on foreign aid or peacekeeping missions

Evidently, FSI has detailed attributes for developing a comprehensive index; hence it was used in this study. Each indicator is ranked from zero to ten, with zero being the most stable and ten being the least stable. The sum of these indicators is tallied for a total of FSI. The 2009 index results range from 18.3 (Norway) to 114.7 (Somalia); with Somalia being the most failed state. All 12 attributes for each country were analyzed for any correlation with the existing number of McDonald's. The FSI ranks 177 states and includes only those that have memberships in the United Nations. Some countries, such as Taiwan that are recognized as sovereign by some states are excluded from the index until their membership in the United Nations is ratified.

The strength of the FSI is in the fact that it is not limited to countries for which statistics are available. Instead, the index is based on thousands of electronic articles and reports that are processed by the Conflict Assessment System Tool (CAST). CAST is a flexible model that has the capability to employ a four-step trend-line analysis, consisting of (a) rating 12, social, economic, and political/military indicators; (b) assessing the capabilities of five core state institutions considered essential for sustaining security; (c) identifying idiosyncratic factors and surprises; and (d) placing countries on a conflict map that shows the risk history of countries being analyzed. CAST used a powerful data-collection system that includes international and local media reports and other public documents, including U.S. State Department reports, independent studies, and even corporate financial filings. The data used in each index are collected from January to December of the preceding year. The thoroughness, general acceptance of the index, and being up-to-date, makes it appropriate for

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our study. The following calculations and observations were used in analyzing and presenting the data in tables and figures included in this paper.

Rank: refers to the ranking of the countries used in FSI and shows the rank of the country among the 177 listed countries.

I-1 to I-12: refers to the number of attribute used in FSI.

McD Index: is calculated by taking the number of McDonald's in each country and dividing by the population of that country and multiplying it by 1,000,000.

# of McD: refers to the number of McDonald's restaurants in respective country.

% Urban Pop: refers to the percentage of urban population in respective country. This figure is obtained from the data provided in the World Fact Book by the CIA (United States Central Intelligence Agency).

Pop count: is the number of individuals 0-14 years of age among the total urban population of the respective country extracted and calculated from the data included in the World Fact Book by the CIA.

McDensity: is calculated by dividing the number of McDonald's in a respective country by Pop count and multiplying it by 100,000.

Pop/McD: is calculated by dividing Pop count by the number of McDonald's in a respective country.

Statistical Analysis

McD index was used as the independent variable in order to account for the variability based on the population. The reason why this index was developed was to normalize the numbers. Simple numbers of restaurants were not used since there were drastic population variances in each country. Taiwan and Hong Kong are not part of the FSI and therefore the numbers have been included as part of China. The United States was excluded from the list of countries examined since it is the country of origin for McDonald's. Countries with no McDonald's have been included in the study because they provide relevant data. Of the 176 countries, 86 have zero McDonald's and 90 have at least one McDonald's restaurant. Based on the preliminary analysis, countries with at least one restaurant have a lower FSI than those with no McDonald's. Additionally a box plot was created for the countries with zero McDonald's and those with at least one McDonald's. The notches in the box plots did not overlap

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further indicating that there is a statistically significant difference between the two groups. For all the benefits mentioned for the selection of FSI, it was selected as dependent variable. In order to assess the relationship between the two variables, Spearman’s ρ was used. The Spearman’s ρ provides a measure of correlation between two variables without making any assumptions on the distribution of the variables. Its use is appropriate when either of the following conditions are met (a) one variable is an ordinal scale and the other is an ordinal scale or higher and (b) one of the distributions is markedly skewed. In this study, both these conditions are met. The variables were made ordinal by ranking them and the distribution shows that it is not normal and is skewed. Also Spearman’s ρ is a ranked correlation coefficient and both variables are continuous. When the correlation coefficient was at or above 0.4, the correlation between the variables was considered to be significant. For most of the research in social sciences this is considered to be a standard value. Microsoft Excel 2008 and SAS program, JMP, were used for analysis.

Findings

The numbers of McDonald’s in each country were tabulated using FSI listings. The overall expansion proceeded from the United States/Canada to Europe and Australia during 1970-1974 and then expanded to selected South American countries during 1975-1979. The rest of the expansion in Eastern, South Asian, and Far Eastern countries, took place in 1990s. FSI 2009 distinguishes countries into four distinct groups (a) Sustainable; (b) Moderate; (c) Warning; and (d) Alert. Countries were separated into each of these groups and compared with the parameters discussed earlier as shown in separate tables later on in this paper.

McD Index Versus FSI

The correlation between McD Index versus FSI was determined by Spearman’s ρ analysis. The highest correlation was between the McD index and “Public Services attribute” (I-8) at -0.8010 and the lowest correlation was for “Legacy of Vengeance-Seeking Group Grievance or Group Paranoia” (I-3) at -0.4438. The values for each FSI attribute are shown in Table 1. The total correlation for all attributes had a value of -0.7099. All correlations were found to be above 0.4 and significant at <0001 level. This coefficient indicates that there is a strong negative relationship between the two variables. Thus higher the FSI index, the smaller the McD index. In other words, the worst off a country is, as indicated by the FSI, the less presence of McDonald's restaurants. The distribution shows increasing number of McDonald's restaurants with the decrease in FSI values. After finding strong negative correlations between all the attributes, the differences between social, economic and political indicators were

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assessed. The results are shown in Table 1. Means were –0.5936 for social, -0.6636 for economical, and -0.6597 for political indicators. Since the greatest correlation was found to be for economical indicators, I-5 and I-6 values were used for further analyses.

Overall presence of McDonald's

The FSI lists countries with FSI scores ranging from 114.7 to 90.0 in a group titled “alert;” those with scores between 89.8 and 60.1 as “warning;” those with scores between 59.7 and 31.2 as “moderate;” and those with scores between 29.0 and 18.3 as “sustainable.” The number of McDonald's in all these group of countries were assessed separately and are shown in respective tables. The most noteworthy point is that there was no McDonald's presence in the countries with “alert” status, except in Pakistan (22), Lebanon (17) Sri Lanka (4), and Georgia (3). The FSI scores for these countries were 104.1; 93.5; 96.7; and 91.8 respectively. Political unrest was seen during the past years in all these countries and most of the restaurants opened before occurrence of the current problems. For further analysis “alert” countries were not included due to their status and relatively higher FSI scores.

One of the problems associated with using the total population of a country is that McDonald's restaurants are exclusively located in highly populated urban cities whereas some countries have large rural population. The percentage of urban population was therefore used in this study based on the data provided by CIA’s World Fact Book. The next consideration involved the selection of the age of the population. The age structures given for the population in the World Fact Book included groups between 0-14 years; 15-64 years; and 65 years and older. Population representing 0-14 years was selected since this would be the most likely target market for McDonald's. The numbers in this age group were calculated from the percentage of urban population and used for further analysis.

McDonald's and “Sustainable” status Countries

With lower FSI scores these countries represent the most preferred markets for business. However there may be over saturation of businesses due to the smaller size of the population. The average for economical indicators was found to be 5.0 (Table 2). The McD Index for this group of countries was 21.52 which is the highest among countries in all groups. This might be an indication of market saturation due to the smaller size of the population in these countries, although there is a high percentage of urban population with an average of 78%. The value for McDensity is 15.61, which is also very high compared to countries in other groups. The pop/McD value is 7,991; which means that on an average

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there is one McDonald's restaurant for every 7,991 individuals. Considering this ratio, there seems to be a very limited scope for further expansion, except for some opportunities in Denmark and Luxembourg, where the economic indicator scores are also low. Canada has the largest density of restaurants compared to the size of the population in that age group. Netherlands can also be a good candidate for expansion, however, the economical indicators scores are above average. Australia and New Zealand also have high restaurant density. According to the news reports the three McDonald's restaurants in Iceland were recently closed. Some of the figures in the table are in bold to highlight points discussed in this paper. Most of the countries in this group belong to the European Union.

McDonald's and “Moderate” status Countries

Table 3. lists the countries listed as “moderate” by FSI. These countries have varying degree of economical indicator scores with an average score of 8.7 which is higher than the average for “sustainable” countries. All countries in this group except Montenegro and Barbados have at least one McDonald's restaurant. Montenegro has favorable economical indicator scores and can accommodate few restaurants; whereas Barbados may not due to limited population. Average McD Index for this group of countries is 10.09, which is lower than that for “sustainable” countries group. The average number of McDonald's restaurants in this group is 322 and McDensity is 7.80. This group has a high percentage of urban population. Based on the population count and the number of McDonald's, the countries which have the potential for expansion are Belgium, Chile, Uruguay, South Korea, Mauritius, Argentina, Oman, Lithuania, Slovakia, and Latvia. Figures for these countries are presented in bold in Table 4. Considering the scores for economical indicators, the most promising country is Oman, which has combined economical indicator score of 2.11. Other countries with considerable economical scores include Belgium, Chile, and South Korea. It is advisable to look at the economical scores in relation to the number of McDonald's per population. This group of countries should be considered for expansion since there are some countries which have good business potential.

McDonald's and “Warning” status Countries

Values for countries listed under “warning” status which does have at least one McDonald's restaurant are presented in Table 4. This group lists countries some of which are rapidly developing and therefore deserve careful consideration for market entry. Since there is considerable variability in figures, median values and total scores of all 12 attributes are presented in this table. The average of economical indicator scores is 13.7 with a median of 14; whereas the average for all 12 attributes is 76.0 with a median value of 78.3. There was a wide

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variation among McD Index with an average of 3.35 and median value of 1.56. This difference is due to the fact that some countries have only one McDonald's restaurant. Countries that deserve consideration for expansion include Paraguay, Peru, Morocco, Dominican Republic, India, Cuba, Ecuador, Belarus, Indonesia, Azerbaijan, Egypt, and Columbia. China is included in this group but considering the existing number of restaurants they seem to be reaching saturation level. Countries that should be considered carefully for expansion include India, Indonesia, Egypt, Paraguay, Columbia, Morocco, Dominican Republic and Azerbaijan, since there are some good potential growth indicators.

“Warning” status Countries with no McDonald's

Countries classified under “Warning” status with currently no McDonald's are listed in Table 5. Potential and prospects of market entry into these countries were assessed taking into consideration economical indicators; total of social, economical, and political indicators; percent urban population; and population count. The median and average values obtained for countries listed under this group as shown in Table 4 were used for comparison. Figures in bold represent values that need consideration. The intent was to examine the potential for McDonald's entry into these countries based on the parameters used for this group of countries. Using the median value of 14.0 for economical indicators, countries with lesser values were highlighted. Similarly for the total of all indicators the median value of 78.3 was used. For minimum required urban population 67% was considered. For population count the cut-off point selected was 79,708. Considering the economical indicators, total of all indicators, and percentage of population, the countries which deserve consideration for market entry include Tunisia, Libya, and Gabon. Vietnam has good scores for attributes and large population but less percentage of urban population. Other countries which have good economical scores and large population are Algeria, Tanzania, Madagascar and Laos. Countries with considerably good economic scores but fair size of population include Turkmenistan and Mauritania. It should be taken into account that even though the percentage of urban population may be low there may be concentration of population in urban cities. By carefully considering all factors markets can be selected from the list of these countries.

Conclusion

The most important conclusion that can be drawn from this study is that the FSI can be a good effective tool for comparing different countries for market entry. Classification used by FSI can be used to select countries from a particular group. Although McDonald's is used in analysis the indices can be used for any type of businesses for global entry, particularly those that are service-oriented.

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Since there may be saturation in the “sustainable” group of countries, other groups should be considered, which include several up and coming countries from the economical, social, and political point of view. Although younger population is used in this study due to the food business, other age groups can be used for analysis. A stepwise approach can be used in the selection of countries by looking at total scores, economical scores, total population and percentage of total population.

Limitations

One of the limitations of this study is that cultural, technological and environmental attributes are not included in the FSI. Also, information for countries included in the FSI is based on published information during the year; there is a chance of countries that are not prominently used in publications, from getting excluded. Also, only countries which belong to the United Nations are used for FSI. The political age of the countries is not taken into consideration thereby the newly formed states may be at a disadvantage. The information available is time bound and can change drastically with circumstances. Changes in political situations can adversely affect the conclusions drawn in this study. Country profiles in light of the discussion could not be included due to the limited scope of this study.

Assumptions made regarding the age of the selected population and percentage of urban population may not be accurate for some countries, particularly where there is a relatively heavy concentration of urban population such as in large cities. Based on the available information and limitations, all efforts were made to provide useful information in this paper.

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Table 1. Overall and Individual values and averages for Social, Economic, and Political Indicators

Table 2. Presence of McDonald's restaurants in countries classified as “Sustainable” by FSI Index 2009

Country Rank I-5 + I-6 McD Index

# of McD

% Urban Pop.

Pop. Count McDensity Pop/McD

Norway 177 3.3 14.64 68 77 664,940 10.20 9779

Finland 176 4.0 15.63 82 63 542,557 15.10 6,617 Sweden 175 4.3 25.76 233 85 1,211,922 19.20 5,201 Switzerland 174 4.6 19.39 147 73 867,365 16.90 5,900 Ireland 173 5.0 18.05 75 61 535,942 14.00 7,146 Denmark 172 4.2 15.13 83 87 867,968 9.60 10,457 New Zealand 171 6.5 34.26 143 87 758,376 18.90 5,303 Australia 170 5.5 37.23 782 89 3,516,215 22.20 4,496 Netherlands 169 5.4 13.16 219 82 2,380,355 9.20 10,869 Luxembourg 168 3.7 14.40 7 82 74,595 9.40 10,656 Austria 167 6.0 19.74 162 67 797,898 20.30 4,925 Canada 166 6.8 42.57 1414 80 4,310,838 32.80 3,049

Iceland 165 5.7 9.86 3 92 58,490 5.12 19,497

AVERAGE 5.0 21.52 262 78 1,275,959 15.61 7,991

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Table 3. Presence of McDonald's restaurants in countries classified as “Moderate” by FSI Index 2009

Country Rank I-5 + I-6 McD Index # of McD

% Urban Pop. Pop. Count McDensity Pop/McD

Japan 164 4.5 29.50 3,755 66 11,318,514 33.17 3,014 Portugal 163 6.2 11.80 126 59 1,030,782 12.22 8,181 Belgium 162 6.2 5.57 58 97 1,629,286 3.55 28,091 United Kingdom 161 7.6 19.53 1,190 90 9,197,898 12.93 7,729 Singapore 160 5.7 23.65 109 100 672,432 16.20 6,169 France 158 7.7 18.58 1,134 77 9,215,864 12.30 8,127 Germany 157 7.7 16.18 1,333 74 8,316,773 14.46 6,239 Slovania 156 8.7 7.47 15 48 130,419 11.50 8,695 Chile 155 6.5 4.25 70 88 3,382,702 2.06 48,324 Uruguay 154 11.0 6.33 22 92 720,539 3.05 32,752 South Korea 153 7.4 4.82 233 81 6,614,539 3.52 28,389 Czech Republic 152 8.8 7.63 78 73 1,011,562 7.71 12,969 Spain 151 6.9 9.73 394 77 4,515,603 8.72 11,461 Italy 150 7.7 6.54 380 68 5,351,754 7.10 14,084 Mauritius 149 8.3 0.78 1 42 121,488 0.82 121,488 Argentina 148 9.5 4.59 186 92 9,652,398 1.77 51,895 Greece 147 9.5 4.10 44 61 933,905 4.71 21,225 Oman 146 3.6 2.11 7 72 1,050,034 0.66 150,005 Lithuania 145 11.4 1.68 6 67 337,370 1.77 56,228 Slovakia 144 11.2 2.93 16 56 483,965 3.30 30,248

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Country Rank I-5 + I-6 McD Index # of McD

% Urban Pop. Pop. Count McDensity Pop/McD

Malta 143 8.9 19.82 8 94 61,388 13.03 7,673 Poland 142 11.0 5.74 221 61 3,518,037 6.28 15,919 Hungary 141 11.1 9.97 99 68 1,008,892 9.81 10,191 Estonia 140 9.4 5.35 7 69 133,721 5.23 19,103 UAE 139 9.0 12.77 59 78 763,866 7.72 12,947 Qatar 138 8.6 20.61 17 96 174,571 9.73 10,269 Costa Rica 137 11.4 7.39 31 63 716,393 4.32 23,109 Latvia 136 11.2 2.67 6 68 202,390 2.96 33,372 Barbados 135 14.1 40 21,894 Montenegro 134 7.1 60 64,495 Bahrain 133 9.6 19.49 14 89 167,734 8.34 11,981 Panama 132 12.3 11.18 37 73 718,101 5.15 19,408 AVERAGES 8.7 10.09 322 73 2,601,228 7.80 27,310

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Table 4. Presence of McDonald's restaurants in countries classified as “Warning” by FSI Index 2009

Country Rank I-5 +

I-6 I-1 to I-12 McD Index

# of McD

% Urban Pop.

Pop. Count McDensity Pop/McD

Croatia 131 10.3 60.1 3.56 16 57 398,121 4.01 24,883 Bahamas 130 12.6 60.9 13.01 4 84 67,237 5.94 16,809 Romania 129 10.9 61.3 2.61 58 54 1,865,226 3.10 32,159 Bulgaria 128 12.3 61.5 3.30 24 71 705,996 3.39 29,416 Kuwait 125 10.4 63.4 18.87 49 98 695,475 7.04 14,193 South Africa 122 12.8 67.4 2.56 125 61 8,634,493 1.44 69,076 Brunei Darussalam 118 11.8 68.1 2.62 1 75 77,567 1.28 77,567

Malaysia 115 10.7 68.9 7.28 184 70 5,648,008 3.25 30,696 Cyprus 114 13.0 68.9 20.19 16 70 106,785 14.98 6,674 Brazil 113 13.9 69.1 2.86 561 86 45,713,406 1.22 81,486 Ukraine 110 13.5 69.7 1.41 65 68 4,287,314 1.51 65,959 Samoa 108 15.0 71.4 13.82 3 23 19,026 15.76 6,342 Paraguay 106 13.9 72.0 0.88 6 60 1,538,805 0.38 256,468 Suriname 103 14.8 73.2 2.10 1 75 97,979 1.02 97,979 Macedonia 100 14.2 74.4 1.46 3 67 266,263 1.12 88,754 Mexico 98 15.2 75.4 3.45 379 77 24,952,130 1.51 65,837 Peru 93 15.5 77.1 0.65 19 71 6,096,974 0.31 320,893 Morocco 92 14.0 77.1 0.64 22 56 5,860,558 0.37 266,388 Honduras 91 14.8 77.2 1.18 9 48 1,423,875 0.63 158,208 El Salvador 90 14.7 77.2 1.42 10 61 1,552,587 0.64 155,259 Saudi Arabia 89 10.4 77.5 3.91 110 82 8,936,415 1.23 81,240 Dominican 88 16.5 77.7 0.84 8 69 2,091,498 0.38 261,437

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Country Rank I-5 +

I-6 I-1 to I-12 McD Index

# of McD

% Urban Pop.

Pop. Count McDensity Pop/McD

Republic India 87 15.6 77.8 0.14 161 29 105,233,740 0.15 653,626 Jordan 86 12.4 77.9 2.10 13 78 1,550,422 0.83 119,263 Turkey 85 13.0 78.2 1.61 116 69 14,438,174 0.80 124,467 Fiji 81 14.2 78.8 3.22 3 52 149,060 2.01 49,687 Thailand 79 12.2 79.2 1.76 115 33 4,521,434 2.54 39,317 Serbia 78 12.9 79.2 1.38 14 52 591,087 2.36 42,221 Venezuela 77 14.9 79.5 5.15 136 93 7,607,204 1.78 55,935 Guatemala 76 14.9 80.6 5.23 68 49 2,566,161 2.64 37,737 Cuba 74 14.0 80.6 0.09 1 76 1,594,585 0.06 1,594,585 Russia 71 14.3 80.8 1.50 211 73 15,140,086 1.39 71,753 Ecuador 69 15.3 81.2 1.15 16 66 2,991,772 0.53 186,986 Belarus 66 12.2 82.3 0.62 6 73 1,003,830 0.59 167,305 Nicaragua 64 15.0 82.6 0.69 4 57 578,705 0.69 144,676 Indonesia 61 15.3 84.1 0.45 108 52 35,099,764 0.30 324,998 China 57 15.3 84.6 1.15 1570 43 11,402,216 13.76 7,263 Azerbaijan 56 13.1 84.6 0.61 5 52 1,023,686 0.48 204,737 Moldova 54 15.0 85.1 0.92 4 42 288,997 1.38 72,249 Philippines 53 14.8 85.8 2.99 287 65 22,436,523 1.27 78,176 Egypt 43 13.8 89.0 0.59 48 43 11,218,432 0.42 233,717 Colombia 41 17.0 89.2 0.87 39 74 9,769,980 0.39 250,512 AVERAGES 13.7 76.0 3.35 109 63 8,815,276 2.50 158,737 MEDIAN 14.0 78.3 1.56 21 67 1,978,362 1.25 79,708

Table 5. Countries classified as “Warning” by FSI Index 2009 with no McDonald's restaurants

Country Rank I-5 + I-6 I-1 to I-12 % Urban Pop. Pop. Count

Mongolia 127 8.1 61.9 57 487,529 Antigua and Barbuda 126 14.3 62.8 30 6,889 Ghana 124 14.7 66.2 50 4,448,218 Trinidad 123 14.7 66.7 13 31,304 Tunisia 121 12.5 67.6 67 1,592,612 Seychelles 120 11.5 67.7 54 10,764 Grenada 119 14.7 67.9 31 8,996 Jamaica 117 13.0 68.6 53 470,521 Botswana 116 13.2 68.8 60 415,474 Libya 112 11.1 69.4 78 1,626,032 Belize 111 14.2 69.5 52 60,621 Albania 109 13.1 70.0 47 395,432 Micronesia 107 15.3 71.9 22 8,232 Kazakhstan 105 10.4 72.5 58 1,949,606 Guyana 104 15.8 73.0 28 55,671 Senegal 102 13.1 74.2 42 2,431,434 Armenia 101 13.2 74.3 64 346,412 Gabon 99 13.8 74.4 85 542,675 Benin 97 14.4 75.5 41 1,630,507 Namibia 96 16.8 75.6 37 280,076 Sao Tome 95 13.4 76.7 61 60,794 Vietnam 94 12.5 76.9 28 6,063,205 Cape Verdi 84 14.4 78.5 60 90,603 Mali 83 14.3 78.7 32 1,956,079 Maldives 82 12.2 78.8 38 33,565 Gambia 80 13.4 79.0 57 443,447 Djibouti 75 13.5 80.6 87 194,426 Algeria 73 13.3 80.6 65 5,652,608 Mozambique 72 15.0 80.7 37 3,552,918 Tanzania 70 12.9 81.1 25 4,414,835 Madagascar 68 12.6 81.6 29 2,605,217 Lesotho 67 12.1 81.8 25 185,358 Swaziland 65 12.0 82.4 25 110,710 Bosnia 63 13.1 83.3 47 313,990 Papua New Guinea 62 16.5 84.1 12 268,459 Zambia 60 14.1 84.2 35 1,870,720 Turkmenistan 59 13.2 84.3 49 691,351 Angola 55 14.9 85.0 57 3,175,701

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Country Rank I-5 + I-6 I-1 to I-12 % Urban Pop. Pop. Count

Comoros 52 12.3 86.3 28 88,859 Bolivia 51 15.6 86.3 66 2,289,576 Togo 50 14.4 87.2 42 1,049,177 Cambodia 49 15.2 87.3 22 1,039,579 Bhutan 48 15.5 87.3 35 73,001 Equatorial Guinea 47 16.4 88.3 39 103,401 Mauritania 46 12.0 88.7 41 525,714 Rwanda 45 14.5 89.0 18 794,282 Laos 44 12.6 89.0 31 863,848 Kyrgyzstan 42 15.8 89.1 36 580,159 Solomon Islands 39 13.0 89.6 18 42,300 Syria 40 14.8 89.8 54 3,906,874

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