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Acknowledgement

I would like to thank all those who in one way or the other has aided in the realization of this research project. I feel particularly indebted to the research methods course facilitator Mr. Shadrack Bett who ably guided the realization of the research project. Equally, my sincere gratitude goes to the project supervisors Messrs. Dr. Patrick Weke and Bwibo Adieri for their kind and able guidance towards fairing up the report to what it has turned out to be.

I wish to acknowledge the enabling and positive support by the Moi University, Nairobi Campus Library staff. Many other contributions have been solicited from many other persons; to them all, the author wishes to pass sincere appreciation. This work, even though is out of my original work, would not have been possible without their contribution.

In this research project where I seem to have used the wordings or phrases of other authors without acknowledgement, if any, is a result of academic upbringing to appreciate their concepts unconsciously and/ or consciously. Thus academics have been allowed appreciation of certain concepts and ideas, not of own origin, and their use have quite unconsciously done, where applicable as part of own-thinking process output. In this regard, faults or expression of ideas and /or spelling or grammatical errors, where they exist, I take full responsibility.

Abstract

This study was focused on identifying measurable social and economic factors influencing visitation to Kenya as a preferred destination and to evaluate the impact of the determinants. Motivated by the importance of tourism in Kenya in relation to the needs for effective and informed planning, developing, operation and management, the study used travel cost method (TCM) as a framework. Thus, with understanding of basic tourism concepts, this study therefore sought explore relevant measurable social and economic factors influencing tourism phenomenon in contexts that can pivot its strategic management. The guiding literature findings of the study underpin and arbores the slow drive towards research informed prudence in tourism management blamed on ‘the inherent weakness in tourism literature’ and ‘factors underlying its emergence and sustenance are still less studied’. The study was thus, motivated by the desire to make a contribution towards the entrenchment and use of TCM in guiding tourism research and research based management in tourism.

The study used Nairobi as a case with about 30% (i.e.470) questionnaire administered. A convenience sampling design was employed on questionnaire survey administration to gather information for the corroboration of economic determinants as conceptualized in the travel cost method. TCM instrument used secondary data of 2005 of 44 independent observations. The study applied descriptive analysis of survey results but the TCM base statistics were subjected to an SPSS regression evaluation.

In conclusion, while the study recommends further investigations in the use of TCM and an understanding of peculiar market characteristics among other areas, underpinned was visitors’ substantive sensitivity to about twelve determinants, namely; travel cost (and of course travel cost rate), currency exchange rate, inflation rate, interest rate, distance traveled, disposable income, literacy level, age, family size, population distribution and population levels in country of origin, the use of only six determinants in TCM framework is found to be contestable. However, as a tool, TCM is successfully used to evaluate the base secondary data and computation of present value albeit weak variable associations with an R-square of 23.4% at 95% confidence level. The use of TCM enabled determination of visit potentials with the base year forecast of 939,988 against observed 827,549 for the year with notable potentials worth marketing attention. Corresponding defining present value of US$3.72 Trillion and a possible expenditure level of US$ US$1,145.6 are derived. TABLE OF CONTENTS Page Title…………………………………………………………………………		i Declaration………………	…………………………………………………. ii Acknowledgement………………………………………………………….. iii Abstract………………………………………………………………………. iv Table of Content………………………………………………………………	v List of Figures and Tables ……………………………………………………	vii Definition……………………………………………………………………. viii Abbreviations and Acronyms…………………………………………………. ix

1.0	INTRODUCTION	…………………………………………………. 1 1.1 Introduction of the Chapter…………………………………………………. 1 1.2 Background to the Study	…………………………………………………. 1 1.3 Statement to the Problem	…………………………………………………. 3 1.4 Objectives of the Study	…………………………………………………. 4 1.4.2 Specific Objectives	…………………………………………………. 5 1.5 Significance of the Study	…………………………………………………. 5 1.6 The Scope and Limitations of the Study	…………………………………. 6

2. 0 LITERATURE REVIEW………………………………………………	8 2.1 Introduction 		…………………………………………………	8 2.2 Tourism Concepts 	…………………………………………………. 8 2.3 Literature Review		…………………………………………………. 9 2.4 Preferred Tourism Destination	………………………………………	11 2.5 Social and Economic Factors Influencing Tourism 	…………………. 12 2.6 Tourism Valuation	…………………………………………………. 13 2.6.1 Travel Cost Method (TCM)	…………………………………………. 16 2.7 Theoretical Frameworks		…………………………………………. 17

3.0 RESEARCH METHODOLOGY	……………………………………. 19 3.1 Introduction		…………………………………………….. 19 3.2 Study Design	……………………………………………………….. 19 3.3 Target Population		…………………………………………………	19 3.4 Sample Design	…………………………………………………………	20 3.5 Data Collection	…………………………………………………………. 20 3.6 Data Analysis Context	…………………………………………………	21 3.6.1 Travel Cost Method Analysis …………………………………………	21

4.0 DATA AND ANALYSIS	………………………………………. 25 4.1 Introduction	…………………………………………………………	25 4.2 General Overview on Data Analysis and Findings	……………………	25 4.3 Demographic Profile of Respondents	…………………………………	25 4.4 Social and Economic Determinants	…………………………………	25 4.4.1 Results – TCM Data and Analysis	…………………………………	29 4.4 Derivative Statistics and Inferences	…………………………………	30

5.0 SUMMARY AND CONCLUSION	………………………………. 32 5.1 Introduction 	…………………………………………………………	32 5.2 Summary of Findings	……….………………………………………	32 5.3 Conclusions 		…………………………….…………………	34 5.4 Recommendations 	………………………………………………	35 5.5 Suggestions for Further Studies 	…………………………………………	36

REFRENCES		……………………………………………………….. I PROGRESSIVE GROWTH RATE 		……………………………….. II QUESTIONNAIRE	……………………………………………………….. III LIST OF STAR-RATED HOTELS 		……………………………….. IV REGRESSION RESULTS			……………………………….. V REGRESSION – INDEPENDENT VARIABLE FORM	……………….. VI TCM BASE STATISTICS	……………………………………………….. VII PROMOTION MIX EFFECTIVENESS	……………………………….. VIII ALTERNATIVE DESTINATIONS EFFECTIVENESS	……………….. IX DIVERSITY OF RESPONDENT’S NATIONALY 	………………………... X DECLARED AMOUNT SPENT PER DAY DURING STAY IN KENYA…	XI ALTERNATIVE PRIORITY DESTINATION 	………………………….. XII PREDICTED AND UN-TAPPED POTENTIAL VBISITATION……………... XIII ANALYSIS OF ANNUAL COST OF TRAVEL AND CONSUMER SURPLUS	XIV List of Figures and Tables

Figures

Figure 1: Schematic diagram illustrating variable relationship for TCM		 ……	18 Figure 2: Visitation demand and consumer surplus ………………………………………. 22

Tables

Table 1: Presentation of Visitor Origin Diversity by Bed Occupation (in ‘000) ……	2 Table 2: Visitor Arrivals		………………………………………………………. 3 Table 3: Tourist Arrivals to Top Ten African Destinations - 2005……………………. 12 Table 4: Social and Economic Stimuli ………………………………………………………	28 Table 5: Base Data for predicting visitor demand ………………………………………. 30 Definition of Terms

This section provide definitions of key words and/ concepts used in the research report.

Visitation (Tourism) - Visitation is used here to subsume all forms of tourism. And tourism has been used here to refer to the activities of persons traveling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes other than routine work, WTO (2002)

Visitor (Tourist) - Visitor or tourist shall be used interchangeably in the report. Where used it shall refer to any person involved in tourism/ visitation or consumes products of tourism within the above definition of visitation.

Destination - Destination shall be used in this report to refer to ‘physical space’ in which a visitor spends at least one overnight. It includes tourism products such as support services and attractions, and tourism resources within one day’s return travel time. It has physical and administrative boundaries defining its management, and images perceptions defining its market competitiveness. And its incorporates various stakeholders often including a host community and can nest and network to form larger destinations’ WTO (2004)

Travel Cost method - Travel Cost method is an econometric (quantitative/measurable) tool that uses measurable socio- economic factors to measure demand for a given item/ service or package of either or both service and item. It relies on neoclassical consumer demand theory that a ‘tourist is a consumer who derives satisfaction from a vector (package) of goods and services, which range from food and medical to travel and recreation’ Var et al (1990).

Star-Rated Hotels - This refers to hotels that have been classified as meeting set bench marks of various rating categories of package (products and services baskets) on the basis of standards specifications as per the Hotels and Restaurants Act, CAP 494, Laws of Kenya. Abbreviations and Acronyms

ACS 		- Annual Consumer Surplus CTDLT	- Catering and Tourism Development Levy Trustees CS		- Consumer Surplus DOT		- Department of Tourism GDP		- Gross Domestic Product GNP		- Gross National Product HDI		- Human Development Index KTB		- Kenya Tourist Board KUC		- Kenya Utalii College MOTW	- Ministry of Tourism and Wildlife MU		- Moi University MBA		- Masters in Business Administration PV		- Present Value WTO		- World Tourism Organization TCM 		- Travel Cost Method TSA		- Tourism Satellite Account US		- United States CHAPTER ONE 1.0  INTRODUCTION

1.1	Introduction

This Chapter presents background on tourism as a phenomenon premising the study area. The Chapter has been structured to provide premising background to the study area and in particular tourism as a concept, phenomena of the moment and as an economic activity. The reporting discerns and, has been steered towards appreciating tourism’s importance, challenges to its growth and effective management in Kenya. The report articulates the specific challenges and the perspective in which influenced and motivated the research. Thus, the Chapter underpins inspiring research questions that founded the four objectives pursued in an attempt to contribute to the evolution of reliable forecasting tools that can premise research-based development and management of the tourism industry in Kenya. Built around current conventional trends and needs amongst tourism industry a player, the Chapter is concluded with the study’s significance and scope.

1.2	Background to the Study

International visitation has grown over the last decades at a progressive average rate of about 10.2% annually with propensity to travel of over one visitor for every one hundred people on global average. The global annual visitation now totaled 736 million in 2005, according to World Tourism Organization (WTO, 2007). This is presenting phenomenal growth opportunities of significant economic benefits. With its economic benefits, third world countries are now giving tourism a serious consideration to provide a gateway to economic empowerment, a development requiring more strategic interventions to sustain the trend.

Many less developed countries like Kenya are fast relying on strategic natural and cultural endowment that make their destinations preferred in the international visitation market to attract the international visitors’ dollars as part of the proactive steps towards solving milliards of socio-economic concerns of the day. Odunga & Folmer (2001) contend that ‘tourism in Kenya contributes 8% to the GDP, provide employment for 470,000 people or 1 in every 15 jobs and generated twenty percent of total exports in 2001’.

Kenya’s tourism is supported by international visitors to the tune of about 70% in room occupancy in tourist accommodation facilities. Moreover, the revenue receipts are of a much higher percentage in favour of the international visitors compared to local residents’ contribution according to KTB. No wonder, in many of the third world destinations, international visitation is the mainstay of the budding tourism. While factors influencing this phenomena still require exploration, that tourism is of international patronage is ‘a fact that is primarily a function of the economy’, Ayala (1995).

As a preferred destination, Kenya is patronized by a diverse supply market with as many unique expectations, needs and wants. The universal patronage is exemplified by the diversity registered in bed occupancy by the various accommodation facilities in the country. Typical here is following tabular presentation of 2003 bed occupation in Table 2 below.

Table 1: Presentation of Visitor Origin Diversity by Bed Occupation (in ‘000) Country of Residence	2003			Country of Residence	2003 1	Permanent Occupants	9.7		16	South Africa	34.4 2	Germany 	420.4		17	Other Africa	39.8 3	Switzerland 	125.9		18	AFRICA	928.9 4	UK	324.3		19	USA	109.6 5	Italy	144.0		20	Canada	17.6 6	France 	113.9		21	Other America	17.5 7	Scandinavia	45.6		22	AMERICA	144.7 8	Other Europe	213.5		23	Japan	26.2 9	EUROPE	1,387.5		24	India	29.2 10	Kenya Residents	738.7		25	Middle East	20.3 11	Uganda 	26.2		26	Other Asia	18.2 12	Tanzania	30.4		27	ASIA	93.8 13	East and Central Africa	27.6		28	Australia and New Zealand	17.0 14	West Africa	15.4		29	All Other Countries	24.2 15	North	16.5			TOTAL OCCUPATION	2,605.9 Source: Adapted from Economic Survey 2004

Kenya has received international visitor patronage since days of hunting four decades ago. The past ten years has seen tourist numbers around one million with 1995 recording 973,614 visitors compared to 827,549 visitors in 2005. As preferred destination Kenya enjoys an ever increasing volume of visitation. In fact computation of progressive growth rate of visitation volume into Kenya over the last three decades stands at 4.5% (see Appendix II). Table 1 below provides a summary of arrivals to destination Kenya for the last two decades.

Table 2: Visitor Arrivals Year	1995	1996	1997	1998	1999	2000 Visitation Volume	973614	1003000	1000600	894300	969300	969300 Year	2001	2002	2003	2004	2005	2006 Visitation Volume	9936000	1001300	1146100		827549	Xxxxx Source: Adapted from Economic Survey (2000, 2004)

1.3	Statement of the Problem

The importance of tourism to the third world countries and in particular, Kenya need not be over emphasized. For Kenya, tourism is the number one economic pillar slated ‘as the leading sector in achieving the (2030) Vision. Kenya aims to be among the 10 long haul destinations in the world offering a high-end, diverse, and distinctive visitor experience that few of her competitors can offer’, Kenya (2007). As a preferred destination, Kenya is enjoying a demonstrated preference with continued steady increase in number of patronizing visitors as well as the diversity of their origin, background and needs. This development come with challenges of sophistication and diverse needs of packages, tourist attractions and engagements as well as services demands in many areas and dimensions that the country, as a destination, is ill prepared for. Responsively and as a result of visitor demands, several and diverse initiatives are rooting amongst visitor hosting and interactions arenas like airlines, airports, conservancies, hospitality facilities, cultural and entertainment centres among others to reduce the negative impacts. Besides, uncoordinated development, many a times of questionable quality and synchrony with existing tourism indulgences, are emerging. This is compounded further by lack of deliberate effort in diversifying tourism activities to include non-traditional attractions, a fact whose solution is registered by Odunga and Folmer (2004) lies in tourism product diversification.

The aforementioned challenges have led to ‘visitors guiding the guides (and host)’ as one report on tour guide survey by CTDLT (2006) observed. Sindiga and Kanunah (1999) puts it thus, ‘tourism has developed in sub-Saharan Africa without planning. Essentially, tourism has grown with government encouragement and private sector participation but without a blue print on the type of tourism desired, growth rate control, and consent by the local communities which are the arenas for guest-host interaction’. To reduce the impact of these problems, certain institutions have resorted to specialized affirmative approaches reactionary to specific needs as they are felt often without much researched basis. In practice guest demands have seen responsive initiatives manifested in dimensions of eco-friendly tourism themes, conservation sensitive practices, language, guest relations handling and animation, cuisine and guest guiding detail and its quality among others. However, these are highly localized and limited reactionary interventions. Therefore, many a cases, the above developments are without a matching deliberate approach towards understanding their emergence or a proactive direction towards desired tourism forms and types at the destination or respective guest-host interaction points. Where a destination seeks to understand the factors leading to emergence of various products and service needs, understanding the customer becomes the central focus. Besides, even if desired tourism types and/ or forms were to guide initiatives, still an understanding of the customer is imperative.

As a preferred destination among many options, Kenya like many destinations is faced with the challenge of understanding her customers and influencing factors. This has led to reduced anticipative and proactive steps necessary to inform an effective tourism management in the destination and its preference enhancement. What is prevailing is a rather reactive mediation to the inherent symptoms often with limited understanding of the visitor and motivating factors. Thus, the need to understand the factors influencing and defining the type of visitors patronizing Kenya as a destination premised this research study. Specifically, attention was paid to social and economic factors influencing demand for (or visit decision to) the destination as contributory step towards proactive and research based management of tourism in Kenya. From the foregoing and with the knowledge that Kenya’s tourist attraction is among the preferred options globally, the following questions arose: What are the social determinants influencing visitation? What are the economic determinants influencing visitation? Can these social and economic determinants be used to provide a basis for predicting future trends to enable proactive tourism plans?

1.4	Objectives of the Study

This research focused on making a contribution towards proactive and research-based management of tourism development, diversification and marketing. The study aimed at understanding and founding use of research-based approaches to tourism management by studying influencing variables and contexts in which they can be of valuable use in tourism management. The use of one of the economic models was put to test as one of the potential tools in aiding forecasting or measurement of preference by various source markets as influenced by their characterizing factors.

1.4.1	Specific Objectives

As a preferred destination, Kenya is receiving visitors from a diverse background and of varied influencing factors and response to these factors. This research is focused on exploring and understanding the social and economic factors influencing the international visitors patronizing Kenya as a destination. Besides, the researched also evaluated the extent and impact of the social and economic factors, particularly measurable factors, on international visit numbers and, the sensitivity and responsiveness to these social and economic factors. Pursuance of these specific areas enabled a comparative analysis of findings of the foregoing to those of evaluated results arising from traditional economic tools – specifically travel cost method in this case.

1.5	 Significance of the Study

As has been mentioned above, ‘tourism is a new recreational phenomenon’ and that factors underlying its emergency and sustenance are still less studied,’ Burkhart & Medlik (1981). For Kenya, studies of the nature of and specific influential factors responsible for international tourism influx are less exploited. Besides, the use of research-based management is still limited amongst key players driving tourism development. Kenya’s national agencies charged with the responsibility of tourism development. Ministry of Tourism and Wildlife (MoTW) - regulating, developing policies and steering overall quality tourism development with a view to diversify the experience and attractions; Catering and Tourism Development Levy Trustees (CTDLT)- developing and regulating national training standards and testing of skills required by the tourism industry; Kenya Utalii College (KUC) - training various disciplines serving the  tourism industry; Kenya Tourist Board (KTB)- marketing destination  Kenya; Tourism Trust Fund (TTF) and Kenya Tourism Development Corporation (KTDC) - both providing finances for tourism  systems and systemic tools development  as well as supporting super-structural development among others, are all limited, to a large extent, by lack of research-driven management. In fact, in relation to tourism research including forecasting international visitation trends, though a fundamental prerequisite and necessity for planning, development and diversification, training and tourism marketing, Sindiga (1994) sums it up thus, ‘no consistent work exist for Kenya’.

As a contribution to embedding research-based management in tourism, the study proposed use of econometrics to conceptualize forecasting tourism based on tourism influencing social and economic factors as a step towards research embracement. Arising inferences from the model would be of significance in enhancing the use of econometric tool in contemporary tourism management. Besides, the study was also motivated by the fact that tourism has become ‘a key sector in economy in the economic development strategy of Kenya’, Kenya (b) (2004), and that consequently there is increasing interest in understanding the main factors that influence visitation demand for Kenya. The value imposed by visitors’ demand on Kenya’s resources is of interest if cost of  marketing and promotion and, need  for conserving as well as preservation of resources devoted for tourism is  to be appreciated. The study evaluated the influence of travel cost and other social and economic factors, namely; travel cost rates, distance and, source country’s population density, gross national products (GNP) and human development index among others. The arising demand function was subsequently used to examine and determine economic value that the visiting tourists attach to the destination.

1.6	The Scope and Limitations of the Study

The area under study and whose tourism demand for resources’ and economic value of tourism resources are being evaluated is Kenya as marked by the political boundary and, commensurate in definition of destination in the preceding parts of this report. Destination Kenya at any one time offers a complex and compounded package that would be difficult to alienate and study singly. Thus, it was convenient, especially for the purposes of this study that destination Kenya, with all the tourism resources as well as influencing factors and attributes that are at play, could be considered from an aggregate demand position. This made it possible to determine overall demand and economic value of the destination.

It should be noted here that the study was conducted with a number of potential constraints. For the research subject and tool used, factors and tenable demand function could be influenced by extrinsic factors. For instance, the instability of oil prices, the volatile and elusive peace in Middle East, unpredictable natural disasters like the occurrence of tsunami have a direct impact on travel to Kenya and even the entire East African region. Travel advisory slaps on Kenya due to perceived porosity of the Kenyan borders, among other security concerns, have served to impact negatively on tourism demand. Moreover, the dilapidated roads and questionable quality of the overall infrastructure and superstructure supporting tourism have not helped.

It was therefore necessary that the research was conducted with assumptive presuppositions in several dimensions. There were assumptions around reliability, relevance, applicability and efficacy in regard to generalizability of the econometric tool proposed for evaluation and, the number of variables deemed to be influencing visitor demand in the used variable form. On the latter, the study assumed that only six variables affect travel demand. While in reality and, as the questionnaire results demonstrated, several quantitative and qualitative factors like politics, and non-measurable socio- culture influence visitation demand. Thus, the econometric model that uses quantitative data has been used in deliberate oblivion. Besides, the econometric tool assumed co-linearity of the variables pointing to applicability of multivariate analysis, a fact that may be contestable in the complexity of issues and their effect and relationships. Moreover, the influence of time value or time value itself has been assumed and hence was discarded as determinant albeit the more that time is money. Besides, many respondents also expressed that time influenced their travel decision in respect to distance to be traveled and planning of travel. For uniformity and in enabling forecasting, the study assumed that air transport is the only mode of transport that the visitors used, which though confirmed by the respondent as the main mode of transport, was pretty presumptuous a position.

The study, though cognizant of inherent limitations of econometric tools particularly generalization, assumed that there would be randomness in the occurrence of variables that the travel cost method rely on. It was therefore assumed that any occurrence of error in measurement on each observation targeted for use in the proposed study also occurred randomly and more so, would be randomly distributed. It is with these in mind that the study focused only on evaluation of inherent travel cost method measurable social and economic determinants influencing international visitation. This was with a view to conceptualizing the forces which impact on visitation and through an evaluation of these forces, provide a broader context for understanding international visitation to Kenya. Thus, the study sought to derive a format that is convergent enough to pivot different but interrelated determinants for Kenya’s international visitation phenomenon as preferred destination and; its measurable social and economic determinants as applicable to travel cost method. CHAPTER TWO 2.0	LITERATURE REVIEW

2.1 Introduction

This Chapter focused on bringing to the fore an appreciation of tourism concepts, tourism perspectives in Kenya in lieu of necessary entrenchment of research-driven management and in particular use of economic techniques as a basis for research. This precedes cultivation of an understanding of travel cost method (TCM), the economic technique that was used to context the study. Besides, this Chapter highlights the perspectives relevant to the technique’s basis for forecasting and evaluating impact of tourism. Entailed here is the revelation of the literature review underscoring the need to embrace research in tourism management and planning. The efficacy of travel cost method and a substantiation of its use in other circumstances are detailed to enable a better understanding of the prevailing situation especially in Kenya. On the basis of the preceding rationale and justification, this Chapter concludes by providing a detailed conceptual framework to enable an in-depth understanding of the travel cost method and the way it was applied in the study.

2.2	Tourism Concepts

The study’s subject area gravitated around tourism phenomenon and, as an economic activity. The term tourism was used synonymous to visitation. The two words have been used here to subsume all forms of tourism. And tourism has been used here to refer to the activities of persons traveling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes other than routine work, WTO (2002). Also related used term is tourist or visitor. Where used, tourist refers to any person involved in tourism/ visitation or consumes products of tourism within the above definition of visitation.

Travel cost method refers to an econometric (quantitative/measurable) tool that uses measurable social and economic factors to measure demand for a given item/ service or package of either or both service and item. It relies on neoclassical consumer demand theory that a ‘tourist is a consumer who derives satisfaction from a vector (package) of goods and services, which range from food and medical to travel and recreation’ Var et al (1990).

In this research, Kenya was viewed under travel cost method as an island destination. Destination here was used to mean ‘physical space’ in which a visitor spends at least one overnight. It included tourism products such as support services and attractions, and tourism resources within one day’s return travel time. It has physical and administrative boundaries defining its management, and images perceptions defining its market competitiveness. And it therefore incorporated various stakeholders often including a host community which can nest and network to form larger destinations’ WTO (2004).

To facilitate in-depth understanding, Nairobi’s star rated hotels were used as reference points to draw sample of target subjects from as a template to discern determinants. The term star-rated hotel used here refers to hotels that have been classified as meeting set bench marks of various rating categories of package (products and services baskets) on the basis of standards specifications as per the Hotels and Restaurants Act, CAP 494, Laws of Kenya.

2.3	Literature Review

Tourism is an economic activity with immense growth potentials the world over. Kenya has benefited from the phenomena with marked growth in arrival volumes. It is therefore prudent that mediating factors are investigated as is the case with destinations experiencing similar trends and phenomena. During these past years, international tourism, typically, has been invisible, yet significant export of the Kenyan economy. These last three decades have seen Kenya enjoy a wave of progress in numbers of overseas visitors with arise from 407,000 in 1975 to 1,523,000 in 2005. Similarly corresponding progressive growth over the last three decades now stands at 4.5% on average (see Appendix IV). Commensurately therefore, Kenya is ‘now putting effort to boost tourism activity in the country…part of which included training of technical personnel that will aid understanding the best way tourism can be taken advantage of to enhance the economy’, Economic Survey (2004). The inherent challenge lies in the fact that ‘tourism is a new recreational phenomenon’ and, that ‘factors underlying its emergence and sustenance are still less studied’, Burkhart & Medlik (1981). This reality has not helped a fast evolution and establishment of informed development of tourism, particularly in Kenya. The situation is worse to the extent that the first and probably the last relevant Government sponsored deliberate national evaluation was last done in 1979 vide Hozlewood (1979). What have since followed are private initiatives mainly of academic orientation.

In view of the foregoing Mowforth and Munt (2003) offers an affirmation and observe that, for third world states like Kenya, ‘the starting point here involves seeking to understand how socio-cultural, economic and political process operates on and through tourism… And ‘the inherent weakness in tourism literature’ is further underscored by the author to be of ‘fundamental importance as it has led to an absence of adequate theoretical critique for understanding the dynamics of tourism and the social activities it involves’. Accordingly, this has premised the evolution of ‘two identifiable groups of research. The first is concerned primarily with auditing, categorizing, listing and grouping the output or consequences of tourism; Part two concludes with conceptualizing the forces which impact on tourism and through an analysis of these forces, providing a broader context for premising research in tourism. Several related studies focusing on Kenya have taken these two dimensions includes Odunga and Folmer (2001) that profiled tourism activities in Kenya; Kareithi (2003) that sought to understand mediating factors and underpin interventions to declining tourism in Kenya in certain niche parts like Mara region.

In Kenya, tourism is predominantly sustained by international tourists,’ a fact that is primarily a function of economy,’ Ayala (1995) yet little, if any, effort has been employed especially by the Government to understand mitigating factors. There is a recorded increased tourist recreation usage of Kenya’s natural and cultural heritage resources, especially in the last three decades, notwithstanding. Little, if any, commensurate sustained proactive investigation has been put in place that matches the increasing need understanding and information. From Sindiga (1994), it can be concluded that first line monitoring and evaluation tools are yet to be identified and entrenched, a fact exacerbated by low availability of conceptual and strategist of tourism faculty and orientation. Recent work by Kareittu (2003) portend that ‘it is clear that the factors contributing to Kenya’s tourism decline are various but interrelated…yet no effort or framework has been employed to understand and come up with mitigating interventions’. This is in agreement with Sindiga and Kanunah (1999) that ‘tourism has developed in sub-Saharan Africa without planning’. This is probably an understatement of the situation.

The evolution of tourism management in Kenya is dogged by dismal or lack of basic systems, systemic tools and management as well as development supporting frameworks. ‘Essentially, tourism has grown with government encouragement and private sector participation but without a blue print on the type of tourism desired, growth rate control, and consent by the local communities which are the arenas for guest-host interaction’, Sindiga and Kanunah (1999).

Past work aimed at understanding the influx or attempts seeking visitation enhancements in Kenya are known to be less and more often not researched. This is due to ‘lack of adequate trained personnel in the field of tourism management’ and, ‘lack of database for research information could be one of the reasons, Sindiga (1994). In fact, the reference study names two other studies of Kiambwarata (1994:10) and; Muya (1994:14) to have reported that ‘to date only 10% of the personnel in the hotel and tourism industry in Kenya have undergone professional training.’ Thus, while tourism returns and noted potential painted by Kenya’s tourism growth are obvious, there seems to be lethargic and advertently no pursuit in pivoting plans, strategic interventions for controlled and directed development as well as its ownership by the host communities! Or else, why the high international visitor volume has not benefited from research and, proactive plans and strategies many a times? The need to embed reliable systematic approaches to tourism research that can credibly inform tourism management is long overdue.

In countries where tourism is as important an industry as it is in Kenya, much has been done in the area of tourism research and research-driven management. Tourism development and diversification with commensurate training for employment in the related areas is, for instance, substantially research-driven deliberately. Marketing Australian destination has been strategically poised to affirmatively base on source market research as TSA (2003) reports. Besides, the same report documents Canada as taking a lead in completing all the twenty-one key parameters in pivoting credible tourism statistics necessary for fully research-based strategic decisions. However, in Kenya, if any and as had been alluded to, whatever information that exist on tourism are incidental; there is hardly any systematic work (in tourism research), Sindiga (1994). Research problem areas remain abound though. And what is of greater importance however is the fact the resources devoted to tourism would be available to produce something else if they were not used for tourism. To merely deduct from the expenditure of tourist the obvious offset such as the import content of goods and services as the benefit would be valid only if Kenyan resources employed in tourism had zero opportunity cost in other words, if they would be producing nothing if they were not employed in tourism, Hazelwood (1979), an observation seconded by Kareithi (2003) in a rather localized work in the Mara region.

2.4	 Preferred Tourism Destinations

Today Kenya is placed sixth amongst African countries as a destination in terms of arrivals. Its preference as a tourist destination primarily relies on natural attractions. In profiling tourism activities premising Kenya’s preference as a tourism destination, Odunga & Folmer (2001) identify natural attractions particularly wildlife, landform and beaches as the key attractions that motivate visitors to destination Kenya. Potential areas requiring attention and diversification are reported to embrace ‘non-traditional products … with culture presenting the greatest untapped potential. In his study focusing on visitation into Maasai Mara Kareithi (2003) while underscoring Kenya’s preference as a destination warns of the dangers of overly relying on wildlife. This challenge notwithstanding Kenya is a preferred destination amongst global choices particularly in Africa. Table 3 below shows arrival into various destinations in Africa, Kenya included. As a preferred destination, Kenya is among the top ten destinations in Africa.

Table 3: Tourist Arrivals to Top Ten African Destinations - 2005 Country	Visitors (000)

2.5	 Social and Economic Factors Influencing Tourism

The preference observed visitor arrival into Kenya witnessed in the past and today is a phenomenon of economic and social importance. As had been mentioned in the previous paragraph the occurrence has presented opportunities worth investing in. It has therefore become imperative in Kenya like many tourist destinations that social and economic determinants at play are understood in the way they influence visitor behaviour, destination choice and management as well as planning. In understanding visitor pattern and factors influencing visitation in Turkey, Var et al contends that besides time, political and legal factors, the authors contend that social and economic factors are key to aiding reliable forecasts of future tourism activities.

In framing demand conditions in a competitive market environment Kotler (1997) presents a number of social and economic factors are projected as affecting choices made. Social factors that are at play or require consideration include demographic aspects - age, family size, gender, population and its distribution. For tourism concerns particularly in evaluation arrivals and visitor behaviour, travel party size, population density and literacy levels. Human Development Index – HDI a compounded parameter, since its inception and acceptance by UN as a more accurate measure of quality of living standards in the late 1990s, is replacing literacy level as a parameter. A number of economic factors are also at play in choice making. Included here are interest rates, inflation rates, natural endowments, income, tax levels, exchange rate and location. Tobias and Mendelssohn (1991) in using travel cost method (focused on here) reduced these social and economic factors to those that are salient and measurable. Particularly, six measurable social and economic factors of travel cost rate, travel cost, distance, HDI, income and population density formed the independent variable to visitation explored. That these are the only influencing factors is debatable and in fact forms part of what this study was set to unravel.

2.6	Tourism Valuation

Tourism like many other human activities requires planning and management informed by reliably predicted future trends. An illustration by Mathieson and Wall (1982) reports on various tourism valuation models relied on here. Estimates of future levels of demand for different commodities, travel volumes, the market share of various source markets, changing visitor tastes and many other economic and social variables are vital to managing and planning tourism development. Thus, valuation or forecasting comes in handing albeit its challenges of underpinning and dealing with the complexity of organizational environment; limited knowledge on factors at play –particularly immeasurable ones as well as the dynamic and diverse nature of various phenomenon requiring forecasting.

Forecasting borrows heavily from the economic concept of demand. As a word the term demand can be ambiguous and subject to many meaning –up to at least four form the Oxford Dictionary (2004). The most traditional definition and the one which this study adopted is that of neo-classical economics that defines demand as ‘a schedule of quantities of some good or service that will be consumed at various specified prices. Thus, various forecasting tools rely on this definition to context independent variables against the dependent variables.

Forecasting tools are several. However, for tourism studies, up to six instruments have been used variously in different circumstances with two on localized economic magnitude of tourism and on tourism demand mainly. The first two which are not the focus area of this study includes Local impact model and, Kreutzwiser estimation. This research focused on one of the second category of four tools which includes simple regression of trend extrapolation; gravity model; the Delphi technique and; probabilistic travel model.

The simple regression of trend extrapolation is used to explore the causal relationship between dependent variable and the influencing dependent variables. Here, regardless of the selected variable set(s), the process followed is the same. The relationship is often of Y = a + bX where Y is the dependent variable, X is the independent variable, a and b are the Y intercept and coefficient to X. Though simple and easy to apply, this model is discredited for being too simplistic. Generalization is also inherent in its data treatment. Gravity model on the other hand is a bit more structured. As the name suggests, it borrows from its analogy to Newton’s law of gravity. Taking from Newton’s gravity model, this gravity model project tourism as being influenced by a pull tourist desire to travel met with equal magnitude of destination attractiveness. Thus, Tij = GPi Aj / Daij, where Tij represents some measure of tourist travel between i and destination j. G and a are the coefficients to be estimated, Pi refers to measure of population size, wealth or propensity to travel. Aj refers to the attractiveness or capacity of the destination while Dij denotes distance between i and destination j. This model is very useful when forecasting travel between a single supply market and a single destination. Besides, this model allows for refinement and modification. However, it becomes a bit limiting where several source markets are to be analyzed against one destination.

The Delphi Model is a forecasting technique used when historical data are unavailable or when existing models require significant levels of subjective judgment. Here, a panel; of experts are assembled by the analyst to respond to a carefully constructed questionnaires designed to move the panel to a consensus on the identity, probability, and timing of future events. A major advantage of the model is that it pivots a controlled setting and an assembly of what can be a very able panel of experts. However, it is highly criticized for being subjective and can be very expensive especially where several source markets or variables are to be evaluated, like was the case in this research.

Probabilistic travel model is another structured forecasting model in which a prediction about travel is made on the basis of a hypothesized structure relating to several travel variables. Here forecasts are in probabilities. It is actually a derivative of a consumer model and accommodates the fact that some consumers may go to other destinations and that travelers will go to all available destinations in varying numbers depending on the level of influence of mitigating factors. An important advantage of this model is that it allows the analyst to avoid the unrealistic assumption that a tourist will always go to the most preferred destination and that all other destinations will be totally ignored. Though criticized for generalization and the assumptions that variables are co-linear and that all visitors have full knowledge of destination. The study uses travel cost method derived from this model backdrop to compare arrivals to Kenya as a preferred destination and forecasts that can be used in planning or to determine impact if social and economic variables are context to inform tourism planning and management decisions.

Kenya’s tourism has suffered perception bias that it is costly a destination. It is important that the perception be corrected from an informed basis. This is to avoid valuing the services on the basis ‘of merely deducting from the expenditure of tourist the obvious offset, such as the import content of goods and services as the benefit would be valid only if resources employed in Kenya’s tourism had zero opportunity cost, in other words, if there sources would be producing nothing if they were not used in tourism’, Hozlewood (1979). Towards this, measurable factors responsible for and are influencing demand that can enable application of TCM, require underpinning and use to proactively evaluate and apply generic results to benchmark tourism performances. For it is actually an irony that the proverbial goose that lay the golden egg is ill understood yet economics and other pure sciences have for generations now provided research frameworks able to shed more light and enrich knowledge in tourism faculty and management tools for its management. In agreement, Tobias & Mendelssohn (1991) had earlier underscored the fact that ‘the aesthetic and the ecological benefits of preserving tourism resources have been acknowledged, yet rarely quantified (in economic terms)’. Surprisingly, as early as the same decade, Var et al (1991) had successfully used econometric tools to pivot research-driven decisions to premise Turkey’s future tourism development.

In fact, in researching for Australian destination, Faulkner and Valero (1995:34) came up with a more elastic ‘target methodology has been developed to provide a bench- mark for analysis of individual markets,’ The Australian tourism authorities recognize the evaluation of demand as a way of directing resources in tourism and, their (resources and visitor) management in tourism system. The bureau of tourism research for Australia has to date subjected quantifiable socio- economic variables in econometrics to arrive at their targets. Tobias & Mendelssohn (1991) did also use econometrics evaluation relying on the econometric methods as the travel cost method. A good account of variables applicable to their study area is identified. Distance and population densities of resource cantons have been deemed most influential. Tobias & Mendelssohn (1991) is credited here for good illustration of the travel cost method.

Thus, this study sought to specifically address the knowledge and knowledge gaps by discerning mediating factors of Kenya’s tourism and, evaluating the factors to provide a systematic basis for forecasting potential demand to premise planning for tourism development in Kenya. Besides, the derivatives have been used here to provide direction for embracing the use of proven frameworks, in particular TCM in tourism forecasting to premise informed and strategic planning, development and management. Historical accounts provided a basis to firm up thoughts that TCM could give favourable and reliable evaluation results that can provide researched facts to pivot informed futuristic decisions are possible. Thus, while discerning and underpinning all or exact determinants that would provide reliable results when subjected to TCM can be elusive, this study, no doubt aided, shedding of light on a number of determinants to consider. More importantly, the study pointed a direction of the usefulness of TCM and what good use of TCM can yield towards tourism management.

2.6.1	Travel Cost Method (TCM)

To premise and put forward a convergent forecasting tool, an export from the economics faculty came in handy. Of relevance were economic tools that could allow evaluation of quantitative data. Such tools fall in the family of econometric tools and/ or techniques. TCM was most prudent and relevant for the purposes of the research context. Besides, TCM was discerned to be a conventional empirical technique that fitted the bill of estimating economic values based on market prices reliant on actual behaviour. The study thus, used questionnaire to corroborate TCM relevant determinants as had been used in previous studies and elsewhere to identify and evaluate relevant measurable social and economic determinants responsible for and influencing international visitation. The results have been used to evaluated and underpin derivable forecasts discussed in subsequent parts below. Attempts were made to use the results to provide evaluated results and conclusions to benchmark tourism planning, strategy formulation and performance.

The research revealed several social and economic determinants are at play. However, there use in TCM as a convergent tool to enable a multivariate evaluation has been guided by past use and economic relevance prescribed by defines and assumption that the determinants are co-linear. To enable this use, observed visits to Kenya as recorded by KTB were used analogously to represent repeat visits. The use of social and economic determinants in TCM was however informed by the results of questionnaire particularly in respect of perceptions and interpretations offered.

2.7	Theoretical Framework

The travel cost method has been used to estimate economic use values associated with ecosystems or sites that are used for recreation. Specifically, TCM can be used to estimate the economic benefits or costs resulting from changes in access costs for a recreational site; elimination of an existing recreational site; addition of a new recreational site and/ or; changes in environmental quality at a recreational site. The basic premise of the travel cost method is that the time and travel cost expenses that people incur to visit a site represent the “price” of access to the site. Thus, peoples’ willingness to pay to visit the site can be estimated based on the number of trips that they make at different travel costs. This is analogous to estimating peoples’ willingness to pay for a marketed good based on the quantity demanded at different prices.

To evaluate value leading to determination of user fees in their study, Knapman & Stoekl (1995), marginal value has been reached via observed demand trends. Demand level identification as a prerequisite to value determination is offered as a way of evading under estimation of attractions, resources and a means of attaining ‘realistic’ value. The study, Knapman & Stoekl (1995), recognizes and applies the successful use of the travel cost method, which though subject to criticism, has been thoroughly tested over more than 25 years and found to be  reasonably an accurate way to estimate empirical demand functions and benefits (value) of recreation.

Travel cost method relies on quantifiable socio- economic factors as a measure on influence on demand. Many factors, most of which are socio- economic in nature, are responsible for observed demand trends in resources’ use in tourism. This is besides mere attractions these resources offer due to their own uniqueness and for strategic location with respect to target market. In evaluation of the factors which ‘most influence’ the international tourism demand for Turkey, Var-et -al (1990), many points stands out. Most important is that a country’s attractions and supporting resources can have demand for – and value of tourism established as an entity. This is by the country being taken as a site evoking desirability for being visited among the tourists. The use of ‘travel cost method’ was an aiding economic tool, Var-et –al (1990).

Travel cost method was used with a neoclassical consumer demand theory that ‘a tourist is a consumer who derives satisfaction (utility) from a vector (package) of goods and services, which range from food and medical to travel and recreation’. This is used in tandem with the a priori definitions advanced of visitation, tourism and visitor or tourist. Econometric evaluations, travel cost being a utilitarian method notably, are part of the management’s problem solving process. Econometric method as is proposed here, have a part to fill in a system’s management that the spontaneous evolvement of tourism presents to concerned authorities.

Figure 1: Schematic diagram illustrating variable relationship for TCM

(Influence)

(Dependent Variable)

(Independent variables) Source: Author 2007 CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1	Introduction

This Chapter details research design used to advance the study’s objectives. The research questions underpinned by measurable relevant social and economic determinants influencing tourism visitation into Kenya are evaluated. The choice and inclusion of questionnaire stimuli (social and economic determinants) were, to some extent, a subject of TCM context and relevance. TCM previous applications and documentations were instrumental and influential. This chapter illustrates dimensions in which determinants were discerned, data collected and treated for analysis. Specific approaches used in data sampling and collection for each level is outlined. The chapter details how the study’s derivatives were structured and prepared for analysis and, concludes with salient expected output of the process.

3.2	Study Design

A descriptive research design was used with data collection premised on questionnaire survey. Besides, to advance applicability of the discerned determinants in forecasting using TCM, secondary data of particularly observed and collated social and economic determinants were used as a basis for analysis. As has been aforementioned, the second level essentially entailed use of secondary data to evaluate the extent and direction of influence of these discerned social and economic determinants on visitation based on observed base year statistic in context of TCM. Observed visitation statistics of year 2005 (to be the base year for the study and subsequent use of the arising generalized form) were used. The secondary data relied more on WTO/UN records on tourism/ visitation and global socio-economic indicators, global distance and web-based air tariffs for air travel cost determination for source countries i.e. between principal exit points and Nairobi, Kenya. Thus, Kenya’s 2005 international visits records collected by KTB were used.

3.3	Target Population

The study targeted international visitors patronizing star rated hotels in Nairobi, Kenya. Thus, the questionnaire administration point was at all the 43 star-rated hotels in Nairobi (defined by the administrative boundary). Visitors in Nairobi’s star-rated hotels totaling 43 (see Appendix IV) representing over 21% of the 232 star-rated hotels in Kenya were targeted. Thus, thirty percent of the potential visitor volume patronizing these star-rated hotels received questionnaires during the administration day(s). The 470 questionnaires that were administered is equivalent to thirty percent of the total rooms occupied (55.9% room occupancy) considering total rooms are 2811. The universal figure was discounted so as to reflect the real situation of room (NOT bed) occupancy during the preceding four months of April, May, June and July 2006 averaging 55.9%. These months (these are shoulder months of low and high seasons) were chosen deliberately to offset seasonality tourism is prone to. While the respondent chances were fairly catholic, only responses from international visitors counted.

3.4	Sample Design

In pursuit of the first two objectives on the relevant (to TCM) measurable social and economic factors, convenience sampling was used to reach about 470 visitors (tourists) mentioned above for the other two objectives requiring evaluation and entrenchment of use of TCM in forecasting in tourism, secondary data was gathered. The secondary data of all visits to Kenya by various citizenries as observed during the proposed base year – 2005 and, data on the same year on TCM relevant social and economic determinants for each visitor’s source country was collected for evaluation. The year 2005 was used as base year rather than 2006 due to data completeness for the said relevant statistics.

3.5	Data Collection

The researcher used six agents inclusive of one supervisor to administer questionnaires and collect responses to aid evaluation. As per the foregoing, the questionnaires were administered universally with the aim of reaching the set target of 30% of total room capacity of the 43 star-rated hotels in Nairobi (see Appendix IX). Three-week duration of questionnaire administration was allowed to the agents. During this period the agents and their supervisor were exposed to salient basics and approaches to take while approaching and administering the questionnaires. Coupled with the researcher’s background in the tourism, the agent formation and capacitation effort did pay dividends. Besides, the ample time of three weeks also enabled the agents an adequate time to ensure response and overcoming the foreseen challenges of translation.

The questionnaire (Annex VII) was designed and administered to capture stimuli responses on measurable social and economic determinants in a manner that was to allow descriptive analysis by simple frequency discernment and weighting. Care was made to ensure inclusion of counter-checking stimuli for key determinants. It should be noted here that the key and relevant stimuli necessary to respond to the needs of TCM could easily be attained from tabulation under question twelve in the questionnaire. The preamble questions were deliberately included necessarily as formative source of information on the overall study area.

3.6	Data Analysis Context

Simple weighted scores provided a basis for corroborating influence of social and economic factors. The secondary data on the other hand were subjected to casual multivariate regression analysis on SPSS package. Relevance and appropriateness used was R-square, standard deviation, p- and f-values derived from the analysis. It suffices to outline how TCM procedure was applied.

3.6.1	Travel Cost Method Analysis

Economic studies that examine the demand for total or holiday/ leisure international tourism generally include some of the following demand determinants; population density,  gross national product, exchange rates, literacy levels (illiteracy level), distance and a host of persistent determinants, Witt et al (1995). While it is true that some of these factors may not particularly be relevant to the proposal’s context, many obviously influence international visitation demand for tourism in Kenya. A specific econometric demand measure for leisure used in this study is the well known travel cost method. TCM estimates the value users place upon the site they visit from their travel behaviour, Tobias & Mendelssohn (1991). TCM, besides being acceptable in many studies, is applicable in this study’s context and is also known to be more accurate in forecasting demand. TCM measures the demand functions for visitors to a destination and empirical relationship between the price of a product and quantity purchased.

Q = ƒ (x1, xs)	 ……….……….……….……….……….………. (3.1)

Where Q is the quantities purchased at price x1 while xs represents the influential socio- economic considerations like income and literacy levels among others.

Subject to budget constrain, economic theories have ‘proved that quantity of a good demanded would go down with increase of price’, Tobias & Mendelssohn (1991). At any given price, the demand function reveals the quantity a consumer would purchase. Thus, the demand function also reveals price a consumer would pay for any specific quantity, Var et al (1990). From figure 1 (in the next page), at price of K£.300 per visit, the consumer is observed to make 10 trips. This implies that the consumer would pay the K£.300 for this 10th trip assuming that he or she is working within a budget constraint represented by line ab. If the price doubles to K£.600 per a visit, the demand function here suggests that the consumer would take only 6 trips per year. Thus, given to same budget constraint, this change of price would see them now paying K£.600 for the 6th trip. For each trip number, the demand curve reveals what the consumer would pay to take that specific trip rather than do without it, given the budget constraint, Tobias & Mendelssohn (1991). It is on this principle that one can use the demand function to gauge the economic value of destination. If the tourism resources are removed, the destination and the visitors both lose all the opportunity to be visited and to visit respectively. Thus, for a consumer facing price per a visit of K£.300, the loss of tourist attractions (or the opportunity to visit) would be equivalent to loss of 10 trips per year.

To this consumer, the 6th trip is worth K£.600 (given the demand curve) but given the current price (see fig.1), cost him or her 300 Kenya pound. This implies that the 6th trip has a net value of K£.300 (K£.600-300).Using demand curve one can value each trips1 through 10 minus transport costs (saved by not going on the trip (s) as an annual loss of the site and / or his customers). Graphically, the loss of the site is the loss of the area underneath the demand function but above the current price, K£.300 represented by the shaded area in figure1.

Figure 2: Visitation demand and consumer surplus y 1600	- a 1400	- 1200	- 1000	- 800	- 600	- 400	- 200 100                                                 b 0 0     2     4     6     8     10    12   x

Source: Adapted from Tobias and Mendelssohn

Therefore, the annual value of losing the tourism resources of the site is the integral area under the demand function Tobias & Mendelssohn (1991). This can be used to establish a consumer surplus accruing from the visitation or consumption phenomenon.

CS (I) = ∫∞x1 (0)  ƒ (x1, xs) d x1 	…….……….……….……….…			(3.2)

Where CS (I) represents individual consumer’s surplus and x…. is the current price of a trip with xs being socio-economic determinants influencing consumption of a given demand, d x1. The shaded area underneath the demand function and above a price is called consumers surplus, Mathieson (1982) and; Tobias and Mendelssohn (1991). Consumption surplus measures the value of making large charges in quantities of goods purchased. However, in order to calculate the economic value of international tourism demand implied on Kenyan tourism resources, one must sum up the consumer’s surplus estimates of all observed users in a year. Thus,

ACS = ∑Ni =1 CS (I)			…….……….……….……….…		(3.3)

ACS refers to the annual consumer surplus across all total number of observed visitations.

Besides the lost value in consumer surplus, loss of tourism resources would also mean the loss of all future recreational opportunities besides the currently realizable recreational opportunity. The entire future streams of annual recreational values must be included as part of value because they will occur in the future; hence should be discounted to make them comparable with the present. Assuming that the annual value of recreation is constant over time, the ‘present value’ (PV) including the stream of future benefits can be calculated by simply dividing the ACS by the real interest rate (r), Tobias and Mendelssohn (1991).

PV = ACS/ r				…….……….……….……….…		(3.4)

So far this analysis has been referring to only one of the many possible destinations the consumers can visit. The discussion, above suggest the many destinations would depend on how much the site is unique among the possible destinations. This is on the basis that the uniqueness would lead to more visitor influx. For demand increasing due to unique aspects, enhanced visitation would be expected. This rate of increase could be at given average, say ‘w’, a fact that would not be disputed for Kenya’s international tourist’ demand. The equation (iv) would therefore underestimate PV over time assuming the increasing tourist influx persist. Thus,

PV=ACS/ r-w				…….……….……….……….…		(3.5)

This implies that the more a destination is visited at an increasing rate, the more ‘w’ would remain positive; suggesting higher PV with time. Thus, as ‘w’ approaches r, PV also approaches infinity, Simons (1981); and Tobias & Mendelssohn (1991). This anomaly suggests that the rate of increase in visitation should be adjusted to reflect the long-term growth, not the high and probably atypical growth in tourism observed in a given year. For instance, in 2004 and 2005 there was an increase in visitation by 15.7% in Kenya, according to Kenya Tourist Board statistics. This is far much higher than the 4.5% of the progressive increase over a long period of time; in this case for the period from 1975 to 2005 for Kenya, as has been referred to above.

Variation of pricing levels of commodities is important if value is to be derived for the subject commodities. However, it is common knowledge that the cost of use of tourism resources, for instance National Park, is common, (the same for all visitors, in these case international tourism). And what varies in international tourism is the travel cost method variables. For instance, potential visitors from far away face a high price per visit than nearby residents. By examining the number of visit taken by those who are closer versus those further from the site, one can estimate the demand, as influenced by socio-economic variables as well, for visitors to a site, in this case Kenya. Variability of determinants, besides their influence on travel, has played a key role in the extent of variable choice for the study.

CHAPTER FOUR 4.0 	DATA ANAYSIS AND FINDINGS

4.1	Introduction

This Chapter provides insight into the specific analyses contexts in which the gathered data from the questionnaire instrument and secondary data have been subjected. This is commenced by a preparatory part of general overview informed by salient information deduced from the gathered data. Questionnaire treatments of the various stimuli numbering about twelve parts follow to provide direction on the overall picture premising the study. Here, due focus is paid to social and economic stimuli that were deliberately tabulated for reaction to corroborate existence and influence on visitation in line with the study objectives. This chapter concludes with analysis of social and economic determinants as in the context of TCM. Derivative information enabled by TCM is presented and analyzed to inform a basis for forecasting and drawing of conclusions and recommendations on the way forward.

4.2	General Overview on Data Analysis and Findings

The research focused on international tourists patronizing star-rated hotels in Nairobi to template an analysis of social and economic determinants influencing visitation into Kenya as a preferred destination. The questionnaire distributed to all the star-rated hotels had quantity and quality data gathered from lower star rated hotels compared to top five star hotels where in fact the few cases of non-response is attributable. Overall, a response rate of about 87% was achieved with 411 questionnaires (mostly well filled) received back after the end of a three-week window of administration. However, it is worth noting here that even though such a high questionnaire return rate was achieved, not all stimuli got desired response attention. In fact, some stimuli did not get decodable response or a response at all.

4.3	Demographic Profile of Respondents

The Questionnaire administration targeted visitors using star-rated hotels indiscriminately. Interestingly, in the one-week span Nairobi’s 43 star-rated hotels hosted a diverse nationality profile. The Questionnaire administration captured a total of 61 different nationals with only 17 cases being Kenyans, 11 of which were those in the diaspora as could be deduced from the country or residence. Majority of the respondents were patrons on holiday. In fact, 71.3% of those who made declaration on their reason for coming to Kenya fell in the holiday group with well over 57% of them specifically interested in safari or wildlife viewing.

Gender balance defined the population visiting Kenya even though males were slightly more. Respondents presented a percentage ratio of 53:47 in male to female numbers. The visitors patronizing Kenya were found to be of fairly youthful and middle age bracket. Average age of star-rated patrons averaged 42 and 38 for male and female respectively. Majority are couples who deductively fall in the category of ‘just married’. Common traveling party size were predominantly of families of 2-4 persons, accounting for a percentage of about 40% and as the most prevalent travel part size to expect. The respondents were of an overall quality of life falling within medium class as per the UN categorization. This is reflected by country average of non weighted rating of 0.784 HDI.

4.4	Social and Economic Determinants

A total of 75% responded to the stimulus on ‘average money spent on holiday while in Kenya’. An average spending per day for the holiday in Kenya reported stood at between US $ 1,000 -3,000 on the party with about 51% declaring US $ 3,000. This level of family income expendable accounts for less than 20% of family income for 76.6% of those who declared their income. At this expenditure rate, about 66.7% declared they are ready to stay in Kenya for between 9 to 30 days. However, it suffices to state that a significant 24.6% of this number declared stay of beyond a month. Interestingly, a definitive 80% of the 265 who responded on the related stimulus on propensity to travel (number of trips in a year) had the capacity to make between 1 to 4 trips annually.

Word of mouth and internet presented a strong two-some promotion mix providing information at the centre in influencing and informing choice of trip to destination Kenya. These two promotion windows accounted for well over half in score for the six options that were coded for response. The respondents had an opportunity to judge how they rated the cost of holiday they were engaged in. The perceptual investigation on the comparative cost of destination Kenya revealed that 84.3% firming that Kenya is either moderately priced or value for their money if not just cheap. It can be discerned that this judgment on perceptual cost of destination was either based on country of origin’s cost of living or alternative destinations that were prioritized by the respondent.

Alternative destinations that respondents considered as alternatives presented a diverse choice base totaling 46 worldwide. Of these alternative destinations that destination Kenya was pitted with, three, namely Egypt, South Africa and Thailand scored significant margins of a combined 35% capture globally. On overall attraction, Egypt was voted (14%) for its pyramid and cultural attractions - as the strongest next alternative followed by South Africa (12%) for mostly wildlife viewing combined with shopping. Thailand (9%) came third for the single leading appeal line being shopping, culture and beach attractions. At 6% overall score, neighbour Tanzania was rated fourth with wildlife viewing being the main reason for consideration. The traditionally speculated Caribbean Islands with beaches as the main attraction featured but scored a poor insignificant 1%.

This study was inspired by the desire to determine measurable social and economic factors influencing visitor decision to travel to Kenya as a preferred destination. Besides, the discerned factors were to be used to template a convergent forecasting instrument in the context of TCM. For the two levels a questionnaire tool, part of whose results has provided the fore–going results, was administered. The prime purpose was to underpin influence and to what extent. Part 4.3.1 below provides details of the relevant findings. On the other hand, a set of secondary data was gathered from different sources to pivot TCM context analysis as had been used previously.

The diversity of source countries used in the study was substantially defined or limited by the data structured as captured and reported by KTB for the study base year. Thus, corresponding necessary data were attained from UN world statistics as provided on the internet. The results arising are presented in detail in subsequent parts following.

A number of social and economic stimuli were put to respondents for corroboration and indication on the extent of influence. The Questionnaire instrument confirmed that social and economic determinants are at play for visitors to Kenya. This was by simple indication by those who respondent to the table of stimuli. Table 4 below provides a summary of the responses. Response results indicate that travel cost and of course travel cost rate; population levels; distance (to be) traveled; literacy level and; level of income.

Table 4: Social and Economic Stimuli Was your decision to visit Kenya affected or influenced by the following (tick if applicable)? And to what extent on a scale of 1-5? 5 represents very much and1 is least) tick accordingly. Stimuli	Factor	5	4	3	2	1	Total	Weighted Score/Total Cost of travel (transport e.g. air ticket)	E	154	62	59	43	44	362 0.732 Currency Exchange rate	E	60	76	77	37	47	297 0.644 Inflation rate in your country of origin	E	42	47	56	50	55	250 0.577 Interest rate	E	40	40	56	43	53	232 0.575 Distance to be traveled	E	61	55	88	54	39	297 0.630 Availability of disposable income	E	71	54	65	36	30	256 0.678 Tourism attractions (natural) in Kenya	E	174	61	38	48	29	350 0.769 Literacy Level (Awareness Education)	S	45	46	46	58	53	248 0.577 Your Age 	S	22	18	43	40	44	167 0.473 Family size	S	15	19	26	25	61	146 0.460 Size of traveling party	S	15	15	27	37	52	146 0.429 Population distribution	S	48	31	30	48	71	228 0.545 Population levels of country of origin	S	33	44	26	46	83	232 0.512 Key: E = Economic factor			S = Social factor Source: Questionnaire Survey

The weighted score of each indicator was done against the possible total (5 by total observations) to provide the extent of influence. ‘Tourism attractions (natural) in Kenya’ as a stimulus scored the highest with a weighted score of .769 against a potential of 1. It is notable that all the six social and economic determinants were all endorsed by interviewees as influencing their travel with all the stimuli scoring significant points. The least score was 0.512 for population levels and, the highest being cost of travel at 0.732. Besides, the cost of travel received the highest response as a stimulus at 362 out of a possible 410 of the responses received. While it is true that all the stimuli had measurable social and economic determinants, the intent of study was to corroborate specific stimuli for their relevance in use in TCM for forecasting. Thus, this deliberation notes the non social and economic responses that were included to inform for enrichment, the study perspectives and conclusion but focuses on applying the relevant determinants in tested TCM context. 4.4.1	Results - TCM Data and Analysis

The use of TCM as an analysis instrument necessitated collection and collation of secondary data on social and economic for 2005 as the base year. While the target listing was intended to be universal with all the 176 UN country listing being the target number, the study’s findings and data availability re-defined and limited scope to statistic reports by KTB on annual visitation. Table 2 below provides a summary of the set of statistics for each country and/ or group of, by determinant. The said visitation was used as a proxy to propensity to visit. The travel costs used were drawn from internet-based global IATA air fares with the cheapest available rate, often economy class fare, for the target principal city of origin to Nairobi taken as the cost of travel. This was guided essentially by the rational norm of price sensitivity.

SPSS regression analysis of the Appendix VI statistics for individual determinants against visitation levels observed all yielded resulted in responses congruent and in tandem with rational economic decision making process on the interviewees. Thus, for distance as an economic determinant, result showed there is reduced volume of visitation as distance increases between source point and the destination. A defining coefficient of -0.8049 showed agreement with previous conclusion by Var et al (1991) that the more the distance to destination increase, the more planning time and financial resources are to be committed for travel. Travel cost, on the other hand had an increase in cost indicating a negative or reduction in number of potential visits from the destination with a coefficient of -5.9273. Related travel cost equally yielded a negative coefficient of -2250.3 in agreement with traditional economic rational behaviour that as price increases, consumption volume diminishes.

Positive relations to increments in visitation volume have been noted with analysis of income, literacy level and population densities with 0.8428, 28376 and 26.85 respectively. These results are in tandem with age old economic findings of rational behaviour in response to price, literacy level and, that as population density increases, there are high pressures in favour of travel. While overall regression analysis negates this result, past use of population density stimulus on TCM instrument showed significantly the same finding. The overall  regression of the determinant yielded an R-Square of 24.3% at 95% confidence level. No doubt, this is a week relation! The arising forecast equation from regression result statistics is-

y = 48380 - 1.768x1 - 0.187x2 -3028.864x3 + 1.792x4 + 17.007x5 – 71378.51x6 ……….. (4.1) Where y, x1, x2, x3, x4, x5 and x6 represents Visitation, Travel distance, Travel Cost, Travel Cost Rate, Income (GDP), Population Density and HDI respectively.

4.5	Derivative Statistics and Inferences

The aforementioned arising equation has been used to compute potential visits for advancing applicability of TCM using the discerned social and economic factors. The base year under study realized a total visitor arrival of 827,549 (see Table 4 above). This is against a total prediction potential of 939,988 (see Table 5 in the next page) that TCM instrument indicates considering the set of determinant statistics characterizing the visits and countries of origin. Thus, while the TCM is resulted in weak association with R-Square being as low as 23.4%, the instrument still yield near comparable predictions and at levels that can premise marketing with a variance of just over 11.96%. This figure underpins as well what would guide levels of expected performance for the year. And, from the predicted potential given a known visitor arrival growth rate of 4.5% (see computation in Annex II), one can predict future targets and related derivative as well as management information for basing decision.

The statistics gathered and collated by KTB does report only significant visitor volumes. Hence many countries though supply visitors, go unreported as individual entities. Comparing the base statistics in Appendix VII and the listing of country diversity of respondents captured in the questionnaire (see Appendix IX) attest to this. It is for this that TCM finds applicability particularly with formula (vi) above. Table 5 exemplifies TCM use. An arbitrarily selected list of ten countries here provides examples of predicted potential for the countries.

Country	Distance	Travel Cost	TC Rate	GDP	Pop Den	HDI	Predicted Visits 1	NIGERIA	1879	750	.399	1188	142	.448	16275 2	NORWAY	3868	1200	.310	42364	12	.965	47618 3	SWEDEN	3732	1200	.322	29926	20	.951	26669 4	HONGKONG	4728	600	.127	33479	6407	.927	142314 5	THAILAND	3890	861	.221	8368	125	.784	1832 6	TURKEY	2482	673	.271	7950	93	.757	4840 7	RUSSIA	3422	679	.198	11041	8.4	.797	4643 8	LIBYA	2448	1000	.408	11624	3.3	.798	6555 9	BOTSWANA	1543	1000	.648	11410	3	.570	23314 10	PORTUGAL	3486	1200	.344	19335	114	.904	13011 11	SINGAPORE	4860	600	.123	29591	6333	.916	134,854 Table 5: Base Data for predicting visitor demand Source: Author (Survey Statistics and Computation) The main relevance in TCM is its applicability in establishing the measurable figures to otherwise elusive qualitative tourism and hospitality service industry. By relying on key measurable social and economic determinants, one is able to discern quantified levels of influence of each determinants and also more importantly the quantity of volume to premise computation of net perceptual worth of the destination. For instance, from formula (vi) above, the weakness of the instrument notwithstanding, the extent to which travel cost, travel cost rate, distance traveled, population density in country of origin, level of income (GDP) and HDI influenced every source market could be noted on the basis of whether it is positive or negative. However, overall, TCM tool enabled not only the determination of untapped potential demand but also prediction of potential demand that premise value determination. Table 6 below provides a summary of computation of both the predicted and un-tapped potential visitation. Where excess supply is being received can also be noted by simple comparison of actual and predicted figures. These results have been used to compute destination value described in the subsequent parts detailing ACS.

Applying the derived TCM formula (vi) to provide predicated demand for use in formulas (iv) and (v) yielded ACS and subsequent PV value. The computation of ACS is presented in Appendix XIV. While visitors actually used a total of US $ 789,331,917 on travel (a pessimistic estimate considering use of the lowest economy rates possible), application of economic principle of diminishing returns and value attached to demand for destination Kenya puts the ACS (Appendix XIV) at US $345,234,938,400 (Ksh.22.80 Trillion). This implies that each of the observed visitors who patronized Kenya during the base year placed an annual value of US $418,131 and thus would have been willing to spend an average of about US$1,145.6 rather than not visit at all. At this rate it is no wonder the surveyed visitors overwhelmingly judged Kenya to not only be a value for money destination but at the very most an average a moderate cost destination. The established ACS above provides a basis for computing PV. Thus, from the average annual interest rate of 13.8% during the base year and a visitor arrival progressive growth rate of 4.5% (applying formula (v)), destination Kenya’s present value by the close of 2005 could be pegged at US $3.72 Trillion (Ksh.245.52 Trillion). This underpins the need to invest in protecting all that feeds into the package that destination Kenya is key of which is the wildlife as the survey revealed.

CHAPTER FIVE

5.0	SUMMARY AND CONCLUSION

5.1	Introduction

This Chapter summarizes the key and salient findings of the study in relation to the study’s objectives. This is followed by articulation of main lessons from the study and the conclusions that can be made from the findings. Thus, informed by the study findings and main lessons, recommendations have been made in the next part especially on key policy and operational areas from the study lessons. Challenges faced, limitations as well as salient academic lessons have been used to premise suggestions on areas for further research to conclude this report. 5.2	Summary of Findings

In studying Kenya as a preferred destination, this study focused on identifying social and economic determinants influencing international visitation and, discerning the level of impact in the context of TCM. With Nairobi as a case where sampling was carried out, the above pursuit enabled an evaluation of the social and economic determinants to provide salient derivative statistics and inferences. Besides, survey results provided informative decision-shaping information. An open-ended question on potential alternative destination choice in the Questionnaire resulted in visitors providing a total of 46 alternatives (see Appendix IX). Egypt was leading for the love of Pyramids, culture and shopping (14%). This was followed by South Africa (12%) for wildlife and, Thailand (9%) came third for culture, shopping and investment opportunities. The youthful (38 and 42 in age of female to male) visitor to Kenya today is attracted mainly by wildlife. In marketing mix options the main promotion window and influence to traveling to Kenya is word of mouth and internet – two of which marketing items accounted for well over 50% in a range of six options.

The first objective of the study was focused on exploring and understanding the social and economic factors influencing the international visitors patronizing Kenya as a destination. Twelve stimuli were put in the study Questionnaire (Appendix II) on social and economic determinants. The respondents endorsed all the twelve social and economic determinants to be influencing their visitation decision. A total of 77% confirmed cost of travel as influencing their decision to travel with currency exchange rate having 63% response by respondents. The lowest responses were for the social factor Family size (35.5%) and; Age and; Size of traveling party each had (31%). While these findings corroborate relevance of the six determinants used in TCM, the research established that other factors are also at play.

The second objective was to evaluate the extent and impact of social and economic determinants. Weights were used to establish the extent of determinants’ influence. Respondents had choice scale of 1 to 5 (with 5 indicating high influence and 1 least). The responding numbers varied for the twelve as has been highlighted under the first objective. While the weights could not be used to establish whether or not the determinants were influencing negatively or positively, weights could be place for the determinants. Results revealed weighted scores of minimum 0.429 for size of traveling party, and a maximum 0.732 for cost of travel. A high response for Kenya’s wildlife attraction of 0.769 was registered as well. In further attempts to understand the nature of influence, the six social and economic determinants of travel cost method (negative), travel cost (negative), distance (negative), income (positive), population density (positive) and HDI (positive) in the theoretical framework were found to influence visitation differently (see Appendix VI).

The third objective focused on comparing forecasts abased on TCM and actual visitations. The study established levels of visitation that should have benchmarked policy on tourism market and product development and diversification as well as operations. This is by its capacity to underpin specific volumes that each source market could supply totaling 939,988 against the realize number of 827,549 visitors. The enabling use of TCM instrument allowed insights into market potentials that can premise product and service preparedness as well as marketing programme diversification on the part of destination. From Table 4 in part 4.4 above, one notes the prudence of going to market Kenya in the Far East. With a predicted potential of 277,168 visitors, pitching tent in Hong Kong and Singapore alone would have earned Kenya just over a third of total annual visitor receipts for the base year. Looking at the statistics, it is needless to overemphasize the prevailing economic and social factors in these particular two countries!

Last objective was on evaluating the impact of social and economic factors on visitation using TCM to evaluate the value of tourism in Kenya as a preferred destination. The latter finding is probably the most telling of potential reasons for the low association between the variables in relation to the dependent variables from the regression results. The said results had a low R-Square of 23.4% at 95% confidence level bringing to focus the association between the independent variables put to test against the dependant variable. However, this limitation notwithstanding, the reliability of TCM in providing a facilitative prediction of forecasts cannot be gainsaid.

Besides the two cases, the use of TCM revealed that Kenya is receiving visitors from some countries beyond predictable levels. Cases notable include UK, USA, Italy, Germany, France and South Africa. While this can be attributed to the prolific ‘word of mouth’, the statistics displayed characteristics of markets worth treating as a ‘niche markets’ with due peculiarities put to perspective and commensurate value sustained and replicated for other markets in deliberate initiatives. The TCM generic and derivative computation based on the survey results and study statistics enabled the realization of not just the total present value attributable to destination Kenya’s worth but more importantly a basis to discern perceptual levels of cost of the destination. The study found Kenya’s present value to be worth about US $3.72 Trillion (Ksh.245.52 Trillion) and at which level visitors of 2005 would have been willing to spend up to a maximum of US $ US$1,145.6 a day rather than miss out. This is in agreement with the survey findings (Annex XV) that currently majority (53.4%) of visitors spent between US$500 and 1500 a day for their stay in Kenya and hence the modal perceptions of Kenya being ‘moderately priced’ destination.

5.3	Conclusions

Visitation to Kenya as a preferred tourist destination is influenced by several measurable social and economic determinants beyond the six-some of travel cost, travel cost rate, distance, income, population density of source country and HDI or literacy level that TCM has relied on in most previous studies and in particular that of Tobias and Mendelssohn (1991). Albeit the inherent limitations of TCM as an economic and the finding of weak association between the tested independent variables and the dependent variable of visitation premising the study, the reliability of TCM instrument in providing an analysis and evaluation framework cannot be gainsaid. TCM if provided with relevant and inclusive quality statistics of influencing determinants can be very instrumental in providing dependable forecasts for planning, destination’s product development and diversification as well as marketing.

The study underpinned visitors’ substantive sensitivity to about twelve determinants, namely; travel cost (and of course travel cost rate), currency exchange rate, inflation rate, interest rate, distance traveled, disposable income, literacy level, age, family size, population distribution and population levels in country of origin. All these determinants were confirmed variously as influencing travel decisions. It is therefore prudent that future use of TCM in forecasting should be more determinants inclusive so as to realize convergent and fairly more dependable results.

5.4	Recommendations

The limitations notwithstanding, the use and subjection of TCM in this study has enabled a basis for debatable statistics on the destination present value and fact based judgment of cost of stay in Kenya, alternative choices to watch and, visit forecasts (by country origin and volume) that readily premise considerations and decisions in conventional perspectives. For instance, the use of TCM derivatives as a standpoint put to focus the question of destination’s value and the issues of value addition. One can readily concede here that just mere deduction that value is tenable from simple increments in visitor numbers, and of course, corresponding earnings, as has been the priority single pursuit of the tourism systems is a naïve. The alternative destinations fronted by the surveyed visitors probably offer strategic initiatives to template forward looking developments worth considering. For instance, beyond the Pyramids, it is common knowledge that Egypt has for the past twenty years progressively invested in artificial attractions to diversify its attraction base and capacity to retain and host the increasing visitor demand. South Africa and Thailand have and are still on course following on the same strategic paths of enhancing visitor stay through diversification of attractions.

Any destination seeking value addition is likely not to go wrong by investing in development and diversification of product and services base. Beckoning options are abound! Culture, second holiday retiree home concept development, artificial scientific and mega entertainment cities, deliberate transport, communication and industrial as well as market (free port/ duty free) hub creation are but a few options. Besides, the foregoing results of the study present strong signals for premising decisions on where to cast marketing nets. This is over and above the potential guidance on performance levels and resource commitment and allocation. With potentials discerned for various destinations, TCM enables current training and resource capacity development and investment in training capacity to be congruent to future needs. This is with particular consideration to customer understanding (culture and expectations), peculiar customer needs of language and cuisine among others source market characterizing uniques. What but what a future would Kenya portend with indulgence in similar strategic efforts!

The use of TCM to context and inform a basis for evaluation was done at its most elementary form. As an evaluation framework, TCM can take more complex levels in up to another two for more responsive and reliable results. While the ‘simple zonal travel cost approach’ has been used here, an ‘individual travel cost approach’ or a ‘random utility approach’ of TCM could be employed in other future research to provide more effective forecasts. Thus, this study recommends explorations in this direction to aid efficacy and effectiveness of TCM applications in solving contemporary tourism management issues. The noble motivation for this drive are the reality and reason that conventional tourism in Kenya has been managed, for the few decades it has been in existence, on the whims of reactionary approaches rather than deliberate undertakings. Overriding insights have also revealed peculiar predictions that certain markets are providing way beyond the definitions of statistics characterizing the destinations. Kenya is receiving way beyond expectations from UK, USA, Italy, Germany, France and South Africa. This calls for a more detailed investigation to at least confirm or disapprove and/ or underpin contributing factors on one hand and, what can be done to fortify or replicate the same capacities for other markets with what can be termed as rational characteristic supply to Kenya.

5.5	Suggestions for Further Studies

The study has presented a number of insights and challenges around the used framework and lessons of the study. Going through the research process, the complexities inherent of tourism and the elusiveness in underpinning influencing behaviour stimuli with precision are probably the areas to hasten firming suggestion for further research on. Specifically the following areas related to the study are recommended for further studies: 1.	Establishing other factors influencing visitation particularly political, legal and cultural determinants on visitation levels; 2.	Profiling attractions contributing to preference of Kenya as a destination of choice; 3.	Comparative evaluation of various economic forecasting instruments and tools with a view to establishing their effectiveness in using various visitation influencing determinants. APPENDIX	I

REFERENCES

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Barbara &Tanya, T. 1995, Australia’s national Eco- tourism strategy’ UNEP Industry and Environment.

Burkhart, A.J. & Medlik, S. 1981. Tourism: past, present and future.2nd Edition Heinemann publishing Ltd, London.

Covey S. R., 2004, ‘The 8th Habit’, Simon and Schuster, New York.

Faulkner, B.& Valerio, P. 1995, ‘An Integrated Approach to Tourism Forecasting’. Tourism Management. Vol.16 No.1 pp 29-37

Hazelwood, P. 1979, ‘Economic Survey of Tourism: Case of Kenya’, Government Printers, Nairobi.

Holloway, J. C. 1989, The Business of Tourism, Pitman, London.

Jorgenson, F. & Solvoll, G. 1995, ‘Demand Models for Inclusive Tour Charters: The Norwegian Case’, Tourism Management. Vol.17 No.1 pp 17-24

Kareithi S., 2003, ‘Coping with declining Tourism, Kenya’ – PPT Working Paper – www.propoortourism.org.uk/3_kenya -2007

Kenya, The Republic of. (a) 2004, ‘Economic Survey 2004’. Government Printers, Nairobi.

Kenya, The Republic of. (a) 2000, ‘Economic Survey 2000’. Government Printers, Nairobi.

Kenya, The Republic of. (c) 2007, ‘Vision 2030: a competitive and prosperous Kenya’. Government Printers, Nairobi.

Kotler P. 1997, ‘Marketing Management, Analysis and Planning’ Ed.10 Prentice Hall, New York.

Mathieson, A. & Wall, G. 1982. Tourism: Economic, Physical and Social Impacts, Longman, London.

Ministry of Tourism and Wildlife, 2005, ‘National Tourism Policy’, Fair Copy, Ministry of Tourism and Wildlife, Nairobi.

Ministry of Tourism and Wildlife, 1997, ‘Hotels’, Ministry of Tourism and Wildlife, Nairobi

Mowforth, M. & Munt, I. 2003, ‘Tourism and Sustainable Development and New Tourism in the Third World’, 2nd Edition, Routledge, London.

Odunga P. and Folmer H. 2001, ‘Profiling tourists for Balanced Utilization of Tourism- Based Resources in Kenya, www.piglaru.it/chia/Odunga.pdf

Simons, I.G. 1981, ‘The Ecology of Natural Resources’, Edward Arnold Publishers Ltd., London, pp 92-115

Sindiga, I. 1994, ‘Tourism Education in Kenya’, Seminar Paper No.1, 1994/95

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Thomson et al. 1995, ‘Tourism in the Gambia’, Tourism Management, Vol.16 No.8 pp 571-581

Tobias, D. & Mendelssohn, R. 1991, ‘Valuing Eco-tourism in a Tropical Rain Forest Reserve’, AMBIO. Vol.20, No.2 pp 91-93.

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Var, T. et al, 1990, ‘Factors Affecting International Tourism Demand for Turkey’, Annals of Tourism Research. Vol.17. pp 606-610

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APPENDIX	II

PROGRESSIVE GROWTH

PROGRESSIVE GROWTH –COMPUTATION

Observations recorded of visitations are thus,

407,000 in 1975 and

1,523,000 in 2005

Let the progressive growth rate be w.

From the formula,

407,000 * (1 + w)30 = 1523,000

Where 30 is the duration in progression growth rate;

(1+w) = 30√ (1,523,000/407,000)

(1+ w) = 1.044969 or 1.045

w = 0.045

w =4.5%

Progressive growth, w, to be used shall be 4.5%

APPENDIX	III

QUESTIONNAIRE

Moi University							Willis Otieno Ondiek MBA RESEARCH							(MU/EMBA/06/05)

Kenya International Visitor Survey Thank you for accepting this questionnaire. Completing the Questionnaire should take approximately fifteen minutes of your time. The questionnaire survey shall be used to help understand factors influencing your decision to visit Kenya with a view of providing a premise to improve Kenya as a visitor destination. The information collected by this survey will be used strictly in confidence. Kindly complete the whole questionnaire.

Please indicate date: …………………………………..

1. Kindly state the following in respect of self: •	Gender:    Female (…)     Male (…) Age………years •	House hold/ family size: …………………….. •	Level Education: Elementary (…….) Secondary (……) Tertiary (……) Other (please specify): ……………………………………………………… •	Nationality……………………	Country of residence ………………. 2.	What was your point of origin (name of principal city and country of origin) to Kenya? ………………………….. (city)…………………………….(country) 3.	How did you know about destination Kenya? (Please specify) …………………………………………………………………… 4.	What is the main purpose of your visit? Holiday (…)	Conference (…)	Business (…)	Education (…) Other (specify): ……………………………………………………… 5.	How many times do you travel to various international destinations in a year? ……………………………………………………………. 6.	What are the two main reasons that prompted your travel? (i)…………………………………… (ii)……………………………. 7.	Name some of the two priority destinations you considered before deciding to come to Kenya and why? 1)………………………………	2)…………….………….. Why? 1)………………………………	2)…………………………

8.	Which mode of transport did you use to come to Kenya? Air Flight (…….) Road (…..)Sea (…….) If other specify……………………………….. 9. What is the estimated cost of your entire trip? US $ ……………………………. •	Cost of stay at in Kenya excluding transport cost, in US$………………. •	Transport cost e.g. flight ticket cost US $…………………. •	Kindly provide an estimate of your annual household earning US $ …………. 10. What is the planned duration of your stay in Kenya? ………………. (Days) 11. On the basis of the services consumed, how do you gage the cost of your stay on the following scale? Very expensive (…) Expensive (….) Moderate (…) Value for money (….) Cheap (…..) 12.	Was your decision to visit Kenya affected or influenced by the following (tick if applicable)? And to what extent on a scale of 1-5? 5 represents very much and1 is least) tick accordingly.	Factor	Tick (√)	5	4	3	2	1 Cost of travel (transport e.g. air ticket)	E						Currency Exchange rate	E						Inflation rate in your country of origin	E						Interest rate	E						Distance to be traveled	E						Availability of disposable income	E						Tourism attractions (natural endowments) in Kenya	E						Literacy Level (Awareness Education)	S						Your Age 	S						Family size	S						Size of traveling party	S						Population distribution	S						Population levels of country of origin	S						Others (Specify): ………………………………….							Key: E = Economic factor			S = Social factor END. Thank you for completing this Questionnaire.

APPENDIX	IV

LIST OF STAR-RATED HOTELS

ESTABLISHMENT	POSTAL ADDRESS	LOCATION	STAR RATING	TOTAL ROOMS 1	HILTON HOTEL INTER	BOX 30624 NAIROBI	MAMA NGINA ST	*****	143 2	HOTEL INTERCON. BOX 30353 NAIROBI	PARLIAMENT RD	*****	385 3	NAIROBI SAFARI CLUB	BOX 43564 NAIROBI	UNIVERSITY WAY	***	89 4	NAIROBI SERENA HOTEL	BOX 46302 NAIROBI	KENYATTA AVENUE	*****	190 5	NEW STANELY HOTEL	BOX 30680 NAIROBI	KENYATTA AVENUE	*****	108 6	NORFOLK HOTEL	BOX 40064 NAIROBI	H/THUKU RD	*****	167 7	SAFARI PARK HOTEL	BOX 45038 NAIROBI	KASARANI	*****	192 8	GRAND REGENCY HOTEL	BOX 57549 NAIROBI	UHURU HIGHWAY	*****	111 9	WINDSOR GOLF HOTEL	BOX 45587 NAIROBI	KARURA	*****	130 10	FAIRVIEW HOTEL	BOX 40842 NAIROBI	COMMUNITY	****	83 11	HOLIDAY INN	BOX 66807 NAIROBI	WESTLANDS	****	171 12	BOULEVARD HOTEL	BOX 42831 NAIROBI	THUKU RD	***	70 13	CASTLE INN	BOX 74411 NAIROBI	GARDEN ESTATE	***	5 14	HOTEL MILIMANI	BOX 30715 NAIROBI	MILIMANI RD	***	73 15	HOTEL SIX EIGHTY	BOX 43436 NAIROBI	KENYATTA AVENUE	***	56 16	KENKO HOTEL	BOX 55127 NAIROBI	HURLINGUM	***	5 17	KENTMERE CLUB	BOX 39508 NAIROBI	TIGONI	***	13 18	LENANA MOUNT HOTEL	BOX 40943 NAIROBI	R. BUNCHE RD	***	50 19	MARBLE ARCH HOTEL	BOX 18755 NAIROBI	LAGOS RD	***	36 20	PANAFRIC HOTEL	BOX 30468 NAIROBI	KENYATTA AVENUE	***	120 21	SILVER SPRINGS HOTEL	BOX 61362 NAIROBI	VALLEY ROAD	***	62 22	ORIENTAL PALACE HOT. BOX 72237 NAIROBI	TAVETA ROAD	***	15 23	DELAMERE CAMPS LTD	BOX 48019 NAIROBI	SAINT ST.	**	16 24	OAK WOOD HOTEL	BOX 40683 NAIROBI	KIMATHI ST	**	24 25	PARKSIDE HOTEL	BOX 53104 NAIROBI	MONROVIA ST	**	45 26	SOLACE HOTEL LTD	BOX 48867 NAIROBI	TOM MBOYA ST	**	28 27	SUNCOURT INN	BOX 51454 NAIROBI	UNIVERSITY WAY	**	30 28	WEST VIEW HOTEL	BOX 14680 NAIROBI	WAIYAKI WAY	**	14 29	ABBEY HOTEL	BOX 75260 NAIROBI	GABERON RD	*	16 30	ESPERIA HOTEL	BOX 14642 NAIROBI	MUTHITHI	*	16 31	FIG TREE HOTEL	BOX 31938 NAIROBI	NGARA	*	31 32	HILLCREST HOTEL	BOX 14284 NAIROBI	WESTLANDS	*	19 33	IMPALA HOTEL	BOX 14144 NAIROBI	PARKLAND	*	21 34	KARANGI HOTEL	BOX 53104 NAIROBI	CHAMBERS ROAD	*	12 35	KENYA CONT.’AL HOTEL	BOX 73893 NAIROBI	RHAPTA	*	15 36	KENYA INTER’L HOTEL	BOX 22411 NAIROBI	MURANGA ROAD	*	24 37	KWALITY HOTEL	BOX 44275 NAIROBI	ARGWINGS KODGE	*	13 38	MILIMANI SAGRET HOTEL	BOX 18324 NAIROBI	MILIMANI RD	*	24 39	NGONG HILLS LTD	BOX 40485 NAIROBI	NGONG ROAD	*	34 40	PERSONIC HOTEL	BOX 28783 NAIROBI	KIRINYAGA ROAD	*	15 41	SAMAGAT HOTEL	BOX 10027 NAIROBI	TAVETA ROAD	*	20 42	SIRONA HOTEL	BOX 20320 NAIROBI 	KEIYO ROAD	*	15 43	PANARI HOTEL	BOX 	MOMBASA ROAD	***	105

TOTAL 2811

APPENDIX	V

REGRESSION RESULTS

Model Summary b

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate

1	493a	.243	.117	27313.942773

Model Summary b

Model Change Statistics

Durbin-Watson R Square Change	F Change	Df1	Df2	Sig. F Change 1	.243	1.930	6	36	.102	1.170

Coefficients a

Model	Un-standardized Coefficients	Standardized Coefficients

t

Sig. B	Std. Error	Beta 1         (Constant) x1 x2 x3 x4 x5 x6	48380.354 -1.768 -.187 -3028.864 1.792 17.007 -71378.510	40736.543 4.482 9.556 32960.774 .729 34.268 59775.355	-.128 -.007 -.023 .804 .081 -.460	1.188 -.394 -.020 -.092 2.458 .496 -1.194	.243 .696 .984 .927 .019 .623 .240

Regression equation

y = 48380 - 1.768x1 - 0.187x2 -3028.864x3 + 1.792x4 + 17.007x5 – 71378.51x6 APPENDIX	VI

REGRESSION – INDEPENDENT VARIABLE FORM

a) Distance

b)	Travel Cost

c) Travel Cost Rate

d) Income (GDP)

e) HDI (Literacy Level)

f) Population Density

APPENDIX	VII

TCM BASE DATA STATISTICS COUNTRY	VISITS	TRAVEL DISTANCE. TRAVEL COST	TRAVEL COST RATE	INCOME (GDP)	POPULATION DENSITY HDI* 1.		ARGENTINA	420	5616	2612	.465	14,838	13.9	.863 2.		AUSTRALIA	11,573	5933	1678	.283	32,686	2.6	.957 3.		AUSTRIA	11,038 3157	600	.190	34,256	98	.944 4.		BELGIUM	14,397 3539	1019	.288	32,468	341	.945 5.		BRAZIL	777	5074	3124	.616	8,826	22	.792 6.		CANADA	17,156 6391	1941	.304	42,402	3.2	.950 7.		CHILE	272	5086	4868	.957	12,254	22	.859 8.		CHINA	11,655 4973	1655	.333	6,760	137	.768 9.		COLOMBIA	420	6656	4550	.684	7,630	40	.790 10.		EGYPT	2,280	1906	460	.241	4,498	74	.702 11.		ETHIOPIA	9,154	629	300	.477	900	70	.371 12.		FRANCE	41,466 3498	1883	.538	30,322	110	.942 13.		GERMANY	74,615 3437	1050	.305	31,472	232	.932 14.		INDIA	32,030 2444	588	.241	3,547	336	.611 15.		ISRAEL	3,500 1986	876	.441	23,800	304	.927 16.		ITALY	67,654 2900	600	.207	29,727	193	.940 17.		JAPAN	12,643 6074	1496	.246	32,640	339	.949 18.		KOREA 	4,774	5455	1500	.275	24,130	480	.912 19.		MALAWI	3,705	783	595	.760	619	109	.400 20.		MALAYSIA	835	3900	1108	.284	11,914	77	.805 21.		MAURITIUS	1,646	1659	564	.340	13,185	610	.800 22.		MEXICO	1,030	7995	2111	.264	10,474	55	.821 23.		NETHERLANDS	29,025 3597	1000	.278	31,989	392	.947 24.		NEW ZEALAND	2,109	7522	1933	.257	25,848	14.9	.936 25.		PAKISTAN	3,666	2754	814	.296	2,706	198	.539 26.		PHILIPPINES	1,101	5080	1139	.224	4968	277	.763 27.		REST OF EUROPE	19,615 3680	979	.266	25787	197	.931 28.		REST OF AMERICA	1,053	6785	2957	.436	15432	124	.751 29.		REST OF ASIA	7,693	3451	1039	.301	8754	114	.637 30.		R. OF AFRICA	59,564	872	496	.569	1534	120	.431 31.		SAUDI ARABIA	490	1376	392	.285	14970	11.4	.777 32.		SCANDINAVIA	24,924 3617	1000	.276	31,989	180	.937 33.		SEYCHELLES	2,435	1134	550	.485	12481	177	.842 34.		SINGAPORE	723	4023	1108	.275	29591	6333	.916 35.		SOUTH AFRICA	26,295	1576	620	.393	11826	39	.653 36.		SPAIN	14,617 3338	1000	.300	26,009	85	.938 37.		SWITZERLAND	20,881 3280	946	.288	34498	176	.947 38.		TANZANIA	21,739	362	351	.970	771	41	.430 39.		UAE	6,900	1852	592	.320	23291	54	.839 40.		UGANDA	20,971	280	333	1.189	1890	120	.502 41.		UK	155,405 3680	800	.217	31528	246	.940 42.		USA	72,772 6388	1493	.234	43555	31	.948 43.		ZAMBIA	6,801	987	595	.602	953	15.5	.407 44.		ZIMBABWE	5,730	1041	595	.572	2576	33	.491 HDI* REFERS TO Human Development Index Source:	 Adapted from UN Statistics 2006; Airline Fare Tariffs and; Distances between Principal Airports

APPENDIX	VIII

PROMOTION MIX EFFECTIVENESS

Statistics on Promotion mix

How did you know about Kenya? Respondents Word of mouth	75

Promotion and promotional materials (brochures)	41

Travel and tour agencies	24

Media and public awareness	25

Through business or work	24

Education	26

Internet	66

Graph of Promotion mix

APPENDIX	IX

ALTERNATIVE DESTINATIONS PREFERENCE

Benin	1		Nigeria	2 Botswana	3		Norway	1 Brazil	1		Oman	4 Caribbean Is. 1		Paraguay	1 China	7		Portugal	1 DRC	1		Rwanda	1 Egypt	22		Saudi Arabia	1 Finland	2		Seychelles	2 France	6		South Africa	18 Ghana	1		Spain	2 Greece	2		Sudan	1 Haiti	1		Sweden	1 India	1		Switzerland	6 Iraq	2		Tanzania	9 Israel	3		Thailand	13 Italy	1		UAE (Dubai)	5 Japan	2		Uganda	5 Korea	1		UK	7 Malawi	1		USA	4 Malaysia	1		Venezuela	1 Morocco	1		West Indies	1 Namibia	1		Zambia	1 Zimbabwe	1

APPENDIX	X

DIVERSITY OF RESPONDENTS’ COUNTRY OF NATIONALITY

1	Afghanistan		22	Germany		43	Pakistan 2	Algeria		23	Ghana		44	Panama 3	Armenia		24	Greece		45	Peru 4	Australia		25	Hungary 		46	Poland 5	Bahamas		26	India		47	Romania 6	Belgium		27	Israel		48	Rwanda 7	Botswana		28	Italy		49	Saudi Arabia 8	Brazil		29	Jamaica		50	South Africa 9	Bulgaria		30	Japan		51	Spain 10	Burundi		31	Korea		52	Sudan 11	Canada		32	Kuwait		53	Sweden 12	China		33	Lebanon		54	Tanzania 13	Costa Rica		34	Liberia		55	Turkey 14	Cuba		35	Libya		56	UAE 15	Cyprus		36	Madagascar		57	Uganda 16	Denmark		37	Mexico		58	UK 17	DRC		38	Morocco		59	USA 18	Egypt		39	Namibia		60	Zambia 19	Ethiopia		40	New Zealand		61	Zimbabwe 20	France 		41	Nigeria 21	Georgia		42	Norway

APPENDIX	XI

DECLARED AMOUNT SPENT PER DAY DURING STAY IN KENYA

Test Question:

What is the estimated cost of your entire trip? US $ ……………………………. •	Cost of stay at in Kenya excluding transport cost, in US$………………. •	Transport cost e.g. flight ticket cost US $…………………. COMPUTER US $ SPENT PER DAY 	Respondents 1-499	21	500 – 1000	70	1000- 1499	67	1500 – 2000	46	2000 – 2499	35	2500 – 3000	32	3000 plus	37 Not declared	103 411

APPENDIX	XII

ALTERNATIVE PRIORITY DESTINATION

S/NO. Score		S/NO. Score 1	BENIN	1		24	NORWAY	1 2	BOTSWANA	3		25	OMAN	4 3	BRAZIL	1		26	PARAGUAY	1 4	CARIBBEAN IS. 1		27	PORTUGAL	1 5	CHINA	7		28	RWANDA	1 6	DRC	1		29	SAUDI ARABIA	1 7	EGYPT	22		30	SEYCHELLES	2 8	FINLAND	2		31	SOUTH AFRICA	18 9	FRANCE	6		32	SPAIN	2 10	GHANA	1		33	SUDAN	1 11	GREECE	2		34	SWEDEN	1 12	HAITI	1		35	SWITZERLAND	6 13	INDIA	1		36	TANZANIA	9 14	IRAQ	2		37	THAILAND	13 15	ISRAEL	3		38	TURKEY	2 16	ITALY	1		39	UAE (DUBAI)	5 17	JAPAN	2		40	UGANDA	5 18	KOREA	1		41	UK	7 19	MALAWI	1		42	USA	4 20	MALAYSIA	1		43	VENEZUELA	1 21	MOROCCO	1		44	WEST INDIES	1 22	NAMIBIA	1		45	ZAMBIA	1 23	NIGERIA	2		46	ZIMBABWE	1

APPENDIX	XIII

PREDICTED AND UN-TAPPED POTENTIAL VISITATION Countries	Actual	Predicted Potential	Un-tapped Potential 1	ARGENTINA	420	1,780	360 2	AUSTRALIA	11,573	27,028	15,455 3	AUSTRIA	11,038 37,783	26,745 4	BELGIUM	14,397 37,590	23,193 5	BRAZIL	777	-3,382	0 6	CANADA	17,156 44,026	26,870 7	CHILE	272	-20,953	0 8	CHINA	11,655 -2,105	0 9	COLOMBIA	420	-8,346	0 10	EGYPT	2,280	3,405	1,125 11	ETHIOPIA	9,154	22,089	12,935 12	FRANCE	41,466 29,183	0 13	GERMANY	74,615 35,002	0 14	INDIA	32,030 11,677	0 15	ISRAEL	3,500 25,021	21,251 16	ITALY	67,654 31,971	0 17	JAPAN	12,643 33,134	20,491 18	KOREA 	4,774	23,929	19,155 19	MALAWI	3,705	18,994	15,289 20	MALAYSIA	835	5,617	4,782 21	MAURITIUS	1,646	21,211	19,565 22	MEXICO	1,030	-5,847	0 23	NETHERLANDS	29,025 37,387	8,362 24	NEW ZEALAND	2,109	13,704	11,595 25	PAKISTAN	3,666	12,206	8,540 26	PHILIPPINES	1,101	-2,341	0 27	REST OF EUROPE	19,615 23,992	4,377 28	REST OF AMERICA	1,053	10,668	9,615 29	REST OF ASIA	7,693	13,331	5,638 30	R. OF AFRICA	59,564	19,048	0 31	SAUDI ARABIA	490	16,570	16,080 32	SCANDINAVIA	24,924 28,344	3,420 33	SEYCHELLES	2,435	10,921	8,486 34	SINGAPORE	723	135,577	134,854 35	SOUTH AFRICA	26,295	19,537	0 36	SPAIN	14,617 22,483	7,866 37	SWITZERLAND	20,881 38,750	17,869 38	TANZANIA	21,739	16,123	0 39	UAE	6,900	26,795	19,895 40	UGANDA	20,971	13,817	0 41	UK	155,405 34,653	0 42	USA	72,772 47,009	0 43	ZAMBIA	6,801	17,621	10,820 44	ZIMBABWE	5,730	14,826	9,096 Source: Author (Survey Statistics and Computation) APPENDIX	XIV

ANALYSIS OF ANNUAL COST OF TRAVEL AND CONSUMER SURPLUS Countries	Annual Cost of Travel (US $) 	ACS (US $) 1	ARGENTINA	1097040	30065481 2	AUSTRALIA	19419494	5905094282 3	AUSTRIA	6622800	11473636740 4	BELGIUM	14670543	11372590560 5	BRAZIL	2427348	102316644 6	CANADA	33299796	15633299300 7	CHILE	1324096	3623610359 8	CHINA	19289025	23695985 9	COLOMBIA	1911000	596709708 10	EGYPT	1048800	94569561 11	ETHIOPIA	2746200	3920455180 12	FRANCE	78080478	6886358922 13	GERMANY	78345750	9864045300 14	INDIA	18833640	1100604849 15	ISRAEL	3066000	5043706397 16	ITALY	40592400	8218210869 17	JAPAN	18913928	8855936758 18	KOREA 	7161000	4628938745 19	MALAWI	2204475	2905187432 20	MALAYSIA	925180	259299073 21	MAURITIUS	928344	3620847028 22	MEXICO	2174330	286568981 23	NETHERLANDS	29025000	11249552280 24	NEW ZEALAND	4076697	1532892133 25	PAKISTAN	2984124	1205020747 26	PHILIPPINES	1254039	46627260 27	REST OF EUROPE	19203085	4640706914 28	REST OF AMERICA	3113721	944426617 29	REST OF ASIA	7993027	1439382975 30	R. OF AFRICA	29543744	2919820660 31	SAUDI ARABIA	192080	2208900119 32	SCANDINAVIA	24924000	6472576388 33	SEYCHELLES	1339250	962702145 34	SINGAPORE	801084	147592464600 35	SOUTH AFRICA	16302900	3073834992 36	SPAIN	14617000	4077180246 37	SWITZERLAND	19753426	12081256960 38	TANZANIA	7630389	2090820536 39	UAE	4084800	5775008879 40	UGANDA	6983343	1535968893 41	UK	124324000	9660040741 42	USA	108648596	17796220000 43	ZAMBIA	4046595	2501125217 44	ZIMBABWE	3409350	1771992928 Source: Survey Statistics and Computation