An Economic Assessment of Tourism in Kenya STANDING OUT FROM THE HERD An Economic Assessment of Tourism in Kenya STANDING OUT FROM THE HERD This volume is a product of the staff of the International Bank for Reconstruction and Development/ The World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Copyright Statement: The material in this publication is copyrighted. Copying and/or transmitting portions or all of this work without permission may be a violation of applicable law. The International Bank for Reconstruction and Development/ The World Bank encourages dissemination of its work and will normally grant permission to reproduce portions of the work promptly. For permission to photocopy or reprint any part of this work, please send a request with complete information to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA, telephone 978-750-8400, fax 978-750-4470, http://www.copyright.com All other queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA, fax 202-522-2422, e-mail pubrights@worldbank.org. Design: Robert Waiharo Acknowledgements T his report was led by Apurva Sanghi (Lead Economist, World Bank) and Richard Damania (Lead Economist, World Bank), who along with Farah Manji (Consultant, World Bank), co-authored this report. The project team comprised of Pasquale Scandizzo (Professor of Political Economy, University of Rome), Cataldo Ferrarese (Consultant, IFAD), as well as Maria Paulina (Ina) Mogollon (Senior Private Sector Development Specialist), and Sid Sapru (Consultant) from the World Bank’s Kenya Trade & Competitiveness Global Practice. The team would also like to acknowledge the useful feedback received from the many tourism stakeholders in Kenya, particularly Agatha Juma (Head of Public-Private Dialogue, KEPSA, and former CEO of the Kenya Tourism Federation), Muriithi Ndegwa (former Managing Director of the Kenya Tourism Board), and Waturi Matu (Director, Business Environment, TradeMark East Africa), as well as the valuable suggestions from the following World Bank colleagues and peer reviewers: John Perrottet (Senior Private Sector Specialist), Claudia Sobrevila (Senior Environmental Specialist), Hermione Nevill (Senior Operations Officer) and Louise Twining-Ward (Senior Private Sector Specialist). A special note of thanks goes to Dr. Richard Leakey and Professor Terry Ryan for their constructive criticism and support throughout. Acronyms CGE Computable General Equilibrium HVLD High-value Low-density LVHV Low-value High-volume KICC Kenyatta International Convention Centre KNBS Kenya National Bureau of Statistics KTB Kenya Tourism Board KWS Kenya Wildlife Service SAM Social Accounting Matrix VW Virtual Water WF Water Footprint WTTC World Travel and Tourism Council Table of Contents Executive Summary ........................................................................................................................................................................ 1 Tourism in Kenya: More than Meets the Eye?.......................................................................................................................... 1 Why this report? ...................................................................................................................................................................................... 1 Methodology............................................................................................................................................................................................... 1 Structure of the report ......................................................................................................................................................................... 2 A snapshot of findings ......................................................................................................................................................................... 2 How intricately is the tourism sector integrated into the Kenyan economy? ................................................. 3 How important is tourism for the economy? ..................................................................................................................... 4 Which product line provides the greatest boost to GDP? ........................................................................................... 5 Would reallocating water from irrigated agriculture to tourism yield economic payoffs? ...................... 6 Do policies aimed at conservation and sustainable development also make economic sense? .......... 8 1. Kenya’s Tourism Sector: Changing Landscapes and Fortunes .................................................................................. 11 Context ......................................................................................................................................................................................................... 11 Challenges and opportunities .......................................................................................................................................................... 12 Concluding comments .......................................................................................................................................................................... 17 2. The Big Question: How Vital is Tourism for the Kenyan Economy? ...................................................................... 19 Context ......................................................................................................................................................................................................... 19 Backward and forward linkages of the tourism sector........................................................................................................ 20 Impacts on GDP......................................................................................................................................................................................... 21 Demand shocks: Decline in foreign demand ...................................................................................................................... 23 Supply-side shocks ............................................................................................................................................................................ 23 Concluding comments .......................................................................................................................................................................... 25 3. Alternative Scenarios for Kenya’s Tourism Development: A Dynamic Assessment ................................... 27 Context ......................................................................................................................................................................................................... 27 Consequences of international tourism growth .................................................................................................................... 28 Alternative strategies of tourism development ..................................................................................................................... 31 Scenario 1: Investments in safari tourism............................................................................................................................. 31 Scenario 2: Infrastructural improvements in the business and beach tourism segments ......................... 34 Concluding comments .......................................................................................................................................................................... 35 4. Resource Rivalry: Water Allocation and the Impacts on Tourism .......................................................................... 37 Context ......................................................................................................................................................................................................... 37 Is water important for tourism?....................................................................................................................................................... 39 Water prices .............................................................................................................................................................................................. 41 Concluding comments .......................................................................................................................................................................... 43 5. Conclusion.................................................................................................................................................................................................. 45 Annexes................................................................................................................................................................................................................. 46 References .......................................................................................................................................................................................................... 59 LIST OF FIGURES Figure 1: Total contribution of travel and tourism to GDP (percent, 2015): Country comparison ...................... 3 Figure 2: The tourism sector’s backward and forward linkages ......................................................................................... 4 Figure 3: Impacts of the different categories of tourism on gross yearly income (KSH, millions) .................. 5 Figure 4: Total contribution of travel and tourism to GDP (Percent, 2015) .................................................................... 12 Figure 5: Tourist numbers in Kenya (2010-2014)............................................................................................................................ 13 Figure 6: (a) Tourist expenditure in Kenya (2010) ........................................................................................................................ 13 Figure 6: (b) Expenditure per category ............................................................................................................................................. 14 Figure 7: Tourism numbers and receipts – Kenya vs. Tanzania (2016) .............................................................................. 14 Figure 8: Comparison of density of tourists at key safari attractions ............................................................................ 15 Figure 9: The tourism sector’s backward and forward linkages ......................................................................................... 21 Figure 10: Comparison between data and simulations of GDP growth (2010-2014) .................................................... 28 Figure 11: Impacts of the simulation results on overall income (KSH, millions)........................................................... 31 Figure 12: Impact of investments in safari tourism on incomes (KSH, millions) ......................................................... 32 Figure 13: Impact of investments in beach and business tourism on incomes (KSH, millions) .......................... 34 Figure 14: Inter-annual variability of water supply – Country comparisons ................................................................... 37 Figure 15: Efficiency of use of water withdrawn ............................................................................................................................ 38 Figure 16: Shadow value of water as % of total value ............................................................................................................... 38 Figure 17: Increase in GDP from improved water allocation ................................................................................................... 41 Figure 18: Value-added impact of the two scenarios ................................................................................................................. 44 Figure 19: Water footprint ......................................................................................................................................................................... 49 LIST OF TABLES Table 1: Summary of simulation scenarios to illustrate the importance of tourism to Kenya’s economy ... 4 Table 2: Alternative investment scenarios by tourism sector (KSH, millions) ........................................................... 6 Table 3: Key travel and tourism performance indicators for Kenya, 2015 .................................................................... 12 Table 4: Impacts of shocks on industry-wide tourism (Percent) ....................................................................................... 24 Table 5: Impacts of shocks on income distribution (Percent) ............................................................................................ 25 Table 6: Terminal year impact of tourism on economy activity levels (KSH, millions).......................................... 29 Table 7: Terminal year impact of tourism on real factor incomes (KSH, millions) ................................................. 30 Table 8: Terminal year impact of tourism on gross incomes (KSH, millions).............................................................. 30 Table 9: Initial investments in tourism by sector ..................................................................................................................... 31 Table 10: (a) Safari tourism development scenario (KSH, millions) ................................................................................... 32 Table 10: (b) Impact of safari tourism strategy on incomes (KSH, millions).................................................................. 32 Table 11: (a) Business and beach tourism scenario (KSH, millions) .................................................................................. 34 Table 11: (b) Impact of business and beach tourism strategy on incomes (KSH, millions) .................................. 34 Table 12: Water Footprint (water resources’ direct and indirect use) ............................................................................. 38 Table 13: Blue water contribution to gross incomes ................................................................................................................. 39 Table 14: Green Water Contribution to Gross Incomes ............................................................................................................ 39 Table 15: Impact on activity levels of a 20% water shift from irrigated agriculture to hotels and lodges . 40 Table 16: Impact on value added of a 20% water shift from irrigated agriculture to hotels & lodges (KSH, millions)............................................................................................................................................................................. 40 Table 17: Impact on gross incomes of 20% water shift from irrigated agriculture to hotels & lodges (KSH, millions)............................................................................................................................................................................. 41 Table 18: Change in factor prices........................................................................................................................................................... 41 Table 19: Impact on (real) value added of a 20% yearly increase in blue water prices for 7 years (KSH, millions) .................................................................................................................................................................................................. 42 Table 20: A 5% discount NPV increase in incomes in response to a 20% yearly increase in blue water prices for 7 Years (KSH, millions)......................................................................................................................................................................... 42 Table 21: Effects on income formation of a 5% yearly increase in the supply of hotel services (KSH, billions)... 47 Table 22: Effects on income formation of a 5% yearly increase in the supply of lodge services (KSH, billions)... 47 Table 23: Alternative investment scenarios in the tourism value chain (5% increase spread over 7 years) (KSH, millions) .................................................................................................................................................................................................. 48 Table 24: Value-added effects of scenario 1 (KSH, millions) ................................................................................................................... 48 Table 25: Value-added effects of scenario 2 (KSH, millions).................................................................................................................... 49 Table 26: Kenya SAM—Backward and forward multipliers ........................................................................................................................ 52 Table 27: Water footprint (direct and indirect use of water resources) .......................................................................................... 54 Table 28: Water use by sector .................................................................................................................................................................................... 55 Table 29: Gross incomes by natural resource sectors (KSH, millions)................................................................................................ 55 Table 30: Effects of water use on gross incomes by sector (KSH, millions) .................................................................................. 55 Table 31: Water use by production sector (KSH, millions)......................................................................................................................... 56 Table 32: Impact of a 20% water transfer from irrigated agriculture to hotels and lodges (KSH, millions)............. 58 LIST OF BOXES Box 1: Description of the CGE Model ............................................................................................................................................. 19 Box 2: The dynamic CGE model ........................................................................................................................................................ 27 Box 3: Water in the SAM and CGE models and some crucial definitions .................................................................. 50 Box 4: Details of the CGE and the water multipliers ............................................................................................................ 51 Tourism in Kenya: More than Meets the Eye: Today, the typical international tourist arrives in Kenya on a package tour that may include a safari, a visit to the beach, or both. Tourism is Kenya’s third largest source of foreign exchange, it dominates the service sector, and contributes significantly to employment. J Sarah Farhat Photo: E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a executive summary Tourism in Kenya: More than Meets impact of the tourism sector may be less than what is often assumed in policy discourse and the Eye? that carrying capacity limits have been reached In recent years, the prospects of Kenya’s tourism industry have been clouded by a perfect storm of misfortunes – at the most popular tourist destinations. In contrast, industry advocates contend that the sector is a critical contributor to the economy insecurity, growing global competition, and that safari tourism provides a win-win and unsustainable tourism development. for both the economy and the environment Security concerns, both within Kenya and by generating tourism revenues through the globally, have dampened international visitor conservation of the country’s natural heritage demand. At the same time, the industry has and resources. become globally more competitive as visitors grow more discerning and rival destinations This report seeks to inform these crucial policy in Africa develop and market their attractions debates from a rigorous economic perspective. more aggressively. While these challenges It attempts to assess the economic role of could be addressed through focused policy the tourism sector in the Kenyan economy attention, of greater concern is evidence of using analytically advanced techniques. The often irreversible damage to sites that attract issues that surround tourism development are tourists. Overcrowding at popular destinations, especially significant in Kenya as choices will combined with degradation of habitats and often have irreversible effects that warrant wildlife corridors, add to concerns about the careful consideration. An alluring beach, for sustainability of the industry’s development example, once contaminated or built over, can trajectory. A case in point is the plan to have seldom be restored as a tourist attraction. the standard gauge railway run through the Likewise, dissecting wildlife migration corridors Nairobi National Park, the only national park to diminishes populations, tourism appeal, and still exist within a city boundary. This plan has the earning potential of natural assets in seen conservationists and the Kenya Railways ways that are often irreparable. Given the Corporation lock horns, with concerns that it significant and long-term implications of such would destabilize the delicate balance between decisions, a rigorous economic assessment humans and wildlife. of the contribution of the tourism sector to the economy can assist in informed decision Why this report? making and be beneficial to the Ministry of It is in this context that the potential and Tourism, the National Treasury, and the industry actual contribution of the tourism sector to the as a whole. country’s development has been questioned, with claims that tourism contributes less to Methodology the Kenyan economy than commonly thought. This study, drawing upon an established There are differences of opinion both within Computable General Equilibrium (CGE) the government and among stakeholders, with approach tailored to the Kenyan economy, suggestions that the economic contribution and seeks to evaluate the tourism sector’s S ta n d i n g O u t F r o m T h e H e r d 1 Executive Summary contribution to the economy in a rigorous and Second, the report examines the importance credible manner. In particular, it computes of tourism to the Kenyan economy through the sector’s backward and forward economic its contribution to GDP. The computable CGE linkages, its contributions to GDP, as well as is constructed to capture some of the key traces the consequences of alternative tourism characteristics and interdependencies of development scenarios. The study also explores the Kenyan economy. The results in Chapter how the sector’s growth prospects might be 2 indicate that the effects on the economy affected by changes in the allocation of water depend on the cross-sectoral linkages. Hence, – an increasingly scarce natural resource in impacts on the economy differ depending on Kenya that is needed both for tourism, which is whether they emanate from changes in foreign a water intensive industry, as well as a host of tourist arrivals, changes in domestic tourist other needs including agriculture, the mainstay demand, oil price shocks, or foreign exchange of livelihoods and employment in the country. shocks. Third, using a dynamic CGE, the analysis traces the consequences of alternative tourism The CGE model is based on a social development scenarios. Chapter 3 attempts accounting matrix, with details of production, to explore how long-term growth and poverty employment, and income distribution, as rates are affected with investments in the well as a number of environmental factors different segments of the tourism industry. that may be of interest to tourism. The CGE Finally, recognizing that growth in the sector is used to develop alternative scenarios where is dependent upon sustainable resource- a supply or demand shock is applied to the use, Chapter 4 contributes to the analysis of tourism sector and the results are compared to alternative policy strategies by investigating a counterfactual baseline. With this technique, policies for the allocation of water. This is a it is possible to approach the measurement highly relevant, though much neglected issue of tourism’s contribution to the economy in a as Kenya is amongst the most water scarce systematic way and to estimate both the direct countries in Africa and also has a highly water and indirect contributions of the sector based intensive economy (when measured in per on a clear set of hypotheses. Data for the CGE capita availability, Kenya is more water scarce model and other chapters was sourced from than land, and projections suggest the former available statistics, notably from the Kenya will get worse faster). The CGE model is also National Bureau of Statistics (KNBS) and the used to examine the growth consequences World Travel and Tourism Council (WTTC). of reallocating water from the highly water- dependent tourism industry to other sectors of Structure of the report the economy. This report investigates four key issues: First, the analysis traces and quantifies the links A snapshot of findings between tourism and other sectors of the Kenya has pioneered the development of economy. Chapter 1 identifies linkages with tourism in Africa. At independence, the sectors that provide inputs into tourism as country was reliant on agricultural exports for well as sectors that benefit from the boost in its foreign exchange revenue and was exposed demand generated by the industry (termed the to the vagaries of commodity price cycles. backward and forward linkages respectively). Nature-based tourism provided an opportunity 2 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Executive Summary Kenya offers three main types of tourism products1 Safari tourism Coastal tourism Business and conference travel • Based on natural and wildlife • Caters to the competitive • Independent business travelers assets. "sun-sea-sand" segment of the from domestic, regional, and global market. international markets, and • The Kenya Wildlife Service, (KWS) conference and meeting attendees. is responsible for management of • Ranges from mass-packaged national parks and executing plans tourism of Mombasa coastal resorts • Kenyatta International Convention to protect biodiversity. to culturally rich destinations such Centre (KICC) in Nairobi is the as Lamu. largest conference facility in East Africa. to diversify export revenues while playing to its contributes about 10 percent of GDP (direct natural comparative advantage. Today, tourism and indirect). However, these values give a is the third largest source of foreign exchange partial picture of the true contribution of the in the country, it dominates the service sector sector as they neglect impacts of backward and (67 percent of the economy), and contributes forward multipliers as well as dynamic effects significantly to employment (1. 5 million jobs), on economic structure. especially in rural areas where economic opportunities are limited. In order to explore the importance of the tourism sector to Kenya’s economy in an Recognizing the potential of the sector to analytically rigorous manner, this report drive development, the country’s Vision 2030 employs a computable general equilibrium identifies tourism as one of the drivers of (CGE) model that computes the sector’s economic growth. According to official GDP economic linkages, its contributions to GDP, data, tourism’s contribution to Kenya is similar and the consequences of alternative tourism to rival destinations with less developed development scenarios. economic structures (Figure 1 below). The World Travel Tourism Council estimates that tourism So, what has the analysis revealed? Figure 1: Total contribution of travel and tourism to GDP (percent, 2015): Country comparison How intricately is the tourism sector 25 integrated into the Kenyan economy? The tourism sector is deeply integrated into 20 Kenya’s economic fabric in complex ways. 15 Figure 2 examines linkages with sectors that provide inputs into tourism as well as 10 sectors that benefit from the boost in demand generated by the tourism industry (termed the 5 backward and forward linkages, respectively). 0 It suggests that tourism has diffuse and deep Kenya Madagascar Tanzania Namibia Gambia South Africa backward links into the economy. This implies Source: World Travel and Tourism Council (WTTC) (2016) that the sector purchases many of its inputs 1 World Bank, (2010), Kenya’s Tourism: Polishing the Jewel. S ta n d i n g O u t F r o m T h e H e r d 3 Executive Summary from domestic sources and is therefore highly How important is tourism for the integrated into the economy. More generally, economy? compared to Tanzania2 and other countries, Since tourism in Kenya has such wide linkages the backward multipliers in Kenya are strong, across the economy, the economic impact or indicating that tourism is well integrated into value of the sector will depend upon the source the economic fabric of the country. of change. Impacts will differ depending upon whether changes emerge due to an exogenous Non-irrigated agriculture has the highest shock to the demand for tourism by foreigners, forward linkages and hence gains the most or exogenous shocks and changes to input costs from the generic increase in demand induced or supplies, or due to some combination of by tourism. In terms of forward linkages, there shocks. To illustrate the importance of tourism are predictable impacts on industries that are to the economy, four hypothetical scenarios “close” to tourism such as agriculture (a key are considered to cover a range of possibilities source of employment for the poor), trade, and (Table 1). The variety of scenarios explored transport, as well as surprising beneficiaries suggest that tourism contributes between 8-14 such as the education sector (Figure 2). percent to Kenya’s GDP, with highly pro-poor distributional impacts in rural areas. Figure 2: The tourism sector’s backward and forward linkages 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Backward linkages Forward linkages Source: Elaboration of Kenya SAM Table 1: Summary of simulation scenarios to illustrate the importance of tourism to Kenya’s economy Scenario number Scenario category Scenario category 1 Demand shock 80% decrease in foreign demand for tourism; capital is immobile 2 Demand shock 80% decrease in foreign demand for tourism; capital is immobile 3 Supply shock 80% decrease in foreign demand for tourism; capital is mobile 4 Supply shock 50% cost increase for both direct inputs and transport 2 For example, in the case of exogenous investment and foreign account, the backward multiplier for tourism is 0.22 for Kenya against 0.18 for Tanzania (see Annex for details). This means that there is a greater impact in Kenya. For instance, $1 spent in Kenya induces a backward multiplier effect of 22 cents in Kenya but 18 cents in Tanzania. 4 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Executive Summary In the first two scenarios, there is assumed The results show that all forms of tourism to be a decline in foreign tourist revenues. have broad economy-wide effects, though the When foreign demand falls, tourism prices sectoral impacts differ. The safari segment decline and this in turn stimulates a strong not only generates greater economic growth increase in domestic tourism, which partly than each of the other forms of tourism, but compensates for fewer foreign tourists. The would do more to address poverty problems end result is a decrease in GDP of between 8 to and create rural economic opportunities. Safari 12 percent. The impacts fall disproportionately tourism, alone or in combination with other upon the rural poor whose incomes decline by forms of tourism, generates the highest GDP, over 10 percent. This partly reflects the links with a plethora of indirect effects, especially on with non-irrigated agriculture, which is the agriculture as the last ring of a well-developed primary employer of the rural poor. The next domestic value chain. It also generates two scenarios consider supply-side shocks significantly greater household income as through cost increases. These suggest that GDP shown in Figure 3, and is also considerably would decline by between 12 and 13 percent — more pro-poor than any of the other forms a result that is robust to wide variations in of tourism as a consequence of its closer the magnitude of the shocks. Again, the rural linkages with the rural economy. In sum, when poor suffer the most with a striking 14 percent tracking the flow of expenditures through the decline in their incomes. In sum, the analysis economy, safari tourism is found to generate a of these scenarios suggests that tourism is greater level and spread of benefits across the well integrated into the economy and makes economy. This is in part a consequence of the a significant contribution, with benefits that kinds of inputs purchased by this sector which accrue strongly to the rural poor. have greater pro-poor benefits (for instance food purchases) and involve fewer imports than While the results are to be interpreted with some of the other tourism sectors. care because the simulated equilibria are Figure 3: Impacts of the different categories of tourism on rather distant from the baseline, they suggest gross yearly income (KSH, millions)3 that tourism is a key sector of the Kenyan 160,000 economy and that its direct and indirect effects 140,000 tend to be considerably larger than those 120,000 100,000 credited by the national accounts. 80,000 60,000 Which product line provides the greatest 40,000 boost to GDP? 20,000 0 The dynamic version of the CGE model Enterprise Government investment Water resources Rural poor Rural non-poor Urban poor Urban non-poor Savings and Natural Capital investigates the consequences of equivalent growth in the safari, beach, business, and Business Beach Safari Other “other” categories of tourism resulting from Source: Elaboration of Kenya SAM an increase in foreign demand: foreign tourist demand (expenditure) is assumed to grow by 5 percent per year in each of the four categories. 3 Enterprise refers to firm enterprises as classified in the SAM. S ta n d i n g O u t F r o m T h e H e r d 5 Executive Summary Expanding tourism will call for investments on the whole, less productive in the aggregate. that will in turn have impacts upon the Moreover, in this case, the incomes of the rural economy. Accordingly, this study explores poor increase by a meager 2.3 percent, which the implications of alternative strategies of is about half as much as under safari tourism. tourism development by simulating different investment scenarios shown in Table 2 The dynamic CGE simulations confirm earlier below. The scenarios are hypothetical and results that safari tourism dominates in are not a description of any policy. Instead, terms of growth and more equitable income they have been developed to determine how distribution benefits. This derives from its investments in alternative subsectors of the complementarity with agriculture, its low industry would impact the economy over the capital intensity, and multiple and intensive medium to long term. backward linkages with the rural poor. The result is highly robust across several scenarios Table 2: Alternative investment scenarios by tourism considered in this exercise. However, the sector (KSH, millions) dynamic simulations suggest that significant Scenario 2: Investments differences in performance can probably Scenario 1: in beach and be obtained only by growing revenue from Type of Investments in business investment safari tourism tourism the tourism industry. If growth is obtained Imports to through increased tourist numbers, problems upgrade park 1,000 of congestion will neutralize any gains. This tourism suggests the need for further consideration on Construction 1,000 5,000 how expansion of the sector occurs. Hotels 5,000 Would reallocating water from irrigated The first scenario, which considers agriculture to tourism yield economic investments in safari tourism, requires payoffs? sacrifices in the first years of implementation, The results regarding water allocation have but its long-term benefits are positive for all far reaching economic implications that go income groups and especially the rural poor beyond the tourism sector. Kenya is a water whose incomes increase by about 5 percent. stressed country, and yet it has a highly water The second scenario allows for the improvement intensive economic structure. Industry and of roads and hotels for the business and beach urban sectors withdraw less than 18 percent of segments and relies on a “business as usual” water but produce most of the country’s value development strategy aimed at attracting the added from the water that they use. And while largest possible number of tourists. For brevity, agriculture consumes 80 percent of water, these two types of tourism are combined it produces less than 25 percent of the value as they use the same types of venues for added generated by water use in the economy accommodation (hotels rather than lodges). as a whole. This suggests that in the context of This growth strategy has similar implications to scarcity there is scope for significant economic the safari-based development, but it appears, gains through the reallocation of water. 6 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Photo: Sarah S ta ndinFarhat g Out From The Herd 7 Executive Summary Allowing for a reallocation of water from Do policies aimed at conservation and irrigated agriculture to the tourism sector sustainable development also make — or at least ensuring that supplies to the economic sense? tourism sector are not diminished further — Tourism has emerged as a significant would yield high economic payoffs. The CGE contributor to the Kenyan economy. The model is used to explore the consequences of intensive modeling undertaken in this report reallocating a small fraction (20 percent) of suggests that the sector is deeply integrated water from irrigated agriculture to tourism. into the economy and much of the direct and The simulation results show that the effects on indirect activity that is induced by tourism the economy are impressive. The productivity generates significant benefits for the poor, of water in tourism is high (roughly 50 KSH of especially in rural areas where poverty incidence product for each KSH worth of water) compared is high. The safari tourism sector stands out to that of agriculture (about 7.6 KSH). Since in terms of its growth dynamism and ability tourism generates more GDP, jobs, and rural to create rural economic opportunities. Thus, incomes per drop of water withdrawn, this further expansion of this sub-sector seems reallocation generates economic growth, higher warranted but it would need to be managed rural incomes, and jobs. Moreover, given that with considerable care. the CGE analysis has shown that safari tourism generates the greatest economic growth, Though tourism is an important economic coupled with the fact that water is important driver today it faces numerous challenges in for sustaining wildlife and ecosystems, the Kenya. There are deep risks that the current reallocation of water for tourism purposes focus on tourist numbers rather than tourist would be beneficial for water-stressed Kenya. revenue will undermine the industry and diminish the sector’s potential. Problems of It is important to note that although the congestion, overcrowding, and ecosystem results are indicative rather than precise, degradation will inevitably worsen as more they serve as a warning sign and highlight tourists crowd into diminishing and degraded the need for a much more careful analysis of habitats. Demand will eventually and inevitably the future problems the country will face in decline for a visitor experience that is inferior allocating water more effectively. This is an to that offered at rival destinations. issue that warrants greater policy discourse and deeper analysis. A number of such trade-offs Much of Kenyan tourism is targeted at the are already being encountered, such as around spectrum of the market where profit margins Naivasha and Mombasa, and more will emerge are low and where volumes need to be in the near future as water demands increase high to break even. Somewhat ambitiously, as a result of population and economic growth, Kenya’s Vision 2030 sets a target of five million while supplies diminish, especially in key water tourists — which would require a four or five- sheds, as a consequence of climate change and fold increase in tourist numbers. Increased habitat erosion. numbers of this magnitude would necessitate a substantial reduction in prices to attract visitors from competing market segments. Higher tourist numbers could result in lower 8 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Executive Summary tourist revenue if prices need to be reduced can raise more revenue per tourist than Kenya. substantially to attract more visitors. It is more However, the HVLD approach is not suitable appropriate to target an increase in revenues for every tourist destination and this will from tourism rather than an increase in the consequently call for a diversified approach to number of tourists. Additionally, the popular tourism development in Kenya, which requires safari destinations remain highly congested a differentiated strategy that plays to the with signs of ecosystem distress and a economic strengths of each attraction and asset. deteriorating experience offered to tourists. This has perpetuated the dependence on low Kenya’s alluring assets have created a robust value-added segments of the market.4 tourism industry that appears to play a pivotal role in sustaining the livelihoods of the rural Kenya operates within a globally competitive poor. To build upon this foundation, Kenya tourism industry, and it faces choices of needs to play to the comparative advantage which market segments to develop and how of each region and attraction. Going forward, it can strategically compete for the global the Government of Kenya (GoK) and tourism tourist dollar. In stark terms, there is a choice stakeholders may consider building and between the high-value low-density (HVLD) differentiating tourism by location (carrying tourist market and the low-value high volume capacity and accessibility), product (wildlife, (LVHV) market. The former calls for restricting beach, culture, conference, and adventure), and supply and targeting the high-end segment of market segment (domestic, international, and the market, while the latter operates on slender conference). Given the variety of assets and the margins, is intensely price competitive, and diversity of customers, products can be designed therefore needs to maximize volumes to make in multiple, interesting, and lucrative ways. The profits or break even. The latter, if unmanaged, HVLD approach is not suitable for every tourist can create a downward spiral of crowding and destination and this will consequently call for site degradation. Tanzania has been more a diversified approach to tourism development successful in targeting the HVLD market, and in Kenya, and to optimize tourism as a source it caters to a segment of the market where of economic dynamism. demand is relatively price inelastic; hence, it 4 A common sight at popular safari destinations is that of tourist vehicles queueing to view wildlife. The experience has often been compared to that of a zoo, lacking in both authenticity and quality, and as a result, few experienced wildlife enthusiasts visit Kenya’s most popular sites in peak season. S ta n d i n g O u t F r o m T h e H e r d 9 Much of Kenyan tourism is targeted at the spectrum of the market 1 where profit margins are low and where volumes need to be high to break even. Of great concern is the irreversible damage that could occur to natural habitats. Many of Kenya’s key tourist attractions are already under pressure and showing signs of degradation. 10 Sarah Farhat Photo: E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Kenya’s Tourism Sector: Changing Landscapes and Fortunes Context typically caters to the highly competitive T he tourism sector, especially nature- “sun — sea — sand” segment of the global based tourism, has played a pivotal role market, and business and conference travel. in shaping Kenya’s development fortunes. Cultural heritage tourism activities are limited Tourism dominates the service sector (67 percent and offer potential for further development. of the economy) and it is the third largest Safari tourism is dependent on natural and source of foreign exchange in the country; the wildlife assets, which are seasonal with World Travel Tourism Council estimates that it peaks and valleys tied to animal migration contributes about 10 percent of GDP, direct and patterns. Capacity is limited by the fragility indirect (as shown in Figure 3 above).5 Tourism of ecosystems, though there is little evidence is also a key part of the country’s economic that such constraints have influenced policies. strategy and is identified in Vision 2030 as one Kenya’s coastal tourism offers products of the drivers of economic growth. However, the ranging from the mass-packaged tourism of contribution of tourism to the economy — much Mombasa’s large coastal resorts to niche of it derived from visits to national parks6 — is destinations. Business and conference travel is open to debate with some claiming that the a segment with much potential. The Kenyatta sector’s contribution is actually much lower. International Conference Centre (KICC) in Nairobi has the largest conference chamber in Kenya’s impressive tourism development is East Africa and recently hosted nearly 40 heads the product of early investments in protected of state and over 10,000 delegates during the areas and a tourism infrastructure that has Sixth Tokyo International Conference on African drawn tourists to the country. At independence Development (TICAD). The Government has also in 1963, Kenya derived the bulk of its foreign recently announced plans to build a convention exchange from exports of tea and coffee. With centre in the city of Mombasa. Moreover, Kenya declining commodity prices, the country turned is an international airline hub with direct to nature-based tourism, which offered an access that far exceeds the capacity of any opportunity to diversify export revenues while other country in East Africa. playing to its natural comparative advantage. Today, the typical international tourist arrives There is recognition that some of the current on a package tour that may include a safari, a products (e.g., nature-based tourism) are visit to the beach, or both. reaching their limits and there is a need to offer a more diverse set of experiences (such Kenya offers three main types of tourism as conference tourism). There are encouraging products: safari tours that vary in quality trends in the industry. Local and international of service and price, coastal tourism that conference tourism has grown significantly, 5 Measuring the contribution of tourism to GDP is not a simple exercise since tourism involves a plurality of value chains that include travel, lodging, leisure activities, and many interlocking sectors and subsectors. For example, in the case of Italy, one of the world’s most sought out tourist destinations, the World Atlas estimates that tourism accounts for 19 percent of GDP, while the government declares “…the sector of tourism, including the activity it generates, contributes approximately one-third to the overall GDP by creating over one million jobs.” (http://www.esteri.it/mae/en/ministero/servizi/benvenuti_in_ italia/conoscere_italia/economia.html). 6 World Travel and Tourism Council, (2012), Travel and Tourism Economic Impact 2012. Also see Figure 2. S ta n d i n g O u t F r o m T h e H e r d 11 Kenya Tourism Sector and the Tourism Cabinet Secretary projects structure. These matters are addressed in conference tourists will hit 130,000 by the end of greater detail in Chapters 2 and 3 of this report. 2016, up from 77,000 in 2015 and 44,000 in 2014.7 In November 2016, the government allocated Sh10 Figure 4: Total contribution of travel and tourism to GDP (Percent, 2015) million to set-up a task force with the aim of GDP share developing a marketing strategy for conference 25 tourism in Kenya.8 In addition, there has been 20 a gradual increase in domestic tourism, helped by rising per capita incomes, which were 15 responsible for the growth of the middle-class population, and there is considerable scope for 10 expanding this market segment as shown in 5 Figure 4. 0 Despite a diversifying economy and mounting Kenya Madagascar Tanzania Namibia Gambia South Africa Source: WTC (2016) challenges, tourism remains an important contributor to the Kenyan economy. Figure 4 illustrates that according to official GDP Table 3: Key travel and tourism performance indicators for Kenya (2015) data, tourism’s contribution to Kenya is not Key Indicators 2015 (%) dissimilar to rival destinations with less developed economic structures. These values Leisure travel spending as % of total 67.5 travel spending give a partial picture of the true contribution of Business travel spending as % of total the sector as they neglect impacts of backward travel spending 32.5 and forward multipliers as well as dynamic Total tourism contribution to GDP (%) 9.9 effects on economic structure. For instance, Tourism contribution to exports (%) 14.9 the bulk of tourists are international travelers, Direct and Indirect (induced) so variations in tourist numbers would impact 9.3 employment (% total) external balances and the exchange rate with Direct and Indirect (induced) 6.2 consequent effects in tradable goods sectors employment (% total) Source: WTTC (2016) as well as the structure of the economy. It is well known that reliance on a single sector for exports (for example mining) can lead to an Challenges and opportunities overvalued exchange rate that in turn impedes The short-term prospects for Kenyan tourism the development and competitiveness of have been clouded by a perfect storm of other export industries9 unless the dominant misfortunes — domestic elections, insecurity, exporter has deep and wide links in the rest of and unsustainable tourism development. the economy. Capturing these trends calls for a Initially, security concerns surrounding the 2013 dynamic CGE model that links each sector of the elections dampened visitor numbers. This was economy and allows for changes in economic soon followed by the global economic slowdown 7 African Travel & Tourism Association, 23 August 2016. http://www.atta.travel/news/7129/conference-tourists-to-double-in-2016-in-kenya 8 Business Daily, “Task force gets Sh10m to review Kenya’s tourism strategy,” November 9, 2016. http://www.businessdailyafrica.com/Taskforce-gets-Sh10-million-to-review-Kenya- s-tourism-strategy/539546-3446864-6rtuhk/ 9 Termed the Dutch Disease. 12 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Kenya Tourism Sector in the Eurozone. In 2013, the damaging fire at Figure 5: Tourist numbers in Kenya (2010-2014) Jomo Kenyatta International Airport and the 2,000,000 much publicized attacks on the Westgate Mall 1,800,000 in Nairobi added to these problems. The April 1,600,000 1,400,000 2015 massacre of college students in Garissa 1,200,000 further fed perceptions of insecurity in the 1,000,000 country. Despite these events, the Ministry of 800,000 Tourism’s tourism diversification strategy has 600,000 400,000 paid some dividends. The strategy is aimed at 200,000 increasing tourism beyond coastal and wildlife - safari destinations, increasing the diversity 2010 2011 Other 2012 Transit visitors 2013 2014 Holiday /business visitors 2015 of tourism products, and increasing tourist Source: Kenya National Bureau of Statistics (2016) arrivals from outside the US, UK, and Western Europe. Regarding the latter, there has since Travelers in the premium wildlife sector been a steady flow of tourists from Asia, albeit spend twice as much per day than those in involving a more price–elastic (cost sensitive) other segments of the market (Figure 6). The segment of the market. In addition, in 2014, bulk of the premium safari spending is on the government launched a Tourism Recovery accommodation, followed by food and beverage. Taskforce, headed by a private sector hotelier, In contrast, the majority of spending in the charged with developing a comprehensive more cost-sensitive beach segment is in the strategy for reviving tourism, including unclassified miscellaneous category as shown initiatives to improve security, develop in Figure 6b.12 The overall economic impacts on infrastructure, and shift the perception of the the rest of the economy of each tourist segment country in foreign markets.10 depend upon these spending patterns, linkages with the rest of the economy, and of course the Overall, however, visitor numbers plummeted sustainability of each market segment. from a peak of about 1.8 million tourists in 2007 Figure 6: (a) Tourist expenditure in Kenya (2010) to 1.3 million in 2014, with the greatest decline Total expenditure per bed night in the lucrative segments of the business 400 and holiday traveler category. In addition, 350 according to a recent report by PwC, since 2011, 300 room revenue in the hospitality industry in US$/day 250 Kenya has fallen cumulatively by 16 percent.11 200 While a decline in the number of tourists 150 may not necessarily be a bad development, if 100 accompanied by an increase in revenue and 50 quality, prospects for the near future may not 0 Wildlife Premium wildlife Beach be favorable unless greater safety assurances Source: Kenya National Bureau of Statistics are provided to boost tourist confidence. Oxford Business Group, (2016), The Report: Kenya 2016. 10 Ibid. 11 According to the standard questionnaire used in statistics for satellite accounts, this category includes sightseeing, visiting friends/relatives, attending 12 sport events, visiting museums, visiting heritage sites, shopping and other. (http://statistics.unwto.org/sites/all/files/pdf/kenya_inbound.pdf ). S ta n d i n g O u t F r o m T h e H e r d 13 Kenya Tourism Sector Figure 6: (b) Expenditure per category approach for some forms of tourism, it has 90 consequences that need to be considered. Figure 80 7 below provides an instructive comparison 70 60 between tourist numbers and revenues in Kenya US$ 50 40 and its nearest neighbor, Tanzania. Kenya and 30 Tanzania offer similar tourism experiences — a 20 10 safari that may be combined with a beach visit. 0 According to a value chain analysis conducted Accommodation Food and beverages Excursion and park fees Miscellaneous Inland transport Out of pocket expenditure by the World Travel and Tourism Council, Tanzania’s higher earnings per visitor are Beach Premium Wildlife Wildlife mostly attributable to lower congestion and its Source: Kenya National Bureau of Statistics (2016) ability to attract travellers who are prepared to pay a higher price for a more authentic and exclusive wilderness experience. In other words, The Kenyan tourism industry is at a crossroads Tanzania caters to a segment of the market and must make a strategic decision on how where demand is relatively price inelastic and to develop its offerings. The industry has hence it can raise more revenue per tourist experienced negative medium-term trends than Kenya.15 There are signs though that this with a decline in per capita spending, average is changing as Tanzania also seeks to target length of stay, and hotel occupancy rates, tourist numbers; this would entail stronger although the last two variables show a reversal competition with Kenya in the medium to in the past few years.13 In part, this reflects the short-term as both countries compete for the type of product that is now on offer and the same niche with similar products. emergence of competitors. The popular safari destinations remain highly congested and are Figure 7: Tourism numbers and receipts – Kenya vs. increasingly targeted towards the low-margin Tanzania (2016) tourist market. Despite the country’s policy 2.5 1.4 of advocating spatial dispersion of tourism, 1.2 2.0 marketing has continued to focus on the International tourist arrivals (M) 1.0 Visitor exports (B, US$) traditional attractions, thereby perpetuating 1.5 0.8 concentration and the deterioration in the 0.6 quality of the tourism products.14 1.0 0.4 0.5 Much of Kenyan tourism is targeted at the 0.2 spectrum of the market where profit margins 0.0 0.0 Kenya Tanzania are low and where volumes need to be high Visitor exports (Billions, US$) International tourist arrivals (Millions) to break even. While this may be a suitable Source: World Travel and Tourism Council (WTC) (2016) 13 According to the World Bank and the Kenya Economic Survey, in the years 2012-2014, tourist arrivals declined from 1.8 million to 1.3 million, while expenditure increased from $197 million to $206 million, with expenditure per-capita increasing from $108 to $153. Other accounts, such as a recent interview with the Minister of Tourism, suggest that earnings from foreign tourism had fallen to $870 million from a peak of $1.2 billion in 2011. (http://www.businessdailyafrica.com/Balala-sees-Kenya-tourism-recovering- in-2018/-/539546/3038300/-/xda2uf/-/index.html). 14 A common sight in the Masai Mara is that of tourists crowding around (say) a pride of lions, trying to get a good view. The experience has often been compared to that in a zoo, lacking in both authenticity and quality, and as a result few experienced wildlife enthusiasts visit Kenya’s most popular sites. 15 During the 2008–09 recession, tourist numbers plummeted across the globe, yet tourist numbers in Tanzania were largely unaffected (World Bank, 2016). 14 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Kenya Tourism Sector Somewhat ambitiously, Kenya’s Vision 2030 economic consequences on revenues and the sets a target of five million tourists — which ecological impacts on the sustainability and would require a four or five-fold increase in future earning capacity of the asset. tourist numbers. Increased numbers of this Figure 8: Comparison of density of tourists at key safari magnitude call for careful consideration of attractions the economic and ecological consequences. Visitor density per m2 Ruaha A dramatic increase in tourist numbers would Tsavo West Na nal Park necessitate a substantial reduction in prices Tsavo East Na nal Park Serenge to attract visitors from competing market Chobe segments. Indeed, higher tourist numbers Aberdare Kruger could well result in lower tourist revenue if Maasai Mara prices need to be reduced (by a proportionately Amboseli Na nal Park greater amount) to attract more visitors. Since Lake Nakuru Na nal Park Hell's Gate the aim is to increase revenues, GDP, and local 0 0.5 1.0 1.5 2.0 incomes, it is more appropriate to target an Kenya Other increase in revenues from tourism rather than Source: Kenya National Bureau of Statistics (2016), TANAPA (2015) an increase in the number of tourists. Many of Kenya’s key tourist attractions Of greater concern is the irreversible damage are under pressure and showing signs of that could occur to natural habitats, which is degradation. Perhaps most troubling, recent the asset base for safari tourism. Increased population monitoring suggests that long-term tourist numbers would further compound declines of many of the charismatic species problems of crowding and congestion at that attract tourists — lions, elephants, giraffe, the key attractions.16 Figure 8 illustrates the impala, and other animals — are occurring at density of tourists at major attractions in the same rates within the country’s national Kenya (highlighted in red) and a few other parks as outside of these protected areas. locations. It shows the ratio of the number Parks in Kenya were established where large of visitors to the area of the attraction. In all aggregations of animals were observed typically these locations, visitor numbers are highest during the dry seasons, but in haste to establish during their respective peak seasons, and in these protected areas, policy makers neglected some cases the attractions are closed during the migratory needs, especially of the ungulate the off-peak periods. What is striking is the herds. Dispersal is a fundamental biological very high density of tourists in Kenya compared process that influences the distribution of to rival countries. This would clearly impact biodiversity in every ecosystem and determines sustainability as well as the country’s ability to whether a species will survive.17 Among attract a more lucrative segment of the market. other things, the process of dispersing It further reinforces the risks of targeting from a natal territory is essential to avoid visitor numbers without consideration of the inbreeding strongly influences individual This is of course a necessary consequence of downward sloping demand curves. It is useful to note that the World Bank’s work in Mexico has shown that 16 mass low-cost tourism generates fewer local benefits (due to lower multipliers). Dispersal is a fundamental behavioral and ecological process. The distance that individuals disperse, and the number of dispersers can be the primary 17 determinant of where and whether species persist. Dispersal fundamentally influences spatial population dynamics including metapopulation and metacommunity processes. Among other things, the process of dispersing from a natal territory to find new space in which to live and avoid inbreeding strongly influences individual fitness. S ta n d i n g O u t F r o m T h e H e r d 15 Kenya Tourism Sector fitnes s. As a result, wildlife depends as much also avoid overcrowding, which has adverse on adjacent land as it does on the protected ecological consequences that diminish the areas for continued viability. Pressures around value of the product. Kenyan parks are affecting the wildlife in the parks. The way land outside of protected areas The HVLD approach is not suitable for every is utilized and managed will become a crucial tourist destination and this will consequently determinant of the industry’s future. Expanding call for a diversified approach to tourism tourism to these areas remains among the most development in Kenya. For HVLD tourism to successful approaches that have been piloted succeed, a host of conditions must prevail: — under the rubric of “ecological easements”. First, the product on offer must be rare or But the feasibility of this approach depends even unique. As a corollary, since such HVLD upon economic incentives and the opportunity tourism assets are rare, by implication there is costs of land. less competition, allowing for higher prices to be charged for the experience. HVLD tourism Kenya operates within a globally competitive attracts people who care more about experience tourism industry, and faces choices of which (for example, wilderness) and less about price market segments to develop and how to (that is, more inelastic demand). This group strategically compete for the global tourist might include the so-called high-net-worth dollar. In stark terms, there is a choice individuals and also includes interest groups between the high-value low-density (HVLD) (hobbyists, birdwatchers, and climbers). Hence, tourist market and the low-value high volume not every destination in Kenya will fit into this (LVHV) market. The former calls for restricting category and there is a need for a differentiated supply and targeting the high-end segment of strategy that plays to the economic strengths of the market, while the latter operates on thin each attraction and asset. Clearly, uncongested margins, is intensely price competitive, and and more pristine parts of the wildlife sector therefore needs to maximize volumes to make can be developed for HVLD tourism in ways profits or break even. that deepen economic linkages, protect the ecosystem, and generate revenues. Much The HVLD approach has several advantages for of beach tourism on the other hand remains wildlife related tourism. High-value visitors are highly competitive, though there are higher typically unaffected by turbulence in the global valued niche segments of the market. The sun- economy. During the 2008-2009 recession, sea-sand product is widely available in almost tourist numbers plummeted across the globe, every tropical coastal country, so competition is yet the high-value added tourist numbers in intense and consumer choices are determined Tanzania were largely unaffected.18 In addition, by costs. A careful segmentation of the market low visitor numbers can minimize congestion based upon the economic potential and at popular sites and preserve the economic capacity of each asset will be needed to strike value of the product by providing visitors with the right balance between volume, value, and an authentic wilderness experience. This can sustainability. 18 World Bank, 2016. 16 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Kenya Tourism Sector Concluding comments consequences of asset depletion. Thus, there is Tourism can be source of economic dynamism. a need for appropriate and sagacious policies, This is evident from the experience of countries laws, and systems that assure stewardship as diverse as Costa Rica, Switzerland, Bhutan, and longer term sustainability of the asset. Australia, and New Zealand. Indeed, each In addition, examples abound where tourism of these countries has built upon its natural and the related investment incentives, when comparative advantage by playing to its managed poorly, create “enclave industries” strengths in ways that have maximized and with few linkages to the rest of the economy. sustained the value of its assets. Among these To maximize economic payoffs, incentives and success stories, there has been recognition that economic policies are needed that promote revenues are generated from natural assets linkages with the rest of the economy to that can be degraded and depleted. Investors boost employment, GDP, and incomes from the and decision makers with short horizons will sector. This has been done in countries with have little incentive to consider the longer-term success stories. S ta n d i n g O u t F r o m T h e H e r d 17 Tourism is a key sector of Kenya’s economy, with wide backward 2 and forward linkages and pro-poor distributional impacts. After the transport and entertainment related sectors, a primary beneficiary of the sector is non-irrigated agriculture, which is a key source of employment for the poor. Photo: Sarah Farhat The Big Question: How Vital is Tourism for the Kenyan Economy? Context by the extent of linkages between sectors T his chapter investigates the importance and the multiplier effects of each. To capture of tourism to the Kenyan economy. It these trends, a standard Computable General recognizes that tourism is a major sector of Equilibrium (CGE) model of the Kenyan economy the economy and that variations in the sector has been built. Box 1 provides a brief description will have impacts that reverberate throughout of the model and the Annex discusses more the economy, with magnitudes that are defined technical details. Box 1: Description of the CGE Model The CGE was formulated using fixed input-output coefficients for intermediate inputs, linear expenditure systems for household consumption, and Cobb-Douglas specifications for demand for factors of production. Trade is modeled assuming that internationally tradable and non-tradable goods are imperfect substitutes. The main structure of the model is based on the reasonable assumption that intermediate inputs are not substitutes for each other in production technology. The main features involve profit maximization by producers, utility maximization by households, mobility of labor, and competitive markets. It is a static and single-country CGE model extended to incorporate both national and international tourism and its relations with the other sectors and environmental variables. Tourism is modeled as an industry providing a specific product and by distinguishing the different components of the domestic value chain. Foreign tourist demand is divided into four components: business, beach, safari, and ‘other’, the latter category being a miscellaneous category that includes the relatively new forms of ecological and domestic tourism. The model has been calibrated using the SAM estimated for 2014 so that the model parameters are such that the CGE solution reproduces the 2014 data as a baseline reference. Two alternative closure rules have been used for the first experiments: (1) exogenous government expenditure with flexible exchange rate, and (2) fixed exchange rate with exogenous foreign trade. In both cases, closure rules are “Keynesian”, in the sense that they are compatible with less than full employment. Because the model is based on an extended Environmental SAM, it generates statistics of both GDP and green GDP (defined as GDP plus the additional value added imputable to the environmental components). An important caveat must be noted when interpreting the results. Projecting future economic performance is a complex and hazardous endeavor. Future changes in economic structures, technological innovations, policies, political priorities, and consumer preferences cannot be known. As with all modeling exercises, the analysis is based on a litany of assumptions, driven by data availability and computational constraints. The exercise provides projections based upon hypothetical scenarios, not forecasts of the economy. S ta n d i n g O u t F r o m T h e H e r d 19 The Big Question At the outset, it must be emphasized that or public expenditure (the full results can be as with any technique, the CGE approach found in the Annex). As tourist expenditure carries both advantages and weaknesses. covers many sectors in the economy, the An advantage of CGE models is that they backward multipliers quantify the indirect are well suited to capture inter-linkages effects of an increase in tourism demand for between sectors of the economy. A second one sector on the demand for inputs into the benefit is that CGE models can also be related value chains. A backward multiplier used for scenario analyses, especially in measures the average response of sectors data sparse environments where statistical supplying outputs or intermediate inputs to the evidence is lacking. The major disadvantage exogenous increase in demand in the tourism of the approach is that the models and results sector. Thus, for each endogenous sector of the remain highly sensitive to assumptions about economy, backward multipliers, following an functional forms and estimates of parameters increase in tourist demand for that particular concerning, for example, demand and supply sector, measure the extent and the depth of functions, elasticities of substitution, and the sector’s response as a component of a degree of non-linearity of model formulation. supply chain.20 For instance, transport has an Furthermore, complexity of the models imply average multiplier of 0.264, suggesting that that interpretation of results are rendered a 10 percent increase in tourism demand for difficult. Notwithstanding these caveats, the transport induces a 2.64 percent increase in the approach is arguably amongst the most robust demand for intermediate inputs directly and and comprehensive ways of understanding indirectly linked to the transport supply chain. sector linkages and determining their importance. The CGE built for this exercise Conversely, a forward multiplier measures draws upon a number of such models that the increase in the supply of one sector in have been developed for Kenya. response to a uniform increase in exogenous demand, spread over all sectors. It thus Backward and forward linkages of the quantifies the relative dependence of each tourism sector sector on a general increase in the activity level The impacts of the tourism sector on the of all other sectors. In the case of tourism, it economy are largely determined by the measures the extent to which tourism benefits linkages between the sectors in the economy. from a boost in the demand of another sector, Figure 9, derived from the Social Accounting which depends on the linkages of tourism with Matrix (SAM)19 of Kenya, which provides a sectors that buy its services. Again, to take static representation of flows of all economic the case of transport, the average forward transactions within an economy, summarizes multiplier of 0.138 implies that a 10 percent these linkages. It provides a snapshot of the increase in expenditure in the transport sector average backward and forward multipliers boosts the demand for and output of tourism for the case where there is an (exogenous) by 1.38 percent. increase in international tourism, investment, 19 Using the 2003 KIPPRA- IFPRI (2003) and the KNBS (2009) SAMs as starting points, we estimated a 2015 SAM for Kenya by applying the entropic methodology described in Scandizzo and Ferrarese (2015) to national and state account data and other statistics (Kiringai et al., 2006, KNBS a,b,c,d, 2014). 20 All results are for total impacts of the economy. 20 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a The Big Question Figure 9: The tourism sector’s backward and forward linkages 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Backward linkages Forward linkages Source: Elaboration of Kenya SAM Tourism has subtle and unexpected impacts More generally, compared to Tanzania21 and on sectors, but there are also more predictable other countries, the backward multipliers in linkages. The greatest (backward multiplier) Kenya are strong, indicating that tourism is impacts are on sectors that provide direct well integrated into the economic fabric of inputs into tourism — agriculture, food, trade, the country. It also implies that tracing and transport, and land-related services. However, understanding these impacts is vital for policy Figure 9 indicates some surprising impacts decisions. If for instance, tourism is found and linkages too. The education sector, for to disproportionately benefit industries that instance, exhibits unexpectedly large forward employ the poor, any decline in the industry linkages, suggesting that demand for skilled may warrant compensating policies to mitigate and educated labor is a positive stimulus for risks on vulnerable sections of the population. increases in economic activity by tourism. Impacts on GDP Non-irrigated agriculture has the highest Since tourism in Kenya has such wide linkages forward linkages and hence gains the most across the economy, the economic impact or from the generic increase in demand induced value of the sector will depend upon the source by tourism. Figure 9 also suggests that since of change. Impacts will differ depending upon tourism provides (final) consumer services and whether changes emerge due to an exogenous goods, the forward multipliers are typically shock to the demand for tourism by foreigners much weaker than the backward multipliers. (perhaps due to fears of insecurity), or exogenous This is shown more clearly in the Annex which shocks and changes to input costs or supplies suggests that on average a 1 percent increase — such as an unanticipated drought, or due to in any tourism sector induces a 0.94 percent some combination of shocks.22 The first case is increase in “backward” factor demand in all an example of a demand-side shock that would other sectors, but only 0.64 percent of activity impact exchange rates, with consequences in forward activity in the industries supplied that would depend upon the spending patterns by tourism. of foreigners in Kenya. The second case is an 21 For example, in the case of exogenous investment and foreign account, the backward multiplier for tourism is 0.22 for Kenya against 0.18 for Tanzania (see Annex for details). 22 The GDP impact is measured by the change in GDP after holding other elements constant. All impacts are economy-wide and not per capita. S ta n d i n g O u t F r o m T h e H e r d 21 The Big Question example of supply-side shocks whose impacts relevant economic parameters such as capital would depend on the extent to which cheap that is available for investment, the exchange substitutes are available for Kenyan agricultural rate, and the distribution and level of factor produce. Since the effects differ, broad demand in the economy. generalizations about the “contribution” of tourism to the economy would be misleading. In the third scenario it is assumed that the The source of change in the sector needs to same level of contraction in tourism revenues be specified and the consequences inferred is caused by a supply-side shock. In this through the many linkages of tourism in scenario, there is an overall cost increase of the economy. Put simply, a statement such 100 percent for direct inputs and 50 percent for as “what is the contribution of tourism to transport. Once the shock occurs, the resources GDP?” has little meaning unless one specifies devoted to tourism accrue to other sectors the source and magnitude of the decline (or (and are optimally allocated by the forces of increase). A snapshot of a single point in time, demand and supply). A 100 percent increase in in fact, as is done in national accounts, would price is chosen because it yields approximately not yield a measure of the real importance of the same reduction of tourism revenue as tourism because it would neglect all dynamic a demand shock that causes a decline of and indirect effects on the economy. 80 percent of foreign tourism (which is the hypothesis of the demand shock scenario). The To illustrate the importance of tourism to impact of a supply-side shock on the economy, the economy, four hypothetical scenarios are however, is different from the demand shock considered to cover a range of possibilities. since it affects industry costs and induces In the first two scenarios, there is assumed to changes in foreign tourist demand given that be an 80 percent decline in foreign demand for it affects the exchange rate (increase in the tourism. This is arguably a relevant scenario ratio of domestic to international prices). This given security concerns within Kenya, global in turn spreads the impacts of the industry cost turbulence, and rising foreign competition. increase to all sectors of the economy. Any of these factors could lead to changes in foreign tourist demand. The eventual Finally, to test the robustness of the results, impacts of an external shock of any kind will in the fourth scenario, the cost increase is depend crucially upon how exchange rates are assumed to be much lower, at 50 percent. impacted. To capture the full range of possible This turns out to be equivalent to the GDP outcomes two extreme cases are considered. impact of a demand shock (under the capital In the first case it is assumed that capital is mobility assumption). The results reinforce the immobile (endogenous interest rates), and conclusion that even when the final GDP impact in the second case capital is assumed to be is the same, the distributional and sectoral mobile (exogenous interest rates). The extent impacts differ depending upon the source of of capital mobility will influence a host of the shock. 22 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a The Big Question Scenario number Scenario category Key assumptions 80% decrease in foreign demand 1 Demand shock for tourism; capital is immobile 80% decrease in foreign demand 2 Demand shock for tourism; capital is immobile 80% decrease in foreign demand 3 Supply shock for tourism; capital is immobile 80% decrease in foreign demand 4 Supply shock for tourism; capital is immobile Demand shocks: Decline in foreign demand 11 to 15 percent, which in turn could constrain The overall results suggest that a demand the government’s capacity to neutralize the shock (80 percent decline in foreign tourist adverse impacts on the poor. revenue) would result in an 8 to 12 percent decline in GDP with impacts that are Supply-side shocks disproportionately felt by the rural poor. The Supply-side shocks of equivalent magnitude results are summarized in columns 1 and 2 in (on tourist industry revenue) have larger, Table 4 below. There is a fall in GDP of between adverse GDP impacts and distributional 8 to 12 percent depending on whether capital is consequences. Two supply side shocks are assumed to be internationally immobile (and considered (scenario 3 and 4). In scenario 3, interest rates endogenous) or internationally tourism sector costs rise by 100 percent. This mobile (with interest rates exogenous). In both generates a loss in tourist revenue of the same cases, when foreign demand falls, tourism magnitude as with the foreign demand shock prices decline, and this in turn stimulates a considered earlier. In scenario 4, industry costs strong increase in domestic tourism, which rise by about 50 percent (this induces a reduction partly compensates for fewer foreign tourists. in value added of the same magnitude as the But since foreign and domestic tourism are demand shock with internationally mobile not perfect substitutes (foreigners spend more capital). In both cases, there is a reallocation of and differently), there is an overall decline in economic activity, with resources absorbed at demand and GDP. The decline is more dramatic the higher prices by other sectors. The results with mobile capital as lower returns to capital are summarized in columns 3 and 4 of Table 4. in Kenya induce a migration of capital overseas. The impacts on the economy are now larger in both scenarios with 12-13 percent loss in GDP. Table 5 charts the resulting distributional impacts. Clearly, the rural poor suffer the most Moreover, despite the large differences in the and experience a 9 to 12 percent decline in their cost increases assumed (100 percent in the incomes. Overall, gross incomes drop by 10 to 15 first scenario versus 50 percent in the second), percent. The clear implication of this exercise the difference in impact on GDP is trivially is that foreign tourism is a pro-poor source of small (less than 1 percent point). This result rural income with non-trivial contributions to has two implications: First, it suggests that the GDP. There is a decline in tax revenues too of outcomes are reasonably robust to changes S ta n d i n g O u t F r o m T h e H e r d 23 The Big Question Table 4: Impacts of shocks on industry-wide tourism (Percent)23 Demand shock with Demand shock with Demand shock with Demand shock with Value added capital internationally capital internationally capital internationally capital internationally components immobile mobile mobile mobile Skilled labor 10.96 13.57 14.03 12.67 Semi-skilled labor 7.32 10.61 13.50 12.48 Unskilled labor 9.98 12.93 10.52 9.66 Capital 8.10 12.11 14.22 13.06 Land 3.88 6.79 7.04 6.44 Blue Water 24 7.78 10.12 7.38 6.76 Green Water25 13.17 14.57 7.22 6.62 Non renewable 26.52 27.77 13.16 12.16 natural resources Savanna and 15.34 18.26 12.72 11.74 forest habitat Direct taxes 9.41 9.97 11.04 10.14 Import tariffs 6.23 10.29 11.05 10.14 Sales taxes 10.77 14.14 12.22 10.83 Green GDP 8.48 11.95 13.01 11.92 GDP 8.06 11.80 13.37 12.0 Source: Elaboration of Kenya SAM in costs, implying that the contribution of the disrupt the tourism value chain and all the tourism sector to GDP is in the range of 12 to 13 value chains related to the tourism industry, as percent when changes originate from the supply well as disrupts both the domestic and foreign side. Second, the result illustrates the self- tourist sub-sectors, the model suggests that correcting (equilibrating) feature of a general the Kenyan economy exhibits a high level of equilibrium system — price changes act as a resilience. shock absorber in a general equilibrium system. If a shock induces positive / negative multiplier Table 5 shows that rural incomes fall by about effects, prices in the relevant sectors tend to 13 to 14 percent. Again, the pro-poor impacts of increase / decrease, which in turn moderates tourism emerge in this scenario too. One reason the impacts of the shock. It is worth noting that for this is shown in Figure 9 (above); observe the differences would be even smaller under that the biggest backward linkage is with non- the assumption of endogenous interest rates irrigated agriculture — which is the source of (and capital internationally immobile) since employment of the poorest in Kenya. Tax and capital prices would also fall in response to the tariff revenues decline by about 30 percent in shocks. Thus, while increases in industry costs these scenarios. 23 These are demand and supply shocks, and by industry we refer to value added. 24 Blue water is surface water. 25 Green water is water held in soils and available for use by plants. 24 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a The Big Question Table 5: Impacts of shocks on income distribution (Percent) Supply shock with capital Demand shock Demand shock Demand shock internationally with capital with capital with capital mobile (demand internationally internationally internationally equivalent in terms immobile mobile mobile of value added) Rural poor 9.15 12.77 14.28 13.06 Rural non-poor 9.06 12.66 14.13 12.97 Urban poor 6.85 9.22 9.78 8.98 Urban non-poor 6.75 9.10 9.66 8.87 Enterprise 14.35 10.74 12.62 11.60 Government 8.68 10.39 9.89 8.91 Savings and investment 7.72 10.58 11.56 10.61 Water resources 7.07 9.12 6.62 6.06 Natural capital 15.54 18.49 12.88 11.89 Total 15.67 10.4 11.56 10.60 Source: Elaboration of Kenya SAM Concluding comments presented in the Annex suggest that the While the results are to be interpreted with care contribution of tourism to GDP is of the order because the simulated equilibria are rather of 12 – 15 percent, and that the overall impacts distant from the baseline, they suggest that are pro-poor with wide diffusion across sectors. tourism is a key sector of the Kenyan economy After the transport and entertainment related and that its direct and indirect effects tend sectors, a primary beneficiary of the sector is to be considerably larger than those credited non-irrigated agriculture, which is a key source by the national accounts. Further simulations of employment for the poor. S ta n d i n g O u t F r o m T h e H e r d 25 3 Kenya is now at a crossroads and must decide how it will develop its lucrative tourism sector. In terms of tourism products, the economic simulations confirm that the safari segment dominates, making a significant contribution to GDP, jobs, and the rural incomes of the poor. Photo: Keziah Muthembwa Alternative Scenarios for Kenya’s Tourism Development: A Dynamic Assessment Context of resources over time will determine how the T he objective of this chapter is to explore economy responds to changes in any of these the dynamic implications of different sub-sectors of tourism. The dynamic component growth strategies in the tourism sector on is also important for policy decisions. Policies that neglect incentives to invest in a sector Kenya’s economic growth and the distribution may end up being counterproductive if they of income. Typically, investments in sectors will dampen incentives and hence growth in the be guided by the returns to investors and the sector. To capture these features it is necessary availability of other inputs such as the supply to introduce a temporal dimension into the of land or labor, that in turn determine the analysis, which calls for a dynamic CGE model, ability to respond to an increase in demand. described briefly in Box 2 below. As an example, if there is insufficient growth in labor supply, the responsiveness of a labor To assess how well the dynamic model intensive industry to higher demand will be performs, it is instructive to compare constrained. In the tourism context, these issues projections with past outcomes. CGE models are especially relevant since the magnitude are not designed to capture short-term and nature of backward linkages vary between volatility, which is better analyzed through the different types of tourism (safari, beach, statistical modeling; instead, the CGE approach business, and other). Hence, the availability seeks to assess long-term growth trajectories. Box 2: The dynamic CGE model The task of introducing dynamic features in a CGE is conceptually straightforward but computationally complex. On the one hand, it amounts to replicating the static model over time periods – or equivalently adding a time subscript to the variables in the static model. On the other hand, solving intertemporal optimization problems are notoriously difficult, so the model needs to be simplified along a number of dimensions to be rendered tractable. Recognizing these challenges, a recursive dynamic version of the static CGE model was developed with decision-making linked across periods. The model contains a capital updating module that allows capital to flow between sectors as a result of increased demand for capital goods in one period that leads to an increase in productive capacity in a future period. The lag between investment and increased production can be interpreted as the time it takes to build new capacity. The demand for investment is assumed to be a function of income and the price of capital. Given these characteristics, the model generates a sequence of static equilibria, which reflect a path of continuous adjustment of the main economic variables. In turn, this can be seen as the result of the attempt of consumers and producers to respond to incentives by updating their decisions in light of the new evidence. To reflect this process, in each iteration, a new static equilibrium and a new SAM are computed as the basis for the computation of next period iteration. S ta n d i n g O u t F r o m T h e H e r d 27 Alternative Scenarios The results for projections from 2010 - 2014 are All forms of tourism have broad economy- shown in Figure 10 below, and suggest that the wide effects, though the sectoral impacts model tracks the trends in the Kenyan economy differ. Tables 6-8 present the summary results with reasonable accuracy, but as would be of the impact of the four different forms of expected, fails to project short-term cyclical tourism, considered as alternatives, on the fluctuations. economic activities for the terminal year of the simulation (which is fixed conventionally Figure 10: Comparison between data and simulations of GDP growth (2010-2014) as the 15th year). While all forms of tourism 9 appear to have comparable effects on the 8 diversification of the economy and affect 7 all sectors, there are significant differences GDP growth rate (%) 6 too. Business tourism has a larger impact 5 on growth and investment in hotels, trade, 4 3 transportation, and package tours. In contrast, 2 safari tourism appears to have a much higher 1 impact on agriculture, both irrigated and non- 0 irrigated, and investment in lodges. Impact on 2011/2010 2012/2011 2013/2012 2014/2013 GDP growth simulated GDP growth 2010-2014 hotels is highest for the generic “other forms Source: Elaboration of the Kenya CGE model and Kenya National Bureau of of tourism” category, which also appears to Statistics favor restaurant and trade. Consequences of international tourism The impacts on the economy are widespread, growth indicating a significant depth of the value The income and distributional consequences chain of the different types of tourism. Safari of further tourism development will vary tourism, alone or in combination with other depending upon the sub-sector of tourism forms of tourism, seems to dominate and that grows. Spending patterns amongst the generates the highest GDP, with a plethora of different classes of tourists vary and the indirect effects, especially on agriculture26 as linkages with the rest of the economy also the last ring of a well-developed domestic value differ. To investigate the consequences of chain (Table 6). This type of tourism seems to developing the different types of tourism, the be much more environmentally benign too and first set of simulations consider four scenarios is less demanding in terms of water and other where foreign tourist demand (expenditure) is natural resources (savanna land and forests). assumed to grow by 5 percent per year (roughly Safari tourism also provides a boost to other 20,000 million Kenyan Shillings) in each of the natural resource sectors such as forestry and four categories of tourism: (i) business tourism, fishing and wildlife, as shown in Table 7. Its (ii) beach tourism, (iii) safari tourism, and (iv) multiplier effect on the hotel component of the other types of tourism. tourism industry, on the other hand, is limited, and the larger effect on this activity appears to originate from the “other” category, which 26 The input output SAM coefficient of Safari Tourism for irrigated agriculture is about 0.014 (i.e. about 1 percent of total tourist expenditure), while for the other types of tourism it is only about 0.2 percent. This large difference is due to a higher direct consumption of fruits and vegetables, and to the fact that agriculture and safari tourism are complements (as agro-tourism) in an increasing number of Safari packages. See, for example: http://www. ecoadventuresafrica.com/safari/AgriTourism 28 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Alternative Scenarios Table 6: Terminal year impact of tourism on economy activity levels (KSH, millions) Types of Tourism Activity/Sector Business Beach Safari Other Irrigated agriculture 3,252 3,046 25,982 2,732 Non-irrigated agriculture 20,495 20,496 30,933 19,583 Forestry and fishing -30 -155 0.550 -129 Wildlife 141 138 2,059 130 Mining 5,902 5,098 7,918 5,073 Food, beverage and tobacco 11,049 12,090 11,879 11,116 Petroleum 1,072 1,214 1,757 843 Textile & clothing 1,174 1,007 1,523 1,018 Leather & footwear 203 164 0,374 167 Wood & paper 2,604 2,411 2,595 2,459 Printing and publishing 5,192 4,870 6,524 4,785 Chemicals 1,678 1,636 2,247 1,692 Metals and machines 1,560 1,463 2,090 1,489 Non metallic products 4,207 3,295 6,808 3,289 Other manufactures 11,922 11,288 12,277 11,395 Distribution water 3,117 2,706 11,330 2,778 Electricity 12,927 12,669 12,613 13,820 Construction 2,167 1,150 5,835 1,327 Trade 58,619 65,106 57,861 55,756 Hotel 28,608 25,403 12,820 39,552 Lodge 1,011 6,567 15,209 6,494 Rent house 13,814 20,185 3,467 19,278 Restaurant 23,164 21,092 19,012 20,860 Transport 136,180 124,230 114,067 122,516 Information and communication 10,085 9,551 9,426 9,548 Financial and insurance activities 18,675 19,798 18,013 18,888 Real estate 2,425 2,292 3,451 2,907 Other services 7,515 6,962 7,314 7,102 Public administration and defence 4,395 5,415 5,562 6,672 Health and social work 33,533 25,205 60,472 24,233 Education -505 889 -3,226 1,916 Package Tours 29,468 29,590 22,870 26,311 Total 455,620 446,871 491,611 445,600 Source: Elaboration of the Kenya CGE model S ta n d i n g O u t F r o m T h e H e r d 29 Alternative Scenarios includes miscellaneous visitor activities. Safari suggests that the income distribution effects tourism also exhibits the highest employment of the various types of tourism are generally multiplier (Table 7), especially for semiskilled of comparable magnitude, even though Safari and skilled labor, while its capital intensity tourism still dominates the scene, especially (in terms of the multiplier effect) is much for its effect on the poor and its contribution smaller than that of other tourism categories, to government revenues. In sum, the overall presumably because of the absence of both effects of Safari tourism dominate other forms direct investment and links with the traditional of tourism. forms of accommodation. Finally Table 8 Table 7: Terminal year impact of tourism on real factor incomes (KSH, millions) Type of Tourism Business Beach Safari Other Skilled labor 32,776 34,091 44,812 35,721 Semi-skilled labor 51,988 49,360 128,698 48,838 Unskilled labor 32,200 28,337 6,156 27,689 Capital 125,601 125,777 21,570 125,039 Land 3,729 3,724 31,036 3,554 Blue Water 3,797 3,403 1,364 3,354 Green Water 5,007 4,521 2,800 4,478 Non renewable natural resources 179 163 1,526 158 Savanna and forest habitat 631 562 21,437 552 Emissions 1,622 1,496 1,766 1,509 Direct taxes 19,215 18,752 32,927 18,706 Import tariffs 1,427 1,374 890 1,342 Sales taxes 34,908 37,069 16,451 40,307 GDP 282,628 279,733 249,613 282,490 Green GDP 313,080 308,629 311,433 311,247 Source: Elaboration of the Kenya CGE model Table 8: Terminal year impact of tourism on gross incomes (KSH, millions) Type of Tourism Business Beach Safari Other Rural Poor 21,747 22,354 29,511 21,959 Rural Non Poor 33,375 34,355 45,467 33,673 Urban Poor 1,684 1,771 2,381 1,694 Urban Non Poor 58,234 61,223 82,309 58,585 Enterprise 121,215 120,510 137,631 121,140 Government 30,531 29,598 34,383 32,231 Savings and investment 22,817 23,746 31,590 22,985 Water resources 5,838 6,458 38,682 5,793 Natural Capital 276 308 1,525 272 Total 295,717 300,323 403,480 298,333 Source: Elaboration of the Kenya CGE model 30 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Alternative Scenarios The simulations suggest that safari tourism be developed but are not considered in this provides the more effective strategy for exercise. The scenarios are structured as boosting growth throughout the economy as investment programs over a period of seven well as the incomes of the rural poor. As Figure years and include an implementation phase 11 shows, the distributional impacts differ of two years and an operational phase of five across the categories of tourism. Safari tourism years. In addition to the physical investment, generates significantly greater household however, which is assumed to be just the start income. It is also considerably more pro-poor of the strategy, the scenarios differ because of than the other forms of tourism. This is perhaps the choice of the sector development patterns. a consequence of the closer linkages with the Thus, while direct outlays for the investment rural economy than those exhibited by other are assumed to be the same, effective costs forms of tourism. In sum, if there is a choice and benefits vary endogenously since further to be made between developing the different investments are generated and interact types of tourism growth, the safari segment differently with the other economic variables. would not only generate greater economic The backdrop is an economy that grows at an growth but would do more to address poverty overall rate of 5 percent a year. problems than the other forms of tourism. Table 9: Initial investments in tourism by sector (KSH, millions) Figure 11: Impacts of the simulation results on overall income (KSH, millions) Scenario 2: 160,000 Investments 140,000 Scenario 1: in beach and Type of Investments in business 120,000 investment safari tourism tourism 100,000 Imports to upgrade 80,000 1,000 park tourism 60,000 40,000 Construction 1,000 5,000 20,000 Hotel 5,000 0 Source: Authors’ hypothesis Urban poor Enterprise Rural poor Government Savings and investment Water resources Urban non-poor Natural capital Rural non-poor Business Beach Safari Other Source: Elaboration of the Kenya CGE model Scenario 1: Investments in safari tourism The first scenario considers investments in Alternative strategies of tourism safari tourism. Physical investment can take development several forms but would in most cases include Expanding tourism will call for investments construction (roads, facilities, and services), that will in turn have impacts upon the some of which could be imported to cater to economy. Accordingly, this section further special tourist niches. This scenario is assumed explores the implications of alternative to attract higher quality lower density tourists. strategies of tourism development by Thus, in this scenario, it is assumed that in the simulating different investment scenarios first two years of implementation, a reduction shown in Table 9. Needless to say, other of safari tourism occurs as a consequence of scenarios involving “soft” investments could the temporary disruption of the safari facilities S ta n d i n g O u t F r o m T h e H e r d 31 Alternative Scenarios and an attempt to reorganize park tourism the rural poor, whose incomes increase by along new and more productive lines. These about 5 percent (Figure 12). would include fewer package tours and more Figure 12: Impact of investments in safari tourism on selective and secure ways of lodging and incomes (KSH, millions) transportation. Income effect of safari tourism strategy (yearly) 8,000 6,000 This has the consequence of thinning tourism 4,000 in the short-run, but boosting it in the KSH, millions 2,000 medium-run. As Tables 10 (a) and (b) show, this 0 scenario requires sacrifices in the first years of -2,000 implementation. The Table shows that in the -4,000 -6,000 initial investment phase, when there is a build- 8,000 up of investments, there is a slow-down of 1 2 3 4 5 6 7 Year activity. In the long-term, however, benefits are Rural Poor Rural Non Poor Urban Poor Urban Non Poor positive for all income groups and especially Source: Elaboration of the Kenya CGE model Table 10: (a) Safari tourism development scenario (KSH, millions) Year 1 2 3 4 5 6 7 Business tourism (international) -231 -292 366 619 916 501 273 Beach tourism (international) -139 -175 219 371 549 300 163 Safari tourism (international) -10,483 -10,611 10,764 16,302 21,940 11,071 5,586 Other tourism (international) -74 -93 117 198 293 160 87 Source: Elaboration of the Kenya CGE model (b) Impact of safari tourism strategy on incomes (KSH, millions) Year 1 2 3 4 5 6 7 Rural poor -2,090.90 -1,021.6s3 1,033.41 1,573.90 2,132.08 1,083.55 548.85 Rural non-poor -3,225.25 -1,578.91 1,599.64 2,438.44 3,305.73 1,681.09 852.14 Urban poor -164.55 -80.87 81.72 124.63 169.23 86.28 43.76 Urban non-poor -5,687.67 -2,795.10 2,824.70 4,308.27 5,849.89 2,982.29 1,512.60 Long Term Net Present Baseline (A) Value (NPV) (B/A)% Rural poor (B) % (B/A) 4.8 Rural non-poor 1,378.728 66,686.7 4.8 Urban poor 91.246 3,422.9 3.8 Urban non-poor 3,200.049 118,327.9 3.7 Source: Elaboration of the Kenya CGE model 32 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a S Keziah Photo: Muthembwa ta n d i n g Out From The Herd 33 Alternative Scenarios Scenario 2: Infrastructural improvements in a meager 2.3 percent, which is about half as the business and beach tourism segments much as under safari tourism. This is explained The second scenario allows for the improvement by the deeper linkages of safari tourism with of roads and hotels for the business and beach non-irrigated agriculture, the sector where segments of tourism and relies on a “business most of the poor are employed. as usual” development strategy aimed at Figure 13: Impact of investments in beach and business attracting the largest possible number of tourism on incomes (KSH, millions) Income effect of business and beach tourism strategy (yearly) tourists. For brevity, these two types of tourism 15,000 are combined as they use the same types of 10,000 venues for accommodation (hotels rather 5,000 than lodges). Figure 13 and Table 11 show that KSH, millions 0 the investment phase is associated with a reduction of tourism followed by a surge that -5,000 converges at a higher level. This growth strategy -10,000 has similar implications to the safari-based -15,000 development, but it appears, on the whole, less 1 2 3 4 Year 5 6 7 productive in the aggregate. Moreover, in this Rural Poor Rural Non Poor Urban Poor Urban Non Poor case, the incomes of the rural poor increase by Source: Elaboration of the Kenya CGE model Table 11: (a) Business and beach tourism scenario (KSH, millions) Year 1 2 3 4 5 6 7 Business tourism (international) -15,523 -15,629 16,168 21,744 10,971 11,064 5,579 Beach tourism (international) -15,315 -15,377 15,699 21,043 10,581 10,636 5,346 Safari tourism (international) -1,093 -1,331 2,464 3,647 2,009 2,191 1,185 Other tourism (international) -167 -202 376 563 315 346 188 Source: Elaboration of the Kenya CGE model (b) Impact of business and beach tourism strategy on incomes (KSH, millions) Year 1 2 3 4 5 6 7 Rural poor -4,558.97 -2,148.06 2,864.64 3,885.21 1,975.03 2,002.82 1,014.70 Rural non-poor -7,030.00 -3,317.93 4,430.20 6,011.22 3,056.50 3,101.26 1,572.04 Urban poor -350.56 -166.30 217.97 295.74 150.35 152.51 77.30 Urban non-poor -12,123.98 -5,750.93 7,539.68 10,229.63 5,200.23 5,275.11 2,673.45 Long Term Net Present Baseline (A) Value (NPV) (B/A) % Rural poor 885.922 20,294.1 2.29 Rural non-poor 1,378.728 31,440.8 2.28 Urban poor 91.246 1,545.9 1.69 Urban non-poor 3,200.049 53,469.0 1.67 Source: Elaboration of the Kenya CGE model 34 Photo: Magical Kenya E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Alternative Scenarios Concluding comments that can judiciously exploit and preserve the Tourism appears to be a robust source of comparative advantage and attraction of the growth with diffuse impacts across the various assets that bring tourists to Kenya. economy. The dynamic CGE simulations confirm earlier results that safari tourism Nonetheless, the results of this exercise dominates in terms of growth and more need to be interpreted with care. They are equitable income distribution benefits. The conditioned by the choice of the terminal year, result is highly robust across several scenarios that is, by the assumption that the economy considered in this exercise. This derives from will be in a steady state at the year chosen its complementarity with agriculture, its low as a final year for the simulations. Second, capital intensity, and multiple and intensive a number of simplifying assumptions are backward linkages with the rural poor. necessary to render the analysis feasible. Third, However, the dynamic simulations suggest the simulations of tourism impact have been that significant differences in performance can obtained against a backdrop of “business as probably be obtained only by growing revenue usual for the other sectors”. It is impossible to from the tourism industry. If growth is obtained project how investments in other sectors could through increased tourist numbers, problems of either negate or complement the contribution congestion and price decreases will neutralize of the tourism sector. Notwithstanding these any gains. This suggests the need for further caveats, the overarching conclusions of the consideration on how expansion of the sector numerous simulations remain consistent and occurs. It would have to entail combinations appear to be robust. S ta n d i n g O u t F r o m T h e H e r d 35 4 Despite limited water resources, Kenya’s economy remains dependent upon water intensive sectors. Agriculture consumes much of the country’s water but it does so with much lower efficiency and job creating potential than other sectors, including tourism. 36 Keziah Muthembwa Photo: E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Resource Rivalry: Water Allocation and the Impacts on Tourism Context The water towers are the source of more than A s one of the most water scarce countries 75 percent of the country’s renewable surface in the world, Kenya’s water availability water resources. It is hard to overstate the stands at a meagre 548 cubic metres (m3) of economic significance of the water towers as renewable freshwater27 per capita, significantly a cheap source of water storage and supply, below the UN’s water scarcity marker of 1,000 especially as water demands rise in sectors such cubic metres per capita. Scarcity of rainfall as tourism and agriculture and because of the is compounded with large annual swings in expanding needs of Kenya’s burgeoning cities. precipitation. Rainfall ranges from as little as However, deforestation and encroachment have 250 mm over much of the country (about 80 taken a severe toll on the region, with impacts percent) that is classified as arid or semi-arid, on stream flow stability, water quality, and soils to more than 2,000 mm per year in isolated at higher elevations. UNEP has estimated that pockets in the high mountain areas — the deforestation in one part of the water towers “water towers” of Kenya. Few countries in the is responsible for the loss of 62 million cubic world, and none in the region (see Figure 14), waters of surface water.29 experience such extreme inter-annual rainfall variability. As a result, the country endures the Despite limited water resources, the inconvenience of floods, as well as the ignominy economy remains dependent upon highly of prolonged droughts. Unsurprisingly, the water intensive sectors. The agriculture impacts fall disproportionately upon rain-fed sector extracts 80 percent of total renewable farmers who, as is well known, constitute the freshwater resources. Industry withdrawals bulk of the rural poor in the country. are at 3 percent of total freshwater supplies, and municipal water supply accounts for the Figure 14: Inter-annual variability of water supply – Country comparisons remainder 17 percent of water withdrawal. 3.5 4-5 Extremely high 3-4 High Tourism is also a thirsty sector in Kenya.30 2-3 Medium to high 3 1-2 Low to medium 0-1 Low In a water scarce country such as Kenya there Inter-anual variability is little by way of “surplus” water that is 2.5 1.5 unused. Much of the water that is presumed to be unused in fact underpins the thirsty tourism 1 industry or goes to subsistence farming. So 0.5 while expanded irrigation is necessary and is rendered more desirable in the face of climate 0 Kenya Somalia Ethiopia Uganda South Sudan Rwanda variability, it will not come without the need to Source: WRI (2014) consider trade-offs — such as allocating more 27 “Renewable freshwater supply” refers to total internal renewable surface water and groundwater resources. Per capital renewable freshwater supply statistics come from the Food and Agriculture Organization (FAO) AQUASTAT survey in 2007. 28 Often these swings in rainfall reflect the warming and cooling effects of the El Niño and the La Niña events in the Southern Pacific, but the correlation is not precise. 29 UNEP, 2012. 30 See Annex for details on SAM. S ta n d i n g O u t F r o m T h e H e r d 37 Resource Rivalry water from one sector to another, such as from can be compromised. A preliminary attempt irrigation to tourism. The issue is of relevance has been made to develop a CGE with water as given the plans to expand irrigated agriculture. part of a broader exercise on growth drivers Going forward, such choices may need to be guided by paying greater attention to the As in most other countries, the productivity economic payoffs from alternative water uses. of water used in agriculture is much lower than the productivity of water used in other To gain a greater understanding of the ways sectors of the economy. Table 12 and Figure in which water impacts the economy, it is 15 below show the importance of water across necessary to explore the role of water in each different sectors of the economy (from the water of the significant sectors of the economy. This component of the Social Accounting Matrix). It is achieved by introducing water in the Social is instructive to note that while industry and Accounting Matrix (described in the Annex). The urban sectors consume less than 18 percent task is especially challenging as, unlike other of water, they produce the most value added commodities in the economy, water performs from the water that they consume, and, while multiple economic functions — it serves as an agriculture consumes 80 percent of water, economic input, a consumption good, and a it produces less than 25 percent of the value renewable natural resource whose productivity added generated by water use in the economy Table 12: Water footprint (water resources’ direct and indirect use) Value ratio Total (water as % Quantity to production of production value ratio value Million KSH value) Million m3 (m3/US$) (Million KSH) Irrigated agriculture 28,874 36.30% 2,887.46 3.630 79,539.40 Non-irrigated agriculture 114,792 8.18% 114,792.08 8.178 1,403,673.04 Industry 34,792 3.01% 1,739.61 0.150 1,157,489.14 Services 153,154 4.50% 5,105.14 0.150 3,406,984.85 Mining 2,679 6.45% 2,679.06 6.451 41,529.19 Source: Elaboration of Kenya SAM Figure 15: Efficiency of use of water withdrawn Figure 16: Shadow value of water as % of total value (value per drop) Value (KSH, million) per drop (m3) Shadow value of water in total production 7 30 6 25 5 20 4 15 3 10 2 5 1 0 0 Agriculture Other Industry Utilities Construction Tourism Services Irrigated Non Irrigated Industry Services Mining primary agriculture agriculture Blue water Green water Source: Elaboration of Kenya SAM Source: Elaboration of Kenya SAM 38 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Resource Rivalry as a whole. This suggests the scope for possible urban poor. Table 14 shows the analogous economic gains through the reallocation of results for green water. Again, though smaller water. Any such, change would of course need than in the blue water case, the contribution to carefully consider impacts on the poor is significant, at almost 6 percent of GDP and and the environment. There are however sub- with distributional impacts concentrated in the sectors such as horticulture where returns may rural areas. be higher but which cannot be identified with existing data. Figure 16 reinforces this message Table 14: Green Water Contribution to Gross Incomes (KSH, millions) and shows the (shadow) value of water in Baseline Impact Ratio total costs, estimated as opportunity costs. (A) (B) (A/B) % There is much variation in the ratios as well Rural poor 885,922 65,562 7,400 as the absolute shadow values in each sector, Rural non-poor 1,378,728 101,018 7,327 suggesting economic gains from reallocation Urban poor 91,246 4,137 4,533 of water across sectors. Urban non-poor 3,200,049 143,501 4,484 Water (blue and green) plays a very significant Total 5,555,945 314,217 5,656 role in the Kenyan economy. In the water Source: Elaboration of the Kenya CGE model geography and hydrology literature, “blue water” is defined as water extracted from Is water important for tourism? aquifers, lakes and rivers, used by irrigated It is useful to examine the economic and agriculture, and “green water” is the rainwater distributional consequences of transferring used by rain fed agriculture. In order to estimate water from the largest user of water (irrigation) the contribution of water to the economy, the to tourism, which is among the more efficient impact of the use of blue and green water is users of water. Tables 15-17 report the results simulated through the CGE model. Table 13 of an experiment where 20 percent of the water summarizes the results for blue water. Its impact is shifted from irrigated agriculture to hotels on the economy is indeed highly significant, and lodges. This simulation aims to provide a with a share of GDP in excess of 8 percent. As general idea of the results of major resource expected, the impact on incomes is especially reallocation and does not take into account high for the rural population and appears to the investment and other costs that such a be important for both for the rural and the shift would entail. Nevertheless, effects on the economy are impressive. The productivity Table 13: Blue water contribution to gross incomes (KSH, millions) of water in tourism is high (roughly 50 KSH of Baseline Impact Ratio product for each KSH worth of water for hotels (A) (B) (A/B) % and lodges) compared to that of agriculture, Rural poor 885,922 91,876 10,371 at about 7.6 KSH. Hence, such a transfer Rural non-poor 1,378,728 141,553 10,267 will inevitably boost GDP so long as water Urban poor 91,246 5,775 6,329 availability is constrained. Urban non-poor 3,200,049 200,366 6,261 Total 5,555,945 439,571 7,912 Source: Elaboration of the Kenya CGE model S ta n d i n g O u t F r o m T h e H e r d 39 Resource Rivalry The fall in the value of irrigated agriculture is (Table 17) would be large and favor the poor and more than compensated from the increase in the rural population in general. Blue water and the tourism industry as well as the consequent water resources (shadow) prices would also growth in the rest of the economy, including decline (Table 18) indicating a lower pull on non-irrigated agriculture. Value added natural resources. Table 18 also suggests that would increase for all factors, except for blue factor prices would tend to increase by about and green water, which would be used less 8 percent. intensively (Table 16). Income distribution effects Table 15: Impact on activity levels of a 20% water shift from irrigated agriculture to hotels and lodges (KSH, millions) Baseline (A) Impact (B) Ratio (A/B) % Irrigated agriculture -91,237 133,853 -68.16 Non-irrigated agriculture 116,245 1,553,870 7.48 Forestry and fishing 9,948 84,187 11.82 Industry 166,908 1,319,853 12.65 Utilities, construction & trade 279,118 1,672,585 16.69 Tourism-related Industries 665,554 916,443 72.62 Services 291,938 2,475,794 11.79 Source: Elaboration of the Kenya CGE model Table 16: Impact on value added of a 20% water shift from irrigated agriculture to hotels & lodges (KSH, millions) Baseline (A) Impact (B) Ratio (A/B) % Skilled labor 163,952 468,237 35.01 Semi-skilled labor 107,808 1,241,740 8.68 Unskilled labor 62,031 576,664 10.76 Capital 434,960 2,509,569 17.33 Land 18,410 279,935 6.58 Blue Water -18,954 57,081 -33.21 Green Water -6,587 71,621 -9.20 Non renewable natural resources 428 3,869 11.07 Savanna and forest habitat 1,799 16,275 11.06 Emissions 2,765 16,586 16.67 Direct taxes 43,352 341,061 12.71 Import tariffs 5,977 52,114 11.47 Sales taxes 68,109 521,342 13.06 Green GDP 840,700 5,815,033 14.46 GDP 743,810 4,735,084 15.71 Source: Elaboration of the Kenya CGE model 40 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Resource Rivalry Table 17: Impact on gross incomes of 20% water shift from irrigated agriculture to hotels & lodges (KSH, millions) Baseline (A) Impact (B) Ratio (A/B) % Rural poor 142,589 885,922 16.09 Rural non-poor 216,826 1,378,728 15.73 Urban poor 9,946 91,246 10.90 Urban non-poor 343,665 3,200,049 10.74 Enterprise 427,303 2,782,427 15.36 Government 127,666 1,150,557 11.10 Savings and investment 141,663 1,105,478 12.81 Water resources -37,831 128,702 -29.39 Natural Capital 1,799 16,066 11.20 Total 1373,625 10,739,176 12.79 Source: Elaboration of the Kenya CGE model Table 18: Change in Factor Prices a year for 7 years with the economy growing Baseline (A) % at 5 percent a year according to its historical Skilled labor 35.01 pattern. As Table 19 shows, there is a temporary fall in unskilled labor wages, land rents, tariffs, Semi-skilled labor 8.68 and taxes for the first year. This is followed Unskilled labor 10.76 by a hefty increase in all real value added Capital 0.00 contributions thereafter. In terms of gross Land 6.58 income growth (Table 20), the results appear Blue water -33.21 even more favorable with income redistribution Green water 0.00 in favor of rural residents (especially rural Implicit GDP deflator 8.6 poor) and water resources. In sum, as shown in Source: Elaboration of the Kenya CGE model other recent work, improving water allocations pays very high economic dividends in terms of Water prices economic growth and distribution. Reallocating water to higher valued uses Figure 17: Increase in GDP from improved water allocation would generate significant increases in Increase in value-added in response to a 20% yearly increase in water prices economic growth. There are several ways in 120,000 which water can be reallocated. One is through 100,000 administrative decree, another is by changing KSH, millions 80,000 prices and the incentives to use water, or by a using a hybrid such as water permits that impose 60,000 caps and regulate prices. The implications of 40,000 water pricing are explored in Tables 19 and 20,000 20 and Figure 17. The tables report the results 0 of a simulation where prices of blue water 1 2 3 4 5 6 7 Year have been assumed to increase at 20 percent Green GDP GDP Source: Elaboration of the Kenya CGE model S ta n d i n g O u t F r o m T h e H e r d 41 Resource Rivalry Table 19: Impact on (real) value added of a 20% yearly increase in blue water prices for 7 years (KSH, millions) Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Skilled labor 140 1,315 1,546 1,829 2,177 2,615 3,176 Semi-skilled labor 270 3,166 3,691 4,328 5,111 6,089 7,337 Unskilled labor -487 284 410 567 765 1,017 1,348 Capital 87 4,351 5,206 6,243 7,519 9,112 11,148 Land -328 459 538 634 753 903 1094 Blue water 3,741 6,629 8,366 10,732 14,017 18,684 25,484 Green water 3,290 6,896 8,877 11,592 15,385 20,801 28,733 Non renewable natural resources -3 8 12 16 22 29 39 Savanna and forest habitat -26 14 26 41 59 83 114 Emissions -29 24 31 39 48 61 76 Direct taxes -23 681 815 979 1,181 1,433 1,755 Import tariffs -40 51 63 78 96 118 147 Sales taxes -380 527 679 865 1,097 1,388 1,765 Green GDP 6,235 23,722 29,444 36,964 47,050 60,900 80,459 GDP 5,895 33,980 41,653 51,547 64,561 82,075 106,323 Source: Elaboration of the Kenya CGE model Table 20: A 5% discount NPV increase in incomes in response to a 20% yearly increase in blue water prices for 7 years (KSH, millions) Baseline (A) NPV (B) B/A % Rural poor 885,922 34,152 3.85 Rural non-poor 1,378,728 53,065 3.85 Urban poor 91,246 2,223 2.44 Urban non-poor 3,200,049 77,124 2.41 Enterprise 2,782,427 75,262 2.70 Government 1,150,557 24,530 2.13 Savings and investment 1,105,478 32,808 2.97 Water resources 128,702 159,515 123.94 Natural capital 16,066 615 3.83 Total 10,739,176 459,294 4.28 Source: Kenya CGE model 42 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Resource Rivalry Concluding comments A CGE approach is particularly well suited to explore the consequences of such structural The results outlined in this chapter have resource deficits and the results for Kenya are far reaching economic implications that go beyond the role of tourism in the economy. noteworthy. Agriculture consumes much of Kenya is a water stressed country and yet it has the country’s water but it does so with much a highly water intensive economic structure. lower efficiency and job creating potential than Businesses therefore encounter both implicit other sectors of the economy, including tourism. and explicit costs when scarcity constraints Allowing for a reallocation of water to the tourism begin to bind upon the economy. The impacts sector — or at least ensuring that supplies to emerge in visible ways, for instance when the sector are not diminished further — would agricultural productivity declines during yield high economic payoffs. The results in this periods of low rainfall, and in less visible and report are indicative and serve as a warning sign less obvious ways, when expansion of business for greater caution and more careful analysis of is constrained, or when firms do not locate in the issues going forward. a part of the country due to resource deficits. S ta n d i n g O u t F r o m T h e H e r d 43 44 Sarah Farhat Photo: E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Conclusion T ourism has emerged as a source of economic dynamism in Kenya. The sector is well integrated into the rest of the economy the comparative advantage of each region and attraction. Going forward, the approach would differentiate tourism by location (carrying with strong links in the rural economy that capacity and accessibility); product (wildlife, are most prominent in safari tourism. A large beach, culture, conference, and adventure); and number of simulations all confirm that the market segment (domestic, International, and sector makes a significant contribution to GDP, conference). Given the variety of assets and rural incomes of the poor, and jobs. the diversity of customers, products can be designed in multiple, interesting, and lucrative Among the key sub-sectors of the industry, ways. The sector also needs access to scarce the safari tourism segment has emerged as resources such as water and land, and in turn the strongest growth driver as well as the needs to demonstrate appropriate stewardship most pro-poor segment. In part, this reflects of the country’s natural assets. Private and the close links that prevail between safari community conservancies are a pragmatic tourism and non-irrigated agriculture, once response to some of these challenges. again reinforcing the advantages of the close linkages of the sector. Finally, this report attempts to answer the big question of how important is tourism to Though tourism is an important economic Kenya’s economy through a rigorous, macro- driver today it faces numerous challenges in economic lens, which captures backward Kenya. There are deep risks that the current and forward linkages between tourism and focus on tourist numbers rather than tourist other parts of the economy. In answering revenue will undermine the industry and this question, the report touches upon several diminish the sector’s potential. Problems of important issues that are beyond the scope of congestion, overcrowding, and ecosystem this report but merit further in-depth analyses. degradation will inevitably worsen as more For example, when it comes to Kenya playing tourists crowd into diminishing and degraded to comparative advantage of each region and habitats. Demand will eventually and inevitably attraction, future work could specify in more decline for a visitor experience that is inferior granularity location, product, and market to that offered at rival destinations. segment opportunities, particularly in the area of cultural / heritage tourism, or which Kenya is now at a crossroads and must decide safari tourism destinations Kenya should how it will develop this lucrative sector. To build diversify into. upon this foundation, Kenya needs to play to S ta n d i n g O u t F r o m T h e H e r d 45 ANNEXES Annexes Annex A: Dynamic CGE - Components of the tourism value chain This annex provides a further robustness check of the results. Much of safari tourism occurs in lodges, while beach and business tourists use the more conventional hotels. Lodges are typically more labor intensive and located in rural areas and may cater to clients with different tastes and spending patterns. This annex shows that impacts on the economy vary depending upon whether investments are made in hotels or lodges. These simulations can also be interpreted as the dynamic counterpart of the supply shocks of Chapter 2. Tables 21 and 22 show the effects on income formation of an annual expansion of investment in hotels and lodges as defined in the Kenya official statistics. Lodges exhibit a higher productivity with better income distribution effects. However, the differences appear relatively small at the level of growth considered. These are likely to become larger only for significantly higher levels of investment in lodges. Table 21: Effects on income formation of a 5% yearly increase in the supply of hotel services (KSH, billions) Year 1 2 3 4 5 6 7 Rural poor 448.68 471.40 472.83 473.19 473.49 473.80 474.10 Rural non-poor 683.79 718.43 720.62 721.18 721.66 722.14 722.62 Urban poor 30.88 32.44 32.54 32.57 32.59 32.61 32.63 Urban non-poor 1,067.78 1,121.85 1,125.24 1,126.09 1,126.82 1,127.54 1,128.26 Source: Elaboration of the Kenya CGE model Table 22: Effects on income formation of a 5% yearly increase in the supply of lodge services (KSH, billions) Year 1 2 3 4 5 6 7 Rural poor 468.44 492.18 493.69 494.08 494.41 494.75 495.08 Rural non-poor 713.79 749.97 752.28 752.89 753.42 753.94 754.46 Urban poor 32.20 33.83 33.93 33.96 33.98 34.00 34.03 Urban non-poor 1,113.27 1,169.68 1,173.24 1,174.17 1,174.96 1,175.74 1,176.52 Source: Elaboration of the Kenya CGE model Table 23 shows the basis of another two scenarios that explore the value added and job creation potential of the tourism industry. In both scenarios, there is a 5 percent expenditure injection in the tourism industry. In Scenario 1, this occurs evenly across all sectors of tourism in accordance to the existing the historical pattern. In the second scenario, there is a higher investment in lodges, whose construction and management is more labor intensive (scenario 2). The results (Tables 24 and 25) indicate that the contribution of a policy package of the second scenario would be far superior in terms of value and job creation, and it would contribute substantially to economic growth. S ta n d i n g O u t F r o m T h e H e r d 47 Annexes Table 23: Alternative investment scenarios in the tourism value chain (5% increase spread over 7 years) (KSH, millions) Scenario 1 Scenario 2 Total investment Share of total Total investment Share of total Hotel 50,980 8.8% 51,000 8.8% Lodge 16,448 2.8% 51,000 8.8% Rent house 24,623 4.2% 24,623 4.2% Restaurant 51,344 8.9% 51,344 8.9% Transport 436,519 75.3% 401,947 69.3% Total 579,914 100.0% 579,914 100.0% Source: Elaboration of the Kenya CGE model Table 24: Value-added effects of scenario 1 (KSH, millions) Year 1 2 3 4 5 6 7 8 Skilled labor 5,397 5,175 5,184 5,206 5,228 5,249 5,271 36,710 Semi-skilled labor 11,251 11,429 11,481 11,526 11,571 11,616 11,661 80,535 Unskilled labor 3,918 3,937 3,958 3,980 4,001 4,023 4,044 27,861 Capital 25,297 25,171 25,259 25,357 25,456 25,555 25,655 177,751 Land 1,002 993 1,000 1,008 1,016 1,023 1,031 7,072 Import tariffs 342 341 343 345 346 348 350 2,415 Sales taxes 3,410 3,385 3,407 3,431 3,454,89 3,479 3,503 24,072 GDP 50,619 50,431 50,632 50,852 51,073 51,294 51,515 356,416 Total Impact (over the seven year Ratio Baseline (A) period) (B) (A/B) % Skilled labor 468,237 36,710 7.84 Semi-skilled labor 1241,740 80,535 6.49 Unskilled labor 576,664 27,861 4.83 Capital 2509,569 177,751 7.08 Land 279,935 7,072 2.53 Import tariffs 52,114 2,415 4.63 Sales taxes 521,342 24,072 4.62 GDP 5649,601 356,416 6.31 Source: Elaboration of the Kenya CGE model 48 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Annexes Table 25: Value-added effects of scenario 2 (KSH, millions) Year 1 2 3 4 5 6 7 8 Skilled labor 5,693 12,793 13,242,13 13,356,37 13,452 13,546,32 13,638,83 85,722 Semi-skilled labor 10,964 20,583 21,258 21,481 21,679 21,873 22,064 139,903 Unskilled labor 3,887 7,636 7,913 8,015 8,108 8,200 8,292 52,052 Capital 25,250 49,168 50,782 51,278 51,717 52,152 52,588 332,936 Land 1,020 2,303 2,402 2,440 2,475 2,508 2,540 15,688 Import tariffs 343 698 724 732 740 747 755 4,740 Sales taxes 3,451 7,430 7,730 7,846 7,954 8,062 8,171 50,645 GDP 50,608 100,612 104,052 105,150 106,125 107,090 108,049 681,686 Total Impact (over the seven year Ratio Baseline (A) period) (B) (A/B) % Skilled labor 468,237 85,722 18.31 Semi-skilled labor 1,241,740 139,903 11.27 Unskilled labor 576,664 52,052 9.03 Capital 2,509,569 332,936 13.27 Land 279,935 15,688 5.60 Import tariffs 52,114 4,740 9.09 Sales taxes 521,342 50,645 9.71 GDP 5,649,601 681,686 12.07 Source: Elaboration of the Kenya CGE model Figure 18: Value-added impact of the two scenarios GDP Sales taxes Import tariffs Land Capital Unskilled labor Semi-skilled labor Skilled labor 0 2 4 6 8 10 12 14 16 18 20 Percent Scenario 1 Scenario 2 Source: Elaboration of the Kenya CGE model S ta n d i n g O u t F r o m T h e H e r d 49 Annexes Annex B: Water in the CGE model This annex describes the manner in which water is integrated into the CGE model and the introduction of water into the SAM. The annex begins with a brief overview of the concepts that are relevant to exploring the effects of water in a general equilibrium context. To gain a greater understanding of the ways in which water impacts the economy, it is necessary to explore the role of water in each of the significant sectors of the economy that consume water. This is achieved by introducing water in the Social Accounting Matrix as described in the boxes below. This is followed by a description of the effects of water on the economy, beginning with the backward and forward linkages. Box 3: Water in the SAM and CGE models and some crucial definitions The incorporation of water into a SAM has traditionally been based on the idea of linking the input-output accounts with the natural system (see, for example, Isard (1969), Ayres & Kneese (1969), Daly (1968), Leontief (1970), and Victor (1972)). For water, as for other environmental goods, the methodology has followed a two stage process, consisting firstly in creating quantity based satellite accounts of the flows, and secondly integrating these flows into components of the income generation cycle by converting them into values. The concept of Virtual Water (VW) was defined by Allan (1993, 1994) as the water embodied in a product; i.e., as the amount of water that is used to generate a given product. The Water Footprint (WF) of a sector, a region, or a country on the other hand is a closely related concept, which is defined as the volume of VW necessary for the satisfaction of one unit of final demand. The consumption pattern of different economies can thus be characterized by the direct and indirect withdrawal of water, taking into account the international water flow involved in commodity trade (Chapagain & Hoekstra (2004), Hoekstra & Hung (2002), and Hoekstra & Chapagain (2007). In CGE models, water may be considered a factor of production (to produce something), as an intermediate input (a good not catering to final consumers), and a consumption good (caters to final demand). Furthermore, in the water geography and hydrology literature, “blue water” is defined as water extracted from aquifers, lakes and rivers, used by irrigated agriculture, and “green water” is the rainwater used by rain fed agriculture, while “grey water” is the amount of water needed to flush pollutants. In some CGE models, water is treated only as a municipal good (Wittwer, 2009) by focusing on municipal water demands as intermediate inputs or final consumption goods where trade-offs between urban water demands and water for crop irrigation are of central concern. Treated water for urban use and untreated water for irrigation enter the production function both as an intermediate input and as a factor of production. 50 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Annexes Using the 2003 KIPPRA-IFPRI and the KNBS SAMs as starting points, an Environmental–Water Resources SAM is built for Kenya by applying the entropic methodology described in Scandizzo and Ferrarese (2015) to national and state account data and other statistics (Kiringai et al., 2006, KNBS a,b,c,d, 2014). These methods are based on the so called “maximum entropy econometrics” (Golan, Judge, and Miller (1996) and are able to handle the “ill-conditioned” estimation problems associated with the lack of the degrees of freedom typical of i-o matrices. The methods are very flexible in combining a variety of specific data with prior information and national accounts. The matrix estimated contains a detailed natural resource account, including water resources, water distribution, and natural capital in various forms. The multipliers of this chapter correspond to the hypothesis that the rest of the world (including international tourists), investment, and the government are exogenous. Under this hypothesis, backward multipliers are on average 0.13, between a minimum of 0.06 for machinery to a maximum of more than 0.22 for distribution water, and highs of around 0.12-0.16 for agriculture, tourism, and most services. For water, in particular, it turns out that for both blue and green water, backward multipliers are very high at about 0.237, or almost twice the average backward multiplier of the other sectors. This means that on average an increase in 1% of demand for water is met with a 0.237% increase in the demand for inputs from other sectors. Forward multipliers are also similar for blue and green water and are around 0.09, which is about 72% of the average forward multiplier. This implies a much weaker linkage of water with the sectors that are closer to the final consumer, with only a 0.09% of increase in water demand in response to an average 1% increase in all other sectors of the economy. Natural resources usually have higher backward than forward multipliers as a consequence of the lower contribution to sector uses compared to their response to exogenous demand increases in terms of demand for conservation, renewal, and management. S ta n d i n g O u t F r o m T h e H e r d 51 Annexes Annex C: Water estimates Table 26: Kenya SAM—Backward and forward multipliers Multiplier impacts of water Forward multipliers Backward multipliers Mean Rasmussen Mean Rasmussen Skilled labor 0.210 1.602 0.170 1.297 Semi-skilled labor 0.477 3.642 0.170 1.301 Unskilled labor 0.175 1.335 0.165 1.269 Capital 0.862 6.581 0.114 0.886 Land 0.124 0.948 0.180 1.392 Blue water 0.093 0.714 0.237 1.842 Green water 0.094 0.720 0.237 1.869 Non-renewable natural resources 0.016 0.125 0.166 1.326 Water resources 0.203 1.551 0.221 1.784 Savanna and forest habitat 0.044 0.335 0.099 0.805 Emissions 0.020 0.156 0.015 0.125 Natural capital 0.028 0.211 0.114 0.913 Rural poor 0.276 2.105 0.167 1.339 Rural non-poor 0.422 3.221 0.165 1.330 Urban poor 0.039 0.296 0.167 1.356 Urban non-poor 0.834 6.368 0.142 1.160 Domestic tourist 0.051 0.391 0.146 1.193 Enterprise 0.864 6.594 0.101 0.831 Direct taxes 0.127 0.970 0.015 0.126 Import tariffs 0.035 0.269 0.015 0.124 Sales taxes 0.227 1.732 0.015 0.121 Irrigated agriculture (international) 0.094 0.721 0.168 1.297 Non irrigated agriculture (international) 0.113 0.859 0.170 1.323 Mining (international) 0.029 0.218 0.142 1.114 Food, beverage & tobacco (international) 0.037 0.282 0.111 0.875 Petroleum (international) 0.018 0.139 0.112 0.878 Textile & clothing (international) 0.019 0.146 0.112 0.873 Leather & footwear (international) 0.019 0.143 0.129 1.006 Printing & publishing (international) 0.020 0.152 0.067 0.524 Chemicals (international) 0.023 0.178 0.074 0.571 Metals & machines (international) 0.018 0.136 0.065 0.497 Non-metallic products (international) 0.016 0.122 0.144 1.076 Other manufactures (international) 0.026 0.202 0.121 0.905 52 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Annexes Multiplier impacts of water Forward multipliers Backward multipliers Mean Rasmussen Mean Rasmussen Irrigated agriculture 0.047 0.356 0.168 1.258 Non-irrigated agriculture 0.494 3.768 0.170 1.285 Forestry & fishing 0.044 0.335 0.142 1.078 Poaching 0.016 0.122 0.142 1.083 Mining 0.029 0.224 0.145 1.107 Food, beverage & tobacco 0.227 1.730 0.112 0.862 Petroleum 0.041 0.313 0.112 0.857 Textile & clothing 0.040 0.305 0.112 0.850 Leather & footwear 0.021 0.164 0.129 0.976 Wood & paper 0.050 0.385 0.101 0.763 Printing & publishing 0.093 0.710 0.067 0.504 Chemicals 0.092 0.702 0.075 0.544 Metals & machines 0.041 0.315 0.065 0.467 Non-metallic products 0.025 0.191 0.149 1.036 Other manufactures 0.100 0.763 0.121 0.839 Distribution water 0.059 0.449 0.228 1.568 Electricity 0.075 0.570 0.130 0.929 Construction 0.021 0.159 0.130 0.927 Trade 0.296 2.258 0.143 1.010 Hotel 0.024 0.180 0.125 0.885 Lodge 0.018 0.138 0.124 0.872 Rent House 0.019 0.147 0.125 0.866 Restaurant 0.024 0.180 0.128 0.873 Transport 0.264 2.018 0.138 0.927 Information & communication 0.102 0.776 0.149 0.996 Financial & insurance activities 0.176 1.346 0.141 0.942 Real estate 0.151 1.156 0.149 0.987 Other services 0.101 0.772 0.147 0.967 Public administration & defence 0.036 0.271 0.137 0.897 Health & social work 0.041 0.316 0.161 1.020 Education 0.079 0.600 0.159 1.016 Package 0.015 0.117 0.154 1.000 Average production activities 0.075 1.000 0.129 1.000 Source: Elaboration of the Kenya CGE model S ta n d i n g O u t F r o m T h e H e r d 53 Annexes Annex D: The water footprint Using historical data on national accounts and data from a plurality of sources, we have estimated the SAM accounts for water. Table 27 below contains the results of the SAM estimates of water withdrawn directly and indirectly by each sector. These values measure the amount of water directly and indirectly withdrawn to sustain the final demand of the sector and, as relative indicators, the ratio between the water value and the sector final demand value (value ratio) and the water quantity and the final consumption value (quantity to value ratio). They can be therefore interpreted as indicators of the water footprints of each sector. As the comparison of figures shows, the apparent domination of the irrigated agricultural sector in terms of direct water withdrawals is mitigated by this water footprint measure. When both direct and direct withdrawals are taken into account, several counterintuitive results follow: (i) traditional agriculture and mining are more water intensive than irrigated agriculture; and (ii) industrial sectors and services are less water intensive in terms of value to production ratio than services. We believe these results are new and highlight the importance of taking into account feedback effects when considering the footprint an economic activity. Stated simply, a sector that consumes less water than another may stimulate other more water-intensive types of economic activity that end up consuming a larger amount of water. This is why non-irrigated agriculture and mining have a larger water footprint than irrigated agriculture. Table 27: Water footprint (direct and indirect use of water resources) Value ratio Total (water as % Quantity to Production of production Value Ratio Value KSH, million value) Million m3 (m3/US$) (KSH, million) Irrigated agriculture 28,874.64 36.30 2,887.46 3.630 79,539.40 Non-irrigated agriculture 114,792.08 8.18 114,792.08 8.178 1,403,673.04 Industry 34,792.11 3.01 1,739.61 0.150 1,157,489.14 Services 153,154.12 4.50 5,105.14 0.150 3,406,984.85 Mining 2,679.06 6.45 2,679.06 6.451 41,529.19 Figure 19: Water footprint 40 35 30 25 20 15 5 0 Irrigated Non-irrigated Industry Services Mining agriculture agriculture Value ratio M2/US$ Source: 54 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Annexes Annex E: SAM water accounts showing the use of different types of water by sector Table 28: Water use by sector Irrigated Non-irrigated Forestry & agriculture agriculture fishing Wildlife Mining KSH, millions Blue water 17,323.29 0 0.00 319.25 292.76 Green water 0 17,986.75 61.35 0.00 0.00 Distribution water 60.76 891.22 0.00 0.00 68.02 Million Cubic Meters Blue water 2,000.00 0 0 319.25 292.76 Green water 0 18,700.00 61.35 0 0 Distribution water 6.76 89.12 0 0 68.02 Table 29: Gross incomes by natural resource sectors (KSH, millions) Non-renewable Blue water Green water natural resources Water resources Blue water 0.00 0.00 778.87 23,615.73 Green water 0.00 0.00 0.00 28,615.83 Water resources 57,081.00 71,620.86 0.00 0.00 Distribution water 0.00 0.00 0.00 21,821.32 Total 57.081.00 71.620.86 778.87 74,052.88 Table 30: Effects of water use on gross incomes by sector (KSH, millions) Urban Rural poor Rural non-poor Urban poor non-poor Government Blue water 297.60 379.45 30.91 0.00 0.00 Green water 869.27 76.87 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 Distribution water 0.00 224.24 0.00 5,924.04 1.141.90 Total 1.166.86 680.56 30.91 5,924.04 1.141.90 S ta n d i n g O u t F r o m T h e H e r d 55 Annexes Table 31: Water use by production sector (KSH, millions) Textile & clothing Food, beverage & (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) (inter-national) manufactures Non-irrigated Non-metallic agriculture agriculture Metals and publishing Petroleum Chemicals Printing & Leather & machines footwear products Irrigated Tobacco Mining Other Blue water 12,695.86 0.00 890.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Green water 0.00 3,109.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution 44.40 161.21 53.41 24.18 158.31 224.96 14.24 1.60 1.87 3.77 0.17 2.69 water Total 12,740.26 3,270.71 943.63 24.18 158.31 224.96 14.24 1.60 1.87 3.77 0.17 2.69 Non- Textile & Leather & Printing and Metals and metallic Other clothing footwear publishing Chemicals machines products manufactures (inter- (inter- (inter- (inter- (inter- (inter- (inter- national) national) national) national) national) national) national) Blue water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Green water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution water 224.96 14.24 1.60 1.87 3.77 0.17 2.69 Total 224.96 14.24 1.60 1.87 3.77 0.17 2.69 Non- Food, Irrigated irrigated Forestry beverage & agriculture agriculture and fishing Wildlife Mining tobacco Petroleum Blue water 5,140.12 0.00 0.00 310.25 304.08 0.00 0.00 Green water 0.00 14,615.04 60.45 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution water 17.97 757.69 0.00 0.00 15.80 221.40 1,361.90 Total 5,158.10 15,372.73 60.45 310.25 319.88 221.40 1,361.90 56 E c o n o m i c A s s e s s m e n t o f To u r i s m i n K e n y a Annexes Non- Textile & Leather & Wood & Printing & Metals & metallic Other man- clothing footwear paper publishing Chemicals machines products ufactures Blue water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Green water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution water 1,402.51 23.49 184.57 25.69 16.47 39.87 1.67 29.02 Total 1,402.51 23.49 184.57 25.69 16.47 39.87 1.67 29.02 Distri- bution Construc- Rent Restau- water Electricity tion Trade Hotel Lodge House rant Blue water 12,637.91 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Green water 19,828.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution water 1,253.49 56.84 265.23 384.11 72.47 25.37 36.51 73.41 Total 33,719.66 56.84 265.23 384.11 72.47 25.37 36.51 73.41 Informa- Finan- Public ad- tion & cial and ministra- Health communi- insurance Real es- Other tion and & social Transport cation activities tate services defence work Education Blue water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Green water 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Distribution water 92.85 1,033.06 254.88 82.61 480.05 1,205.00 1,871.28 1,803.27 Total 92.85 1,033.06 254.88 82.61 480.05 1,205.00 1,871.28 1,803.27 Package Business tourism Beach tourism Safari tourism Other tourism tourism (international) (international) (international) (international) Blue water 0.00 0.00 0.00 0.00 0.00 Green water 0.00 0.00 0.00 4.445.63 0.00 Water resources 0.00 0.00 0.00 0.00 0.00 Distribution water 43.69 175.88 86.51 258.28 52.37 Total 43.69 175.88 86.51 4,703.91 52.37 S ta n d i n g O u t F r o m T h e H e r d 57 Annexes Annex F: Effects of water transfer to tourism Table 32: Impact of a 20% water transfer from irrigated agriculture to hotels and lodges (KSH, millions) Impact (A) Baseline (B) Ratio (A/B) % Irrigated agriculture -91,237.86 133,853 -68.16 Non-irrigated agriculture 116,245.10 11,553,870 7.48 Forestry & fishing 9,947.62 84,187 11.82 Wildlife 255.18 1,896 13.46 Mining 18,766.77 172,713 10.87 Food, beverage & tobacco 64,240.14 441,772 14.54 Petroleum 3,281.00 53,597 6.12 Textile & clothing 5,628.61 50,280 11.19 Leather & footwear 3,146.15 34,891 9.02 Wood & paper 8,689.17 58,116 14.95 Printing and publishing 19,639.76 137,929 14.24 Chemicals 11,684.12 101,682 11.49 Metals and machines 10,592.84 100,782 10.51 Non-metallic products 10,614.65 101,621 10.45 Other manufactures 29,392.07 239,183 12.29 Distribution water -557.68 43,508 -1.28 Electricity 70,668.34 176,434 40.05 Construction 64,852.27 683,530 9.49 Trade 144,154.65 769,114 18.74 Hotel 312,186.69 39,053 799.40 Lodge 281,785.01 12,373 2277.38 Rent House 1,181.85 18,606 6.35 Restaurant 2,521.94 39,577 6.37 Transport 63,911.52 773,604 8.26 Information & communication 37,357.19 259,815 14.38 Financial & insurance activities 92,556.40 459,857 20.13 Real estate 50,924.71 452,791 11.25 Other services 27,878.44 251,935 11.07 Public administration & defence 45,260.45 411,537 11.00 Health & social work 11,706.32 160,127 7.31 Education 26,254.30 479,733 5.47 Package 3,966.61 33,230 11.94 Total 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