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Tourism Contribution to Poverty Alleviation in Kenya: A Dynamic Computable General Equilibrium Analysis

Tourism Contribution to Poverty Alleviation in Kenya: A Dynamic Computable General Equilibrium... The aim of this article is to investigate the claim that tourism development can be the engine for poverty reduction in Kenya using a dynamic, microsimulation computable general equilibrium model. The article improves on the common practice in the literature by using the more comprehensive Foster-Greer-Thorbecke (FGT) index to measure poverty instead of headcount ratios only. Simulations results from previous studies confirm that expansion of the tourism industry will benefit different sectors unevenly and will only marginally improve poverty headcount. This is mainly due to the contraction of the agricultural sector caused the appreciation of the real exchange rates. This article demonstrates that the effect on poverty gap and poverty severity is, nevertheless, significant for both rural and urban areas with higher impact in the urban areas. Tourism expansion enables poorer households to move closer to the poverty line. It is concluded that the tourism industry is pro-poor. Keywords Kenya, tourism development, poverty, dynamic computable general equilibrium, CGE, microsimulation, Foster-Greer- Thorbecke Index p. xvii). The authors propose that economic growth needs to Introduction be, a priori, inclusive and the benefits need to accrue to the Nobel Prize laureate Amartya Sen’s definition of poverty poor for this to be achievable. moves the concept from merely “lowness of income” to “the The Millennium Development Goals (MDGs) advocate deprivation of basic capabilities” (Sen 2001, 87). He pro- economic development to reduce extreme poverty by tack- poses that inadequate income is a “strong predisposing con- ling the problem of capability deprivation through better dition for an impoverished life,” and lack of capabilities access to education, health, and better opportunities for all often resulting from lack of income is the underlying cause (UNWTO 2005). It has been acknowledged that tourism will of poverty. By redefining poverty, Sen puts people at the play an important role in the achievement of MDGs. heart of development. He links capability with freedom of However, whether resources allocated to the tourism indus- choice and access to opportunities that empowers individu- try in fact lead to pro-poor development is an empirical ques- als, giving them the ability to choose the type of life that they tion. Mitchell and Ashley (2010) provide some evidence have “reason to value.” Hence, according to Sen, any policy supporting this claim. They state that in most destinations aiming at achieving poverty reduction needs to address the 10% to 30% of in-country tourist spending accrues to poor issue of capability deprivation rather than merely targeting people. This is facilitated by the economic, political, and the level of household income. On the other hand, Croes and Rivera (2015), who take a Department of Logistics Operations and Hospitality Management, taking a purely economic perspective, postulate that poverty University of Huddersfield, Huddersfield, United Kingdom is a form of underutilization of productive resources. It repre- Department of Tourism and Hospitality, Faculty of Management, sents the underdevelopment of the pool of skills and reduces Bournemouth University, Poole, Dorset, United Kingdom the productive capacity of a nation. They argue that the poor Corresponding Author: should be helped in order to expand the wealth-creating Neelu Seetaram, Department of Tourism and Hospitality, Faculty of capacity of nations and raise the standard of living and quality Management, Bournemouth University, Talbot Campus, Poole, Dorset of life for the whole country. In other words, “the poor should BH12 5BB, United Kingdom. Email: [email protected] be helped out of self-interest” (Croes and Rivera 2015, 514 Journal of Travel Research 57(4) cultural context as well as the other factors pertaining to the using headcount indices, such as the proportion of house- implementation of tourism development strategies. On the holds below an identified poverty line, as in Blake et al. other hand, Hall (2007), Scheyvens (2007), and Schilcher (2008) and Vanegas, Gartner, and Senauer (2015). While the (2007) argue that tourism is not necessarily pro-poor. Croes headcount measures offer valuable information, they are and Rivera (2015) state that while the poor may benefit from deemed to be too crude. tourism-led economic growth by accessing employment The most widely used index in the development econom- opportunities, in times of economic slowdown they tend to ics literature is the Foster-Greer-Thorbecke (FGT) index suffer the most and in periods of economic growth they ben- (1984), which is a multidimensional index combining three efit the least. classes of measurements: headcount index (P ), income gap The literature on pro-poor tourism continues to grow as index (P ), and poverty severity index (P ). The index has a 1 2 more research focus on individual pro-poor tourism projects simple additive structure where aggregate poverty is a popu- at destinations and examine their outcomes. However, lim- lation-weighted mean of subpopulation groups. This allows ited evidence is available on the relationship between tour- for the decomposition of the index and analysis of each sub- ism development and poverty reduction at a macro level. group individually. Such information is more relevant to The advantage of studying the poverty reduction capacity of policy holders as it allows the identification of the subgroup tourism development at the macro level is that it enables the that contributes most to poverty, and therefore, more targeted researcher to trace the mechanism through which tourism measures of poverty reduction can be designed. The simple expenditure affects the different industries at the destina- structure of the index makes it easy to apply and interpret tions and, hence, account for those that are the most affected, and, hence, it is surprising that the FGT has not yet been used positively or negatively. This provides policy makers with in the tourism context. This article fills in the gap. detailed information on the transmission mechanism of tourism expenditure and, can be an important tool in formu- The Tourism and Poverty Nexus lating and targeting policies that aim at increasing the eco- nomic benefits and reducing the economic cost of tourism Cross-country studies have verified that sustained economic expansion. growth reduces poverty (Kraay 2004). However, there is a The few studies that have investigated the problem at the widespread consensus that not all forms of growth have the macro level have applied static techniques to investigate same impact on poverty. Economic growth is pro-poor when the relationship. Static modeling techniques analyze the it is balanced with equity but to be achievable, it requires the contribution of the tourism industry but ignore the effect of careful implementation of targeted macroeconomic policies policy changes on these contributions in the postimplemen- on education and health, nutrition and infrastructure (Croes tation years. This article argues that the effect on the poor and Rivera 2015). Sectoral pattern of growth affect the extent may occur with a time lag, making dynamic modeling a of poverty reduction (Coxhead and Warr 1995; Fane and more appropriate approach. This approach assesses the Warr 2002; Loayza and Raddatz 2006). If, for example, the effect on an annual basis and allows for more effective tourism sector in a destination is low-skilled and labor-inten- monitoring and analysis of the effect of policy changes. sive, it is likely that its expansion will generate high income Blake (2009) points out that a detailed household modeling flows to the poor. There are many different ways by which using a microsimulation approach provides a more compre- tourism can engage the poor, boost local economic develop- hensive assessment of the impact of tourism on economic ment, or affect the physical and social environment of local development. Hence, this approach is better suited for the communities. assessment of the effect of tourism expenditure on the stan- The link between tourism and the reduction of poverty is dard of living of households at the destination. The micro- best understood by considering the link between trade liber- simulation approach, however, is yet to be to be implemented alization and poverty reduction (McCulloch, Winters, and in the tourism context. Cirera 2001). Figure 1 shows the channels through which This article aims to investigate the connection between tourism may affect the poor. These include income, tax, tourism policies and poverty reduction by developing a price, and risk channels (Blake et al. 2008). Poor households dynamic general equilibrium model of the Kenyan economy earn income through direct or indirect participation in tour- by integrating the microsimulation approach of Cockburn ism (International Trade Centre [ITC] 2009). Tourism also and Decaluwé (2006) to analyse the extent to which the contributes to the tax base of local or national government, Kenyan tourism industry is benefiting poor households. It is and the additional revenue can be used to provide or improve one of the first studies in the tourism literature that uses a the social infrastructure. ITC (2009) argues that positive dynamic approach and contributes to the literature by not effects can include better social infrastructure, education, only providing the evidence as to whether tourism develop- stronger local institutions, and gender equality. ment is pro-poor in Kenya but also analysing the magnitude The third channel is the price paid by the poor for the of the effect over time. Furthermore, the existing literature consumption bundle goods they purchase. Tourism expan- on tourism and poverty reduction has measured poverty by sion leads to an increase in the demand for local products, Njoya and Seetaram 515 Participation of poor people in Income Positive tourism industry or Channels in supply chains Tax collection and provision Positive Tax of social Channels infrastructure Positive An increase Pressure on Price in tourism local prices Channels spending Negative Vulnerability to exogenous Negative (internal and Risk/ external) shocks Dynamic Channels Positive Social, cultural and natural environment of Negative the poor Figure 1. Channels by which tourism spending may affect the poor. Source: Authors’ own illustration. such as food, land, and construction, which in turn can cause employed in the affected non-tourism sectors, therefore, tend an increase in local prices (ITC 2009). As tourism increases, to find their real earnings fall following the expansion of the the demand for goods and services that the tourists use tourism industry. increases and, as a result, the prices of those goods will rise. This effect is empirically proven by Wattanakuljarus and The impact of the price channel on the poor will depend on Coxhead (2008), who simulate the effects of a boom in the the amount of tourism-related goods and services among the inbound tourism demand on the Thai economy. The authors goods and services purchased by the poor (Blake et al. 2008). show that an increase in tourism arrivals of 10% would lead The fourth channel relates to risks and other long-term to an increase in household income accompanied by a wors- dynamic influences. The dynamic impact of tourism on local ening of the income distribution. Benefits from the tourism economic development can be positive (e.g., biodiversity industry do not trickle down to the poorer household because conservation measures; allocation of funds for natural, cul- tourism is not a notably labor-intensive sector in comparison tural, and historical resources) and negative (e.g., destruction to key tradable sectors such as agriculture and labor-inten- of environmental resources, pollution of air, water, noise). sive manufacturing, and its expansion triggers the Dutch A number of studies have developed theoretical models to Disease effect, which undermines profitability and reduces show that expansion of the tourism sectors can be immiser- employment in tradable sectors, notably agriculture, from izing (Chen and Devereux 1999; Copeland 1991; Hazari and which the poor derive a substantial percentage of their Kaur 1995; Hazari and Nowak 2003; Sahli and Nowak, income. 2007). These models assume that a boom in the tourism On the other hand, Blake et al. (2008), who apply a CGE industry will have a negative effect on poorer households model of the Brazilian economy to assess the distributional when it leads to the appreciation of the local currency. A effects, find that poorer households will benefit from an higher value of the local currency erodes the international increase of 10% in tourism spending. Increasing demand competitiveness of non-tourism exports and, therefore, limits from tourism causes prices of goods and services to rise but their growth and capacity for generating employment. This have no effect on the price of the bundle of consumption of phenomenon, also known as the Dutch Disease effect, is the poorer households. However, the poorer households are often a result of the expansion of an export industry in the not the main beneficiaries of the earnings and price channel presence of market distortions such as monopoly power, effects of tourism expansion. The authors note that transfer- repatriation of profits by foreign companies, increasing ring all additional government revenue to the poorest house- returns to scale in non-tourism export activities, crowding- hold group can double the benefits for poor people, giving out effects, and trade distortions. Poorer households who are them around one third of benefits in total. 516 Journal of Travel Research 57(4) This recommendation is challenged by Mahadevan, Amir, reduction. They use headcount indices, which according to and Nugroho (2016). The authors use a CGE model of the Haughton and Khandker (2009), are basic measures and Indonesian economy to show that expansion of the tourism need to be complemented with other indices to accurately sector reduces poverty but increases income inequality at quantify the effect of growth on poverty reduction. national level. The authors undertook several types of simu- Schilcher (2007) stipulates that economic growth can lations aimed at exploring complementary policies likely to reduce poverty and help poor households cross the poverty improve the poverty impact of tourism growth and reduce line while making the extreme poor worse off. The effect on income inequality gaps. They found that poverty reduction the latter, however, has not been analyzed empirically in the can be achieved faster by investment that raises the labor tourism literature. Studies that focus on headcount measure- productivity of the poor as compared to monetary transfers. ment of poverty only have mostly concluded that tourism This result offers a solution to the problem identified by growth provides limited benefits to the poor. The effect on Croes (2014) who finds that in Costa Rica jobs created by the the extreme poor and their income distribution are ignored. A tourism industry are filled by higher educated local labor policy that helps the extreme poor move closer to the poverty force and foreigners, thus excluding the poor. Croes con- line though not having a significant contribution in reducing cludes that economic growth in Costa Rica is followed by poverty count is a valid mean for poverty alleviation, but declining opportunities for the poor. failure to account for the benefits accrued in the form of Similary, Kweka (2004) finds that in Tanzania, urban improved income distribution for the poorest leads to the (higher income) households will benefit more from a 20% erroneous conclusion that the policy is ineffective. According increase in tourism than their rural (lower income) counter- to Schilcher (2007), there is a need to distinguish between parts, confirming the argument of Croes and Rivera (2015). the poor and the extreme poor. On the other hand, Vanegas, Gartner, and Senauer (2015) In this study, it is proposed to use the FGT index, which apply an autoregressive lag model and find that in Costa is multidimensional and incorporates three indices of pov- Rica and Nicaragua, tourism development is negatively erty, (P ), (P ), and (P ). P measures the proportion of the 0 1 2 0 related to the prevalence of extreme poverty and that the population of a country that is below the poverty line. It poverty reduction effect of the tourism industry is higher measures the mean gap between the income of the poor than that of the agricultural sector in both countries. Croes households and the poverty line. That is, the gap between (2014), using an error correction model to assess the effect the poverty line and the income of each poor household is of tourism growth on absolute poverty, concludes that in summed and divided by the population of the country. Nicaragua a 1% increase in tourism receipts reduces the While P measures the incidence of poverty, P measures 0 1 poverty headcount index by 1.23 points. He explains that in the incidence and depth of poverty. P is a measure of Nicaragua, where the proportion of poor is higher, the tour- income distribution among the poor. It is the square of P , ism industry creates jobs in the informal sector providing and it places higher weights on the poorest households. It opportunities for the poor to join the supply chain. His is expected that using the FGT to assess the effect of tour- results confirm those of Croes and Vanegas (2008). In ism development on poverty reduction adds additional Croes and Rivera (2017), the authors use the social account- dimensions to the analysis that are more relevant for policy ing matrix of Ecuador to show that tourism development makers. Furthermore, the analysis is carried out in a benefits the poor disproportionately by improving their dynamic setup, and, therefore, the time lag that may be income. The authors conclude that tourism development is needed for poverty reduction effect to be noticeable is a viable tool for reducing poverty in developing countries. taken into account, allowing for the study of poverty alle- However, the use of SAM may overestimate the poverty viation nexus path over time. reduction effect of tourism development as this method is based on restrictive assumptions that do not take into Modeling the Economic Impact of account the price effect on the consumption of households. Tourism in Kenya Croes and Rivera (2017) use poverty headcount as their measure of poverty in Ecuador. The World Bank (2010) estimates that Kenya has one of the From the empirical studies above, it is seen that the effect world’s highest rates of population growth below the age of of tourism expansion on poorer households is mixed. These 25 at 2.6% (on average per annum), with approximately studies, while offering invaluable insight on the ways in three quarters of the population living in rural areas. The which poorer households are affected by tourism, use static Kenyan social accounting matrix (SAM) shows that the analysis and, therefore, cannot provide more information on highest total consumption expenditure shares of poor house- the yearly longer-term effect. The current study seeks to holds in rural areas are found in agricultural products (32%), address this gap. Furthermore, the studies discussed above followed by transport (12.8%). The richest rural household have focused on one measurement of poverty, which does spends more on services than on agricultural and manufac- not necessarily provide a comprehensive assessment of the tured goods. The urban households spend a large percentage effect of an expansion of the tourism industry on poverty of their budget on services such as transport (17.7%) and Njoya and Seetaram 517 restaurants (11.9%). The poorest urban deciles, on the other demand value plus the value of stock changes hand, spend 51% of their consumption expenditure on food. that are defined as being fixed, usually in volume Tourism is one of the fast growing sectors in Kenya’s terms at the levels in the base period. economy, and it is directly responsible for creating about half c. Exports: Aggregate domestic output is allocated be- a million jobs. It has been earmarked as one of the strategic tween domestic and export markets. This is done un- sectors for economic growth and development in Kenya. der the assumption that suppliers maximize the sales According to World Travel and Tourism Council (WTTC revenue for any given aggregate output level, subject 2015), the travel and tourism sector contributed approxi- to imperfect transformability between exports and mately 4.1% directly and 10.5% indirectly to GDP in 2014. domestic sales, expressed by a constant elasticity of Export earnings from international tourists generated 18.3% transformation function. of total exports in the same year. Income from tourism grew d. Imports: It is assumed that the institutions in the econ- by 126% between 1995 and 2014, attaining US$2.1 billion. omy consume a composite good, made up of domes- This study investigates the impact of sustained tourism tic goods and imports. Imports and domestic goods in growth on poverty from 2003 to 2015, with 2003 as the base the same sector are imperfect substitutes, an approach year and using poverty indicators in 2004–2005 as a baseline called Armington assumption. estimate. e. A group of equations describing net transfers, in- The effect of tourism expansion on the Kenyan economy comes, expenditures and savings, GDP, trade balance, will be assessed using a recursive dynamic Computable consumer price index, real exchange rate and market General Equilibrium (CGE) that draws on CGE models by clearing for composite commodities and primary fac- Decaluwé et al. (2010), Savard (2003), Cockburn (2001), tors. Robinson et al. (1999), and Dervis, de Melo, and Robinson (1982), as well as the contributions to tourism-based dynamic Thus, the model developed has 115 block equations and the CGE model by Blake (2009). It involves the specification of same number of variables that are solved simultaneously a multihousehold and multisectorial CGE model by means of using GAMS, Generalized Algebraic Modeling System soft- nonlinear algebraic equations and addressing these equations ware. For reasons of space, the article only describes how directly with numerical solution techniques. The basic model tourism, dynamics, and poverty are modeled. illustrates consumption and production-related behavior, interinstitutional transactions, and trade relationships. Thus, Modeling Tourism Demand the model has the following key structural elements: A Cobb-Douglas (C-D) utility function is used to determine a. Production and factor demand: Production technol- how tourists substitute between commodities. C-D and ogy is specified in a multilevel nesting structure. Constant Elasticity of Substitution (CES) functions have been b. Product demand: The final demand is composed of widely used in the tourism-based CGE models. The C-D util- demand by households, investment, inventory, the ity function exhibits a constant and unitary elasticity of sub- government, the rest of the world, and tourists. stitution. With the exception of the demand for tourism, which - Household demand: Households are assumed to is modeled using a C-D function because of lack of data, con- choose the consumption of different commodities sumption and production behavior are modeled using CES, according to an Engel expenditure function. The LES, and constant elasticity of transformation functions. demand functions are derived from maximization From the modeling point of view, two categories of tour- of a Stone–Geary utility function (often called ism demand (domestic tourism demand and international Linear Expenditure System) subject to the budget inbound tourism demand) are considered, assuming that constraint. Most empirical literature on the link there are differences in the structure of their spending. Hence, between household consumption patterns of dif- the assumption is that there are two categories of tourism ferent goods and level of income in developing demand accounting for the consumption of a certain quantity countries applies Engel’s law, which suggests that of a composite good and service at an aggregated tourism as households become more affluent, the share of price level PTOU . Analogous to household demand, () () t household spending dedicated to necessities such domestic CDD as well as international inbound () () t as food declines (Banerjee and Duflo 2011). In an CDF tourism demand is obtained by maximizing the () () t LES specification, consumers first set aside sub- utility function of each individual tourist function to its bud- sistence levels of goods, then allocate the remain- get constraint. Following Blake et al. (2008), the demand for ing budget in proportion to preferences. tourism is defined by the following equations: - Government demand: The government collects taxes and receive transfers from other institutions. d   PTOU () t - Investment demand: The value of investment CDDC =⋅ χ DD ⋅  () tt () (1)   PIXCON expenditure is equal to the sum of investment () t   518 Journal of Travel Research 57(4) dynamic-recursive adjustment is solved recursively from the  PTOU  () t base year 2003 to the year 2022. CDFC =⋅ χ DF ⋅   (2) () tt ()   pop e Moreover, there is a population index , which is () t () t   updated exogenously and growing in each period at a rate (, ci ) η . This index is used in the model to update the values of CTOU = ω tcom (, it )(i ) (, ci,) t () t ∏ (3) variables, parameters, and constants that are assumed to grow at the same rate η as the population index pop . () t Following World Bank’s current and projected annual growth tcom PC =⋅ ε CTOU PTOU (4) (, ci,) tc (,tc )( )(it ,) () t rate of approximately 2.6%, the Kenyan population is assumed to grow at a rate of 0.026 per year in the model. (, ci ) Total labor supply becomes an endogenous variable and is TOUP = PC (, it ) (, ct ) ∏ (5) assumed to grow at the exogenous rate η , which is the () t labor force growth rate. where CDD and CDF are parameters equal to the base () t () t Unlike the static model, capital stock is endogenous in the level of domestic and international inbound tourism con- dynamic model. In every period, capital stock is the result of sumption, respectively, except where tourism demand shocks the stock of the preceding period, minus depreciation, plus are introduced into the modeling system by changing these the volume of new capital investment in the preceding period parameters. The price elasticity of demand for domestic tour- (equation (6)). ism is captured by the parameter ς with ς  1 , while is d d χ= 1 () a shift parameter in the base year. Domestic tourists KD =− () 1 δ KD + IND ka ,,tk +1 ,, ak at ,, ka,t (6) () () () () are concerned with how the composite price changes relative to the consumer price index PIXCON . ω is a shift () () t i () parameter, calibrated to ensure that the model replicates the j j IT = PK IND (7) ∑ kj ,,t () t () t () benchmark; ε is the share of each commodity in each (, ci ) kj , CTOU tourism consumption; and the aggregate tourism (, it ) consumption by each i category of tourism (the index i refers () c   PC 1 ct , () to the type of tourism, i.e., domestic or inbound). j   PK = (8) t K ∏ j () () It is assumed that international inbound tourists are con-   () c   cerned with how their composite price changes relative to a INV real exchange rate. Thus, international inbound tourism () kj ,  IR  demand is modeled in a similar way to export demand and is (, kj ,) t IND = ϕ KD (9)   (, kj ) (, kj ,tt ) () kj ,,t assumed to be inversely proportioned to the price of foreign  (, kj ,) t    exchange in the domestic market (equation (2)). The utility of the two categories of tourists is modeled using a Cobb– Douglas function, determining how they substitute between UP =+ KI δ R (10) () kj ,,t kj , t () () t () () commodities. Following Blake et al. (2008), tourism con- sumption by sector tcom can be specified as indicated () (, ci,) t where KD is the demand for type k capital by activity () ka ,,t +1 in equation (3). a and the volume of new type k capital investment IND () ka ,,t Thus, the total value of total tourist expenditure of each to activity a. The amount of each investment expenditures tourism category CTOU ⋅ PTOU must equal the total () (, it )(t ) category (the index j refers to the type of capital, i.e., IT expenditure of each tourism category of different commodi- ( ) () ties tcom ⋅ PC . Additionally, the price paid by tour- public or private) is determined by equation (7) as the price () (, ci,) tc (,t ) ists in each category can be related to the prices of the of each investment category PK times the aggregate ( ) () PC individual commodities as indicated in equation (5). ct , () volume of the new type k of each category of capital invest- is the purchasing price of composite commodity i (including IND ment . () () kj ,,t all taxes and margins). The impact of changes in the tourism The prices of new private and public capital are given by sector on the rest of the economy is captured through their equation (8). These prices are obtained from the investment effect on the aggregate prices (see equation (5)). demand functions, whose forms imply that the production () function of new capital follows a Cobb-Douglas form. A Dynamic Setup are scale parameters for each category of investment and The dynamic setting follows Decaluwé et al. (2010) and are positive parameters calibrated on the basis of the () takes into account accumulation and growth effects. It is investment elasticity and the investment equilibrium equa- INV established by means of lagged variables and by updating tion. σ is the elasticity of investment demand. The vol- (, kj ) exogenous variables and parameters that are either fixed or ume of new capital allocated to a sector is proportional to the absent in the base-year solution. In this study, the existing stock of capital. The proportion varies according to Njoya and Seetaram 519 where P is the simple headcount index, as it measures the the ratio of the rental rate to the user cost of that capital. The incidence of poverty as the proportion of total population investment demand follows a modified version of below the poverty line. In other words, the headcount ratio Bourgignon, Branson, and Melo (1989). Equation (10) gives the percentage of the population that is below the pov- defines the capital user cost U as a function of the () () ka ,,t erty line. price of new capital (the replacement cost of capital), the rate of depreciation δ , and the interest rate IR 1 zy − ()   () () t () ka , i For α= 1,, P = ∑  (Decaluwé et al. 2010). N z   i=1 where P is the poverty gap index. It reflects how far the Model Closure poor are from the poverty line. For any individual, the pov- Simulations are carried out under the following assumptions. erty gap is the distance between the poverty line and his/her The current account is fixed, reflecting the scarcity of for- income. Aggregating individual poverty gaps for all individ- eign credit in developing countries. Government expendi- uals gives the aggregate poverty gap. tures are assumed to be fixed in real terms in the first period. However, this increases as the same rate of population 2 1  zy −  growth. Tax policy instruments are free. It is assumed that And for α= 2,, P = 2   N z investment expenditures are endogenous and adjust to   i=1 changes in savings. Past investments influence economic where P is the poverty severity index. It gives an indication growth owing to the inclusion of dynamics into the model. It of the degree of inequality among the poor. Moreover, pov- is assumed that there is perfect mobility of labor and capital erty severity captures how difficult it is to get out of poverty. between rural and urban areas. This implies that the model Building on poverty gap measures, the poverty severity has three specific prices for payment for factors, namely, index gives more weight to the extreme poor by squaring the wages, return on agricultural capital (land), and return on distance to the poverty line. In other words, it measures other capital. The nominal exchange rate (i.e., the rest of the inequality between subpopulations of the poor. The micro world’s imports price index) is chosen to be the numéraire. model is solved using DAD 4.6, a Software for Poverty and Distributive Analysis. Poverty Analysis Simulation Design In order to assess the mechanism through which tourism growth may affect poverty in the country, the identified Tourism expansion is generally modeled as an increase in “macro effects” from the CGE growth scenario are fed back total tourism spending or a reduction or elimination of trade into a microsimulation model, based on household survey restrictions on the tourism industry or related industries. data. The construction of the micro household module relies This requires information on both the economic structure on data sets from the Kenya Integrated Household Budget and the size of tourism as well as the likely path for the Survey (KIHBS). The KIHBS is based on a representative future growth of the economy and the sectors within it. sample of 13,430 households. The impact of tourism growth International tourist arrivals and spending in Kenya grew on poverty is captured by changes in the FGT poverty indices on an average of 4.6% per annum between 2003 and 2013, (Foster, Greer, and Thorbecke 1984). FGT is one of the most totalling approximately 1.5 million arrivals in 2013 (WTTC important poverty measures, which is widely applied in 2015). With respect to future growth, it is forecasted that empirical work because of its simplicity. It is based on nor- domestic and foreign travel spending will rise by 4.7% on malized poverty gaps, that is, the term in the parentheses in average p.a. from 2013 to 2023 (WTTC 2015). In this con- equation (11). Poverty gaps are then raised to the power to text, the effects of a 5% annual growth of tourism spending capture how deep poverty is. The definition is as follows: on the Kenyan economy are simulated. With 2003 as the baseline, this corresponds to a yearly increase in tourism spending of Kenyan shilling (KES) 2,723 million (or 0.2% zy − 1   Py; z = () (11) α ∑  of GDP) (KES 100 = US$1.08). It should be noted that the N z   i=1 tourism sector accounted for 4.15% of total GDP in 2003, namely, KES 1,311 billion. where y is a vector of household incomes in increasing order, z is the poverty line (in income units), N is the total number of households, q is the number of poor households and is Simulation Results a parameter. The simulation results are reported in terms of macroeco- nomic and sectoral impacts and in terms of income, con- For α= 0,, P = sumption, and poverty impacts. N 520 Journal of Travel Research 57(4) which when combined with increasing domestic prices Macroeconomic Impacts (0.1%), rental returns (0.11%), and wage rates (0.18%) per A 5% increase in tourist spending generates an annual per- annum sees traditional export sectors, such as agriculture, centage change in GDP of 0.24% on average, aggregating to experiencing a decrease in international competitiveness. an overall percentage change in GDP of 4.87% from the first Consequently, export earnings decline. Agricultural exports (2003) to the last (2022) period. On the expenditure side, show an annual percentage decrease of –0.11%, while export total real investments increase by 0.52% per year, accumu- demand for manufacture and services shows annual percent- lating to an overall percentage change in aggregate invest- age increase of 0.09% and 0.21%, respectively. At the same ments of 10.44% for the whole period. Regarding the time, the higher value of the Kenyan dollar makes imports contribution of each investment aggregate to total invest- cheaper, increasing the demand for imports of agricultural, ment, it is found that private investments make the largest manufactured, and services products by 0.26%, 0.17%, and contribution to total investment (0.66% per year on average) 0.31% respectively. compared with public investments (0.09% per year on aver- The growth of the manufacturing and services sectors is age). Both domestic and international inbound tourism have followed by a 0.03% average annual increase in the demand a positive impact on the Kenyan economy and there seem to for labor. The results are consistent with other studies that be no marked differences between domestic and interna- have investigated the relationship between tourism and agri- tional inbound tourism with respect to the overall economic culture in developing economies (e.g., Bowen, Cox, and Fox impact. 1991; Sahli and Nowak 2007; Wattanakuljarus and Coxhead For non-tourism exporters and some manufactured goods, 2008). Sahli and Nowak (2007) argue that in developing the simulated percentage changes are negative. The changes economies where the tourism sector is relatively more labor to total export are lower in the first period (0.09% on over- intensive than the agricultural sector, the net benefit from age) and positive in the last period (0.11% on average). Total inbound tourism growth on national welfare will be positive imports, on the other hand, increase on average by 0.24% as is the case for Kenya. annually, leading to a trade deficit. Over the whole period, The patterns of demand for the different types of labor are total imports accumulate up to 49.06%, outweighing the identical to patterns of output growth. Demand for all types increase in total exports (19.94%). Government income of labor increases in industries closely associated to the tour- increases per annum by 0.21% in the first period and 0.22% ism industry as well as industries in the supply chain and in the last period, accumulating to 4.36% over the whole decreases in non-tourism sectors. However, demand for time period. There is an increase in savings of all household unskilled labor increases faster than demand for skilled and groups, which increase on average by 0.18% annually. semi-skilled labor, both in the first period and last period. Enterprise savings also increase (0.24%), while government Hotel has the largest impact (0.92%), followed by transport savings decline on average by 0.92% per year. (0.7%), construction (0.59%), and retail trade (0.29%). This suggests that service exports are relatively (unskilled) labor intensive and that households endowed with these factors, Sectorial Impacts that is, low-income households in service industries will be An increase in tourism demand is associated with the shifting the main beneficiaries. Semi-skilled labor experiences the of scarce resources from non-tourism sectors, such as agri- lowest growth in the service industries and the highest culture, towards tourism-related sectors, construction, trans- decline within non-service in all periods. port, mechanical repair work, crafts, entertainment and shopping. Clearly tourism, especially rural tourism impacts Impact on Income and Consumption on agriculture in many ways. Both industries compete for The simulation results indicate that the nominal income of all resources, including land, labor and capital. For instance, the household categories rises. A comparison of households by designation of parks and recreation areas for the visitors deciles and region reveal that the changes are uneven. The reduces the economic opportunity of the farmers. Fishermen poor households in urban areas receive a 0.25% rise in their have to compete for shore space with tourism development. nominal income in the first period as compared to only Outputs of the agricultural activities fall by 0.01% on aver- 0.19% for the poor households in rural areas. In the rural age annually, manufacturing increases by 1.65% and the ser- area, middle- and upper-income households gain more than vices sectors increase by 12.11% over the whole period. In low-income ones. In the urban area, middle- and upper- terms of annual percentage change in gross value added, the income households as well as households at the lowest decile largest positive impact is in transport (0.4% on average), fol- gain the most. As a result, low-income agricultural house- lowed by construction (0.38%) and trade (0.11%). holds experience the least changes, while low-income non- The results confirm those of Wattanakuljarus and Coxhead agricultural households (i.e., those who derive their income (2008). The expansion of the tourism industry introduces the from services industries) and high-income households gain Dutch Disease phenomenon into the Kenyan economy the most. These results are in line with the results by Blake through an annual appreciation of the exchange rate (0.34%), Njoya and Seetaram 521 Table 1. Poverty Results—Kenya: All Households. Poverty Count Poverty Gap Poverty Severity P P P 0 1 2 (%) (%) (%) Base year 45 15.2 7.7 Year 1 −0.092 −0.17 −0.15 Year 5 −0.103 −0.19 −0.17 Year 9 −0.102 −0.2 −0.19 Year 13 −0.09 −0.18 −0.19 Year 17 −0.08 −0.24 −0.18 Year 20 −0.07 −0.26 −0.21 Poverty indices, Year 20 43.16 12.22 4.93 Change in poverty indices −1.84 −2.98 −2.77 Source: Authors’ simulations results. Note: Italicized values represent the key results from the study. et al. (2008), Kweka (2004) and Wattanakuljarus and poverty in Kenya but also reduce the poverty gap and Coxhead (2008), and show that, in general, tourism expan- improve the income distribution among the poor. In other sion benefits all household groups, but the poorest (rural) words, a larger number of households will be able to move household group gains less than other household groups. closer to the poverty line. Increased income allows rural consumers to enjoy an annual increase in aggregate real consumption of 0.11%. Impact on Poverty Urban households, on the other hand, register an annual The poverty effects are assessed against the base year (2005). increase in aggregate real consumption of 0.14%, reflecting Table 1 presents a summary of the poverty incidence using the higher increase in their annual income. The poverty the standard Foster-Greer-Thorbecke FGT poverty indica- effects are assessed against the base year (2005) using the tors, that is, headcount (P ), income gap (P ), and severity poverty line of KES 1,562 per month per person for rural and 0 1 (P ). In 2005, 45% of the population of Kenya lived below KES 2,913 per month for urban areas (in adult-equivalent the poverty line, and the poverty gap shows that the average terms, which at the time was approximately US$0.75 and shortfall of income of the population from the poverty line is US$1.40 a day per person) and include minimum provisions 15.2%. Poverty severity is 7.7 in Kenya in 2005. The simula- for both food and nonfood expenditures (GOK 2007). The tion effect indicates that tourism development reduces the results are reported in Table 2. poverty count by 1.83% to 43.16 in year 20. This means a In the base year, all three indices indicate that poverty is steady 5% increase in arrivals in Kenya will enable 1.83% of more prevalent and severe in the rural areas of Kenya. In the population to cross the poverty line. A priori, the results 2005, 70% of the rural population of Kenya lived below the indicate the poverty reduction effect of the growth of the poverty line as opposed to 34.5% in the urban areas. The tourism industry is very small. results clearly demonstrate that the urban population of However, during the same period of time, the poverty Kenya will benefit more from the development of the tour- gap is reduced by approximately 3%, meaning that not only ism industry. The poor households in urban areas are more 1.83% of the population will no longer be poor but that favored than the poor households in rural areas. Moreover, among the poor, the average income needed to close the low-income agricultural households experience the least poverty gap will have fallen as well. Furthermore, poverty improvement, while low-income nonagricultural households severity will have fallen, implying that the poorest house- and high-income households gain the most. It can be pre- holds of the country will experience an improvement in dicted that 1.56% of the rural population and 1.89% of the their welfare. The effect on poverty count is highest in urban population will move out of poverty by year 20. The years 3 to 11. These results validate the use of a dynamic effect on poverty gap and severity is significantly higher. The model and show that application of statics models as is poverty gap is reduced by 5.8% and 5.15% for rural and common in the literature will underestimate the poverty urban households, respectively. Poverty severity falls faster reduction effect of tourism expansion as the higher effect in the urban area by 4.43 as opposed to 3.72 in the rural occurs with a lag. This is understandable, as growth of the areas. The urban poor benefit more than that poor household manufacturing and services sector leads to an increase in in the rural areas. demand and create job opportunities for the poor but the These effects indicate that the tourism industry is inclu- effect occurs mostly in the postsimulation years. The results sive and benefit the poorest household of Kenya. It has the indicate that the tourism industry will not only reduce potential of significantly reducing poverty severity in the 522 Journal of Travel Research 57(4) Table 2. Poverty Results—Rural and Urban Households. Rural Urban P P P P P P 0 1 2 0 1 2 (%) (%) (%) (%) (%) (%) Base year 69.9 17.6 8.9 34.5 11.6 5.4 Year 1 −0.06 −0.41 −0.25 −0.15 −0.42 −0.25 Year 5 −0.07 −0.39 −0.3 −0.17 −0.38 −0.21 Year 9 −0.08 −0.34 −0.35 −0.16 −0.32 −0.24 Year 13 −0.08 −0.37 −0.28 −0.15 −0.29 −0.25 Year 17 −0.09 −0.38 −0.21 −0.14 −0.27 −0.2 Year 20 −0.09 −0.34 −0.21 −0.13 −0.25 −0.2 Poverty indices, year 20 68.34 11.76 5.17 32.61 5.85 0.97 Change in poverty indices −1.56 −5.83 −3.72 −1.89 −5.15 −4.43 Source: Authors’ simulations results. Note: Italicized values represent the key results from the study. urban area and drastically reduces the income gap of the Tourism expansion and the resulting economic growth poorer households in the rural and the urban areas. However, principally trickle down to both the urban and rural poor, while the condition of the poorer households are improved, through increases in income and in labor demand. This leads only a very small proportion of the households will cross the to a fall in poverty headcount and an even greater fall in pov- line, implying that tourism will not have a very significant erty gap and severity. Tourism in Kenya has the potential to role in improving the incidence of poverty. Furthermore, the reduce poverty at the national and at urban and rural levels. gap between poverty in rural and urban households is not However, poverty falls faster in the urban area, and the effect significantly narrowed. The gap in the headcount ratio and in the rural area is dampened by a fall in labor demand and the poverty severity ratio between rural and urban house- earnings for the poor working in the agricultural sector. holds increases marginally and the poverty gap for rural The results of the present article have important implica- improves slightly compared to that of the urban area. It indi- tions for policies. First, it demonstrates that the use of only cates that tourism development will benefit the urban house- one poverty index as is common in the literature does not hold disproportionately. This can result from the fact that provide a complete picture. For example, based on poverty investments in hotels and transport will grow the fastest and count index only, it can be concluded that the tourism indus- as these investments are more likely to take place in the try is only marginally beneficial for the Kenyan poor. larger towns and cities of Kenya, this is where opportunites However, taking into account the poverty gap and poverty for employment and consumption will be created. On the severity indices can change that conclusion. It is clear from other hand as the agricultural sectors contracts in the rural the results that policies aiming at attracting more tourists or areas, the positive effect of the expansion of the tourism boosting the discretionary spending of tourists alone will industry may be dampened. have relatively minor impacts on rural poverty although it will enable a proportion of households to move closer to the poverty line and reduce poverty severity. Recommendations and Conclusion One of the main policy implications that emerge from This article investigates the impact of increase in inbound these findings is that tourism development strategies need to tourism on Kenyan households using a dynamic GCE model. give due consideration to agricultural production. Results The aim is to find out the extent to which the tourism indus- indicate that there is a significant pull of labor from agricul- try affects the poorer households of the country. ture to sectors with higher linkages to the tourism industry. Results indicate that as a nation Kenya will benefit from Fostering collaborations and reducing competition between the higher growth in its GDP and export earnings. However, the two sectors has the potential of benefiting both. Tourism the higher growth will come at a cost. As a result of Dutch can stimulate the development of new agriculture-based ser- Disease, other exportable sectors will find their competitive- vices, such as tours of agricultural production and processing ness eroded and appreciation of the currency will make facilities. Strengthening linkages between the agricultural import cheaper, leading to a worsening of the balance of and the tourism sector may dissipate the negative impact on trade of the country. Resources move from traditional sectors the former. New synergies between these two competing sec- to tourism industries and its supply chain. These industries tors can take the form of favoring locally sourcing food expand creating employment opportunities and offering needed from the increased demand in the tourism sectors higher wages. The agricultural sector declines whereas non- over import, as suggested by Belisle (1983). This will create tourism exporters will experience a weak to negative growth. opportunities in the agricultural sector through the expansion Njoya and Seetaram 523 of its market while reducing the leakage rate from the tour- Funding ism sector. The author(s) received no financial support for the research, author- Although it can be expected that different types of tourism ship, and/or publication of this article. generate different distributional impacts on poor people, the combination of size and linkage strength is likely to amplify Note the beneficial effects of any type of tourism. Exploiting the 1. Labor migration between rural and urban owing to the structure linkages between tourism and the local economy toward of data is not explicitly modeled. Thus, the data does not pro- poverty reduction requires a diversified growth strategy that vide information about the spatial location of different types of expands tourism while improving the competitiveness of labor. However, the assumption of perfect mobility within agri- other sectors and ensuring a better distribution of income. culture (industries mainly classed as urban) and nonagriculture From a technical point of view, it is worth noting that the (industries mainly classed as rural) sectors may be considered as a proxy for rural–urban labor mobility. application of a dynamic model offered additional informa- tion on the impact on poverty alleviation, which becomes References more apparent from the third year. It is clear that the poverty effect occurs with a lag, and it is recommended that when Banerjee, Abhijit V., and Esther Duflo. 2011. Poor Economics: A possible, dynamism be included in future models developed. Radical Rethinking of the Way to Fight Global Poverty. New The model developed in this study, however, is not without York: PublicAffairs. Blake, A. 2009. “The Dynamics of Tourism’s Economic Impact.” limitations. 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Kweka, J. 2004. “Tourism and the Economy of Tanzania: A CGE Analysis.” Paper prepared for the CSAE Conference on Author Biographies Growth, Poverty and Human Development in Africa, 21–22 Eric Tchouamou Njoya, PhD, is a lecturer in Air Transport March, Oxford, UK. Economics and Management at the Department of Logistics, Loayza, N., and C. Raddatz. 2006. “The Composition of Growth Operations, Hospitality and Marketing, University of Huddersfield. Matters for Poverty Alleviation.” Mimeo, World Bank, He received his PhD from the Karlsruhe Institute of Technology, Washington, DC. Germany.His current research focuses on the economic impact of Mahadevan, R., H. Amir, and A. Nugroho. 2016. “Regional Impacts tourism, the tourism benefits of air transport liberalisation and the of Tourism-Led Growth on Poverty and Income: Inequality: role of air cargo in achieving regional economic growth. A Dynamic General Equilibrium Analysis for Indonesia.” Tourism Economic 1–18. Neelu Seetaram, PhD, is the head of research and professional McCulloch, N., L. A. Winters, and X. Cirera. 2001. Trade practice at the Department of Tourism and Hospitality, Bournemouth Liberalization and Poverty: A Handbook. London: Centre University. She is an elected council member of the International Econ. Policy Research. Association for Tourism Economics. She is an economist, who Mitchell, J., and C. Ashley. 2010. Tourism and Poverty Reduction: regularly publishes in the area of tourism economics and has co- Pathways to Prosperity. London: Earthscan. edited two books. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Travel Research Pubmed Central

Tourism Contribution to Poverty Alleviation in Kenya: A Dynamic Computable General Equilibrium Analysis

Njoya, Eric Tchouamou; Seetaram, Neelu
Journal of Travel Research , Volume 57 (4) – Apr 4, 2017
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Abstract

The aim of this article is to investigate the claim that tourism development can be the engine for poverty reduction in Kenya using a dynamic, microsimulation computable general equilibrium model. The article improves on the common practice in the literature by using the more comprehensive Foster-Greer-Thorbecke (FGT) index to measure poverty instead of headcount ratios only. Simulations results from previous studies confirm that expansion of the tourism industry will benefit different sectors unevenly and will only marginally improve poverty headcount. This is mainly due to the contraction of the agricultural sector caused the appreciation of the real exchange rates. This article demonstrates that the effect on poverty gap and poverty severity is, nevertheless, significant for both rural and urban areas with higher impact in the urban areas. Tourism expansion enables poorer households to move closer to the poverty line. It is concluded that the tourism industry is pro-poor. Keywords Kenya, tourism development, poverty, dynamic computable general equilibrium, CGE, microsimulation, Foster-Greer- Thorbecke Index p. xvii). The authors propose that economic growth needs to Introduction be, a priori, inclusive and the benefits need to accrue to the Nobel Prize laureate Amartya Sen’s definition of poverty poor for this to be achievable. moves the concept from merely “lowness of income” to “the The Millennium Development Goals (MDGs) advocate deprivation of basic capabilities” (Sen 2001, 87). He pro- economic development to reduce extreme poverty by tack- poses that inadequate income is a “strong predisposing con- ling the problem of capability deprivation through better dition for an impoverished life,” and lack of capabilities access to education, health, and better opportunities for all often resulting from lack of income is the underlying cause (UNWTO 2005). It has been acknowledged that tourism will of poverty. By redefining poverty, Sen puts people at the play an important role in the achievement of MDGs. heart of development. He links capability with freedom of However, whether resources allocated to the tourism indus- choice and access to opportunities that empowers individu- try in fact lead to pro-poor development is an empirical ques- als, giving them the ability to choose the type of life that they tion. Mitchell and Ashley (2010) provide some evidence have “reason to value.” Hence, according to Sen, any policy supporting this claim. They state that in most destinations aiming at achieving poverty reduction needs to address the 10% to 30% of in-country tourist spending accrues to poor issue of capability deprivation rather than merely targeting people. This is facilitated by the economic, political, and the level of household income. On the other hand, Croes and Rivera (2015), who take a Department of Logistics Operations and Hospitality Management, taking a purely economic perspective, postulate that poverty University of Huddersfield, Huddersfield, United Kingdom is a form of underutilization of productive resources. It repre- Department of Tourism and Hospitality, Faculty of Management, sents the underdevelopment of the pool of skills and reduces Bournemouth University, Poole, Dorset, United Kingdom the productive capacity of a nation. They argue that the poor Corresponding Author: should be helped in order to expand the wealth-creating Neelu Seetaram, Department of Tourism and Hospitality, Faculty of capacity of nations and raise the standard of living and quality Management, Bournemouth University, Talbot Campus, Poole, Dorset of life for the whole country. In other words, “the poor should BH12 5BB, United Kingdom. Email: [email protected] be helped out of self-interest” (Croes and Rivera 2015, 514 Journal of Travel Research 57(4) cultural context as well as the other factors pertaining to the using headcount indices, such as the proportion of house- implementation of tourism development strategies. On the holds below an identified poverty line, as in Blake et al. other hand, Hall (2007), Scheyvens (2007), and Schilcher (2008) and Vanegas, Gartner, and Senauer (2015). While the (2007) argue that tourism is not necessarily pro-poor. Croes headcount measures offer valuable information, they are and Rivera (2015) state that while the poor may benefit from deemed to be too crude. tourism-led economic growth by accessing employment The most widely used index in the development econom- opportunities, in times of economic slowdown they tend to ics literature is the Foster-Greer-Thorbecke (FGT) index suffer the most and in periods of economic growth they ben- (1984), which is a multidimensional index combining three efit the least. classes of measurements: headcount index (P ), income gap The literature on pro-poor tourism continues to grow as index (P ), and poverty severity index (P ). The index has a 1 2 more research focus on individual pro-poor tourism projects simple additive structure where aggregate poverty is a popu- at destinations and examine their outcomes. However, lim- lation-weighted mean of subpopulation groups. This allows ited evidence is available on the relationship between tour- for the decomposition of the index and analysis of each sub- ism development and poverty reduction at a macro level. group individually. Such information is more relevant to The advantage of studying the poverty reduction capacity of policy holders as it allows the identification of the subgroup tourism development at the macro level is that it enables the that contributes most to poverty, and therefore, more targeted researcher to trace the mechanism through which tourism measures of poverty reduction can be designed. The simple expenditure affects the different industries at the destina- structure of the index makes it easy to apply and interpret tions and, hence, account for those that are the most affected, and, hence, it is surprising that the FGT has not yet been used positively or negatively. This provides policy makers with in the tourism context. This article fills in the gap. detailed information on the transmission mechanism of tourism expenditure and, can be an important tool in formu- The Tourism and Poverty Nexus lating and targeting policies that aim at increasing the eco- nomic benefits and reducing the economic cost of tourism Cross-country studies have verified that sustained economic expansion. growth reduces poverty (Kraay 2004). However, there is a The few studies that have investigated the problem at the widespread consensus that not all forms of growth have the macro level have applied static techniques to investigate same impact on poverty. Economic growth is pro-poor when the relationship. Static modeling techniques analyze the it is balanced with equity but to be achievable, it requires the contribution of the tourism industry but ignore the effect of careful implementation of targeted macroeconomic policies policy changes on these contributions in the postimplemen- on education and health, nutrition and infrastructure (Croes tation years. This article argues that the effect on the poor and Rivera 2015). Sectoral pattern of growth affect the extent may occur with a time lag, making dynamic modeling a of poverty reduction (Coxhead and Warr 1995; Fane and more appropriate approach. This approach assesses the Warr 2002; Loayza and Raddatz 2006). If, for example, the effect on an annual basis and allows for more effective tourism sector in a destination is low-skilled and labor-inten- monitoring and analysis of the effect of policy changes. sive, it is likely that its expansion will generate high income Blake (2009) points out that a detailed household modeling flows to the poor. There are many different ways by which using a microsimulation approach provides a more compre- tourism can engage the poor, boost local economic develop- hensive assessment of the impact of tourism on economic ment, or affect the physical and social environment of local development. Hence, this approach is better suited for the communities. assessment of the effect of tourism expenditure on the stan- The link between tourism and the reduction of poverty is dard of living of households at the destination. The micro- best understood by considering the link between trade liber- simulation approach, however, is yet to be to be implemented alization and poverty reduction (McCulloch, Winters, and in the tourism context. Cirera 2001). Figure 1 shows the channels through which This article aims to investigate the connection between tourism may affect the poor. These include income, tax, tourism policies and poverty reduction by developing a price, and risk channels (Blake et al. 2008). Poor households dynamic general equilibrium model of the Kenyan economy earn income through direct or indirect participation in tour- by integrating the microsimulation approach of Cockburn ism (International Trade Centre [ITC] 2009). Tourism also and Decaluwé (2006) to analyse the extent to which the contributes to the tax base of local or national government, Kenyan tourism industry is benefiting poor households. It is and the additional revenue can be used to provide or improve one of the first studies in the tourism literature that uses a the social infrastructure. ITC (2009) argues that positive dynamic approach and contributes to the literature by not effects can include better social infrastructure, education, only providing the evidence as to whether tourism develop- stronger local institutions, and gender equality. ment is pro-poor in Kenya but also analysing the magnitude The third channel is the price paid by the poor for the of the effect over time. Furthermore, the existing literature consumption bundle goods they purchase. Tourism expan- on tourism and poverty reduction has measured poverty by sion leads to an increase in the demand for local products, Njoya and Seetaram 515 Participation of poor people in Income Positive tourism industry or Channels in supply chains Tax collection and provision Positive Tax of social Channels infrastructure Positive An increase Pressure on Price in tourism local prices Channels spending Negative Vulnerability to exogenous Negative (internal and Risk/ external) shocks Dynamic Channels Positive Social, cultural and natural environment of Negative the poor Figure 1. Channels by which tourism spending may affect the poor. Source: Authors’ own illustration. such as food, land, and construction, which in turn can cause employed in the affected non-tourism sectors, therefore, tend an increase in local prices (ITC 2009). As tourism increases, to find their real earnings fall following the expansion of the the demand for goods and services that the tourists use tourism industry. increases and, as a result, the prices of those goods will rise. This effect is empirically proven by Wattanakuljarus and The impact of the price channel on the poor will depend on Coxhead (2008), who simulate the effects of a boom in the the amount of tourism-related goods and services among the inbound tourism demand on the Thai economy. The authors goods and services purchased by the poor (Blake et al. 2008). show that an increase in tourism arrivals of 10% would lead The fourth channel relates to risks and other long-term to an increase in household income accompanied by a wors- dynamic influences. The dynamic impact of tourism on local ening of the income distribution. Benefits from the tourism economic development can be positive (e.g., biodiversity industry do not trickle down to the poorer household because conservation measures; allocation of funds for natural, cul- tourism is not a notably labor-intensive sector in comparison tural, and historical resources) and negative (e.g., destruction to key tradable sectors such as agriculture and labor-inten- of environmental resources, pollution of air, water, noise). sive manufacturing, and its expansion triggers the Dutch A number of studies have developed theoretical models to Disease effect, which undermines profitability and reduces show that expansion of the tourism sectors can be immiser- employment in tradable sectors, notably agriculture, from izing (Chen and Devereux 1999; Copeland 1991; Hazari and which the poor derive a substantial percentage of their Kaur 1995; Hazari and Nowak 2003; Sahli and Nowak, income. 2007). These models assume that a boom in the tourism On the other hand, Blake et al. (2008), who apply a CGE industry will have a negative effect on poorer households model of the Brazilian economy to assess the distributional when it leads to the appreciation of the local currency. A effects, find that poorer households will benefit from an higher value of the local currency erodes the international increase of 10% in tourism spending. Increasing demand competitiveness of non-tourism exports and, therefore, limits from tourism causes prices of goods and services to rise but their growth and capacity for generating employment. This have no effect on the price of the bundle of consumption of phenomenon, also known as the Dutch Disease effect, is the poorer households. However, the poorer households are often a result of the expansion of an export industry in the not the main beneficiaries of the earnings and price channel presence of market distortions such as monopoly power, effects of tourism expansion. The authors note that transfer- repatriation of profits by foreign companies, increasing ring all additional government revenue to the poorest house- returns to scale in non-tourism export activities, crowding- hold group can double the benefits for poor people, giving out effects, and trade distortions. Poorer households who are them around one third of benefits in total. 516 Journal of Travel Research 57(4) This recommendation is challenged by Mahadevan, Amir, reduction. They use headcount indices, which according to and Nugroho (2016). The authors use a CGE model of the Haughton and Khandker (2009), are basic measures and Indonesian economy to show that expansion of the tourism need to be complemented with other indices to accurately sector reduces poverty but increases income inequality at quantify the effect of growth on poverty reduction. national level. The authors undertook several types of simu- Schilcher (2007) stipulates that economic growth can lations aimed at exploring complementary policies likely to reduce poverty and help poor households cross the poverty improve the poverty impact of tourism growth and reduce line while making the extreme poor worse off. The effect on income inequality gaps. They found that poverty reduction the latter, however, has not been analyzed empirically in the can be achieved faster by investment that raises the labor tourism literature. Studies that focus on headcount measure- productivity of the poor as compared to monetary transfers. ment of poverty only have mostly concluded that tourism This result offers a solution to the problem identified by growth provides limited benefits to the poor. The effect on Croes (2014) who finds that in Costa Rica jobs created by the the extreme poor and their income distribution are ignored. A tourism industry are filled by higher educated local labor policy that helps the extreme poor move closer to the poverty force and foreigners, thus excluding the poor. Croes con- line though not having a significant contribution in reducing cludes that economic growth in Costa Rica is followed by poverty count is a valid mean for poverty alleviation, but declining opportunities for the poor. failure to account for the benefits accrued in the form of Similary, Kweka (2004) finds that in Tanzania, urban improved income distribution for the poorest leads to the (higher income) households will benefit more from a 20% erroneous conclusion that the policy is ineffective. According increase in tourism than their rural (lower income) counter- to Schilcher (2007), there is a need to distinguish between parts, confirming the argument of Croes and Rivera (2015). the poor and the extreme poor. On the other hand, Vanegas, Gartner, and Senauer (2015) In this study, it is proposed to use the FGT index, which apply an autoregressive lag model and find that in Costa is multidimensional and incorporates three indices of pov- Rica and Nicaragua, tourism development is negatively erty, (P ), (P ), and (P ). P measures the proportion of the 0 1 2 0 related to the prevalence of extreme poverty and that the population of a country that is below the poverty line. It poverty reduction effect of the tourism industry is higher measures the mean gap between the income of the poor than that of the agricultural sector in both countries. Croes households and the poverty line. That is, the gap between (2014), using an error correction model to assess the effect the poverty line and the income of each poor household is of tourism growth on absolute poverty, concludes that in summed and divided by the population of the country. Nicaragua a 1% increase in tourism receipts reduces the While P measures the incidence of poverty, P measures 0 1 poverty headcount index by 1.23 points. He explains that in the incidence and depth of poverty. P is a measure of Nicaragua, where the proportion of poor is higher, the tour- income distribution among the poor. It is the square of P , ism industry creates jobs in the informal sector providing and it places higher weights on the poorest households. It opportunities for the poor to join the supply chain. His is expected that using the FGT to assess the effect of tour- results confirm those of Croes and Vanegas (2008). In ism development on poverty reduction adds additional Croes and Rivera (2017), the authors use the social account- dimensions to the analysis that are more relevant for policy ing matrix of Ecuador to show that tourism development makers. Furthermore, the analysis is carried out in a benefits the poor disproportionately by improving their dynamic setup, and, therefore, the time lag that may be income. The authors conclude that tourism development is needed for poverty reduction effect to be noticeable is a viable tool for reducing poverty in developing countries. taken into account, allowing for the study of poverty alle- However, the use of SAM may overestimate the poverty viation nexus path over time. reduction effect of tourism development as this method is based on restrictive assumptions that do not take into Modeling the Economic Impact of account the price effect on the consumption of households. Tourism in Kenya Croes and Rivera (2017) use poverty headcount as their measure of poverty in Ecuador. The World Bank (2010) estimates that Kenya has one of the From the empirical studies above, it is seen that the effect world’s highest rates of population growth below the age of of tourism expansion on poorer households is mixed. These 25 at 2.6% (on average per annum), with approximately studies, while offering invaluable insight on the ways in three quarters of the population living in rural areas. The which poorer households are affected by tourism, use static Kenyan social accounting matrix (SAM) shows that the analysis and, therefore, cannot provide more information on highest total consumption expenditure shares of poor house- the yearly longer-term effect. The current study seeks to holds in rural areas are found in agricultural products (32%), address this gap. Furthermore, the studies discussed above followed by transport (12.8%). The richest rural household have focused on one measurement of poverty, which does spends more on services than on agricultural and manufac- not necessarily provide a comprehensive assessment of the tured goods. The urban households spend a large percentage effect of an expansion of the tourism industry on poverty of their budget on services such as transport (17.7%) and Njoya and Seetaram 517 restaurants (11.9%). The poorest urban deciles, on the other demand value plus the value of stock changes hand, spend 51% of their consumption expenditure on food. that are defined as being fixed, usually in volume Tourism is one of the fast growing sectors in Kenya’s terms at the levels in the base period. economy, and it is directly responsible for creating about half c. Exports: Aggregate domestic output is allocated be- a million jobs. It has been earmarked as one of the strategic tween domestic and export markets. This is done un- sectors for economic growth and development in Kenya. der the assumption that suppliers maximize the sales According to World Travel and Tourism Council (WTTC revenue for any given aggregate output level, subject 2015), the travel and tourism sector contributed approxi- to imperfect transformability between exports and mately 4.1% directly and 10.5% indirectly to GDP in 2014. domestic sales, expressed by a constant elasticity of Export earnings from international tourists generated 18.3% transformation function. of total exports in the same year. Income from tourism grew d. Imports: It is assumed that the institutions in the econ- by 126% between 1995 and 2014, attaining US$2.1 billion. omy consume a composite good, made up of domes- This study investigates the impact of sustained tourism tic goods and imports. Imports and domestic goods in growth on poverty from 2003 to 2015, with 2003 as the base the same sector are imperfect substitutes, an approach year and using poverty indicators in 2004–2005 as a baseline called Armington assumption. estimate. e. A group of equations describing net transfers, in- The effect of tourism expansion on the Kenyan economy comes, expenditures and savings, GDP, trade balance, will be assessed using a recursive dynamic Computable consumer price index, real exchange rate and market General Equilibrium (CGE) that draws on CGE models by clearing for composite commodities and primary fac- Decaluwé et al. (2010), Savard (2003), Cockburn (2001), tors. Robinson et al. (1999), and Dervis, de Melo, and Robinson (1982), as well as the contributions to tourism-based dynamic Thus, the model developed has 115 block equations and the CGE model by Blake (2009). It involves the specification of same number of variables that are solved simultaneously a multihousehold and multisectorial CGE model by means of using GAMS, Generalized Algebraic Modeling System soft- nonlinear algebraic equations and addressing these equations ware. For reasons of space, the article only describes how directly with numerical solution techniques. The basic model tourism, dynamics, and poverty are modeled. illustrates consumption and production-related behavior, interinstitutional transactions, and trade relationships. Thus, Modeling Tourism Demand the model has the following key structural elements: A Cobb-Douglas (C-D) utility function is used to determine a. Production and factor demand: Production technol- how tourists substitute between commodities. C-D and ogy is specified in a multilevel nesting structure. Constant Elasticity of Substitution (CES) functions have been b. Product demand: The final demand is composed of widely used in the tourism-based CGE models. The C-D util- demand by households, investment, inventory, the ity function exhibits a constant and unitary elasticity of sub- government, the rest of the world, and tourists. stitution. With the exception of the demand for tourism, which - Household demand: Households are assumed to is modeled using a C-D function because of lack of data, con- choose the consumption of different commodities sumption and production behavior are modeled using CES, according to an Engel expenditure function. The LES, and constant elasticity of transformation functions. demand functions are derived from maximization From the modeling point of view, two categories of tour- of a Stone–Geary utility function (often called ism demand (domestic tourism demand and international Linear Expenditure System) subject to the budget inbound tourism demand) are considered, assuming that constraint. Most empirical literature on the link there are differences in the structure of their spending. Hence, between household consumption patterns of dif- the assumption is that there are two categories of tourism ferent goods and level of income in developing demand accounting for the consumption of a certain quantity countries applies Engel’s law, which suggests that of a composite good and service at an aggregated tourism as households become more affluent, the share of price level PTOU . Analogous to household demand, () () t household spending dedicated to necessities such domestic CDD as well as international inbound () () t as food declines (Banerjee and Duflo 2011). In an CDF tourism demand is obtained by maximizing the () () t LES specification, consumers first set aside sub- utility function of each individual tourist function to its bud- sistence levels of goods, then allocate the remain- get constraint. Following Blake et al. (2008), the demand for ing budget in proportion to preferences. tourism is defined by the following equations: - Government demand: The government collects taxes and receive transfers from other institutions. d   PTOU () t - Investment demand: The value of investment CDDC =⋅ χ DD ⋅  () tt () (1)   PIXCON expenditure is equal to the sum of investment () t   518 Journal of Travel Research 57(4) dynamic-recursive adjustment is solved recursively from the  PTOU  () t base year 2003 to the year 2022. CDFC =⋅ χ DF ⋅   (2) () tt ()   pop e Moreover, there is a population index , which is () t () t   updated exogenously and growing in each period at a rate (, ci ) η . This index is used in the model to update the values of CTOU = ω tcom (, it )(i ) (, ci,) t () t ∏ (3) variables, parameters, and constants that are assumed to grow at the same rate η as the population index pop . () t Following World Bank’s current and projected annual growth tcom PC =⋅ ε CTOU PTOU (4) (, ci,) tc (,tc )( )(it ,) () t rate of approximately 2.6%, the Kenyan population is assumed to grow at a rate of 0.026 per year in the model. (, ci ) Total labor supply becomes an endogenous variable and is TOUP = PC (, it ) (, ct ) ∏ (5) assumed to grow at the exogenous rate η , which is the () t labor force growth rate. where CDD and CDF are parameters equal to the base () t () t Unlike the static model, capital stock is endogenous in the level of domestic and international inbound tourism con- dynamic model. In every period, capital stock is the result of sumption, respectively, except where tourism demand shocks the stock of the preceding period, minus depreciation, plus are introduced into the modeling system by changing these the volume of new capital investment in the preceding period parameters. The price elasticity of demand for domestic tour- (equation (6)). ism is captured by the parameter ς with ς  1 , while is d d χ= 1 () a shift parameter in the base year. Domestic tourists KD =− () 1 δ KD + IND ka ,,tk +1 ,, ak at ,, ka,t (6) () () () () are concerned with how the composite price changes relative to the consumer price index PIXCON . ω is a shift () () t i () parameter, calibrated to ensure that the model replicates the j j IT = PK IND (7) ∑ kj ,,t () t () t () benchmark; ε is the share of each commodity in each (, ci ) kj , CTOU tourism consumption; and the aggregate tourism (, it ) consumption by each i category of tourism (the index i refers () c   PC 1 ct , () to the type of tourism, i.e., domestic or inbound). j   PK = (8) t K ∏ j () () It is assumed that international inbound tourists are con-   () c   cerned with how their composite price changes relative to a INV real exchange rate. Thus, international inbound tourism () kj ,  IR  demand is modeled in a similar way to export demand and is (, kj ,) t IND = ϕ KD (9)   (, kj ) (, kj ,tt ) () kj ,,t assumed to be inversely proportioned to the price of foreign  (, kj ,) t    exchange in the domestic market (equation (2)). The utility of the two categories of tourists is modeled using a Cobb– Douglas function, determining how they substitute between UP =+ KI δ R (10) () kj ,,t kj , t () () t () () commodities. Following Blake et al. (2008), tourism con- sumption by sector tcom can be specified as indicated () (, ci,) t where KD is the demand for type k capital by activity () ka ,,t +1 in equation (3). a and the volume of new type k capital investment IND () ka ,,t Thus, the total value of total tourist expenditure of each to activity a. The amount of each investment expenditures tourism category CTOU ⋅ PTOU must equal the total () (, it )(t ) category (the index j refers to the type of capital, i.e., IT expenditure of each tourism category of different commodi- ( ) () ties tcom ⋅ PC . Additionally, the price paid by tour- public or private) is determined by equation (7) as the price () (, ci,) tc (,t ) ists in each category can be related to the prices of the of each investment category PK times the aggregate ( ) () PC individual commodities as indicated in equation (5). ct , () volume of the new type k of each category of capital invest- is the purchasing price of composite commodity i (including IND ment . () () kj ,,t all taxes and margins). The impact of changes in the tourism The prices of new private and public capital are given by sector on the rest of the economy is captured through their equation (8). These prices are obtained from the investment effect on the aggregate prices (see equation (5)). demand functions, whose forms imply that the production () function of new capital follows a Cobb-Douglas form. A Dynamic Setup are scale parameters for each category of investment and The dynamic setting follows Decaluwé et al. (2010) and are positive parameters calibrated on the basis of the () takes into account accumulation and growth effects. It is investment elasticity and the investment equilibrium equa- INV established by means of lagged variables and by updating tion. σ is the elasticity of investment demand. The vol- (, kj ) exogenous variables and parameters that are either fixed or ume of new capital allocated to a sector is proportional to the absent in the base-year solution. In this study, the existing stock of capital. The proportion varies according to Njoya and Seetaram 519 where P is the simple headcount index, as it measures the the ratio of the rental rate to the user cost of that capital. The incidence of poverty as the proportion of total population investment demand follows a modified version of below the poverty line. In other words, the headcount ratio Bourgignon, Branson, and Melo (1989). Equation (10) gives the percentage of the population that is below the pov- defines the capital user cost U as a function of the () () ka ,,t erty line. price of new capital (the replacement cost of capital), the rate of depreciation δ , and the interest rate IR 1 zy − ()   () () t () ka , i For α= 1,, P = ∑  (Decaluwé et al. 2010). N z   i=1 where P is the poverty gap index. It reflects how far the Model Closure poor are from the poverty line. For any individual, the pov- Simulations are carried out under the following assumptions. erty gap is the distance between the poverty line and his/her The current account is fixed, reflecting the scarcity of for- income. Aggregating individual poverty gaps for all individ- eign credit in developing countries. Government expendi- uals gives the aggregate poverty gap. tures are assumed to be fixed in real terms in the first period. However, this increases as the same rate of population 2 1  zy −  growth. Tax policy instruments are free. It is assumed that And for α= 2,, P = 2   N z investment expenditures are endogenous and adjust to   i=1 changes in savings. Past investments influence economic where P is the poverty severity index. It gives an indication growth owing to the inclusion of dynamics into the model. It of the degree of inequality among the poor. Moreover, pov- is assumed that there is perfect mobility of labor and capital erty severity captures how difficult it is to get out of poverty. between rural and urban areas. This implies that the model Building on poverty gap measures, the poverty severity has three specific prices for payment for factors, namely, index gives more weight to the extreme poor by squaring the wages, return on agricultural capital (land), and return on distance to the poverty line. In other words, it measures other capital. The nominal exchange rate (i.e., the rest of the inequality between subpopulations of the poor. The micro world’s imports price index) is chosen to be the numéraire. model is solved using DAD 4.6, a Software for Poverty and Distributive Analysis. Poverty Analysis Simulation Design In order to assess the mechanism through which tourism growth may affect poverty in the country, the identified Tourism expansion is generally modeled as an increase in “macro effects” from the CGE growth scenario are fed back total tourism spending or a reduction or elimination of trade into a microsimulation model, based on household survey restrictions on the tourism industry or related industries. data. The construction of the micro household module relies This requires information on both the economic structure on data sets from the Kenya Integrated Household Budget and the size of tourism as well as the likely path for the Survey (KIHBS). The KIHBS is based on a representative future growth of the economy and the sectors within it. sample of 13,430 households. The impact of tourism growth International tourist arrivals and spending in Kenya grew on poverty is captured by changes in the FGT poverty indices on an average of 4.6% per annum between 2003 and 2013, (Foster, Greer, and Thorbecke 1984). FGT is one of the most totalling approximately 1.5 million arrivals in 2013 (WTTC important poverty measures, which is widely applied in 2015). With respect to future growth, it is forecasted that empirical work because of its simplicity. It is based on nor- domestic and foreign travel spending will rise by 4.7% on malized poverty gaps, that is, the term in the parentheses in average p.a. from 2013 to 2023 (WTTC 2015). In this con- equation (11). Poverty gaps are then raised to the power to text, the effects of a 5% annual growth of tourism spending capture how deep poverty is. The definition is as follows: on the Kenyan economy are simulated. With 2003 as the baseline, this corresponds to a yearly increase in tourism spending of Kenyan shilling (KES) 2,723 million (or 0.2% zy − 1   Py; z = () (11) α ∑  of GDP) (KES 100 = US$1.08). It should be noted that the N z   i=1 tourism sector accounted for 4.15% of total GDP in 2003, namely, KES 1,311 billion. where y is a vector of household incomes in increasing order, z is the poverty line (in income units), N is the total number of households, q is the number of poor households and is Simulation Results a parameter. The simulation results are reported in terms of macroeco- nomic and sectoral impacts and in terms of income, con- For α= 0,, P = sumption, and poverty impacts. N 520 Journal of Travel Research 57(4) which when combined with increasing domestic prices Macroeconomic Impacts (0.1%), rental returns (0.11%), and wage rates (0.18%) per A 5% increase in tourist spending generates an annual per- annum sees traditional export sectors, such as agriculture, centage change in GDP of 0.24% on average, aggregating to experiencing a decrease in international competitiveness. an overall percentage change in GDP of 4.87% from the first Consequently, export earnings decline. Agricultural exports (2003) to the last (2022) period. On the expenditure side, show an annual percentage decrease of –0.11%, while export total real investments increase by 0.52% per year, accumu- demand for manufacture and services shows annual percent- lating to an overall percentage change in aggregate invest- age increase of 0.09% and 0.21%, respectively. At the same ments of 10.44% for the whole period. Regarding the time, the higher value of the Kenyan dollar makes imports contribution of each investment aggregate to total invest- cheaper, increasing the demand for imports of agricultural, ment, it is found that private investments make the largest manufactured, and services products by 0.26%, 0.17%, and contribution to total investment (0.66% per year on average) 0.31% respectively. compared with public investments (0.09% per year on aver- The growth of the manufacturing and services sectors is age). Both domestic and international inbound tourism have followed by a 0.03% average annual increase in the demand a positive impact on the Kenyan economy and there seem to for labor. The results are consistent with other studies that be no marked differences between domestic and interna- have investigated the relationship between tourism and agri- tional inbound tourism with respect to the overall economic culture in developing economies (e.g., Bowen, Cox, and Fox impact. 1991; Sahli and Nowak 2007; Wattanakuljarus and Coxhead For non-tourism exporters and some manufactured goods, 2008). Sahli and Nowak (2007) argue that in developing the simulated percentage changes are negative. The changes economies where the tourism sector is relatively more labor to total export are lower in the first period (0.09% on over- intensive than the agricultural sector, the net benefit from age) and positive in the last period (0.11% on average). Total inbound tourism growth on national welfare will be positive imports, on the other hand, increase on average by 0.24% as is the case for Kenya. annually, leading to a trade deficit. Over the whole period, The patterns of demand for the different types of labor are total imports accumulate up to 49.06%, outweighing the identical to patterns of output growth. Demand for all types increase in total exports (19.94%). Government income of labor increases in industries closely associated to the tour- increases per annum by 0.21% in the first period and 0.22% ism industry as well as industries in the supply chain and in the last period, accumulating to 4.36% over the whole decreases in non-tourism sectors. However, demand for time period. There is an increase in savings of all household unskilled labor increases faster than demand for skilled and groups, which increase on average by 0.18% annually. semi-skilled labor, both in the first period and last period. Enterprise savings also increase (0.24%), while government Hotel has the largest impact (0.92%), followed by transport savings decline on average by 0.92% per year. (0.7%), construction (0.59%), and retail trade (0.29%). This suggests that service exports are relatively (unskilled) labor intensive and that households endowed with these factors, Sectorial Impacts that is, low-income households in service industries will be An increase in tourism demand is associated with the shifting the main beneficiaries. Semi-skilled labor experiences the of scarce resources from non-tourism sectors, such as agri- lowest growth in the service industries and the highest culture, towards tourism-related sectors, construction, trans- decline within non-service in all periods. port, mechanical repair work, crafts, entertainment and shopping. Clearly tourism, especially rural tourism impacts Impact on Income and Consumption on agriculture in many ways. Both industries compete for The simulation results indicate that the nominal income of all resources, including land, labor and capital. For instance, the household categories rises. A comparison of households by designation of parks and recreation areas for the visitors deciles and region reveal that the changes are uneven. The reduces the economic opportunity of the farmers. Fishermen poor households in urban areas receive a 0.25% rise in their have to compete for shore space with tourism development. nominal income in the first period as compared to only Outputs of the agricultural activities fall by 0.01% on aver- 0.19% for the poor households in rural areas. In the rural age annually, manufacturing increases by 1.65% and the ser- area, middle- and upper-income households gain more than vices sectors increase by 12.11% over the whole period. In low-income ones. In the urban area, middle- and upper- terms of annual percentage change in gross value added, the income households as well as households at the lowest decile largest positive impact is in transport (0.4% on average), fol- gain the most. As a result, low-income agricultural house- lowed by construction (0.38%) and trade (0.11%). holds experience the least changes, while low-income non- The results confirm those of Wattanakuljarus and Coxhead agricultural households (i.e., those who derive their income (2008). The expansion of the tourism industry introduces the from services industries) and high-income households gain Dutch Disease phenomenon into the Kenyan economy the most. These results are in line with the results by Blake through an annual appreciation of the exchange rate (0.34%), Njoya and Seetaram 521 Table 1. Poverty Results—Kenya: All Households. Poverty Count Poverty Gap Poverty Severity P P P 0 1 2 (%) (%) (%) Base year 45 15.2 7.7 Year 1 −0.092 −0.17 −0.15 Year 5 −0.103 −0.19 −0.17 Year 9 −0.102 −0.2 −0.19 Year 13 −0.09 −0.18 −0.19 Year 17 −0.08 −0.24 −0.18 Year 20 −0.07 −0.26 −0.21 Poverty indices, Year 20 43.16 12.22 4.93 Change in poverty indices −1.84 −2.98 −2.77 Source: Authors’ simulations results. Note: Italicized values represent the key results from the study. et al. (2008), Kweka (2004) and Wattanakuljarus and poverty in Kenya but also reduce the poverty gap and Coxhead (2008), and show that, in general, tourism expan- improve the income distribution among the poor. In other sion benefits all household groups, but the poorest (rural) words, a larger number of households will be able to move household group gains less than other household groups. closer to the poverty line. Increased income allows rural consumers to enjoy an annual increase in aggregate real consumption of 0.11%. Impact on Poverty Urban households, on the other hand, register an annual The poverty effects are assessed against the base year (2005). increase in aggregate real consumption of 0.14%, reflecting Table 1 presents a summary of the poverty incidence using the higher increase in their annual income. The poverty the standard Foster-Greer-Thorbecke FGT poverty indica- effects are assessed against the base year (2005) using the tors, that is, headcount (P ), income gap (P ), and severity poverty line of KES 1,562 per month per person for rural and 0 1 (P ). In 2005, 45% of the population of Kenya lived below KES 2,913 per month for urban areas (in adult-equivalent the poverty line, and the poverty gap shows that the average terms, which at the time was approximately US$0.75 and shortfall of income of the population from the poverty line is US$1.40 a day per person) and include minimum provisions 15.2%. Poverty severity is 7.7 in Kenya in 2005. The simula- for both food and nonfood expenditures (GOK 2007). The tion effect indicates that tourism development reduces the results are reported in Table 2. poverty count by 1.83% to 43.16 in year 20. This means a In the base year, all three indices indicate that poverty is steady 5% increase in arrivals in Kenya will enable 1.83% of more prevalent and severe in the rural areas of Kenya. In the population to cross the poverty line. A priori, the results 2005, 70% of the rural population of Kenya lived below the indicate the poverty reduction effect of the growth of the poverty line as opposed to 34.5% in the urban areas. The tourism industry is very small. results clearly demonstrate that the urban population of However, during the same period of time, the poverty Kenya will benefit more from the development of the tour- gap is reduced by approximately 3%, meaning that not only ism industry. The poor households in urban areas are more 1.83% of the population will no longer be poor but that favored than the poor households in rural areas. Moreover, among the poor, the average income needed to close the low-income agricultural households experience the least poverty gap will have fallen as well. Furthermore, poverty improvement, while low-income nonagricultural households severity will have fallen, implying that the poorest house- and high-income households gain the most. It can be pre- holds of the country will experience an improvement in dicted that 1.56% of the rural population and 1.89% of the their welfare. The effect on poverty count is highest in urban population will move out of poverty by year 20. The years 3 to 11. These results validate the use of a dynamic effect on poverty gap and severity is significantly higher. The model and show that application of statics models as is poverty gap is reduced by 5.8% and 5.15% for rural and common in the literature will underestimate the poverty urban households, respectively. Poverty severity falls faster reduction effect of tourism expansion as the higher effect in the urban area by 4.43 as opposed to 3.72 in the rural occurs with a lag. This is understandable, as growth of the areas. The urban poor benefit more than that poor household manufacturing and services sector leads to an increase in in the rural areas. demand and create job opportunities for the poor but the These effects indicate that the tourism industry is inclu- effect occurs mostly in the postsimulation years. The results sive and benefit the poorest household of Kenya. It has the indicate that the tourism industry will not only reduce potential of significantly reducing poverty severity in the 522 Journal of Travel Research 57(4) Table 2. Poverty Results—Rural and Urban Households. Rural Urban P P P P P P 0 1 2 0 1 2 (%) (%) (%) (%) (%) (%) Base year 69.9 17.6 8.9 34.5 11.6 5.4 Year 1 −0.06 −0.41 −0.25 −0.15 −0.42 −0.25 Year 5 −0.07 −0.39 −0.3 −0.17 −0.38 −0.21 Year 9 −0.08 −0.34 −0.35 −0.16 −0.32 −0.24 Year 13 −0.08 −0.37 −0.28 −0.15 −0.29 −0.25 Year 17 −0.09 −0.38 −0.21 −0.14 −0.27 −0.2 Year 20 −0.09 −0.34 −0.21 −0.13 −0.25 −0.2 Poverty indices, year 20 68.34 11.76 5.17 32.61 5.85 0.97 Change in poverty indices −1.56 −5.83 −3.72 −1.89 −5.15 −4.43 Source: Authors’ simulations results. Note: Italicized values represent the key results from the study. urban area and drastically reduces the income gap of the Tourism expansion and the resulting economic growth poorer households in the rural and the urban areas. However, principally trickle down to both the urban and rural poor, while the condition of the poorer households are improved, through increases in income and in labor demand. This leads only a very small proportion of the households will cross the to a fall in poverty headcount and an even greater fall in pov- line, implying that tourism will not have a very significant erty gap and severity. Tourism in Kenya has the potential to role in improving the incidence of poverty. Furthermore, the reduce poverty at the national and at urban and rural levels. gap between poverty in rural and urban households is not However, poverty falls faster in the urban area, and the effect significantly narrowed. The gap in the headcount ratio and in the rural area is dampened by a fall in labor demand and the poverty severity ratio between rural and urban house- earnings for the poor working in the agricultural sector. holds increases marginally and the poverty gap for rural The results of the present article have important implica- improves slightly compared to that of the urban area. It indi- tions for policies. First, it demonstrates that the use of only cates that tourism development will benefit the urban house- one poverty index as is common in the literature does not hold disproportionately. This can result from the fact that provide a complete picture. For example, based on poverty investments in hotels and transport will grow the fastest and count index only, it can be concluded that the tourism indus- as these investments are more likely to take place in the try is only marginally beneficial for the Kenyan poor. larger towns and cities of Kenya, this is where opportunites However, taking into account the poverty gap and poverty for employment and consumption will be created. On the severity indices can change that conclusion. It is clear from other hand as the agricultural sectors contracts in the rural the results that policies aiming at attracting more tourists or areas, the positive effect of the expansion of the tourism boosting the discretionary spending of tourists alone will industry may be dampened. have relatively minor impacts on rural poverty although it will enable a proportion of households to move closer to the poverty line and reduce poverty severity. Recommendations and Conclusion One of the main policy implications that emerge from This article investigates the impact of increase in inbound these findings is that tourism development strategies need to tourism on Kenyan households using a dynamic GCE model. give due consideration to agricultural production. Results The aim is to find out the extent to which the tourism indus- indicate that there is a significant pull of labor from agricul- try affects the poorer households of the country. ture to sectors with higher linkages to the tourism industry. Results indicate that as a nation Kenya will benefit from Fostering collaborations and reducing competition between the higher growth in its GDP and export earnings. However, the two sectors has the potential of benefiting both. Tourism the higher growth will come at a cost. As a result of Dutch can stimulate the development of new agriculture-based ser- Disease, other exportable sectors will find their competitive- vices, such as tours of agricultural production and processing ness eroded and appreciation of the currency will make facilities. Strengthening linkages between the agricultural import cheaper, leading to a worsening of the balance of and the tourism sector may dissipate the negative impact on trade of the country. Resources move from traditional sectors the former. New synergies between these two competing sec- to tourism industries and its supply chain. These industries tors can take the form of favoring locally sourcing food expand creating employment opportunities and offering needed from the increased demand in the tourism sectors higher wages. The agricultural sector declines whereas non- over import, as suggested by Belisle (1983). This will create tourism exporters will experience a weak to negative growth. opportunities in the agricultural sector through the expansion Njoya and Seetaram 523 of its market while reducing the leakage rate from the tour- Funding ism sector. The author(s) received no financial support for the research, author- Although it can be expected that different types of tourism ship, and/or publication of this article. generate different distributional impacts on poor people, the combination of size and linkage strength is likely to amplify Note the beneficial effects of any type of tourism. Exploiting the 1. Labor migration between rural and urban owing to the structure linkages between tourism and the local economy toward of data is not explicitly modeled. Thus, the data does not pro- poverty reduction requires a diversified growth strategy that vide information about the spatial location of different types of expands tourism while improving the competitiveness of labor. 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Kweka, J. 2004. “Tourism and the Economy of Tanzania: A CGE Analysis.” Paper prepared for the CSAE Conference on Author Biographies Growth, Poverty and Human Development in Africa, 21–22 Eric Tchouamou Njoya, PhD, is a lecturer in Air Transport March, Oxford, UK. Economics and Management at the Department of Logistics, Loayza, N., and C. Raddatz. 2006. “The Composition of Growth Operations, Hospitality and Marketing, University of Huddersfield. Matters for Poverty Alleviation.” Mimeo, World Bank, He received his PhD from the Karlsruhe Institute of Technology, Washington, DC. Germany.His current research focuses on the economic impact of Mahadevan, R., H. Amir, and A. Nugroho. 2016. “Regional Impacts tourism, the tourism benefits of air transport liberalisation and the of Tourism-Led Growth on Poverty and Income: Inequality: role of air cargo in achieving regional economic growth. A Dynamic General Equilibrium Analysis for Indonesia.” Tourism Economic 1–18. Neelu Seetaram, PhD, is the head of research and professional McCulloch, N., L. A. Winters, and X. Cirera. 2001. Trade practice at the Department of Tourism and Hospitality, Bournemouth Liberalization and Poverty: A Handbook. London: Centre University. She is an elected council member of the International Econ. Policy Research. Association for Tourism Economics. She is an economist, who Mitchell, J., and C. Ashley. 2010. Tourism and Poverty Reduction: regularly publishes in the area of tourism economics and has co- Pathways to Prosperity. London: Earthscan. edited two books.

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