The economic impacts of tourism in Botswana, Namibia and South Africa: Is poverty subsiding?

The economic impacts of tourism in Botswana, Namibia andSouth Africa: Is poverty subsiding?

Edwin Muchapondwa and Jesper Stage

Abstract

Tourism in southern Africa is based on the region’s wildlife and nature assets and is generally environmentally sustainable,but the extent to which it contributes to other aspects of sustainable development — overall income generation or povertyeradication — is less well explored. In this paper, we use social accounting matrices to compare the economic impacts offoreign tourism in Botswana, Namibia and South Africa. Overall impacts on GDP range from 6% (South Africa) to 9%(Namibia). However, South Africa’s economy is more diversified than its neighbours’ and more of the goods and services usedby tourists and by the tourism industry are supplied domestically. Consequently, the impact per Rand spent is considerablylarger for South Africa than for Botswana or Namibia. The poorer segments of the population appear to receive shares oftourism income that are smaller than their share of overall income in all three countries.

Keywords: Tourism; multiplier effects; Botswana; Namibia; South Africa; poverty reduction.

1. Introduction1

Over the past six decades, tourism has become one of thelargest and fastest growing economic sectors in the world.The number of international tourist arrivals has continuedto grow. The 25 million recorded in 1950 jumped to 277million in 1980, 438 million in 1990, 684 million in 2000,and an estimated 990 million in 2011. International touristarrivals are forecasted to reach nearly 1.6 billion worldwideby 2020. More importantly, the share of international touristarrivals received by developing countries has grown from31% in 1990 to 47% in 2011.

International tourist arrivals in Africa grew by an averageof 12% annually from 2000 to 2011, totalling an estimated50 million. The African share in the world tourism marketin 2011 was only 5%, and Africa’s international touristarrivals are currently concentrated in relatively fewdestinations. Only Algeria, Botswana, Kenya, Morocco,Mozambique, Nigeria, South Africa, Tunisia and Zimbabwehave received over 1 million arrivals per year in the last few

years, together attracting at least 66% of 2009 internationalarrivals to Africa.2 It is forecast that Africa’s tourist arrivalswill reach 77 million by 2020. International tourismreceipts by Africa amounted to US$ 32.6 billion, whichaccounted for 3.2% of total world tourism earnings in 2011.

These statistics indicate the ever-growing significance ofthe tourism sector in most developing economies. Tourismhas become a key driver of socioeconomic progress throughthe export income earned, infrastructure development, andthe creation of jobs and enterprises (Saarinen et al., 2009).For many developing countries, tourism is one of the mainsources of income and the leading export category. Roeet al. (2004) find it to be the principal foreign exchangeearner for about 83% of developing countries. Tourism canalso contribute to the development of other economicsectors such as transport and construction. In addition,the tourism sector presents a comparative advantage todeveloping countries as it is built around natural resourcesand has labour-intensive characteristics (Spenceley andMeyer, 2012).

The prospect of tourism growth in developing countrieshas created enormous attention in tourism as a toolfor poverty alleviation. In fact, a number ofinstitutional stakeholders, like the United Nations World

Edwin Muchapondwa is at the School of Economics, University of CapeTown, South Africa. E-mail: [email protected] Stage is at the Department of Social Sciences, Mid SwedenUniversity, Sweden. E-mail: [email protected] The statistics and forecasts cited in this section are drawn from UNWTO(2009; 2010a; 2010b; 2011a; 2011b; 2012a; 2012b) unless otherwisestated.

2 Note that Egypt also receives well over a million arrivals per year but itis classified by the UNWTO under Middle East.

Natural Resources Forum 37 (2013) 80–89

© 2013 The Authors. Natural Resources Forum © 2013 United Nations

Tourism Organization, Governments and developmentorganizations, regard tourism as a major driver of povertyalleviation (Hall, 2007; Spenceley and Meyer, 2012). Onthe flipside, Tosun (2001) argues that tourism should reducepoverty in local destinations, inter alia, for it to besustainable. In fact, there is a huge amount of literatureadvocating for pro-poor tourism (e.g., Ashley et al., 2001;Cattarinich, 2001; Mahony and Van Zyl, 2001; Nicanor,2001; Ashley, 2002; 2006; Spenceley and Seif, 2003;Saarinen et al., 2009).3 Ultimately, the desirability oftourism as a sustainable development mechanism will bejudged based on the extent to which it helps the poorparticipate in the national economy and pulls them out ofpoverty. Whether it actually has this impact at present is anopen question. Spenceley and Meyer (2012) outline some ofthe theoretical discussions and findings from practitionersthat have emerged on tourism and poverty reduction duringthe past two decades. For example, works by Goodwin(2006), Dixey (2008) and Spenceley (2008) paint apessimistic picture of tourism as a potential tool for povertyreduction. Furthermore, Blake (2008) finds that althoughlinkages from tourism to the domestic economy arerelatively important in three studied East African countries(Kenya, Tanzania and Uganda), the share of income fromtourism accruing to the poor is in fact smaller than thepoor’s overall share of national income.

If the desirability of tourism will ultimately be judged onthe extent to which it helps the poor participate in thenational economy and pull them out of poverty, it isnecessary to know the economic significance of tourism tothe national economy and craft the tourism policy in such away as to promote tourism’s contribution to the nationaleconomy. Visitor expenditure on accommodation, foodand drink, local transport, entertainment and shoppingis an important variable which could be influenced to createthe much-needed employment and opportunities fordevelopment in Africa. In the absence of concrete policymeasures, the income generated by this expenditure maywell contribute mainly to increased incomes for the affluent,or for foreign tourism operators, rather than for the poorersegments of the population.

Wildlife and nature-based tourism has been identifiedas a possible key sector for the achievement of sharedeconomic growth and poverty alleviation in Africa(Mitchell and Ashley, 2006; World Bank, 2006). This paperseeks to make a contribution to research on tourism inAfrica with particular emphasis on three southern Africancountries, namely Botswana, Namibia and South Africa.These three neighbours have tourism sectors dominated bywildlife and nature tourism. They also have reasonably gooddata sets on tourism, and in all three countries high hopesare attached to tourism as a driver for poverty alleviation.

The paper investigates the contribution of tourism to thispoverty alleviation, and on the overall national economiesof these three countries, by studying the direct and indirecteffects of tourism on income generation and incomedistribution using multiplier analysis. In addition tocomparing the effects per tourist on each economy, thepaper discusses the implications of these effects for tourismpolicy in the different countries.

2. Background on the three southern Africancountries under study

2.1. Botswana

Botswana is a semi-arid country with a total land area of582,000 km2 and a population density of 2.7 per km2

(Atlhopheng and Mulale, 2009; Moswete et al., 2009).Temperatures are very high and rainfall is low and erratic.Only 5% of the total land surface area is arable. Thepopulation, which is characterized as being low-density, isestimated at 1.85 million, with a rate of growth of 3.5% perannum. The majority of the inhabitants live in rural areas.Some 80% are concentrated in the fertile eastern region ofthe country, where they subsist on pastoralism and cropagriculture (Central Statistics Office, 2008; Moswete et al.,2008). In 2003, the overall incidence of poverty inBotswana was 30%, while rural poverty for the same periodwas estimated at 45% (Moepeng, 2007).

Botswana is endowed with bountiful and diverse wildliferesources with the potential to contribute to the growth anddiversification of the economy. The wildlife resourcesoccupy 37% of the total land area, of which 17% is nationalparks and game reserves and 20% is wildlife managementareas (Atlhopheng and Mulale, 2009). Most populations ofwildlife species were recorded as stable during the period1989-2007, which reflects efforts to conserve this naturalresource (Central Statistics Office, 2008). Botswana’sNational Development Plan 9 recognizes wildlife as one ofthe main valuable natural resources together with mineralsand rangeland, and as the principal tourist attraction(Atlhopheng and Mulale, 2009).

Tourism is based on wildlife and wilderness resourcesthat are strictly controlled (Moswete, et al., 2009). Thetourism attractions in Botswana are mainly to the north,around the Okavango Delta (Atlhopheng and Mulale, 2009;Mbaiwa and Darkoh, 2009; Moswete et al., 2009). Someof the popular protected areas that have experiencedsubstantial increases in visitations are the KgalagadiTransfrontier Park, Central Kgalagadi Game Reserve,Chobe National Park, Moremi Game Reserve andMakgadikgadi National Park (Moswete et al., 2009). Thegovernment is committed to nature-based tourism and hasinvested in wildlife conservation and associated tourisminitiatives. The country’s policy emphasizes low-impactand high-value tourism. Rather than promoting enclave

3 Pro-poor tourism is broadly defined as tourism that generates net benefitsfor the poor (Ashley et al., 2001).

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tourism,4 the policy attempts to encourage and supportindustry that benefits local communities (Moswete et al.,2008; WTTC, 2007). Thus, income impacts and povertyreduction impacts are stated targets of tourism policy.However, Botswana is heavily dependent on intraregionaltourists and largely remains a secret to their long-haulcounterparts; long-distance international tourists tend tospend more money, but also frequently pursue more“enclave”-style tourism of the type that Botswana wishes toavoid. Overall impacts on income generation and incomedistribution therefore may be different from those of themore long-distance tourism seen in the other two countries.

2.2. Namibia

Namibia is situated in south-western Africa. It is a large andmainly arid country that borders not only the AtlanticOcean, but also Angola, Botswana and South Africa. Thecountry has a land surface area of about 824,000 km2. Thelandscape is mainly dominated by the shifting sand dunes ofthe Namib Desert. In the interior, the escarpment of a north-south plateau slopes away to the east and north into the vastinterior sand basin of the Kalahari. Other than a few malariaareas, there are relatively few health concerns needingtravel prerequisites that might discourage tourists. In 2005,the country’s population was estimated at 2 million.Mining, fishing, tourism and agriculture are the pillars ofthe Namibian economy (Karemaker and Whitehead, 2007).In 2003/2004, 38% of the population was below the povertyline, while the same statistic was 49% for the ruralpopulation (Schmidt, 2009).

Tourism in Namibia has a history of being developedaround state-owned resorts in protected areas. The producthas predominantly been of a self-catering nature, mainly fornational and regional travellers. Today, tourism is becomingan increasingly vital component of livelihood strategies forfarmers on both communal and private land. Tourism atso-called guest farms has been a popular activity since the1960s, and starting in the late 1990s residents of communalareas were able to organize themselves into communalconservancies to reap benefits from wildlife tourism(Republic of Namibia, 2001; Samuelsson and Stage, 2007).

Namibian tourism policy aims to guarantee that tourismserves as a vehicle for securing definite social gains for thepopulation, particularly the poorer segments of thepopulation, while simultaneously avoiding and minimizingas far as possible the negative aspects of tourismdevelopment activities (Republic of Namibia, 2001). It aimsto, inter alia, ensure that all sections of the Namibian

community benefit from tourism, and encourage thedevelopment of those cultural forms and expressions whichare distinctly Namibian in origin and their development intonew attractions. In Namibia, too, there is a goal of ensuringthat the income benefits from tourism are widely diffusedthroughout the population and that tourism shouldcontribute to overall development.

2.3. South Africa

South Africa has scenery that spans from mountain rangesto vast grass plains, from coastline to meandering rivers anddesert dunes. The country’s diverse climates range fromtropical in the south-east to desert in the central region.Being at the southern tip of a large continent, South Africaoffers 3,000 km of coastline. The country’s diverse terrainand wide range of possible recreational activities hasfostered a diverse tourism industry catering to an array ofdifferent tourism niches supported by numerous specializedoperators.

The country’s wildlife is far more varied than thecelebrated “Big Five”, and is supported by an extraordinarybiological diversity. The country’s national parks —including the world-famous Kruger National Park — havebeen enlarged, and the Government is committed toincreasing the total terrestrial and marine protected areasfurther. The country’s private game lodges have also grownsubstantially in number and scope since 2000, withstandards from middle to very upmarket, including ultra-luxury lodges catering almost exclusively to foreigntourists. Community-managed nature and wildlife tourismis less well established than in the other two countries.

Tourism has the potential to act as a catalyst for othersectors of the economy. These include the agriculture sector,which benefits from increased demand for new agriculturalproducts and services such as organic agriculture and farmtourism; the manufacturing sector, which benefits from thesupply of furniture and fittings, construction, linens, pots,pans, etc.; and the fine art and crafts sector, which producesitems such as woodworking and curios.

The tourism sector has been identified as vital to ensuringthat the country achieves the goals set out in the Acceleratedand Shared Growth Initiative for South Africa, which aimsto halve unemployment and poverty in the country by 2014(Republic of South Africa, 2007). At present, however, poorinvolvement of local communities and previously neglectedgroups in the industry is seen as a major problem facing theindustry. This would have to change in order for SouthAfrica to deal effectively with national poverty, which stoodat 54%, and rural poverty, which stood at 77% in 2010(Leibbrandt et al., 2010).

3. Data: The social accounting matrices

In order to assess the impacts of tourism (or any othereconomic activity) on income levels or income distribution,

4 Enclave tourism is concentrated in remote areas in which the types offacilities and their physical location fail to take into consideration theneeds and wishes of surrounding communities. Any foreign currencycreated may have only a minimal effect upon the economy of the hostlocation. Facilities are characterized by foreign ownership and aredesigned to meet the needs and interests of foreign tourists(Ceballos-Lascurain, 1996; Mbaiwa and Darkoh, 2009).

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information on current income levels and distribution isessential. For multiplier analysis, the standard practice is touse social accounting matrices (SAMs), which are detaileddatabases on the flows of expenditure and income within aneconomy. For all three countries, relatively recent SAMs areavailable; however, all three countries also have importantlimitations in the data available, as discussed below.

Botswana’s 1996-1997 SAM has 46 activities and 54commodities. The main difference between the sectoralclassifications used for these two groupings is the treatmentof agriculture. On the activity side, Agriculture issubdivided not only into livestock and crop farming, butalso by land ownership: traditional communal agricultureremains an important activity and is measured separatelyfrom agriculture carried out on freehold land. On thecommodity side, the multi-output character of manyagricultural enterprises means that there are more types ofagricultural commodities than there are agricultural sectors.On the factor side, Botswana separates labour income notonly by type of labour but also by citizenship, reflecting thelarge importance of foreign expertise for the economy.Communal agriculture, for which it is difficult to separatelabour and capital income, has its own mixed incomecategory. Household categories are subdivided not byincome level but by location (rural or urban) and main typeof income (those who are principally wage-earners, thoseusually self-employed, and those largely subsisting ontransfers). This means that it is not possible to assessdirectly whether and to what extent tourism-related incomeaccrues to the poorer segments of the population, as theseare not classified separately in the SAM; however, it ispossible to assess impacts on population groups classifiedby income source, and income distribution tends to followthese income sources fairly closely.

Botswana has compiled input-output tables and SAMs ona regular basis for quite some time. However, the countryhas yet to switch fully to the 1993 System of NationalAccounts (SNA) in its national accounting. In practice, thishas meant that, although SAMs were compiled regularlywhile the old SNA was still in place, efforts after the 1993standard was introduced have focused on updating theoverall national accounts system. Thus, the most recentSAM remains the one developed for 1996-1997 (CentralStatistics Office, 2002), with work on the next SAM put onhold for the time being.

Namibia does not compile official input-output tables orSAMs. The first input-output table, for 1980 (Hartmann,1986), was compiled as a private research project whileNamibia was still controlled by South Africa. Subsequently,three SAMs have been constructed, namely for 1998, 2002and 2004 (Conningarth Economists, 2001; Lange et al.,2004; Lange and Schade, 2008). The 1998 SAM was a pilotexercise which relied largely on estimates based onexperiences from other countries, while the two later SAMshave relied increasingly on domestic data sources and,although donor-funded and constructed by an independent

research institute rather than by government agencies, werenonetheless the result of extensive cooperation withgovernment institutions with regard to data collection.However, even the latest SAM continues to rely on theSouth African supply-and-use tables for some estimates.

The latest and most reliable Namibian SAM, the one for2004 (Lange and Schade, 2008), has 32 commodities and30 activities. The sectoral classification is largely similar forthe two. Notably, however, one of the activities is a genericForeign tourism dummy industry which exports goods andservices to foreign visitors. This reflects the fact that,although the researchers compiling the SAM had data onoverall foreign visitor expenditure in the country, they didnot have access to detailed information on the distributionof spending. This is discussed further in the next section.Factor incomes are subdivided into five different categories:Skilled and Unskilled labour, Capital income, and twoMixed income categories for Freehold and Communalagriculture, respectively. This can be contrasted to theBotswana SAM, which allocates income in the freeholdfarming sector to the capital income category. Thehousehold classification is the same as that in the BotswanaSAM. As for Botswana, this means that the directestimation of impacts on income distribution is impossible,and that only indirect assessments can be made.

Of the three countries, South Africa has the longestcontinuous tradition of compiling input-output tables andSAMs. The first official South African input-output tablewas for the years 1956-1957, after which such tables werecompiled every few years (Bouwer, 1999). Supply-and-usetables are now published on an annual basis.

Several SAMs have been compiled as well, but on a lessregular basis: the first SAM was compiled for the year 1978,and SAMs subsequently have been compiled by StatisticsSouth Africa for the years 1988 (based on the 1968 SNA),1998, 2002 and 2005 (Statistics South Africa, 2009a). Theexercise of compiling SAMs has successively expanded toinclude additional data sources. The latest SAM, namelythat for the year 2005, is closely linked to the supply-and-use tables and integrated economic accounts published forthe same year. Apart from this, data sources include the2005 Household Income and Expenditure Survey, the 2005Labour Force Survey, the 2007 Community Survey, andvarious other published and unpublished data fromStatistics South Africa and the South African Reserve Bank.Although we use a variant of this SAM, rather than theofficial version, for reasons discussed below, the twoversions are similar in their classifications and we thereforedescribe the official SAM first.

The 2005 SAM has 27 industrial sectors. Of these, theservice sectors (including most of the tourism-relatedactivities, such as transportation services and hotels andrestaurants, but also including domestic spending on thesesectors, such as financial services, real estate services, anddomestic personal services) account for over 70% of overalllabour income in the economy. Households are classified



both by income level (mostly deciles, although the lowestand highest income groups are disaggregated further intothe lowest 5% and the next 5%, and the highest 5% andsecond highest 5%, respectively) and by self-classifiedethnic group (Black, Coloured, Indian/Asian, and White,following the old racial classification used in the apartheidsystem), for a total of 48 household groups. The lowestincome percentiles are negligible for all racial categoriesexcept the Black category. Labour income is disaggregatedinto 11 different categories by type of employment, andfurther into the same four racial categories used forhouseholds.

In principle, therefore, it should be possible to study theincome effects of tourism in some detail. Unfortunately forour purposes, Statistics South Africa has chosen toaggregate sectoral incomes in a fashion that makes adetailed analysis of the particulars of income distribution(and estimates of the second-round impacts of householdspending induced by changes in income) difficult. Alllabour income is assumed to accrue to a genericHouseholds and non-profit institutions serving householdscategory, which then redistributes this income to differenthousehold groups. There is, thus, no way of using the SAMto capture the direct link from changes in labour income ina specific sector to changes in the incomes of specifichousehold groups; all that can be done is to estimate theoverall changes in household income, and then assume thatthis income is subsequently redistributed among differenthousehold categories in the same proportions as the overalldistribution of household income in the economy. Thismeans that the official SAM cannot be used for multiplieranalysis aimed at studying whether the tourism sector, forexample, is more “pro-poor” than others.

Instead of using the official SAM published by StatisticsSouth Africa, therefore, we use another recent SAM for theSouth African economy (Thurlow, 2009). This SAM islargely based on the same data, but has been developedspecifically to permit research into issues related to incomedistribution. The latter SAM originates in the work that theInternational Food Policy Research Institute (IFPRI) hasbeen carrying out in recent years to develop standardizedSAMs for numerous countries in sub-Saharan Africa. TheIFPRI originally developed a SAM for the South Africaneconomy (Thurlow and Van Seventer, 2002) for 1998.Thurlow (2009) represents an update of this work, andprovides a SAM for 2000 as well. Thus, even though thisSAM provides data for an earlier year than the latest officialone, we chose to use it because of its more useful format.The IFPRI SAM for South Africa has a wealth of provincialdata that are not used in the official SAM. It has 39commodities, but 351 activities (each of the nine Provinceshas its own activity account for each commodity produced).Similarly, households are disaggregated not only by ethnicgroup, income quintile and rural vs. urban location, but alsoby Province. There are four skill levels, disaggregated byethnic group.

4. Data: The tourism satellite accounts

Tourism satellite accounting is a recent development for allthree countries, despite a pilot study (Poonyth et al., 2002)which concluded that tourism satellite accounts (TSAs)would be beneficial for tourism planning in all threecountries. It was several years before this translated intoconcerted efforts to compile such accounts, and TSAs arestill only at the draft stage for all three countries.

The levels of detail and reliability in the TSAs for thethree countries are reminiscent of those for the SAMs, andthe limited information-sharing between differentgovernment agencies seen in many countries in the regionhas meant that the work on compiling TSAs has had toinclude efforts to reconcile data from different governmentsources that are inconsistent with each other. This is linkedto the general capacity constraints facing compilation ofeconomic statistics in these three countries, as is the case inmany other developing countries. It does mean, however,that even though all three countries describe tourismdevelopment as important components of their nationaldevelopment strategies, none of them have economicstatistics on tourism that permit easy assessment oftourism’s actual economic impacts.

Botswana compiled TSAs for 2005/2006 as a joint effortbetween the United Nations World Tourism Organization(UNWTO) and the Botswana Government (Millingtonet al., 2007). The effort was led by a UNWTO team. Datafrom the Central Statistics Office (national accounts data,household income and expenditure data, and data frommore specialized surveys) were supplemented with data ontourist arrivals, visitor expenditure figures, and targetedestablishment surveys by the Department of Tourism. Thereport identifies a number of remaining data issues. Notableamong these is that domestic tourism is completelyexcluded from the analysis because of the lack of reliabledata. The report also points out the benefits of combiningthe data collection for TSAs with the simultaneous efforts toset up a new data collection framework for the SAM. Thepreliminary TSAs presented in the 2007 report provideexpenditure breakdowns subdivided by type of foreignvisitor, namely Day visitors and Overnight visitors. Dayvisitors are mostly not tourists per se, but rather, businesstravellers from South Africa or, alternatively, traders fromZimbabwe who come to Botswana in order to purchasegoods which they then transport across the border for resale.Overnight visitors are assumed to be mostly tourists. Thecategories for commodities and services used to classifyvisitor expenditure are selected based on their importancefor tourist spending and do not follow those in the SAM ornational accounts.

The regional pilot study (Poonyth et al., 2002) mentionedabove included an extensive attempt to develop preliminaryTSAs specifically for Namibia (Suich, 2002). This activitywas carried out within the country’s Ministry ofEnvironment and Tourism and, as with Botswana, based on



data both from the government’s tourism agencies and fromits Central Bureau of Statistics. The report from this projectincluded detailed estimates of tourist spending in 1991,1992, and 1996, following the categories in the country’snational accounts. However, the subsequent work ontourism accounts has not built on this effort; instead, theNamibia Tourism Board collaborated with the World Traveland Tourism Council (WTTC) to develop a new series ofaccounts, based on WTTC methodology and not explicitlylinked to the earlier accounting work. Thus far, accountshave been compiled for the years 2001 through 2009, withfinalized figures published for the years up to 2007 (WTTC,2006; 2009). These reports only present estimates of overalltourist spending and do not provide sectoral expenditurebreakdowns. Moreover, the reports do not provide anyinformation on how the estimates have been compiled,leading people in the country’s tourism industry —government officials and private sector operators — to viewthem with some suspicion (pers. comm.). However, thesedata are, at present, the only source of recent estimates ontourist spending in Namibia.

South Africa has compiled detailed — albeit still onlydraft — TSAs for 2005, the same year as that of their latestSAM (Statistics South Africa, 2009b). These accounts relyon data from several different sources. The supply-and-usetables form an important source of data for these accounts,while other sources consist of targeted surveys of tourismestablishments, a visitor survey which is conducted on anongoing basis, and (for domestic tourism) the householdincome and expenditure survey. The draft report fromStatistics South Africa provides estimated touristexpenditure, disaggregated both by foreign and domestictourists as well as by type of commodity or servicepurchased. Classification of the characteristic Touristproducts purchased is more detailed than the correspondingcategories in the SAM; accommodation and restaurants aregiven separate categories, rather than being collapsed intoone category as in the SAM; and different types oftransportation are classified in considerable detail, againin contrast with what occurs in the SAM. However,expenditure data for other products, which constitute thebulk of tourist expenditure, follow a sectoral classificationthat is slightly less specific than that used in the SAM.Frequently, two or three SAM categories, such as the twoFood and beverage categories, are aggregated into a singleexpenditure category in the TSAs.

5. Multiplier effects of tourism

In order to assess the impact of tourist spending on theeconomies of the three countries, we use standard multiplieranalysis. We use overall spending by foreign tourists and thedistribution of this spending, as measured in the TSA for thethree countries, to estimate the overall impact of tourism.We also calculate the average spending per foreign tourist

and use this to estimate the economic impact per tourist inthe three countries. This means that, owing to the limiteddetail in tourism expenditure data, we study the impacts ofall tourism, rather than the impacts of specifically nature-based tourism or community-based tourism. On the otherhand, studying the impacts of all foreign tourism in all threecountries makes us less sensitive to differences inclassification schemes between the countries.

For Botswana, we use the 1996-1997 SAM and the 2005-2006 tourism expenditure data from the TSA. There arerelatively few categories in the TSA. Expenditure in thefairly broad Shopping category is assumed to be distributedbetween different goods categories in the SAM according tothe same proportions as Botswana households’ spending ongoods, while the similarly broad Other spending category isdistributed among all goods and services in the SAM,following Botswana’s household spending proportions.This means that some 40% of overall tourist spending isapportioned between different SAM categories usingBotswana’s household spending as a benchmark. This isunfortunate but difficult to avoid given the low resolution ofthe reported data. Average spending per tourist is calculatedusing the tourist numbers reported in the same study.

For Namibia, as noted in Section 4 above, there are norecent estimates of the sectoral breakdown of touristspending. Therefore, we use the 2005 figures for overallspending by foreign tourists in Namibia reported in WTTC(2009) and subdivide this spending according to theproportions in the 1996 accounts. The 1996 expenditurecategories could be directly linked to the correspondingsectors in the SAM, except for the Other tourism-relatedindustries category, which was assumed to correspond tothe Tourism dummy sector in the SAM. Average spendingper tourist in different categories is calculated by using theexpenditure figures arrived at in the above fashion anddividing by the tourist number for 2005 in the WTTC(2008) report.

For South Africa, we use the 2000 IFPRI SAM for dataon the overall structure of the economy and the 2005 TSAscompiled by Statistics South Africa for information ontourist spending. The total visitor number for 2005 reportedin the TSAs is then used to calculate average expenditurefigures per tourist for each expenditure category. Where thetourist expenditure categories were more detailed than thosein the SAM, we allocated the entire expenditure to therelevant SAM category; where a TSA expenditure categoryincluded several SAM categories, we used the sameproportions as for South African household spending onthose categories. This also means that tourist spending onOther goods and Other services were spread over a fairlylarge number of different expenditure categories.

For all three countries, the overall economic impacts oftourism and economic impacts per tourist were estimatedusing standard multiplier analysis (Leontief, 1966; Wagner,1997; Wanhill, 1994; Dwyer et al., 2004; Miller and Blair,2009). Demand in one sector uses inputs from other sectors,



thereby affecting production and employment in thosesectors. These latter sectors are, in turn, dependent on inputsfrom other sectors. This means that the overall impact oftourism on national income (or GDP) can be greater thanthe initial tourist expenditure, if that expenditure inducessubstantial additional production and income generation inother parts of the economy. However, the overall impact onGDP might also be smaller than the initial expenditure, ifthe expenditure is mostly on foreign-produced goods (suchas petrol or equipment) which do not generate employmentand income in the domestic economy. Multiplier analysiscan be used to estimate the sizes of these direct and indirectlinkage effects. This means that by studying the types ofspending that tourists incur in the three countries and thelinkage impacts of these different types of spending onproduction and income, we can assess what the overallimpacts of tourism are for the local economy and how muchof the impacts accrue to local households rather than, e.g.,foreign providers of goods and services or foreign capitalowners.

The Namibia Dollar is pegged to the South African Randon a one-to-one basis (both amounted to US$ 0.16 at thetime). In order to make the Botswana results comparablewith those for the other two countries, they were convertedinto Rand, using an average of the 2005 daily Pula/Randexchange rates. We also calculate crude estimates of theproportional importance of the overall economic impactsfor the three countries’ economies by inflating grossdomestic product (GDP) and other aggregate indicatorsfrom the SAM by the change in consumer price index inthe countries concerned from the year of the SAM untilmid-2005.

6. Results

South Africa generated R51 billion in direct tourismreceipts from about 7.4 million tourists in 2005 (StatisticsSouth Africa, 2009b). Namibia generated R3.3 billion fromabout 0.8 million tourists (WTTC, 2008 and 2009), whileBotswana generated about R3.8 billion from approximately1.8 million tourists in the same year (Millington et al.,2007). Overall impacts, including indirect effects, arepresented in Table 1.

Tourism plays a relatively important role in all threecountries’ economies but is by no means a dominant sectorin any of the countries. As a share of the overall economy,tourism is more important in Namibia and Botswana, wherewe estimate that it accounts, directly or indirectly, for some8-9% of GDP. However, the overall impact is, notsurprisingly, greatest in South Africa, where the largestnumber of tourists come. The South African economy alsohas the largest indirect linkage effects: the impact on GDPper Rand spent in South Africa is estimated at 1.24, whichis considerably larger than the 0.95 and 0.76 estimated forNamibia and Botswana, respectively. The South African

economy is considerably more diversified than that of itsneighbours, and a greater share of the goods and servicesused by tourists and by the tourism industry can be supplieddomestically.

The results indicate that, in all three countries, the idea ofusing tourism as a pro-poor development strategy has yet tobear fruit. Impacts on poor households per se can only beevaluated directly for South Africa, where the lowestincome quintile is a separate category in the SAM. We seethat this share of the population only receives a 4% share ofthe income from tourism, which is less than its overall shareof national income, 6.2%. For the other countries, we canonly use proxy indicators, but the results from looking atthese indicators are discouraging. For all three countries,the share of income generated by tourism that labourreceives, for example, is less than its share of the overallincome in the country, whereas capital and mixed incomereceive shares of tourism income that are greater than theirshares of overall income. Similarly, the share of tourismincome received by the rural segment of the population —which is poorer, on average, than the urban segment in allthree countries — is less than its share of overall nationalincome. Botswana is the closest to at least havingproportional shares of income from tourism accruing tothese categories, but even here, the shares are slightly lowerthan those for national income. Thus, there is nothing in theresults to suggest that tourism currently generates greaterbenefits for the poorer parts of these countries’ populationsthan do other industries.

Table 1. Overall economic impacts of tourism

South Africa Botswana Namibia

Number of tourists 7,368,742 1,759,000 777,890Direct spending (millions of 2005

Rands)51,089 3,776 3,267

Selected multiplier effects:Overall effect on GDP (millions of

2005 Rands)63,368 2,859 3,102

as share of overall GDP (%) 6.2 7.6 8.6as multiple of direct spending 1.24 0.76 0.95

Effect on labour income 29,160 569 1,148as share of overall labour

income (%)5.4 7.3 7.9

Effect on rural income 7,701 370 569as share of overall rural

income (%)5.0 7.4 6.7

Effect on lowest quintile’s income 1,134 NA NAas share of its overall

income (%)4.0

Effect on government revenue 19,050 1,126 889as share of overall government

revenue (%)6.3 5.8 6.6

Effect on savings 12,091 550 907as share of overall savings (%) 6.7 0.9 10.0

Effect on imports 19,948 1,658 1,768as share of overall imports (%) 5.7 7.4 8.1

Source: Author’s elaboration based on tourism data for 2005, asreported in the text.



Impacts per tourist are presented in Table 2. These figuresshow that there are striking differences in average spendingper tourist: an average tourist in Botswana spends less thana third of what an average tourist in South Africa does,reflecting the fact that many of the tourists visitingBotswana are relatively low-spending tourists fromneighbouring countries rather than high-spendinginternational tourists. Namibia occupies an intermediateposition.

The pronounced linkages between different sectors of theSouth African economy mean that the overall impact oftourism on GDP is greater than the direct spending of thetourists. The induced overall spending (which includes bothinduced GDP and induced intermediate spending, andwhich is therefore always greater than induced GDP) isespecially pronounced for manufactured goods and forservices. For Namibia and Botswana, on the other hand,direct tourist spending is greater than the generated GDP,because so much of the tourist spending is on goods that areimported from other countries and that have no impact onthe local economy.

The large differences in per-tourist spending, and thedifference in the sizes of the indirect linkages, ensure thatthe overall economic impact per tourist is far greater inSouth Africa than in either of the other countries. Theoverall impact per tourist in South Africa is over five timesthat for Botswana. If one looks at impacts on selectedeconomic activities, the effects on overall sales are smallestfor the primary sectors. Despite (presumably) being themain suppliers of food for tourists, the agricultural sector inthe three countries appears to garner few of the benefitsfrom tourism. In all three countries, impacts on the servicessector dominate.

7. Discussion

In this paper, we have attempted to estimate the overall andper-tourist impacts of foreign tourism on overall incomeand on income distribution in Botswana, Namibia andSouth Africa. For all three countries, relatively poor data

both on income distribution and on details of touristspending present challenges to this analysis. Nonetheless,based on the data that are available, tourism may not havethe huge beneficial impacts on the three countries that aregenerally assumed.

Tourism multiplier analysis determines the impactgenerated by every Rand that a tourist spends on a touristproduct in a given destination. The calculation is influencedby three levels of expenditure: (1) An initial expenditurecreates direct income for the industry; (2) a significantportion of that income is used to pay salaries and wages,replenish stocks, and purchase services necessary forcontinued production — indirect expenditure; and (3) theincome that is thus generated results in greaterconsumption and a higher local level of economic activity— induced expenditure. Higher tourism multipliers areobtained in cases where the amount of local resourceutilization is large and the proportion of imported goodsthat enters local consumption and production expenditureis low (Tooman, 1997). Our results show that the indirectlinkages are more important in South Africa than in theother two countries, where more of the goods used by thetourism industry are imported. Given that many of theimports by Botswana and Namibia probably come fromSouth Africa, our study reveals a potential need to extendthe multiplier analysis methodology. Linking the dataregionally and identifying imports from specific countriesin the Southern African Development Community — ratherthan only having national-level data with The rest of theworld as a single import category — might be useful foranalysing indirect regional impacts, both for tourism andfor other industries. More disaggregated tourism statistics,permitting analysis of the impacts of different types oftourism, would of course also be beneficial for strategictourism planning.

Tourism is expected to grow in Botswana, Namibia andSouth Africa (UNWTO, 2012a). To the extent that thedesirability of tourism as a sustainable developmentmechanism will ultimately be judged on the extent to whichit contributes to sustainable development by increasingincomes and reducing income disparities, the results of thisstudy suggest that the sector has so far failed the threecountries. Overall income generation from tourism remainsfairly limited, and induced economic activity in othersectors, in the form of multiplier impacts, remains small.For higher tourism multipliers, there is a need to deepen thelinkages between tourism and other sectors in eachcountry’s economy. This needs to be considered whenplanning new tourism infrastructure and when deciding onchanges in tourism policy.

Impacts on income distribution appear, if anything, to beeven more disappointing. The rural population in the threecountries, which account for a majority of the poor, receiveshares of tourism income which are less than their shares ofoverall national income. Furthermore, the primary sector, inwhich the majority of the poor participate in each of these

Table 2. Economic impacts per tourist (2005 Rands)

South Africa Botswana Namibia

Direct spending per tourist 6,933 2,146 4,200Selected multiplier effects:Impact on GDP per tourist 8,600 1,625 3,987Sales of primary sector goods 1,635 208 321

as multiple of direct spending 0.24 0.10 0.08Sales of secondary sector goods 9,562 1,440 3,613

as multiple of direct spending 1.38 0.67 0.86Sales of tertiary sector services 11,781 2,230 7,346

as multiple of direct spending 1.70 1.04 1.75

Source: Authors’ elaboration.



economies, appears to receive negligible benefits fromtourism; this is despite the stated emphasis on community-based tourism in national tourism policies, which aimsspecifically to ensure that rural communities benefit fromtourism. It appears that, in the absence of meaningfulbenefits from tourism by the poor, taxes on tourism profitsmay be the simplest way through which the poor couldbenefit substantially from tourism. Proceeds from such atax could be used for government poverty reductionprogrammes. This is something that the three Governmentscould consider further if the idea of using tourism as apro-poor development strategy is to bear fruit.

In the presence of a more supportive private sector, pro-poor tourism might simply require better partnershipsbetween the government, local communities and the privatesector, aimed at influencing the distribution of touristexpenditure to create more employment and otheropportunities for the poor. The distribution of touristexpenditure needs to change in such a way that it is morefocused on components that have a direct bearing on thepoor. One way in which this can be done is by developingtourists’ tastes for local products. These can then besupplied by locals, thereby reducing the need for importedproducts. Thus, in order for tourism in the region to bepro-poor, expenditure patterns within the tourism industryneed to change. Multiplier analysis helps illustrate thelimited benefits generated by current expenditure patternsbut can also help demonstrate the effects of changes inexpenditure.

Acknowledgements

Financial support from the Swedish InternationalDevelopment Co-operation Agency (Sida) through itsEnvironment for Development initiative, from Formasthrough its COMMONS programme, and from Elforsk isgratefully acknowledged. The authors are also grateful tothree anonymous reviewers for constructive comments onan earlier version of the paper. The usual disclaimers apply.

References

Ashley, C., 2002. Methodology for pro-poor tourism case studies. WorkingPaper No. 10. Pro-Poor Tourism Partnership, London.

Ashley, C., 2006. Facilitating pro-poor tourism with the private sector:Lessons learned from ‘Pro-Poor Tourism Pilots in Southern Africa.Pro-Poor Tourism Report No. 2. Pro-Poor Tourism Partnership,London.

Ashley, C., Roe, D., Goodwin, H., 2001. Pro-poor tourism strategies:Making tourism work for the poor: A review of experience. Pro-PoorTourism Report No. 1 Pro-Poor Tourism Partnership, London.

Atlhopheng, J., Mulalae, K., 2009. Natural resource-based tourism andwildlife policies in Botswana. In: Saarinen, J., Becker, F., Manwa, H.,Wilson, D. (Eds.), Sustainable Tourism in Southern Africa: LocalCommunities and Natural Resources in Transition. Channel ViewPublications, Bristol, pp. 134–149.

Blake, A., 2008. Tourism and income distribution in East Africa.International Journal of Tourism Research, 10(6): 511–524.

Bouwer, G., 1999. The implementation of the supply and use tables inSouth Africa. Unpublished paper presented at the RegionalInternational Input-Output Conference, Arusha, Tanzania, 12–17September 1999.

Cattarinich, X., 2001. Pro-poor tourism initiatives in developing countries:Analysis of secondary case studies. Working Paper No. 8. Pro-PoorTourism Partnership, London.

Ceballos-Lascurain, H., 1996. Tourism, Ecotourism and Protected Areas.IUCN Publication, Gland.

Central Statistics Office, 2002. The 1996/97 Social Accounting Matrix.Central Statistics Office, Gaborone.

Central Statistics Office, 2008. Botswana Environment Statistics. CentralStatistics Office, Gaborone.

Conningarth Economists, 2001. Pilot Social Accounting Matrix forNamibia. Final Report. Pretoria: Natural Resource AccountingProgramme of Southern Africa.

Dixey, L., 2008. The unsustainability of community tourism donorprojects: Lessons from Zambia. In: Spenceley, A. (Ed.), ResponsibleTourism: Critical Issues for Conservation and Development. London:Earthscan, pp. 323–341.

Dwyer, L., Forsyth, P., Spurr, R., 2004. Evaluating tourism’s economiceffects: New and old approaches. Tourism Management, 25(3):307–317.

Goodwin, H., 2006. Community-based Tourism: Failing to Deliver? (id21Insights, No. 62). Brighton: Institute of Development Studies.

Hall, M., 2007. Pro-poor tourism: Do tourism exchanges benefit primarilythe countries of the south? Current Issues in Tourism, 10 (2&3):111–118.

Hartmann, P.W., 1986. The role of mining in the economy of South WestAfrica/Namibia: 1950 to 1985. Unpublished MSc thesis, University ofStellenbosch, South Africa.

Karemaker, W., Whitehead, W., 2007. Evaluating and benchmarkingNamibia’s positioning and branding relative to key competitors (Part ofthe Adding Value to the Destination Competiveness of NamibiaProject). Windhoek/Pretoria: Namibia Tourism Board & Departmentof Tourism Management, University of Pretoria.

Lange, G.-M., Schade, K., 2008. A social accounting matrix for Namibia,2004: A tool for analysing economic growth, income distribution andpoverty. Namibian Economic Policy Research Unit Working Paper112. NEPRU, Windhoek.

Lange, G.-M., Schade, K., Ashipala, J., Haimbodi, N., 2004. A socialaccounting matrix for Namibia, 2002: A tool for analysing economicgrowth, income distribution and poverty. Namibian Economic PolicyResearch Unit Working Paper 97. NEPRU, Windhoek.

Leibbrandt, M., Finn, A., Argent, J., Woolard, I., 2010. Changes in incomepoverty over the post-apartheid period: An analysis based on data fromthe 1993 project for statistics on living standards and development andthe 2008 base wave of the national income dynamics study. Journal ofStudies in Economics & Econometrics, 34(3): 25–43.

Leontief, W. 1966. Input-output Economics. Oxford University Press, NewYork.

Mahony, K., Van Zyl, J., 2001. Practical strategies for pro-poortourism. Case studies of Makuleke and Manyeleti tourism initiatives:South Africa. Working Paper No. 2. Pro-Poor Tourism Partnership,London.

Mbaiwa, J.E., Darkoh, M.B.K., 2009. The socio-economic impacts oftourism in the Okavango Delta, Botswana. In: Saarinen, J., Becker, F.,Manwa, H., Wilson, D. (Eds.), Sustainable Tourism in Southern Africa:Local Communities and Natural Resources in Transition. ChannelView Publications, Bristol, pp. 210–230.

Miller, R.E., Blair, P.D., 2009. Input-output Analysis: Foundations andExtensions 2nd edn. Cambridge University Press, Cambridge.

Millington, K., McEwen, D., Libreros, M., Heym, J., 2007. Botswana:Strengthening tourism statistics and formulation of an experimentalTSA — 2005/2006. United Nations Development Programme,



Government of Botswana and United Nations World TourismOrganization, Gaborone.

Mitchell, J., Ashley, C., 2006. Can tourism help reduce poverty in Africa?Overseas Development Institute, London.

Moepeng, P.T., 2007. Rural poverty and economic deprivation inBotswana. PhD Thesis, The University of Queensland.

Moswete, N., Thapa, B., Lacey, G., 2009. Village-based tourism andcommunity participation: A case study of the Matsheng Villages inSouthwest Botswana. In: Saarinen, J., Becker, F., Manwa, H., Wilson,D. (Eds.), Sustainable Tourism in Southern Africa: Local Communitiesand Natural Resources in Transition. Channel View Publications,Bristol, pp. 189–209.

Moswete, N., Thapa, B., Toteng, E.N., Mbaiwa, J.E., 2008. Residentinvolvement and participation in urban tourism development: Acomparative study in Maun and Gaborone, Botswana. Urban Forum,19(4): 381–394.

Nicanor, N., 2001. Practical strategies for pro-poor tourism: NACOBTAthe Namibian case study. Working Paper No. 4. Pro-Poor TourismPartnership, London.

Poonyth, D., Barnes, J.I., Suich, H., Monamati, M., 2002. Satellite andresource accounting as tools for tourism planning in southern Africa.Development Southern Africa, 19(1): 123–141.

Republic of Namibia, 2001. Draft Tourism Policy 2001–2010. Ministry ofEnvironment and Tourism, Windhoek.

Republic of South Africa, 2007. Accelerated and Shared Growth Initiativefor South Africa: Annual Report. The Presidency, Pretoria.

Roe, D., Ashley, C., Page, S., Meyer, D., 2004. Tourism and the poor:Analysing and interpreting tourism statistics from a povertyperspective. Working Paper No. 10. Pro-Poor Tourism Partnership,London.

Saarinen, J., Becker, F., Manwa, H., Wilson, D., 2009. Introduction: Callfor sustainability. In: Saarinen, J., Becker, F., Manwa, H., Wilson, D.(Eds.), Sustainable Tourism in Southern Africa: Local Communitiesand Natural Resources in Transition. Channel View Publications,Bristol, pp. 3–19.

Samuelsson, E., Stage, J., 2007. The size and distribution of the economicimpacts of Namibian hunting tourism. South African Journal ofWildlife Research, 37(1): 41–52.

Schmidt, M. 2009. Poverty, inequality and growth linkages: National andsectoral evidence from post-independence Namibia. Institute forPublic Policy Research Briefing Paper 48, IPPR, Windhoek.

Spenceley, A. 2008. Local impacts of community-based tourism inSouthern Africa. In: Spenceley, A. (Ed.), Responsible Tourism: CriticalIssues for Conservation and Development. London: Earthscan, pp.285–303.

Spenceley, A., Meyer, D., 2012. Tourism and poverty reduction: Theoryand practice in less economically developed countries. Journal ofSustainable Tourism, 20(3): 297–317.

Spenceley, A., Seif, J., 2003. Strategies, impacts and costs of pro-poortourism approaches in South Africa. Working Paper No. 11. Pro-PoorTourism Partnership, London.

Statistics South Africa, 2009a. Final social accounting matrix, 2005.Report 04-03-02. Statistics South Africa, Pretoria.

Statistics South Africa, 2009b. National accounts: Draft tourism satelliteaccount for South Africa, 2005. Discussion document D0405.7.Statistics South Africa, Pretoria.

Suich, H., 2002. Development of preliminary tourism satellite accounts forNamibia. Development Southern Africa, 19(1): 105–121.

Thurlow, J., 2009. South African social accounting matrices for 1993 and2000. Mimeo. International Food Policy Research Institute,Washington, DC.

Thurlow, J., Van Seventer, D.E., 2002. A standard computable generalequilibrium model for South Africa. Trade and MacroeconomicsDivision Discussion Paper 100. International Food Policy ResearchInstitute, Trade and Macroeconomics Division, Washington, DC.

Tooman, L.A., 1997. Multipliers and life cycles: A comparison of methodsfor evaluating tourism and its impacts. Journal of Economic Issues,31(4): 917–932.

Tosun. C., 2001. Challenges of sustainable tourism development in thedeveloping world: The case of Turkey. Tourism Management, 22(3):289–303.

UNWTO/United Nations World Tourism Organisation, 2007. Climatechange and tourism: Responding to Global Challenges. UNWTO, NewYork.

UNWTO/United Nations World Tourism Organisation, 2009. Tourismhighlights. UNWTO, New York.

UNWTO/United Nations World Tourism Organisation, 2010a. Tourismhighlights. UNWTO, New York.

UNWTO/United Nations World Tourism Organisation, 2010b. UNWTOworld tourism barometer (January). UNWTO, New York.

UNWTO/United Nations World Tourism Organisation, 2011a. UNWTOworld tourism barometer (January Advance Release). UNWTO, NewYork.

UNWTO/United Nations World Tourism Organisation, 2011b. UNWTOWorld Tourism Barometer (April Interim Update). UNWTO, NewYork.

UNWTO/United Nations World Tourism Organisation, 2012a. Tourismhighlights. UNWTO, New York.

UNWTO/United Nations World Tourism Organisation, 2012b. UNWTOWorld tourism barometer (November). UNWTO, New York.

Wagner, J., 1997. Estimating the economic impacts of tourism. Annals ofTourism Research 24(3): 592–608.

Wanhill, S. 1994. The measurement of tourism income multipliers.Tourism Management 15(4): 281–283.

World Bank, 2006. Ethiopia: Towards a strategy for pro-poor tourism.Africa Private Sector Development Note 24. World Bank, Washington,DC.

WTTC/Torld Travel and Tourism Council, 2006. Namibia: The Impact ofTravel and Tourism on Jobs and the Economy. WTTC, London.

WTTC/Torld Travel and Tourism Council, 2007. Botswana: The Impactof Travel and Tourism on Jobs and the Economy. WTTC,London.

WTTC/Torld Travel and Tourism Council, 2008. Namibia Tourism SatelliteAccount, 2nd edn. WTTC, London.

WTTC/Torld Travel and Tourism Council, 2009. Namibia: Travel andTourism Economic Impact. WTTC, London.



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