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IS TOURISM A KEY SECTOR IN TANZANIA?

INPUT-OUTPUT ANALYSIS OF INCOME, OUTPUT, EMPLOYMENT

AND TAX REVENUE

Josaphat Kweka, Oliver Morrissey and Adam Blake∗

Abstract

In most developing countries endowed with significant tourist attractions, tourism has emerged as a

new impetus for economic growth, given its ability to generate foreign exchange and employment.

This study uses input-output analysis to estimate the economic impact of tourism and assess whether

it is a key sector for the Tanzanian economy. The findings show that tourism has a significant

impact on output, which is due to its strong inter-sector and linkage effects. The income impact of

tourism is insignificant, presumably due to low value added in production. However, tourism is

identified as a key sector in the economy, which attests its potential to enhance economic growth.

Keywords: Tourism, Economic Impact, Sectoral Linkages, Tanzania

JEL Classification: D57, L83, O10, O55.

∗ We are grateful to Thea Sinclair for comments on the initial draft. The responsibility for any errors and views are, ofcourse, our own. Josaphat Kweka is a Graduate Researcher and Adam Blake is a Research Fellow in the ChristelDeHaan Tourism and Travel Research Institute, Nottingham University Business School. Oliver Morrissey is a Readerin Development Economics and Director of CREDIT, in the School of Economics, University of Nottingham.

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1. IntroductionTourism is seen by many developing countries as an impetus for economic growth. In Tanzania, thetourism sector has become a pillar of the economy, particularly in the last decade. Economic policiesand government efforts to support tourism have been emphasised for several reasons. First, Tanzaniais endowed with various natural resources that form a mainstay of tourist attractions1. Second, over-dependence on a few agricultural exports has meant that tourism may diversify the sources offoreign exchange earnings2. Third, tourism generates many other economic benefits, includingincome, employment, and tax revenue. However, despite its increasing importance in the Tanzanianeconomy, there has been no serious study to examine its economic impact3.

In a destination country such as Tanzania, tourism can be broadly defined to include the provision ofgoods and services necessary to maintain tourists: internal transport, accommodation, food andbusinesses catering specifically for tourists, for example art and crafts. The tourism sector may alsobenefit non-tourists. The growing business of providing goods and services to meet tourism demandprovides a clear reason for recognising the increasing role of tourism in the economy. The sectorallinkages and aggregate demand attributed to this business add an important dimension to theanalysis of the economic impact of tourism in economies such as Tanzania. It is in this context thatthe need to measure the role of the tourism industry in a destination economy is clear.

In contrast to the economic benefits are the costs of tourism, including its foreign exchange leakage,promotion and development, and the opportunity costs of the resources involved in its expansion.Quantification of such costs is often limited by lack of data. Uncertainty about tourism demand andits effects on employment has also contributed to the pessimism about the role of tourism ineconomic development (Dieke, 1993). In addition, growth of tourism can have adverseenvironmental (depletion and degradation of resources) and social (erosion of local culture andtraditions) impacts. Such concerns are acknowledged, but are beyond the scope of this paper.

The economic impact of tourism can be examined by analysing its impact on the growth ofproduction, use of the factors of production and on the country’s balance of payments (Miki�,1988:302). In tourism economics, examination of the economic impact of tourism has occupied acentral place, and, subsequently the multiplier effects of tourist spending constitute one of the mostresearched issues (Sinclair, 1998:26). Most of the previous empirical studies of the economic impactof tourism have concentrated on estimating multiplier values of tourism for different countries usinginput-output (IO) analysis4. However, examination of multiplier estimates for tourism offers alimited understanding of the extent in which expansion of tourism enhances the overall objective of

1 Tanzania is the largest of the East African states and nearly a third of its territory is occupied by wildlife reserves andcontains some of the world’s greatest natural wonders.2 Despite its economic significance, tourism remains an understudied aspect of international trade (Carey, 1989:59).3 Curry’s (1986) study was concerned with measuring the opportunity cost of tourism expansion in the 1970s, butsubstantial changes have occurred in the economy since then.4 One of the key advantages of IO analysis is its stress on interdependence, an important feature of an economic system(Miernyk, 1965:125; Augusztinovics, 1995:277; Lahiri, S, 2000).

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increasing economic growth. This question is important in the context of a developing country suchas Tanzania on two grounds.

First, the fact that tourism is a composite product of many other industries implies that itscontribution to the growth process depends not only on the productivity levels within it, but on theextent to which its expansion can be converted into productivity increases in other sectors. Thispresupposes a concise analysis of tourism’s relationships with other sectors. Sectors that benefitfrom tourism expansion should be identified for policy purposes, as they are likely to enhance thegrowth impact of tourism5. Second, the recent growth of tourism and its significant contribution tothe economy has attracted investment and government policy initiatives to support its development.This implies that tourism will receive higher priority as a key sector in the economy, but doestourism qualify to be a key sector for growth? Determination of key sectors is important for adeveloping country where, given scarcity of resources, investment decisions have to be selective.

In this paper, we use IO analysis to examine the significance of tourism in the economy of Tanzaniaand identify whether its interaction with other sectors may enhance economic growth. Our analysisis focused on three related issues. First, we use multiplier analysis to assess the relative significanceof tourism in creating output, income, employment and government revenue, distinguishing theimpact occurring within the sector and the spread to other sectors. In this way we can also identifythe sectors that are important for tourism output impact. Second, we carry out linkage analysis toexamine the interdependence between tourism and other sectors. Finally, we use a Multi-criteriaapproach to identify whether tourism is a key sector. In section 2 we describe the salient features ofthe economy and recent trends of international tourism in Tanzania. The IO model used in impactestimation is then outlined in section 3, with a brief review of its applications in section 4. The dataand results are discussed in section 5, before concluding in section 6.

2. The Economy of Tanzania and Growth of TourismTanzania’s economy is characterised by a large traditional rural sector and a small modern urbansector. Agriculture is the primary economic activity. The sector accounts for about 50 percent ofGDP and about 80 percent of export earnings. The manufacturing sector is still small, oncedominated by textile industries, now by consumables and beverages. Similarly, infrastructure,particularly the transport sector, is still underdeveloped. Exports are largely dependent onagricultural production. The size and role of the public sector has changed over time. Until the mid-1980’s, the public sector was dominant as parastatals were involved in direct production andcommercial activities. The level of government spending as a proportion of GDP has been high,albeit growing at a slower rate in recent years. Donor financing has assumed greater importanceafter adoption of economic reforms in 1986. Servicing of foreign debt absorbs an increasing share ofrecurrent revenue, which relies heavily on indirect taxes.

The Tanzanian tourism industry is based mainly on wildlife attractions. Tourism activities arelargely concentrated in the Northern Wildlife area (NWA), the city of Dar es Salaam and the

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historic isle of Zanzibar. Government control of the industry was high until in the early 1990s, whenmajor institutional changes were implemented that allowed for significant participation by theprivate sector. International tourism expanded rapidly in the early 1970s, particularly due to thesignificant expansion of the (state owned) hotel programme6. This growth was brought to a halt in1977 when the border with Kenya was closed (Curry, 1986:55), and only recovered from the late1980s. These trends have been reversed since 1990, partly as a positive response to the economicreform policies and government initiatives to promote the sector. Tourism policy became moreproactive, seeking to offer a low-density, high quality and high-priced tourism experience.

The European and Americas (USA and Canada) are the major source market for Tanzania’sinternational tourism7. We summarise the recent trends in the growth and role of tourism in theTanzanian economy in figures 1 and 2. Nominal earnings from foreign tourism increased from US$95 million in 1991 to over US$ 500 million in 1998 compared to tourist arrivals of about 190,000 to480,000 respectively. Although earnings from international tourism have grown more rapidly thanarrivals in nominal terms (due to policy measures to attract high spending tourists), real earningshave grown less significantly, reflecting a general increase in price levels. Expenditure per tourist ishigh in Tanzania, increasing from US$ 425 in 1990 to over US$ 1,000 in 1998, compared with theaverages of US$ 338 to about US$ 400 for Africa (WTO, various years). Furthermore, employmentfrom tourism, although small, has grown rapidly in the 1990s. The increase in earnings rather thanarrivals may explain the rising number of jobs, showing a simultaneous growth of tourism relatedbusinesses.

Fig.1: International Tourism Arrivals and Earnings (1991-1998)

0

100

200

300

400

500

600

700

1991 1992 1993 1994 1995 1996 1997 1998

Year

Inde

x (1

991=

100)

No. tourists Tourism earnings (US$)

Real earnings (Tshs) Tourism employment

Source: Calculated using data from tourism department, Dar es Salaam.

5 Assuming they can increase output to meet tourism demand; if not, bottlenecks will emerge that can constrain growth.6 Expansion of hotel sub-sector in most developing countries occurred in the late 1960s motivated by the potentialforeign exchange earnings from tourism (Carey, 1989:59).7 See Wade et al, 2001 for a detailed discussion of the historic and market analysis of Tanzanian tourism.

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Fig.2: Trends in the Role of Tourism (1991-1998)

0

50

100

150

200

250

300

350

400

1991 1992 1993 1994 1995 1996 1997 1998

Year

Inde

x (1

991=

100)

Real GDP Earnings/GDP Tourism/total exports

Source: Calculated using data from tourism department and Economic Survey (various years), Dares Salaam.

Tourism earnings as a share of GDP increased significantly from about 1% in the 1986-92 period toover 6% in the 1993-98 period8. As a share of total exports, tourism earnings increased from about15% in the 1980s to over 40% in the 1990s, becoming the second largest foreign exchange earnerafter agriculture. Tourism GDP grew from about 2% to over 15% respectively. The numbers ofhotels and beds have increased more slowly than the growth of arrivals/receipts, suggesting a rise incapacity utilisation of accommodation9.

3. Input-Output Multipliers and Linkage Analysis

3.1: Standard input-output (IO) modelIn the study of economic development, IO analysis shows in detail how changes in one or moresectors of the economy will affect the total economy (Miernyk, 1965:102; Sadoulet and de-Janvry,1995:285). As a theory, IO analysis is built on a number of assumptions. First, the economy iscomposed of N endogenous sectors producing N different commodities, and one exogenous sector(final demand). Second, each commodity is produced by one production sector. Third, there is astable and linear relationship between the inputs and the level of output of that sector. Fourth, thereare constant returns to scale such that there are no external economies or diseconomies and finally,there is no substitution of intermediate inputs.

Denote the total output of each sector i as qi that is sold to other sector j called inter-industrytransactions (denoted as zij), and to final demand sector denoted as f. Where i, j = 1, 2…N,composition (in sales) of qi can simply be represented as:

iiNiiii fzzzzq ++++= .....321 (1)

8 Comparable data for East Africa region and African countries on average show that tourism earnings as a share of GDPincreased marginally from 1.5% and to about 2% respectively.9 The room occupancy rate also increased slightly from 55% in 1990 to about 60% in 1998.

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If we extend (1) to an N-sector economy, we have:

NNNNNNN

N

N

fzzzzq

fzzzzq

fzzzzq

+++++=

+++++=+++++=

L

M

M

L

L

321

222322212

111312111

(2)

By assumption zij is a unique linear function of qj:

j

ij

ij q

za = (3)

The ratio aij is called the technical or input-output coefficient. When computed for all sectors in theinter-industry transactions, we obtain an N by N matrix of technical coefficients, which we denote as

= M

L

LM

NNN

N

aa

aa

1

111A . Each element of A (aij) represents the direct input requirements from sector i

per unit of final demand for the output of sector j.

Substituting (A) in equation (3) yields in matrix form:

fAqq += (4)

where: q and f are (N by 1) vectors of total output and final demands respectively. Equation (4) canbe rearranged as:

[ ]qAIf −= (5)

where, I is an identity matrix. Assuming that an inverse of [ ]AI − exists, we can write (5) as:

[ ] [ ] [ ] fAIqAIAI 11 −− −=−− (6)

Thus

[ ] fAIq 1−−= (7)

Equation (7) represents the standard IO model used for multiplier analyses, where [ ] 1−− AI is the

familiar Leontief inverse. It represents the mechanism through which f is converted to q (assumingthe existence of at least one non-zero element in f). This mechanism underlies the multiplier

concept. We can present elements of the Leontief inverse matrix wij as

= M

L

LM

NNN

N

ww

ww

1

111W . We

may therefore write

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Wfq = (8)

Each element of W, (wij) is called an inter-dependence coefficient, which measures the totalstimulus (direct and indirect) to the gross output of sector i when sector j’s final demand changes by

one unit (i.e. jiij fqw δδ= ). Consequently, the output multiplier for sector j is given by the column

sum of wij (denoted by Oj):

∑=i

ijj wO (9)

We note that Oj can be decomposed between the effects occurring within the sector (intra-sectoreffects) and those that spread to all other sectors (inter-sector effects). We consider the significanceof each to be different for the economy. We can express intra-sector and inter-sector effectsrespectively as rj and nj, where:

rj = wij for i = j (10)nj = Oj - rj (11)

In this case, a sector with high Oj may not possess growth potential in the economy as, say, a sectorwith relatively high nj.

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At this stage, mention of primary inputs in relation to the standard IO model can be made. IOanalysis also assumes a constant relationship between primary inputs requirements per unit of grossoutput in each sector. If we let V be a k by N matrix of the shares of k primary inputs in total inputs,total income that accrues to a particular primary input may be expressed generally as:

[ ] KffAIVVq =−= −1 (12)

where K is a k by N matrix, whose elements kij show the direct and indirect requirement for the ith

primary input when jth final demand changes by one unit. With equation (12) it is possible toestimate different primary input multipliers, including income, employment, tax or import. Inaddition to employment effects, we estimate four primary income multipliers: labour, non-labour,taxes and import multipliers. We will denote the income multiplier by Y and each share of labour,non-labour, indirect tax and import in total input of sector i as hi, nhi, ti and mi respectively.

The employment multiplier can be calculated in the same way as the income multiplier, provided wehave data on sectoral employment. The former is estimated using physical units of labour while thelatter are estimated using the monetary value of labour. As in the case of the output multiplier, wedistinguish between intra-sector and inter-sector employment impacts, denoted respectively, as Erj

and Enj. The total employment multiplier is Ej. The procedure to separate the two effects is

10 Schultz (1974) recommended that those sectors of the economy that are in a position to promote or generate growth inother sectors through that of their own, owing to their close technology-related ties, can be called strategic for achievinghigher growth of the economy.

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analogous to that of output multipliers, but we need to re-organise V in equation (12) to obtain thesquare matrix of the employment multiplier (E) by formulating a diagonal matrix (L) with li (sectoremployment) on the diagonal, so that:

[ ] EffAIL =− −1 (13)

Each element of E, (eij) measures the direct and indirect employment effects of sector i when sectorj’s final demand changes by one unit.

The above analysis is made using open as opposed to closed IO static models. The latter incorporateinduced effects of increased household consumption (Keynesian multiplier effects)11. We do not useclosed models for several reasons. Calculation of induced effects assumes all household income isspent on consumption but, in practice, income is also spent on tax, insurance and so on. In addition,closing the model for households assumes the average propensity to consume the output of sector iis constant and equal to the marginal propensities to consume, an untenable restriction on consumerbehaviour. Moreover, induced effects exaggerate the magnitude of multiplier estimates, and may notalter the ranking of the multiplier values estimated using open models (Miller and Blair, 1985:109).

3.2: Linkage analysisThe basis for measuring linkages between sectors rests on the assumption that the goal of rapidindustrialisation, hence growth, may be achieved if countries concentrate on promoting those sectorswith high linkage effects (inducement) on others. Given a shortage of information or entrepreneurialskills, such an inducement mechanism might stimulate the economic activity of others and have amultiplier effect on growth (Jones, 1976:324). There are two types of linkages: forward andbackward linkages. Backward linkages measure the (demand) stimuli given to supplying sectors as aresult of increased demand by sector j. Forward linkages measure the (supply) stimuli given to usersectors as a result of an increase in the output of the supplying sector.

Earlier measures of backward linkage by Chenery (1958) take into account the direct linkage onlyand have therefore, been modified to incorporate indirect stimuli and measures of variation (see Al-Momen, 1997; Jones, 1976; Yotopoulos and Nugent, 1976; Schultz, 1974 and Hazari, 1970).Further, as we are interested in comparing the linkage effects of different sectors, the average stimuliby a particular sector should be normalised and compared with the overall average of all sectors. Wedenote backward and forward linkage indices as BLj and FLj respectively, which we compute usingmeasures suggested by Bulmer-Thomas (1982). Backward linkage is given by the formula:

∑∑∑

=

i jij

iij

jwN

wN

BL21

1

(14)

11 Usually, IO models are closed with respect to households, but may also be closed with respect to other final demands.

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So BLj > 1 implies above average linkage and vice versa. However, the index assumes linkages areevenly distributed over many sectors. Dominance of the linkage effects by a few sectors can betaken into account by using the coefficient of variation formula:

∑∑ ∑−−

=

iij

i iijij

vjwN

wNwN

BL1

)1()1(1 2

(15)

where the lower the value of BLvj, the more even are the stimuli across sectors in the economy.

In the case of forward linkage, previous measures computed FLj as the sector’s row sum in W suchthat capacity expansion in the supplying sector will induce output expansion in using sectors due toincreased supply. The approach assumes final demand for all sectors increases by one unit, whichhas been considered misleading as not all sectors are of equal importance in the structure of demand.The measure by Bulmer-Thomas (ibid.) addresses this problem (originally suggested by Jones,1976), and can be given as:

∑∑∑

=

i jij

iij

jbN

bN

FL*1

*1

2(16)

where, bij* is the total (direct and indirect) increase in the output of using sectors as a result of a unitincrease in the output of the supplying sector, as opposed to wij which shows the impact due to achange in final demand12. Thus, a high forward linkage exists for FLj >1, and vice versa.The coefficient of variation is given as:

∑∑ ∑−−

=

jij

j jijij

vjbN

bNbN

FL*1

)*1*()1(1 2

(17)

4. The Literature on IO Analysis of the Economic Impact of TourismIO analysis has been considered a particularly suitable method for studying the economic impact oftourism (Fletcher, 1989) and it has been widely used. Comprehensive reviews of the literature onmultiplier analysis of the economic impact of tourism are provided in Fletcher (1989) and Archerand Fletcher (1990); and can be conveniently categorised into three types. First, (the majority), is anapplication of the standard IO technique to examine the economic impact of tourism in differentregions/countries. These studies indicate evidence of the economic impact of tourism either at the

12 This measure is computed using output (as opposed to input) coefficients (bij), which shows that a sector’s output isdistributed to all using sectors in fixed proportions.

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sub-regional or national level of analysis. Table 1 summarises the main findings from selectedstudies.

Table 1: Summary of selected studies on economic impact of tourism

Author Region/country IO analysis used Main resultsFrechtling andHarvàth (1999)

Washington City Standard IOA (compute bothratio and normal multipliers)

Normal multiplier is morereliable impact indicator

Henry andDeane (1997)

Ireland Standard IOA, allowing forinduced effects ofgovernment and household

International tourism shows ahigher GNP impact thanaggregate exports

West (1993) Queensland combine SAM witheconometric analysis

Integrated approach give superiorresults than traditional one

Var andQuayson (1985)

Okanagan -Canada

Standard IOA with inducedeffects

Primary effects are important inincome and secondary effects inemployment generation

Mescon andVozikis (1985)

Dade County –Miami

Standard IOA with linkageanalysis

Strong linkages with significantincome and employment effects.

Fletcher andFreeman (1997)

Israel Multi-regional IO model(MRIO)

Regional multipliers are smallerthan the MRIO’s.

Wagner (1997) Brazil Standard IOA and SAM Low impact of tourism due to itshigh import content

Archer (1995) Bermuda Standard IOA Significant employment effects.Rising income multiplier due toefforts to increase value added.

Kammas andEsfahani (1992)

Cyprus Standard IOA and SAM Significant linkage and valueadded effects.

Khan et al(1990)

Singapore andsmall Islands

standard IOA Significant economic impacts,but high import leakage inSingapore

Heng and Low(1990)

Singapore standard IOA (allowing fordifferent tourist origins)

Increasing importance of tourismregardless of rich or poor origins

Summary (1987) Kenya Standard IOA High linkage effects, but lowemployment and income effectsdue to low wages

Curry (1986) Tanzania Standard IOA (allowing foropportunity cost)

Significant output impact due tostrong linkages. Sectors linkedwith tourism have low valueadded

Notes: IOA denotes Input-output analysis

Second, studies concerned with methodological issues of IO analysis. Such studies identifyweaknesses and practical limitations in the application of IO framework. Examples include Archer(1984), Fletcher (1989), Archer and Fletcher (1990), Briassoulis (1991) and Hughes (1994). Mostcriticisms of IO analysis are of its restrictive assumptions. However, there has been confusion andmisunderstanding associated with the interpretation and analysis of multipliers. This confusion

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arises due to the use of different approaches, such as normal vs. ratio multipliers13. Thus, cautionmust be exercised when comparing multiplier values from different studies (Fletcher, 1989:526) asmultiplier values may differ across destinations or time, even for studies using the same approach(see, for example, Table 13).

Finally, there are studies that modify the standard IO analysis for different purposes. For instance(examples in brackets) a model that takes into account capacity constraints in the productive sectors(Wanhill, 1988), costs and benefits of tourism (Bryden, 1973) or opportunity costs of resources(Mitchell, 1970; Curry, 1986). Alternative methodologies have also been suggested. Andrew (1997)used Linear Programming and IO analysis to model the impact of tourism in Cornwall. Zhou et al

(1997) used both IO and computable general equilibrium (CGE) model to analyse the impact oftourism in Hawaii. Wagner (1996) estimated social accounting matrix (SAM) multipliers, in effecttaking into account the distributional aspects of tourism impact. Adams and Permenter (1991, 1995),Blake and Gillham (2000) and Blake (2000) have used CGE models to examine the impact oftourism in Australia, Mauritius and Spain respectively.

5. Data and results

Two sets of data are required for estimating IO multipliers: the first is the inter-industry flow oftransactions among the sectors of the economy, traditionally organised as IO tables. We make use ofthe IO Table of Tanzania for 1992 (most recent available) as our major source of this information.The second data set is the value of tourist expenditures (see Appendix Table A.1). The original IOTable contained 79 sectors, which we aggregated to 23 sectors as listed in Table 214. Two remarksare in order regarding data. First, the sector we term tourism (sector 15) is the ‘Hotels andRestaurants’ sector. Obviously, not all the activity in this sector is due to tourists and not all tourists’activities are in this sector. Nevertheless, it is the sector that most closely corresponds to tourism(see Carey, 1989:63) and, especially, the magnitude of multipliers associated with tourism.

13 The two are different approaches used in expressing multipliers. The normal multiplier is a number that shows by howmuch the initial effects are multiplied to get the total effects. The ratio multiplier is a share (proportion) of the initialeffects in the total effects. The ratio multiplier can be ‘type I’ or ‘type II’ if the total effects include only direct andindirect effects, or in addition, the induced effects respectively. Other types of ratio (e.g. type III income) multiplier existthat take into account different disaggregation of household income groups.

14 In aggregating the original IO sectors, two main criteria were followed: first, grouping the IO sectors according to themain economic classification of industrial activities; second, availability of employment data by sectors from the LabourForce Survey (LFS) to match the two.

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Second, although the IO Table that we use is the most recent one for Tanzania (published in 1999),its data refer to the year 1992. In practice, IO tables take a number of years to construct, especiallyin the LDCs where delays of 5 to 7 years are common (Bulmer-Thomas, 1982:156). The IO theory,in its original form (Leontief, 1951) deals with the constancy of IO coefficients in physical forms(e.g. tons of iron per ton of steel). It is, thus, reasonable to expect little or no changes in technologyor relative prices in the short to medium terms, particularly in LDC’s where the basic structure ofthe economy changes slowly:

Each sector or industry thus has its own ‘cooking recipe’. The recipe is determined in the main bytechnology; in a real economy it changes slowly over the periods of time usually involved ineconomic forecasting and planning” (Leontief, 1986:165).

In Tanzania, 1992 was a ‘normal’ and a base year for macroeconomic series of the later years, hencean ideal year to describe the typical features of the economy. The impacts estimated by equation (8)through (13) give an indication of the importance of tourism in Tanzania.

Table 2: The aggregated 23 IO Sectors

Code IO Sector Activity1 Coffee Agriculture2 Cotton Agriculture3 Staple food Agriculture4 Oil seeds Agriculture5 Other cash crops Agriculture6 Cattle and other animal Agriculture7 Fish, hunting and forestry Agriculture8 Mining and quarrying Mining9 Food and Beverages Manufacturing10 Textile and Leather products Manufacturing11 Wood, pulp and paper products Manufacturing12 Other manufactures Manufacturing13 Water, Electricity and gas Public Utilities14 Construction Construction15 Tourism Services16 Land Transport Services17 Other transport & communication Services18 Financial and Business services Services19 Public administration Services20 Education services Services21 Health services Services22 Wholesale and retail trade Services23 Other services Services

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5.1: Output significance of tourismEstimates of output multipliers are shown in Table 3 and ranked accordingly. We find that thetourism sector, relative to other sectors, has a share of nj (44.6%) far above the average of 25.7% forall sectors. The output multiplier for tourism is 1.84, ranking third, and ranks second in terms of itsinter-sector effects. Tourism’s inter-sector effects dominate its output in the economy15. Theseresults are generally a reflection of the nature of tourism as a composite product of many sectors.

Table 3: Total, Intra and Inter- sector output multipliers

Total Intra-sector Inter-sectorSector Oj Rank rj Rank % (rj/Oj) nj Rank %(nj/Oj)1 1.464 9 1.050 12 71.7 0.414 9 28.32 1.514 8 1.028 16 67.9 0.486 7 32.13 1.226 20 1.087 5 88.7 0.138 22 11.34 1.122 23 1.062 10 94.7 0.059 23 5.35 1.384 15 1.116 4 80.7 0.268 17 19.36 1.264 18 1.003 22 79.3 0.262 18 20.77 1.185 22 1.046 13 88.3 0.139 21 11.78 1.203 21 1.016 20 84.5 0.186 20 15.59 1.912 1 1.085 6 56.7 0.828 1 43.310 1.723 5 1.163 2 67.5 0.561 5 32.511 1.585 7 1.068 8 67.4 0.517 6 32.612 1.445 11 1.141 3 78.9 0.305 16 21.113 1.392 14 1.065 9 76.5 0.327 14 23.514 1.459 10 1.037 15 71.1 0.422 8 28.915 1.840 3 1.019 18 55.4 0.821 2 44.616 1.367 16 1.040 14 76.1 0.327 13 23.917 1.441 12 1.059 11 73.5 0.381 10 26.518 1.901 2 1.586 1 83.4 0.315 15 16.619 1.773 4 1.081 7 61.0 0.692 3 39.020 1.668 6 1.007 21 60.4 0.661 4 39.621 1.350 17 1.000 23 74.1 0.350 12 25.922 1.252 19 1.019 19 81.4 0.233 19 18.623 1.394 13 1.025 17 73.5 0.369 11 26.5

For the output significance of tourism to be transformed into growth of the economy, sectors thatbenefit from it should be equally vibrant. Identification of such sectors is important for policypurposes, as they may constrain the growth impact of tourism. We identify them by examiningelements of the Leontief inverse, wij (for j = 15), where the share of each sector in Oj (for j = 15) iscomputed in Table 4. We find sectors 9 (food & beverage) and 7 (fishing & hunting) constitute 12%and 8% respectively of Oj for tourism. Others include sector 3 - staple food (7.2%), and sector 22 -wholesale and retail (4%).

15 The relationship between the total and inter-sector output effect is positive and significant (correlation coefficientbetween nj and Oj is 0.9, unlike 0.5 for rj and Oj).

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Table 4: Distribution of Tourism output effects by sector

Code wij (j = 15) Rank % (wij/Oj) for j = 15)1 0.002 21 0.12 0.002 18 0.13 0.132 4 7.24 0.002 19 0.15 0.034 7 1.96 0.033 8 1.87 0.148 3 8.08 0.002 20 0.19 0.229 2 12.410 0.012 13 0.611 0.003 16 0.212 0.031 9 1.713 0.027 10 1.414 0.009 14 0.515 1.019 1 55.416 0.024 11 1.317 0.015 12 0.818 0.038 6 2.119 0.003 17 0.220 0.001 22 0.021 0.000 23 0.022 0.068 5 3.723 0.008 15 0.4

In addition to multiplier values, we provide estimates of different output linkage indices in Table 5.Our results show that tourism has significant backward linkage (BLj = 1.25), for which it ranks third.The degree of dispersion shows that tourism’s backward linkage is the most evenly distributed of allsectors (BLvj = 0.75). In general, most of the sectors with high BLj are also found to have low BLvj.Tourism has above average forward linkage (FLj = 1.08) ranking seventh, and fifth in terms of FLvj.Contrary to the backward linkage case, most sectors with high FLj have high FLvj values16.

These results reveal two things. First, the tourism sector is assuming a more important role, not onlyas a producer of foreign exchange but also as a potential avenue through which important structuralchanges in the economy may be possible. This is shown by the significant stimuli tourism exertsover many other sectors in the economy. Second, the results are consistent with some notablecharacteristics of the economy: the manufacturing sector has not exerted the necessary structuraltransformation of the economy; the agriculture sector produces traditional exports (with low levelsof processing) and the few manufacturing industries continue to depend on imported inputs. Foodprocessing and beverage industries, however, have better linkage effects. In addition, tourism hasweaker forward linkages and this may be expected as a considerable share of sales is to visitors. 16 Linkages can provide a stimulus to growth only if the interdependence among sectors is causal (Yotopoulos andNugent, 1976:335). Jones (1976:325) considered backward linkages as being more causal, and forward linkages aspermissive. We find that the correlation coefficient between the backward and forward output linkages for the tourismindustry is notably high (0.98), while for most other sectors, it is insignificant.

15

Table 5: Backward and Forward Output Linkages

Backward Linkage Forward LinkageSector BLj Rank BLvj Rank FLj Rank FLvj Rank1 0.995 9 0.863 10 0.715 21 0.991 202 1.028 8 0.836 5 1.540 2 0.932 163 0.833 20 0.978 21 0.943 14 0.920 144 0.762 23 1.001 22 0.755 19 0.976 185 0.940 15 0.943 19 1.090 6 0.921 156 0.859 18 0.888 14 0.959 13 0.860 117 0.805 22 0.956 20 0.901 17 0.859 108 0.817 21 0.921 17 1.057 9 0.777 49 1.299 1 0.816 4 0.722 20 1.013 2110 1.170 5 0.885 13 0.927 16 0.939 1711 1.077 7 0.853 8 1.031 10 0.822 712 0.982 11 0.941 18 1.117 5 0.826 813 0.945 14 0.895 15 1.565 1 0.653 114 0.991 10 0.852 7 0.704 22 0.982 1915 1.250 3 0.751 1 1.078 7 0.781 516 0.929 16 0.880 12 1.070 8 0.773 317 0.978 12 0.873 11 1.199 4 0.754 218 1.291 2 1.141 23 1.213 3 1.098 2319 1.204 4 0.796 3 0.664 23 1.060 2220 1.133 6 0.773 2 0.979 11 0.901 1321 0.917 17 0.851 6 0.974 12 0.877 1222 0.850 19 0.904 16 0.857 18 0.856 923 0.947 13 0.858 9 0.942 15 0.818 6

5.2: Significance of Tourism in Income generationIncome multipliers translate the impacts of final demand spending into income received by differentfactors of production. However, official estimates of household income in developing countries maybe highly underestimated due to the existence of significant informal activities and traditional (non-waged) agricultural labour17. Measures of income effects should also include these activities. Weuse the Tanzanian Labour Force Survey (LFS) data (1991/2) to obtain the estimates of the labourforce by sector18.

17 The prevalence of informal sector activities (believed to be over 30% of GDP) implies that official figuresunderestimate actual employment levels.18Description of the LFS data indicates that non-wage labour accounts for 84% of the total labour force, reflecting thefact that over 80% of the Tanzanian labour force is in the rural sector. The public sector represents the largest share ofwage employment. Tourism accounts for about 5% of wage employment and over 10% of self-employment. The labourforce in the tourism industry is mostly self-employed (62.3%), and only one-third is in wage employment, and shows theprevalence of informal activities in tourism, that may undermine its income effects.

16

In the IO Table, the share of labour income is only 16.6% of value added compared with theoperating surplus (OS) 73.1%19. We consider the former to be too low, given the prevalence of non-waged labour in Tanzania. Similarly, the latter is unrealistically high and is likely to containsignificant non-wage labour income. We adjust the labour income in the IO Table (H) to take intoaccount non-paid employment. The total value added for a particular sector and other primaryincome figures are taken as given, so revised estimates of labour income will involve adjusting theOS values. The adjusted household (labour) income (H*) is computed as a sum of waged (WE), non-waged (NW) and self employed (SE) labour income, and each is a product of their respectivelyestimated average wage rates and the numbers of people employed:

iiiiiiiii SEwNWwWEwH βα ++=* (18)

where, for each sector i, w is the average wage of paid labour and α and β the ratio of the average

wage rate for non-wage and self-employed labour to that of paid labour respectively. We

hypothesise that 10 ≤< α , and 21 ≤≤ β . The estimated H* is found to be significantly higher than

H and labour’s share in value added increased from 16.6% to 47.7%. We use values of H* toestimate labour income multipliers.

We provide estimates for both labour (Yhi) and non-labour income (Ynhi) in Table 6. Labour incomeeffects are dominant in the agricultural sectors, which is expected given the prevalence of rurallabour in Tanzania. Non-labour income effects are stronger in the service sectors. For tourism, Yhi isweaker, hence Ynhi dictates the total income effects20. Hotels (the principal tourist establishments)have a relatively low share of variable costs (Carey, 1989:59); thus a larger proportion of initialexpenditures results in profit rather than labour incomes. In general though, our results indicate thatthe labour income effect is insignificant. This may be explained by the low level of wages or leakagethrough imports21.

19 Other elements in value added (and their shares) are net indirect taxes (6.6%) and consumption of fixed assets (3.6%).20 The significance of tourism non-labour relative to labour income effects has also been observed for Kenya (seeSummary, 1987).21 The relationship between the import multiplier and the total income multiplier is negative and significant (correlationcoefficient is –0.9), and that between Yhi and Ymi is insignificant (-0.24).

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Table 6: Estimates of Income Multipliers

Sector Yhi Rank Ynhi Rank Yhi + Ynhi Rank

1 0.428 9 0.396 10 0.824 92 0.300 16 0.271 14 0.570 193 0.473 6 0.435 8 0.907 44 0.862 1 0.119 20 0.980 15 0.096 22 0.740 2 0.836 86 0.378 13 0.572 4 0.951 27 0.147 21 0.771 1 0.918 38 0.836 2 0.070 23 0.906 59 0.382 12 0.454 7 0.836 710 0.478 5 0.142 18 0.620 1711 0.530 3 0.259 15 0.788 1012 0.278 18 0.185 16 0.462 2213 0.178 19 0.287 12 0.465 2114 0.459 7 0.094 21 0.553 2015 0.404 11 0.286 13 0.690 1316 0.093 23 0.663 3 0.756 1117 0.288 17 0.462 6 0.750 1218 0.158 20 0.476 5 0.634 1619 0.506 4 0.134 19 0.640 1520 0.428 10 0.158 17 0.586 1821 0.369 14 0.089 22 0.458 2322 0.449 8 0.406 9 0.855 623 0.346 15 0.341 11 0.687 14

Estimates of tax and import revenue leakage are in Table 7. Tourism is second in importance ingenerating indirect tax revenue22. Bird (1992) examined the economic case for taxing tourism indeveloping countries, and noted the problems that limit the ability of tourism to generate revenue.First, much tourist expenditure goes to international airlines and tourist agencies, not the destinationcountry. Second, the multitude of small (and in some cases informal) businesses in tourismexacerbates the administrative difficulty in extracting revenue from them. Third, the linkagebetween tourism and the rest of the economy may be weak, limiting the revenue impact of increasedtourist spending. Finally, the generous fiscal incentives (notably in the hotel sub-sector) for investorshave eroded the tax revenue base for developing countries. Our study clearly indicates that thelinkage effects of tourism to other sectors explain its significance in tax revenue creation23.

22 The tax/output ratio for tourism was 5.3% ranking third after trade and land transport.23 The correlation between the tax multiplier and the backward linkage is significantly positive (0.6).

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Table 7: Import and Indirect Tax (Leakage) Multiplier Estimates

Sector Ymi Rank Yti Rank

1 0.120 14 0.041 182 0.352 4 0.061 83 0.066 18 0.020 224 0.007 23 0.009 235 0.089 16 0.059 116 0.023 22 0.021 217 0.054 19 0.022 208 0.047 20 0.022 199 0.086 17 0.057 1310 0.260 6 0.067 611 0.118 15 0.052 1612 0.403 2 0.067 413 0.213 10 0.042 1714 0.358 3 0.060 1015 0.209 11 0.078 216 0.148 13 0.069 317 0.163 12 0.061 918 0.223 8 0.055 1519 0.241 7 0.067 520 0.298 5 0.064 721 0.435 1 0.055 1422 0.041 21 0.081 123 0.221 9 0.058 12

Most of the agricultural sectors have the lowest Yti, perhaps due to the significant proportion of own-consumption in rural produce. Values of Ymi indicate the extent of import leakage, which in the caseof tourism is approximately 0.21. This implies that out of one shilling of income, 21 cents leaks outto pay for imports and domestic income earners retain 79 cents. This value can be consideredreasonable compared with that of other important sectors such as manufacturing (for instance sector12, other manufactures, has Ymi = 0.40)24.

24 The direct and indirect import coefficients for some developing countries vary, ranging from 0.11 for the Philippinesto 0.45 for the Bahamas (Sinclair, 1998:29).

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5.3: Employment significance of TourismIn addition to the IO table, estimation of employment multipliers requires data on the number ofpeople employed in each of the IO sectors (li). We use the LFS data for 1991/2 to provide estimatesof li. The value of the employment multiplier is interpreted as the (full time equivalent) number ofemployees per one million TShs increase in final demand for any sector j. Employment multiplierestimates are shown in Table 8, and the employment linkage indices in Table 9. Overall, theagricultural sectors have the highest values of Ej, and intra-sector components (Erj) are high.Tourism’s employment impact is more significant in its inter-sector components (Enj = 69%), forwhich it ranks third (compared to intra-sector and total employment effects for which it rankstwelfth and ninth respectively). Half the employment impact of tourism is generated in sector 3,staple foods.

Table 8: Total, Intra- and Inter- sector Employment Multipliers

Total effects Intra-sector effects Inter-sector effectsSectorEj Rank Erj Rank % (Erj/Ej) Enj Rank % (Enj/Ej)

1 45.933 1 45.210 1 98.4 0.723 7 1.62 17.054 3 16.609 3 97.4 0.445 12 2.63 18.151 2 17.964 2 99.0 0.186 21 1.04 12.407 4 12.325 4 99.3 0.082 23 0.75 4.517 8 4.297 5 95.1 0.220 20 4.96 5.808 6 3.731 7 64.2 2.077 4 35.87 0.937 19 0.659 16 70.3 0.278 18 29.78 4.179 10 4.044 6 96.8 0.135 22 3.29 8.313 5 0.206 23 2.5 8.107 1 97.510 4.596 7 0.709 15 15.4 3.887 2 84.611 1.646 16 1.218 13 74.0 0.428 13 26.012 0.715 22 0.331 20 46.3 0.384 16 53.713 0.635 23 0.309 21 48.7 0.326 17 51.314 0.876 20 0.456 19 52.1 0.420 14 47.915 4.245 9 1.325 12 31.2 2.920 3 68.816 1.067 18 0.583 17 54.6 0.485 9 45.417 1.217 17 0.742 14 60.9 0.476 10 39.118 0.752 21 0.222 22 29.5 0.530 8 70.519 1.859 15 0.508 18 27.3 1.351 5 72.720 3.495 12 2.447 10 70.0 1.048 6 30.021 3.179 13 2.731 9 85.9 0.448 11 14.122 1.866 14 1.619 11 86.8 0.246 19 13.223 3.611 11 3.209 8 88.9 0.402 15 11.1

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Table 9: Backward and Forward Employment Linkages

Backward Linkage Forward LinkageSector EBLj Rank EBLvj Rank EFLj Rank EFLvj Rank

1 7.184 1 6.666 23 7.116 1 6.701 232 2.667 3 4.017 21 3.253 3 3.682 223 2.839 2 4.215 22 4.827 2 3.387 204 1.940 4 3.498 20 1.952 4 3.487 215 0.706 8 2.017 18 0.791 5 1.906 196 0.908 6 1.708 16 0.701 7 1.755 177 0.147 19 0.690 8 0.160 14 0.658 78 0.654 10 1.975 17 0.707 6 1.893 189 1.300 5 2.397 19 0.045 23 0.384 110 0.719 7 1.669 14 0.135 17 0.758 1011 0.257 16 0.945 9 0.225 13 1.009 1312 0.112 22 0.413 3 0.104 18 0.395 313 0.099 23 0.397 2 0.074 21 0.439 414 0.137 20 0.487 5 0.087 19 0.605 615 0.664 9 1.196 11 0.343 12 0.889 1216 0.167 18 0.572 6 0.159 15 0.561 517 0.190 17 0.668 7 0.149 16 0.750 918 0.118 21 0.295 1 0.049 22 0.392 219 0.291 15 0.471 4 0.084 20 0.693 820 0.547 12 1.305 12 0.422 11 1.488 1421 0.497 13 1.522 13 0.453 10 1.602 1522 0.292 14 1.179 10 0.571 9 0.808 1123 0.565 11 1.681 15 0.594 8 1.634 16

The employment multiplier estimates provided by most past studies ignore the extent to whichemployment effects are spread evenly across other sectors (Diamond, 1975). Employment linkageindices can be estimated to overcome this shortcoming. The formulae for calculation of employmentlinkage indices with their associated measures of dispersion (prefixed by E) are analogous to thosefor output linkages. Most of the employment stimuli are found to be concentrated in the agriculturalsectors, shown by high absolute values of EBLj and EFLj, and very high values of EBLvj and EFLvj.

25

Tourism’s EBLj is stronger (ranking ninth) than EFLj (ranking twelfth), but is not evenly distributedacross sectors. In general, most sectors with significant employment linkage have a relatively highdegree of dispersion. Calculation of the capital-labour ratio (KL) indicates that tourism is a labour-intensive industry (KL = 0.01), ranking fifth most labour-intensive. This suggests the potential fortourism to provide employment opportunities. However, our results suggest that the prospects fortourism to be a reliable employment generator are small.

5.4: Empirical Identification of Key Sectors in the EconomyDetermination of key sectors is important for a developing country where, given scarcity ofresources, investment decisions have to be selective. Key sectors play an important role in initiatingthe process of economic growth and diversification of the industrial structure of the economy, and asubstantial part of investment should be made in the key sectors (Hazari, 1970:301). The concept of

21

key sector is closely associated with the famous linkage hypothesis advocated by Hirschman (1958).Empirical studies of the identification of key sectors of the economy are generally few and almostnon-existent for tourism. They include Al-Momen (1997) for the UAE and, both Dhawan andSaxena (1992) and Hazari (1970) for India. A few other studies were concerned with measurementof the linkage hypothesis (see Jones, 1976; Bulmer-Thomas, 1982; Matalla and Proops, 1992, and,Soofi, 1992) or testing it (see Yotopoulos and Nugent, 1973, 1976).

Two principal criteria have been used to identify key sectors. First, is the linkage indices criterion.Based on the Hirschman linkage hypothesis, sectors with significant linkage effects are consideredto be key sectors. Second, is the multiplier criterion, a proxy for planner’s objective(s). This methodwas originally proposed by Hazari (1970) who argued that, as multipliers give the total impact of anincrease in final demand of a given sector, one can rank sectors according to this impact to reflectthe planner’s objective function26. Thus, a sector ranking high in say, output multipliers will beconsidered a key sector27. Many development economists have used linkage indices in theidentification of key sectors, where key sectors are defined as the sectors with above averageforward and backward linkages (Soofi, 1992:351). Hirschman used only linkage indices to definekey sectors. Hazari (1970) and Jones (1976) used both linkage and output multiplier criteria; Al-Momen (1997) also included income multipliers.

Three shortcomings are evident from the previous studies. First, the concept of key sector impliesexistence of non-key sectors. The cut-off point between the two is not identified in any of thesestudies. Second, they relied on cardinal indices to identify key sectors. As all sectors in the economyare important in one way or another, identification of key sectors may only be justifiable on ordinalterms. That is, some sectors are key relative to others. Finally, they used more than one methods buteach yields different results, so it may be difficult to choose key sectors. Thus, we define key sectorsin ordinal terms (i.e. by ranking).

We determine key sectors for growth by using a Multi-rank index (MRI). Sectors are rankedaccording to a particular index to generate a list of sectors by order of importance. We use four mainindices: output, employment, income and tax revenue multipliers, which we supplement (whererelevant) with linkage and degree of dispersion indices28. We determine the number of sectors thatwould be considered key under each criterion (in this case we chose the top 10), so that, if we have pindices, the sample size becomes S = p10. Then we determine the frequency with which each of thesectors appears in S. 25 In general, it has been noted that agricultural sectors in LDCs have few employment linkage effects (see Sadoulet andde-Janvry, 1995 p.273).26 Matalla and Proops (1992:263) note that ranking sectors is common in the identification of key sectors.27 The basic IO framework ignores the possibility of there being serious capacity constraints or strategic reasons for notinvesting in some sectors. In the context of a developing country such as Tanzania, capacity constraints are likely.Nevertheless, since our primary objective is to evaluate the tourism sector on a comparative basis, conclusions can bedrawn concerning the relative values of multipliers for different sectors.

28 The import multiplier is not included as a criterion for identifying key sectors since we do not consider it important inthe assumed objectives of government.

22

We can identify key sectors according to both individual criteria and all the four criteria. Thus, thehigher the frequency of a sector in S, the more it is likely to be identified as key. Finally we establishthe cut-off point between key and non-key sectors. The most convenient way to determine the cut-off point in S is to calculate the simple average frequency so that if a sector has above averagefrequency it is considered ‘key’. Identification of key sectors by each criterion is shown in Table 10and indicates that tourism ranks as the second most significant sector in terms of the output criterion.Tourism also ranks as the second most significant sector for government tax revenue. However,tourism was less significant in terms of the employment criterion, and was insignificant according tothe income criterion29. In the second case, we identify key sectors according to all four criteria, theresults of which are shown in Table 11. The MRI identified the four most key sectors as coffee,cotton, textiles and leather, and tourism. This attests to the significance of tourism in enhancing thegrowth of the economy. However, these results should not be interpreted to mean that non-keysectors are not important.

Table 10: Key sectors by individual criteria

Output Employment Income Tax RevenueSector Freq Sector Freq Sector Freq Sector Freq

11 7 1 5 1 2 22 115 6 6 5 3 2 15 11 5 2 4 22 2 16 12 5 3 4 12 1

19 5 4 4 19 19 4 5 4 10 1

10 4 8 4 20 114 4 9 4 2 118 4 10 4 17 120 4 15 3 14 1

16 317 318 319 3

Sample statisticsIndex Output Employment Income Tax RevenueSample size (S) 70 70 20 10Mean 3.2 3.0 1.2 1

Note: The sectors (shown by their respective codes) ranks from highest to the lowest.

29 Key sectors for income generation are identified as coffee, staple food and wholesale and retail trade.

23

Table 11: Key Sector by all criteria

Rank Sector Freq. Description1 1 3 Coffee2 2 3 Cotton3 10 3 Textile and leather products4 15 3 Tourism5 3 2 Staple food6 9 2 Food and beverage7 14 2 Construction8 19 2 Public administration9 20 2 Education services10 22 2 Wholesale and retail trade11 4 1 Oil seeds12 5 1 Other cash crops13 6 1 Cattle and other animal14 8 1 Mining and quarrying15 11 1 Wood, pulp and paper products16 12 1 Other manufactures17 16 1 Land Transport18 17 1 Other transport and communication19 18 1 Financial and Business services20 7 NA Fish, hunting and forestry21 13 NA Water, Electricity and gas22 21 NA Health services23 23 NA Other services

Sample Size = 33 Average = 2

Note: NA – not applicable.

5.5: The Impact of International TourismIn measuring the economic impact of tourism, we focus on international tourist expenditure.Expenditure by international tourists in 1992 amounted to US $120 million (or Tshs 42,014 mill).We simulate (by multiplying the Leontief inverse by final demand) the level of economic activitysupported by this expenditure, distinguishing between direct and ‘direct plus indirect’ effects. Theoutput, employment and income impacts are provided in Table 12.

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Table 12: Output and Employment Multiplier Impact

(a) Output impactLevel of effect Sectoral effect Multiplier estimate Impact (Tshs. Mill) %share of GDPDirect Total 0.545 21,930.5 1.7

Intra 0.013 540.0 0.0Inter 0.532 21,390.5 1.7

Direct + indirect Total 1.840 74,012.0 5.8Intra 1.019 40,983.5 3.2Inter 0.821 33,028.5 2.6

(b) Employment impactDirect NA 1.296 52,131 0.5Direct + indirect Total 4.245 170,718 1.6

Intra 1.325 53,279 0.5Inter 2.920 117,440 1.1

(c) Income impactType of income Level of effect Multiplier Impact (Tshs. Mill) %share:Labour income Direct 0.236 9,471.2 0.7 (of GDP)

Direct + indirect 0.404 16,247.0 1.3None Labourincome

Direct 0.002 85.0 0.0

Direct + indirect 0.286 11,484.8 0.9Tax revenue Direct 0.053 2,126.5 2.7 (of net indirect taxes)

Direct + indirect 0.078 3,149.3 4.1Import leakage Direct 0.156 6,291.0 1.6 (of total imports)

Direct + indirect 0.209 8,410.2 2.1NA – not applicable

Output and employment impact

At the direct level, tourism expenditure resulted in output of Tshs 21,930.5 mill in 1992 (1.7% oftotal GDP), mainly contributed by the inter-sector impact. When direct and indirect effects areconsidered, the total impact on output was Tshs 74,012 mil (5.8% of GDP). Intra-sector impactsincreased from almost zero to 3.2% of GDP and inter-sector impacts increased from 1.7% to 2.6%respectively. Thus, the output impact of tourism increases as indirect effects are added; for instance,intra-sector effects are significant only when indirect effects are considered.

In the case of employment, tourism expenditure in 1992 directly supported 52,131 jobs (0.5% oftotal labour; estimated to be about 10.9 million people in 1991/92). The direct plus indirectemployment impact was 170,718 jobs, i.e. 1.6% of the labour force. The number of jobs createdwithin the tourism sector was 53,279 (0.5%) and the remaining 117,440 (1.1%) were created inother sectors. Our results compare well with the share of tourism employment in other developingcountries, which range from 0.9% (Philippines), 1.4% for Sri Lanka and 1.3% for Zimbabwe(Sinclair, 1998:30). In Kenya, it was found that tourism was not particularly effective in creatingjobs (Summary, 1987:537).

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Income impact

Tourist spending in 1992 created direct labour income worth Tshs 9471.2 mil, accounting for 0.7%of GDP. This increased to Tshs 16,247 mil (1.3%) when the indirect effects were included.However, the impact on non-labour income was not as significant; at the direct level, this was below0.1% of GDP, and was below 1% at the direct and indirect level. Tourist spending generatedgovernment tax revenue amounting to TShs 2,126.5 mil at the direct level (2.7% of net indirecttaxes) and, when indirect effects are included, increased to 3,149.3 mil or 4.1% of total net indirecttaxes. The corresponding impact on imports is Tshs 6,291 mill (1.6% of total imports) and 8,410.2mill (2.1% of total imports) 30.

Since about 21 cents leaks out of the economy as import revenue, the net foreign exchange earningswould be 79 cents at the direct and indirect level, and 85 cents at the direct level. Given that totaltourist expenditure in 1992 was US$ 120 mill (in nominal terms), the direct tourism net foreignexchange earnings would be US$ 102 mill and US$ 94.8 mill when indirect effects are included.

5.6. Comparison of resultsWe verify the above results by using a different (higher) level of aggregation to four sectors, whichalso allows for direct comparison of the main sectors: agriculture, manufacturing, tourism and otherservices. The results are listed in Appendix B and reveal no significant changes, but confirm tourismas the most significant of the four sectors. Tourism is found to have higher labour income effects. Inaddition, tourism is slightly more import intensive than in the 23-sector case. Although a serviceindustry, tourism benefits little from other service sectors compared with linkages to agriculture andmanufacturing. This is further evidence of its significant output impact. Manufacturing has higherbackward linkages than forward linkages, and vice versa for the agricultural sector. The results foremployment effects are mixed; agriculture has the largest total employment impact and smallestinter-sector impact (presumably due to low level of skills in peasant labour), while manufacturinghas the largest inter-sector employment effects. Both have an unfavourably high dispersion ofemployment stimuli, for which other services are the most evenly distributed. Tourism is average inall these indicators. Overall, tourism is found to be the most key sector of the four in the economy.

In Table 13, we also compare our results with some other studies that use similar techniques. Ouroutput multiplier is one of the highest and compares well with that of the neighbouring Kenya. Ingeneral, the income multiplier value is similar to that in other studies and is shown to be lower at aregional as opposed to national level of analysis. However, the employment impact of tourism isexceptionally low in Tanzania compared with other studies.

30 Tourism has sometimes been considered to have low direct import content but a high import content when indirecteffects are considered (Miki, 1988:308).

26

Table 13: A comparison of Tourism Multipliers Estimates with other selected studies

Author Country Year Output Employment* Income Tax ImportsCurry (1986) Tanzania 1976 1.591 -- 0.849 -- --Summary (1987) Kenya 1976 1.81 0.0007b 0.641 -- --

Khan et al (1990) Singapore 1983 1.482 24.842 0.688 -- --Fletcher (1989) Jamaica 1984 -- 0.04a 0.603 0.069 --Var and Quayson (1985) Okanaganc 1985 1.3 0.052a 0.486 -- --Heng and Low (1990) Singapore 1986 1.464 26.66 0.748 0.086 0.171Henry and Deane (1997) Ireland 1990 0.751 41.85 0.391 0.252Frechtling and Harvàth(1999)

WashingtonD.C.d

1994 1.184 18 0.348 -- --

This study Tanzania 1992 1.84 4.245 0.69 0.078 0.209*Employment measured per million of tourist expenditure, except in a which is measured per ‘000 units or b

per one unit of tourist expenditure. c, d denotes a region in Canada and USA respectively.

6. Conclusions and implicationsThe value of tourism in the development process has become a matter of substantial debate, giventhe benefits and costs involved in its development. In Tanzania, tourism is growing fast, and itscontribution to the economy is significant. As a result, it has attracted investment and policyinitiatives to support its development. In this paper we used IO analysis to examine the significanceof tourism in creating output, income, employment and government tax revenue relative to othersectors. We distinguish between intra-sector and inter-sector impacts of tourism. Using linkageanalysis, we further examined the interdependence between tourism and other sectors. Based on themultiplier and linkage estimates, the paper used a Multi-rank index approach to determine whethertourism is a key sector in the economy. Finally, we measured the impact of international tourismexpenditure in Tanzania for 1992.

Our analysis has shown that tourism has a significant impact on output and this importance liesmainly in its inter-sector effects, i.e. tourism output impact is realised through increases in theoutput of many other sectors relative to that of hotels and restaurants. This is enhanced by itssignificant backward and forward output linkage effects, which are found to be widely and evenlydistributed in the economy. The sectors most important for tourism output impacts are food andbeverages, fishing and hunting, staple food and the wholesale and retail trade.

However, the different estimates of income multipliers showed that tourism is not particularlysignificant in terms of income generation. This may be due to the low level of wages in the sector.Tourism is found to be the second most important sector in generating indirect tax revenue. Overall,tourism has a relatively low employment impact because the employment linkage in the economy isgenerally low. The total output impact associated with international tourism in 1992 was 5.8% ofGDP, and the employment impact was 1.6% of the labour force. Tourist spending created labourincome worth Tshs 16,247 mil or (1.3% of GDP), 4.1% of GDP in indirect tax revenue, and 2.1% inimports.

27

Overall, our results imply that the tourism sector is assuming a more important role in the economy,not only as a generator of foreign exchange, but an avenue through which important structuralchanges may be possible. Our MRI results identified tourism as one of four key sectors of theeconomy, which implies that its potential for enhancing economic growth is significant. Others arecoffee, cotton, and textiles and leather. Clearly, the effectiveness of the tourism sector in enhancinggrowth depends on the output responses of other sectors, particularly those linked significantly withtourism. However, greater linkages do not imply greater incomes, indicating that sectors linked withtourism have low value added per unit of output.

However, in the context of increasing competition among destinations, it may be argued that, inorder to sustain the current impressive growth of tourism, several measures need be taken. Theseinclude diversification of the tourism product, development of infrastructure and skilled personneland an increase in promotional expenditure. Government expenditure on promotion of tourism isone of the lowest in the region and has been declining over time. Interviews with Tanzania TouristBoard (TTB) officials revealed this as the greatest constraint in realising their full potential inadvertising and developing Tanzanian tourism. The above findings point to the need for governmentto promote particular sectors through such instruments as fiscal and investment policies, and tocreate an enabling environment for tourism enterprises to increase value added in production.

ReferencesAdams, P.D. and Parmenter, B.R. (1995) ‘An applied general equilibrium analysis of the economic

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Appendix A: Trends of tourism business in Tanzania (1990-98)

Table A.1: Growth of International Tourism in Tanzania (1970-98)Index (1991=100)

Year(s) No. of ArrivalsNominalEarnings (US$ mill)* Arrivals Earnings

1970-79 131,117 14.7 70 161980-85 74,522 14.8 40 161986-90 131,089 43.3 70 461991 186,800 94.7 100 1001992 201,744 120.0 108 1271993 230,166 146.8 123 1551994 261,595 192.1 140 2031995 295,312 259.4 158 2741996 326,188 322.4 175 3401997 359,096 392.4 192 4141998 482,331 570.0 258 602Source: National Bureau of Statistics, and Tourism Department

Appendix B: Four Sector Results(Note: Table numbers corresponding to those of the main results are shown in brackets)

Table B.1: Total, Inter and Intra-sector output multipliers (Table 3)Sector Total Inter-sector Intra-sector

Oj Rank nj Rank %(nj/Oj rj Rank % (rj/Oj)Agriculture 1.267 4 0.161 4 12.7 1.107 3 87.3Manufacturing 1.702 2 0.472 2 27.7 1.230 2 72.3Tourism 1.827 1 0.804 1 44.0 1.023 4 56.0Other services 1.532 3 0.211 3 13.7 1.321 1 86.3

Table B.2: Distribution of Tourism output effects by sector (Table 4)Sector wij (j = 15) % (wij/Oj) for j = 15) RankAgriculture 0.314 17 3

Manufacturing 0.326 18 2Tourism 1.023 56 1Other services 0.163 9 4

Table B.3: Backward and Forward output linkages (Table 5)Sector Backward Linkage Forward Linkage

BLj Rank BLvj Rank FLj Rank FLvj RankAgriculture 0.801 4 0.938 3 1.036 2 0.768 2Manufacturing 1.076 2 0.848 2 0.889 4 0.974 3Tourism 1.155 1 0.570 1 1.139 1 0.710 1Other services 0.968 3 1.011 4 0.936 3 1.029 4

Table B.4: Estimates of Income Multipliers (Table 6)Sector Yhi Rank Ynhi Rank Yhi + Ynhi RankAgriculture 0.385 4 0.517 1 0.902 1Manufacturing 0.413 2 0.282 3 0.694 3Tourism 0.454 1 0.208 4 0.662 4Other services 0.395 3 0.343 2 0.738 2

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Table B.5: Import and Net-indirect Tax (leakage) Multipliers (Table 7)Sector Ymi Rank Yti Rank

Agriculture 0.067 4 0.025 4Manufacturing 0.211 2 0.057 3Tourism 0.238 1 0.079 1Other services 0.153 3 0.070 2

Table B.6: Total, Inter and Intra-sector Employment Multipliers (Table 8)Sector Total effects Inter-sector effects Intra-sector effects

Ej Rank Enj Rank % (Enj/Ej) Erj Rank % (Erj/Ej)

Agriculture 13.334 1 0.118 4 0.9 13.216 1 99.1Manufacturing 4.698 3 4.160 1 88.5 0.538 4 11.5Tourism 5.388 2 4.061 2 75.4 1.326 3 24.6Other services 2.031 4 0.682 3 33.6 1.349 2 66.4

Appendix B: Four Sector Results (continued)

Table B.7: Backward and Forward Employment Linkages (Table 9)Sector Backward Linkage Forward Linkage

EBLj Rank EBLvj Rank EFLj Rank EFLvj RankAgriculture 2.096 1 3.609 4 12.373 1 2.653 4Manufacturing 0.738 3 1.762 3 0.389 4 0.644 1Tourism 0.847 2 1.461 2 1.476 2 0.809 2Other services 0.319 4 0.850 1 0.956 3 1.040 3

Table B.8: Key Sector by Individual Criteria (Table 10)Output Employment Income Tax RevenueSector Freq. Sector Freq. Sector Freq. Sector Freq.Tourism 6 Tourism 6 Manufacturing 2 Tourism 1Manufacturing 5 Agriculture 4 Other services 2 Other services 1Agriculture 2 Manufacturing 2 Agriculture 1 Agriculture 0Other services 1 Other services 2 Tourism 1 Manufacturing 0Sample size (S) 14 14 6 2Mean 3.5 3.5 1.5 0.5

Table B.9: Key Sector by all criteria (Table 11)Sector Freq. RankTourism 3 1Agriculture 2 2Manufacturing 2 3Other services 1 4Sample size (S) 8Mean 2