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Water Policy 13 (2011) 220–231
Market efficiency and welfare effects of inter-sectoral waterallocation in South Africa
James S. Juanaa, Kenneth M. Strzepekb and Johann F. Kirstenc
aCorresponding author. Department of Economics, University of Botswana, P/Bag 0022, Gaborone, Botswana
Tel: (267)355 2150/(267)74 58 79 82; E-mail: [email protected]; [email protected] of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, USA
cDepartment of Agricultural Economics, Extension and Rural Development, Faculty of Agriculture and Natural Sciences,
University of Pretoria, Pretoria 0002, South Africa
Abstract
The need for increased agricultural production to meet the growing demand for food, coupled with concernsfor environmental sustainability, economic growth and poverty reduction has increased demand on the alreadyscarce water in South Africa. At the same time, because of agriculture’s minimal contribution, compared to theindustrial and mining sectors, to South Africa’s GDP and employment, the call to reallocate water from agricul-ture to non-agricultural use has been intensified.
This study updates the 1998 Social Accounting Matrix (SAM) for South Africa and uses the computablegeneral equilibrium model to analyze the impact of water reallocation from agriculture to the non-agriculturalsectors on output growth, value added at factor cost, which captures the payments from the production sectorsto the factors of production, and households’ welfare.
Using different water reallocation scenarios, the simulation results indicate that water reallocation from agri-culture to non-agricultural sectors beyond the level of a market allocation scenario will lead to a decline in sec-toral output and a significant deterioration in the welfare of poor households. It thus undermines developmentefforts aimed at reducing the existing level of poverty in the country.
Keywords: Computable general equilibrium; Households’ welfare; Sectoral output; Value added
1. Introduction
The need to maintain a sustainable environment, economic growth and to increase agriculturalproduction to meet global food requirements has increased the demand for the world’s water resources.This has raised concerns about increasing the efficiency of water use. In the last decade, the number of
doi: 10.2166/wp.2010.096
�C IWA Publishing 2011
countries facing the problem of water scarcity and insufficient water supply has increased sharply. At theglobal level, while per capita water availability is declining, withdrawals are projected to increase morerapidly, especially in developing countries (Rosegrant et al., 2003). Generally, water scarcity raises twoquestions: (i) to what extent can water resources be efficiently, equitably and sustainably allocated andused? and (ii) what are the possible ways and means by which water scarcity can be alleviated or mitigatedin support of further development? The answers to these questions enable water managers to designappropriate water development policies and allocation strategies.
In South Africa, as the economy grows, the competition for water amongst agriculture, mining,manufacturing industries, and domestic and environmental uses increases, while water supply sources areprojected to be inelastic in the future. These factors increase the value of water and, hence, the benefits thatcould be achieved from efficient water allocation amongst the different sectors.
Table 1 shows that while, irrigation accounts for about 62% of the total water requirements in South Africa,the agricultural sector contributes only 4% to the gross domestic product (GDP) and employs only about 11%of the total number of employees in the country. At the same time, the mining and manufacturing industrieswhich contribute about 8% and 23%, respectively, to the GDP and employ about 7% and 19%, respectively,of the total number of employees, account for only 10% and 5%, respectively, of total water use in the country(DWAF, 2004: 16–18). Thus, there is an economic reason to reallocate water from agriculture to the non-agriculture sectors to promote sectoral water use efficiency. Under efficiency considerations, the issue ofreallocating water from low to high-value uses often emerges as rational. In most cases, however, efficiencyconsiderations fail to consider distributional or equity issues. Therefore, the question is not only how muchdoes a particular sector contribute to the GDP but also how can a given water resource be allocated such thatthe standard of living of the critical mass of people is improved. This justifies the inclusion of welfareconsiderations into the economic valuation framework. Against this background, this study uses thecomputable general equilibrium model to critically analyse the impact of water reallocation from agricultureto the non-agriculture sectors on sectoral output, added value, and households’ welfare in South Africa.
2. Data, theoretical framework and modelling procedure
This section discusses the data and model used in the study. Particular attention is given to the standardComputable General Equilibrium (CGE) conditions, and the behavioural assumptions for the study. It alsobriefly discusses the transformation of water from a production sector, as recorded in previous CGE modelsin South Africa, to a factor of production.
2.1. Description and sources of data
The study uses an updated version of the 1998 social accounting matrix (SAM) for South Africa whichwas compiled by Thurlow & van Seventer (2002). The Trade and Industrial Policy Secretariat (TIPS) 2003
Table 1. Water use, contribution to GDP and employment, by sector (source: DWAF, 2004: 16–18).
Sector Water use (%) Share of GDP (%) Employment (%)
Agriculture 62 4 11Mining 10 8 7Manufacturing 5 23 19
221J. S. Juana et al. / Water Policy 13 (2011) 220–231
supply-use table was used to update the 1998 activities and commodities accounts (TIPS, 2004).Information on household income and expenditure patterns was extracted from Statistics South Africa2001 census figures (STATSSA, 2004).
The updated SAM was aggregated to 13 activities/commodities consisting of agriculture (AGR), mining(MIN), beverages and tobacco (AGI), textiles and wearing apparel (TEX), wood, paper and printing (PPP),petroleum products (PET), chemicals (CHM), heavy manufacturing (HEV), machinery and equipment(MAC), other manufacturing (OHM), electricity (ELE), construction (CON) and services (SER). Theseaggregations reflect the structure of water use by these sectors or sub-sectors.
2.1.1. Water. As a key factor in this study, the use of water requires a detailed description of the waterdata sources and adjustments made to the SAM to properly account for sectoral water allocation and use inthe economy. The water supply information from the municipalities’ billing records grossly understates theactual water used by the different sectors, because most sectors use self-supplied water. These entries weretherefore replaced by the information published in Statistics South Africa water accounts for the nineteenwater management areas (STATSSA, 2004). Using the municipal water tariff schedule, the monetary valueof the physical quantities of water used by each production sector was computed. This was done toascertain the resource cost of water used in each sector. Therefore, each water account entry indicates thecost of water used in each sector.
In Thurlow & van Seventer (2002), water is treated as a production sector, with the row accountsshowing water used as a fixed intermediate input by each of the other production sectors and as a final goodby households. It also shows payments by these sectors and institutions to the water sector. The columnentries show payments by the water sector to the other sectors for the use of other intermediate inputs andfactors services. A key objective in this study is to investigate the macroeconomic implications of waterreallocation from agriculture to the non-agriculture sectors on the basis of efficiency. Consequently,treating water as a fixed intermediate input (as is usually the approach in standard CGE models) is notsuitable. Therefore, water enters into the production process as a third factor of production (a value addedfunction) and not as a fixed intermediate input.
As a factor of production, the row accounts represent distribution of water among the production sectorsand the respective payments by these production sectors for the use of this factor. Households initially usedwater as a final good and made payments to the water sector for this good. These payments are removedfrom the water accounts and transferred to government which provides the service via its municipal watersupply networks. The initial account payments from the other production sectors to the water sector aremaintained in the adjusted SAM as payments for the use of this factor. Water no longer pays for factorservices as well as for fixed intermediate inputs. To balance the SAM again, the study assumes that allfactor payments to water accrue to government, which is recorded as a part of government receipts. Thisincreases government revenue. In its expenditure accounts, government’s net investments as well aspayments to the services sector increases. This is followed by a corresponding increase in investments inwater delivery infrastructure and services payments to the factors of production. The increased factorpayments are finally redistributed among the various household categories. Government also pays the restof the world for the use of water from sources outside South Africa. The adjusted SAM is presented inTable A1 in the Appendix.
The adjusted SAM has three factors of production (water, capital and labour, which itself comprisesunskilled, medium-skilled and high-skilled labour). There are also five household accounts (the first to thefifth quintile). Each quintile represents 20% of the households in that category. Ranked from first to fifth,
222 J. S. Juana et al. / Water Policy 13 (2011) 220–231
the quintiles represent the least-income, low-income, middle-income, high-income and highest-incomehouseholds, respectively.
Households receive income from wages and from both local sources (government and inter-personaltransfers) and international transfers. Their disposable income is allocated to consumption and savings.Households’ consumption is divided into food and non-food consumption. Food consumption isdetermined by households’ expenditure on agriculture and on the beverages and tobacco sectors. Non-food consumption expenditures are those incurred on the other sectors, which are further divided intodurables and non durables. These divisions are the basis for welfare policy investigations. Sectoral output issold to the production sectors as intermediate input, consumed domestically, or exported. Governmentaccounts, which were broken down into expenditure and income accounts in the original SAM, aremaintained.
2.2. The theoretical framework
A Computable General Equilibrium (CGE) model is used to investigate the impact of efficient waterallocation on households’ welfare in South Africa. The study adopts the CGE framework developed byStrzepek & Carbone (2007). This framework uses the mathematical programming system for generalequilibrium (MPSGE), which is GAMS extension developed by Rutherford (1995) (note: the 1995 versionis freely available on the website. 1998 version can only accessed within the University of ColoradoCampus), with the MCP GAMS solver. MPSGE is a library of functions and Jacobian evaluation routineswhich facilitate the formulation and analysis of Applied General Equilibrium (AGE) models. The MPSGEprogram provides a relatively simpler way to write down and analyze complicated systems of nonlinearinequalities. The language is based on nested constant elasticity of substitution utility functions andproduction functions. The data requirements for a model include share and elasticity parameters for all theconsumers and production sectors included in the model. These may or may not be calibrated from aconsistent benchmark equilibrium dataset (Rutherford, 1995).The model uses multi-level nested productionfunctions to determine the level of production. Sectoral outputs are represented by a Leontief’scombination of fixed intermediate consumption and value added. The model also specifies a constantelasticity of substitution (CES) function to establish the relationship between inputs and output. However,the use of capital is modelled by a Leontief’s fixed proportions function, because the short-run use ofcapital is fixed and sector specific. Conversely, water and the three labour categories are freely mobileacross sectors, except where specified. Therefore, the use of these inputs is modelled by the CES function.This allows the functioning of a competitive market to efficiently allocate the mobile factors. Therefore,these mobile factors move to sectors where factor returns are highest. The free movement of these factorsof production enhances the adjustment of wages for each of the three labour categories to achieveequilibrium in the factor markets.
The model uses the constant elasticity of transformation (CET) function to formulate the imperfectsubstitution between domestic consumption of sectoral output and export. The CET function is also used tomodel the imperfect substitution between domestically produced and imported goods. The imperfectsubstitutability modelled above enhances the importation and exportation of the same goods.
The factor market for water is closed by assuming that the quantity of water used is fixed and that totalsectoral water use is equal to the total sectoral water supply; hence, there are no reserves. The capitaland labour markets are closed by assuming that the demand for each of these factors is equal to theirsupply. These assumptions imply full employment of the factors. The saving-investment closure assumes
223J. S. Juana et al. / Water Policy 13 (2011) 220–231
that savings equal investment and that government income (receipts) equals government spending(payments).
2.3. The experimental simulations
The situation documented in the adjusted SAM is the base situation which reflects the current sectoralwater allocation in South Africa. All input and output prices, including water are normalized in this baseperiod. This situation represents water market inefficiency, because the price paid by the production sectorsdoes not reflect the competitive market price of water.
2.3.1. Scenario 1. To achieve the market efficient level of water allocation, the study uses the sectoralmarginal values of water estimated in a related study as shadow prices to calibrate the SAM and allowwater use, output, output prices, and input prices to adjust to equilibrium levels. This is referred to as the‘‘market allocation of water’’. Changes in the indices of output, value added at factor cost, and householdwelfare under the market efficient allocation of water scenario are all compared to the base indices.
2.3.2. Scenario 2. This is the water reallocation scenario. In this scenario 30%, 20%, 10% and 5% ofwater in the agriculture sector is reallocated to, and redistributed among the non- agriculture sectors usingthe computed sectoral marginal values as coefficients. The study then investigates the impact of thesereallocations on sectoral output, value added, and households’ income and consumption.
2.4. Analysis of households’ welfare
The study uses the concept of equivalent variation (EV) discussed in Chitiga & Mabugu (2006) toanalyze the impact of the different water reallocation scenarios on households’ welfare. EV compares thelevel of households’ consumption at the base prices and incomes to the levels of consumption at the newprices and incomes in the water reallocation scenarios. It provides an estimate of the amount of income(money) that should be given to an individual, a group or an economy to make them as well off as theywere before the specified change. In this study the transfer of water from the agriculture sector to non-agriculture sectors is likely to impact the welfare of the different household categories through its impact onfactor remuneration and income/consumption. Hence, EV measures the loss of well-being resulting fromthe income decrease associated with the transfer of water from the agriculture to non-agriculture sectors.Alternatively, it measures the gain in well-being resulting from the income increase associated with thetransfer of water from agriculture to non-agriculture sectors.
Functionally, EV is denoted as:
EV ¼ P01
P11
� �g P02
P12
� �1�gY1 � Y0 ð1Þ
where P10 is the price of good 1 in the base model; P1
1 is the price of good 1 after the simulation; P20 is the
price of good 2 in the base model; P21 is the price of good 2 after the simulation; Y0 is the income in the
base model and Y1 is households’ income after the simulation.
224 J. S. Juana et al. / Water Policy 13 (2011) 220–231
A positive EV implies welfare improvement (gain), while a negative EV implies welfare deterioration(loss). An increase in households’ expenditure or income implies welfare improvement, while a decreaseimplies welfare deterioration.
3. Presentation of results
In this section, the study discusses potential changes in sectoral output, added value and households’welfare that would result from the different sectoral water reallocation scenarios.
3.1. The impact of water reallocation on sectoral output
The first block of Table 2 shows the impact of water reallocation on sectoral output. Columns 2 and 3show the base sectoral output and base indices respectively, and column 4 shows the impact of marketallocation of water on sectoral output. The results indicate that the market allocation of water among theproduction sectors can potentially lead to an overall increase in sectoral output by 6.8%. However, marketallocation of water can potentially lead to a significant decline in the output of agriculture, beverages andtobacco, and the services sectors. Columns 5, 6, 7 and 8 show that further reallocation of water fromagriculture to the non-agriculture sectors beyond market allocation leads to an overall decline in sectoraloutput, although output increases in some of the water recipient sectors.
3.2. The impact of water reallocation on added value
The alterations in sectoral output have consequences for payments to the factors of production (valueadded at factor cost). The price of a factor of production is determined by the demand for the factor relativeto its supply, which is also determined by the level of sectoral output. Growth in sectoral output leads to acorresponding increase in factor demand, and vice-versa. Since the model assumes full factor employment,factor prices keep adjusting until a competitive equilibrium is achieved. These consequences are presentedin the second block of Table 2. Column 2 shows the base factor payments, while column 3 shows the baseindices. Column 4 shows the percentage changes in base indices due to market allocation of water, andcolumns 5, 6, 7 and 8 show the percentage changes to base indices with further reallocation of water fromthe agriculture to the non-agriculture sectors on the basis of efficiency.
The results show that market allocation of water would potentially increase overall value added by about11%. Specifically, water tariff increases by about 15%, interest on capital by about 5%, and wages ofunskilled, medium-skilled and high-skilled labour by about 9%, 10% and 12%, respectively. Althoughfurther water reallocation would lead to an overall increase in value added, there are mixed results forspecific factors. For example, water tariffs significantly increase for all the water reallocation scenarios.Also, the wages of medium-skilled and high-skilled labour significantly increase, while interest payment oncapital and the wages of unskilled labour decline.
3.3. The impact of water reallocation on households’ welfare and agricultural trade
The alterations in sectoral output as a consequence of sectoral water reallocations on the basis ofefficiency, and the implied impacts on value added are transmitted to the various household categories.
225J. S. Juana et al. / Water Policy 13 (2011) 220–231
Tab
le2.
Perc
enta
gech
ange
sin
outp
ut,
adde
dva
lue
and
hous
ehol
ds’
wel
fare
byse
ctor
,un
der
the
diff
eren
tw
ater
real
loca
tion
scen
ario
s.
Wat
erre
allo
catio
nsc
enar
ios
Sect
orB
ase
figur
es(R
mill
ion)
Bas
ein
dex
Mar
ket
allo
catio
nm
30m
20m
10m
05
1Se
ctor
alO
utpu
tA
gric
ultu
re10
7549
.30
1.00
00�
1.04
78�
1.39
16�
1.21
31�
1.12
97�
1.04
36M
inin
g18
6475
.60
1.00
001.
2203
1.16
121.
1457
1.09
421.
0784
Agr
o-in
dust
ry23
8395
.70
1.00
00�
1.02
75�
1.06
73�
1.04
95�
1.02
87�
1.01
92L
eath
er&
wea
ring
appa
rel
8031
2.64
1.00
001.
0001
1.01
811.
0109
1.00
971.
0046
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ran
dpu
lp79
506.
521.
0000
�1.
0136
1.06
351.
0316
1.02
441.
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oleu
m82
195.
241.
0000
�1.
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0627
�1.
0264
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asic
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ical
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8622
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0446
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eavy
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alm
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ring
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57.8
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981.
1988
1.13
681.
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1.00
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achi
nery
&eq
uipm
ent
2952
22.1
01.
0000
1.00
171.
0388
1.02
021.
0223
1.01
47O
ther
man
ufac
turi
ng10
0214
.20
1.00
001.
0044
1.07
541.
0423
1.04
881.
0326
Ele
ctri
city
5731
1.97
1.00
001.
0105
�1.
0249
�1.
0153
�1.
0005
1.00
27C
onst
ruct
ion
1504
34.8
01.
0000
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1.02
721.
0134
1.00
481.
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Serv
ices
1831
373.
001.
0000
�1.
0249
�1.
0896
�1.
0796
�1.
0175
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0011
Tot
al35
3357
1.37
1.00
001.
0679
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0597
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0239
�1.
0168
1.00
51
2A
dded
valu
eC
apita
l18
218.
111.
0000
1.04
72�
1.03
27�
1.01
90�
1.01
601.
0001
Wat
er37
0416
.40
1.00
001.
1481
1.08
151.
0583
1.03
941.
0293
Uns
kille
dla
bour
1415
14.5
01.
0000
1.08
74�
1.10
40�
1.08
40�
1.05
011.
0188
Med
ium
-Ski
lled
labo
ur16
9071
.90
1.00
001.
1043
1.03
921.
0175
1.01
651.
0149
Hig
h-Sk
illed
labo
ur86
538.
551.
0000
1.12
351.
0273
1.01
040.
0096
1.00
37T
otal
7857
59.4
61.
0000
1.10
901.
0139
1.02
601.
0290
1.03
20
3H
ouse
hold
s’W
elfa
reL
east
-inc
ome
1767
4.90
1.00
001.
0973
�1.
0649
�1.
0317
�1.
0264
�1.
0110
Low
-inc
ome
3355
3.95
1.00
001.
0897
�1.
0611
�1.
0291
�1.
0240
�1.
0158
Mid
dle-
inco
me
2819
96.4
01.
0000
1.04
181.
0605
1.07
261.
0754
1.07
63H
igh-
inco
me
1468
35.8
01.
0000
1.02
631.
0647
1.06
261.
0608
1.05
99H
ighe
st-i
ncom
e11
4287
.00
1.00
001.
0104
1.07
981.
0561
1.04
431.
0435
Tot
al59
4348
.05
1.00
001.
0639
1.00
121.
0315
1.05
261.
0128
4A
gric
ultu
ral
Tra
deA
gric
ultu
ral
Exp
ort
5928
6.73
1.00
00�
1.03
74�
1.19
83�
1.11
73�
1.07
42�
1.05
07A
gric
ultu
ral
Impo
rt37
517.
381.
0000
1.05
541.
1261
1.08
521.
0349
1.01
32A
gric
ultu
ral
Supp
lies
1012
30.2
21.
0000
�1.
0199
�1.
1139
�1.
0868
�1.
0635
�1.
0357
226 J. S. Juana et al. / Water Policy 13 (2011) 220–231
Generally, market allocation of water among the production sectors leads to an overall increase inhouseholds’ welfare. This is indicated by a positive EV of 1.0439, which implies a 4.39% welfareimprovement. The simulation results show that further reallocation of water from agriculture to the non-agriculture sectors generally leads to households’ welfare improvement, although the welfare of the leastand low-income households deteriorates. The third block of Table 2 presents details of the households’welfare implications of inter-sectoral water reallocations. For example, a 40% transfer of water fromagriculture to the non-agriculture sector leads to a 6% deterioration in the welfare of the least and low-income households’ welfare. On the other hand, the welfare of the middle-income, high and highest-income households improve by 4%, 4.6% and 1%, respectively. The same trend of welfare changes arerecorded for the other water reallocation scenarios beyond the market allocation scenario.
Inter-sectoral water reallocation on the basis of efficiency impacts domestic supply of agriculturalcommodities, agricultural exports and imports. Generally, reallocation of water from agriculture to the non-agriculture sectors leads to a decline in the agriculture sector’s output. Consequently, domestic supply ofagricultural commodities decreases relative to the demand for these commodities. Therefore, agriculturalexports decrease. To meet the domestic demand, agricultural imports increase. For example, when 5% ofthe agriculture sector’s water is reallocated to the non-agriculture sectors, domestic supply of agriculturalcommodities decreases by 3.57%, agricultural exports decline by 5% and agricultural imports increase by1.32%. All the other reallocation scenarios, including market allocation, show a similar pattern of changesin domestic agriculture supplies, exports and imports of agricultural commodities.
4. Discussion of research findings, summary and conclusions
The simulation results show that market allocation of water among the production sectors generally leadsto a growth in sectoral output, although agriculture and related sectors’ output decline. With the marketmechanism, water is reallocated from agriculture to the non-agriculture sectors, because of the lowmarginal productivity of water in that sector. Therefore, output declines in agriculture due to the decreaseof water availability. The output of the sectors that are highly dependent on agriculture as a main source oftheir intermediate input or as a purchaser of their inputs also decline. With continued reallocation of waterbeyond the market allocation, total sectoral output falls. This is because the further increase in sectoralwater use by these sectors does not lead to a proportionate increase in output.
Alterations in sectoral output due to changes in sectoral water use have consequences for factorremuneration (value added). With alterations in sectoral output, factor prices keep changing until fullemployment market equilibrium is attained. The prices of factors which are in over supply fall, while theprices of those in excess demand increase. With the market allocation of water, value added at factor costincreases for each of the production factors. However, further reallocation from agriculture to the non-agriculture sectors leads to a decline in total added value. A decrease in agriculture sector’s output leads toa decrease in the demand for unskilled labour. To clear the unskilled-labour market, wages decline leadingto a decrease in the total wages paid to this labour category. Conversely, the demand for capital, medium-skilled and highly-skilled labour increases more than the supply. Therefore, to clear these markets, theirprices increase leading to an overall increase in the added value for these factor categories.
Alterations in sectoral output and corresponding changes in value added at factor costs are transmitted tothe various household categories, which are the owners of the resources. With the market allocation, wateris utilized efficiently, hence there is growth in sectoral output and value added. This leads to a welfare
227J. S. Juana et al. / Water Policy 13 (2011) 220–231
improvement for all the household categories. Although further reallocation leads to an improvement in theoverall welfare of households, the welfare of the least and low-income households deteriorates. This iscaused by the decline in value added for unskilled labourers due to a decline in the output of the agricultureand related sectors. The main source of income for the least and low-income households are the wages ofthe unskilled labourers. Therefore, a decline in unskilled labour wages leads to a decline in the income andconsumption expenditures of these household categories.
A decline in the agriculture sector’s output leads to a decline in domestic agriculture supplies. Therefore,agricultural exports decrease and imports increase to satisfy the domestic demand for agricultural products.In summary, further reallocation from agriculture to the non-agriculture sectors beyond the marketallocation level not only leads to a decline in sectoral output but also to a deterioration in the welfareof the most poverty-stricken households.
Acknowledgements
The authors are grateful to the International Food Policy Research Institute (IFPRI), Statistics SouthAfrica (STATSSA), Trade and Industrial Policy Strategy of South Africa (TIPS) and the Department ofWater Affairs and Forestry (DWAF), for the use of their different data sources; and to the AfricanEconomic Research Consortium (AERC), United States Agency for International Development (USAID)and the University of Colorado, Boulder, for jointly funding the study.
References
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Department of Water Affairs and Forestry (DWAF) (2004). Towards a Framework for Water Allocation Planning: A draft positionpaper for water allocation reform in South Africa. Water Allocations Directorate, DWAF, Pretoria, South Africa.
Rosegrant, M. W., Cai, X. & Cline, S. A. (2003). World water and food to 2025: Dealing with scarcity. International Food PolicyResearch Institute, Washington D.C.
Rutherford, R. T. (1995). Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem: An overview of theModelling Framework and Syntax Department of Economics. Working Paper, University of Colorado. Available atwww.gams.com/solvers/mpsge/syntax.htm.
Statistics South Africa (STATSSA) (2004). Natural resource accounts: Water accounts for nineteen water management areas.Report No. 04-05-01 (2000), STATSSA, Pretoria, South Africa.
Strzepek, K. M. & Carbone, J. (2007). A CGE model for South Africa with explicit modelling of water as a factor of production.Department of Civil, Environmental and Architectural Engineering working paper 2007-22, University of Colorado.
Thurlow, J. & van Seventer, D. E. N. (2002). A standard computable general equilibrium model for South Africa. Trade andMacroeconomics Division Paper No.100, International Food Policy Research Institute, Washington DC and Trade andIndustrial Policy Strategies, Johannesburg, South Africa.
Trade and Industrial Policy Secretariat (TIPS) (2004). Online resources for trade and industrial policy research in SouthAfrica: South African economic indicators. Trade and Industrial Policy Secretariat, Pretoria, South Africa Available at,www.Tips.org.za
Received 13 July 2009; accepted in revised form 31 October 2009. Available online 20 October 2010
228 J. S. Juana et al. / Water Policy 13 (2011) 220–231
Tab
leA
1.So
uth
Afr
ican
1998
Soci
alA
ccou
ntin
gM
atri
x.
AG
RM
INA
GI
TE
XPP
PPE
TC
HM
HE
VM
AC
OH
ME
LE
CO
NSE
RW
AT
AG
R51
179.
4439
3123
6.81
614.
5512
57.4
41.
3444
1.12
83.3
920
6.14
2292
.17
9.71
713.
2413
973.
90
MIN
174.
4984
078.
3911
7.13
23.8
921
6.72
1193
5.15
2368
.04
9466
.06
156.
3716
69.4
6439
52.1
641
73.3
1685
8.40
190
AG
I49
17.8
8960
.46
9982
3.11
1178
.62
134.
750
1103
.543
16.9
0133
8.93
41.3
6907
29.7
40
1261
0.38
0T
EX
536.
2545
0.43
109.
4447
3344
2.74
38.1
2039
024
0.58
4144
.69
770.
6393
1304
.204
6.35
523.
9929
35.8
210
PPP
322.
9815
3.46
2824
.19
353.
0498
4196
6.02
1.58
1332
.618
281.
6255
383.
1449
905.
4129
28.8
949
0.88
2155
5.07
0PE
T29
07.7
312
56.0
752
3.55
110.
127
0.22
2332
8.16
5421
.384
1399
.62
526.
3528
7.05
103.
7219
87.1
219
221.
590
CH
M45
44.8
332
54.8
296
7.39
2557
.774
2313
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362.
9285
6899
6.97
2654
.39
2799
.127
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64.8
785
314
098.
830
HE
V58
5.69
2808
.21
2070
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295.
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139.
5681
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3715
26.5
2194
345.
8515
303.
4119
23.5
718
0.92
1259
5.27
7760
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2005
.96
5118
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517.
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8.70
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9.6
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1.00
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4511
5517
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3.31
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63.2
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59.4
231
216.
80
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0.75
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2465
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4579
1292
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26.2
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8.55
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LE
452.
2737
33.2
362
4.92
279.
1751
2.28
257.
9211
25.7
346
65.7
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7.06
474.
7228
993.
2610
67.5
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77.4
040
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6.05
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00
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00
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50.8
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9847
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0.78
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3.53
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2.47
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9.26
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4.49
2223
8.34
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0.81
2009
5.73
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810
0W
AT
225.
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6.54
217.
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87.2
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CA
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246.
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15.7
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28.8
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34.2
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38.9
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875
62.6
4630
27.2
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465.
582
81.7
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LA
BL
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01.7
8716
898.
249
72.9
3252
65.0
6518
95.6
0754
2.89
1721
70.9
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21.9
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27.5
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07.7
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29.1
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40.5
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10
LA
BM
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1040
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3456
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922.
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2003
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HI
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2348
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2859
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822.
2685
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855.
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22.8
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90.9
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37.4
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14.7
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270.
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00
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RO
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19.8
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962.
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46.4
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11.2
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AL
1075
49.3
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75.6
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95.7
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2.64
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6.52
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5.24
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22.5
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57.8
2952
22.1
1002
14.2
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1.97
1504
34.8
1831
373
1821
8.11
229J. S. Juana et al. / Water Policy 13 (2011) 220–231
CA
PL
AB
LO
LA
BM
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LA
BH
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HH
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H3
HH
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VR
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61.3
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00
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75.9
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H2
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00
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2.65
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1996
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429.
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00
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00
00
330.
4448
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4023
6.15
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0.71
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7.12
3576
7.22
2100
36.8
017
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V0
00
09.
1047
4121
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3273
9.60
5272
5.47
3755
5.38
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7.36
3248
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3398
1437
61.7
RO
W27
305
010
00.7
8370
5.21
70.
0743
990.
3803
1423
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3657
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7644
.237
1789
5851
00
2191
09T
OT
AL
3704
16.4
1415
14.5
1690
71.9
8653
8.55
1767
4.9
3355
3.95
2819
96.4
1468
35.8
1142
8734
3111
.444
1560
.314
3761
.721
9109
6061
221
Tab
leA
1.(c
ontin
ued)
230 J. S. Juana et al. / Water Policy 13 (2011) 220–231
KEY FOR THE SAM
PRODUCTION SECTORAGR AgricultureMIN: MiningAGI: Agricultural ManufacturingTEX: Clothing and TextilePPP: Pulp and Paper ManufacturingPET: Petrol Chemical ManufacturingCHM: Other Chemical ManufacturingHEV: Heavy ManufacturingMAC: Machinery and Equipments ManufacturingOHM: Other ManufacturingCON: ConstructionELE: ElectricitySER: Services
FACTORS OF PRODUCTIONWAT: WaterCAP: CapLABLO: Unskilled LabourLABMED: Medium Skilled LabourLABHI: High Skilled Labour
HOUSEHOLD CATEGORIESHH1: Least Income HouseholdsHH2: Low Income HouseholdsHH3: Middle Income HouseholdsHH4: High Income HouseholdsHH5: Highest Income Households
OTHER INSTITUTIONSFIRMS: FirmsGOV: GovernmentROW: Rest of the World
INVESTMENTINV: Investment
231J. S. Juana et al. / Water Policy 13 (2011) 220–231