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Socioeconomic Impact of Drought in Botswana
Article in International Journal of Environment and Sustainable Development · June 2014
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International Journal of Environment and Development
Vol. 11, No. 1, June 2014, pp. 43-60
The Socio-economic Impact of Drought in Botswana
J. S. JUANA1, P. M. MAKEPE2, K. T. MANGADI3 & N. NARAYANA4
Abstract: Drought has serious consequences onthe economy of a country as a whole and
particularly the socio-economic lifeof agricultural communities. In addition to the economy
and people, drought also affects the environment as plants and animals depend on water, just
as people do. Understanding drought conditions, societal vulnerability and their related effects
on one another provide us with lessons that can aid in dealing with recurrent drought conditions.
Using a computable general equilibrium model, this study investigates the economy-wide impact
of drought in Botswana. The simulation results show that periodic drought leads to a significant
decline in sectoral output, factor remuneration and deterioration in households’ welfare. Shortrun policy interventions that dampen the adverse consequences of drought on households’
welfare include food stamp or food aid, which is equivalent to the loss in households’ income/
consumption.
JEL: C68, D 60, E16, H31
Keywords: Computable general equilibrium, sectoral output, factor payments, households
’income/consumption, households’ welfare.
1. INTRODUCTION
Drought is a serious socio-economic challenge to many countries in the world. Drought
is the result of the deficiency in water supply, whether atmospheric, surface or ground
water. A drought can occurs when a region receives consistently below average
precipitation. It can have a substantial impact on the ecosystem and agriculture of the
affected region. Although droughts can persist for several years, even a short, intense
drought can cause significant damage and harm to theeconomy. Prolonged droughts
have caused mass migrations and humanitarian crises. Agriculture is affected adversely
1
2
3
4
Senior Lecturer, Department of Economics, University of Botswana, P/Bag UB 705, Gaborone, Botswana,
(corresponding author), E-mail: james.juana@mopipi.ub.bw/jamessjuana@yahoo.com
Associate Professor and Head, Department of Economics, University of Botswana, P/Bag UB 705,
Gaborone, Botswana, E-mail: makepepm@mopipi.ub.bw
Lecturer, Department of Economics, University of Botswana, P/Bag UB 705, Gaborone, Botswana,
E-mail: Kagiso.mangadi@mopipi.ub.bw
Professor of Economics, Department of Economics, University of Botswana, Private Bag UB 705,
Gaborone, Botswana, E-mail: narayana@mopipi.ub.bw
44 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
because of drought. However, the effects of drought can beminimised to a greater
extentby extending irrigation facilities and adopting crop rotation methods. Failure to
develop appropriate drought mitigation strategies results in loss of production and
livelihood to people.
Botswana is a semi-arid country in the centre of Southern Africa, with a total area
of 58,173,000 hectares. The country is water stressed, with an average annual
precipitation rate of 416 mm/year, which ranges from 650 mm/year in the northwest
to 250 mm/year in the southwest. The country’s total internal renewable water is 2.4
km3/year. Of this total, surface water produced internally accounts for 0.8 km 3, while
groundwater accounts for 1.7km3/year and an overlap of 0.1 km3/year (FAO, 2005).
Apart from the perennial rivers and wetlands in the north, and the over utilized
Limpopo River and its tributaries in the east, Botswana lacks enough surface water for
both socio-economic and environmental sustainable development.
The problem of water scarcity in Botswana is exacerbated by the persistent
recurrence of drought. For the purpose of this study, drought is characterized by low
precipitation compared to the normal amount, low humidity, high temperatures, and
high wind velocity (Masike and Urich, 2008). When these conditions occur over an
extended period, drought causes low water supplies that are inadequate to support
economic activities.
It has been established that ElNino Southern Oscillation (ENSO) modulates rainfall
variability over southern Africa (Nicholson et al, 2001), and the effects have been adverse
for Botswana, where research has shown that a high correlation exists between severe
drought in the country and ENSO events (Nicholson and Kim, 1997). Specifically, in
fifteen out of twenty ENSO events, rainfall (precipitation) has been below normal in
Botswana (Nicholson et al, 2001).
Studies have shown that drought-induced water deficiency affects production,
sales and business operations in a variety of economic sectors (Dung et al, 2010). The
effects of drought could be categorized into two; direct or primary, and indirect or
secondary and tertiary effects. The direct effect is evidenced by crop failure, livestock
death or weight reduction in agriculture and its related sectors, while the secondary
effect is evidenced by the inter-sectoral linkages, value added at factor cost, and
households’ income and general welfare. Mogotsi, et al (2010) and Masike andUrich
(2008) have shown that drought in Botswana leads to livestock death, reduced crop
yield and livestock weight, low pasture production and increased distances to water
livestock. These studies also show that droughts in Botswana affect the livelihoods
and revenues of the rural population who are the most vulnerable. However, these or
other studies found in the available literature have not quantified the primary,
secondary or tertiary effects of droughts in Botswana. Therefore, questions of research
interest include;
(i) What is the economy-wide impact of drought on sectoral output in Botswana?
(ii) How does the impact of drought on sectoral output affect factor remunerations?
The Socio-economic Impact of Drought in Botswana • 45
(iii) Which households are the most vulnerable to the impact of drought in the
country?
(iv) What is the drought-induced loss in households’ income and welfare?
The answers to these questions necessitate an empirical inquiry into the socioeconomic impact of drought in Botswana, with the view to formulating and testing
both short and long-term policy interventions that can help the vulnerable population
to cope with or adapt to the adverse consequences of drought in the country. The
specific objectives of the study are;
(i) To quantify the economy-wide impact of drought on sectoral output in
Botswana.
(ii) To investigate which household categories are adversely impacted by drought
in the country
(iii) To analyse the income and welfare impacts of drought on the vulnerable
households.
(iv) The simulate the impact of short-term public policies implemented to dampen
the potential impact of drought in Botswana
2. PREVIOUS STUDIES
Many empirical studies have used different approaches to assess the impact of drought
on state and national economies. Using the input-output model, Dirsen et al (2002)
examined the economic impact of the 2002 drought in South Dakota, USA. The authors
measured both the direct and indirect impacts of drought on crops and livestock
production, as well as the secondary effects on the whole economy. The direct and
indirect impacts were estimated at US$1.8 billion. In a related study, Dirsen and Taylor
(2003) re-examined the drought impact in South Dakota. In this study, the authors
considered the improved market conditions and the direct food commodity aid of
US$100 million provided by the federal government of USA to the state. The re-assessed
impact estimated a loss of US$1.4 billion. This shows that, with improvement in market
conditions, the full potential impact of drought might be reduced, because some regions
in the economy might benefit from the drought-induced supply shortages, which
increases the price of basic food and non-food commodities.
Most climate change studies have estimated the impact of drought, through its
impact of water resources. Garcia-Vilanas (2006) used a stylized CGE approach to
analyse the impact of water use restrictions and water quality reductions on consumer
welfare during the drought period of the early 1990s in Spain. The empirical results
show that the average welfare losses were €138.3 per water user.
Bou-Zeid and El-Fadel (2002) used simulations of climate change predictions from
several GCMs to investigate the potential impacts of climate change on water resources
in the Middle-East Region in the 2020s, with Lebanon as a case study. The study found
that as a result of drought, existing water shortages in the region would be further
exacerbated.
46 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
Zhu and Ringler (2010) used a semi-distributed hydrological model and the Water
Simulations Model of the International Model for Policy Analysis of Agricultural
Commodities and Trade (IMPACT-Water) to analyse the impact of climate change
on the hydrological and water resource systems of the Limpopo River Basin, which
covers the four Southern African nations of Botswana, Mozambique, South Africa
and Zimbabwe. The findings of the study were that annual rainfall and water
availability will be significantly reduced for all four countries by the year 2030. This
will have implications for the supply and reliability of irrigation water within the
basin and on agricultural planning, as this will require changes in crop varieties,
planting dates and crop patterns. The study concluded that as a way to mitigate the
adverse effects of climate change, water infrastructure and management could be
improved.
Butt et al. (2005) investigated the economic and food security impacts of drought
on agricultural sector in Mali. The study made use of a collection of biophysical and
economic models. The analysis focused on crops, forages, livestock and the sectoral
economics and risk of hunger. Butt et al. (2005) found that drought resulted in a
reduction in crop yields; a decline in forage yields and livestock weights. There were
also substantial economic losses, with producers gaining and consumers losing; and
an increase in the percentage of population at the risk of hunger. The study
recommended that there should be a promotion of heat resistant cultivars, migration
of cropping patterns and an expansion of crop land in order to mitigate the negative
effects of climate change in Mali.
Winters et al. (1998) used a CGE model with a multi-market agricultural sector to
analyse the impact of global climate change and extreme climate events on developing
countries. This study was carried out for three poor cereal exporting nations in Africa,
Asia and Latin America. The objective was to compare the effects of climate change on
macroeconomic performance; sectoral resource allocation and household welfare across
the continents. The main findings of the study were that climate change will lead to a
substantial reduction in global incomes, which will in turn lead to global reduction in
trade. There will also be a fall in agricultural production. Africa will be the most affected
continent. This is because for the majority of countries in the, Agriculture is the largest
contributor to GDP. Furthermore,it has the lowest substitution possibilities between
imported and domestically produces cereals. Africa is also the only continent subjected
to negative shocks in the price of its export crops, and has the lowest elasticities of
supply response. Winters et al. (1998) therefore recommended that mitigation strategies
should focus on the production of food crops in Africa, and the production of export
crops in Asia and Latin America.
Perez y Perez and Barreiro-Hurle (2009) used the Input-Output framework to assess
the socio-economic impacts of the 2005 drought in the Ebro River Basin. The study
examined both the direct impact of drought on agriculture and energy production
and the indirect impacts on wider economic activities within the river basin. The
empirical results showed a direct impact of a loss of output of €482 million in the
agriculture and energy sectors, and an induced production loss of €377 million in other
The Socio-economic Impact of Drought in Botswana • 47
production sectors. The reduction in economic activity due to drought resulted in a
reduction of over 11,000 jobs in the basin.
In Botswana different studies have been undertaken to assess the impact of
drought in the country. Masike and Urich (2008) assessed the vulnerability of the
traditional beef sector to rainfall variability or failure in the Kgatleng District of
Botswana. The empirical results show that the rainfall reliability index in the study
district is 0.5 and the vulnerability index of cattle is 8000 cattle heads per year.
However, the study did not attempt to investigate how this vulnerability leads to an
induced-output loss in other sectors and loss in the incomes of the vulnerable
households.
Tsheko (2003) studied the patterns of rainfall reliability, drought and flood
vulnerability in Botswana. using time series data, on drought, floods and rainfall
patterns for 72 years, the study found that most of the study stations had a reliability
index exceeding 0.5, but less than 0.7. The study also found out that while some stations
experienced drought, other stations experienced floods. However, there is an
insignificant difference between socio-economic impacts of droughts and floods. In a
similar study, Parida and Moalafhi (2008) using time series data on rainfall (19612003) at 11 synoptic stations in Botswana found out that rainfall has been decreasing
since 1981. These studies assessed the reliability of rainfall in Botswana, but did not
assess how the low rainfall in the country affects production of crops and the rearing
of animals, and the welfare effects of these losses in output.
Holm and Cohen (1988) examined how equity could be established in the midst of
drought in Botswana. The study looked at the impact of government’s drought relief
programmes in the country, and found out that most of the relief items do not get to
the targeted vulnerable households.
While the above studies mainly focused on the direct and the induced impact of
drought on sectoral output, the extent to which these impacts affect households’
incomes, prices and welfare have not been adequately considered. Juana et al. (2012) is
among the few studies that have investigated the households’ welfare consequences
of climate change. The study used a CGE model to analyse the socio-economic impact
of climate change on water resources in South Africa. The study showed that water
scarcity due to climate change or drought can lead to a significant decline in sectoral
output, factor remuneration, and deterioration in the welfare of poor households in
South Africa. The study also investigated the impact of both internal and external
welfare policy interventions.
All the above studies reviewed show that drought or water scarcity due to climate
change has adverse consequences for output growth and households’ welfare. They
used the direct impact of drought on crops and livestock production or the indirect
impact through reduced water availability for economic activities. The current study
adopts the modeling technique used in Juana et al (2012), to assess the socio-economic
impact of drought on sectoral output, factor payments and households’ welfare.
48 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
3. DATA, THEORETICAL FRAMEWORK AND
MODELING PROCEDURE
This section discusses the data used for the study, the aggregation method and the
modeling techniques applied by the authors. Because the study uses the CGE model,
particular attention is given to the standard CGE conditions and the specific model
closures assumed for the study.
Data
The study uses the 2006/7 SAM for Botswana, developed by the Central Statistics
Office (CSO) of Botswana. This highly disaggregated SAM was aggregated to 28
production sector accounts, which include agriculture sub-divided into traditional
agriculture (livestock and crops), commercial agriculture (livestock and crops), hunting
and fishing; mining, which is sub-divided into diamond mining and other mining;
manufacturing, which is sub-divided into meet processing, dairy and other agricultural
processing, beverages, textiles, chemicals, metal products, bakery, leather, wood and
paper, village industries and other manufacturing; water; electricity, construction. The
services sector sub-divided into trade, hotel and restaurants, transport,
communications, financial, ownership of dwellings, government and miscellaneous
services. The highly disaggregated SAM has nine labour categories, which have been
aggregated into citizen professional employees, citizen administrative employees,
citizen clerical employees, skilled citizen employees, unskilled employees and noncitizen employees. The capital account in the original SAM is maintained. More details
are found on Table 1A in the appendix.
The aggregated SAM has three household categories, which are urban households,
rural households and non-citizen households. The other institution accounts include
government, firms and the rest of the world. The aggregated SAM also has net trade,
which subtracts, total imports from exports.
Households receive income from wages and from both local sources
(governmentand inter-personal transfers) andinternational transfers. Their disposable
income isallocated to consumption and savings. Household consumption is divided
into food andnon-food consumption. Food consumption is determined by household
expenditure onmanufactured agricultural commodities and beverages and tobacco.
Non-food consumptionexpenditures are those spent on the other sectors, which are
further divided intodurables and non-durables. These divisions are the basis for welfare
policy investigations.
Sectoral output is sold to the production sectors as intermediate input, consumed
domestically, or exported. Government accounts, which were broken down into
expenditure andincome accounts in the original SAM, are maintained.
Theoretical Framework
This study adopts the Computable General Equilibrium model in Juana et al (2012) to
investigate the impact of drought on sectoral output,value added and households’
The Socio-economic Impact of Drought in Botswana • 49
welfare, using the semi-aggregated 2006/7 Botswana SAM described in the preceding
sub-section. In Botswana drought could be categorized as mild, medium and extreme
(Nicholson et al, 2001).
As in Juana et al (2012)the framework uses theMathematical Programming System
for General Equilibrium (MPSGE), a GAMS extensiondeveloped by Rutherford (1995),
with the Mixed ComplementarityProgramming(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 MPSGE program
provides a relatively simpler way to write down and analyzecomplicated systems of
nonlinear inequalities.The language is based on nested constant elasticity of substitution
(CES) utility and production functions. The data requirements for this type of model
include share andelasticity parameters for all the consumers and production sectors
included in the model.These may or may not be calibrated from a consistent benchmark
equilibrium data-set(Rutherford 1995).The model uses multi-level nested production
functions to determinethe level of production. Sectoral outputs are represented by a
Leontief combination offixed intermediate consumption and value added. The model
also specifies a CES functionto establish the relationship between inputs and output.
However, the use of capital is modelledby a Leontief fixed proportions function, because
the short-run use of capital is fixedand sector specific. Therefore, the use of capital can
only be increased by increasing thenumber of hours of use. Conversely, the sixlabour
categories are freely mobileacross sectors, except where specified; hence, the use of
these inputs by the productionsectors is modelled by the CES function. This allows the
functioning of a competitive marketto efficiently allocate the mobile factors(different
labour categories). This implies that these mobile factors move fromsectors where factor
returns are low to sectors where factor returns are highest. Similarly,the CES function
allows the production sectors to substitute factors based on comparativefactor prices.
The free movement of these factors of production enhances the adjustmentof
wages (for each of the six labour categories) to achieve equilibriumin both the labour
market.
The adapted model uses a constant elasticity of transformation (CET) function to
formulatethe imperfect substitution between domestic consumption of sectoral output
and export, and between domesticallyproduced and imported goods. The imperfect
substitutability modelled above enhances theimportation and exportation of the same
goods.
The capital and labour markets are closed by assuming that the demand foreach of
these factors is equal to their supply. If they are not equal, then factor prices adjust
until equilibrium is achieved in the factor markets. These assumptions imply full
employment ofthe factors. The saving–investment closure assumes that savings equal
investment and thatgovernment income (receipts) equals government spending
(payments). This implies that government transfer payments and taxes are fixed and
do not affect household incomes in the experimental simulations.
50 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
Drought, Agriculture Sector and the Economy
Masike and Urich (2008) predict a reduction of about 11 per cent in precipitation by
2050. That is a decline of 1960-1990 average of 377 mm per annum to an average of 337
mm per annum in 2050. These averages are stated for normal years and do not include
the impact of drought on water availability in Botswana. Masike and Urich (2008) also
predict the return period of mild drought to be two years, and that drought intensity
and frequency are projected to increase in the future. The empirical study of McDonalds
(2000) suggests that during the drought of the 1980scrop production declinedby 35
per cent of the long-term mean in 1982 and 1983, and by 15 per cent in 1884 ( McDonalds,
2000). Drought has been persistent since the early 1980s (Government of Botswana,
1997). The empirical study of Chanda et al (1999) shows that drought years in Botswana
have been 1959/60, 1961/62, 1963/64, 1964/65, 1969/70, 1972/73, 1978/79, 1981/82,
1982/83, 1983/84, 1984/85, 1985/86, 1991/92. Mogotsi, et al (2011) extends the drought
years to include 1993/94, 1994/95, 1995/96, 1997/98, 1998/99, 2001/02, 2004/5, 2005/
06 and 2007/08. In addition to these, the year 2012 was declared as a drought year in
Botswana. These figures show that the frequency of droughts in Botswana has increased
over the years, with shorter returning periods.
Drought generates severe problems for rural population of Botswana. There is the
direct effect on both the livestock and crop production sub-sectors, and the indirect or
induced effect on the other sectors of the economy that directly or indirectly depend
on agriculture sector. The direct and indirect impacts have welfare consequences that
have been understudied in Botswana. While the droughts of the 1980s, 1990s and 2000s
led to a significant decrease in livestock and crop production, the country’s GDP has
continued to significantly grow in these drought periods. This is because of agriculture’s
minimal contribution to GDP and the upsurge of economic activities in the mining
and manufacturing sectors. McDonalds (2000) states that although agriculture’s
contribution to Botswana’s GDP, national accounts may tend to understate the true
impact of drought on the economy of Botswana, because of the forward and backward
linkages the sector has with other production sectors, and the role the agriculture sector
plays in the welfare of the most vulnerable population in the country.
The Experimental Simulations
The situation documented in the adjusted SAM is the base situation which reflects
the2006/7economyof Botswana. All input and output prices are normalized at the
base level.
Drought scenario:The study assumes a mild drought situation in Botswana because
this is the situation that is prevalent in drought literature in the country. The study
simulatesthe impact of a 10 per cent, reduction in the outputs of all the agriculture
sub-sectors. These are; traditional crop, traditional livestock, freehold crops and freehold
livestock. These simulations are based on the findings of Masike and Urich (2008) and
McDonalds (2000). The simulations are doneby reducing output of the affected subsectors recorded in the SAM by ten per cent and re-running the modelto equilibrium
The Socio-economic Impact of Drought in Botswana • 51
levels.The percentage changes in sectoraloutput, value added, and household income/
consumption are compared to the base scenario. Also, using equation 1 in section 3.5,
households’ welfare changes are computed.
Policy simulations: The study investigates both short-term and medium term
policies. While short-term policies focus on the immediate response to the adverse
consequences of drought, these policies are not sustainable in the long-run. Conversely,
medium to long-term policies do not immediately address the adverse consequences
of drought, but focus on more sustainable ways of dealing with the drought impact.
Short-term policies: In the drought scenario, households’ income; hence, their level
of consumption is expected to decrease, because prices increase. However, some
households (producers) income may increase, while the incomes of others (consumers)
may decrease. Therefore, it is expected that while some households’ welfare may
improve, the welfare of others may deteriorate. However, the net welfare effect is
expected be negative. Therefore, this study estimates the welfare impacts on the
different household categories, to determine which household categories are the most
vulnerable to the consequences of drought. To form a hedge against these adverse
consequences of drought, the experiments were re-run assumingthat food consumption
for the vulnerable households is maintained atbase consumption levels. The two policy
interventions used to maintain base food consumptionlevels for the vulnerable
households are food stamps and food aid.
The food stamp experiment includes the distribution of food vouchers, equivalent
to the loss in welfare, among the affected households. This experiment is recorded as a
government transfer to the targeted households. In this scenario, government’sinterdepartmental expenditure is reduced by an amount equivalent to the deterioration in
the vulnerable households’ welfare, and distributed among the targeted household
categories as food vouchers, or government uses its reserve to make transfer payments
to affected households in terms of food vouchers.
Conversely, food commodity aid is recorded as an externalshock to the model.
That is, emergency food distribution,from external sources,among the affected
households. However, from past history, Botswana has implemented the food stamp
policy more frequently as compared to the food commodity aid.
Welfare Analysis
The study uses the concept of equivalent variation (EV) discussed in Chitiga and
Mabugu(2006) to analyze the impact of droughts on household welfare in Botswana.
EV compares thelevel of household income/consumption at the given price and income
in the base scenario tothe levels of price and income/consumption in the drought
scenarios.
In principle, EV can be interpreted as the minimum amount of money that has to
be given tohouseholds to renounce a utility-increasing change, or the maximum amount
thathouseholds are willing to pay to prevent a utility-decreasing change. As used in
52 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
this study,EV is defined as the minimum amount that should be given to households
to make them as well off as they were before the drought.Functionally, equivalent
variation is formulated as:
EV
P11
(Y 1
P10
Y0)
(1)
Where P10 is the price index in the base model,
P11 is the price index after the simulation,
Y0 is the households’ income in the base model and
Y1 is households’ income after the simulation
A positive EV implies welfare improvement, while a negative EV implies welfare
deterioration (loss). The first part of the equation shows the ratio of the price index in
the drought period to the price index in the non-drought period. A ratio of 1 shows
that there is no change in the prices of the two periods. A ratio of more than 1 shows
that prices generally increased in the drought period. If less than 1, the opposite is the
case. The second part of equation 1 shows whether or not incomes increased or
decreased during the drought period. If incomes increased, the net effect is positive,
implying welfare improvement, but if households’ incomes decreased, the net effect is
negative, implying welfare deterioration (Chitiga and Mabugu, 2008).
4. PRESENTATION AND DISCUSSION OF THE
SIMULATION RESULTS
In this section, the results from the simulation on the impact of climate change on
sectoral output, factor payments and household income and welfare are presented
and discussed. The impact of drought on sectoral output, factor payments and
households’ income/consumption is presented on Table 1.
The Impact of Drought on Sectoral Output
Table 1 presents the impact of drought on sectoral output in Botswana. Column two
shows the base case output prior to the drought period. This is followed by column
three which shows the impact of a 10 per cent decline in agricultural output due to
drought in the different sectors. The 10 per cent decline in agricultural output (crops
and livestock) is the direct impact of drought, while the absolute changes in the other
sectors or sub-sectors are presented in column 2 of Table 1. Column three presents the
percentage changes in the other sectors, and this shows the indirect or induced impact
of drought.
This is divided into the direct impact and the indirect impact. The direct impact is
captured by the 10% decrease in agricultural output in Table 1, the figures in column
4shows that the value of sectoral output in Botswana declines by 2.44 because of 10
per cent decline in agricultural output as a result of drought. In monetary terms, this
The Socio-economic Impact of Drought in Botswana • 53
translates into a loss of P514.77 million in sectoral output. This shows the total impact
of drought, which divided into the direct impact of P64.27 decline in agricultural output
and the induced impact of 386.23 million decline in the output of other sectors.
Specifically, the 10 per cent reduction in agricultural sector’s output; due to drought
induces a reduction of about 5.55 per cent, 9.32 per cent, 8.15 per cent, 6.12 per cent ,
4.8 per cent, 4.99 per cent, 7.29 per cent , 5.57 per cent, 4.07 per cent, 4.38 per cent, 3.76
per cent, 11.08 and 3.79 per cent in the outputs of hunting, fishing and gathering; meat
processing, dairy and other agriculture processing, beverages, Textiles,
chemicals;bakery products, leather, wood and paper, village industries, electricity,
Hotels and restaurants, communications, ownership of dwellings and miscellaneous
services respectively. These are the most indirectly impact sectors in the economy of
Botswana. The other sectors that are mildly impacted are the; other manufacturing,
transport, and financial services. These secondary or indirect impact impacts are
associated with the forward and backward linkages of the agriculture sector. For
example, the livestock sector supplies the meat processing sector with meat. Similarly,
the crop sector provides the primary inputs for dairy, other agro-processing sub sectors,
bakery, beverages, and hotel and tourism sectors. These are the forward linkages
between agriculture and the other sector. On the other hand, the agriculture
sector (crops and livestock) receives inputs from chemicals, like fertilizers
and veterinary medicines; transportation and financial services. Therefore, a decline
in the output of the agriculture sector induces a decline in the demand for the output
of the allied sectors. These forward and backward linkages are the invisible impact of
drought.
As expected, the traditional sector is more adversely affected than the commercial
sector probably because the traditional sector relies more heavily on rainfed agriculture
than its commercial or freehold counterpart, in which some method of irrigation is
applied to compliment for the low rainfall. Furthermore, owing to their risk averseness,
resource poorness and practice of low-input agriculture, it is not surprising that
traditional farmers are more negatively affected by the effects of drought than their
counterparts in the freehold sector who are more likely to adapt and invest in
technologies that can help them better cope with the drought.
As can be seen on Table 1, the impact of drought on the mining; metal products;
and construction sectors are minimal and insignificant. This may be due to these sectors
using less water than the highly impacted sectors or have poor inter-sectoral linkages
with the agriculture sector.
Hotels and restaurants, which are normally a proxy for the tourism sector, were
also negatively affected by the drought, probably because tourism sector primarily
depends on the renewable natural resource base of the country and requires water for
survival. This finding does not support the country’s diversification efforts because as
a shift from diamonds to tourism the demand for water significantly increases.
Consequently, if investments are not made now to cope with the foreseeable water
shortages because of drought and demand management improved, there will be a
huge cost to pay in the future.
54 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
Table 1
Economy-wide Impact of Drought in Botswana
Sector/Sub-sector
(1)
Traditional Agriculture
- Cattle and other livestock
- Crops
Freehold farms
- Cattle and other livestock
- Crops
Impact on Agriculture
Hunting, fishing and gathering
Mining
- Diamonds
- Other mining
Meat processing
Dairy and other agric. processing
Beverages
Textiles
Chemicals
Metal products
Bakery & products
Leather, wood and paper
Village industries
Other manufacturing
Electricity
Construction
Hotels and restaurants (Tourism)
Transport
Communications
Financial services
Ownership of dwellings
Miscellaneous services
Indirect Impact
Total Impact
Base Figure
(2)
Change in Output
(3)
Percentage Change
(4)
438.18
103.93
-43.8184
-10.3932
-10
-10
76.84
23.73
642.68
161.36
-7.6839
-2.3729
-64.2684
-8.956
-10
-10
-5.55
6675.53
1043.85
454.04
433.37
406.73
211.01
302.33
242.78
160.57
184.19
63.28
474.22
302.16
3228.44
475.79
1564.6
309.29
1788.02
315.13
1046.33
19843.02
21128.38
-0.1164
-0.4124
-42.22
-35.3011
-24.8807
-10.1322
-15.0834
-0.0527
-11.7064
-10.2603
-2.5741
-9.9436
-21.1854
-22.0578
-20.8456
-18.0184
-11.6321
-46.2675
-34.93
-39.6577
-386.2338
-514.7706
-0.0017
-0.04
-9.32
-8.15
-6.12
-4.80
-4.99
-0.02
-7.29
-5.57
-4.07
-2.10
-7.01
-0.68
-4.38
-1.15
-3.76
-2.59
-11.08
-3.79
-1.95
-2.44
Source: Extracted from authors’ simulations
Changes in Factor Payments
The impact of drought on factor payments is presented in Table 2.The percentage
changes are presented in Column 3, while Column 4 presents the absolute changes in
factor payments. When compared to the base case drought leads to an overall decline
of 1.18 per cent in factor payments, which is equivalent to P196 million. Specifically,
the most impacted factors are unskilled labour, which declines by about 7.48 per cent,
and skilled manual labour, which declines byabout 5.88 per cent. The other factors are
insignificantly affected. However, payments to capital insignificantly increased. The
study assumes full employment of the factors of production, and that factor payments
are determined by output. Therefore, when output falls because of drought, the affected
sectors, lay off some factor. Because of the assumption of full employment of the factors,
The Socio-economic Impact of Drought in Botswana • 55
factor prices adjust to clear the factor market. This leads to an overall decline in factor
payment. Capital is assumed to be fixed in the short-run; hence, payment to capital is
unaffected.
Similar results are recorded in Juana et al (2008, 2012). Agricultural production in
Botswana is labour intensive; hence, for a decline in the sector’s output due to extreme
climatic events, the unskilled labourers become are the most vulnerable factor.
Table 2
Impact on Factor Payments
Section 2. Impact of factor paymentsor payments
Factor
Base payment
% Change
Absolute Change
479.30
99.96
291.14
1513.17
762.35
1336.40
12146.90
16629.22
-0.2462
-0.0515
-0.8496
-5.8772
-0.5214
-7.4765
0.0013
-1.1819
-1.18004
-0.05148
-2.47353
-88.932
-3.97489
-99.9159
0.15791
-196.3699
Prof. & technical employees – citizens
Admin & mgt. employees – citizens
Clerical employees – citizens
Skilled manual – citizens
Non – citizens
Unskilled employees
Capital
Total
Source: extracted from authors’ simulation results
Households’ Income/consumption and Welfare Analysis
In this sub-section, the welfare effects of drought on households are discussed. Table 3
below shows the impact of drought on households’ income and welfare.
Table 3
Households’Income and Welfare
Section 3. Households’ Income and Welfare
Household Category
Urban households
Rural households
Non-citizen households
Total
Base Income
% Change
Absolute Change
Welfare Change
2797.40
2383.47
923.98
6104.85
-2.3022
-3.1379
-0.5473
1.8539
-64.4017
-74.7909
-5.05694
-144.25153
-3.4264
-5.1209
-0.0567
-3.7040
Source: Extracted from authors’ simulation results
There are three household categories; urban, rural and non-citizen households.
The percentage change in households’ consumption/consumption, the absolute change
in income/consumption and the associated welfare changes are presented in columns
3, 4 and 5 respectively. In percentage terms, rural households are the most affected
household category. In analysing the welfare effects, we compared both the change in
income/consumption, which shows direction of welfare. Consumption/incomedeclines
by 3.14 per cent. This translates into 5.12 per cent deterioration in welfare. This implies
that, generally, as the price index increases, households’ consumption/income declines,
leading to general deterioration in welfare. The income of urban households also
decreases by 2.23 per cent leading to 3.45 per cent deterioration in welfare.According
56 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
to the simulation results, the non-citizen households are the least affected by drought.
This is because a significant percentage of non-citizens are either technical or
professional employees. Conversely, the majority of the unskilled and skilled manual
employees are urban and rural households and are employed in the agriculture sector.
Therefore, a decline in the payments of these labour categories is directly transmitted
to the urban and rural households.
Secondly, rural households depend more on the environment or the natural
resources in which they live in. In particular, they tend to be more reliant on agriculture
and its associated industries, and other livelihoods that are adversely impacted by
extreme climatic events.. Consequently, they are more affected by drought than their
counterparts who live and work in the urban areas.
Food Stamp Policy Analysis
There are two short-run policy simulations; food stamp and food commodity aid. As
discussed earlier, the distribution of food stamp among the vulnerable households is a
short-run public policy, and the government of Botswana has always implemented
this policy during drought periods in the country.
Table 2
Welfare Analysis of Short-run Policy Interventions
Household
Category (1)
Urban
Rural
Non-citizen
Total
Base
Income
(2)
2797.4
2383.47
923.98
6104.85
A: Food Stamp
%
Absolute
Change
Change
(3)
(4)
Welfare
change
(5)
B: Food Aid
%
Absolute
Change
Change
(6)
(7)
2.3022
3.1379
-1.1667
2.1035
1.6597
2.5575
-2.2490
1.2673
2.3022
3.1379
0.0000
2.2939
64.4017
74.7909
-10.7813
128.4131
64.4017
74.7909
0.0000
139.1926
Welfare
Change
(8)
2.8384
3.7672
0.0000
2.5257
This section investigates the distribution of food among both the rural and urban
vulnerable population. While rural food distribution is quite easy, it is difficult to
identify highly drought impacted households among the urban population. The
approach therefore is to select unskilled and skilled manual labourers in urban cities,
because the empirical results show that these are the vulnerable working population.
The results of these policy simulations are presented in part A of Table 2. Column
4 of Table 2 shows that, generally, households’ income/consumption increase by
P128.413 million. That is urban and rural households’ incomes/consumption increase
by about P64 million and P75 million respectively. However, the income of non-citizen
households income/consumption decline by about P11 million. In percentage terms,
there is an overall increase in income/consumption by 2.1 per cent, with urban and
rural households’ income/consumption increasing by 2.3 and 3.1 per cent respectively.
On the contrary, non-citizen households’ income/consumption declines y 1.17 per
cent.
The Socio-economic Impact of Drought in Botswana • 57
The above results have welfare implications. Generally, the policy of food stamps
leads to an overall improvement in households’ welfare by 1.27 per cent. Specifically,
urban and rural households’ welfare improves by 2.56 and 1.66 per cent respectively.
Nonetheless, the welfare of non-citizen households’ welfare deteriorates by about 2.25
per cent. The possible economic reason for these results is price changes. When food
stamps are distributed among the vulnerable households, the demand for food
increases, but the short-run supply remains unchanged. Therefore, to clear the market,
food prices increase, but the urban and rural households are unaffected by these price
increase. The non-citizen households, income/consumption decline by 1.17 per cent,
because their expenditure on food increases, leaving them with less income to purchase
other goods. The price increase, coupled with the decline in income makes the
deterioration in welfare to be more than the percentage decline in come.
The analytical results for the urban and rural households are different. The general
increase in prices counters the income/consumption gains from the welfare programme;
hence, the percentage improvement in welfare of these two household categories is
less than the percentage increase in their income/consumption.
Food Aid Policy
Unlike the food stamp policy, food aid is treated as an external shock to the model. An
amount equivalent to the income/consumption loss (in the form of food aid) is
distributed among the vulnerable households (Urban and rural households). As in the
case of food stamp, the non-citizen households are exempted from this welfare
programme.
Columns 6, 7 and 8 of Table 2 present the income/consumption and welfare changes
of food aid policy. From column 1, it is show that urban and rural households’ incomes
increase by 2.3 and 3.13 per cent respectively. The income/consumption and welfare
of non-citizen households are unaffected. Generally, households’ income/consumption
increase by 2.29 per cent. This is translated to a general improvement of households’
welfare by 2.53 per cent. The percentage improvement in welfare is more than the
percentage increase in income/consumption. This is due to the price effects. Food aid
increases the supply of food. Food prices fall to clear the market. A decrease in the
price leads to an increase in the demand for food, hence improvement in the general
welfare of the households.
5. SUMMARY, CONCLUSIONS AND RESEARCH GAPS
This section is sub-divided into summary and conclusions, and research gaps.
Summary and Conclusions
This study used the computable general equilibrium approach to analyse the socioeconomic impact of drought in Botswana. The results suggest that the impact of drought
on the agriculture sector has induced impacts on sectoral output, factor remuneration
and general households’ welfare. Specifically, the impact of drought leads to a decline
58 • J. S. Juana, P. M. Makepe, K. T. Mangadi and N. Narayana
in agricultural output (primary impact) and those sectors that have forward (recipients
of inputs from agriculture sector) and backward (suppliers of inputs to the agriculture
sector) linkages. These have consequences for factor payments. The empirical results
show that unskilled labour and skilled manual labour are the most impacted factors.
This is transmitted to different household categories. Those households who receive a
significant proportion of their income from unskilled and skilled manual labour services
are the most vulnerable households todrought in the country. Furthermore, rural and
urban households are also found to be vulnerable to the impact of drought.. The noncitizen households are insignificantly affected. However, the implementation of
domestic policies aimed at dampening the adverse drought consequences; improve
the welfare of both rural and urban households, while leading to decline in the
consumption/income of non-citizen households. Conversely, the implementation of
external food aid policy improves the welfare of the vulnerable households, while
maintaining the welfare of the unaffected households.
The above findings suggest that while food distribution policies can dampen the
potential impact of drought in the country, the food aid policy has more desirable
effect. Therefore, it is more effective to pursue both policies at the same time to address
the adverse consequences of especially prolonged droughts in the country.
Limitations and Research Gaps
The study estimates the impact of drought on gross output and general welfare at
national level. However, the socio-economic consequences of drought differ from one
agro-ecological zone to the other. Botswana has three agro-ecological zones and five
basins. The impact of drought in each zone or basin may be different. While zones
known for pastoral/livestock farming experience more livestock losses, zones that
practice arable farming are likely to experiences more crop failures. Therefore, a national
level study may not reflect the situation in different basins or zones. There is the need
to develop an integrated socio-economic and drought model that considers zone or
basin specific drought impacts.
Also, farmers’ immediate reaction to drought differs for both arable and pastoral
farming in the country. While pastoral farmers can easily reduce their livestock by
selling more during the drought period and restocking after the drought, arable farmers
cannot easily switch to drought resilient crops during the drought period. Therefore,
considering a concurrent empirical estimation of drought for both sub-sectors during
a single study may understate or overstate the true impact. There is a need to look at
the livestock and arable sectors separately.
Furthermore, there is the need to develop livelihood typologies at basin or zonal
level and assess the extent to which drought will affect these livelihoods.
Acknowledgements
The authors are grateful to the African Economics Research Consortium (AERC) and the United
Nations University-World Institute for Development Economic Research (UNU-WIDER) for
The Socio-economic Impact of Drought in Botswana • 59
funding the study and providing the technical experts on climate change and CGE modelling.
We would also like to thank our research assistants Samuel Chingoiro and Njoku Chidozie for
collecting and helping to analyse data.
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