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CLIMATE VOLATILITY AND POVERTY VULNERABILITY IN TANZANIA
Syud Amer Ahmed
1
,
Noah S. Diffenbaugh
2
,
Thomas W. Hertel
3
,
Navin Ramankutty
4
,
Ana R. Rios
5
, and
Pedram Rowhani
6
Selected Paper prepared for presentation at the
Agricultural & Applied Economics Association 2009 AAEA & ACCI Joint Annual Meeting,
Milwaukee, Wisconsin, July 26-29, 2009
Copyright 2009 by S.A. Ahmed, T.W. Hertel, N.S. Diffenbaugh, N. Ramankutty, A.R. Rios and P.
Rowhani. All rights reserved. Readers may make verbatim copies of this document for non-
commercial purposes by any means, provided that this copyright notice appears on all such
copies
1
Development Research Group, World Bank, Washington DC. Email: sahmed20@worldbank.org (contact
author).
2
Purdue Climate Change Research Center and Department of Earth and Atmospheric Sciences, Purdue
University, IN
3
Center for Global Trade Analysis and Department of Agricultural Economics, Purdue University, IN
4
Department of Geography and Earth System Science Program, McGill University, Montreal, Canada
5
Inter-American Development Bank, Washington DC
6
Department of Geography, Universite de Louvain, Belgium
2
Abstract
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Climate volatility will increase in the future, with agricultural productivity expected to
become increasingly volatile as well. For Tanzania, where food production and prices are
sensitive to the climate, rising climate volatility can have severe implications for poverty. We
develop and use an integrated framework to estimate the poverty vulnerabilities of different
socio-economic strata in Tanzania under current and future climate. We find that households
across various strata are similarly vulnerable to being impoverished when considered in terms of
their stratum’s populations, with poverty vulnerability of all groups higher in the 21
st
Century
than in the late 20
th
Century. When the contributions of the different strata to the national
poverty changes are taken into account, the rural and urban households with diversified income
sources are found to account for the largest poverty changes due to their large shares in initial
total poverty.
KEYWORDS: climate, volatility, poverty vulnerability, Tanzania
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This research was funded by the World Bank’s Trust Fund for Environmentally and Socially Sustainable
Development. The views and opinions expressed in this paper are solely those of the authors. The authors
are grateful to Tasneem Mirza for research assistance and to Oliver Morrissey and Vincent Leyaro for
price data.
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1. INTRODUCTION
There is substantial evidence that rising atmospheric GHG concentrations are likely to
increase temperature and precipitation extremes in the future (IPCC, 2007), with Easterling et al
(2000) making the case that greater climate volatility is already occurring. These changes to the
distribution of climate outcomes in a given year are particularly important for agriculture (White
et al, 2006; Mendelsohn et al, 2007). They also have implications for developing countries where
agriculture is important for the poor as both a source of income as well as for consumption –
since the majority of the poor reside in rural areas where farming is the dominant economic
activity and because the poor may spend as much as two-thirds of their income on food.
This is particularly true for Tanzania, where agriculture accounts for about half of gross
production, and employs about 80 percent of the labor force (Thurlow and Wobst, 2003).
Agriculture in Tanzania is also primarily rain-fed, with only 2 percent of arable land having
irrigation facilities – far below the potential irrigable share (FAO, 2009). Tanzanian yields,
especially of staple foods like maize, are particularly susceptible to adverse weather events. This
threat has been recognized by policy makers, with Tanzania’s National Strategy for Growth and
Reduction of Poverty identifying droughts and floods as among the primary threats to
agricultural productivity and poverty vulnerability.
Despite its significance for developing countries, like Tanzania, the effects of changes in
climate volatility on agriculture and development are not well-understood. While there is a
substantial literature examining the effects of shifts in mean climate variables, fewer studies
focus on the economic effects of increased volatility of those climate variables, and their
impacts on the populations most vulnerable to climate volatility, namely the poor.
This paper fills an important gap in the literature by developing a quantitative
framework for examining the impact of greater volatility of key climate variables on agricultural
productivity, and the subsequent effect on poverty. After outlining the methodology, this
framework is used to determine how greater climate volatility affects the vulnerability of the
poor in Tanzania. This is achieved by first characterizing the poverty vulnerability of different
socio-economic groups under the climate volatility of the late 20
th
Century as it affects grains
productivity, and then showing how these vulnerabilities will evolve in the first few decades of
the 21
st
Century.
The next section describes the poverty profile of Tanzania, while section 3 provides
details of the integrated assessment strategy that is developed and used. Section 4 analyzes the
vulnerability of the poor under the current and future climate volatility. Section 5 outlines some
ideas for future work with this framework, and section 6 concludes.
2. POVERTY PROFILE OF TANZANIA
Following the approach of Hertel et al (2004), the population as a whole can be divided
into seven distinct strata, reflecting the pattern of household earnings specialization:
Agricultural self-employed (more than 95 percent of income from farming), Non-Agricultural
(more than 95 percent of income from non-agricultural self-employment), Urban Labor (more
than 95 percent of income from wage labor), Rural Labor (more than 95 percent of income from
wage labor), Transfer dependent (more than 95 percent of income from transfer payments),
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Urban Diverse, and Rural Diverse. As determined by the Household Budget Survey 2000/01,
there were 12.3 million Tanzanians living below the poverty line in 2001 (NBS, 2002).
Table 1 reports some key estimates of the structure of poverty in Tanzania, based on
Tanzania’s national poverty line
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. The rows in this table correspond to the seven strata and are
therefore exhaustive of the Tanzanian population. The first column reports the poverty
headcount rate in each stratum. This shows that the overall poverty headcount in Tanzania was
about 36 percent. The estimated headcount was highest in the agriculture-specialized stratum
(68 percent), followed by the transfer-dependent households (56 percent), the rural diversified
stratum (51 percent) and then rural labor, urban diversified, non-agriculture self-employed and
urban labor. Based on these figures, it is not surprising that the agriculture, transfer and rural
diversified households all account for a larger share of the total poor in Tanzania (column II)
than in the total population (column III). Taken together, the agricultural specialized and rural
diversified households account for two-thirds of total poverty in Tanzania.
Table 1: Earnings-Based Socio-Economic of Tanzania by (in percent)
Stratum
Stratum
Poverty
Rate
Share in
Total Poverty
Share in
Total Population
I II III
Agriculture 68.79 29.95 15.54
Rural Labor 24.15 0.74 1.09
Rural Diversified 51.43 30.34 21.05
Non-Agriculture 23.71 10.02 15.08
Urban Labor 12.24 3.40 9.91
Urban Diversified 23.24 23.44 35.99
Transfers 56.01 2.11 1.35
National 35.68 100.00 100.00
Source: Authors’ estimates based on data from NBS (2002)
From Thurlow and Wobst (2003), we know that grains are among the most important
crops for Tanzanian households, as shares of both household income and consumption, and
especially so for the rural poor. So, changes in the volatility in the productivity of the Grains
sector will have different poverty implications for each of the seven strata of Tanzania’s poor.
For example, a drought-like climate event would reduce agricultural productivity, and
push up food prices. To a first-order approximation, whether a particular household gains or
loses income from this change depends on whether it is a net buyer or seller of the commodity.
Higher prices will simply push up the cost of living for net consumers, like urban populations.
It is thus difficult to ascertain, in the absence of more specific knowledge of the
situation, how climate volatility would affect poverty, and empirical methods are necessary. For
a comprehensive analysis of the poverty implications of prospective climate volatility changes
over the course of the 21
st
Century, we need to use an integrated analytical framework that
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The national poverty line is the basic needs poverty line defined in the Household Budget Survey
2000/01 (NBS, 2002), and is TShs 7253 (2001) without correcting for PPP.
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incorporates analyses of crop production, climate science, and general equilibriums of economic
systems. Such a method is described in the following section.
3. METHODOLOGY
The analytical framework used in this paper relies on several empirical methods
implemented in sequence. The first is to characterize interannual agricultural productivity
volatility under current climate (section 3.1), followed by the volatility under future climate
(section 3.2). A global computable general equilibrium model is then developed (section 3.3) to
implement stochastic simulations productivity volatility in the Grains sector under current and
future climates.
3.1 AGRICULTURAL PRODUCTIVITY VOLATILITY UNDER CURRENT CLIMATE
There are many potential ways to capture climate volatility and the resulting impacts on
agriculture. In this work we focus on the distribution of interannual changes in agricultural
productivity. For our purposes, under any given climate regime, this will be characterized via a
mean-zero normal distribution, with an empirically estimated variance. Of course as climate
change occurs, both the variance of this distribution and its mean, are subject to change.
Agricultural productivity is difficult to observe, and so we use interannual output
changes as a proxy. An alternative would be to use yields. However, in the available data sets,
yields are defined as production divided by harvested area. Since harvested area is also subject
to climate volatility (some planted area may not be harvested in a bad year), we view the
interannual random change in production as a better measure of the overall impact of climate
on grains productivity. To determine the standard deviation of the interannual output changes,
production data is obtained for Tanzanian Grains (maize, paddy rice, and sorghum)
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from
FAOSTAT for the years 1971 to 2001 (FAO, 2009). These are treated as an aggregate to simplify
our task and in order to better match up with the poverty analysis framework to be used below.
The interannual percentage changes are then calculated for the Grains aggregate and tested for
time trends
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, before the standard deviations of the percentage change time series is
determined.
While climate-induced productivity volatilities are fundamentally unobservable, they
result in price volatility which can be observed. Since price effects are important mechanisms by
which supply side climate shocks to agriculture affect poverty, it is necessary to understand the
price volatilities to put the poverty vulnerability in perspective. We also utilize these observed
price volatilities to validate our economic model. The price volatility for the aggregated Grains
crop is determined through a more complex approach, involving data from a variety of sources
for the period 1990 to 2003, and is the value share weighted average of the real price of the
disaggregate grains crops
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.
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These three crops collectively proxy for the Grains sector that we use in our CGE analysis, aggregated
from the Paddy Rice, Wheat, and Other Grains GTAP sectors.
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None were found.
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The time series for the price volatility estimation is smaller than the series for the productivity volatility
estimation due to the unavailability of reliable data necessary for the estimation. Details on the aggregate
price determination can be found in Appendix A.
