Essam, Timothy M.

Tondel, Fabien

English

Research Papers in Economics

Impact of El Niño on Staple Food Prices in East 
and Southern Africa 
 
 
 
 
Fabien Tondel1 
Famine Early Warning Systems Network (FEWS NET) 
Michigan State University 
Institute of International Agriculture 
Tim Essam 
University of Maryland 
Department of Agricultural and Resource Economics 
 
 
 
 
Selected Paper prepared for presentation at the Agricultural and Applied Economics 
Association’s 2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24 – 26, 
2011 
 
 
 
 
Copyright 2011 by Fabien Tondel and Tim Essam. 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 FEWS NET is a USAID-funded activity. The views expressed in this publication do not necessarily reflect the views of the 
United States Agency for International Development or the United States Government. Impact of El Niño on Staple Food Prices in East 
and Southern Africa 
 
 
 
 
PRELIMINARY DRAFT  
1  Overview of the effects of El Niño in East and Southern 
Africa 
This paper presents evidence on the impact of the El Niño-Southern Oscillation (ENSO) phenomenon 
on staple food prices in East and Southern Africa, highlighting differences across markets within these 
regions in relation to geography and market access and pointing to its lagged effects. These findings 
contribute to strengthening capacities for early warning of vulnerability to food insecurity in these 
regions. 
El Niño, a recurrent warming of the central and eastern Pacific’s intertropical waters, typically causes a 
shift in rainfall patterns worldwide resulting in important, yet mixed effects on agricultural production 
and local and international agricultural prices. With El Niño, rainfall normally increases in the southern 
United States and South America, whereas Central America, Indonesia, Australia, India, and South Africa 
tend to experience dry spells. Because some of those countries and regions produce and exports large 
amounts of food commodities, El Niño translates into fewer exports and higher export prices. Research 
showed statistically and economically significant effects on international food commodity prices with 
abnormally high prices, especially eight to 16 months after the onset of El Niño (see Brunner, A.D. El 
Niño and world primary commodity prices: Warm water or hot air? The Review of Economics and 
Statistics, February 2002, 84(1): 176 – 183.) El Niño events tend to occur every four to seven years and 
to last 12 to 18 months. They typically peak towards the end of the calendar year. 
El Niño generally brings about anomalous rainfall throughout Africa (see Figures 1 through 3, based on 
various measures). El Niño events usually coincide with above-average precipitation in the Horn of 
Africa and in Central Africa, although there are exceptions. In Southern Africa, the tendency is for El 
Niño to be associated with below-average precipitation, especially in the eastern part of the region. 
Other regions of Africa are also concerned, albeit to a lesser extent. The Gulf of Guinea region of West 
Africa tends to experience dryer-than-normal weather, whereas the Sahel region may receive more 
precipitation than normal. However, there are large variations from one El Niño event to another, and 
within region, there are significant differences as well. 
During El Niño events, rainfall tends to be above-normal in most of East Africa—especially during the 
October-December period and in the eastern and central parts of this region (see Figure 4). However, 
some areas are generally unaffected, and others affected by below-normal rainfall (the western part of 
Ethiopia, parts of Sudan, and areas of western Kenya notably) or early cessation of rainy seasons. For 
instance, in Ethiopia and in parts of Sudan in 2009, rainfall during the June – September rainy season was 
poor, erratic, and ceased earlier than normal, which led to a poor meher harvest in October – 
December. 
Additional rainfall can be beneficial to East Africa as it is chronically subject to drought conditions. It 
brings about a rise in cereal production in the Lake Victoria basin and in flood-recession cropping areas 
(flood-prone areas of southern Sudan and southern Somalia for instance), leading to lower prices. 
Reduced demand for feed and lower feed prices benefit pastoral and agro-pastoral areas, especially in 
the eastern part of the region (southeastern lowlands and coastal areas of Kenya, southern and southeastern Ethiopia, south and central Somalia). It can also improve access to drinking water and 
hydroelectric power generation. However, excessive, unseasonal rainfall following El Niño can damage 
standing crops and induce greater post-harvest losses, especially when it occurs during the meher 
harvest, in Ethiopia, and in the northern Rift Valley area of Kenya and in western and southwestern 
Uganda during the long-rains harvests and second harvests, respectively, during the period October – 
December. It can also lead to flooding, inflicting further crop losses. This possibility concerns lowlands 
like the Juba and Shabelle basins in Somalia. Excessive rainfall and flooding can also favor the emergence 
of epizooties like the Rift Valley fever  
In Southern Africa, El Niño tends to bring about prolonged dry spells between January and March, in the 
midst of the rainy season, when rainfall is most critical for cereal crops, thus adversely affecting crop 
production, leading to a deterioration of pastoral conditions, and lower demand for agricultural labor. 
There is some evidence that when SST anomalies are high in the central and eastern Pacific, maize 
production is relatively low in Southern Africa (Cane, Eshel and Buckland 1994). The most affected areas 
generally are in the southern half of the Southern Africa region, that is, southern Malawi, southern 
Mozambique, Zimbabwe, Lesotho, South Africa, Swaziland, Botswana, and Namibia. In Tanzania, 
however, like in East Africa, El Niño is generally associated with above-normal rainfall between October 
and December, leading to above-normal crop production. 
La Niña, a phase of cooling of the central and eastern Pacific’s intertropical waters that usually follows El 
Niño, also affects rainfall throughout Africa. In East Africa, it typically leads to good precipitation early 
during the rainy season October – December, but rainfall being erratic later during that rainy season and 
ending early (FEWS NET, 2010). It also affects negatively the main rainy season in the region, from 
March to May. La Niña events are usually associated with dryer-than-normal conditions in the eastern 
part of East Africa, and with wetter-than-normal conditions in the western and northern sector of the 
region. 
Given its impact of local agro-climatic conditions, the impact of the ENSO cycle on staple crop prices in 
East Africa could be complex. Food commodity prices in most of East Africa exhibit little integration 
with international export markets. However, as one gets closer to the coast, prices show more co-
movement with international prices. Because El Niño also affects international prices, and thus, 
indirectly, prices in East Africa, its total effect is ambiguous a priori. Maize is the main staple crop in the 
region (see Figure 5). It is provides a good case study, especially as it is an internationally traded 
commodity. 
 
2  Data analysis 
This study is based on retail prices for maize, millet, sorghum, and wheat from markets in Burundi, 
Ethiopia, Kenya, Malawi, Mozambique, Tanzania, Rwanda, Somalia, Uganda, Zimbabwe, and Zambia 
compiled by the Famine Early Warning System Network. The Oceanic Nino Index (ONI) data are from 
the National Weather Service Climate Prediction Center. The primary question of interest is whether a 
relationship exists between local cereal prices and a measure of ENSO. To analyze the influence of 
ENSO on cereal prices, a series of time-series tests and models are applied to unravel the structure of 
the data and the potential relationship between ENSO and cereal prices. As a first pass in our analysis, we start by considering prices in both real terms (deflated by national 
consumer price indices) and in terms where the month and market fixed-effects are removed, essentially 
transforming price levels into price anomalies (or deviations from the long-run mean by month and 
market). Both series of prices are then tested for unit roots using an augmented Dickey-Fuller test 
methodology explained in Enders (1995). 
With both sets of data we then apply cross correlegrams between ONI and cereal prices from each 
market, across various countries. The results from this analysis are found in Figure 6. The most striking 
results from the cross correlogram summary appear to be found in Tanzania as a strong cyclical pattern 
is observed. Negative peaks occur first at about 3 lags followed by a much stronger, positive peak near 
18 lags. In other words, it appears that there is a rather strong relationship between the ONI data and 
maize prices in Tanzania. The negative correlation in the short-term could indicate that El Nino leads to 
lower prices, as rainfall conditions improve. But in the longer term, El Nino is followed by a La Nina 
event, which leads to lower rainfall, leading to a reduction in yields and higher prices. Hence, the effects 
appear to reverse themselves as the lag length grows. 
The relationship between cereal prices and ONI in Kenya and Uganda appear to exhibit a similar 
relationship, in terms of the shape of the lag pattern, however the magnitude is much smaller from that 
in Tanzania. Similarly, for Malawi, the degree of correlation is very weak. 
 Cereal prices in Zambia, on the other hand, and ONI appear to be positively correlated and the 
magnitude of the correlation appears to be the greatest in Kasama. The positive correlation between 
ONI and prices seem to be consistent with the fact that El Nino typically leads to dryness in Southern 
Africa, lower yields, and higher maize prices. 
 
3  Further analysis (TBC) 
Autoregressive models are used where a price anomaly (in levels) is regressed on the contemporaneous 
Oceanic Niño Index (ONI), a measure of ENSO based on a three-month moving average of sea surface 
temperature (SST) anomalies in a zone of the eastern equatorial Pacific, lagged ONI values at multiple 
horizons, and lagged values of the price anomaly. Various specifications are employed for robustness 
check. Future work may include the use of vector autoregressions (VARs) to account for the 
interdependence among commodity prices from different markets, within a region or across market 
sheds. 
Preliminary results based on the analysis of maize price anomalies in Tanzania suggest that ENSO 
substantially influences maize prices, but at a considerable lag (after 12 months). Positive ONI 
realizations, that is, an El Niño episode, appear to exert upward pressure on maize prices but with a 12-
month lag, while negative ONI realizations exert downward pressure on prices at a similar lag. 
 
4  Implications for early warning and food security analysis References 
Enders, W. 1995. Applied Econometric Time Series, John Wiley & Sons, Inc. USA. 
Famine Early Warning Systems Network. "Pre-emptive livelihood support could mitigate La Nina impacts 
in the eastern Horn." East Africa Food Security Alert. Washington, November 2, 2010. 
 Tables 
 
Table 1. Augmented Dickey-Fuller Test results 
   Type  ADF Test Statistic  Critical value  Critical value  Critical value 
     
1%  5%  10% 
Kenya           
Nairobi  level  -2.907  -4.018  -3.441  -3.141 
Nairobi  f.d.  -7.365  -4.018  -3.441  -3.141 
Mombasa  level  -3.007  -4.018  -3.441  -3.141 
Mombasa  f.d.  -7.295  -4.018  -3.441  -3.141 
Malawi 
          Mzuzu  level  -4.99  -3.991  -3.43  -3.13 
Lilongwe  level  -3.491  -4.004  -3.436  -3.136 
Rwanda 
          Kigali  level  -2.551  -4.033  -3.447  -3.147 
Kigali  f.d.  -7.846  -4.033  -3.447  -3.147 
Tanzania 
          Dar Es Salaam  level  -4.205  -4.018  -3.441  -3.141 
Mbeya  level  -4.421  -4.016  -3.441  -3.141 
Songea  level  -5.496  -4.018  -3.441  -3.141 
Uganda 
          Kampala  level  -2.846  -4.028  -3.445  -3.145 
Kampala  f.d.  -7.974  -4.028  -3.445  -3.145 
Zambia 
          Kasama  level  -4.427  -4.011  -3.438  -3.138 
Kalulushi  level  -4.611  -4.012  -3.439  -3.139 
Lusaka Urban  level  -4.037  -4.013  -3.439  -3.139 
*f.d. = first difference 
         Figures 
Figure 1: GPCP precipitation anomalies during El Nino years, October to September 
 
Source: Image provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at 
http://www.esrl.noaa.gov/psd/. 
Note: Precipitation anomalies are deviations from long-term averages. Precipitation data are from the Global Precipitation 
Climatology Project (GPCP). Figure 2: GPCC precipitation anomalies during El Nino years, October to September 
 
Source: Image provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at 
http://www.esrl.noaa.gov/psd/. 
Note: Precipitation anomalies are the deviations from long-term averages. Precipitation data are from the Global Precipitation 
Climatology Centre (GPCC). Figure 3: Arkin-Xie precipitation anomalies during El Nino years, October to September 
 
Source: Image provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at 
http://www.esrl.noaa.gov/psd/. 
Note: Precipitation anomalies are the deviations from long-term averages. Figure 4: GPCP precipitation anomalies during El Nino years, October to December 
 
Source: Image provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at 
http://www.esrl.noaa.gov/psd/. 
Note: Precipitation anomalies are the deviations from long-term averages. Precipitation data are from the Global Precipitation 
Climatology Project (GPCP). Figure 5: Maize producing areas in East and Southern Africa 
 
Source: Data on area planted are from HarvestChoice. 
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Figure 6: Cross-correlograms 
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Impact of El Niño on Staple Food Prices in East and Southern Africa

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