This paper proposes a technique to decompose short-run fluctuations in the terms of trade. Using Ethiopia as an example, we decompose the commodity terms of trade into various components to measure the impact of price and volume shifts as well as export diversification. We use monthly data from the past decade, including periods during the global food and financial crises. Our findings suggest that diversification out of traditional coffee exports to other export commodities successfully mitigated a terms of trade shock. Continued export diversification will be beneficial.Terms of Trade, Food Price Crisis, Financial Crisis, Ethiopia

Josef L. Loening

Masato Higashi

English

Research Papers in Economics

Ibero-Amerika Institut für Wirtschaftsforschung   
Instituto Ibero-Americano de Investigaciones Económicas   
Ibero-America Institute for Economic Research   
(IAI) 
     
Georg-August-Universität Göttingen 
(founded in 1737) 
 
 
 
Nr. 205 
 
Decomposing Terms of Trade Fluctuations in Ethiopia 
 
Josef L. Loening, Masato Higashi 
 
October 2010 
Diskussionsbeiträge  ·  Documentos de Trabajo  ·  Discussion Papers 
Platz der Göttinger Sieben 3    37073 Goettingen    Germany    Phone: +49-(0)551-398172    Fax: +49-(0)551-398173
e-mail: uwia@gwdg.de    http://www.iai.wiwi.uni-goettingen.de   -1- 
 
Decomposing Terms of Trade Fluctuations in Ethiopia 
 
Josef L. Loening*    Masato Higashi 
         World Bank                        Columbia University 
 
August 2010 
 
This paper proposes a technique to decompose short-run fluctuations in the terms of 
trade. Using Ethiopia as an example, we decompose the commodity terms of trade into 
various components to measure the impact of price and volume shifts as well as export 
diversification. We use monthly data from the past decade, including periods during the 
global food and financial crises. Our findings suggest that diversification out of 
traditional coffee exports to other export commodities successfully mitigated a terms of 
trade shock. Continued export diversification will be beneficial. 
 
Keywords: Terms of Trade, Food Price Crisis, Financial Crisis, Ethiopia. 
JEL classification: F14, O11, O55. 
 
* Corresponding author’s address: World Bank, 1818 H Street NW, Washington DC 
20433, USA; Email: jloening@worldbank,org. 
 
1. Introduction 
Developing countries were heavily affected by the food price crisis of 2006-2008. 
And when global and domestic food prices started to decline, the financial crisis of 
2008-2009 once again hit developing economies in several ways, in particular by 
slowing the demand for export commodities, resulting temporarily in the biggest 
decline in world trade of the past 60 years. 
The aim of this paper is to assess the impact of these two global shocks on the 
terms of trade. Few studies have examined this topic; after the food and financial 
crises, we know little about how price and volume fluctuations affected terms of trade 
in developing countries, especially sub-Saharan Africa. Using a novel monthly data 
set for Ethiopia, during the past decade, we decompose the country’s terms of trade to 
study the short-run impact of the two global shocks on external trade. Ethiopia is an 
interesting case study — it allows us to focus solely on how the crises affected trade 
because there were few effects on the financial sector. At the same time, it is a 
relevant case because inflation of domestic food prices reached an historical record of -2- 
 
92 percent in July 2008, followed by significant pressures on the balance of payments 
(Loening, Durevall, and Ayalew, 2009).  
The remainder of the paper is structured as follows: Section 2 gives an 
overview of Ethiopian trade patterns, Section 3 discusses the methodology of our 
terms of trade decomposition, Section 4 presents the empirical results, and Section 5 
concludes. 
2. Trade in Ethiopia 
 
During 2009, Ethiopia’s total export volume was about US$ 1,600 million, while the 
total import volume was US$ 7,600 million, thus producing a trade deficit of US$ 
6,000 million, equivalent to about 23 percent of GDP. 
Arabica coffee not only originated in Ethiopia, but is also the country’s largest 
export commodity. Nevertheless, as shown in Figure 1, the coffee share of exports 
decreased from 54 to 36 percent between 2000 and 2008.
1 At the same time, the share 
of oil seeds, pulses, and flowers substantially increased. Ethiopia’s export structure 
has thus diversified. On the import side, the petroleum share increased from 20 to 25 
percent, food imports also increased, but machinery and vehicles imports decreased 
from 25 to 20 percent.  
We generally find that at low levels of disaggregation — sufficient for the 
purpose of this paper — Ethiopian Customs Authority Data, information provided by 
the International Monetary Fund’s Direction of Trade Statistics (DOTS), and the 
United Nation’s Commodity Trade Statistics (COMTRADE) database are consistent. 
We adopt Ethiopian Customs Authority data as well as price information from the 
Central Statistical Agency and exchange rate data from the National Bank of Ethiopia 
for the following terms of trade decomposition. 
                                                 
1 The export volume of coffee stayed roughly the same whilst other commodities, such as pulses, rose. -3- 
 
Figure 1. Ethiopia: Export and Import Share by Goods, 2000 and 2008 
 
                            Exports            Imports 
0
10
20
30
40
50
60
70
80
90
100
2000 2008
Others Leather Chat Flower
Pulses Oil Seeds Coffee
%
0
10
20
30
40
50
60
70
80
90
100
2000 2008
Others Machinery & Vehicle
Metal Fertilizers
Petroleum Food
%
Source: Ethiopian Customs Authority and  COMTRADE.
 
3. Terms of Trade Decomposition: Methodology 
 
The commodity terms of trade measures the number of units of imports that can be 
exchanged for a unit of exports. Terms of trade are thought to be among the key 
determinants of a country’s macroeconomic performance, but are often volatile and a 
traditional source of concern for policy-makers in developing countries (Cashin, 
McDermott, and Pattillo, 2004; Blattman, Hwang, and Williamson, 2007). Despite the 
importance of understanding terms of trade changes, there are few studies that apply 
decomposition analysis to analyze the approximate causes of their fluctuations. 
One notable exception is Baxter and Kouparitas (2006), who decompose 
commodity terms of trade volatility for a panel of countries.
2 They divide the terms of 
trade into a goods price effect, measuring differences in the composition of import 
and export baskets, and a country price effect, measuring the relative prices of exports 
and imports. A country tends to obtain a gain (or loss) in the terms of trade if it is 
more (or less) relatively specialized in goods whose prices are growing faster (or 
                                                 
2 Another, more data-intensive approach to analyze income terms of trade, using a two-step variance 
decomposition technique, was proposed by Lloyd and Procter (1983).  -4- 
 
slower). The goods price effect measures the size of this effect. A country tends to 
obtain a gain (or loss) in the terms of trade if it has the ability to import at low cost in 
intra-sector international trade. The country price effect measures the size of this 
effect. Following this approach, Cardoso and Soares Esteves (2008) divide Portuguese 
commodity terms of trade movements into inter-sector specialization and intra-sector 
segmentation effects, in the same sense as the former goods and country price effects.  
Developed countries export and import similar goods, thus the intra-sector 
segmentation (or country price) effect is a useful result. However, in our view, a 
limitation of this approach is that developing countries typically export and import 
different goods. In addition, both papers use ready-made, officially published export 
and import price index data, which is often not available. For an analysis of a 
developing country such as Ethiopia, we need to decompose the terms of trade 
differently. As shown in Section 2, relative specialization does not matter because of 
absolute specialization in coffee and a few other agricultural goods. Trading mainly 
commodities, it is important to evaluate the effect of changes on commodity prices 
and export diversification.  
To illustrate the issues involved a terms of trade decomposition, we show a 
simplified situation in which there are only two goods in each export and import 
category. The commodity terms of trade ( t TT ) are: 
 
t m t x t P P TT , , /                                                    
where Px,t  represents the export price index, and Pm,t  represents the import price 
index at time t. TTt, Px,t , and Pm,t are indices. This definition is also called net barter 
terms of trade or simple terms of trade index (Jemma and Zieschang, 2004). In this 
paper, we define the terms of trade for convenience of the following decomposition as: 
(1) -5- 
 
 
t m t x t P P TT , ,    
 
This terms of trade index shows the difference of accumulated growth rates 
between the export price and the import price. For small values, both terms of trade 
indices are similar. However, when the export and import prices show a very large 
gap from the base level or a gap between each other, there will be a small difference 
between the indices. Throughout this paper, this difference should be recognized. 
Next, we calculate export and import price indices. We use the export unit 
value index as the export price index. The export unit value index of the component i 
is: 
 
t xi
t xi t xi
t xi
q
q p
p
,
, ,
,
* 
 
 
where Pxi,t and qxi,t represent the export unit value and the quantity of the 
component i at t. The overall export price index (Px,t) is a weighted average of all 
components: 
t xi
n
i
t xi t x p w P ,
*
1
, , 

 , 


 n
i
t xi t xi
t xi t xi
t xi
q p
q p
w
1
, ,
, ,
,  
 
Similarly, for the import side, the terms of trade ( t TT ) are expressed as: 
t mi
n
i
t mi t xi
n
i
t xi
t m t x t
p w p w
P P TT
,
*
1
, ,
*
1
,
, ,
 
 
 
 
 
where wi,t represents the export/import value weight of the i component at t. 
When decomposing the terms of trade into an export and import component the 
equation becomes: 
(2) 
(3) 
(4) 
(5) -6- 
 
 
Efffect Goods port
base x t m t m
Efffect Goods port
base x t m t m
Efffect Goods Export
base x t x t x
Efffect Goods Export
base x t x t x
n
i
base mi t mi t mi base xi t xi
n
i
t xi
t m t x t
p p w p p w p p w p p w
p p w p p w
P P TT
2 Im
, 1
*
, 2
*
, 2
1 Im
, 1
*
, 1
*
, 1
2
, 2
*
, 2
*
, 2
1
, 1
*
, 1
*
, 1
1
,
*
,
*
, ,
*
,
*
1
,
, ,
) ( ) ( ) ( ) (
) ( ) (
       
   
 
 
 
 
We next divide the terms of trade into a price change effect and a share weight 
change effect of the main export and import goods. If the prices of export 
commodities rise or the prices of import commodities fall, the terms of trade improve 
— and vice versa. The price change effect measures the size of this effect. In addition, 
changing the export and import goods basket has an impact, measured by the share 
weight changes effect. When decomposing the terms of trade into these two effects, 
the expression becomes: 
 
) ( * ) ( ) ( * ) (
) ( * ) ( ) ( * ) (
* ) ( * ) (
* ) ( * ) (
, 2 , 2 , 2 , 2 , 1 , 1 , 1 , 1
, 2 , 2 , 2 , 2 , 1 , 1 , 1 , 1
, 2 , 2 , 2 , 1 , 1 , 1
, 2 , 2 , 2 , 1 , 1 , 1
1
, ,
*
1
, ,
*
, ,
base m t m base m t m base m t m base m t m
base x t x base x t x base x t x base x t x
base m base m t m base m base m t m
base x base x t x base x base x t x
n
i
t mi t mi
n
i
t xi t xi
t m t x t
w w p p w w p p
w w p p w w p p
w p p w p p
w p p w p p
w p w p
P P TT
     
     
   
   
 
 
 
   
 
where pi,base represents the export and import unit value index of the 
component i at July 2005 (pi,base = 100). The first half of the equation shows the price 
change effect, which is expressed as the accumulated change of price multiplied by 
the share. The latter half of the equation shows the share weight change effect, which 
is written as the accumulated change of the price, multiplied by the accumulated 
change of share. 
Price change effect 
Share weight change effect 
(6) 
(7) -7- 
 
4. Decomposing Ethiopia’s Terms of Trade: Results 
 
An overview of the movement of Ethiopia’s terms of trade is given in Figure 2. From 1999 
to 2003, Ethiopia’s terms of trade showed a downward trend. During the food crisis and the 
financial crisis, Ethiopia’s terms of trade index showed some volatility, but appears stable 
without a visible significant shock. Since mid-2008 the terms of trade increased. Figure 2 
also shows 95 percent confidence bands, suggesting that small changes should be 
interpreted with caution. 
 
Figure 2. Ethiopia: Terms of Trade, 1999:7-2010:3 
 
20
40
60
80
100
120
140
160
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Terms of Trade(Px ‐ Pm)
Note: Shadow areas show 95% confidence bands of the terms of trade (Px - Pm).
Source: Ethiopian Customs Authority.
Commodity Terms of Trade Index (Base = 2005:7)
 
 -8- 
 
Figure 3 shows a decomposition of the terms of trade by goods.
3 This 
decomposition is based on Equation (6). Coffee and other agricultural commodities 
mainly influence the movement of export prices. The key imports are petroleum 
products, manufactured metal goods, and grains. These items mainly determine the 
movement of import prices.  
Figure 4 analyzes the situation in more detail. The decomposition is based on 
Equation (7). From 1999 to 2002, export prices dropped because of a decrease in 
coffee prices. During this period, the world market price fell because the world coffee 
supply increased rapidly, but on the other hand, prices of imported goods, including 
petroleum products, increased. Altogether, these factors caused a decrease in the 
terms of trade. High prices for key import contributions worsened the terms of trade 
from 2002 to 2004. The effects include grain imports (summarized in “others”).  
From 2005 to July 2008, the export price of coffee and other key commodities 
(defined as oil seeds, flowers, and pulses), as well as the import price of petroleum 
products, increased. But the effects of coffee, key export commodities and petroleum 
products cancel each other out, and the terms of trade remained relatively stable. 
Similarly, since mid-2008, the export price of coffee and key commodities was stable 
and the import price of petroleum dropped. By contrast, the price of key imports rose, 
thus, their effects are cancelling each other out. 
                                                 
 
3 In the decompositions we prefer to use a terms of trade series omitting the Machinery and 
Transportation Equipment Unit Value Index because data on quality changes of manufactured goods 
are limited. Nevertheless, if we choose to include this index and make assumptions about quality 
changes (such as fluctuations within ±15 percent) our quantitative overall results would only be 
affected marginally. -9- 
 
Figure 3. Ethiopia: Terms of Trade Decomposition by Commodity Goods, 
1999:7-2010:3 
0 
20 
40 
60 
80 
100 
120 
140 
160 
180 
200 
220 
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
OTHERS Import of OTHER KEY IMPORT
Import of GRAIN Import of PETROLEUM
Export of OTHER KEY EXPORT Export of COFFEE
Terms of Trade
Source: Ethiopian Customs Authority.
(Base = 2005:7)
 
 
Figure 4. Ethiopia: Terms of Trade Decomposition by Price and Share Weight 
Change, 1999:7-2010:3 
0 
20 
40 
60 
80 
100 
120 
140 
160 
180 
200 
220 
240 
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
OTHERS WEIGHT(Import of OTHER KEY IMPORT)
PRICE(Import of OTHER KEY IMPORT) WEIGHT(Import of PETROLEUM)
PRICE(Import of PETROLEUM) WEIGHT(Export of OTHER KEY EXPORT)
PRICE(Export of OTHER KEY EXPORT) WEIGHT(Export of COFFEE)
PRICE(Export of COFFEE) Terms of Trade
(Base = 2005:7)
Source: Ethiopian Customs Authority.-10- 
 
Figure 5. Ethiopia: Dynamics of Export Commodities, 2002-2008 
-5 
0 
5 
10 
15 
20 
25 
02468 1 0 1 2
D
i
f
f
e
r
e
n
c
e
 
b
e
t
w
e
e
n
 
E
t
h
i
o
p
i
a
 
a
n
d
 
W
o
r
l
d
'
s
 
A
v
e
r
a
g
e
A
n
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a
l
 
G
r
o
w
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R
a
t
e
 
o
f
 
2
0
0
2
-
2
0
0
8
,
 
%
Ethiopia's Share of World Trade, Average of 2002-2008, %
Pulses
Coffee
Oil seeds
Leather
Source: COMTRADE.
Ethiopia export growth rate 
is greater than World
Ethiopia export growth rate 
is less than World
400
450
Flowers
 
 
To confirm these findings with alternative data, Figure 5 plots Ethiopia’s 
shares of key commodity exports in world trade, and the growth rate differences 
between Ethiopian exports and world exports. Coffee and leather exports from 
Ethiopia had about the same growth rate as world exports. On the other hand, oil seed, 
flower and pulse exports exceeded the growth rate of the world trade. Thus, Ethiopia 
expanded, in a relative sense, exports of selected commodities, except coffee.  
5. Conclusion 
 
We decompose Ethiopia’s terms of trade fluctuations, especially during periods of the 
global food and financial crises. The results suggest that agricultural export 
diversification was instrumental in helping to keep the terms of trade stable, and 
eventually increase since mid-2008. -11- 
 
Commodities will remain subject to future price fluctuations. To prevent risks 
from terms of trade volatility and shocks, continued export diversification can be 
beneficial. Diversification can be both horizontal, involving alternative economic 
activities, and vertical, involving the development of new products or marketing. For 
example, an estimated 95 percent of the Ethiopian coffee is organic, but is not labelled 
as such, and may suggest development potential.  
In addition, Ethiopia needs to continue to improve domestic competitiveness, 
benefitting from policies and investments that support private and financial sector 
development in this area. Overall, the Ethiopian case underlines the benefits of export 
diversification. The methodology applied in this paper could be of interest to other 
countries. 
*** 
 
We would like to thank Yohannes Ayalew Birru, Fátima Cardoso and Paulo Esteves for helpful 
comments and discussions, as well as Yuya Oshima for excellent research assistance. The views 
expressed in this paper are entirely those of the authors; they do not necessarily represent the views of 
the World Bank and its affiliated Organizations, or those of the Executive Directors of the World Bank 
or the governments they represent. 
 
 
References 
 
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Decomposing Terms of Trade Fluctuations in Ethiopia

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