Ethiopian exports growth characteristics, dynamics and survival berihu-assefa_sep30_2016

An Economic Inquiry into Ethiopian
Exports: Pattern, Characteristics,
Dynamics and Survival
Berihu Assefa Gebrehiwot (PhD)
Mekelle University, Mekelle
30 September 2016
ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE (EDRI)

1 Introduction
2 Objectives and Questions
3 Data
4 Results and Discussions
Structure
5 Conclusions

Introduction
 International trade is a key driver of economic
growth and poverty reduction [Reis & Farole, 2012; Frankel &
Romer, 1999; Samuelson, 1939; Dollar & Kraay, 2003; Irwin & Terviö, 2002; Krueger, 1998;
Balassa, 1982]
 Some of the mechanisms through which trade
contributes to economic growth include (Samen, 2010;
Brenton & Newfarmer, 2009; Reis & Farole, 2012):
 Trade increases potential market size (via exports) - increasing market size
means more profits and scale effects leading to growth
 Trade increases domestic competition (via imports) – efficiency gain
 Trade increases specialization and expands efficiency-raising benefits of
comparative advantage (via factor price equalization)
 Trade affords greater capacity utilization
 Trade induces rapid technological change, and productivity gains leading
to wage premiums and job creation (via DCA)

Introduction
 Empirical literature also shows a wide consensus
on the positive association between trade and
economic growth (Edwards, 1992; Krueger, 1997; Wacziarg & Welch,
2003)
 Special emphasis on the role of exports
• Rapid and sustained economic growth through productivity
gains and technological upgrading (Spence, 2007; Brenton &
Newfarmer, 2009)
» Export sectors have higher productivity and
» And within sectors, exporting firms tend to be more
productive than non-exporters

Introduction
 Following these spectrum of theoretical and empirical
grounds and the drive for industrialization, many
developing countries have started promoting exports
through direct policy incentives and export
institutions
 Particularly, since the 1980s, most developing countries abandoned import
substitution strategies in favor of export-led growth and start to undertake
trade reform measures as part of the Structural Adjustment Program (SAP)
 Since then designing policies that promote trade and trade
competitiveness have been at the heart of growth strategies for developing
countries
 Likewise, since the early 1990s Ethiopia has been
striving to set the right conditions for increasing
exports
 Overall economic liberalization and macroeconomic stability reforms
 Introduction of several export incentive schemes
 Set up a number of export support institutions (ESI) aimed at boosting
exports in general and manufactured exports in particular

Research Objectives and Questions
 So our goal in this paper is to analyze
Ethiopia’s export performance and
dynamics over the period 1995 to 2014
 Ethiopia’s trade/export patterns and
Characteristics
 What characterizes and drives export growth/contraction?
 Export Dynamics and Survival
 How concentrated/diversified are Ethiopia’s exports in terms
of exporters, products, and markets?
─ Or over the past decade or so, has Ethiopia added
economically significant numbers of new products and
markets to its export portfolio?
 And to what extend do Ethiopian exporters survive beyond
their first year of entry to the export market?

 Our dataset come mostly from ERCA
administrative data
 We use both
 Aggregate data – provide a good picture of the trade flows
 Firm-level data - has the merit of giving a richer and more
accurate picture of export dynamics
 We used complementary data from international
data sources – Uncomtrade, WB, IMF, UNCTAD, etc
 When Customs data is not available
 When comparison is necessary and
Dataset

Two Main Sections
 Section 1: Aggregate Data
– Trends, Patterns and Key Performance Indices
• Overall Openness
• Trade Composition (sectoral, geographical, etc)
 Section 2: Firm-Level Data
– Export Diversification
– Export Dynamics
– Export Survival

Trends in Ethiopia’s Exports, Imports and
GDP
Figure 1: Trends in Exports, Imports and GDP of Ethiopia (1995- 2013)
• High import growth following the rapid increase in GDP after 2003
• Increase in exports not equally remarkable
• As a result, trade deficit widened

Ethiopia: Trends in Trade Growth
Figure 6: Annual Growth Rates of Exports of Goods and Services in Ethiopia (1995-2013)
• The growth rates in exports over the reviewed period have been positive
except for the negative growth rates observed in 1999 and 2012 and the
close to zero rates in 2008 and 2009.

Figure 7: Export Value and Annual Growth Rate of Merchandize Exports (2005/2006 - 2014/2015).
Source: ERCA

 We observe large annual fluctuations in the level and
growth of exports (see Figures 6 and 7)
– This is driven largely by changes in international commodity
prices of major exports such as Coffee
• For example, the country's strong export performance recorded in
2010 and 2011 can largely be attributed to the rise in international
commodity price in general and that of coffee in particular
• Similarly, the negative export growth rates observed in 2012 appears
to coincide with the fall in international commodity price particularly
that of coffee
– This co-movement of international commodity price and
Ethiopia's export performance clearly shows how the
country is heavily reliant on commodity exports and signals
the need for further diversification of the country’s exports
base

Figure 10: Coffee Commodity prices, annual, 1960 - 2014. Source: UNCTAD

Figure 9: Indices of Non-Fuel Primary Commodity Prices (2005 = 100). Source: IMF Commodity Prices
Notes: (1) Indices comprise 60 price series for 44 non-fuel primary commodities; (2) Weights are based on the
2002-2004 average of world export earnings; and (3) Real means that it is deflated by U.S. CPI

Key Trade Performance Indices
 Discussion on trade performance commonly
revolves around 3 questions – how much, what
and with whom a country trades
 We focus on two key performance measures
1) Trade Openness (at the aggregate level)
 Measures a country’s ability to integrate in global trade
2) Trade Composition
 Sectoral and geographic compositions

(1) Overall Openness Measures
 Raw Trade Openness – measured as the sum of imports
and exports over GDP
 Common measure but has defects (e.g., cross country
comparison is difficult because openness is correlated with
country characteristics such as income, location, size, etc)
 For example, while smaller countries (such as Singapore,
Belgium, etc) appear to have higher levels of trade share in
GDP, large countries such as the U.S. and Japan appear to have
the lowest level of trade share in GDP. (This is so because larger
countries trade more with themselves)
 Relative Trade Openness – measures a country’s trade
share in world trade
 Corrects for some of the weaknesses of the raw measure but
cross-country comparisons still remain difficult
 Adjusted Trade Openness – controls for the influence of
country characteristics through regression analysis
 Generates meaningful comparisons

Raw Measure: Ethiopia’s Trade Openness
Figure 2: Trends in Shares of Trade, Exports and Imports of Goods and Services in GDP Ethiopia
(1995-2013)
26%
51% 40%Mainly attributed to the rapid
increase of imports
During the 2006-2009, rapid growth rate of GDP was largely
driven by growth in the non - tradable (service sector)
Improvements in
international
commodity prices.

Raw Measure: Ethiopia’s Trade Openness
Figure 3: Trends in Shares of Merchandise Trade, Exports and Imports in GDP Ethiopia (1995-2013)
• Trend is similar but the share of merchandise exports in GDP remains flat and its
contribution to the overall merchandise trade openness is meagre
• In the case of merchandise, it is clear that the overall trade openness is to a greater
extent dictated by trends in share of merchandise imports in GDP.

Raw Measure: Ethiopia’s Trade Openness Relative
to Other Countries
Trade openness: Merchandise Trade Share in GDP (%GDP)
Period Ethiopia Kenya Zambia Tanzania Mozambique Uganda Zimbabwe Madagascar
1994-1998 21.67 44.9 47.98 34.44 34.42 29.38 65.83 32.98
1999-2003 27.61 39.02 51.31 24.32 45.1 30.41 55.32 39.03
2004-2008 37.74 44.36 56.81 41.45 63.93 36.07 86.52 53.88
2009-2013 33.5 43.29 66.67 54.51 72.28 41.78 66.78 46.49
Export Share in GDP (%GDP)
1994-1998 5.95 18 26.39 10.53 6.52 8.88 29.6 14.57
1999-2003 5.87 14.89 24.59 8.1 14.63 7.76 27 16.83
2004-2008 6.64 15.04 27.58 12.84 27.47 9.98 39.31 17.66
2009-2013 6.83 12.29 35.73 17.26 26.33 11.51 30.17 14.95
Import Share in GDP (%GDP)
1994-1998 15.72 26.9 21.59 23.91 27.91 20.49 36.23 18.41
1999-2003 21.74 24.13 26.72 16.22 30.47 22.64 28.32 22.2
2004-2008 31.1 29.32 29.23 28.61 36.45 26.09 47.21 36.22
2009-2013 26.68 31 30.94 37.25 45.95 30.26 36.61 31.54
Table 1: Ethiopia's Trade Openness Relative to Selected Sub-Saharan African Countries (1994 - 2013). Source:
World Bank

Relative Measure: Comparing Ethiopia's Share in
World Trade with Selected Sub-Saharan African
Countries
 Taking all countries in the world, 𝑗 = {1, 2, … , 𝑁}, country 𝑖’𝑠, world
trade share is given by:
𝐖𝐨𝐫𝐥𝐝 𝐓𝐫𝐚𝐝𝐞 𝑺𝒉𝒂𝒓𝒆𝒊 =
(𝐗 + 𝐌)𝒊
𝐗 + 𝑴 𝒋
𝑵
𝐣=𝟏
, where i ∈ j
 This measure corrects for some of the weaknesses of trade share in
GDP as a measure of openness
Trade Openness: share in world trade (%)
1994-1998 0.016 0.046 0.017 0.02 0.01 0.016 0.047 0.011
1999-2003 0.017 0.039 0.016 0.019 0.015 0.014 0.027 0.013
2004-2008 0.025 0.044 0.026 0.026 0.02 0.015 0.018 0.013
2009-2013 0.037 0.058 0.044 0.042 0.028 0.022 0.021 0.013
Table 2: Ethiopia's Trade Share in World Trade Relative to Selected Sub-Saharan African Countries (1994 -
2013). Source: World Bank

Adjusted Trade to GDP Ratio: Regression
Analysis to Control for Country
Characteristics
 Having a lower trade share in GDP doesn't necessarily mean that
the country in question is less open to international trade
 In fact, these countries might be trading as much as they would be expected to trade
given their size, level of development and geographical location etc
 In order to measure correctly how much a country trades relative
to how much it can be expected to, given its fundamentals, one can
run a trade-openness regression of the type
𝑂𝑖 = 𝛼0 + 𝛼1 𝑌𝑖 + 𝛼2 𝐴𝑖 + 𝛼3 𝑅𝑖 + 𝛼4 𝐿𝐿𝑖 + 𝛼5 𝐼𝐿𝑖 + 𝑈𝑖
 Where Yi is GDP per capita (2005 USD); Ai is area in sq. kms; Ri is remoteness index; LLi
and ILi are dummies for landlocked and island; and Ui is an error term.
 Remoteness index measures a country’s average weighted distance from its trading
partners (Head, 2003), where weights are the partner countries’ shares of world GDP
 Since the distance variable used in calculating the remoteness index is measured
between capitals of trading partners, it is can also capture internal cost of
transportation
 Country size measured by total population and land size

Adjusted Trade to GDP Ratio:
Regression Analysis to Control Country
Characteristics
 The difference between 𝑂𝑖 and its predicted value,
𝑂𝑖, can be taken as an adjusted indicator of openness,
i.e., adjusted for the structural factors included in the
model
 Positive – trades more than it can be expected to
 Negative – trades less
 Zero – trading as expected

Log Openness: Log Trade (% of GDP)
(1) (2) (3) (4) (5)
Log GDP per capita (2005$)
0.053 0.027 0.046 0.033 0.012
(0.010)∗∗∗ (0.013)∗∗ (0.013)∗∗∗ (0.020)∗ -0.027
Log Population
-0.07 -0.089 -0.086 -0.077 -0.079
(0.016)∗∗∗ (0.019)∗∗∗ (0.018)∗∗∗ (0.025)∗∗∗ (0.033)∗∗
Log Area in sq. kms
-0.045 -0.04 -0.056 -0.047 -0.1
(0.013)∗∗∗ (0.014)∗∗∗ (0.014)∗∗∗ (0.023)∗∗ (0.029)∗∗∗
Remoteness Index
-0.179 -0.09 -0.108 -0.092
(0.044)∗∗∗ (0.050)∗ -0.071 -0.08
Dummy for Landlocked
0.064 0.179 0.1
-0.04 (0.059)∗∗∗ -0.066
Dummy for Islands
-0.218 -0.224 -0.235
(0.057)∗∗∗ (0.078)∗∗∗ (0.092)∗∗
Log Cost to Exp. & Imp.
-0.197
(0.059)∗∗∗
Log Time to Exp. & Imp.
-0.089
(0.045)∗∗
Number of Obs. 715 715 715 346 222
Adj. R2 0.31 0.33 0.35 0.35 0.44
• Robust standard errors in parentheses. Note that clustering at Country level doesn’t change the result in any meaningful way. The numbers of observations in
these two last columns are greatly reduced as data on these two variables is available only for the last two five year periods. All regressions include Period
FixedEffects. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
• Based on data for all countries (where data is available) over the four five-year periods
Table 3: Trade Openness and its Major Determinants
Adjusted Measure: Controlling for
Country Characteristics

Adjusted Openness BasedonModel 3: Trade share in GDP(% GDP)
Period Ethiopia SSA S. Asia N.America LAC MENA ECA EAP
1994-1998 -20.84 8.64 -6.34 -8.53 3.66 4.92 5.59 24.61
1999-2003 -12.92 4.83 2.47 -8.36 -4.60 -0.06 9.87 34.07
2004-2008 -8.90 0.96 1.59 -16.37 -6.57 5.28 6.78 34.43
2009-2013 -10.12 2.71 -0.57 -16.65 -11.51 5.07 10.68 33.43
Adjusted Openness Based on Model 4: Trade share in GDP (% GDP)
Period Ethiopia SSA S. Asia N.America LAC MENA ECA EAP
1994-1998
1999-2003
2004-2008 -10.84 3.27 -1.43 -16.81 -0.59 0.79 4.67 33.34
2009-2013 -13.18 5.20 -1.93 -17.06 -6.64 2.73 8.53 29.91
NOTE: We don’t have data on Cost of Importing and exporting for the first two periods
Data Source: WB and Ethiopian Revenues and Customs Authority (ERCA)
Table 4: Comparing Adjusted Openness across Regions
Adjusted Measure: Ethiopia’s Trade
Openness

Openness
 2009-2013 period
 Ethiopia traded 10 percentage points less than what
would be expected once the influence of the factors is
accounted for
 Using this adjusted measure, Sub Saharan African
countries (including Ethiopia), on average, appear to be
relatively more open compared to North America and
Latin America and Caribbean regions once the influence
of these factors is taken into account (contrary to what
the "raw" measure of openness reported earlier)
 When we further control for inland costs of trading (local
costs associated with exporting and importing), Ethiopia
becomes relatively less open - trades 13 percentage
points less than would be expected.

Figure 5: Adjusted Openness to Trade for Sub-Saharan African Countries (Average of
2009 - 2013)
Openness
 Ethiopia - 14th least open based on models 3 & 4
 Ethiopia - 6th open when comparison is made based on
the "raw" measure of openness.

(2) Trade Composition: Sectoral Composition of
Exports
• Agriculture still largest contributor to Ethiopia’s
merchandise exports - accounting for close to
87 percent, on average, during the past decade
(2005/06 – 2014/15)
• While the manufacturing sector contributed
about 7 percent for total merchandise exports,
all the other sectors (minerals, base metals,
etc) account for about 7 percent during the
same period
• The other interesting observation is that
although still dominant the export value share
of Agriculture fell from 88 percent in
2005/2006 to 85 percent in 2014/2015
• On the other hand, the export value share of
Manufacturing rose from 6.6 percent in
2005/2006 to 10 percent in 2014/2015.
– Although these are small changes, this would
suggest that Ethiopian exporters are slightly
diversifying into higher value sectors and sectors
that are not heavily affected by external price
and demand shocks.
– Likewise, in terms of the number of exporters,
agriculture has the largest number of exporters
followed by the manufacturing sector.
– On the contrary, the number of exporters is
smallest in Mineral and Base Metals sectors.

(2) Trade Composition: Geographical
Composition of Exports
 In terms of geographic composition, modest diversification away from traditional
partners such as Europe
Figure 14: Regional Share of Ethiopia's Merchandise Export over Periods

(2) Trade Composition: Geographical
Composition of Exports
.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
2005/2006
2006/2007
2007/2008
2008/2009
2009/2010
2010/2011
2011/2012
2012/2013
2013/2014
2014/2015
Year
Overall Export Value Share BY Region (%)
South & Central Asia
Europe
East Asia & Pacific
Latin America & Caribbean
Middle East & North Africa
Sub-Saharan Africa
North America
COMESA

1 Overall Trade Pattern
2 Export Base and Diversification
3 Export Dynamics
4 Export Survival
Results & Analysis
5 Conclusions

 Export Base – measured in terms of number of exporters,
products and markets
 Export diversification – a measure of risk and vulnerability
arising from relying on a narrow range of exporters, products
and markets
– It makes countries less vulnerable to demand shocks and price swings in
overseas markets by stabilizing export revenues (Ghosh & Ostry, 1994)
– Creates greater opportunities through knowledge spillovers and increasing
returns to scale, which are critical to long-run growth (Amin Gutiérrez de
Piñeres & Michael, 2000; Hausmann & Klinger, 2006)
 As argued by (Samen, 2010), export diversification takes the
following forms:
– Horizontal diversification - within the same sector (primary, secondary or
tertiary)
– Vertical diversification entails a shift from the primary to the secondary or
tertiary sector (e.g., moving from basic commodity extraction to commodity
processing)
– Diagonal diversification entails a shift from imported inputs into the
secondary and tertiary sector (e.g., using imported goods to produce
manufactured products for exports)
Export Base and Diversification

Export Base and Diversification
 Diversification is critical in any trade
strategy (Reis & Farole, 2012)
 Justified to avoid the so-called “natural resource curse”
(a negative correlation between growth and
dependence on primary commodity exports) (Sachs and
Warner, 1999)
 In addition, diversification at the extensive margin
reflects “export entrepreneurship” (Hausman & Rodrik,
2003)
 History: few countries have developed quickly on the
basis of exports of primary products alone

Ethiopia’s Export Base and
Diversification
 In order to investigate export diversification
performance in Ethiopia more robustly, we
consider the following diversification measures:
1) Number of exporters, export products and destination
markets
 a simple count
2) Share of top exporters, products and markets
 Top 1, 5 and 25 percent
3) Theil Diversification Index
 Within and between-group diversification
4) Extensive and Intensive Margins: Export Growth
Decomposition
 What is the contribution of the extensive margin?

1. Ethiopia’s Export Base - A Simple Count
Figure 15: Export Base of Ethiopia (2005/06 - 2014/2015). Source: ERCA

2. Ethiopia’s Export Base and Diversification: Export Value Share of
Top 1, 5 and 25 percent Exporters, Products and Markets
Export Years Based on Ethiopian Fiscal Year Calendar
2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015
Firm
Top 1%
Exporters
Number 13 15 16 17 21 22 20 19 18 18
Export
Value Share
(%)
31 35 34 32 38 35 32 31 28 28
Top 5%
Exporters
Number 62 75 80 85 101 106 96 94 90 89
Export
Value Share
(%)
68 69 67 65 69 68 65 65 61 60
Top 25%
Exporters
Number 306 375 396 423 504 528 479 468 448 443
Export
Value Share
(%)
96.0 95.4 95.0 94.3 94.7 93.7 92.7 93.3 92.9 92.5
Product
Top 1%
Exporters
Number 8 7 7 7 7 8 8 7 7 8
Export
Value Share
(%)
77.7 72.3 68.3 76.9 77.1 76.8 75.9 73.2 67.3 76.8
Top 5%
Exporters
Number 36 35 35 33 34 38 36 35 35 37
Export
Value Share
(%)
95.0 93.7 92.4 95.1 95.2 94.1 94.5 93.7 91.7 92.3
Top 25%
Exporters
Number 179 174 174 163 169 187 177 172 175 181
Export
Value Share
(%)
99.5 99.5 99.5 99.5 99.6 99.5 99.7 99.6 99.4 99.4
Market
Top 1%
Exporters
Number 2 2 2 2 2 2 2 2 2 2
Export
Value Share
(%)
21.6 19.4 17.5 22.4 22.5 23.8 22.9 21.1 27.8 24.1
Top 5%
Exporters
Number 6 7 6 6 7 7 7 7 7 7
Export
Value Share
(%)
50.0 49.1 44.1 53.0 58.5 56.5 60.1 53.2 57.1 56.3
Top 25%
Exporters
Number 29 31 28 30 31 32 33 34 31 34
Export
Value Share
(%)
95.7 96.4 94.5 96.3 96.9 96.6 96.7 96.5 95.5 96.5

3. Ethiopia’s Export Base and Diversification –
Theil Index
 The Theil Export Diversification Index
 Theil’s index is another widely used diversification measure – upper
limit is ln(n) and lower limit is zero
 The higher the Theil index, the higher the concentration (or the lower
the diversification)
 Theil’s index is decomposable - additively to measure diversification
within (intensive) and among groups (extensive) of exports
 Changes in the within-group component of Theil index measure changes at the
intensive margin (i.e., changes in concentration among active lines only)
 Changes in the between-groups component of Theil’s index measure changes at the
extensive margin (i.e., proportional changes in the number of active lines)
 Our results for the intensive (within) and extensive
(between) Theil indices are based on the definitions
and methods used in (Cadot, Carrère, & Strauss-Kahn,
2011)

Theil Index
 Following (Cadot et al., 2011), Theil’s entropy index (Theil,
1972) is given by:
 Let 𝑛 denote the number of export products (products are defined at the 4-digit SITC (Rev.
1) level), 𝑛𝑗
the number of export lines in group 𝑗, µ the average dollar export value, µ𝑗
group 𝑗’𝑠 average dollar export value, and 𝑋𝑘
the dollar value of export line 𝑘. Then the
between-groups component is:
 And the within-group (intensive margin) component is
Where

3. Ethiopia’s Export Base and Diversification – Theil
Index
 Overall Theil index and its components are calculated for Ethiopia
for the period 1993 – 2010 (where data is available)
– Results based on an updated version of the UN–NBER dataset as compiled
by the IMF, which harmonizes COMTRADE bilateral trade flow data at the 4-
digit SITC (Rev. 1) level
– Results are consistent with earlier measures - Ethiopia’s export
diversification has generally remained low through out the period
 Compared to the Sub-Saharan average, Ethiopia performed lower than
the average until the year 2001, beyond which Ethiopia’s diversification
appears to be better than the Sub-Saharan average
 Compared to many Sub-Saharan African Countries including Kenya,
Tanzania, Uganda, Mauritius, Zimbabwe and Madagascar
 However, Ethiopia does better than some Sub-Saharan countries such as
Mozambique, Ghana, Rwanda, Malawi and Sudan in terms of export
diversification
 Since 2001 Ethiopia has shown some improvements in terms of
diversifying its exports compared to the period 1994 – 2000

Theil Index
Figure 16: Theil Diversification Indices: Overall, Intensive and Extensive (1993 - 2010). Source: IMF Diversification
Database

3. Ethiopia’s Export Base and Diversification – Theil Index
Figure 19: diversification index: Ethiopia against the Sub-Saharan average (1962 - 2010). Source: IMF Diversification
Database

Figure 17: Diversification Index for Selected Sub-Saharan (COMESA) Countries (1962 - 2010). Source: IMF
Diversification Database

Figure 18: Diversification Index for Selected Sub-Saharan (COMESA) Countries (1962 - 2010). Source: IMF
Diversification Database

4. So, What Drives Export Growth? Roles of IM and EM
 To understand what drives short run export growth (or contraction), we consider a simple
decomposition of the change in exports between consecutive years
 Following (Cebeci, Fernandes, Freund, & Pierola, 2012), if 𝑡 designates a year, 𝑋 designates total
exports and 𝑛 is the number of exporters, then total exports in a given year (𝑋𝑡) is simply the product
of the number of exporters in a given year (𝑛 𝑡) and the average exporter size in a given year (𝑆𝑡):
𝑋𝑡 = 𝑛 𝑡 ∗ 𝑆𝑡
 Thus, the change in exports between years 𝑡 − 1 and 𝑡 can be written as:
𝑑𝑋𝑡 = 𝑛 𝑡 ∗ 𝑑𝑆𝑡 + 𝑆𝑡 ∗ 𝑑𝑛 𝑡
– where 𝑑𝑆𝑡 is the change in the average exporter size between years 𝑡 − 1 and 𝑡, 𝑑𝑛 𝑡 is the change in the number of
exporters between years 𝑡 − 1 and 𝑡, 𝑛 𝑡 is the average number of exporters across years 𝑡 − 1 and 𝑡, and 𝑆𝑡 is the
average exporter size across years 𝑡 − 1 and 𝑡.
 The contribution of the intensive margin (IM) to a change in exports is given by:
𝐼𝑀 = 𝑛 𝑡 ∗
𝑑𝑆𝑡
𝑑𝑋𝑡
 And the contribution of the extensive margin (EM) to a change in exports is given by:
𝐸𝑀 = 𝑆𝑡 ∗
𝑑𝑛 𝑡
𝑑𝑋𝑡

4. So, What Drives Export Growth? Roles of IM and EM
Figure 11: Decomposition of Export Growth into Intensive and Extensive Margins (2006/07 - 2014/15). Source: ERCA
o While expansions in existing trade flows (the intensive margin) explained more than 92
percent of the overall changes in merchandize exports, the extensive margin explained,
on average, only about 8 percent
o Indicates the existence of weak diversification in terms of new products and markets

Average Exporter Size – How Large is the Average
Ethiopian Exporter?
 Exporter size (measured by mean exports per exporter) is about 1.15 million
USD (WB: average for developing countries is about 2.21 million USD)
 Difference in Average Exporter Size: The Top vs the Bottom
– In terms of contribution to the national export value, the top 10 percent
of exporters (an average of 175 firms over the period 2005/2006 –
2014/2015) contributed a very high share of exports – close to or more
than 80 percent over the past decade (2005/2006 – 2014/2015)
– The difference in the average exporter size (measured by mean exports
per exporter) in the top 10 percent and bottom 90 percent of exporters
is simply tremendous
 In 2014/2015, while the average export value among firms in the top 10
percent of exporters is 13.7 million USD, the corresponding value for firms
in the bottom 90 percent is only 0.5 million USD
 Considering a longer period (2005/2006 – 2014/2015), the mean export
per exporter (or average firm size) has been 9 million USD for the top 10
percent and 0.26 million USD for the bottom 90 percent
 This means that over the entire period, an exporter in the top 10 percent
of exporters is, on average, about 35 times larger in size than an exporter
in the bottom 90 percent of exporters (see Table 7)

Average Exporter Size – How Large is the Average
Ethiopian Exporter?
Ethiopian Fiscal Years Ten Year
Average2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015
Top 10%
Total Number of
Firms
123 150 159 170 202 211 192 188 179 178 175
Total Export Value
(USD Millions)
792 918 1,206 1,147 1,526 1,928 2,121 2,072 2,227 2,430 1,637
Average Value
(USD Million)
6.44 6.12 7.58 6.75 7.56 9.14 11.05 11.02 12.44 13.65 9.17
Export Value
Share (%)
84.6 83.5 82.2 80.0 81.4 80.7 78.7 79.3 76.8 75.5 80.3
Bottom
90%
Total Number of
Firms
1100 1348 1423 1521 1811 1898 1720 1683 1609 1593 1571
Total Export Value
(USD Millions)
145 181 260 287 349 460 574 542 674 788 426
Average Value
(USD Million)
0.13 0.13 0.18 0.19 0.19 0.24 0.33 0.32 0.42 0.49 0.26
Export Value
Share
15.4 16.5 17.8 20.0 18.6 19.3 21.3 20.7 23.2 24.5 19.7
Table 7: Export Performance of the Top 10 and Bottom 90 percent Exporters (2005/2006 - 2014/2015). Source: ERCA

Export Dynamics: Entry and Exit
 Explores to what extent Ethiopia has been able to add
economically significant new exporters, new products and
new markets to its export portfolios
 Firm entry and exit
– Entrants as exporting firms that exported in the current period but
did not export in previous periods
– Exiters are firms that exported in the previous period but did not
export in the current period,
– Firm Entry Ratet = Number of Entrantst / Number of Exporterst
– Firm Exit Ratet = Number of Exiterst / Number of Exporterst-1

Export Dynamics - Firm Entry and Exit Rates
Years
Average
2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015
Total Number
of Exporters
1498 1582 1691 2013 2109 1912 1871 1788 1771 1804
Total Number
of Entrants
690 511 684 844 762 604 524 430 494 616
Total Number
of Exiters
415 427 575 522 666 801 565 513 511 555
Net Exporter
Entry
275 84 109 322 96 -197 -41 -83 -17 61
Exporter Entry
Rate (%)
46.1 32.3 40.5 41.9 36.1 31.6 28.0 24.1 27.9 34.3
Exporter Exit
Rate (%)*
33.9 28.5 36.4 30.9 33.1 38.0 29.6 27.4 28.6 31.8
Net Exporter
Entry Rate (%)
12.1 3.8 4.1 11.1 3.1 -6.4 -1.5 -3.4 -0.7 2.5
Table 8: Entry and Exit of Ethiopian Exporters (2006/2007 - 2014/2015). Source: ERCA

Export Dynamics - Firm Entry and Exit Rates
 Ethiopian exporters exhibited high degree of exporter churning over
the past nine years (2006/2006 - 2014/2015)
– Export firm entry rate averaged 34.3 percent
– Firm Exit Rate averaged 31.8 percent
– Consistent with a number of studies who find rates of entry into
exporting of 30-60 percent a year (e.g., see Alvarez and Lopez, 2008 for
Chile; Eaton, Eslava, Kugler and Tybout, 2008 for Colombia; Volpe-
Martincus and Carballo, 2008 for Peru; and Albornoz et. Al., 2010 for
Argentina)
 Both entry and exit rates initially increased and peaked in 2010/2011
and slowed down since then until the current period 2014/2015.
– But, the number of entrants is consistently higher than the number of
exiters between 2006/2007 and 2010/2011.
– The opposite is true for the period between 2011/2012 and 2014/2015
– However, the gap between the two rates shrinks towards the end of the
analysis period.
 Overall, net firm entry has been 2.5 percent on average over the past
nine years

Export Dynamics - Product and Market Entry
and Exit Rates
Years
Average
2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015
Product
Dynamics
Product
Entry Rate
59.52 58.50 65.04 59.46 60.21 55.18 54.21 55.17 55.51 57.92
Product
Exit Rate
58.78 59.20 59.90 59.55 60.17 56.79 54.80 55.30 55.10 57.63
Net
Product
Entry Rate
0.74 -0.7 5.14 -0.9 0.05 -1.61 -0.59 -0.14 0.41 0.28
Market
Dynamics
Market
Entry Rate
(%)
57.8 56.5 57.4 57.7 58.1 54.6 54.8 54.9 54.2 56.2
Market
Exit Rate
(%)
54.3 56.3 57.2 56.9 57.5 54.1 54.0 54.3 53.7 55.4
Net Market
Entry/Exit
Rate (%)
3.5 0.2 0.2 0.8 0.6 0.5 0.8 0.5 0.5 0.9
Table 9: Product and Market Entry and Exit Rates (2006/2007 - 2014/2015). Source: ERCA

Export Dynamics - Product and Market Entry
and Exit Rates
 Similar to the exporter churning, product and market
churning is also relatively high in the Ethiopian export
market
– Product entry rate averaged at 57.9 percent over the past nine
years (2006/2007 – 2014/2015)
– While product exit rate has been 57.6 percent
– Relatively high product entry, however, most of these new products
die out as can be seen from the high product exit rates, which
suggests accidental deaths beyond experimentation
 Likewise, market entry and exit rates have been high,
56.2 and 55.4 percent respectively
 Overall
– Net Product entry rate averaged at 0.28 percent
– Net market entry rate has been positive in all the years over the
past 9 years (2006/2007 – 2014/2015) averaging at about 1 percent

Ethiopia: Exporter Firm Survival
• For any developing country, survival (the ability
of those new export portfolios to survive and
thrive in export markets) is as important as
entry (Reis J. G., 2011; Cebeci, Fernandes, Freund, & Pierola, 2012)
• Firm Survival Ratet = Number of Survivorst / Number
of Entrantst
 1-year Survival Ratet = Number of Survivorst / Number of
Entrantst
 2-year Survival Ratet = Number of 2-year Survivorst / Number of
Entrantst
 3-year Survival Ratet = N. 3-year Survivorst / N. Entrantst

Table 10: Exporter Survival (2007/2008 - 2014/2015). Source: ERCA
Firm Type 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015 Average
1-Year
Survival
Rate (%)
85.5 37.9 47.2 61.8 64.2 62.0 70.7 50.2 59.9
2-Year
Survival
Rate (%)
34.4 18.8 31.1 41.6 46.0 51.4 39.1 37.5
3-Year
Survival
Rate (%)
21.7 15.0 23.3 37.8 43.3 26.7 28.0
Definition

 1-Year Survival Rate
 On average, about 60 percent of the new entrants survive for just
one year
 High variability in firm survival rate (ranging from 37.9% to 85.5%)
 In the current year 2014/2015, firm survival rate is 50 percent,
which is lower than the eight-year average, suggesting that
entrant firms have recently been experiencing trouble remaining
in the export market
 2-year and 3-year Survival Rates
 Again, high variability in firm survival (ranging from 18.8 to 51.4
percent) and the 3-year firm survival rate (ranging from 14.9 to
43.3 percent)
 Survival beyond the first year becomes more and more difficult
 And the survival rate for 1-year, 2-year and 3-year survivors in the
current year 2014/2015 is much lower than the previous three
years; indicating that firms recently have had trouble remaining in
the export market relative to the last 3 years

What Drives Export Dynamics and
Survival?
• We saw entry, exit and survival rates, but drives
them? What are the Key factors?
• Theory and Literature (e.g., see Roberts and
Tybout, 1997; Bernard and Wagner, 1998; Bernard
and Jensen, 1999; 2001 and Campa, 2004)
– sunk entry costs , firm-specific characteristics and
industry categories are the key factors affecting entry,
exit and survival dynamics
• Following this, we adopt a heterogeneous
regression approach of the following type to
investigate the key determinants of exporter
dynamics

Survival?
𝑌𝑖𝑗 = 𝛼𝑖 + 𝛽𝑙𝑛𝐸𝑛𝑡𝑟𝑦𝐶𝑜𝑠𝑡𝑖𝑗
+ 𝜃(𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒_𝑆𝑖𝑧𝑒_𝐸𝑛𝑡𝑟𝑎𝑛𝑡)𝑖𝑗
+ 𝜇 ln 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑠𝑗
+ 𝑋
𝑗
+ 𝐸𝑖𝑗
– Where Y is the dependent variable for entry, exit and survival rates,
– i is exporter and j is industry (sector);
– αi and Xj are a series of firm and industry fixed effects or controls,
– EntryCostij, which is proxied by average incumbent size, captures the effect of entry costs to
the export sector.
– And Relative_Size_Entrantij captures whether size (or starting with small trials) affects entry,
exit and survival rates.
• Since our dependent variable is a proportion, we use a generalized
linear model (GLM) with a logit link and report robust standard errors

What Drives Export Dynamics and Survival?
Explanatory
Variables
Dependent Variables
Model 1 Model 2 Model 3
Entry
Rate
Exit Rate
Entrant
Survival
Rate
Entry Rate Exit Rate
Entrant
Survival Rate
Entry Rate Exit Rate
Entrant
Survival Rate
Ln(Entry Cost)
-
0.122***
(0.0136)
-
0.131***
(0.0129)
0.112***
(0.0172)
-
0.0897***
(0.0181)
-0.109***
(0.0215)
0.0748***
(0.0175)
-0.102***
[0.0145]
-0.112***
[0.0151]
0.077***
[0.0169]
Relative size of
entrant
0.197**
[0.0984]
0.131***
[0.103]
0.296***
[0.108]
0.217**
[0.0875]
0.188**
[0.090]
0.316***
[0.119]
ln(Number of
Exporters)
-0.017**
(0.008)
-0.015*
(0.007)
0.013***
(0.005)
Ln(Firm age)
0.0002
(0.0037)
0.0000
(0.0037)
0.0001
(0.0032)
0.0005
(0.0037)
0.0003
(0.0038)
0.0002
(0.0032)
0.0003*
(0.0038)
0.0001
(0.0038)
0.0002
(0.0035)
Firm, Sectoral and
Destination
Controls
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Number of
Observations
412 390 305 412 390 305 412 390 305
R-Squared 0.21 0.18 0.14 0.24 0.20 0.17 0.23 0.20 0.19
Table 11: Determinants of Entry, Exit and Survival Rates - Regressions Results
(i) Robust standard errors in parentheses; and ***, ** and * indicate significance at 1%, 5% and 10% confidence levels respectively.
(ii) Entry, exit and survival rates are as defined in previous sections

Survival?
• The key finding from the regression analysis is that entry
and exit rates are highest in sectors where average size of
firms is smallest.
– Put differently, entering firms that start with relatively small
trials, as compared with other entrants and incumbents, are
more likely to exit.
– In contrast, the survival rates of entrants are highest in sectors
with large average transactions, where presumably only the
most productive firms can overcome entry costs.
– This implies that when trade costs are large, the ability to enter
a foreign market with small transactions is relatively more
important.
– Likewise, when entry costs are low, the industry is very likely to
have high entry and exit rates and low survival rates.

1 Overall Trade Pattern
2 Export Base and Diversification
3 Export Dynamics
4 Export Survival
Results & Analysis
5 Conclusions and implications

Conclusions
 Several key patterns emerge from our analysis
– Falling Agriculture and rising Manufacturing
 Although these are small changes, this would suggest
that Ethiopian exporters are slightly diversifying into
higher value sectors and sectors that are not heavily
affected by external price and demand shocks
– Increasing roles of more dynamic and emerging
markets in Asia, Latin America, Sub-Saharan, etc
 EAP, Sub-Saharan Africa, LAC

Conclusions
 Several key patterns emerge from our analysis
– In any one year, almost all export expansion or contraction
comes from changes in exports by existing firms (the
intensive margin)
– This dominance of existing firms is despite the fact that one-
third to one-half of all exporters are new entrants in a typical
year
– These new firms by and large do not add much to export
growth simply because (i) the majority do not last more than
2 years and (ii) their export sales are very small
– Each year, large numbers of new exporters enter the export
market, but most drop out after 2 years of export. And even
much smaller remain in the market after 3 years
 Death beyond experimentation

Conclusions
• But Still Sources of Vulnerability Remain
– Ethiopia’s export sector remains heavily dependent on
primary commodity exports and a few large exporting
firms
• The top 10 percent exporters (only 175 firms on average
between 2005/06 – 2014/15) contributed close to or more than
80 percent of the country’s exports
• So, while export value has shown a modest increase, it is
dominated and driven by the top tier of larger exporters and few
export products
– Similarly, in terms of destination markets, Ethiopia’s
exports are focused on a small number of countries (135
markets out of a total of 247 potential markets)
– Limited diversification – the extensive margin’s
contribution to overall export growth is weak, unable to
expand the country’s export base

Conclusions
• In an attempt to explain what drives entry, exit
and survival rates, we found that entry and exit
rates are highest in sectors where average size
of firms is smallest.
– Entering firms that start with relatively small trials,
as compared with other entrants and incumbents,
are more likely to exit.
– In contrast, the survival rates of entrants are highest
in sectors with large average transactions, where
presumably only the most productive firms can
overcome entry costs.

Some Key Policy Implications
• First, a dollar export value from Djibouti Vs a
dollar export value from the US or Japan?
– Policies should encourage exporters not only to
diversify away from traditional commodity exports
into higher value sectors and sectors that are not
heavily affected by external price and demand
shocks but also to diversify into more dynamic
markets where learning and spillover effects can
be maximized.
– In other words, in terms of export promotion
policy, knowing where to export is as important as
knowing what to export.

• Second, export promotion must be viewed as a continuum in the sense
that it should target not only more entry by making entry cost lower but it
should also target longer survival of new entrants in the export market
– Each year, large numbers of new exporters enter the export market, but most drop out
after 2 years of export.
– It must be noted that the achievement of virtuous export growth hinges not only on more
entry but also on longer survival.
• Third, Export Diversification must be followed as a key policy objective.
– This is so because expansions along the intensive margin – i.e., increases in the
average exporter size – contribute more to export growth in the short run than
expansions along the extensive margin – i.e., increases in the number of
exporters.
– In any given year, almost all export expansion or contraction comes from changes in
exports by existing firms (the intensive margin).
– Large number of new firms enter but they do not add much to export growth simply
because (i) the majority do not last more than 2 years and (ii) their export sales are very
small.
– The high attrition rates may suggest that the export environment is so rough; and this
constitutes part of the diversification challenge for developing countries like Ethiopia.
– There is a growing evidence that export diversification must be followed as a key policy
objective by developing countries.

• Fourth, improve export environment
– Attrition in the export sector is healthy if it reflects
strong experimentation at the extensive margin; but if
the attrition (or failure) rates are very high, beyond
experimentation, it may suggest that the export
environment is so rough that it is bound to entail a
large proportion of accidental or immature deaths of
new entrants

Ethiopian exports growth characteristics, dynamics and survival berihu-assefa_sep30_2016

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