An Economic Inquiry into Ethiopian Exports: Pattern ... · An Economic Inquiry into Ethiopian...

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EDRI Working Paper 14 May 2016 An Economic Inquiry into Ethiopian Exports: Pattern, Characteristics, Dynamics and Survival Berihu Assefa a and Kiflu Gedefe b a Senior Associate Researcher at the Ethiopian Development Research Institute (EDRI) b Senior Associate Researcher at the Ethiopian Development Research Institute (EDRI)

Transcript of An Economic Inquiry into Ethiopian Exports: Pattern ... · An Economic Inquiry into Ethiopian...

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EDRI Working Paper 14

May 2016

An Economic Inquiry into Ethiopian Exports: Pattern,

Characteristics, Dynamics and Survival

Berihu Assefaa and Kiflu Gedefeb

a Senior Associate Researcher at the Ethiopian Development Research Institute (EDRI)

b Senior Associate Researcher at the Ethiopian Development Research Institute (EDRI)

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THE ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

WORKING PAPERS

ABOUT EDRI

Founded in August 1999 as a semi-autonomous government development research institute, EDRI’s primary mission is to conduct quality research on the development of the Ethiopian economy and disseminate the results to key stakeholders within and outside of Ethiopia. EDRI is sponsored by the Ethiopian government, ACBF, UNDP, IDRC-TTI and IFPRI/ESSP. For more information as well as other publications by EDRI staff and its affiliates, see http://www.edri.org.et/

Ethiopian Development Research Institute (EDRI) P.O. Box 2479 Tel: 251-11-550-6066 Fax: 251-11-550-5588 Email: [email protected]: http://www.edri.org.et

ABOUT THESE WORKING PAPERS

The Ethiopian Development Research Institute (EDRI) Working Papers contain peer-reviewed material from EDRI and/or its partners. They are circulated in order to stimulate discussion and critical comment. The opinions are those of the authors and do not necessarily reflect those of their home institutions or supporting organizations.

Editors in Chief: Newai Gebre-ab, Executive Director of EDRI Bart Minten, Program Leader of IFPRI–ESSP II

Editor: EDRI

Editorial Board: Alebel Bayrau, EDRI, Tadesse Kuma, EDRI, Alemayehu Seyoum, IFPRI–ESSP, Tassew Woldehanna, AAU, Mulu Gebreeyesus, Binyam Bedasso,Girum Abebe, EDRI

Paper citation: Berihu Assefa and Kiflu Gedefe. 2016. An Economic Inquiry into Ethiopian Exports: Pattern, Characteristics, Dynamics and Survival. EDRI Working Paper 14. Addis Ababa: Ethiopian Development Research Institute.

About the Author(s)

Berihu Assefa and Kiflu Gedefe are both Senior Associate Researchers at EDRI.

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An Economic Inquiry into Ethiopian Exports: Pattern, Characteristics, Dynamics and Survival Berihu Assefa and Kiflu Gedefe

Copyright © 2016 Ethiopian Development Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not- for-profit use without the express written permission of but with acknowledgment to EDRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, please contact Eden Fitsum at: [email protected].

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Table of Contents

ABSTRACT .................................................................................................................................................... 5

1. INTRODUCTION ................................................................................................................................... 6

2. OVERVIEW OF ETHIOPIA’S FOREIGN TRADE SECTOR ............................................................................. 8

2.1. TRENDS IN ETHIOPIA’S EXPORTS, IMPORTS AND GDP ...................................................................................... 8 2.2. ETHIOPIA’S TRADE OPENNESS ...................................................................................................................... 9 2.3. MERCHANDISE EXPORT GROWTH ............................................................................................................... 17 2.4. SECTORAL AND GEOGRAPHICAL COMPOSITION OF EXPORTS ............................................................................ 23

3. ETHIOPIA’S EXPORT BASE AND DIVERSIFICATION ............................................................................... 26

3.1. CONCEPT AND MEASUREMENT .................................................................................................................. 26 3.2. NUMBER OF EXPORTERS ........................................................................................................................... 27 3.3. NUMBER OF PRODUCTS AND MARKETS ...................................................................................................... 28 3.4. SHARE OF TOP EXPORTERS, PRODUCTS, AND MARKETS ................................................................................. 30 3.5. DIFFERENCE IN AVERAGE EXPORTER SIZE: THE TOP VS THE BOTTOM ................................................................ 32 3.6. THE THEIL EXPORT DIVERSIFICATION INDEX .................................................................................................. 34

4. EXPORT DYNAMICS AND SURVIVAL .................................................................................................... 37

4.1. EXPORTER ENTRY AND EXIT ....................................................................................................................... 37 4.2. PRODUCT AND MARKET ENTRY AND EXIT .................................................................................................... 38 4.3. EXPORTER SURVIVAL DYNAMICS ................................................................................................................ 40 4.4. EXPLAINING THE DETERMINANTS OF ENTRY, EXIT AND SURVIVAL ..................................................................... 42

5. CONCLUSIONS AND IMPLICATIONS .................................................................................................... 45

BIBLIOGRAPHY ........................................................................................................................................... 47

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An Economic Inquiry into Ethiopian Exports: Pattern, Characteristics, Dynamics and Survival1

Abstract

Using both aggregate and firm-level Customs data, this paper examines Ethiopia’s export performance and dynamics over the period 1995/1996 – 2014/2015 from various dimensions. Specifically, we attempt to address the following issues:

(i) 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.

(ii) To what extent do Ethiopian exporters survive beyond their first year of entry to the export market?

(iii) And finally we decompose export growth/contraction into intensive and extensive margins to see what drives export change in Ethiopia.

Several key patterns emerge from our analysis. First, we observe that Ethiopia’s export base remains quite limited, both in absolute terms and compared to countries with similar level of development. This is reflected in Ethiopia’s small number of exporters and export products as well as its low ratio of merchandise exports to GDP relative to its size and compared to its peers. The country not only has very few exporters, but the average exporter size is also very small. Second, the export diversification analysis reveals that despite a 15.5 percent growth in merchandize exports over the past decade, most of this growth is driven by expansion of exports of existing products in existing markets (growth at the intensive margin) rather than by diversification into new products and into new geographic markets (growth at the extensive margin). Third, the firm-level data analysis shows that the Ethiopian export market shows a high degree of exporter and export product churning but exporter exit and product death are also high. As a result the annual average net exporter entry is found to be about 2.5 percent. Likewise, survival beyond the first export year continues to be a challenge to new entrants. And finally, entry costs and relative size of entrants are found to be key determinants of entry, exit and survival rates.

Key Words: Ethiopia, export diversification, export dynamics, export survival

1 The views and opinions expressed in this article are those of the authors and do not necessarily

reflect the official position of the Ethiopian Development Research Institute (EDRI)

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1. Introduction

The role of international trade as a key driver of economic growth and hence poverty reduction is well established in the literature (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): (a) increasing specialization and expanding the efficiency-raising benefits of comparative advantage, (b) offering greater economies of scale due to an enlargement of the effective market size, (c) affording greater capacity utilization, (d) inducing rapid technological change, and (e) productivity gains leading to wage premiums and job creation.

There has also been a wide consensus on the positive association between trade and economic growth in the empirical literature as well. In this regard, the literature especially emphasized the role of exports in rapid and sustained economic growth through productivity gains and technological upgrading (Spence, 2007). In many developing countries shifting resources into exports has a strong impact on growth since export sectors have higher productivity and within sectors exporting firms tend to be more productive than non-exporters (Brenton & Newfarmer, 2009). According to these same authors, the 16 fastest growing economies over the 25 year period leading up to 2005 experience export growth that was 25% more rapid than the average for all developing countries.

Basing on these 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). Ever 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. For example, in addition to overall economic liberalization and macroeconomic stability reforms, Ethiopia has introduced several export incentive schemes and set up a number of government institutions aimed at boosting exports in general and manufactured exports in particular. Despite these efforts, however, Ethiopian export has remained less diversified, less vibrant and hence falls short of securing enough foreign exchange earnings to achieve the goals of GTP.

Our goal in this paper is to analyze Ethiopia’s export patterns, dynamics and growth over the period 1995 to 2014 and give insight into the sector’s performance and potential. We are particularly interested in the research questions: (i) what is the extent of firm entry and exit in Ethiopia compared to other countries? (ii) What is the level of survival rate of firms in the export sector in Ethiopia and to what extent it defers from other countries that succeeded in that venture? (iii) How do the typical pattern of firm exit and entry, and the survival rate relate to the overall less satisfactory performance of the Ethiopian export? And (iv) What determines the entry, exit, and survival of firms in the Ethiopian export? To this end we have employed firm-level administrative data that mainly comes from the Ethiopian Revenues and Customs Authority (ERCA) as well as complementary aggregate level data from domestic and international data sources.

Several key patterns emerge from our analysis. First, despite a multitude of institutions involved in promoting the export sector and a set of export incentives, the sector has shown limited progress in terms of growth and diversification. Between 1995 and 2013 Ethiopia’s exports of goods and services grew, on average, by about 12.6 percent. Merchandize exports alone grew by about 15.5 percent on average in the past decade (2005/2006 – 2014/2015). Export growth is predominantly driven by growth at the intensive margin. Looking at the size of the export sector, Ethiopia’s export base remains quite small with only

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1,771 exporters compared to 4600 in Kenya in the most recent year 2014/2015. These 1,771 exporters exported an export value of 3.2 billion USD in 2014/2015. Given its level of development and size, Ethiopian exports remain relatively small. For example, Ethiopia’s export of goods and services to GDP ratio remains at about 14 percent, with service and merchandize exports contributing almost equally to the national export pool. Given its per capita income level, Ethiopia exports (or trades) 10 to 13 percentage points less than what would be expected.

In terms of export diversification, Ethiopia’s export sector is 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 a considerably larger share of exports – close to or more than 80 percent of the country’s exports. These top exporters are very large in size - about 35 times larger than the bottom 90 percent exporters. So, while export value has shown a modest increase, it is dominated and driven by the top tier of larger exporters. 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). The fact that Ethiopia has a relatively small export base implies low export earnings and proneness to demand shocks and price swings in overseas markets.

Second, Ethiopia’s export sector is characterized by a high degree of dynamics in terms of export firm, product and market churning. Firstly, exporter entry and exit rates have been high throughout although both rates have declined a bit towards the end of the analysis period. While firms’ export market entry rate ranges from 46 to 28 percent, the exit rate lies in the range 34 to 29 percent between 2005/06 – 2014/2015. In the period between 2006/2007 and 2010/2011, the number of entrants has been consistently larger than the number of exiters. However, the opposite was true for the period between 2011/2012 and 2014/2015. Nonetheless, the gap between the two rates shrinks towards the end of the analysis period and as a result the overall net export firm entry rate averaged at 2.5 percent between 2005/2006 and 2014/2015.

Third, we have explored the survival of exports in Ethiopia. This is important as export base expansion or successful export diversification requires not only new products and markets, but also survival and growth of existing ones (Brenton, Saborowski, & Uexkull, 2010). To a larger extent, virtuous export growth depends on the ability of new firms, products and markets to survive beyond the first export year. In this regard, one characteristics of the Ethiopian export sector is low survival rate of new entrants. This is especially true for exporters beyond their first year of exporting; the survival rate is only about 26 percent for 3 year survivors. This indicates high attrition rate of new entrants after one year in export business. However, it should be noted that attrition is not unique to the Ethiopian export sector and that it is not necessarily bad by definition because it also reflects strong experimentation at the extensive margin. It can be a problem if attrition is largely due to reasons other than experimentation. That is, 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. As a result, the extensive margin’s contribution to overall export growth would be weak and a country’s export base cannot be significantly expanded.

The rest of the paper proceeds as follows. While Section two provides background information and basic snapshot of Ethiopia’s foreign trade sector, Section three examines whether Ethiopia’s export base has expanded and diversified relative to its peers and past performances. Section four assesses Ethiopia’s export dynamics and survival. Finally, Section five concludes by providing some policy implications and directions for future research in the area.

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2. Overview of Ethiopia’s Foreign Trade Sector

2.1. Trends in Ethiopia’s Exports, Imports and GDP

Figure 1 presents the trend in Ethiopia’s exports, imports and GDP over the period 1995-2013. The rapid increase in GDP observed after 2003 has brought with it a rapid increase in demand for imports of goods and services. As can be seen from the figure, starting from 2003, imports and GDP have been increasing rapidly. Trends in exports of goods and services, on the other hand, have not been equally remarkable. Except for the moderate increase in exports observed in the years 2004 and 2005 and the jump observed in 2010 and 2011, the trend in exports do not keep pace with the growth of GDP and imports. As a result, the country’s trade deficit widened during this period. The dramatic increase in exports observed in 2010 and 2011 did not help to narrow down the country's trade deficit as imports also increased sharply in these years. Moreover, since exports of goods and services showed a decline in 2012, while imports of goods and services continued to grow, the trade deficit increased further in 2012. Specifically, Ethiopia’s trade deficit has increased from below 2 billion USD (15% of GDP) in early 2000s to 4.5 billion USD (18% of GDP) in 2012. Figure 1: Trends in Exports, Imports and GDP of Ethiopia (1995- 2013)

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2.2. Ethiopia’s Trade Openness

(i) Trade-to-GDP ratio

The ratio of Exports and Imports to GDP, (X + M)/GDP, is a widely used measure of outcome based openness to trade and it gives an indication of how integrated a country is in global trade. According to this measure, Ethiopia’s openness to trade (or integration with the rest of the world) has shown an upward trend starting from its lows of about 26 percent in 1995 and 1996 reaching a peak of 51 percent in 2005 and 2006. During this period, the rise in Ethiopia’s trade to GDP ratio (openness to trade) can mainly be attributed to the rapid increase of imports of goods and services that went in tandem with GDP and to a lesser extent to an increase in exports of goods and services (see Figure 1 and Figure 2). After 2006, however, the rapid increase in GDP that was not matched up with a similar increase in trade appear to put a downward pressure in Ethiopia’s upward trending trade openness observed in the years leading up to 2006. In particular, despite the continued increases in imports of goods and services in the same period, Ethiopia’s trade to GDP ratio dipped to 40 percent in 2009, falling by about 10 percentage points from its peak in 2006. This was mainly because, during the 2006-2009 period, the rapid growth rate of GDP was largely driven by growth in the non - tradable (service sector) and the growth rate in exports of goods and services has been nearly zero. After 2009, however, Ethiopia’s openness to trade has been on the rise and this has mainly been due to a strong export performance that can by and large be attributed to improvements in international commodity prices.

If one compares the trend in the share of imports with that of the share of exports in GDP, unlike the upward sloping trend observed in the share of imports in GDP, the overtime trend in the share of exports has been relatively flat. While the share of imports in GDP increased from about 16 percent in 1995 - 1996 to its highest point of 37 percent in 2006 and remained above 30 percent since 2010, the share of exports in GDP started from about 10 percent in 1995 and remained around 15 percent and below for most of the years except in 2011 when it reached 17 percent (see Figure 2).

Figure 2: Trends in Shares of Trade, Exports and Imports of Goods and Services in GDP Ethiopia (1995-2013)

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On the other hand, Error! Reference source not found. shows Ethiopia’s level of openness as measured by the respective shares of merchandise exports, imports and trade in GDP. Although the general trend is similar to what is observed in the case of shares of trade in goods and services in GDP, the apparent lack of an upward sloping trend in the share of merchandise exports in GDP and its meager contribution to the overall merchandise trade openness is worth pointing out. In the case of merchandise, it is clear from the Error! Reference source not found. that the overall trade openness is to a greater extent dictated by trends in share of merchandise imports in GDP.

Figure 3: Trends in Shares of Merchandise Trade, Exports and Imports in GDP Ethiopia (1995-2013)

(ii) Ethiopia’s Openness to Trade Relative to Other Countries

In Table 1 Ethiopia's degree of openness to trade as measured by the share of total trade as well as exports and imports in GDP is compared with the average trade shares of seven other Sub-Saharan African (SSA) countries. This is done taking averages of four five-year periods covering years 1994-1998, 1999-2003, 2004-2008 and 2009-2013.

Ethiopia's degree of openness, measured by share of trade in GDP, improved substantially over the years. Compared to the first five-year period (1994-1998), Ethiopia's openness to trade in the last five-year period (2009-2013) has increased by some 11.83 percentage points reaching 33.5%. Despite the improvement in trade to GDP share, Ethiopia’s level of integration with global trade still stands at a lower level compared to all the seven SSA countries. For the last five-year period the second and third lowest level of openness is observed in Uganda and Kenya (more than 40% in each) whereas Mozambique recorded the highest percentage share (72.28%) in the same period. Ethiopia’s export share in GDP has shown very little improvement over periods. It has only increased from 5.95 percent in

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the first five-year period to 6.83 percent in the last five-year period. Ethiopia’s export share is also disappointing when compared to the selected other Sub-Saharan African countries. Out of the selected seven SSA countries, while Zambia (36%), Zimbabwe (30%) and Mozambique (26%) has the top three export shares in GDP, Tanzania (17), Kenya (12) and Uganda (12) has the lowest export shares in GDP.

Table 1: Ethiopia's Trade Openness Relative to Selected Sub-Saharan African Countries (1994 - 2013). Source: World Bank

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)

Period Ethiopia Kenya Zambia Tanzania Mozambique Uganda Zimbabwe Madagascar

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)

Period Ethiopia Kenya Zambia Tanzania Mozambique Uganda Zimbabwe Madagascar

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

As can be seen from Table 1, it is difficult to make cross-country comparisons of openness based on trade shares in GDP and make implications about the level of integration of a country in world trade. For instance, when comparing openness across-regions, North America (the USA in particular) appears to have the lowest level of trade share in GDP. Similarly, when making comparison among SSA countries, countries with larger size (like Sudan, Ethiopia and Nigeria) appear to have among the least levels of trade shares in GDP among SSA countries. On the other hand, smaller countries like Luxembourg and Singapore and, in Sub Saharan Africa, Seychelles, Swaziland and Equatorial Guinea appear to have trade shares in GDP in excess of 100 percent of their GDP.

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(iii) Comparing Ethiopia's Share in World Trade with Selected Sub-Saharan African Countries

A country’s share in world trade is another important measure of a country’s openness or integration in global trade. This measure corrects for some of the aforementioned weakness of trade share in GDP as a measure of openness, which measures a country’s level of openness relative to its domestic economy size. According to this alternative measure of openness, USA and China respectively has the highest (around 10%) share in world trade. Taking all countries in the world,j = {1,2,…,N}, country i's world trade share is given by:

Table 2 compares Ethiopia's world trade share with the selected seven SSA countries over the four five-year periods indicated above. Ethiopia’s world trade share has increased from 0.02 percent in the first five periods (1994-1998) to 0.04 percent in the most recent five-year period (2009-2013). Compared to the seven east African countries, Ethiopia's RWTI ratio is larger than Mozambique, Uganda, Zimbabwe, and Madagascar for the 2009-2013 period. Whereas Ethiopia's degree of openness appears to be lower than that of Kenya, Zambia, and Tanzania.

Table 2: Ethiopia's Trade Share in World Trade Relative to Selected Sub-Saharan African Countries (1994 - 2013). Source: World Bank Trade Openness: share in world trade (%)

Period Ethiopia Kenya Zambia Tanzania Mozambique Uganda Zimbabwe Madagascar

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

(iv) Adjusted Trade to GDP Ratio

As we have seen above, it is clear that 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 what follows, we have estimated an Ordinary Least Squares (OLS) regression model of trade openness with the objective of calculating an adjusted measure of trade openness. By comparing the observed level of openness with what is predicted (or would be expected) given the structural factors facing each country, one can get a better idea about a country's level of openness and make reasonable comparisons across countries. To this end, an indicator of openness is generated after controlling/adjusting for the fundamentals (structural factors) that can potentially determine countries’ degree of openness.

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Figure 4: Openness to Trade and GDP per Capita: (Based on Average for 1997-2013)

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Data Source: World Development Indicators (WDI)

For example, Figure 4 shows the relationship between per-capita income and Trade shares in GDP (both measured in logs) for all countries where data is available. From this figure it is clear that richer countries, on average, have higher levels of trade share than poorer countries - i.e.; there is a positive relationship between countries’ levels of development as measured by their per-capita income levels and trade share in GDP. While countries placed above the fitted line trade more than what would be expected given their level of development, others, especially countries with very large economic size like the USA and Japan, trade much less than what would be expected given their per-capita incomes.

Ethiopia is also among the countries that trade less than what is predicted given its level of income. It should, however, be noted that income is not the only determinant of a country's level of openness to trade. Apart from countries' income, other factors like size, distance from potential trading partners (remoteness), whether the country is landlocked or not could matter for a country's level of trade openness. Large countries normally trade less than smaller countries as they are more likely to be self-sufficient. Unlike smaller countries, large economies do not have strong incentives to specialize as they are expected to be endowed with diversified resources. In the trade openness model estimated below, country size is measured by total population and land size of countries.

Another important determinant of a country's level of trade openness is its distance with potential trading partners; countries that are far from major global trading centers, like North America and Europe, are likely to trade less as the cost of trading (transport cost) is likely to be higher. To this end, a remoteness index is generated based on a weighted average distance of countries from each of its partners. That is,

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is the trade share of each of country si potential trading partners.

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.

Dummy variables for landlocked and island nations are also included in the estimated model to capture the potentially higher cost of trading these type of countries are likely to face. Holding other factors constant, these types of countries are expected, on average, to trade less than others. Finally, the model includes internal costs of exporting and importing based on data prepared by World Bank's doing business project. This variable measures costs, other than tariffs or trade taxes, associated with importing and exporting such as administrative costs as well as costs related to terminal handling and inland transport. Alternatively, internal costs associated with importing and exporting are captured by the time it takes to import and export.

The openness regression results reported in Table 3 are estimated based on data for all countries (where data is available) over the four five year periods indicated above. While the first model (see Column One of Table 3) controls for countries level of income and size, models two and three (Columns Two and Three) respectively controls for the roles of international transport costs as measured by remoteness index and dummy variables for landlocked and island nations. In the last two columns, alternative measures of costs associated with importing and exporting are added as additional controls.

Coefficient estimates of the variables included in the regression model, for the most part, have the theoretically expected signs and are statistically significant. As indicated above, deviations of observed openness from the predicted level of openness can be taken as an adjusted indicator of openness; i.e., adjusted for the structural factors included in the model. Accordingly, Table 4 presents an adjusted measure of trade openness. An adjusted trade share that is close to zero indicates that the country/region in question is trading as would be expected given the variables included in the respective models. While negative values of adjusted trade openness indicates that the country in question is trading less, positive values indicates that the country is trading more than what would be expected given the factors (variables) included in the model.

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Table 3: Trade Openness and its Major Determinants

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 Fixed Effects. ∗ p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗ p < 0.01

The upper part of Table 4, for example, presents openness adjusted for countries’ levels of income, size and geographical location (remoteness, landlocked and island). Accordingly, in the last five year period (2009-2013) Ethiopia trades 10 percentage points less than what would be expected once the influence of these factors is accounted for. According to this adjusted measure of openness, Ethiopia can be considered relatively more open than North America and Latin America and Caribbean regions and this is contrary to what the "raw" measure of openness reported in Section 2.4 indicates. Similarly, Sub Saharan African countries, 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. That is, this region's degree of openness is close to what would be expected given its fundamentals.

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Table 4: Comparing Adjusted Openness across Regions

Adjusted Openness Based on Model 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)

When we further control for inland costs of trading (local costs associated with exporting and importing), Ethiopia becomes relatively less open; according to the prediction of this model it trades 13 percentage points less than would be expected. On the other hand, the degree of openness of Sub Saharan Africa and Latin America and Caribbean regions, according to this model improved (now Sub Saharan African countries are found to trade 5 percentage points more than what would be expected). This can also be interpreted as suggesting that these two regions (according to the prediction made in Model 3) looked more closed than they actually are; and this is because of the high inland costs of trading.

As can be seen from Figure 1, Ethiopia is only 14th least open country in Sub Saharan Africa once the influence of income, country size (land area and population) as well as geographic locations are taken into account, i.e., based on the estimation done in model 3. A similar result is found when Model 4 is used instead and this is in contrast to its 6th place when comparison is made based on the "raw" measure of openness.

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Figure 1: Adjusted Openness to Trade for Sub-Saharan African Countries (Average of 2009 - 2013)

2.3. Merchandise Export Growth

(v) Total Export Value and Annual Growth Rate of Exports

Although trends in Ethiopia’s exports did not match the rapid increase in imports and GDP, the level of exports has increased substantially over the years. For example, the real value of exports of goods and services has more than doubled in the period 2005 to 2012. Similarly, the nominal value of merchandise exports has increased more than threefold over the same period. Figure 6 presents annual growth rates of total exports of goods and services over the period 1995-2013. During this period, the real average growth rate of exports of goods and services was 12.6 percent. Due to dependence of Ethiopia on a small number of agricultural commodity exports, growth rate of its exports has not been stable over the years. 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. As can be seen from Figure 6, the average growth rate of the reviewed period is clearly inflated by the strong performances observed in 1995, 1997, and 2004 as well as the exceptionally strong performances of 2010 and 2011. In four of these five well-performing years, the growth rate of exports has been more than 30 percent having a disproportionately larger contribution to the average export growth observed in that period.

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Figure 2: Annual Growth Rates of Exports of Goods and Services in Ethiopia (1995-2013)

On the other hand, Error! Not a valid bookmark self-reference. presents the level and annual growth rate of nominal merchandise export values together with the average annual growth rate over the period 2005/06 – 2014/2015. As can be seen from the figure, export growth fluctuates significantly perhaps because most of Ethiopia’s exports are primary commodities with little value addition, which tend to be prone to international price and demand shocks. Over the past 9 years (2005/06 – 2014/2015), the annual merchandize export growth has averaged at 15.5% (see Error! Not a valid bookmark self-reference.).

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Figure 3: Export Value and Annual Growth Rate of Merchandize Exports (2005/2006 -

2014/2015). Source: ERCA

As can also be seen from Error! Not a valid bookmark self-reference., Ethiopia's nominal annual growth rate of merchandise exports has not been different from that of (nominal) growth rates of exports of goods and services. The average growth rate of merchandise exports observed over the period 1995-2013 was about two percentage points lower than the growth rate of total exports observed over the same period. The percentage difference is mainly attributed to the strong performance to total exports in 1995.

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Figure 4: Annual (Nominal) Growth Rates of Exports for Ethiopia: 1995-2013

We observe large annual fluctuation in the level and growth of exports (see

On the other hand, Error! Not a valid bookmark self-reference. presents the level and annual growth rate of nominal merchandise export values together with the average annual growth rate over the period 2005/06 – 2014/2015. As can be seen from the figure, export growth fluctuates significantly perhaps because most of Ethiopia’s exports are primary commodities with little value addition, which tend to be prone to international price and demand shocks. Over the past 9 years (2005/06 – 2014/2015), the annual merchandize export growth has averaged at 15.5% (see Error! Not a valid bookmark self-reference.).

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Figure 3, Figure 2 and As can also be seen from Error! Not a valid bookmark self-reference., Ethiopia's nominal annual growth rate of merchandise exports has not been different from that of (nominal) growth rates of exports of goods and services. The average growth rate of merchandise exports observed over the period 1995-2013 was about two percentage points lower than the growth rate of total exports observed over the same period. The percentage difference is mainly attributed to the strong performance to total exports in 1995.

Figure 4). The large annual fluctuation is driven largely by changes in international commodity prices. Trends in international commodity price including coffee (see Figure 5 & Figure 6) appear to play a major role in Ethiopia's export growth performance. 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. As can be seen from these figures, after a dramatic increase in two consecutive years (2010 and 2011), the price of coffee started to fall in 2012, 2013 and reached its lowest point towards the end of 2013. It is the lowest point recorded after 2009. 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.

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Figure 5: 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

Figure 6: Coffee Commodity prices, annual, 1960 - 2014. Source: UNCTAD

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(vi) What Drives Export Growth? Decomposing Export Growth

To understand whether the short run export growth – and contraction – is driven by the expansions of existing trade flows (the intensive margin) or through the addition of new products and markets (the extensive margin), we consider a simple decomposition of the change in exports between consecutive years. Following (Cebeci, Fernandes, Freund, &

Pierola, 2012), if designates a year, designates total exports and is the number of

exporters, then total exports in a given year ( ) is simply the product of the number of

exporters in a given year ( ) and the average exporter size in a given year ( ):

years and can be written as: Thus, the change in exports between

where is the change in the average exporter size between years and , is the

change in the number of exporters between years and , is the average number of

exporters across years and , and is the average exporter size across years

and . The contribution of the intensive margin (IM) to a change in exports is given by:

And the contribution of the extensive margin (EM) to a change in exports is given by:

The contribution of the IM to a change in exports arises from (1) an increase in export values of existing products in established markets, (2) a decrease in export values of existing products in established markets, or (3) extinction of exports of products in established markets. On the other hand, the contribution of the extensive margin to a change in exports could be due to (1) introduction of new products in new markets, (2) introduction of new products in established markets, or (3) introduction of existing products in new markets.

The decomposition of export growth into intensive and extensive margins over the period 2006/2007 – 2014/2015 is indicated in Figure 11. On average, for the past decade (2006/2007 – 2014/2015), expansions in existing trade flows (the intensive margin) explained more than 92 percent of the overall changes in merchandize exports. On the other hand, the extensive margin explained, on average, only about 8 percent of the overall variation in merchandize export. This indicates the existence of weak diversification in terms of new products and markets.

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Figure 7: Decomposition of Export Growth into Intensive and Extensive Margins (2006/07 - 2014/15). Source: ERCA

2.4. Sectoral and Geographical Composition of Exports

Agriculture still remains the 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 (see Figure 8). The other interesting observation from Figure 8 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.

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Figure 8: Export Value Share of Major Sectors in Ethiopia (2005/06 - 2014/2015). Source: ERCA

In terms of geographic composition, a country's export orientation is said to be favorable if the growth rates of imports of its major trading partners is relatively higher. That is, the country is exporting towards destinations that have had faster import growth. If, on the other hand, the import growth rate of a country’s major trading partners is relatively slow, that country is said to have unfavorable export orientation.

To make a similar assessment for Ethiopia's export orientation, export shares of each of Ethiopia's trading partners is plotted against the average growth rate of imports of these destination countries. Both of these variables are measured in logarithm and the import growth rate of the destination countries is taken over the four years period covering 2009-2012. To calculate the share of destination countries in Ethiopia's exports, bilateral export data for 2012 (the recent available bilateral trade data) is used. To make comparisons with other countries, the same exercise is done for three additional countries in the region (Uganda, Tanzania, and Rwanda).

Generally Ethiopia's export orientation can be considered unfavorable. Its major trading partners (like Germany and other European countries) had relatively slower average import growth over the period 2009-2012. This can be explained by the Global financial crisis that started in 2008 and hit Europe relatively strongly. It can be the case that Ethiopia's exports will turn favorable once the region fully recovers from the economic crisis. This in general underlines the need to diversifying export destinations and/or the need to create trade

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relationships with emerging markets that have registered relatively faster growth rates of imports (see Figure 9). In this regard Ethiopia is showing some progress in that its trade share with East Asian and Pacific and Sub Saharan Africa regions is increasing in recent years while its reliance on Europe and Central Asia as its major export destination is diminishing (see Figure 10).

Figure 9: Geographical Orientation of Exports: Ethiopia and Selected African Countries (2012)

On the other hand, Ethiopia has potential benefits to reap arising from export destination diversification to the Middle East and Asia, and particularly with the increase in importance of China in global trade. Ethiopia is reducing its heavy reliance on exports to countries like Germany, Japan, Saudi Arabia and Italy. These shifts in export orientation can be viewed positively in so far as it does not result from reductions in exports to these regions/countries and/or that it does not create heavy reliance on few other countries like China.

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Figure 10: Regional Share of Ethiopia's Merchandise Export over Periods

3. Ethiopia’s Export Base and Diversification

This section aims to analyze Ethiopia’s overall export base and examine how it has done in terms of diversifying its export base. We especially explore: (i) trends and changes in the total number of exporters, (ii) trends and changes in the total number of export products and markets, and (iii) Ethiopia’s overall degree of diversification across products and markets relative to its peers using measures such as the share of top exporters, products and markets and the Theil index.

3.1. Concept and Measurement

Export diversification reflects the degree to which a country’s exports are concentrated on a small number of products or a small number of trading partners (UNDP, 2011) or more broadly the change in the composition of a country’s existing export product or destination mix (Berthelemy & Chauvin, 2000). The degree of diversification measures risk and vulnerability arising from relying on a narrow range of exporters, products and markets. Consequently, most researchers in the area would agree that export diversification is

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especially important for developing countries because it makes countries less vulnerable to demand shocks and price swings in overseas markets by stabilizing export revenues (Ghosh & Ostry, 1994); and 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: (a) horizontal diversification takes place within the same sector (primary, secondary or tertiary) and entails an adjustment in the country’s export mix by adding new products to existing export baskets and within the same sector; (b) vertical diversification entails a shift from the primary to the secondary or tertiary sector (e.g., moving from basic commodity extraction to commodity processing), which typically entails increasing value added in activities such as processing, marketing or other services; and (c) 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).

We use several diversification measures to investigate the export diversification performance of the Ethiopian export sector. We start with simple indicators such as the number of exporters, export products and destination markets, which provide a simple count of how many exporters there are in the Ethiopian export market, how many products Ethiopia exports in a given year and the number of destination markets Ethiopian exporting firms export to. The second measure is the share of exports by the top 1, 5 and 25 percent exporters, products and markets, which tells us whether exports are dominated by a few exporters, products and markets. And thirdly, we use the Theil diversification index.

3.2. Number of Exporters

The number of exporters in Ethiopia has increased substantially over the last 10 years from a low base of 1,223 export firms in 2005/2006 to 1,771 exporter in 2014/2015 (see Figure 15). The period between 2005/2006 and 2010/2011 is characterized by high growth in the number of exporters; reaching a peak of 2109 in 2010/2011. It then started to decline constantly until 2014/2015, which recorded 1771 exporters. Compared to its past performance, Ethiopia has registered some progress over the past decade. However, compared to other developing countries, Ethiopia’s export base, measured by number of exporters, still remains quite small. For example, among developing countries, the largest numbers of exporters are found in Turkey and Mexico followed by South Africa and Brazil (around 20,000) then by Pakistan, Bulgaria, and Iran (with more than 13,000 each) (Cebeci, Fernandes, Freund, & Pierola, 2012).

Ethiopia’s merchandise export value increased from 0.94 Billion USD in 2005/2006 to 3.2 Billion USD in 2014/2015, growing at an annual average rate of 15.5 percent. Unlike the total number of firms, which grew until 2009 and declined thereafter, the total export value increased continuously and its value for 2014/2015 is higher than the ten years’ average. This suggests that growth was mainly been driven by existing firms and firms that exit are extremely small in size having very little impact on overall export value.

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Figure 11: Export Base of Ethiopia (2005/06 - 2014/2015). Source: ERCA

3.3. Number Of Products And Markets

This measure provides a simple count of how many products, at the Harmonized System (HS) 6-digit level Ethiopia exported in a given year and the number of destination markets Ethiopian exporters export to. To start with the number of export products, over the past decade (2005/2006 – 2014/2015), Ethiopian exporters as a whole exported more than 700 products annually to more than 135 destination markets. Mirroring the overall trend in the number of exporters, the total number of products was highest in 2010/2011. Except for the period between 2011/2012 and 2013/2014, where we observe significant product deaths, the range of products exported over the past decade (2005/2006 – 2014/2015) remained largely stable. Trends in the number of destination markets have also been stable over the period 2005/2006-2014/2015. The number of destination markets showed a modest increase over the past decade; from 115 in 2005/2006 to 135 in 2014/2015. Ethiopian exporters, on average, accessed about 124 destination markets (out of a total of 247 potential markets) between 2005/2006 – 2014/2015 (see Table 5).

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Table 5: Ethiopia's Export Base and Diversification (2005/2006 - 2014/2015). Source: ERCA

Year 2005/ 2006 2006/ 2007 2007/ 2008 2008/ 2009 2009/ 2010 2010/ 2011 2011/ 2012 2012/ 2013 2013/ 2014 2014/ 2015

Export

Base

Number of Exporting Firms 1,223 1,498 1,582 1,691 2,013 2,109 1,912 1,871 1,788 1,771

Number of Export Products 713 692 693 649 675 745 706 686 699 721

Number of Destinations 115 120 110 117 123 125 129 132 122 135

Total Export Value (Billion USD) 0.9 1.1 1.5 1.4 1.9 2.4 2.7 2.6 2.9 3.2

Export

Div

ers

ific

atio

n

Share of top 5% Exporters in total Export Value (%) 67.9 69.0 67.3 65.2 68.6 67.8 64.5 64.7 61.4 59.8

Share of top 10% Exporters in total Export Value (%) 84.6 83.5 82.2 80.0 81.4 80.7 78.7 79.3 76.8 75.5

Share of top 25% Exporters in total Export Value (%) 96.0 95.4 95.0 94.3 94.7 93.7 92.7 93.3 92.9 92.5

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3.4. Share of Top Exporters, Products, and Markets

(i) Share of Top 1, 5 and 25 Percent of Exporters

Ethiopia’s merchandize export sector is characterized by a high concentration of exporters, products and markets; markets making up a vast bulk of the export value with a small number of exporters and products (see Error! Reference source not found.). As can be seen from Error! Reference source not found., the top 1, 5 and 25 percent of exporters critically shape Ethiopia’s export patterns as these firms account for an overwhelmingly larger share of the export value and export relatively larger number of products to a larger number of markets with a higher export frequencies. In 2014/2015, the top 1 percent of exporters represented just 18 firms out of the total number of 1771 exporters, but they account for 28 percent of the country’s total export revenue. In the same year, the top 5 percent of exporters (89 firms) account for 60 percent of the total export value. Likewise, a whopping 93 percent of the country’s total export value is accounted for by the top 25 percent (443 in number) of exporters. During the last decade, there has been a slight improvement in the degree of concentration as measured by the top 1, 5 and 25 percent exporters For example, the share of the top 1, 5 and 25 percent of exporters in the total export values declined from 31 to 28 percent, 68 to 60 percent and 96 to 92.5 percent respectively between 2005/2006 and 2014/2015. However, this slight improvement in the degree of diversification does not change the overall picture that the Ethiopian export sector is highly dominated by a few top exporters.

(ii) Share of Top 1, 5 and 25 Percent of Products

We have generated a simple measure of the degree of export concentration at the product level by calculating the export shares of the top 1, 5 and 25 percent of products exported from Ethiopia. As we have indicated above products are defined at the HS6 level and there has been an average of 700 products over the period covering 2005/2006 – 2014/2015. Accordingly, the top 1 percent of products (only 8 export products), the top 5 percent of products (37 export products) and the top 25 percent of products (181 products) account for 77, 92 and 99.4 percent of the country’s total export values in the current year 2014/2015. The trend has been similar for the past 10 years (see Error! Reference source not found.) and in general Ethiopia’s degree of export concentration at the product level is also concerning.

(iii) Share of Top 1, 5 and 25 of Percent Markets

Likewise, in terms of market concentration, while the top 1 percent of markets (i.e., only 2 countries, namely China and Somalia) contributed for almost a quarter (about 24 percent) of the total export value in 2014/2015. The concentration is also evident when one looks at the contributions of the top 5 and 25 percent of export destinations. Specifically, the top 5 percent of markets (which are 7 in number), the top 25 percent of markets (which are 34 in number) contribute about 56 and 97 percent of total export value in 2014/2015, respectively. The trend over the past 10 years has been similar (see Error! Reference source not found.).

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Table 6: Number and Export Value Share of Top 1, 5 and 25 percent Exporters,

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

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3.5. Difference in Average Exporter Size: The Top vs the Bottom

How do exporters in the top 10 percent category and firms in the bottom 90 percent category compare in terms of size of exports? 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 over exporters) 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, the mean export per exporter (or average firm size) has been 9 million USD over the period 2005/2006 – 2014/2015. The corresponding value for an exporter in the bottom 90 percent exporters is just 0.26 million USD. This means that 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).

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Table 7: Export Performance of the Top 10 and Bottom 90 percent Exporters (2005/2006 - 2014/2015). Source: ERCA

Ethiopian Fiscal Years

Ten Year Average

2005/2006 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

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3.6. The Theil Export Diversification Index

The other widely used measure of export diversification/concentration is the Theil’s Entropy. The higher the Theil index, the higher the concentration (or the lower the diversification). If

one good is all that a country exports, the Theil index is . If goods have an equal share, the Theil index is zero. Theil’s Entropy can be computed for subgroups of exports and decomposed additively to measure diversification within (intensive) and among groups (extensive) of exports. While changes in the within-group component of the 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). If the Theil index is calculated over active export lines only, it measures diversification at the intensive margin. On the other hand, diversification at the extensive margin can be measured simply by counting the number of active export lines. Theil’s entropy index (Theil, 1972) is given by:

Where

Following (Cadot, Carrère, & Strauss-Kahn, 2011), the Theil index can be decomposed into its intensive (with-in) and extensive (between) components as follows:

Let n denote the number of export products (products are defined at the 4-digit SITC (Rev. 1) level), n_j the number of export lines in group j, µ the average dollar export value, µ_j group j’s average dollar export value, and X_k the dollar value of export line k. Then the between-groups component is:

And the within-groups component is:

Where

stands for Theil’s sub-index for . It can easily be shown that

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Based on this, the overall Theil index and its components, the intensive and extensive margins, are calculated for Ethiopia for the period 1993 – 2010 (where data is available). The results are 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. Note that higher values for all the three indices indicate lower diversification. Consistent with the results presented in sections 3.2, 3.3 and 3.4, Ethiopia’s export diversification has generally remained low throughout the period. This is also the case when compared to many Sub-Saharan African Countries including Kenya, Tanzania, Uganda, Mauritius, Zimbabwe and Madagascar (see Figure 13). However, Ethiopia does better than some Sub-Saharan countries such as Mozambique, Ghana, Rwanda, Malawi and Sudan in terms of export diversification. On the other hand, 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 (see Figure 15).

Although Ethiopia’s diversification is generally low given its size and compared to neighboring non-oil exporting countries, recently (more specifically since 2001) Ethiopia has shown some improvements in terms of diversifying its exports compared to the period 1994 – 2000. This is consistent with the results obtained in section 3.4. On the other hand, although export diversification started to pick up since 2001, compared to the most diversified economies in the world (such as Germany and South Korea), Ethiopia has a long way to go in terms of diversifying its economy and hence reducing vulnerability to demand and price swing shocks that could affect Ethiopia’s overall development (see Figure 14) .

Figure 12: Theil Diversification Indices: Overall, Intensive and Extensive (1993 - 2010). Source: IMF Diversification Database

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Figure 13: Diversification Index for Selected Sub-Saharan (COMESA) Countries (1962 - 2010). Source: IMF Diversification Database

Figure 14: Diversification Index for Selected Sub-Saharan (COMESA) Countries (1962 - 2010). Source: IMF Diversification Database

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Figure 15: Diversification Index: Ethiopia against the Sub-Saharan Average (1962 - 2010) (Source: IMF Diversification Database

4. Export Dynamics and Survival

This section explores to what extent Ethiopia has been able to add new exporters, new products and new markets that are economically significant to its export portfolios; and the ability of those new export portfolios to survive and thrive in export markets. This is important because successful export diversification requires not only introducing new products and/or entry into new markets but also survival and growth of those new export portfolios. In the literature, persistence and resilience of a country’s exporters, products and destination markets is taken as a good sign of economic maturity.

4.1. Exporter Entry and Exit

We begin with some definitions. Entrants are 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. Continuing exporting firms (or incumbents) are firms that exported in previous period and continue to export in the current period. Based on these definitions, we calculate entry rate as the number of firms that export in the current year but did not export in the previous year, relative to the total number of firms in the current year. On the other hand, the Exit rate is the number of firms that exported in the previous year but have not exported in the current year, relative to the total number of firms in the previous year.

The results are presented in Table 8. The Ethiopian export market exhibits high degree of export churning as can be seen from the high number of entrants and exiters in Table 8 . Export firm entry and exit rates averaged 34.3 and 31.8 percent respectively, yielding a net entry rate of 2.5 percent over the past nine years (2006/2006 - 2014/2015). This result is consistent with a number of studies who find rates of entry into exporting of 30-60 percent a year.2 We see that both rates initially increased and peaked in 2010/2011 and slowed down since then until the current period 2014/2015. 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. But more generally the gap between the two 2 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.

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rates shrinks towards the end of the analysis period. And overall, net firm entry has been 2.5 percent on average over the past nine years (see Table 8).

4.2. Product and Market Entry and Exit

Similar to the exporter churning, product and market churning is also relatively high in the Ethiopian export market. While the product entry rate averaged at 57.9 percent over the past nine years (2006/2007 – 2014/2015), the corresponding figure for product exit rate has been 57.6 percent. This means that, on average, net product entry rate has been 0.28 percent. The evidence in table 10 points to a substantial degree of experimentation by exporters in introducing new export products. However, most of these new products die out as can be seen from the high product exit rates. Also apparent from table 10 is that product entry and exit rates slightly decline over time and the gap between them seems to shrink quickly towards the end of the analysis period (see Table 9).

Likewise, market entry and exit rates have been high, 56.2 and 55.4 percent respectively. However, the overall 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 (see Table 9).

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Table 8: Entry and Exit of Ethiopian Exporters (2006/2007 - 2014/2015). Source: ERCA

* Total number of exporters in the year 2005/2006 was 1,223. Exit rate = (415/1223) = 33.9 percent

Table 9: Product and Market Entry and Exit Rates (2006/2007 - 2014/2015). Source: ERCA

Years

Average 2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 2014/2015

Product Dynamics

Average Product Entry Rate 59.52 58.50 65.04 59.46 60.21 55.18 54.21 55.17 55.51 57.92

Average Product Exit Rate 58.78 59.20 59.90 59.55 60.17 56.79 54.80 55.30 55.10 57.63

Average 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

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

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4.3. Exporter Survival Dynamics

For any developing country, firm survival is as important as firm entry (Reis J. G., 2011; Cebeci,

Fernandes, Freund, & Pierola, 2012). We define a one year survival rate as the number of year

entrants that survive until year , relative to total number of entrants in the year . It is important to note that while Exit Rate incorporates exit of all firms (new and old), the Survival Rate measures of the ability of new firms to continue exporting for more than one year.

As indicated in Table 10, one year firm survival rate has been generally high (about 60 percent per year on average) over the period 2008/2009 – 2014/2015. Over the course of the analysis period, firm survival rate shows some degree of volatility ranging from as low as 37.9 percent in in 2009/2010 to as high as 85.5 percent in 2008/2009. 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.

Looking at firm survival beyond the first year, specifically 2-Year and 3-Year Survival rates, first, we see high variations among the 2-year firm survival rates (ranging from 18.8 to 51.4 percent) and the 3-year firm survival rate (ranging from 14.9 to 43.3 percent). Second, on an 8-year average (2008/2009 – 2014/2015) basis, the 3-year survivors have the lowest survival rate followed by 2-year survivors and 1-year survivors respectively; suggesting that survival beyond the first year becomes more and more difficult. And thirdly, 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.

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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(4)

1-Year Survival

Rate (%)(1)

85.5 37.9 47.2 61.8 64.2 62.0 70.7 50.2 59.9

2-Year Survival

Rate (%)(2) 34.4 18.8 31.1 41.6 46.0 51.4 39.1 37.5

3-Year Survival

Rate (%)(3) 21.7 15.0 23.3 37.8 43.3 26.7 28.0

(1) Survivors are Incumbent Firms that did not Export 2 Years Earlier but Exported in the Past & Current Years

(2) 2-Year Survivors are Incumbent Firms that did not Export 3 Years Earlier but Exported the Past 2 Consecutive Years

(3) 3-Year Survivors are Incumbent Firms that did not Export 4 Years Earlier but Exported the Past 3 Consecutive Years

(4) The eight- year average is the average of the values presented in this table

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4.4. Explaining the Determinants of Entry, Exit and Survival

In previous sections, we generated a lot of information on exporter dynamics in terms of entry, exit and survival rates. Next we want to show what drives entry, exit and survival rates using some regression analyses. Theory and empirical evidences show that sunk entry costs3, firm-specific characteristics and industry categories are the key factors affecting entry, exit and survival dynamics (e.g., see Roberts and Tybout, 1997; Bernard and Wagner, 1998; Bernard and Jensen, 1999; 2001 and Campa, 2004). Following the literature, we put together the following econometric estimation to investigate the key determinants of exporter dynamics. We use the average size of exports of an incumbent in a sector as a proxy for entry costs. We use incumbent size as a proxy for entry costs because entry barriers arise from incumbent exporters. In sectors where average exports are larger, entry is likely to be more difficult. Next, we include the relative size of entrants in the regression not only to distinguish between informational uncertainty and entry costs but also to examine if entering firms that start with relatively small trials (as compared with incumbents) are more likely to enter, exit or survive. Furthermore, in all specifications, we include firm and industry-fixed effects to control for firm and industry characteristics that may influence entry, exit, and survival rates. Since exporters vary by size, industry, market and so on, we adopt a heterogeneous regression approach of the following type:

Where Y is the dependent variable for entry, exit and survival rates, i is exporter, j is industry (sector); αi and Xi 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 proportion4, we use a generalized linear model (GLM) with a logit link and report robust standard errors. We report the results of the regression in Table 11. The coefficients have their expected signs in all the three specifications. Entry costs and relative size of entrant are highly significant but the number of exporters seems to affect only survival rate. The results imply that a 10 percent increase in entry cost (as measured by the average incumbent size) results in 1.2 percentage points lower entry rates and 1.3 percentage points lower exit rates and 1 percentage points higher survival rate according to model 1 (see Table 11). All the other models produced similar results. For example, using model 2, a 10 percent increase in entry costs results in about 1 percentage points lower in entry and exit rates and 0.75 percentage points higher survival rates. On the other hand, relative size of entrant comes with a positive sign, suggesting that that variation in entry costs across sectors is more important in determining entry and exit rates. Small incumbent exports could be associated with other industry characteristics, such as lots of firms and

3 Sunk costs, typically, represent the costs of setting up a distribution and service network, of establishing a brand name

through advertising, or of bringing the product in conformity with health and safety regulations of the foreign country. 4 Entry rate is defined as as the number of firms that export in the current year but did not export in the previous year,

relative to the total number of firms in the current year. On the other hand, the Exit rate is the number of firms that exported in the previous year but have not exported in the current year, relative to the total number of firms in the previous year. And firm Survival Rate = Number of Survivors / Number of Entrants

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lots of entry and exit. Perhaps these may be simply industries where many small firms participate and both entry and exit are high. We included the log of the number of firms in each sector to control for this possibility. It is not significant, neither for entry nor for exit, implying that entry and exit rates are not correlated with the number of firms. However, survival is positively correlated with the number of firms. This makes sense because when more firms survive the number of firms increases.

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.

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Table 11: Determinants of Entry, Exit and Survival Rates - Regressions Results

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 (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

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5. Conclusions and implications

Several key patterns emerge from our analysis. First, in terms of export composition, we observe that while the role of agriculture in export earning has been falling, the role of the manufacturing sector has been rising. 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. Similarly, in terms of export market destinations we observe increasing roles of more dynamic and emerging markets in Asia, Latin America and Sub-Saharan. Second, in any given 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 close to one-third to one-half of all exporters are new entrants in a typical year. These new export firms by and large do not add much to export growth simply because (i) the majority do not last for more than 2 years and (ii) their export sales are very small. Third, given its level of development and size, Ethiopian exports remain relatively small in number and average size. Controlling for country characteristics (per capita income level, size, location, etc),Ethiopia trades 10 to 13 percentage points less than what would be expected. In terms of its export base, Ethiopia’s export base remains quite small with only 1,771 exporters compared to 4600 in Kenya in the most recent year 2014/2015.

Third, in terms of export diversification, Ethiopia’s export sector is 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 a considerably larger share of exports – close to or more than 80 percent of the country’s exports. These top exporters are very large in size - about 35 times larger than the bottom 90 percent exporters. So, while export value has shown a modest increase, it is dominated and driven by the top tier of larger exporters. 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). The story is the same or even worse when we look at the findings of the product and market concentration analyses. The top 1 percent products, which are only 8 in number, account for 77 percent of total exports in the current period. Likewise, the top 5 percent products (which are 35 in number) account for 92 percent of total exports. We find similar results when we look at market concentration. This indicates that a few export products and markets drive total export value and growth and export pattern and flow in general. The fact that Ethiopia has a relatively small export base implies low export earnings and proneness to demand shocks and price swings in overseas markets.

Fourth, the other issues we addressed in this paper are export dynamics and survival. In the case of export dynamics, Ethiopia’s export sector is characterized by a high degree of dynamics in terms of export firm, product and market churning. Exporter entry and exit rates have been high throughout although both rates have declined a bit towards the end of the analysis period. While firms’ export market entry rate ranges from 46 to 28 percent, the exit rate lies in the range 34 to 29 percent between 2005/06 – 2014/2015. In the period between 2006/2007 and 2010/2011, the number of entrants has been consistently larger than the number of exiters. However, the opposite was true for the period between 2011/2012 and 2014/2015. Nonetheless, the gap between the two rates shrinks towards the end of the analysis period and as a result the overall net export firm entry rate averaged at 2.5 percent between 2005/2006 and 2014/2015. And in the case of export survival, the Ethiopian export sector is characterized by low survival rate of new entrants. This is especially true for exporters beyond their first year of exporting; the survival rate is only about 26 percent for 3 year survivors. This indicates high attrition rate of new entrants after one year in export business. However, it should be noted that attrition is not unique to the Ethiopian export sector and that it is not necessarily bad by definition because it also reflects strong experimentation at the extensive margin. It can be a problem if attrition is largely due to

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reasons other than experimentation. That is, 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. As a result, the extensive margin’s contribution to overall export growth would be weak and a country’s export base cannot be significantly expanded.

In an attempt to explain what drives entry, exit and survival rates, we run a simple yet powerful regression. The key finding that emerged from this regression analysis is 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. 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.

Some key policy implications emerge from our analysis. First, 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, 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). 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. 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.

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