metadata: authors: 0: author: Martins,Pedro Miguel Gaspar
count: Ethiopia
docna: 0: docna: Structural change in Ethiopia : an
            employment perspective
docty: Policy Research Working Paper
subtopic: Construction Industry,Mining & Extractive Industry (Non-Energy),Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,International Trade and Trade Rules,Food Security,Business Cycles and Stabilization Policies
teratopic: Social Protections and Labor,Macroeconomics and Economic Growth,Private Sector Development
abstracts: cdata!: This paper investigates whether the
            Ethiopian economy is undergoing a virtuous process of
            structural change. In particular, it assesses the relative
            contributions of within-sector and between-sector
            productivity to output per capita growth. Based on data
            disaggregated into eight sectors for the period 1996-2011,
            the analysis suggests that the structure of output has
            changed considerably -- predominantly from agriculture to
            services -- but changes in the composition of employment
            have lagged behind. Labor productivity growth has been
            strong across most sectors, albeit mainly driven by
            within-sector productivity improvements. Nonetheless, the
            pace of structural change is accelerating and its relative
            contribution to output growth is increasing.
geo_reg_key: 517191
repnme: repnme: Structural change in Ethiopia : an
            employment perspective
display_title: Structural change in Ethiopia : an
            employment perspective
listing_relative_url: /research/2014/01/18832342/structural-change-ethiopia-employment-perspective
topicv3: Agriculture and Food Security m1326205 435,Mining Services m1326410 1701,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Labor Productivity m1504060 2756
publishtoextweb_dt: 2014-01-01T00:00:00Z
historic_topic: Social Protections and Labor,Macroeconomics and Economic Growth,Private Sector Development
no_of_pages: 42
author_keys: 0: author_key: 1655352
model_extractions: WPS6749 Policy Research Working Paper 6749 Structural Change in Ethiopia An Employment Perspective _Pedro Martins_ The World Bank Africa Region Poverty Reduction and Economic Management Department January 2014
Policy Research Working Paper 6749

# **Abstract**

This paper investigates whether the Ethiopian economy is undergoing a virtuous process of structural change. In particular, it assesses the relative contributions of withinsector and between-sector productivity to output per capita growth. Based on data disaggregated into eight sectors for the period 1996–2011, the analysis suggests that the structure of output has changed considerably— predominantly from agriculture to services—but changes in the composition of employment have lagged behind. Labor productivity growth has been strong across most sectors, albeit mainly driven by within-sector productivity improvements. Nonetheless, the pace of structural change is accelerating and its relative contribution to output growth is increasing.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at p.martins@odi.org.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Structural Change in Ethiopia: An Employment Perspective**

## _Pedro Martins_<sup>_1_</sup>

JEL classification: O11, O40, O55 Keywords: Structural change, labor productivity, Ethiopia Sector board: Economic Policy Acknowledgements: The author would like to thank Lars Christian Moller for the overall guidance in preparing this paper. Taye Mengistae and Alun Thomas kindly served as peer reviewers. Helpful comments on a previous draft were also received from Asya Akhlaque, Louise Fox, Pablo Fajnzylber, Michael Geiger, and Ruth Hill.

> 1 Research Fellow, Overseas Development Institute, UK (p.martins@odi.org.uk)

1
|**Contents**|
|---|
|1.<br>Introduction ..................................................................................................................................................................... 4|
|2.<br>Literature Review .......................................................................................................................................................... 5|
|3.<br>Main Trends ..................................................................................................................................................................... 7|
|3.1.<br>Gross Value Added ............................................................................................................................................... 7|
|3.2.<br>Employment ......................................................................................................................................................... 11|
|3.3.<br>Productivity and Elasticities .......................................................................................................................... 14|
|4.<br>Productivity Analysis ................................................................................................................................................. 19|
|4.1.<br>Decomposition at the Aggregate Level ...................................................................................................... 19|
|4.2.<br>Decomposition at the Sectoral Level .......................................................................................................... 20|
|5.<br>Conclusions..................................................................................................................................................................... 25|
|References ................................................................................................................................................................................ 27|
|Annex 1: Employment Characteristics ......................................................................................................................... 29|
|Annex 2: Additional Tables and Graphs ....................................................................................................................... 37|

|**Tables**|
|---|
|Table 1: Gross Value Added ................................................................................................................................................. 7|
|Table 2: Sectoral GVA Shares .............................................................................................................................................. 8|
|Table 3: Demographic Statistics ...................................................................................................................................... 12|
|Table 4: Labor Market Status of the Working Age Population (10+)............................................................... 13|
|Table 5: Employment by Sector ....................................................................................................................................... 14|
|Table 6: Productivity by Sector........................................................................................................................................ 15|
|Table 7: Labor Productivity in Sub-Saharan Africa (2005).................................................................................. 17|
|Table 8: Aggregate Decomposition of GVA per capita ............................................................................................ 19|
|Table 9: Sectoral Decomposition of GVA per capita (1996–2011) ................................................................... 21|
|Table 10: Sectoral Contributions to Inter-Sectoral Shifts (1996-2011) ......................................................... 22|
|Table 11: Sectoral Decomposition of GVA per capita (Several Periods) ........................................................ 22|
|Table 12: Decomposition of Productivity Growth (unweighted averages, 1990-2005) .......................... 23|
|Table 13: Country Rankings of Contributions to Productivity Growth (1990-2005) ............................... 24|
|Table 14: Interpreting Employment Elasticities ...................................................................................................... 37|
|Table 15: Poverty and Inequality .................................................................................................................................... 37|
|Table 16: Consumption Shares ........................................................................................................................................ 37|
|Table 17: Contributions to Output Per Capita Growth in the Republic of Korea (1970-1990) ............ 38|
|Table 18: Studies on Structural Change ....................................................................................................................... 39|
|Table 19: Productivity, Output and Employment Growth (%) ........................................................................... 40|
|Table 20: Within-Sector and Reallocation Effects on Labor Productivity Growth (%) ............................ 40|

2
# **Figures**

|Figure 1: Sectoral Value Added (constant birr, billions) ......................................................................................... 8|
|---|
|Figure 2: Sectoral Value Added (% GVA) ....................................................................................................................... 9|
|Figure 3: Sectoral Value Added (% GVA) ....................................................................................................................... 9<br>|
|Figure 4: Sectoral Value Added (% Agriculture GVA and % Other Services GVA) ..................................... 10|
|Figure 5: Industry and Manufacturing (% GDP) in Sub-Saharan Africa ......................................................... 10|
|Figure 6: Labor Market Shares (1995/96, 1999, 2005 and 2010/11) ............................................................ 14|
|Figure 7: Total Labor Productivity ................................................................................................................................. 16|
|Figure 8: Productivity (Y-axis) and Labor Share (X-axis) – 1995/96 and 2010/11 .................................. 17|
|Figure 9: Counterfactual Impact on Productivity (% of 2005 levels) .............................................................. 18|
|Figure 10: Aggregate Decomposition of GVA per capita (constant birr) ........................................................ 20|
|Figure 11: Decomposition of Productivity Growth: Inter- and Within-Sectoral (1996-2011) ............. 21|
|Figure 12: Decomposition of Productivity Growth (1990-2005) ...................................................................... 23|
|Figure 13: Gender Breakdown by Sector (1999 and 2005) ................................................................................. 29|
|Figure 14: Sector of Employment by Gender (1999 and 2005) ......................................................................... 30|
|Figure 15: Age Breakdown by Sector (1999 and 2005) ........................................................................................ 30|
|Figure 16: Sector of Employment by Age Group (1999 and 2005) .................................................................. 31|
|Figure 17: Type of Education by Sector (1999 and 2005) ................................................................................... 31|
|Figure 18: Sector of Employment by Education Level (1999 and 2005) ....................................................... 32|
|Figure 19: Type of Employment by Sector (1999 and 2005) .............................................................................. 32|
|Figure 20: Sector of Employment by Type of Employment (1999 and 2005) ............................................. 33|
|Figure 21: Sector of Activity by Economic Sector (1999 and 2005) ................................................................ 33|
|Figure 22: Employment Sector by Sector of Activity (1999 and 2005) .......................................................... 34|
|Figure 23: Location by Economic Sector (1999 and 2005).................................................................................. 34|
|Figure 24: Employment Sector by Location (1999 and 2005) ........................................................................... 35|
|Figure 25: Labor Productivity and Average Wage Indices in Manufacturing (2000=100) .................... 36<br>Figure 26: Decomposition of Growth in Output per Worker for the Rep. of Korea (1970-1990) ....... 38|

3
# **1. Introduction**

While many economists tend to focus on the structure of output, little attention is usually paid to the importance of changing the structure of employment. In the particular context of African economies, relative shifts in production – from agriculture to industry and services – have rarely been followed by commensurate changes in sectoral employment. This undermines the impact and sustainability of the growth process, since research has shown that structural change (in the labor market) is vital for sustaining economic and social development.

In this paper, we conduct an investigation of the structural changes that have taken place in Ethiopia over the past 15 years. For that purpose, we use macroeconomic and household survey data disaggregated into eight sectors to answer the following key questions:

- How has the sectoral composition of output and employment changed over time?

- What are the trends in labor productivity?

- What have been the relative contributions of within-sector productivity and structural change (i.e. labor shifting from low- to high-productivity sectors) to output per capita growth?

Our results suggest that the structure of output has changed considerably. For instance, the share of agriculture in total output declined from 66 percent in 1991 to 45 percent in 2011, while the services have expanded considerably – especially commerce and real estate activities. However, changes in the composition of employment have lagged behind. The share of agricultural employment in total employment remains very high, at about 78 percent. Nonetheless, labor productivity growth has been strong across most sectors (including agriculture), with total output per worker doubling in the past decade. This performance has been mainly driven by within-sector productivity improvements, although the relative contribution of structural change is increasing.

This paper is structured as follows. Section 2 provides a brief overview of the literature on structural change. Section 3 assesses trends in the level, growth and composition of output (value added) and employment, as well as sectoral productivity and employment elasticities by economic sector. In section 4 we use suitable analytical tools to decompose labor productivity growth into within-sector and between-sector changes, while also taking into consideration demographic effects. Section 5 concludes.

4
# **2. Literature Review**

‘A powerful historical pathway of structural transformation is experienced by all successful developing countries.’ (Timmer and Akkus, 2008) ‘The speed with which this structural transformation takes place is the key factor that differentiates successful countries from unsuccessful ones.’ (McMillan and Rodrik, 2011) It is a historical inevitability. All successful developing countries have undergone a fundamental and potent process of structural transformation. Structural transformation – or ‘structural change’ – is therefore a critical prerequisite for economic and social development. Through its impact on labor productivity, structural change plays an instrumental role in sustaining economic growth, generating productive employment, and raising living standards. In addition, economic processes that support structural transformation and economic diversification are also more likely to foster inclusive and sustainable growth – especially through the expansion of modern sectors and higher value added exports.

In simple terms, structural change can be defined as the reallocation of labor from low-productivity sectors to more dynamic (higher-productivity) economic activities.<sup>2</sup> For most developing countries, this would usually require shifting labor from subsistence agriculture to commercial agriculture, manufacturing and modern services. This process has strong implications for the economy (e.g. what is produced and how), but also for how society is organized. According to Timmer and Akkus (2008), structural transformation entails four interrelated processes: (i) a declining share of agriculture in total output and employment; (ii) rapid urbanization as people migrate from rural to urban areas; (iii) the rise of a modern industrial and service economy; and (iv) a demographic transition that leads to a spurt in population growth. Hence, structural transformation has implications that go beyond the economic sphere.

Despite its crucial role, the potential contribution of structural change to productivity growth has often been neglected by researchers and policy makers. There is significant (micro-level) research focusing on the reallocation of resources within industries – i.e. within-sector productivity – but inter-sectoral shifts can be an equally important driver of aggregate productivity. In fact, increases in labor productivity can be accounted by several factors, including: increased efficiency in the use of labor, increased use of other inputs (such as physical and natural capital), and labor moving to more productive activities (structural transformation).<sup>3</sup> Moreover, there are several possible growth paths that could support structural change.<sup>4</sup> Timmer (2012) illustrates this with the following hypothetical scenarios:

- a. Labor productivity remains constant in industry and services as these sectors absorb labor from agriculture at the same rates as each sector itself expands. This labor-intensive growth (akin to the Lewis Model) leads to the fastest pace of structural transformation, while agricultural productivity increases significantly.

> 2 The term can also refer to the changing composition of output. However, since shifts in production tend to precede shifts in employment, it can be argued that this transformative process is only underway once labor starts to relocate. 3 Given the interdependence between the different inputs, low labor productivity may not necessarily reflect low labor efficiency, but possibly the lack of complementary inputs (e.g., physical capital).

> 4 UNRISD (2010) argues that the classic employment-generating growth path – from agriculture to manufacturing – is not being replicated in most developing economies. The report identifies five broad development paths: (i) transition to manufacturing (e.g. East Asia); industrialisation with dualist labor market regimes (e.g. Latin America); service-led growth (e.g. India); agrarian economies (e.g. many African countries); and mineral-rich countries (e.g. Botswana).

5
- b. Labor productivity in industry and services grows at the same rate as the sectors themselves. Hence, neither sector absorbs new workers, while the entire increase in the labor force remains in agriculture – its share in total employment increases. Agricultural GDP grows faster than the labor force, so labor productivity in the sector does rise slightly. This pattern is somewhat closer to the African experience.

- c. Labor productivity in industry and services grows at half the rate of increase in sectoral output. The agricultural labor force rises, but so does labor productivity in the sector. This scenario is half way between the extremes of paths ‘a’ and ‘b’.

Meanwhile, it is important to note that productivity growth in isolation is not a sufficient condition for improving living standards. It is vital to ensure that ‘surplus labor’ can relocate to more productive jobs elsewhere in the economy. This is because labor productivity growth has a dual character: while it is an important source of economic growth and dynamism, it can also have a labor-saving effect – thus potentially reducing employment demand (Martins and McKinley, 2011). Hence, productivity growth needs to be accompanied by employment growth.

There are a few studies exploring the sources of aggregate productivity by decomposing labor productivity growth. Some of them use the Groningen Growth and Development Center (GGDC) 10Sector Database, which contains disaggregated (real) value added and employment data for 29 countries – although none of them in Africa. Timmer and de Vries (2009) used part of the dataset to investigate structural transformation in Asia and Latin America, concluding that growth accelerations are mostly explained by within-sector productivity rather than labor reallocation (i.e. structural change). Moreover, they argued that productivity growth in market services is more important than productivity improvements in manufacturing.

McMillan and Rodrik (2011) complemented the full GGDC dataset with national sources for China, Turkey and nine African countries (including Ethiopia). Their main conclusion is that Africa and Latin America have experienced growth-reducing structural change since 1990 – which largely explains the difference between these regions’ productivity performance and that of Asia. This means that labor has moved to lower-productivity sectors, therefore dampening economic and productivity growth.<sup>5</sup> Kucera and Roncolato (2012) constructed a sample of 81 countries with data from the UN National Accounts and the ILO, as well as the GGDC. The authors find that within-sector productivity growth is more important than labor reallocation across all regions. Moreover, aggregate labor productivity growth in Asia has been driven by industry (e.g. China) as much as by services (e.g. India).

Despite the broad findings of these studies, there is significant heterogeneity across countries. For instance, McMillan and Rodrik (2011) observe that – despite the negative aggregate results for Africa – Ethiopia, Ghana and Malawi have experienced growth-enhancing structural change. Therefore, it is important to take into consideration specific country contexts, especially since African countries are seldom well-represented in the study samples.

> 5 However, a recent revision of the estimates for Sub-Saharan Africa does suggests a more positive picture for the period between 2000 and 2005 – see AfDB et al (2013).

6
# **3. Main Trends**

This section provides an overview of key trends on output and employment in Ethiopia. In particular, it tracks changes in the level, growth and composition of production (value added) and employment. In addition, it investigates sectoral productivity and provides estimates of employment elasticities by economic sector.

## **3.1. Gross Value Added**

Table 1 presents data on Gross Value Added (GVA). Total GVA increased four-fold in merely two decades, from 128 billion birr in 1991 to 510 billion birr in 2011 (in constant prices).<sup>6</sup> GVA in construction grew eight-fold, while the value added generated by trade, transport & communications, and other services nearly increased by a factor of seven. Agriculture registered the lowest relative expansion – less than tripled its initial value. However, in terms of each sector’s contribution to the overall GVA increase (i.e. 383 billion birr), agriculture accounted for nearly 38 percent of this expansion, while other services and trade accounted for 24 and 21 percent, respectively. Hence, despite its relatively lower growth – 5 percent over the entire period – agriculture still provided the greatest contribution to the rise in GVA. This is due to its large weight in the economy, which we will show below.

<u>Table 1: Gross Value Added</u>

||Constan|t birr(bi|llion,20|10/11pr|ices)|Average an|nualgrowth|(%)|||
|---|---|---|---|---|---|---|---|---|---|---|
||1991|1996|2001|2006|2011|1991-96|1996-01|2001-06|2006-11|1991-11|
|Gross Value Added<sup>*</sup>|127.7|160.2|204.3|282.4|510.2|4.8|5.2|6.9|10.7|6.9|
|Agriculture|83.9|98.8|111.9|145.2|223.1|3.5|2.9|5.9|7.9|5.1|
|Mining & Quarrying|1.4|1.5|2.0|2.6|7.7|4.4|6.5|5.6|24.1|10.2|
|Manufacturing|4.0|6.4|8.3|11.2|20.5|10.3|5.5|6.3|10.1|8.1|
|Electricity & Water|1.3|1.5|2.5|3.6|6.5|2.6|11.3|7.6|9.0|7.6|
|Construction|2.7|3.7|5.9|11.1|21.5|7.3|10.1|13.5|11.5|10.6|
|Trade|14.6|21.3|30.8|46.2|99.3|9.2|7.7|8.6|13.8|9.8|
|Transport & Comms|3.2|4.6|7.4|11.9|22.3|8.1|9.9|10.1|10.8|9.7|
|Other Services|16.7|22.3|35.6|50.7|109.3|6.1|9.9|7.4|14.5|9.5|

* Values not adjusted for financial intermediation services indirectly measured (FISIM) Source: Government of Ethiopia

Economic growth has been accelerating steadily over the past 20 years. In the early 1990s, real GVA grew at an average of about 4.8 percent, compared with 5.2 percent in the late 1990s. This decade witnessed a transition in political power and consequently a first wave of economic reforms. Between 1998 and 2000, the Ethiopia-Eritrea war had a significant impact on the economy. Economic performance continued to improve in the early 2000s, reaching an average growth rate of 6.9 percent – despite facing one of the most severe droughts in two decades. In the second half of the 2000s, economic growth accelerated substantially to over 10 percent per year. The annual data suggest a clear trend break around 2003 – the year of the drought (Figure 1). From that point forward, economic activity has accelerated at an unprecedented pace.

> 6 GVA is notoriously difficult to measure. Estimates often depend on critical assumptions and may lack consistency across space and time – e.g. due to changes in methodology and/or failure to update prices. Nonetheless, the (official) data used in this paper are the best estimates available for Ethiopia.

7
<!-- Start of picture text --> 600.0<br>500.0<br>m Other Services<br>400.0 Transport and Communications<br>mTrade<br>300.0 = Construction<br>Electricity and Water<br>200.0 = Manufacturing<br>m Mining and Quarrying<br>100.0 Agriculture, Hunting and Forestry<br>0.0<br>eek RRR RRERESSESSESS<br>= = = = = = = - = _ N nN Nn nN N nn<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70% m Other Services<br>S Transport and Communications<br>60%<br>STrade<br>50% eConstiction<br>40% m Electricity and Water<br>= Manufacturing<br>30%<br>@ Mining and Quarrying<br>20% w Agriculture, Hunting and Forestry (96)<br>10%<br>0% r<br>=Bohhha ic] u Lied&GF ~2RF -_FRhhcarl wu&& Load&& ~&& aad6SSm_S&Sir&6 cxSs& as& &-_Sa<br><!-- End of picture text --> <!-- Start of picture text --> GVA Shares<br>° s a & = & 3 z<br>Agriculture<br>Mining& Quarrying<br>Manufacturing 1995/96<br>Flectricity & Water 91999<br>Construction m2005<br>Transport& CommsTrade 2010/11<br>Other Services<br><!-- End of picture text --> <!-- Start of picture text --> ®0<br>70}<br>60} —— Agriculture, Hunting and Forestry<br>so + —— Mining and Quarrying<br>—— Manufacturing<br>> —— Electricityand Water<br>—— Construction<br>ball —Trade<br>20‘oy+SS =erent Transportand Commmmications<br>ae = = t =<br>BReREEEE EE E2228 8 =F<br><!-- End of picture text -->
<!-- Start of picture text --> 50.0<br>45.040.0 ++<br>40.0 + ——— Financial Intermediation<br>35.0 + —— Real Estate, Renting and<br>- Business Activities<br>30.0 + —— Public Administration<br>Defense and<br>25.0 > —Education<br>20.0 +<br>——Health and Social and Social Social Work<br>15.0 +<br>—— Other Community<br>10.0 7 Personal Services<br>5.0 +———————————————— —— Private Households, Socialwith & Households, Socialwith &, Socialwith & Socialwith &with &<br>a Braployed Persone<br>eoeooseoeoeeFeEFRFFSFFESFFESSSSSE-SSSESZSS25252R-mewMeenrmeewree=-eseFeEFRFFSFFESFFESSSSSE-SSSESZSS25252R-mewMeenrmeewree=-ese-SSSESZSS25252R-mewMeenrmeewree=-eseSSSESZSS25252R-mewMeenrmeewree=-eseeoeoOhUcCOUCUCUCOUCUCOCUCSCUCUrHDmeyerRRRKRmeyerRRRKRRRRKR se Se ae eoeoOhUcCOUCUCUCOUCUCOCUCSCUCUrHDmeyerRRRKRmeyerRRRKRRRRKR SRK=RK=<br><!-- End of picture text --> <!-- Start of picture text --> 80.0 ><br>i ,auw_wTaauw_wTauw_wTa<br>60.0<br>$0.00 ee ——trap<br>40.8 AnimalFartingFarting and Hurting<br>0 ——Forestry<br>~~ Fishing<br>20.010.010.0 ©©oe©oeoe<br>00<br>apSeBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFSeBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFSStsgsaRkeaRRRRRRRRERPForPF SeoFoF FeseeseererereereraestNMNM THe Re BPO<br><!-- End of picture text --> <!-- Start of picture text --> 80.0 > 50.0<br>i ,auw_wTaauw_wTauw_wTa 45.040.0 ++ ——— Financial Intermediation<br>60.0 35.0 + —— Real Estate, Renting and<br>- Business Activities<br>$0.00 ee 30.0 + —— Public Administration<br>——trap Defense and<br>40.8 AnimalFartingFarting and Hurting =| 25.0 > —Education<br>0 ——Forestry 20.0 +<br>~~ Fishing ——Health and Social and Social Social Work<br>15.0 +<br>—— Other Community<br>10.0 7 Personal Services<br>20.010.010.0 ©©oe©oeoe 5.0 —— &<br>+———————————————— Private Households, Socialwith & Households, Socialwith &, Socialwith & Socialwith &with &<br>00 a Braployed Persone<br>PForPForPFPF SeoFoF FeseeseererereereraestNMNM THe Re BPO -mewMeenrmeewree=-esese se Se ae =<br>apSeBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFSeBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFBRERESESSESESESESEESEESSStsgsaRkeaRRRRRRRRERPForPFSStsgsaRkeaRRRRRRRRERPForPF eoeooseoeoeeFeEFRFFSFFESFFESSSSSE-SSSESZSS25252R-mewMeenrmeewree=-eseFeEFRFFSFFESFFESSSSSE-SSSESZSS25252R-mewMeenrmeewree=-ese-SSSESZSS25252R-mewMeenrmeewree=-eseSSSESZSS25252R-mewMeenrmeewree=-eseeoeoOhUcCOUCUCUCOUCUCOCUCSCUCUrHDmeyerRRRKRmeyerRRRKRRRRKR SRK=RK=<br>45<br>°<br>40<br>=~a, 35<br>S<br>30<br>2<br>—<br>pp 25<br>é.<br>E20 .(*,<br>ae *<br>315 >. See<br>5 +<br>. . i*<br>><br>><br>° og . ewesa » § WwW,>» . ."sal 7<br>0 ,<br>020 40 60 80 100<br>Industry (% GDP)<br><!-- End of picture text -->
# **3.2. Employment**

In this paper, we predominantly use data from two labor force surveys (LFS) and two household income consumption and expenditure surveys (HICES), namely: HICES 1995/96, LFS 1999, LFS 2005 and HICES 2010/11. The HICES 1999/00 and HICES 2004/05 are not included because the labor force surveys cover similar time periods and provide more comprehensive information on employment issues.<sup>7</sup> Moreover, the HICES 1999/00 does not provide data on the sector of employment, which is a crucial variable for our analysis on structural transformation. In addition, we do not use Census data. The 2007 Census also does not provide data on the sector of employment,<sup>8</sup> while the 1984 Census includes data for Eritrea. The 1994 Census is fairly close to the first HICES and is not included to minimize the use of multiple data sources. The use of the HICES 2010/11 survey is particularly crucial, since it enables us to investigate recent sectoral employment dynamics (and thus structural change).

Before analyzing employment dynamics, it is important to investigate the underlying demographic trends. However, as we will note below, there can be significant discrepancies across data sources. For instance, while Census data is the primary source of population statistics, the methodology we utilize later in the paper requires consistency between data on total population, working age population and sector of employment. We therefore use the data included in survey reports and describe broader trends rather than focus on exact values and magnitudes.

Table 3 suggests that the Ethiopian population increased from about 53 million in 1996 to 63 million in 2005 and 76 million in 2011. The 2007 Census estimated total population at 73.8 million – with those under 10 years of age at 22.8 million (about 31 percent) and those under 15 at 33.2 million (45 percent) – while the 1994 census calculated total population at about 53 million. The World Development Indicators – which interpolates 5-year data from the UN Population Division’s World Population Prospects – estimated total population at 77 million in 2007.<sup>9</sup> This seems to highlight some uncertainty around population estimates in Ethiopia.

Nonetheless, there are some important points that can be made. First, Ethiopia’s population remains predominantly rural, even if its weight is slowly declining – from 86 percent in 1996 to 83 percent in 2011. Second, Ethiopia’s population is very young, with about 65 percent below 25 years of age. Finally, the proportion of the population in the 10-65 age group has recently increased, which led to a falling dependency ratio. This is partly due to the fact that the proportion of 10-14 year-olds is increasing, while the 0-9 age group is declining in relative terms. In conclusion, there are some signs that Ethiopia might be undergoing a period of demographic transition, and thus it is crucial to seize this opportunity.<sup>10</sup> The table also presents data from the WDI, which uses the more traditional age brackets for the working age population – i.e. 15-64. While the broad conclusions above still apply, there is significant variation with regard to the specific values – e.g. dependency ratio – mainly due to the treatment of the 10-14 age group.

> 7 Labor force surveys have larger sample sizes and, quite naturally, larger employment modules – which is likely to lead to more consistent estimates. Nevertheless, our results are robust to the use of HICES 2004/05 instead of the LFS 2005. 8 The questions relating to occupation and industry were dropped from the questionnaire when it was observed that some trainers had difficulty in understanding concepts and definitions relating to the occupational and industrial classifications (CSA, 2012: 49). <u>http://unstats.un.org/unsd/censuskb20/Attachment489.aspx</u> 9 Although the WDI-UN series are regularly revised to take into consideration recent data (e.g. Census), the modelled estimates for the late 2000s seem far off the Census estimates.

> 10 In order to secure a demographic dividend, it is important to provide adequate skills and create productive employment opportunities for this youth bulge. Otherwise, unemployment and underemployment will undermine recent economic and social achievements and potentially fuel social unrest.

11
Table 3: Demographic Statistics

||1<br>(<br>|995/96<br>HICES)<br>|<br>||1999<br>(LFS)<br>|||2005<br>(LFS)<br>||2<br>(<br>|010/11<br>HICES)<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|
|Total Population, million|52.7|7.6|45.1|54.5|7.4|47.1|63.2|9.0|54.3|76.1|12.6|63.5|
|Ages 0-9, %|32.2|25.3|33.4|33.8|23.7|35.4|35.1|23.5|37.0|32.4|22.2|34.4|
|Ages 10-24, %|33.0|38.4|32.0|31.6|38.8|30.4|30.6|39.0|29.1|32.6|37.8|31.5|
|Ages 25-64, %|31.4|32.8|31.2|31.2|34.3|30.7|31.1|33.4|30.6|31.3|36.6|30.3|
|Ages 65 and above, %|3.4|3.5|3.3|3.4|3.3|3.4|3.2|3.2|3.3|3.7|3.4|3.7|
|Age dependency Ratio (%)|0.55|0.40|0.58|0.59|0.37|0.63|0.62|0.37|0.68|0.56|0.34|0.62|
|Child (0-9)|0.50|0.36|0.53|0.54|0.32|0.58|0.57|0.32|0.62|0.51|0.30|0.56|
|Old-age (65+)|0.05|0.05|0.05|0.05|0.04|0.06|0.05|0.04|0.06|0.06|0.05|0.06|
|World Development Indicators|||||||||||||
|Population, million|58.8|8.2|50.5|63.9|9.3|54.6|74.3|11.7|62.6|84.7|14.4|70.3|
|Ages 0-14, %|46.0|..|..|46.0|..|..|44.5|..|..|40.8|..|..|
|Ages 15-64, %|51.1|..|..|51.0|..|..|52.4|..|..|55.8|..|..|
|Ages 65 and above, %|2.9|..|..|2.9|..|..|3.1|..|..|3.4|..|..|
|Age dependency ratio|0.96|..|..|0.96|..|..|0.91|..|..|0.79|..|..|
|Young (0-14)|0.90|..|..|0.90|..|..|0.85|..|..|0.73|..|..|
|Old(65+)|0.06|..|..|0.06|..|..|0.06|..|..|0.06|..|..|

Sources: Compiled from survey reports (HICES 1995/96, LFS 1999, LFS 2005, and HICES 2010/11) and WDI (2013).

Table 4 provides information on the labor market status of the population. The surveys suggest that the working age population increased from 36 million in 1996 to 41 million in 2005 and 52 million in 2011 – which corresponds to 68, 65 and 68 percent of total population, respectively. However, the caveats noted above also apply. In terms of its composition, the labor force participation rate – i.e. the share of the economically active population in the working age population – increased significantly up to 2005 and then declined sharply. The employment rate – i.e. the proportion of the employed population in the working age population – followed a similar trend.<sup>11</sup> International data suggests less pronounced patterns for these two variables, possibly due to the different age brackets. A potential explanation of the recent decline of both participation and employment rates relates to the recent increase in the student population.<sup>12</sup> Strong investments in education (including in rural areas) are likely to have contributed to increases in enrolment and attendance rates, therefore increasing the proportion of those that are economically inactive. This is particularly noticeable when the data includes the 10-14 age group. Hence, this should be seen as a positive development.

> 11 These trends are also observed when using the HICES 2004/05 instead of the LFS 2005.

> 12 The magnitude of these shifts may also be partly due to differences in methodology or definitions. For instance, the HICES data seems to underestimate the level of unemployment (self-declared), which could mean that economic inactivity may not be as high as suggested in 2011. Nonetheless, Molsa (2011) does suggest a large increase in inactivity rates from 2005 (LFS) to 2007 (Census) – from 15.3 to 25.6 per cent (for 15+) – in both rural and urban areas. This is particularly strong for the 15-19 age group. The inactive population is mainly composed of students (more than half) and home makers (about one-quarter).

12
<u>Table 4: Labor Market Status of the Working Age Population (10+)</u>

||<br>1<br>(|<br>995/96<br>HICES)|<br>||<br>1999<br>(LFS)|||2005<br>(LFS)||2<br>(|010/1<br>HICES)|1<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|Tot.|Urb.|Rur.|
|Working Age Population, mln|35.7|5.7|30.0|36.0|5.6|30.4|41.0|6.9|34.2|51.5|9.8|41.6|
|Economically Active (LF), %|_63.8_|_49.4_|_66.5_|71.1|55.4|74.0|78.4|57.7|82.6|_67.2_|_55.0_|_70.1_|
|Employed, %|_62.3_|_41.6_|_66.3_|69.7|47.7|73.7|76.6|50.2|82.0|66.5|51.9|69.9|
|Unemployed, %|_2.3_|_15.9_|_0.4_|2.0|13.8|0.3|5.1|22.6|2.7|_1.0_|_5.7_|_0.2_|
|Economically Inactive, %|_36.2_|_50.6_|_33.5_|28.9|44.6|26.0|21.0|40.6|17.0|_32.8_|_45.0_|_29.9_|
|_of which: students_, %|_8.8_|_28.5_|_5.1_|10.5|26.3|7.6|9.7|26.0|6.4|_17.9_|_28.2_|_15.4_|
|World Development Indicators|||||||||||||
|LF Participation Rate (%, 15+)|81.0|..|..|81.0|..|..|84.6|..|..|84.0|..|..|
|_Ratio of female to male (%)_|78.5|..|..|78.7|..|..|86.1|..|..|87.3|..|..|
|Employment-to-Pop. Ratio (15+)|74.4|..|..|74.4|..|..|80.1|..|..|79.2|..|..|
|Unemployment Rate(%)|..|..|..|8.2|..|..|5.4|..|..|..|..|..|

Sources: Compiled from survey reports (HICES 1995/96, LFS 1999, LFS 2005, and HICES 2010/11) and WDI. Values in italic were directly calculated from survey data and should be treated with great caution.

Unemployment statistics are usually quite controversial, since they are dependent on the specific definition utilized. The official unemployment rate increased from 3.1 percent in 1994 to 8.2 percent in 1999, but has since then declined to 5.4 percent in 2005 and 3.7 percent in 2007 (MoLSA, 2011). Unemployment is typically associated with the (literate) youth in urban areas. Denu et al. (2005) argue that open unemployment is a middle-class phenomenon, with people queuing for government jobs.

# <u>Box 1: Population and Labor Force Growth</u>

Population growth has recently decelerated – from an average of about 3 percent per year in the 1990s to nearly 2.5 percent in the 2000s – although it remains relatively high. The average for SubSaharan Africa was also 2.5 percent per annum. Nonetheless, the labor force is growing at an even faster pace (at about 3.5 percent), which is considerably high even for regional standards. This is partly a consequence of Ethiopia’s demographic profile. Employment growth increased in the early 2000s – to above 4 percent per year – although it has fallen substantially in recent years.

Sources: Census (1994 and 2007) and WDI (2013) Table 5 provides data on the sectoral distribution of employment. Employment in agriculture has increased significantly, from 18 million in 1996 to nearly 27 million in 2011. This ensured that the share of agricultural employment in total employment remained very high – declining by only 3 percentage points from 81 percent in 1996 to 78 percent 2011 (see also Figure 6). Trade and other services accounted for 3.0 and 2.7 million jobs in 2011, respectively, nearly doubling their 1996 values. Jointly, these two sectors currently account for almost 17 percent of total employment. Manufacturing and construction have also experienced a noticeable expansion, albeit from a lower base. Total employment increased by 11.6 million between 1996 and 2011, with agriculture absorbing 72 percent of this increase, followed by trade (12 percent), other services (10 percent), manufacturing (4 percent) and construction (3 percent).

13
<!-- Start of picture text --> 100%<br>90%<br>80% = Other Services<br>70% | Transport & Comms<br>60% Trade<br>50% = Construction<br>40% mElectricity & Water<br>30% = Manufacturing<br>20% @ Mining & Quarrying<br>m Agriculture<br>10%<br>0%<br>1995 /96 1999 2005 2010/11<br><!-- End of picture text -->

<!-- Start of picture text --> eae i<br>— ae ome Agriculture<br>ees a \ Sitining® Quarrying<br>fh \ manufacturing<br>| MElectricity & Water<br>\ i Mm Construction<br>,hs /i ETrade<br>aie 4 @Transport & Comms<br>mOther Services<br><!-- End of picture text -->
GVA and employment terms. The productivity of the manufacturing sector remains very low – in fact, the lowest after agriculture – despite nearly trebling between 2005 and 2011. In terms of average productivity growth, four main sectors stand out in the past 5 years or so: mining & quarrying, manufacturing, construction, and trade.

<u>Table 6: Productivity by Sector</u>

||La|bor Pro<br>(‘000|ductivi<br>birr)<sup>*</sup>|ty<br>|<sup>Average P</sup><br>(|<sup>roductivity</sup><br>% per year)|<sup>Growth</sup><br>|E<br>E|mploymen<br>lasticities|t<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||1996|1999|2005|2011|1996-99|1999-05|2005-11|1996-99|1999-05|2005-11|
|Agriculture|5.4|4.5|5.2|8.0|<br>-5.9|2.4|7.4|<br>-0.90|0.58|0.10|
|Mining & Quarrying|20.0|102.6|29.4|87.5|<br>72.6|-18.8|19.9|<br>-7.65|8.47|-0.03|
|Manufacturing|12.4|6.3|6.6|17.8|<br>-20.2|0.8|17.8|<br>12.92|0.85|-0.43|
|Electricity & Water|40.8|84.6|100.4|108.9|<br>27.5|2.9|1.4|<br>-0.41|0.44|0.80|
|Construction|22.3|22.8|22.5|40.4|<br>0.7|-0.2|10.2|<br>0.93|1.02|0.07|
|Trade|13.3|11.0|16.3|29.5|<br>-6.3|6.8|10.4|<br>2.27|0.05|0.19|
|Transport & Comms|58.2|48.4|77.1|95.1|<br>-5.9|8.1|3.6|<br>1.90|0.21|0.56|
|Other Services|15.1|25.5|28.5|36.7|<br>19.1|1.9|4.3|<br>-0.56|0.68|0.59|
|Total|7.1|6.7|8.0|13.7|<br>-1.7|3.0|9.3|<br>2.24|0.52|0.10|

* Constant 2010/2011 prices Source: Author’s calculations

Employment elasticities can provide useful information about the economy’s capacity to generate employment and can be a useful complement to productivity data. During periods of positive economic growth, employment elasticities between 0 and 1 indicate that there is both productivity growth and employment growth (see Table 14 in Annex 2). Higher elasticities in this range – i.e. towards 1 – correspond to more employment-intensive growth, while elasticities in the low positive values indicate strong labor productivity growth (Kapsos, 2005).<sup>14</sup> It is therefore important to evaluate economic performance beyond a simple ‘productivity’ lens.

In the late 1990s, the overall elasticity value was rather high, although it was accompanied by negative productivity growth – which is undesirable. The elasticity for the early 2000s compares positively with several countries in the region and was achieved together with fairly strong productivity growth. Perhaps unsurprisingly, employment elasticities have dramatically declined in the late 2000s. This is intrinsically related to the strong acceleration of economic growth and the slowdown in employment growth in that period. However, the values still fall within the ‘ideal’ range, which means that productivity growth was accompanied by employment growth. Nonetheless, it should be noted that employment growth is likely to be correlated with population (and labor force) growth and this information needs to be complemented by data on the quality of employment – e.g. the type of employment (wage vs. self-employment), level of earnings, and job security (see Annex 1).

> 14 Khan (2001) argues that employment elasticities should be around 0.7 in developing countries. However, African countries might require even higher employment elasticities – for any given rate of growth – due to strong labor force growth.

15
<!-- Start of picture text --> Productivity (‘000 birr)<br>o =] —)<br>o N i=}+ woSo ooi} i—)al NSn<br>Agriculture<br>Mining & Quarrying<br>Manufacturing m1995/96<br>Electricity & Water 1999<br>m2005<br>Construction<br>m2010/11<br>Trade<br>Transport& Comms<br>Other Services<br><!-- End of picture text -->
<!-- Start of picture text --> 120<br>100<br>~ BAgriculture<br>5 80 Mining & Quarrying<br>s B Manufacturing<br>2 60 WElectricity & Water<br>é B Construction<br>8 40 Trade<br>*“ BTransport & Comms<br>20 B Other Services<br>0<br>Labour Share (%)<br><!-- End of picture text -->
<!-- Start of picture text --> ETH<br>GHA<br>KEN<br>MUS<br>Mwi<br>NGA<br>SEN<br>ZAF<br>2ZMB<br>0 500 1,000<br><!-- End of picture text -->
# **4. Productivity Analysis**

This section decomposes labor productivity growth into within-sector and between-sector productivity changes. The methodology utilized also enables an assessment of the contribution of demographic change and employment rates, thus providing a richer perspective on the ongoing changes in the Ethiopian labor market.

## **4.1. Decomposition at the Aggregate Level**

Our starting point for decomposing output per capita is the following equation: where _Y_ is total output, _N_ the total population, _A_ the working-age population, and _E_ is total employment. These three components represent the relative size of the working-age population ( _A_ / _N_ ), the employment rate ( _E_ / _A_ ) and output per worker ( _Y_ / _E_ ). In order to calculate the contribution of each of these components to GVA per capita growth, we employ an analytical tool that uses Shapley decompositions.<sup>15</sup> This complements the employment elasticities presented previously.

Table 8 presents estimates for the full period (1996-2011) as well as its three sub-periods. The results for the entire period suggest that the majority of the change in GVA per capita can be explained by changes in output per worker – i.e. labor productivity growth. In fact, out of the 103 percent increase in GVA per capita, over 90 percent of this was due to higher labor productivity. Changes in the employment rate also played an important (though smaller) role, with a contribution of nearly 10 percent. Perhaps surprisingly, the impact of demographic change was negligible (Figure 10).

<u>Table 8: Aggregate Decomposition of GVA per capita</u>

||<br>GVA per capita|<br>% <br>|<br>Contribution of<br>||
|---|---|---|---|---|
|Period|<br>(total change, %)|Demographic<br>Change|Employment<br>Rate|Output per<br>worker|
||∆(Y/N)|∆(A/N)|∆(E/A)|∆(Y/E)|
|1996-2011|103.11|<br>-0.38|9.72|90.67|
|1996-1999|1.06|<br>-231.49|987.55|-656.06|
|1999-2005|29.85|<br>-7.51|39.61|67.90|
|2005-2011|54.77|<br>9.71|-33.27|123.56|

Note: The values for 1999-2005 differ slightly from Martins (2012), especially for the demographic change component, due to the use of different data sources.

A breakdown of these results into sub-periods provides additional insights. The values for the initial three years (1996-1999) are quite striking. However, this is likely to be due to the 1998-2000 Ethiopia-Eritrea war, when output fell and productivity levels declined. GVA per capita growth averaged only 0.3 percent a year, and therefore the high contribution shares are likely to be a consequence of that. The period from 1999-2005 observed a rebound in GVA per capita growth – nearly 30 percent over 6 years, or 4.4 percent per year – partly due to increased productivity growth and a rise in the employment rate. In the most recent period (i.e. 2005-2011), the economy

> 15 See World Bank (2012) for further details on the methodology.

19
<!-- Start of picture text --> i ‘Output per<br>|) worker YE<br>_|BEmploymentrate<br>| Ba<br>'|D.Demographic|| change AN<br>2841.9<br>-500.0 0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0<br>Contribution to Growth in Value Added per capita, in 1000's of Birr<br><!-- End of picture text -->
<!-- Start of picture text --> Other Services<br>.<br>Transport and Comms | | |<br>Trade 7 |<br>Construction | | |<br>Electricity and Water | |<br>Manufacturing | | |<br>Mining and Quarrying | | |<br>AgricultureaSO<br>0.0 500.0 1000's of1,000.0Birr 1,500.0 2,000.0 2,500.0<br><!-- End of picture text -->
million, (ii) keeping constant the working age population ratio (67.6 percent), yielding a working age population of about 58 million, (iii) keeping constant the employment rate (66.5 percent), putting total employment at about 38.7 million, and (iv) keeping constant the sectoral employment shares, therefore changing the amount of labor in each sector proportionally. These changes do not change the results in any considerable way, which is rather reassuring.<sup>17</sup> Table 10 provides information on the sectoral contribution to structural transformation – the between-sector component. The negative signs in the first column indicate that the employment shares of agriculture, mining and electricity & water were lower in 2011 than in 1996. In the case of agriculture, this actually generated a positive momentum for structural change, since it is the sector with the lowest productivity. In the other two cases, the outcome was a fairly small negative impact. Moreover, construction, trade, transport & communications and other services have provided solid contributions to structural change.

<u>Table 10: Sectoral Contributions to Inter-Sectoral Shifts (1996-2011)</u>

||Direction of<br>Employment<br>Share Shift<br>|Contribution to<br>Inter-sectoral<br>Shifts(%)|
|---|---|---|
|Agriculture|–|19.94|
|Mining and Quarrying|–|-5.45|
|Manufacturing|+|3.70|
|Electricity and Water|–|-1.48|
|Construction|+|16.68|
|Trade|+|20.48|
|Transport and Comms|+|20.85|
|Other Services|+|25.28|
|_Total_||_100.00_|

Table 11 repeats the same exercise for its sub-periods. We have already described some of these estimates, so we will focus on the decomposition of output per worker. The decline in labor productivity that occurred in the 1996-1999 period was due to both within- and between-sector productivity decreases – especially the former. Productivity levels recovered in the 1999-2005 period, although mostly due to within-sector improvements. Since 2005, productivity growth has accelerated, to which both within- and between-sector productivity contributed. Although productivity improvements within economic sectors have provided the main contribution to total productivity, structural change appears to be gaining an important momentum. In fact, the gap between the contributions of within- and between-sector productivity is narrowing.

<u>Table 11: Sectoral Decomposition of GVA per capita (Several Periods)</u>

||Within-Sector<br>Productivity|% Contributionof<br>Changes in<br>Employment|<br>Inter-sectoral<br>Productivity|Demographic<br>Component (%)|Change in GVA<br>per capita (%)|
|---|---|---|---|---|---|
|1996-2011|79.50|9.72|11.17|-0.38|103.11|
|1996-1999|-491.20|987.55|-164.85|-231.49|1.06|
|1999-2005|62.51|39.61|5.40|-7.51|29.85|
|2005-2011|102.86|-33.27|20.71|9.71|54.77|

> 17 In addition, we re-calculated the decomposition for the period 2005-2011 using the HICES 2004/05 instead of the LFS 2005. The conclusions are robust to this change, even though the structural change component increases from 21 to 27 per cent.

22
<!-- Start of picture text --> LAC<br>AFRICA<br>ASIA<br>(9 within<br>HI (0) Structural change<br>—0.02 0.01 0 0.01 0.02 0.03 0.04 0.05<br>Percentage<br><!-- End of picture text -->
- <u>Table 13: Country Rankings of Contributions to Productivity Growth (1990 2005)</u>

|<br>|<br>Ranked by‘wit<br>|<br>hin’ contribution<br>|<br> <br> <br>|<br>|<br>Ranked by‘structural cha<br>|<br>nge’ contr<br>|ibution<br>|
|---|---|---|---|---|---|---|---|
|Rank|Country|Region|Within<br>(%)<br>|Rank|Country|Region|Structural<br>Change(%)|
|1|China|Asia|7.79|1|Thailand|Asia|1.67|
|2|Zambia|Africa|7.61|2|Ethiopia|Africa|1.48|
|3|Korea, R.|Asia|5.29|3|Turkey|..|1.42|
|4|Nigeria|Africa|4.08|4|Hong Kong SAR, China|Asia|1.25|
|5|Peru|Latin America|3.85|5|Indonesia|Asia|1.06|
|6|Chile|Latin America|3.82|6|China|Asia|0.99|
|7|Singapore|Asia|3.79|7|India|Asia|0.99|
|8|Senegal|Africa|3.61|8|Ghana|Africa|0.59|
|9|Malaysia|Asia|3.59|9|Taiwan, China|Asia|0.54|
|10|Taiwan,China|Asia|3.45|10|Malaysia|Asia|0.49|

Source: Adapted from McMillan and Rodrik (2011).

24
# **5. Conclusions**

_Over four years, I’ve seen Ethiopia gradually embrace structural transformation and its practical application. Leaders there are acutely aware that, if they are to maintain a robust growth rate, they must move away from agriculture, the dominant sector, toward industrial upgrading and technological innovation, often by imitating economies just a few rungs up the economic ladder. Ethiopia’s agriculture sector is important and should not be neglected, but that alone won’t get the country onto a path toward middle income and finally to high income status._ (Lin, 2012) This paper provided insights on recent output, employment and productivity trends in Ethiopia. For that purpose, it used data from two labor force surveys and two household income consumption and expenditure surveys. A number of key stylized facts were uncovered (Box 2). First, output growth has accelerated at an unprecedented pace since 2003. Its average annual growth rate of about 10 percent places Ethiopia as one of the fastest growing economies in the world. This fact is even more remarkable when considering that Ethiopia’s growth is not fueled by natural resources, such as oil or gas. Second, agriculture continues to be a very large production sector, although its weight in total output has been steadily declining – from 66 percent in 1991 to 45 percent in 2011. The service sector has expanded considerably (especially commerce and real estate activities), but the share of industry remains one of the lowest in Africa. Third, Ethiopia’s population is considerably young (about two-thirds are below the age of 25) and predominantly rural (over 80 percent of the population). Nonetheless, the dependency ratio has been declining, as the youth bulge becomes part of the working age population, while the proportion of people living in urban areas increased by 3 percentage points in the past 15 years.

## <u>Box 2: Key Stylized Facts</u>

1. Output growth strongly accelerated since 2003.

2. Agriculture still a very large economic sector, but a range of service activities are growing. 3. Very young and largely rural population, although the dependency ratio is declining and urbanization slowly increasing.

4. Participation and employment rates have recently declined (with employment growth slowing down), mainly due to an increase in student numbers.

5. Share of agricultural employment remains very high, despite a small decline in recent years.

6. Labor productivity has increased considerably (contributing to strong GVA growth), while employment elasticities have fallen.

7. Structural transformation has been relatively slow, but increasingly important.

8. Demographic changes are supporting economic growth (demographic dividend).

Fourth, the pace of employment growth has recently slowed down, which is partly due to a growing student population. In fact, this is behind the recent declines in the participation and employment rates. Fifth, the share of agricultural employment in total employment remains very high (at about four-fifths), even if it has declined by 2 percentage points in the past 6 years. Trade and other services jointly account for nearly 17 percent of total employment. Sixth, labor productivity growth has been strong, with output per worker doubling in the past decade. In fact, GVA growth has been mainly driven by labor productivity growth. Conversely, employment elasticities have fallen but remain positive – suggesting that productivity growth was accompanied by employment growth. Moreover, productivity gaps across economic sectors are very large. Seventh, while within-sector productivity growth accounts for much of the aggregate productivity performance, structural change (i.e. between-sector productivity growth) is playing an increasingly important role. Finally, there is also evidence that the economy is starting to benefit from a demographic dividend.

25
Therefore it can be concluded that, while economic growth has generated some employment, the growth-employment link has been weakened. This is not necessarily a disappointing development, since employment growth in Ethiopia is intrinsically linked to demographic trends. Not only has population growth slowed down, but the proportion of people outside the labor force has also increased (e.g. students). Nonetheless, unemployment remains an important concern, especially for young people and in urban areas. This may suggest that, despite strong growth, the urban economy might be struggling to create sufficient productive employment opportunities for an increasingly educated urban youth. Moreover, changes in the sectoral composition of employment have significantly lagged behind that of output – despite broadly similar tendencies. In terms of labor productivity growth, while most of it has been accounted by higher within-sector productivity, labor shifts from low- to high-productivity sectors (i.e. structural change) seem to be increasingly important. In fact, it is unlikely that the sectors with the highest productivity levels will be able to absorb a significant amount of labor, due to their intrinsic high capital intensity.

To some extent, the evidence uncovered in this paper supports the three ongoing shifts (on the labor supply) highlighted by the World Bank (2007): (i) a demographic shift, which has the potential to generate significant benefits – a demographic dividend – although it is not without challenges (e.g. a need to create productive employment opportunities for new entrants in the labor market); (ii) a geographic shift, as illustrated by an increase in rural-urban migration; and (iii) a qualitative shift, as the skill level of the labor force is rising. In addition, there is growing evidence of a structural shift in the composition of the labor force – which, albeit still limited, appears to be picking up pace.

26