PSI

Ethiopia
Productivity
Report2020

©2020 Policy Studies InstituteAll rights reserved
Printed in Addis Ababa
ISBN: 978–99944–77–53–1
Contact:
Policy Studies Institute
General Abebe Damtew Street (Near National Stadium), Addis Ababa, Ethiopia
Phone: +251-11-5506066
Email: psiinfo@psi.gov.ethttps://www.psi.gov.et/Ethiopia
Productivity Report

2020

Policy Studies InstituteNational Graduate Institute for Policy Studies

Table of contents
Table of contents ................................................................................................ iii
List of tables......................................................................................................... v
List of figures ...................................................................................................... vi
Abbreviations and Acronyms.......................................................................... vii
Acknowledgements ............................................................................................ ix
About the Authors .............................................................................................. x
Executive Summary ............................................................................................ 1
1. Introduction ................................................................................................. 11
2. Productivity: Concept, Measurement and Significance ......................... 15
2.1

The concept of productivity ............................................................................ 15

2.2

Measuring productivity ................................................................................... 15

2.3

Decomposition of labor productivity .............................................................. 17

2.4

The importance of productivity in general and in the context of Ethiopia ..... 18

2.5

Conceptual issues and pragmatic caveats........................................................ 19

3. Economy-wide Labor Productivity in Ethiopia..................................... 21
3.1

Data sources and limitations ........................................................................... 21

3.2

Trends of economy-wide labor productivity in Ethiopia ................................ 21

3.3

Ethiopia’s economy-wide labor productivity compared with peer countries . 24

3.4 Decomposition of economy-wide labor productivity ..................................... 26
3.4.1 The growth accounting method ................................................................... 26
3.4.2 Shift-share analysis ...................................................................................... 29
3.5

Summary of findings ....................................................................................... 33

4. Zooming in on the Manufacturing Sector ............................................... 35
4.1 Review of past policy interventions ................................................................ 35
4.1.1 Benchmarking, Kaizen and Twinning ......................................................... 35
4.1.2 Small business support schemes.................................................................. 38
4.2 Description of the manufacturing survey data ................................................ 38
4.2.1 The survey data, source and limitations ...................................................... 38
4.2.2 General characteristics of the manufacturing sector ................................... 41
4.3 Productivity in the Manufacturing Sector ....................................................... 46
4.3.1 Methodology................................................................................................ 46
4.3.2 Productivity in time series ........................................................................... 47iii

4.3.3 Productivity by sub-sector ........................................................................... 50
4.4 Manufacturing labor productivity: Ethiopia and other countries .................... 54
4.4.1 Pattern and growth of manufacturing labor productivity ............................ 54
4.4.2 Labor productivity pattern in selected labor intensive sectors .................... 55
4.5 The wage-productivity nexus in manufacturing ............................................. 58
4.5.1 Trends in manufacturing labor cost ............................................................. 59
4.5.2 The wage-productivity nexus ...................................................................... 62
4.5.3 Labor cost for different stakeholders........................................................... 64
4.5.4 The wage-productivity nexus in international comparison ......................... 65
4.6

Summary of findings ....................................................................................... 68

5. Labor Productivity Challenges in the Emerging Ethiopian Apparel
Industry: Survey Results ........................................................................... 71
5.1

Introduction ..................................................................................................... 71

5.2

Survey method and basic characteristics of the sampled firms ........................ 72

5.3 Labor mindset and quality ............................................................................... 75
5.3.1 Workers’ mindset ........................................................................................ 76
5.3.2 Workers selection, education, trainability, and skills Workers selection andcomposition ............................................................................................................ 79
5.3.3 Training and trainability .............................................................................. 81
5.3.4 Incentives and working conditions .............................................................. 82
5.4 Management practices ..................................................................................... 86
5.4.1 Composition of managers ............................................................................ 86
5.4.2 Approaches to management and guidance of workers ................................ 86
5.4.3 How firms upgrade workers to management positions ............................... 91
5.4.4 Adaptability of foreign managers to local conditions ................................. 91
5.4.5 Mindset problems of Ethiopian managers ................................................... 92
5.5 Policy and external conditions ........................................................................ 93
5.5.1 Labor laws ................................................................................................... 93
5.5.2 Logistic performance ................................................................................... 94
5.5.3 Custom’s clearance ...................................................................................... 95
5.5.4 Power supply ............................................................................................... 95
5.5.5 Other business related problems.................................................................. 95
5.6

Summary of findings ....................................................................................... 96

6. Policy Measures Towards Enhanced Productivity ................................ 99
References ........................................................................................................ 107
Annexes ............................................................................................................ 111

iv

List of tables
Table 3.1 Labor productivity of Ethiopia, by industry (in thousand ETB, 2011 prices) ........ 29
Table 3.2 Decomposition of labor productivity growth: shift-share analysis ......................... 31
Table 3.3 Decomposition of labor productivity growth by sector: shift-share analysis ......... 32
Table 4.1 Large and medium scale manufacturing: real value added and employment share byindustrial group ............................................................................................................... 43
Table 4.2 Share of export sales of the manufacturing sector, 2016-2017 ............................... 45
Table 4.3 Labor productivity by sub-sector for selected years (in thousand ETB) ................ 51
Table 4.4 TFP level and growth by sub-sector for selected years........................................... 53
Table 4.5 Manufacturing labor cost per employee by sub-sector in thousand ETB (real labor
cost in 2011 prices) ......................................................................................................... 61
Table 4.6 Annual growth rate of labor cost per employee by sub-sector (%, real labor cost in2011 prices)..................................................................................................................... 61
Table 5.1 Firms sampled in the survey ................................................................................... 73
Table 5.2 Number of workers by type of work, gender and location ..................................... 75
Table 5.3 Percentage of workers by task category .................................................................. 75
Table 5.4 Average line efficiency and labor productivity of selected products, by country...... 75
Table 5.5 Framework of analysis for labor quality and incentives ......................................... 76
Table 5.6 Attrition and absenteeism by location ..................................................................... 78
Table 5.7 Sewing workers, by education and age ................................................................... 80
Table 5.8 Firm rating of worker skills (number of firms) ....................................................... 82
Table 5.9 Salary and benefits by task category (ETB) ............................................................ 83
Table 5.10 World Bank Ease of Doing Business rankings ..................................................... 96v

List of figures
Figure 2.1 Components of Real GNI per capita ...................................................................... 18
Figure 3.1 Economy-wide labor productivity (2011 prices) ................................................... 22
Figure 3.2 Economy-wide growth of GDP and labor productivity ......................................... 23
Figure 3.3 Labor productivity by major sectors (2011 prices) ................................................ 23
Figure 3.4 Comparison of Ethiopia’s labor productivity and that of peer countries, by income
category (Output per worker, GDP measured in constant 2011 international $ in PPP) ..... 24
Figure 3.5 Comparison of labor productivity growth of Ethiopia and peer countries, byincome category .............................................................................................................. 25
Figure 3.6 Comparison of Ethiopia’s labor productivity and that of selected peer countries, by
category (Output per worker, GDP measured in constant 2011 international $ in PPP) 26
Figure 3.7 Growth rate of labor productivity, capital intensity, and TFP (%) ........................ 27
Figure 3.8 Contributions of capital intensity and TFP to Ethiopia’s labor productivity ........... 28
Figure 4.1 Trends in number of establishments and employees ............................................. 42
Figure 4.2 Regional distribution in number of manufacturing establishments, 2016-2017 .... 44
Figure 4.3 Average capacity utilization of the manufacturing sector ..................................... 45
Figure 4.4 Labor productivity in the manufacturing sector (real value added per employee) ...... 48
Figure 4.5 Labor productivity growth in the manufacturing sector, Ethiopia......................... 48
Figure 4.6 The level of manufacturing TFP ............................................................................ 49
Figure 4.7 The growth of manufacturing TFP ........................................................................ 50
Figure 4.8 Labor productivity and capital intensity by sub-sector, 1996-2016 ...................... 52
Figure 4.9 Labor productivity by firm size ............................................................................. 52
Figure 4.10 Manufacturing labor productivity: selected countries ......................................... 55
Figure 4.11 Value added per employee: food & beverages .................................................... 56
Figure 4.12 Value added per employee: textiles ..................................................................... 56
Figure 4.13 Value added per employee: apparel ..................................................................... 57
Figure 4.14 Value added per employee: leather ...................................................................... 57
Figure 4.15 Manufacturing labor cost (real labor cost in 2011 prices) ................................... 58
Figure 4.16 Sectoral share of labor cost in total manufacturing ............................................. 59
Figure 4.17 Real and nominal labor cost per employee (real labor cost in 2011 prices) ........ 60
Figure 4.18 Labor productivity and labor cost per employee ................................................. 63
Figure 4.19 Growth of labor productivity and labor cost per employee ................................. 63
Figure 4.20 Comparison of four labor cost indicators (1996 = 100)....................................... 65
Figure 4.21 Labor productivity and real and nominal wages in 2015: selected countries ...... 66
Figure 5.1 Labor mindset: rating by managers ....................................................................... 77
Figure 5.2 Number of firms providing incentives (out of 15 surveyed) ................................. 84viAbbreviations and Acronyms
ADLI

Agricultural Development Led Industrialization

APO

Asian Productivity Organization

CEO

Chief Executive Officer

CMT

Cut, Make and Trim

CSA

Central Statistical Agency

CPI

Consumer Price Index

DFID

Department for International Development

EDRI

Ethiopian Development Research Institute

EIC

Ethiopian Investment Commission

EKI

Ethiopian Kaizen Institute

EP

Enterprise Partners

EPU

Economic Planning Unit

ETB

Ethiopian Birr

FDI

Foreign Direct Investment

GDP

Gross Domestic Product

GNI

Gross National Income

GRIPS

National Graduate Institute for Policy Studies

GTP

Growth and Transformation Plan

ICT

Information and Communication Technology

ILO

International Labour Organization

INDSTAT

Industrial Statistics Database

IPDC

Industrial Parks Development Corporation

IPRIntellectual Property Rights

ISIC

International Standard for Industrial Classification

JICA

Japan International Cooperation Agency

LIDI

Leather Industry Development Institute

LMSMI

Large and Medium Scale Manufacturing Industries

MIDI

Metal Industry Development Institute

MSE

Micro and Small Enterprise

NEPAD

New Partnership for Africa’s Development

NPC

National Planning Commission

NPO

Nonprofit Organization

OECD

Organisation for Economic Co-operation and Development

PASDEP

Plan for Accelerated and Sustained Development to End Poverty

vii

PDC

Planning and Development Commission

PPP

Purchasing Power Parity

PSI

Policy Studies Institute

QPC

Quality, Productivity and CompetitivenessR&D

Research and Development

SME

Small and Medium Enterprise

TFP

Total Factor Productivity

TIDI(Ethiopian) Textile and Industry Development Institution

TVET

Technical and Vocational Education and Training

ULC

Unit Labor Cost

UNDP

United Nations Development Program

UNIDO

United Nations Industrial Development Organization

USD

United States Dollar

WDI

World Development Indicatorsviii

Acknowledgements
This report was prepared jointly by the Policy Studies Institute (PSI) in Addis Ababa and the
National Graduate Institute for Policy Studies (GRIPS) in Tokyo. The drafting team consistedof Kidanemariam Berhe Hailu (lead author), Mulu Gebreeyesus, Tsegay Gebrekidan
Tekleselassie (all from the PSI) and Kenichi Ohno (from the GRIPS). Kenichi Ohno also
provided content editing and overall guidance throughout the preparation of the report. The
authors benefited greatly from the feedback from key government officials in Ethiopia. We
would like to particularly thank the officials of the Planning and Development Commission,
the Ministry of Finance, the Office of the Prime Minister and the Ethiopian Investment
Commission. The authors also received valuable comments and suggestions from theparticipants of the consultation workshops held in February and August 2019. They included
representatives from government offices, private businesses, international development
institutions and bilateral donors. We would also like to acknowledge the all-round support of
PSI leadership, especially Dr. Yohannes Ayalew who provided comments and followed up on
the progress during the course of report preparation. Survey respondents who spared theirprecious time to answer our questions and government officials who supported us inorganizing the interviews are greatly appreciated. Zenaye Tekle deserves our special thanks
for her excellent work as research assistant and in designing the cover page. We are equally
thankful to Akemi Nagashima for her determined effort in initiating this research project andpreparing earlier drafts, and Mieko Iizuka for style editing, finalizing the report cover, andformatting of the report. We appreciate Lawrie Hunter’s help in thoroughly editing the report.
Finally, the authors remain deeply thankful to the Japan International Cooperation Agency(JICA) and the PSI which jointly financed this research.

ix

About the Authors
Kidanemariam Berhe Hailu (PhD) is a Lead Researcher at the Policy Studies Institute. He
was a lecturer at Aksum University before assuming his current position. His main work
involves conducting policy-oriented research in the areas of banking and finance, productivity,
structural transformation and industrialization and industrial policy, among others. He has
produced a number of policy reports and published in international peer-reviewed journals.
Kidanemariam earned his PhD in Public Policy (Economics) from the National Graduate
Institute for Policy Studies in 2014.
Mulu Gebreeyesus (PhD) is a Lead Researcher at the Policy Studies Institute. He has
thirteen years of international experience in post-doctoral research at the United Nations
University (UNU-MERIT), Maastricht, and elsewhere and has published widely in the field ofdevelopment economics and actively engaged in policy-oriented research. Gebreeyesus holds
a PhD in Economics from the Department of Economics, University of Gothenburg, Sweden
in 2006.
Tsegay Gebrekidan Tekleselassie (PhD) is a Lead Researcher at the Policy Studies Institute.
Tsegay has produced several policy reports and published in international peer-reviewed
journals. His empirical research covers human capital, labor markets, productivity, industrial
policy and structural transformation in developing countries, among others. He obtained a
PhD in Economics from the University of Sussex in 2016.
Kenichi Ohno is a professor at the National Graduate Institute for Policy Studies. He was at
the International Monetary Fund, Tsukuba University and Saitama University before taking
up his current position. He specializes in comparative research on the industrial strategies in
Asia and Africa. He conducts policy dialogue with Ethiopia and Vietnam. He received a PhD
in Economics from Stanford University, California, in 1987. His recent books include
Learning to Industrialize (2013), The History of Japanese Economic Development (2018) andHow Nations Learn (2019, co-edited with Arkebe Oqubay).

x

Executive Summary
For the economic development of any country, productivity is an issue that must be fully
understood, addressed, and pursued. This report provides, for the first time, a broad set ofscientific evidence on Ethiopia’s productivity, for use by policymakers as well as thoseinterested in the Ethiopian economy and its growth history and future directions. We examine
both economy-wide productivity and the productivity of the manufacturing sector with its
sub-sector details. Among many productivity indicators, labor productivity and total factor
productivity (TFP) are featured as the most appropriate measures for policy attention and also
as the most frequently cited and compared data types across countries. The two main
analytical methods used here are the decomposition of labor productivity into capital
deepening and TFP and the shift-share analysis of labor productivity—explained below and in
the main text.
International comparisons are performed in several topics to clarify Ethiopia’s current
position among developing countries attaining or about to attain global competitiveness inlight manufacturing. In addition, an in-depth survey of management and labor capacities andmindsets at garment factories in Hawassa, Bole Lemi, and Mekelle are conducted to explore
the causes of the mindset problem of Ethiopian workers, and how firms are coping with it. We
also offer ten policy suggestions for the Ethiopian government to consider for further research
and actual implementation. These are outlines of policy directions, without much detail. If the
proposals are accepted, there should be a drafting of concrete action plans towards enactmentof those proposals as a next step in Ethiopia’s industrialization.
Our sources of domestic data are national accounts data compiled by the Planning andDevelopment Commission (PDC), supplemented by the World Bank’s World Development
Indicators (WDI) and data from the International Labor Organization (ILO). For
manufacturing, the annual Large and Medium Scale Manufacturing Industries (LMSMI)
Survey, published by the Central Statistical Agency (CSA), covering the period 1996-2016,
was extensively utilized. Before analysis, our team carefully checked and cleaned thismanufacturing survey data and re-constructed it into a connected and sufficiently reliable
panel dataset, the result of which should be useful to any researcher interested in the
Ethiopian manufacturing sector. For USD-based international comparison, the sources areextensive industrial databases of ILO and the United Nations Industrial Development
Organization (UNIDO). For researching the Ethiopian garment sector and its workers’

1Ethiopia Productivity Reportmindset, we conducted an original firm survey as touched above. We do not claim that ourdata is free of errors and inconsistencies. Information is often less than sufficient topersuasively interpret our various results. But we have analyzed as carefully as possible andare ready to admit remaining limitations and ambiguities. Given the current status of
Ethiopian data, it is difficult to attain precision. Improvement of productivity-related data is
one of the most urgent agenda for Ethiopia.
**********The ten key findings of the analysis are presented in outline form below.
1. Reasonably high productivity growth but low absolute productivity level
From 2000 to 2016, Ethiopia’s economy-wide labor productivity grew at an annual
average of 4.9%, which was reasonably high though not outstanding (section 3.2). However,
the absolute level of Ethiopia’s labor productivity is still low even by the standard oflatecomer countries. International comparison indicates that productivity in Ethiopia is much
lower than that of other industrializing countries such as Myanmar, Vietnam, Cambodia, andTanzania whose productivity for the same period is between 1.7 and 3.4 times that of Ethiopia.
The results are similar or even worse for the manufacturing sector (section 4.2).
Manufacturing labor productivity in Ethiopia, measured in real Birr, has grown an average of
4.6% per year over the last two decades. Measured in the USD-based labor productivity data
published by UNIDO, Ethiopia’s labor productivity in overall manufacturing and selected
sub-sectors has remained low and stagnant, and in some sub-sectors has even declined. As aresult, the gap between Ethiopia and the other countries in our sample (China, Malaysia,
Indonesia, Kenya, Sri Lanka, and Vietnam) has widened dramatically. In 2000, Ethiopia’s
manufacturing labor productivity was roughly equal to that of China, but in 2015 it was only
13%. Ethiopia must accelerate productivity growth if it is to catch up with the high performers
in the developing world. Its current moderate productivity growth is not sufficient to achieve
this goal (sections 3.3 and 4.4).
2. Heavy investment as a main driver of labor productivity
Labor productivity growth can be separated into two components by means of the growthaccounting method: capital deepening (heavy investment) and TFP growth (overall
efficiency). Capital deepening increases labor productivity by giving each worker access tomore machines and structures to work with, even without acquisition of additional skills or
technology. Meanwhile, TFP growth is regarded as efficiency improvement in the true sense.

2

Executive Summary

Our data reveals that in recent years the increase in Ethiopia’s labor productivity was mainly
the result of capital deepening rather than TFP improvement. True efficiency is not achieved
even through large investments in public infrastructure and private physical assets sustain
labor productivity growth. Aggressive investments are also likely to cause fiscal and balanceof-payments problems and real estate and land bubbles. This is alarming for Ethiopia, as it is asign of over-investment and inefficient resource use (section 3.4).
3. Limited labor mobility from low- to high-productivity activities
From another angle, part of Ethiopia’s less than exemplary productivity performance canbe attributed to a lack of internal labor movement from low-productivity to high-productivity
sectors. This can be confirmed by means of shift-share analysis, which shows that Ethiopia’slabor productivity growth is largely the result of the “within effect,” i.e., efficiency
improvement within individual sectors (section 3.4). That outcome is welcome, but therelative contribution of within effect declined over time. In a country at an early stage ofdevelopment like Ethiopia, labor productivity in every sector must be stimulated. Moreover,productivity can—and should—be additionally much enhanced by the “shift effect,” in whichlabor is allowed to move freely from traditional sectors plagued by labor surplus to globallycompetitive modern sectors with increasing labor demand. The shift effect did increase
productivity in Ethiopia, but the effect was not as extensive as that commonly observed inhigh-performing Asian economies. Ethiopia’s labor mobility is limited, partly because themodern industrial sector is still young and hence does not absorb so much labor, and partly
because significant ethnic and cultural barriers exist between rural and urban areas and among
regional states. Latecomer industrializing nations are expected to experience dynamic internal
labor migration, and the “within effect” and the “shift effect” should be large and mutuallyenhancing, as witnessed in Japan in the 1960s and Taiwan and Korea in the 1970s-80s. This
has not yet been observed in Ethiopia.
4. Fear of premature de-industrialization as rural labor migrates to services
Another general pattern observed in rapidly industrializing economies is a steady declineof agriculture as a percentage of national employment and output, concurrent with a strong
emergence of modern industry—especially manufacturing—as the principal creator of jobs
and value added, absorbing a large amount of the labor migrating from family farms. In
Ethiopia, there are few signs of such dynamic structural transformation despite the very fast
GDP growth recorded over the last one-and-half decades, and despite the government’s
sustained effort in the form of Agricultural Development Led Industrialization (ADLI)

3

Ethiopia Productivity Reportstrategy and manufacturing sector promotion. Data shows that, in 2016, real value added perperson was highest in services (33,100 Birr measured in 2011 real Birr, the same for below)
followed by industry (27,900 Birr), and lowest in agriculture (8,400 Birr) although there has
been an upward trend in each of those sectors (section 3.2). Furthermore, between 2000 and2017, the share of agriculture in total employment fell from 86% to 68%, while that ofindustry rose from 3.5% to 10% and that of services rose from 11% to 22% (World Bank data
derived from ILO). Much of the increase in the Ethiopian industrial labor pool must be
construction workers rather than factory operators. Further detailed evidence on inter-sectoral
labor migration was unavailable, but the above information suggests that the majority of
Ethiopian rural labor migrants are moving to services and construction and only moderately to
manufacturing (section 3.4). The services sector is an amalgam of high-tech and low-tech
activities, from Ethiopian Airlines and software engineering to local shops, restaurants, androad transport. Data revealing the share of rural migrants absorbed by each service sub-sector
were not available, but the main labor absorbers are likely to be the low-tech type.
In rapidly industrializing economies, the share of manufacturing in GDP and employment
typically rises to 30-50% before falling back when the nation achieves high income. This has
been observed in advanced economies in Europe and America and more recently in East
Asia’s high-performers: Japan, Korea, and Taiwan. However, many developing countries
today are experiencing shrinkage of manufacturing even before they arrive at upper middle
income, let alone high income. This premature de-industrialization phenomenon can be
regarded as a primary symptom of middle income trap (section 3.4). Ethiopia, still at low
income, may fall into this trap if structural transformation is not strongly supported by
improvements in quality and productivity. Some argue that there is nothing wrong witheconomic development driven by services, but for a latecomer country with a large rural labor
surplus like Ethiopia, robust growth of manufacturing is crucial for meaningful job creationand income generation in the decades to come. This is more urgent when the large share ofcurrent labor migration is moving to low-tech services rather than high-value professional
services.
5. Diverse performance within manufacturing
The report finds heterogeneity in productivity performance across manufacturing subsectors. Motor vehicles, basic metals, fabricated metal, and food & beverages have relatively
high labor productivity, while lower labor productivity was found for the garment, wood,textiles, furniture, and leather & footwear sectors (section 4.3). However, these results should

4Executive Summary

be interpreted with caution because they also reflect factors other than efficiency. One
obvious factor is difference in the capital-labor ratio. Capital-intensive sub-sectors such as
steel and cement may show high labor productivity because they hire fewer workers, usuallyengineers or similar, who work with a large amount of sophisticated machines—although thisfact alone does not mean that those workers are efficient by each industry’s standard. In
contrast, labor-intensive operations, such as garment and footwear production that require lessmachines and more workers, naturally show low labor productivity, although there is a needfor an accurate measure of their efficiency by benchmarking competitor firms and countries.
Another difficulty is the aggregation problem. Production in each sub-sector is a mixture of
traditional and modern techniques and of large-scale production and family-based
proprietorship, hence average performance is difficult to interpret. If we turn to TFP,
seemingly capital-intensive industries such as machinery & equipment, fabricated metal,
publishing & printing, and motor vehicles perform poorly despite their relatively good laborproductivity rating. This partially corrects our tentative conclusion, derived from laborproductivity data, that those sub-sectors are more efficient than labor-intensive ones.
6. The risk of losing wage-productivity balance
The wage-labor productivity nexus refers to a balance between the level and the growth ofwages and of labor productivity. In neoclassical economic theory, equilibrium under perfectcompetition ensures that real wage is determined by and equal to the value of the marginal
productivity of labor. In the real world, this does not happen due to such technical problems
as imperfect competition, non-constant returns to scale, externalities, and information
asymmetry as well as underdevelopment of the market economy and political pressure andlobbying. When the wage-labor productivity balance is disturbed, sound development isundermined. Competitiveness is lost if wages rise faster than labor productivity, and living
standards are suppressed if workers do not receive compensation proportional to the value
they create. Global firms are mainly interested in Ethiopia’s wages and labor productivity
measured in USD or Euro, while workers are sensitive to their wages deflated by domestic
consumer prices (i.e., how many consumer goods they can buy). The matter becomes more
complicated when the exchange rate deviates from the equilibrium level (purchasing power
parity), generating different results when measured in ETB and USD.
Ethiopia’s manufacturing labor productivity and wages are both lower than those of China,
Kenya, Vietnam, Indonesia, Malaysia and Sri Lanka (section 4.5). Some FDI garment firms
complain that Ethiopian wages are low but labor productivity is even lower. To attract high-5Ethiopia Productivity Reportquality manufacturing FDI, low wage advantage alone is not sufficient; labor productivity
must rise far above the current level. Furthermore, political demands often push up wages(including the minimum wage) without attaining equivalent labor productivity growth. Thiscan damage national competitiveness as has been observed in Indonesia, Vietnam, andCambodia. As Ethiopia prepares to institute a minimum wage system, this mistake should be
avoided at all cost. Minimum wage settings must be based on economic data and scientificreasoning, not on whose voice is loudest. Data is currently insufficient for pursuit of this very
important issue. There is a need for more comprehensive data and more thorough analyses.
7. Ethiopian workers are trainable in terms of technical skills, but attitude and discipline arewanting
An in-depth survey of firms in the apparel industry was conducted to identify possible
causes of Ethiopia’s low labor productivity (section 5.2). Eighteen firms from Hawassa, Bole
Lemi, and Mekelle, all but one fully foreign owned, were surveyed. Average number ofworkers per firm in the sample was 1,614. The operation ratio of the firms relative to capacity
was generally low in the range of 30% to 40%. Through preliminary interviews and study,
three major factors affecting labor productivity in light manufacturing were identified: (i)labor mindset and quality; (ii) management style and strategy (item 9 below); and (iii) policy
and external factors (item 10 below). Among these, the lack of proper labor mindset andinsufficient quality are great concerns for Ethiopia. The possibility of enhancing labor
motivation and productivity by means of financial and non-financial incentives and goodworking conditions was also examined.
Most garment workers are engaged in sewing (75% in our sample firms), which requires
only basic numerical and reading skills. Approximately 60% of the workers have high school
diplomas or better. Almost all firms agree that Ethiopian workers are sufficiently schooled
and are quick learners of technical skills. There is, however, a strong need for thedevelopment of soft skills, including industrial work discipline and motivation (section 5.3).
Poor work attitude manifests in forms including high attrition, absenteeism, lack of sense ofurgency for work, and low motivation to work overtime. In interviews workers reported
extreme dissatisfaction with very low wages and poor working conditions. The averagemonthly salary and non-wage benefits for sewing operators are about USD 30 and below
USD 20, respectively. Workers do not regard the garment job as permanent but onlytransitory. The low wage and non-wage compensation as well as poor working conditions
create the vicious circle of low work motivation, high attrition, and low productivity. The

6

Executive Summary

issue of housing and dormitories is especially critical for factory garment workers who are
predominantly young women. Failure to provide free or cheap accommodation with decent
quality near workplaces negatively affects workers’ enthusiasm and productivity. Refusal byworkers to do overtime is not only due to low compensation but also security problems young
females encounter in traveling to their residence at night.
8. Foreign methods in improving workers
Foreign factory managers bring different attitudes, work cultures and experiences fromhome countries. This may improve enterprise management in Ethiopia but it may also raise
tension with local workers (section 5.4). On the positive side, labor management methods
proven to be effective abroad may also improve Ethiopian workers. Each country and eveneach firm has a different corporate philosophy. Regarding labor mindset and productivity,
there are such various approaches as (i) top-down order and punishment, (ii) creation of
corporate family oneness, (iii) mindset reform through instruction and persuasion, and (iv)mindset reform through monetary rewards and incentives. All of these approaches are at leastconsidered, and sometimes tried, partially or fully, by FDI garment factories we interviewed
in Hawassa, Bole Lemi, and Mekelle. The results range significantly from firms that aresatisfied with the progress Ethiopian workers have made to firms which continue to complain
about the quality of workers. Another important method of facilitating communication andtrust is to mobilize Ethiopian line supervisors as an interface between foreign managementand Ethiopian labor. Such middle managers are typically picked and trained from among bestline workers, and are already producing good results at a number of interviewed FDI garment
factories.
On the negative side, some foreign managers impose their home methods without due
respect to local customs and conditions. Some even shout at or insult female workers which
are taboo in the local culture. Meanwhile, Ethiopian managers unexposed to global businesspractices also have weaknesses in the sense of purpose and responsibility, the lack ofconsistency in learning, poor time management and the lack of global mindset. Both foreignand domestic managers must learn and improve. It should be highly useful if systematic
research is conducted on the impact of imported labor management on the transformation of
Ethiopian mindset, and what pitfalls to be avoided. Ethiopia should create its own way of
improving worker quality by learning different foreign methods and adopting them to
Ethiopian cases with selectivity and proper adjustments.
9. Locational differences in worker type7

Ethiopia Productivity Report

In any rapidly industrializing economy, industrial parks in the suburbs of a principal city
tend to suffer from labor shortage and footloose workers. This is partly because big cities
offer many other job opportunities to those with low skills, including restaurant and café staff,
shop keepers, house maids, construction workers, shuttle traders, and informal vendors, someof which are far more lucrative than sewing operators. Another reason is that most workers
are migrants from rural villages with the main objective of earning cash to bring back home,
and this makes them very sensitive to wages and other financial conditions. Meanwhile,
factories located in rural areas have less problems in recruiting and retaining workers, whousually commute from their own home nearby and are happy to earn extra income withoutleaving the village—at least until local industrial labor demand rises so much to outstrip local
labor supply. Factory wages are naturally higher in urban areas than rural areas due to higher
urban prices and living costs. This dual geographical pattern is clearly visible in Asia, wheresome FDI firms even relocate from the capital city to rural areas in the same country in searchof more workers with relatively low wages and less job hopping. The same phenomena arealso detected in Ethiopia, as Bole Lemi is challenged with highly selective and fast-moving
workers while Mekelle presently faces less problems of that kind. The situation in Hawassa isin between the two poles (section 5.3).
10. Impediments of productivity improvement outside factories
Our re-constructed manufacturing database shows that, from 1996 to 2016, Ethiopia’s
manufacturing labor productivity on average rose 4.6% annually. However, this growth was
highly volatile year-to-year due to factors beyond the control of individual firms. Meanwhile,
manufacturing TFP remained stagnant over the sample period except temporary spikes in1999 and 2015, with an average annual growth of 2.5%. Ethiopian manufacturing
entrepreneurs encounter many external impediments. Some are caused by inappropriate policy
actions and others are generated by international political and economic situations, for which
factory managers and workers bear no responsibility. Problems include shortage of foreign
currency, unstable power supply, slow and expensive logistics, bureaucratic customs
clearance, unavailability of materials, supplies and spare parts, and a horde of labor law-basedheadaches concerning the minimum wage (or lack thereof to date), and workers’ overtime
limits, leaves, and income tax. Interviewees in our firm survey all stressed that these werevery serious business barriers which lowered productivity at their firms as well as for thewhole nation (section 5.5). Each problem must be solved with appropriate policy resolve andmeasures. The government is currently working hard on the World Bank’s Ease of Doing

8

Executive Summary

Business ranking, which is desirable, but this ranking deals mainly with the speed and cost ofadministrative procedures. Other impediments cited above must also be tackled.
**********At the end of the report, we offer ten suggestions for policy directions. For setting up a
policy framework for nationwide productivity improvement, we propose the following: (i)establish a policy organization and an operational organization; (ii) improve data collection
and publication; and (iii) set medium-term targets. For concrete policy areas in which
substantive action is required, we propose the following: (iv) adjust investment policy forproper pace and more private projects; (v) speed up structural transformation; (vi) maintainwage competitiveness; (vii) deepen and broaden Kaizen into a national productivity
movement; (viii) construct an effective enterprise support system, especially for small andmedium enterprises (SMEs); (ix) simultaneously pursue productivity and ethical standards;and (x) transform the mindset of workers and management.
It is hoped that this report contributes to the formulation of Ethiopia’s of productivity
policy by making basic data and useful analyses accessible to all.

9

1. Introduction
Ethiopia has designed and implemented several national development plans and strategies
since the early 2000s, including A Plan for Accelerated and Sustained Development to End
Poverty (PASDEP) during 2005/06-2009/10; the First Growth and Transformation Plan (GTP
I) covering the period 2010/11-2014/15; and the current Second Growth and Transformation
Plan (GTP II) (2015/16-2019/20). During those plan periods, the Ethiopian economy
generally performed well, achieving fast economic growth, improving the per capita income
and living standard of citizens, and reducing the poverty level. Ethiopian growth was not only
continuous and broad-based but also much higher than the regional average. For instance,
during the PASDEP and GTP I periods, the Ethiopian economy grew an average of 10.1% per
year. In the first three years of the GTP II period, the economy grew an average of 8.8% per
year. Growth was accompanied by a substantial improvement in access to health andeducation facilities. Regarding the economic structure of the GDP, agriculture used to havethe lion’s share, followed by services and industry. More recently, however, the service sector
has become the dominant sector, despite the country’s great ambition towards
industrialization. A report by the National Bank of Ethiopia in 2017/18 showed that the GDP
shares of agriculture, service and industry were 34.9%, 39.2%, and 27.0% respectively.
Despite Ethiopia’s high economic growth, its productivity remains well below the averageof developing countries. That high growth has largely been driven by substantial public
investment in physical infrastructure and the strong performance of the service sector, which
absorbed a modest migration of labor from the agricultural sector (World Bank, 2016).
Among its African peers, Ethiopia stands out for its very rapid development of infrastructure,but its overall economic efficiency has not improved at the same rate. This is alarming
because productivity improvement is an important contributor to sustainable economic growthand hence a crucial indicator for policymakers (Conway, 2016).
Realizing this, Ethiopia made pursuit of productivity its key policy direction under GTP II,
with the enhancement of productivity of agriculture and manufacturing one of the major focus
areas. However, concrete policy measures to enhance productivity remain unclear. In order toconcretize productivity policies, a comprehensive and detailed study of productivity is needed.
This report is intended to contribute to that research direction. The objectives of this report are
to examine the evolution of productivity in Ethiopia, with particular emphasis on the
manufacturing sector; and to qualitatively analyze the effect of worker mindset and11Ethiopia Productivity Reportmanagement mindset on labor productivity in Ethiopia’s emerging garment sector.
Our analysis focuses on two main elements: economy-wide productivity andmanufacturing sector productivity. The macro-level part of the analysis focuses on thedeterminants of labor productivity using national account and shift-share analysis. The
manufacturing part examines the evolution of productivity of the manufacturing sector using
various productivity metrics, both descriptive and statistical, that provide insights into theconstraints on structural transformation. Analyzing productivity at the macro-level affords aview of the state of the manufacturing sector in the context of the overall economy, as the
manufacturing sector is closely linked with both the agricultural and service sectors. The
focus on the manufacturing sector is in line with the government’s policy of prioritizing that
sector. The report offers basic but crucial information and a deeper understanding of theconcept of productivity and the Ethiopian approach to it. These should help Ethiopian
policymakers to formulate an appropriate productivity strategy.
The main data sources for the economy-wide productivity analysis are official data fromthe Planning and Development Commission (PDC) and the World Bank’s World
Development Indicators (WDI). The main data source for the manufacturing study is theLarge and Medium Scale Manufacturing Industries (LMSMI) Survey conducted annually by
the Central Statistical Agency (CSA) of Ethiopia for the period 1996–2016. The LMSMI
Survey is a census of medium and large manufacturing industries (i.e. employing ten or moreworkers) and contains a rich set of information on inputs and outputs crucial for the
calculation of productivity. Our team checked, cleaned and reconstructed this survey data tocreate an integral database before conducting panel data analysis. We also use the USD-based
UNIDO database for international comparisons.
The rest of the report is structured as follows. Chapter 2 offers a literature review which
covers the concept of productivity, productivity measurements, and the importance of
productivity in general and in the context of Ethiopia. It also identifies some conceptual issues
and warns of pragmatic caveats related to the measurement of productivity. Chapter 3
presents an analysis of economy-wide labor productivity, characterizing the current status oflabor productivity and decomposing it into capital deepening and total factor productivity(TFP). Economy-wide labor productivity growth is further decomposed by means of shiftshare analysis. Chapter 4 discusses productivity in Ethiopian manufacturing. It examines
Ethiopia’s manufacturing labor productivity and TFP in time-series and subsector
disaggregation, and compares Ethiopia’s performance with that of selected peer countries.

12

Introduction

The issue of Ethiopia’s wage-productivity nexus is also discussed, and held up forinternational comparison. Chapter 5 reports the results of an in-depth survey of garment
factories in Hawassa, Bole Lemi and Mekelle, mostly operated by FDI. Various aspects of theEthiopian worker mindset problem are identified and discussed, and possible causes anddirections for remediation are explored. In Chapter 6, we propose ten policy directions forenhancement of Ethiopian productivity, some related to policy framework creation, and others
to areas for policy implementation.

13

2. Productivity: Concept, Measurement
and SignificanceIn this section, we examine the concept of productivity and its measurement. We go on to
discuss the importance of productivity and the decomposition techniques of labor productivity.
We also present some caveats related to measuring productivity.

2.1

The concept of productivity

Productivity is a fundamental concept in economic analysis. It is a key measurement of
economic effectiveness, revealing how well resources are combined and utilized to achieve
the desired and expected results. For countries, productivity is a tool for value creation fromavailable resources such as raw materials, labor, skills, capital equipment, land, intellectual
property, managerial capability and financial capital. If the right choices are made, higher
production, higher value and higher income can be achieved for every hour worked.
According to Krugman (1994), “productivity is almost everything in the long run.”
Productivity can be studied at three levels: international, national and enterprise.
Productivity at the international level reflects a view of competition among countries to attainhigh technology, quality products, high-value services and low production costs. At thenational level, productivity uses the available resources to maximize overall yield, increase
employment and improve the living standard of the citizens. At the enterprise level,
productivity is associated with the optimal utilization of in-company resources, aimed at
superior business performance.
Productivity is defined as the link between the output resulting from a production process
or service system, and the inputs used to generate that output (ILO, 2015; Prokopenko, 1987).
It reflects how much is produced per unit of input. A productivity-enhancing society
selectively mobilizes new ideas, technological innovations and competing business models togenerate more value by realizing a better combination of a country’s resources (Conway,
2016; Cusolito & Maloney, 2018; OECD, 2015a).

2.2

Measuring productivity

Productivity is defined as the ratio of output to input.
𝑃𝑃𝑃𝑃𝑃𝑃𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 =𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
15

Ethiopia Productivity Report

Despite the simplicity of the formula, there is no unique way of measuring productivity inthe real world. The productivity literature provides us with many different measures of
productivity, and selection of the optimum measure depends on the purpose of the analysis
and the availability of data. Generally, tools for the measurement of productivity are classified
into single factor productivity measures and multi-factor productivity measures. Single factor
productivity, also referred to as partial productivity measure, pairs a measure of output with asingle input such as labor or capital. Multi-factor productivity, a measure of output combinedwith more than one input, is commonly known as total factor productivity, or TFP (OECD,
2001). This report focuses mainly on labor productivity, invoking TFP as need arises. Labor
productivity is important for policymakers because it is a key economic indicator closely
associated with a country’s economic growth, competitiveness and living standards (ILO,
2015; OECD, 2001).
Labor productivity is defined as the amount of output produced divided by the amount oflabor expended to produce that output. It reveals how efficiently labor is used to produce
gross output or value added (OECD, 2001). Here, gross output is the entire physical productemerging from the production process, and value added is the part of this product value which
is newly added by the current production process after deduction of the value of purchasedmaterials and components. Labor productivity based on gross output captures required labor
per unit of output, while the value added measure enables the gauging of living standards andincome per capita. From a policy viewpoint, value added-based productivity is also a usefulindicator for wage negotiation and setting.
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂(𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

Labor productivity as characterized above is relatively easy to calculate with reasonableaccuracy and to compare across countries. For this reason, it is widely used in policy work by
governments of both developing and developed nations, and in international comparisons as
well. The ILO Manual on Productivity Improvement identifies labor as the key among all
factors of production, including capital, land, equipment and materials. It likens the role
played by labor to child delivery—just as a child cannot be delivered without labor, allelements of production remain unrealized unless they are combined with labor.

16

Productivity: Concept, Measurement and Significance

2.3

Decomposition of labor productivity

Labor productivity can be decomposed in a variety of ways. Most frequently, it isdecomposed into two main factors: capital deepening and TFP (Anderson & Kliesen, 2006;
OECD 2001, 2015a, 2015b).
Capital deepening (capital intensity) is defined as the amount of capital available to each
unit of labor. It contributes to labor productivity improvement by giving each worker more
capital—tools, machinery and equipment—to work with in the production process (Conway,2016). The rise of labor productivity due to capital deepening occurs naturally in any sector orcountry, and is basically unrelated to the attainment of higher skills or to the dexterity of labor
itself. Meanwhile, TFP is defined as the overall efficiency with which all productive inputs—
not just labor—are combined in the production process to generate output (OECD, 2015a).
TFP reflects many aspects such as management style and strategy, different production
methods, scale merits, accumulation of skill and technology, and the elimination of muda
(wasteful actions or things in production). The conceptual ambiguity and multi-facet nature of
TFP makes it far more difficult than labor productivity to estimate statistically.
Labor productivity can also be decomposed into three components: capital deepening,labor quality and TFP (Jorgenson & Stiroh, 2000; APO, 2010; Fernald & Matoba, 2009).
Jorgenson & Stiroh, for example, consider labor quality as time allocation in terms of numberof hours worked by workers who have higher marginal products (and therefore enjoy higher
wages) than those with lower marginal products. This concept of labor quality is directly
connected with both labor productivity enhancement and labor compensation.
This report adopts the two-way decomposition of labor productivity into capital intensity
and TFP, given the paucity of data such as total hours worked or classification of labor by
education and skill levels, which are necessary for the measurement of labor quality. This
two-way decomposition reflects labor quality indirectly in TFP rather than as a separate entity.
The national-economy context of labor productivity and its decomposition is illustrated in
Figure 2.1. In broad terms, higher income per capita can be achieved by producing more
output per person, or by getting higher world prices for what is produced via improvements inthe terms of trade (the prices of exported domestic products relative to those of imported
foreign products). Increasing the amount of output produced per person can be achieved byincreasing hours worked per capita (higher labor utilization) and/or by achieving more output
from each hour worked (higher labor productivity).

17

Ethiopia Productivity Report

Figure 2.1 Components of Real GNI per capita
INCOME
Real GNI per capita

OUTPUT
Real GDP per capita

TERMS OF TRADE
Relative prices ofexports to imports

LABOR
PRODUCTIVITYCAPITAL/LABOR
Contribution ofcapital deepening

MULTIFACTORPRODUCTIVITY
Real GDP/total
inputsNET FOREIGN INCOME
Net investment income

LABOR UTILIZATION
Amount of labor

HOURS
WORKED
Average hours
per worker

PARTICIPATION
RATE
Size of workforce

EMPLOYMENT% of workforce
employed

Source: Conway & Meehan, 2013.

2.4

The importance of productivity in general and in the context
of Ethiopia

Concern with productivity is ubiquitous. National leaders, bureaucrats and public
administrators talk about productivity improvement as a solution to many problems affecting
society. Economists consider productivity as an important source of economic growth andincreased real income in the different segments of society. Business executives and managers
consider it as a viable response to increased global competition and a means to reduce
production costs and improve profitability. Industrial supervisors and engineers are interested
in keeping abreast of production schedules, reducing the frequency of defects and rejections,achieving high quality and cutting expenses—all through productivity improvement.
The ILO describes productivity as the chief source of real economic growth, social
progress and better standard of living. Given its direct relationship with national income andwelfare, productivity has become an important concept in national policy formulation andcorporate strategy. Productivity appears to provide the only explanation why some countries
with scarce resources can enjoy a better standard of living than others with ample resources(ILO, 2015). Enhanced productivity is good for sustainable economic growth and as such is a
crucial element of policymaking (Conway, 2016). Productivity was highlighted as “a gamechanger to achieving high economic growth” in the Eleventh Malaysia Plan (EPU, 2017). In
August 2019, in Hanoi, Vietnam, Prime Minister Nguyen Xuan Phuc officially declared the18

Productivity: Concept, Measurement and Significancestart of the National Labor Productivity Improvement Movement to accelerate economic
growth.
The pursuit of quality, productivity and competitiveness (QPC) became Ethiopia’s key
policy direction in GTP II in which enhancing the productivity of agriculture andmanufacturing is one of the major focus areas. Ethiopia aims to become a light manufacturing
hub of Africa by 2025. To achieve this, the Ethiopian government has prioritized thedevelopment of the manufacturing sector, in which productivity improvement of the
manufacturing sector should be a key policy pillar. Specifically, GTP II targeted structuraltransformation involving the shifting of activities from low to high productivity sectors,
especially the manufacturing sector, in a relatively short period of time.
Ethiopia’s current industrial activities are mostly labor-intensive since the country has asubstantial workforce with a high percentage of young people but it has very limited capital
accumulation except in simple tools and light-duty machines. Industries can attain acompetitive advantage if equipped with a productive labor force with comparatively low
wages (Rao & Tesfahunegn, 2015). In a country where labor is a dominant factor of
production, labor productivity is a suitable indicator of a firm’s productivity (Bernolak, 1997).
Hence, the measurement of labor productivity has particularly high practical relevance in
Ethiopia. Competitiveness cannot be realized unless these activities are accompanied by highlabor productivity.
There are additional reasons why labor productivity is a strong policy focus in virtually all
nations, not just in those with labor-intensive industries. First, the human factor is considered
critical among factors of production, so labor productivity should be the starting point inproductivity analysis (ILO, 2015). Second, labor productivity is a widely used indicator as amajor determinant of living standard and economic growth. Third, labor productivity can beunderstood intuitively; it is relatively easy to calculate with reasonable accuracy; and it isamenable to international comparison. Fourth, labor productivity is also easier to measure anddiscuss at all levels including national, sectoral and enterprise levels (OECD, 2001, 2008).
In short, among the productivity measures, this report focuses on Ethiopia’s laborproductivity, with TFP also highlighted as a supplementary indicator of productivity.

2.5

Conceptual issues and pragmatic caveats

Although labor productivity is the most powerful and widely accepted measure of
productivity today, it has limitations which must be mentioned upfront.

19Ethiopia Productivity Report

First, labor productivity is a partial measure of productivity which does not account forthe contribution and cost of capital growth or any other important inputs to the enhancementof output (OECD, 2001). Findings based on labor productivity alone could be misleading as
they do not directly address the role of other factors of production. For example, at certain
stages of economic development, labor may become less important and the focus may shift toanother input (e.g., machinery, IT, AI) whose contribution to economic growth has risen.
Thus, the analytical value of labor productivity may vary over time and across industries dueto the emergence of other important inputs (Conway & Meehan, 2013).
Second, labor productivity calculations may be influenced by the accuracy of statistics aswell as the method of defining inputs and outputs. While it is relatively easy to measure value
added in terms of current price, it is more difficult to measure it in terms of constant price, as
separate price indexes are required to arrive at real outputs and inputs for different sectors andsubsectors. Another problem relates to the measurement of labor itself. Different statisticalsources, with different concepts and definitions, are used in different countries, which impedeinternational comparability. Ideally, the measurement of labor inputs should adjust forworkers’ education, qualifications, skills and experience, but in practice, only number ofhours worked is available in most advanced countries and only number of employees isavailable in many developing countries, including Ethiopia.
Theoretically, TFP is superior to labor productivity as an indicator of overall economic
efficiency and performance. However, TFP too has major limitations. First, differences indata and technical assumptions adopted by various researchers often produce very different
measurements of productivity growth, which renders TFP impractical for policymakers.
Second, factors other than purely technical change, such as increasing returns to scale,markups due to imperfect competition, or gains from sectoral reallocations, contaminate TFP
and lead to difficulty in interpretation (World Bank, 2000). Third, estimation of TFP demands
substantially larger amounts of data than estimations of labor productivity, yet it is less able toidentify causes of, or appropriate policy actions to cope with, productivity problems, as it
deals with multiple input variables (EPU, 2017). Hence, caution is required when interpreting
both labor productivity and TFP.

20

3. Economy-wide Labor Productivity in
Ethiopia
This chapter focuses on economy-wide labor productivity. Data sources and limitations areprovided in Section 3.1. Section 3.2 presents trends in economy-wide labor productivity in
Ethiopia, and it is further disaggregated into major sectors of agriculture, industry and service.
Ethiopia’s economy-wide labor productivity is compared with that of selected countries in
Section 3.3. In Section 3.4, economy-wide labor productivity is decomposed using growthaccounting and shift-share methods.

3.1

Data sources and limitations

In our economy-wide labor productivity analysis, the key variables are value-added (GDP),value-added share by sector, GDP deflator, labor engaged in each sector, and capital stock.
The main sources of data for this analysis are official data from the Planning andDevelopment Commission (PDC: formerly the National Planning Commission), the WorldBank’s World Development Indicators (WDI), and the International Labor Organization(ILO).
Data for value-added and GDP deflator are obtained from the national accounts dataset
prepared by PDC. The year 2011 is the base year for the GDP deflator. Employment data is
obtained from WDI, sourced from ILO by the World Bank. National labor force surveys wereconducted in 1999, 2005, and 2013 in Ethiopia. ILO estimates labor data for other years using
the standard model it applies to all member countries.
The national accounts data of PDC does not provide capital stock. Due to the absence ofinitial capital stock, it is difficult to compute capital stock using the inventory method eventhough NPC provides investment data. Moreover, that procedure would require a reliable
depreciation rate. Hence, we resorted to the Penn World Table to source data for capital stockfor Ethiopia from 2000-2014 1.

3.2

Trends of economy-wide labor productivity in Ethiopia

Using national accounts and employment data, the level and growth of economy-wide laborproductivity are calculated as the ratio of output produced in any year to the total number of
persons mobilized.
1

Unless specified otherwise, we compute the growth rate of any variable by the change in its natural logarithm(i.e. ∆𝑙𝑙𝑙𝑙𝑌𝑌𝑡𝑡 = ln 𝑌𝑌𝑡𝑡 – ln 𝑌𝑌𝑡𝑡−1 ).

21Ethiopia Productivity Report

Figure 3.1 Economy-wide labor productivity (2011 prices)(in thousand ETB)

18
16
14
12
10
8
6
4
2
2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Source: authors’ calculation based on data from PDC and WDI.

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =𝐺𝐺𝐺𝐺𝐺𝐺
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

Figure 3.1 shows the result. Economy-wide labor productivity increased from 7,080 Birr
per worker in 2000 to 15,610 Birr per worker in 2016. This translates to an annual average
growth of 4.94 % (Figure 3.2). Except for 2002 and 2003, when labor productivity growth
was negative, the overall trend was fairly stable with slight declines in 2009, 2012, and 2016.
The negative growth of labor productivity in 2002 and 2003 can be explained by the drought
during that period which resulted in a significant drop in value added in both years. The slightdeceleration in 2009 coincided with the global financial crisis. The decline in 2016 is related
to another drought in 2015-16 which affected the agricultural sector in particular and theinstability caused by violent protests within the country.
Figure 3.2 plots GDP growth and labor productivity. It is evident that labor productivity ishighly correlated with the movement of real GDP, whereas changes in employment are not.
Labor productivity by major sector is provided in Figure 3.3. The service sector stands out
for its relatively high labor productivity. Even though the industrial sector accounts for thesmallest share of GDP, its value added per unit of labor has been approaching the level ofthe service sector. The data indicate that the agricultural sector had the lowest laborproductivity. In terms of average labor productivity growth during 2005-2016, theproductivity of the industrial sector rose faster than that of the other two sectors. In 2016, the22Economy-wide Labor Productivity in Ethiopia

Figure 3.2 Economy-wide growth of GDP and labor productivity
%
15

10

5

0

-5

Labor Productivity Growth

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

-10

GDP Growth

Source: authors’ calculation based on data from PDC and WDI.

Figure 3.3 Labor productivity by major sectors (2011 prices)(in thousand ETB)

35
30
25
20
15
10
5
0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Agriculture

Industry

Services

Economy-wide

Source: authors’ calculation based on data from PDC and WDI.

23

Ethiopia Productivity Reportlabor productivity of the services sector and the industrial sector were 3.9 and 3.3 times more,
respectively, than that of agriculture. Nevertheless, the pace of structural transformation has
been slow. The large variation in labor productivity among the three major sectors suggests
considerable potential for structural transformation through inter-sectoral labor movements
accompanied by overall productivity gain and economic growth.

3.3

Ethiopia’s economy-wide labor productivity compared withpeer countries

To assess Ethiopia’s position among its peer countries, we use ILO’s labor productivity
estimation in constant USD. Ethiopia’s economy-wide labor productivity for the period 20002018 is at the low end of the distribution of global and regional productivity. As shown in
Figure 3.4, it has been consistently lower than the average for low-income countries although
the gap has almost closed in recent years. However, it continues to be substantially lower thanthat of other groups. In 2018, the labor productivity of Ethiopia was 40% of the average for
Sub-Saharan Africa, a quarter of the average for lower-middle income countries, and 10% of
the average for upper middle income countries.
Figure 3.4 Comparison of Ethiopia’s labor productivity and that of peer countries, byincome category (Output per worker, GDP measured in constant 2011 international $ in PPP)
40,00035,00030,00025,00020,00015,00010,0005,000

Sub-Saharan Africa

Lower middle income

Upper middle Income

Low income

2018

2017

2016

2015

2014

2013

2012

2011

2010

Ethiopia

Source: ILO estimation of economy-wide labor productivity.

24

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Economy-wide Labor Productivity in Ethiopia

Figure 3.5 provides a comparison of the labor productivity growth of Ethiopia and that ofits peer countries. Even though Ethiopia’s labor productivity remains low compared to that ofits peer countries, it has registered slightly higher productivity growth than the other countries
examined here. Labor productivity growth has been positive in the last two decades, with the
exception of 2002-03. The countries compared here experienced positive labor productivity
growth through most of the last two decades.
Ethiopia’s labor productivity relative to that of selected individual peer countries is
illustrated in Figure 3.6. Ethiopia’s labor productivity was lower than that of every othercountry selected.

In 2018, Myanmar, Vietnam, Cambodia, and Tanzania had laborproductivity 3.4, 2.9, 1.7, and 1.6 times higher, respectively, than that of Ethiopia.

Figure 3.5 Comparison of labor productivity growth of Ethiopia and peer countries, byincome category
10
8
6
4
2
0
-2
-4
-6

Ethiopia

Sub-Saharan Africa

Lower middle income

Upper middle Income

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

-8

Low income

Source: authors’ calculation from ILO economy-wide labor productivity

25

Ethiopia Productivity Report

Figure 3.6 Comparison of Ethiopia’s labor productivity and that of selected peer
countries, by category (Output per worker, GDP measured in constant 2011 international$ in PPP)
14,00012,00010,000
8,000
6,000
4,000
2,000

Ethiopia

Vietnam

Cambodia

Myanmar

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Tanzania

Source: authors’ calculation from ILO economy-wide labor productivity.

3.4

Decomposition of economy-wide labor productivity

As discussed in the preceding sections, economy-wide labor productivity in Ethiopia grew on
average by 4.9% between 2000 and 2016. In this section, we focus on the period 2000-2014,
given that capital stock data are not available for the period after 2014 (economy-wide laborproductivity grew on average by 4.8% between 2000 and 2014). We analyze the sources oflabor productivity growth using growth accounting and shift-share analysis methods 2.
Growth accounting theory holds that labor productivity growth can be explained bychanges in labor quality; capital intensity (also known as capital deepening, which is anincrease in the use of capital per unit labor); and TFP growth (which reflects innovation or an
overall improvement of efficiency). In the absence of data on labor quality, we follow
previous researchers and decompose labor productivity growth into two factors, capital
deepening and TFP growth.

3.4.1 The growth accounting method
Our growth accounting decomposition is derived from a Cobb-Douglas type production
function which assumes constant returns to scale (see Annex 3.1 for more information on
2

For growth accounting decomposition, we examine the period 2000-2014 due to data limitations for capitalstock, while for the shift-share analysis we extend the period to 2016.

26

Economy-wide Labor Productivity in Ethiopia

methodology). We copy capital stock data for Ethiopia from the Penn World Tables, which
report capital stock and real GDP in 2011 prices. We use the national accounts data torecalculate capital stock with the Penn World Table estimate of the ratio of capital stock toGDP. This recalculation is necessary as national accounts data are in local currency in 2011
prices.
Determination of the capital share of national income is challenging due to the lack of data.
Most studies assume a certain share of capital in GDP. For example, Collins et al. (1996) and
Thanh et al. (2018) both assume capital share 𝛼𝛼 = 0.35 for East Asia and Vietnam,
respectively. We adopt 𝛼𝛼 of 0.3 from Mengistu et al. (2018); that value was calculated fromthe 2015/16 Input-Output and Social Accounting Matrix of Ethiopia.

National accounts data expresses real GDP in constant 2011 price. Our employment datais the number of employed persons of age 15 and higher, as reported in WDI, which in turn isbased on ILO modelled estimates. Details of the data constructed from national andinternational sources are provided in Annex 3.2.
Results of the decomposition are given in Figures 3.7 and 3.8. In Figure 3.7, capitalintensity rose throughout the sample period, while TFP growth declined from 2004 onward.
In 2011, capital intensity replaced TFP growth as the largest determinant of labor productivity
growth.
Figure 3.7 Growth rate of labor productivity, capital intensity, and TFP (%)
%

20
15
10
5
0
-5

Labor Producvity

Capital intensity

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

-10

TFP

Source: authors’ calculation based on data from PDC, WDI and the Penn World Tables.

27

Ethiopia Productivity Report

Figure 3.8 shows the same information in terms of contribution to labor productivity
growth. Until 2011, the contribution of TFP growth was dominant, at 70% or larger,
suggesting that Ethiopian productivity at that time was driven by improvements in efficiency.
From 2012 onward, however, the main driver of labor productivity growth was capitalintensity, with a contribution of 62% in 2014. The shift from TFP to capital intensity as themain driver of labor productivity growth indicates an efficiency slowdown accompanied byvigorous investment in infrastructure and other capital, equipping each worker with moremachinery and structures, which sustained labor productivity. This is not good news for
Ethiopia as such enhancement of labor productivity cannot continue in the long run.
This trend may have more than one cause. One possible explanation is that labor-intensive
manufacturing industries, such as garments and leather, were sluggish while the capitalintensive manufacturing sector, such as machinery and equipment, and perhaps also thecapital-intensive service sector, fared better. If this explanation is valid, a rising contributionof capital deepening is not necessarily a bad thing, as it signifies a structural transformationfrom light manufacturing to capital-intensive manufacturing (and services). To date, however,such a transformation has not been corroborated by other data.
Another possible explanation is excessive public investment with low efficiency. Ethiopia
recently invested heavily in physical infrastructure, particularly after the launch of thecountry’s First Growth and Transformation Plan in 2009/10. This might have led to excessive
Figure 3.8 Contributions of capital intensity and TFP to Ethiopia’s labor productivity
100%
80%
60%
40%
20%
0%

Capital intensity

TFP

Source: authors’ calculation based on data from PDC, WDI and the Penn World Tables.

28

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

-20%

Economy-wide Labor Productivity in Ethiopia

investment, low capital efficiency and inefficient use of capital by labor. A World Bank report(2016) finds that while public investment has been one of the drivers of growth in Ethiopia,
the estimated marginal return to public investment has been low even by the standard of lowincome countries. The report also suggests that Ethiopia would benefit from directing its
resources to more private investment. A number of large-scale public power and transport
investment projects in recent years will surely have positive effects on private sector
production and investment in the future. However, questions regarding appropriate speed andsize, cost performance, debt sustainability, and the risk of crowding out private activities mustalso be addressed.
The Asian Productivity Organization (APO) reports the labor productivity of Asian
countries annually. Its 2017 report finds that the situation surrounding labor productivity is
not uniform across countries or over time. From 1970 to 2015, capital intensity was the main
driver of labor productivity growth in China, Vietnam, and Indonesia. Meanwhile, during thesame period, TFP appears to have been the main driver in Cambodia, India, and Sri Lanka.
Sub-period observations also reveal changes of driver. In Vietnam, for example, TFP mainly
contributed to labor productivity from 1970 to 1995 but capital intensity was more importantfrom 1996 to 2015. The Vietnamese case, where capital intensity overtook TFP as the main
driver of labor productivity growth, is similar to the Ethiopian situation we examined above.

3.4.2 Shift-share analysis
In this section, we use the shift-share method to examine another strand of laborproductivity: labor mobility between sectors and subsectors.
Before discussion of the results, it is useful to briefly check how the three major sectors of
the Ethiopian economy fared in terms of labor productivity. Figure 3.2 in the preceding
section and Table 3.1 below show the relevant data.
Between 2000 and 2005, productivity in the agricultural sector increased while that in the
Table 3.1 Labor productivity of Ethiopia, by industry (in thousand ETB, 2011 prices)Year

Agriculture

Industry

Services

Economywide

2000

4.47

18.08

24.45

7.08

2005

4.93

12.11

21.75

7.61

2010

6.54

17.73

28.81

10.93

2016

8.44

27.88

33.13

15.63

Source: authors’ calculation based on data from PDC and WDI.

29

Ethiopia Productivity Reportindustrial and service sectors declined. After 2005, all three sectors showed positive growth inproductivity. Between 2005 and 2016, industrial sector productivity grew by a factor of 2.3
while the service sector and the agricultural sector grew by factors of 1.5 and 1.7, respectively.
In 2016, labor productivity in the service sector and the industrial sector was respectively 3.9
and 3.3 times more than that of agriculture. The service sector comprises heterogeneous subsectors including Ethiopian Airlines, financial institutions, transport services, professional andconsultation services, tourism, wholesale trade, small shops and restaurants—some of which
are high-tech and capital intensive while others are low-tech and labor-intensive. The
relatively high labor productivity in the service sector may reflect the dominance and growthof high-tech services but this cannot be confirmed. Due to data paucity, disaggregatedanalysis of service sub-sectors is hardly possible.
Economy-wide productivity can be expressed as the sum of the productivity levels of thesectors weighted by their employment shares. Labor continuously moves within sectors, e.g.,
from low productivity firms to high productivity ones, or across industries from low
productivity sectors to high productivity ones—both of which affect the labor productivity ofthe entire economy (‘sector’ and ‘subsector’ are used interchangeably in this section). This
process can be captured by means of the shift-share method (see Annex 3.3 for the derivationof shift-share method), decomposing labor productivity growth into three factors: (i) thewithin effect; (ii) the shift effect; and (iii) the interaction effect.
The within effect reflects the contribution of the labor productivity growth of each sector
to economy-wide labor productivity, assuming the labor share of each to be constant. This
captures improvement in each sector as a driver of economy-wide labor productivity. If thereis improvement in technology, management or production method, the within effect will havea positive sign.
The shift effect is the effect of labor reallocation across sectors, assuming unchangedlabor productivity in each sector. Economy-wide labor productivity increases due to the
movement of labor from low productivity to high productivity sectors.
The interaction effect captures the second-order impact of labor mobility, namely, thecorrelation between changes in labor share and labor productivity growth in each sector. A
positive sign means that the within effect and the shift effect are complementary, or that
sectors with rising (not necessarily high) labor productivity are absorbing more labor andexpanding. If the interaction effect is negative, the two effects are substitutes, or sectors withslow or falling labor productivity growth are receiving more labor.

30

Economy-wide Labor Productivity in Ethiopia

Table 3.2 Decomposition of labor productivity growth: shift-share analysis
Sources of labor productivity
growth (%)

Contribution shares to Labor
productivity growth (%)

WithineffectShift
EffectInteraction
EffectWithineffectShift
EffectInteraction
EffectPeriodProductivity
growth

2004-2007

7.92

21.73

4.56

0.54

80.99

17.00

2.00

2008-2011

6.61

14.18

7.22

0.52

64.70

32.93

2.37

2012-2016

5.96

15.70

9.94

1.28

58.31

36.92

4.77

2004-2016

6.60

79.50

26.68

15.52

65.32

21.93

12.75

Source: authors’ calculation based on data from PDC and WDI

As shown in Table 3.2, in the period 2004-2007, the major contributor was the within
effect, accounting for 81% of labor productivity growth 3. The shift and interaction effects
contributed the remaining 17% and 2%, respectively. For the period 2008-2011, the within
effect dominated, but its contribution decreased by about 16 percentage points, while thecontribution of the shift effect increased to about 33%. For the recent period of 2012-2016,
the within effect was still large but its share decreased further, to about 58%. The contributionof the shift effect rose to about 37%.
For the whole period under consideration, the within effect contributed 65% of laborproductivity growth, the shift effect contributed 22%, and the smallest contribution, 13%,came from the interaction effect. Ferede and Kebede (2015) find similar results for Ethiopia,
and stress the importance of the within effect for aggregate labor productivity in Ethiopia.
In sum, labor productivity growth has been driven mainly by the within effect, whichsuggests that there have been efficiency improvements within individual sectors. This may
have been due to capital accumulation, technological change or improved allocation of
resources across firms, but the precise causes and their relative weights cannot be identified.
However, the contribution of the within effect gradually declined and the importance of theshift effect gradually rose over time. This suggests labor movement from low productivity
sectors to high productivity ones. The large but gradually falling share of the within effect,
combined with the rising share of the shift effect, signifies that Ethiopia is in an early stage ofstructural transformation. The small share of the interaction effect, which stood at 10% for
2004-2016, combined with the still relatively small contribution of the shift effect, implies
that massive inter-sectoral labor mobility has not yet occurred and the structural bonus3

Due to the drought in 2002/2003, labor productivity growth between 2000 and 2004 was negative. Hence, forease of interpretation we start the period of analysis for the shift-share model from 2004.

31

Ethiopia Productivity Report

hypothesis is yet to be realized in Ethiopia. If Ethiopia’s industrialization is sustained into thenext stages of structural transformation, the shift effect and the interaction effect are expectedto account for the lion’s share of economy-wide labor productivity enhancements.
It would be preferable to conduct shift-share analysis on individual sectors instead of theentire economy. Table 3.3 reports the results of such an analysis, with disaggregation limited
to the major categories of agriculture, industry and services. Due to the lack of data, further
disaggregation is not possible. Agriculture had the largest contribution to the within effect(58.3%) followed by the services sector (26.5%) and the industrial sector (15.3%). In terms of
the shift effect, labor movement was mostly from the agricultural sector to services, while
movement to the industrial sector was modest. The negative interaction effect in the
agricultural sector suggests the possibility of labor shifting to less productive industrial andservices sectors such as informal trading. Overall, our results are in line with those of Feredeand Kebede (2015), who use the national labor force survey to study labor productivity
drivers during 2005-2013.
Data on sectoral labor shares, obtained from WDI data based on ILO computation, also
confirms our suspicion. It shows movement of labor away from agriculture to service andindustry. In 2000, the agricultural sector employed about 86% of the labor force, while service
and industry absorbed the rest, 11% and 3.5%, respectively. By 2017, the share of agriculture
had declined to about 68% while the shares of service and industry had risen to 22% and 10%,respectively.
This Ethiopian trend deviates from the past pattern of structural transformation
experienced by today’s developed economies, where the path of development wascharacterized by a strong rise of manufacturing industries and a transformation from agrariansocieties to urban ones. Over time, services overtook manufacturing as high income wasattained, with labor moving initially from agriculture to manufacturing and then to the service
sector.
Table 3.3 Decomposition of labor productivity growth by sector: shift-share analysis
2004-2016

Sources of labor productivity
growth (%)

Contribution shares of laborproductivity (%)

Agriculture

Industry

Services

Agriculture

Industry

Services

Within effect

46.31

12.14

21.05

58.25

15.28

26.48

Shift Effect

-8.03

5.89

28.82

-30.0822.08

108.00Interaction Effect

-7.21

6.56

16.16

-46.4742.28

104.18Source: authors’ calculation based on data from PDC and WDI.

32

Economy-wide Labor Productivity in Ethiopia

In “The Perils of Premature Deindustrialization,” Rodrik (2013) finds that the
manufacturing share of employment in today’s developed countries (before theirdeindustrialization) peaked at 45%, 27%, 33%, 40% and 28% in Britain, the United States,
Sweden, Germany, and South Korea, respectively. However, Rodrik notes that the pattern ofindustrialization in today’s developing world is different: not only has the process been slow,
but deindustrialization sets in much sooner, even before economic maturity is reached. He
calls this “premature deindustrialization.” Similarly, Tran and Karikomi (2019) point to therisk of premature deindustrialization as one of the major symptoms of middle income traps.
They compare high performing economies of Northeast Asia, such as Japan and Korea, which
did not experience premature deindustrialization, with industrializing but less spectacular
economies of Southeast Asia, which are trapped or about to be trapped in middle income andmay deindustrialize prematurely in the future.
The low but rising level of the shift effect and the high within effect are at least welcome
indicators for Ethiopia, at a very early stage of industrialization where a strong emergence of
manufacturing and massive internal labor movement associated with it has not yet begun.
Given that labor mobility across sectors and from rural to urban areas is at present limited, it
is important to promote and sustain the high level of the within effect along with a rising
contribution of the shift-effect for an extended period. These two effects must be more
dynamic and mutually enhancing to achieve overall economic growth and visible structuraltransformation.

3.5

Summary of findings

This chapter presented our analysis of the evolution and the current state of economy-widelabor productivity in Ethiopia, using data from PDC, the World Bank’s WDI and the Penn
World Tables for growth accounting and shift-share methods.
Ethiopia’s economy-wide labor productivity grew by an average of 4.94% per annum
from 2000 to 2016. However, in terms of absolute level, Ethiopia’s labor productivity is still
low even by the standard of latecomer countries. International comparison indicates that
Ethiopia’s economy-wide labor productivity is lower than that of Myanmar, Vietnam,
Cambodia, and Tanzania.
Our growth accounting analysis revealed that in recent years the source of economy-widelabor productivity growth was mainly capital deepening rather than TFP growth. This meansthat efficiency improvement slowed, while large investments in infrastructure and otherphysical assets equipped workers with more machinery and buildings, and thus sustained33

Ethiopia Productivity Reportlabor productivity growth. This is alarming as it is a sign of declining efficiency in the overall
economy.
Furthermore, our shift-share analysis shows that Ethiopia’s labor productivity growth has
been predominantly driven by the within effect. This suggests that there has been an
improvement in efficiency in individual sectors. However, that contribution has declined overtime. Instead, the shift effect increased over time, with labor moving away from agriculture,largely to the service sector and to a smaller extent to the industrial sector. The service sector
which receives labor may be informal and low productivity rather than high-tech and highproductivity, but this suspicion cannot be corroborated due to lack of data. The large share of
the within effect, combined with an increasing share of the shift effect, is in line with theprofile of Ethiopia, which is at an initial stage of industrialization and structuraltransformation. In the following stage, the shift effect and the interaction effect should bothbe further activated to contribute greatly to the improvement of economy-wide laborproductivity. If they do not, there will be a risk of growth slowdown before sufficientdevelopment is attained, premature deindustrialization, and a middle income trap after
Ethiopia reaches lower middle-income in 2025.
While a nascent process of structural transformation is visible in Ethiopia, so far labor
shift has mainly been from agriculture to services rather than to manufacturing, despite thegovernment’s aim of vitalizing the industrial sector based on the dynamic achievement of the
agricultural sector. This points to the need to identify the constraints on the industrial sector,
particularly the manufacturing sector, and measures to remove them, in order to ensure thatstructural transformation proceeds at a suitable pace and scope.

34

4. Zooming in on the Manufacturing Sector
In this section, we focus on manufacturing sector productivity. The section is divided into six
parts. The first part reviews three policy instruments adopted by Ethiopia to enhance the
manufacturing sector. The second part describes the dataset we have reconstructed together
with its limitations, and the general characteristics of the Ethiopian manufacturing sector. The
third part presents a time-series disaggregation of manufacturing labor productivity and TFP
into sub-sectors. The fourth part compares Ethiopia’s manufacturing labor productivity withthat of peer countries. The fifth part discusses the wage-productivity nexus of Ethiopia andcompares it with that of the benchmarking countries. The last part is a summary of the main
findings.
This section works with data from the Ethiopia Large and Medium Scale Manufacturing
Industries (LMSMI) survey of the Central Statistical Agency (CSA), covering the period
1996-2016. The manufacturing sector discussed below is defined as the large and medium
scale manufacturing industries reported by the LMSMI survey, which do not include microand small establishments. For international comparison, we will use the UNIDO database(INDSTAT 2 2018, ISIC Revision 3).

4.1 Review of past policy interventions
This section presents a review of the impact of policy interventions undertaken by the
Ethiopian government, which include benchmarking, kaizen, twinning, and the small businesssupport scheme. The main objective of these policy instruments was enhancement of the
productivity of the manufacturing sector.

4.1.1 Benchmarking, Kaizen and Twinning
The Government of Ethiopia has adopted benchmarking, kaizen and twinning as policy
tools for the enhancement of quality, productivity, and competitiveness (QPC) in theindustrial sector of Ethiopia. The three are explained briefly below.
Benchmarking is the systematic comparison of current reality with the situation of target
countries and/or companies, and the setting of clear numerical goals for improvement definedas the addressing of identified gaps. With UNIDO support, the government launched abenchmarking program in 2005 with the initial target of the leather sector. The program aimed
to upgrade technology and raise the capacity of prioritized sectors, thereby raising theirinternational competitiveness. Selected garment and leather enterprises received direct35

Ethiopia Productivity Report

support from globally renowned companies and experts. However, the impact of the programon the performance of implementing factories was moderate. In a commissioned study, IPE
Global (2017) stated that despite some modest results, benchmarking led to neither thesolution of targeted problems nor the achievement of the pre-set goals due to high staff
turnover, insufficient commitment by the implementing firms and supporting institutes, andinsufficient funding for continuation after the completion of the benchmarking project.
Moreover, some firms saw benchmarking as imposition from outside, which reduced thesense of ownership and interest of the beneficiaries, posing a risk to sustainability.
Twinning is an institutional cooperation agreement between a domestic institution and an
exemplary foreign institution of the same kind, intended to raise domestic capacity throughtraining, visits, institution-building, and experience-sharing. Twinning brought significant
improvements in certain Ethiopian organizations, most notably the Leather Industry
Development Institute (LIDI), which received the support of the Central Leather ResearchInstitute of Chennai, India from 2011 to 2014. Through research and product development aswell as industrial consultancy services, the twinning arrangement significantly raised LIDI’s
institutional capacity. Beneficiary companies supported by LIDI witnessed improved
productivity and motivation on LIDI’s part to engage in new product development. Following
the success of the LIDI project, the Textile Industry Development Institute (TIDI) in 2014 andthe Metal Industry Development Institute (MIDI) in 2017 also entered similar twinningarrangement. However, problems related to complementarity have been observed in theimplementation of the scheme. LIDI and TIDI in particular are busy institutions,
simultaneously conducting many projects and receiving many kinds of international support,
which tends to generate uncertainty among beneficiary factories when new initiatives are
introduced in parallel, giving rise to confusion as to relationships with and integration withexisting programs (IPE Global, 2017).
Kaizen: Ethiopia officially introduced kaizen in 2009 in collaboration with Japan. Kaizen,
a Japanese management philosophy with many concrete tools, features continuous quality andproductivity improvement at low cost and requiring little investment. It has brought
remarkable qualitative and quantitative achievements wherever it is seriously embraced. One
advantage of kaizen is that results are quickly obtained, concrete and quantifiable. For
example, Otsuka et al. (2018) reports initial achievements at kaizen implementing factories in
Ethiopia from 2012-2014 as follows: (i) securing additional workspace ranging from 52.6 to
9,053 square meters depending on firms; (ii) an increase in labor productivity ranging from36

Zooming in on the Manufacturing Sector

1.29% to 60%; (iii) a rise in capacity utilization of machinery ranging from 25% to 75%; (iv)
decline in the defect ratio ranging from 57.1% to 5.0%; (v) cost reduction ranging from 6% to33%; and (v) decline in accident occurrences from 49.5% of firms to 14.3%. Improvement inlabor productivity was especially notable in the textiles and leather sectors. Kaizen alsobrought improvements in product quality and customer satisfaction.
The Ethiopian Kaizen Institute (EKI), established in 2011, has grown to be a capable
implementing agency with an expanding scope of work. Ethiopian Kaizen Consultants can
now teach basic kaizen without Japanese support. National, regional, city-wide andinstitutional kaizen movements are guided by EKI. Other African countries, and the African
Union and New Partnership for Africa’s Development (NEPAD) as well, began to learnkaizen from Ethiopia. Achievements so far owe much to the strong commitment of Ethiopia’s
national leaders, to the adequate budgeting, staffing and incentivization of EKI, and to
Japanese cooperation.
However, kaizen has also encountered many challenges, including unwillingness of
selected firms to introduce kaizen, workers’ reluctance to participate, frequent turnover ofmanagers and kaizen leaders, imperfect understanding of the kaizen philosophy 4 , andlimitations associated with hasty learning and poor information management. Companies are
sometimes wary of EKI consultants, suspecting they might report sensitive information to tax
agencies. At other times, companies become heavily dependent on EKI consultants, expecting
them to do everything for them. Good managers and kaizen leaders may be poached by otherfirms that want to implement kaizen, which can lead to high personnel turnover and lack ofcontinuity in kaizen effort. There are also reports of problems at companies that introduce
kaizen without including everyone from top executives to line workers.
Perhaps the greatest challenge for Ethiopia, even after implementing kaizen for more thanten years, is that of mindset transformation. Despite awareness campaigns, not all kaizen
implementers at factories and other workplaces have developed a mindset based on a fullunderstanding of kaizen philosophy and the proper conduct of kaizen in every instance. In
Ethiopia, the spread of kaizen is still viewed as a forced efficiency tool rather than a spiritual
awakening. Kaizen in Ethiopia has not yet emerged as a genuine national productivity
movement.

4

In policy dialogue sessions with Japan, former prime ministers Meles and Hailemariam both regularly stressedthat kaizen was not merely a convenient toolbox for achieving quick results but a deeply ingrained life and work
philosophy which should be embraced by all Ethiopians at heart.

37Ethiopia Productivity Report

4.1.2 Small business support schemes
In 1997, Ethiopia formulated a Micro and Small Enterprises (MSEs) development strategy
to address the issues of unemployment and inequality, and to enhance economic growth. This
strategy was revised in 2011, with a reiteration of its key objectives of raising the income of abroad segment of society, creating far-reaching job opportunities, and thereby reducing
poverty and inequality. The revised strategy also emphasizes the role of MSEs in laying afoundation of sustainable and productive industrial development. It pays special attention to
manufacturing enterprises, particularly those that are export-oriented or substitute imports inline with the priorities of GTP I (2010/11-2014/15).
The MSE strategy comprises several key interventions and support programs, including
the promotion of human capital and technology development through entrepreneurial,
technical and marketing training, and provision of an industrial extension service that reaches
out to MSEs. This package consists of four components: entrepreneurship training, technicalskill training, kaizen, and technology support. The Ethiopian government has mandated thetechnical and vocational education and training (TVET) colleges to provide these support
services. Other interventions include improved access to financing, rental working premises,
and market linkages.
Hundreds of thousands of MSEs, established through the successive GTPs and the MSE
strategy, created millions of jobs. However, despite such quantitative achievements, themajority of workers are engaged in low skill and low technology activities. MSEs generally
have weak linkages with medium and large enterprises, and they contribute little to exports asmost of them target the domestic market. Due to their low quality and productivity, not many
MSEs are able to compete in the international market.
Gebreeyesus, et al. (2018) argue that the current business development services provided
to small firms are insufficient in coverage and lack diversity and depth. They only offer
generic guidance and are supply (or donor) driven, giving rise to a mismatch of supply anddemand, duplication and a lack of coordination among multiple service providers, and thus
are not sustainable. More importantly, their support instruments do not differentiate dynamic,
growth-oriented entrepreneurs from the rest, which leads to a blanket approach that does not
identify the individual capacity and needs of potential entrepreneurs.

4.2 Description of the manufacturing survey data
4.2.1 The survey data, source and limitationsa) Data compilation process and limitations

38

Zooming in on the Manufacturing Sector

The main source of data for the manufacturing study is the Large and Medium Scale
Manufacturing Industries (LMSMI) survey of CSA. The CSA has been conducting thissurvey annually since the mid-1970s, but at present the data is only available from 1996
onward. Thus, our analysis is for the period 1996-2016. The survey, a census of medium andlarge manufacturing industries, provides a rich set of information about their inputs andoutputs and other key indicators of productivity5.
The CSA defines manufacturing as “the physical or chemical transformation of materials
or components into new products, whether the work is performed by power-driven machines
or by hand, whether it is done in a factory or in the worker’s home, and whether the products
are sold at wholesale or retail. The assembly of the component parts of manufactured products
is also considered as a manufacturing activity” (CSA, 2018). This definition is based on theInternational Standard Industrial Classification (ISIC Revision-3.1). The CSA defines large
and medium scale manufacturing industry as all manufacturing establishments which employ
ten or more people and use power for production.
Although this survey is the main source of information about the structure and function of
the manufacturing industries in Ethiopia, it suffers from many shortcomings. The main issue
in using the CSA’s LMSMI data is that variables often change in terms of structure, code andname across years due to new variable coming to the data, omitted variables, changes invariable names, and so on. Among these, the greatest problem arises with the wholesalechange in the establishment numbers of surveyed firms between 2011 and 2012. Hence,merging the data across this gap poses a particular challenge. We approached this problem bysystematically matching the features of each establishment before and after the gap—features
such as line of business, establishment year, address, telephone number, etc.—to rediscover
the old number. We are confident that this procedure successfully re-connected the two partsof the database at the establishment level.
In addition, there are inconsistencies in the assignment of ISIC codes to different activities,
which creates problems for sub-sector level analysis. Aggregate features of our final dataset
were compared with their equivalents in the summary reports prepared by the CSA. The
matching was reasonably close, suggesting reliability of data reconstruction.
During the productivity analysis, we encountered several outliers which could have been aresult of entry errors regarding units or variable names. We eliminated such outliers as much

5

In this report the manufacturing sector refers to large- and medium-size manufacturing firms unless indicatedotherwise.

39

Ethiopia Productivity Reportas possible. In some cases, we took the average of preceding and subsequent years for aparticular variable or firm. In other cases, we decided to remove the variable or the firm fromthe dataset. For the 2016 data, it was more difficult to determine whether a variable was an
outlier or not, as new firms were included in 2016 with no data history as a basis fordetermination of whether the data was an error or a genuine figure.
The reconstructed dataset presented here is a result of our painstaking effort to get an
accurate picture of the manufacturing sector. Even though we can hardly claim the revised
dataset to be completely free of errors, we now have a connected and reasonably good dataset
spanning twenty-one years from 1996 to 2016. The final panel dataset comprises 3,378
establishments and 30,609 observations. It should be noted that the structure of the panel data
is unbalanced, given a significant number of firm entries to and also firm exits from the
LMSMI Survey every year.
b) Variable Definitions
Large and medium industry price deflator: we compute price deflators for medium andlarge manufacturing enterprises using data obtained from PDC. The PDC computes GDP data
disaggregated by sector at both current and constant prices. We compute the deflator for
medium and large enterprises by dividing nominal values by real values for medium and large
enterprises. These disaggregated price deflators are more appropriate than the aggregate GDP
deflator because the manufacturing sector, and in particular large and medium sub-industries,
may not experience the same price changes as the rest of the economy. However, the key
shortfall of such price deflators is that they assume the same price changes for all sub-sectors
within the large and medium manufacturing enterprise group. Moreover, applying the same
price deflator for different inputs such as materials, labor, and capital is also questionable.
This problem may be mitigated if the prices of different inputs and outputs within the samelarge and medium enterprises group are highly correlated, which we believe to be the case.
Labor: labor engaged in the large and medium-manufacturing industry is computed as thesum of paid employees, working proprietors, active partners, and unpaid family workers. We
have converted temporary workers into the equivalents of full-time workers.
Value added in the National Account concept at market price: this is defined as thedifference between the gross value of production and the sum of industrial and non-industrial
costs. In addition to total sales values, gross value of production includes: the value ofcontracted work done for others using the firm’s materials, receipts for repair andmaintenance works done for others, receipts for products bought and resold without further

40

Zooming in on the Manufacturing Sector

processing, value of capital goods produced by firm’s own employees and materials for own
use, interest received, rental income from the lease of machinery and equipment, insurance
claims, and other income, but excludes subsidies received. Industrial cost is the sum of thevalues of total raw materials, fuel and lubricating oil, electricity consumed, wood andcharcoal for energy, cost of repair and maintenance, water consumed, goods bought andresold, and contracted works done by others for the establishment. Non-industrial cost
includes: license fees, cost of advertising, stationery, telephone and mailing, accounting andlegal commissions, and rent payable for rental of structures and equipment. Real value added
is computed by deflating nominal values using the medium and large manufacturing industry
deflator mentioned above.
Capital stock: this refers to the total year-end book value of fixed assets as provided byrespondents in the survey. The assumption is that the respondents compute capital stock as net
value at the beginning plus new capital expenditure minus capital sold, disposed anddepreciated. However, some respondents may simply estimate the current value without
following the appropriate procedure. Therefore, there may be inaccuracies in capital stock
declared by some firms. Like the other variables, the sub-sector price deflator for large andmedium industry is applied to capital stock so as to arrive at real capital stock 6. The data on
capital stock is used mainly for TFP computation.
Our sub-sector analysis is based mainly on two-digit ISIC. The sub-sectors included arefood & beverages, textiles, garment, leather, wood, paper, publishing & printing, chemicals,
rubber & plastics, other non-metallic minerals, basic metals, fabricated metals, and machinery& equipment. The analysis is done on a total of 30,315 observations, sorted into 21 years and
15 sub-sectors.

4.2.2 General characteristics of the manufacturing sector
Figure 4.1 presents the number of establishments and employees in the LMSMI sector
covering the period 1997-2016. During that period, the number of establishments rose from
741 to 3,596, a 4.7-fold increase. Employment also increased 2.8-fold over the same period
and reached a quarter million by 2016.

6

We tried to extract capital good price deflators from the World Bank’s WDI by examining the nominal and realcapital formation (investment) data. However, that data is available only for the period after 2011. Moreover, it
is for capital formation (investment) in the whole economy and is not unique to medium and large manufacturing
enterprises, which raises another representation problem. We decided to stick to the medium and large
enterprises deflator computed from the PDC data even though it does not distinguish price movements ofdifferent inputs and outputs.

41Ethiopia Productivity Report

Figure 4.1 Trends in number of establishments and employees
4,000

350,000

3,500

300,000

3,000

250,000

2,500

200,000

2,000

150,000

1,500

100,000

1,000

50,000500
0Number of employees (right axis)

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

0

Number of establishments (left axis)

Source: authors’ calculation based on the CSA’s LMSMI Survey.

The first three columns of Table 4.1 give real value added 7, adjusted by the manufacturing
deflator, by industrial group for selected years. In 2014/15, the overall manufacturing realvalue added was 25.8 billion Birr, which was 1.7 times and 6.8 times larger than value added
generated in 2004/2005 and 1995/1996, respectively. In the sub-sectoral distribution of value
added, food & beverages accounts for over 40% of total manufacturing value added. This
share has changed little over the last twenty years.
With a value added share of 14.7%, the second largest sector in 2014/15 was the nonmetallic mineral products industry including cement, clay and glass products. The share ofthis sub-sector increased over time. On the other hand, the shares of the priority sub-sectors
designated by the government—textiles, garment and leather—declined over the sample
period. From 1995/1996 to 2014/2015, the value added share of these sectors dropped sharply,
9.8% to 3.2% for the textiles sector, from 1.0% to 0.6% for the garment sector, and from 9.2%
to 3.3% for the leather sector.
Table 4.1 also reports employment share by industry group. In the last twenty years
LMSMI employment tripled, reaching 333,084 in 2014/2015. Similarly with value added, thefood & beverages industry performed relatively well over the last twenty years, increasing
its employment share from 25.8% to 39.0%. Furniture was the second-best performer in terms
7

Value added in the national account concept is defined as the difference between gross value of production andintermediate consumption, adjusted for tax on products, e.g., license tax.

42

Zooming in on the Manufacturing Sector

Table 4.1 Large and medium scale manufacturing: real value added and employment
share by industrial group
Value added in national
account concept share (%)

Employment share (%)

1995/96

2004/05

2014/15

1995/96

2004/05

2014/15

Food & beverages

41.15

44.05

42.83

25.76

29.14

39.00

Tobacco

4.83

4.36

1.45

1.09

0.63

0.67

Textiles

9.83

5.66

3.18

31.53

18.82

9.23

Garment

0.95

0.53

0.59

4.50

2.40

1.87

Leather & footwear

9.15

3.85

3.34

8.61

7.23

5.17

Wood

2.02

0.65

0.46

2.77

1.43

1.13

Paper

6.65

7.07

2.95

6.16

6.83

2.89

Chemicals

3.18

5.17

5.36

3.14

5.43

4.28

Rubber & plastics

3.32

4.91

3.72

2.46

5.16

5.94

Non-metallic minerals

9.49

8.80

14.70

6.71

8.32

8.77

Basic metals

3.28

7.66

5.14

1.25

1.60

2.45

Fabricated metal

2.02

3.25

5.86

2.23

3.55

2.84

Machinery & equipment

0.23

0.08

0.41

0.48

0.20

0.55

Motor vehicles

2.35

1.01

6.63

0.78

1.12

1.38

Furniture

1.55

2.94

3.35

2.53

8.13

13.83

Total share

100.00100.00100.00100.00100.00100.00Total value added (million ETB)

3,795

15,06625,842
101,155

110,160

333,084

Total employment
Source: authors’ calculation based on the CSA’s LMSMI Survey.

of employment generation, increasing its share from 2.5% in 1995/1996 to 13.8% in
2014/2015.
In contrast, the employment share of the textile industry, initially the largest employment
source, declined continuously over the sample period. Its share dropped from 31.5% in
1995/1996 to 9.2% in 2014/2015. The employment shares of the garment and leather sectors
also fell over the sample period, declining in both value added and employment despite thepolicy effort of the government to promote those sectors.
Figure 4.2 presents the regional distribution of manufacturing establishments for the mostrecent available year of 2016/2017. Addis Ababa and Oromia account for 39% and 26%,
respectively, suggesting high concentration of manufacturing establishments in these regions.
Since most of the establishments in the Oromia region are in the areas surrounding Addis
Ababa, it is clear that the capital city and its vicinity are Ethiopia’s dominant manufacturinghub. This is a point of concern given the government’s desire for more balanced spatial
economic distribution.

43

Ethiopia Productivity Report

Figure 4.2 Regional distribution in number of manufacturing establishments, 2016-2017
Somale, 0.7%
Harari, 1.0%

Benishan., 0.2%

Afar, 0.4%

Gambella, 0.0%

Dire Dawa, 3.5%

Tigray,7.0%
SNNP, 8.2%
Addis Ababa, 39.4%
Amhara, 13.5%

Oromiya, 26.0%

Source: authors’ calculation based on the CSA’s LMSMI Survey.

Table 4.2 gives the export share in total sales of LMSMI by sub-sector, based on the
CSA’s 2016/2017 Survey report. The total export of the LMSMI sector was 5.86 billion Birr,
equivalent to 261.6 million USD at the average official exchange rate of that year (22.4
Birr/USD). The average share of export in the total sales reported in LMSMI remained verysmall at 3.7%. In recent years, manufactured exports, and for that matter total merchandise
exports, have not shown any tendency to rise. Manufactured exports are dominated by a few
sub-sectors including food & beverages (41.7% of total manufactured exports) and leather(24.5%) in the reference year. The contributions of textiles, which have been expected to
become leading export sub-sectors, remained small, accounting for 9.1% and 4.5% of total
manufactured exports, respectively.
Finally, we examine the level of capacity utilization in the manufacturing sector. As canbe seen in Figure 4.3, despite small signs of improvement, the capacity utilization rate of
manufacturing has remained below 67%. When firms are asked to give the main reason for
their low capacity utilization, they often cite the shortage of input supply. In 2016, about onethird of the respondents replied that the shortage of input supply was their largest obstacle toachieving greater capacity.

44

Zooming in on the Manufacturing Sector

Table 4.2 Share of export sales of the manufacturing sector, 2016-2017
Revenue from sales in 2016-2017(million ETB)

Exportshare in
total sales(%)

Industry
share in
total
export (%)

Local

ExportTotal

Food & beverages

49,844.88

2,445.40

52,290.28

4.68

41.69

Tobacco1,606.67

568.762,175.44

26.14

9.70

Textiles

3,031.93

536.293,568.22

15.03

9.14

Garment

8,131.86

263.838,395.69

3.14

4.50

Leather & footwear

3,223.09

1,434.11

4,657.20

30.79

24.45

Wood

549.880.38

550.26

0.07

0.01

Paper

7,635.72

0.37

7,636.09

0.00

0.01

Chemicals

9,342.54

157.469,500.01

1.66

2.68

Rubber & plastics

11,481.50

40.29

11,521.79

0.35

0.69

Non-metallic minerals

26,505.30

164.4026,669.70

0.62

2.80

Basic metals

10,041.32

0.11

10,041.42

0.00

0.00

Fabricated metal

7,012.53

82.85

7,095.38

1.17

1.41

773.22113.67886.89

12.82

1.94

Motor vehicles

4,364.03

0.00

4,364.03

0.00

0.00

Furniture

9,727.57

57.37

9,784.93

0.59

0.98

153,272.00

5,865.29

159,137.30

3.69

100.00Machinery & equipment

Total

Source: authors’ calculation based on the CSA’s LMSMI Survey.

Figure 4.3 Average capacity utilization of the manufacturing sector
70

%

60
50
40
30
20
10
0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014 2015 2016

Source: authors’ calculation, based on the CSA’s LMSMI Survey.

45

Ethiopia Productivity Report

4.3 Productivity in the Manufacturing Sector
4.3.1 Methodology
In this sub-section, we analyze the current state of labor productivity and TFP in the
manufacturing sector of Ethiopia, based on data from the reconstructed LMSMI survey of the
CSA covering the period of 1996-2016.
We compute the labor productivity of the manufacturing sector as the ratio of real value
added of production to the size of labor force as measured by number of workers,
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖𝑡𝑡 =𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖𝑡𝑡
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑡𝑡(1)

where 𝑡𝑡 refers to year and 𝑖𝑖 refers to sector or sub-sector depending on the context of theanalysis. For the entire manufacturing sector, total manufacturing value added is divided bytotal number of workers engaged in the sector.
The limitations of labor productivity as a productivity measure, discussed in Section 2.5
for the case of the whole economy, should be reiterated here for the manufacturing sector.
Labor productivity based on value added is supposed to capture the efficiency with whichlabor is mobilized and performs work. However, as it is a partial measure rather than amultiple-factor one, it reflects a number of other influences as well, such as changes in capitaland technology within and across firms, economies of scale, degree of capacity utilization,and measurement errors (OECD, 2001). Therefore, manufacturing labor productivity is animperfect indicator of what we actually want to measure, namely the contribution of workers
as they improve capacity and are allocated to appropriate tasks, as well as the degree of effort
they expend in the manufacturing process.
Following the World Bank enterprise survey report on productivity (Saliola & Seker,2011), we estimate a Cobb-Douglas production function with three factors of production:
capital, labor, and intermediate goods.
The gross value of production as measured by total sales is used as a measure of output.
The total book value of the fixed assets at the end of each year, estimated by the survey
respondents, is used as the value of capital. This is assumed to capture the replacement valueof machinery, vehicles, buildings, and equipment. We measure labor as the total number ofpaid employees, working proprietors, active partners, and unpaid family workers, withtemporary workers converted to fulltime equivalents. Intermediate goods are assessed by thecost of raw materials and intermediate goods purchased. All variables are adjusted for pricelevels and hence are real values.

46

Zooming in on the Manufacturing Sector

TFP is computed as the residual of the estimated Cobb-Douglas production function.
Specifically, we compute TFP at the firm level as
𝑌𝑌𝑖𝑖 = 𝛽𝛽𝑙𝑙 𝐿𝐿𝑖𝑖 + 𝛽𝛽𝑘𝑘 𝐾𝐾𝑖𝑖 + 𝛽𝛽𝑚𝑚 𝑀𝑀𝑖𝑖 + 𝛾𝛾𝑠𝑠 + 𝜔𝜔𝑖𝑖 +𝜀𝜀𝑖𝑖(2)where Yi is the logarithm of the firm’s output, measured by gross sales value; Lit , K it , and Mit

are the logarithms of firm 𝑖𝑖′𝑠𝑠 costs of labor, capital, and materials, respectively; β is a vectorof input elasticities of firms; 𝛾𝛾𝑠𝑠 is a vector of industry-specific effects; 𝜔𝜔𝑖𝑖 represents firm 𝑖𝑖′𝑠𝑠

total factor productivity as a logarithm; and 𝜀𝜀𝑖𝑖 denotes an i.i.d. component capturingidiosyncratic deviations from the mean due to unexpected events such as external factors andmeasurement errors. We estimate (2) to solve for TFP (𝜔𝜔�𝑖𝑖 ):
�𝑙𝑙 𝐿𝐿𝑖𝑖 − 𝛽𝛽�𝑘𝑘 𝐾𝐾𝑖𝑖 − 𝛽𝛽�𝜔𝜔�𝑖𝑖 = 𝑌𝑌𝑖𝑖 − 𝛽𝛽𝑚𝑚 𝑀𝑀𝑖𝑖 − 𝛾𝛾𝑠𝑠

(3)

� 𝑖𝑖 = 𝑒𝑒 𝜔𝜔�𝑖𝑖 .
Estimated TFP in normal scale can be expressed as the exponential of 𝜔𝜔�𝑖𝑖 , that is, ΩWe estimate TFP using equations (2) and (3) for each year for the period 1996-2016.

Estimating TFP separately for each year permits exploration of the trend over time. We also
report estimated TFP by sector and sub-sector.

4.3.2 Productivity in time seriesThis section depicts the past history of labor productivity and TFP of the manufacturingsector. In Ethiopia, manufacturing labor productivity has shown an upward trend over time,
particularly in recent years. Between the two end periods, 1996 and 2016, labor productivity
increased from 66,400 Birr per employee to 167,600 Birr per employee (Figure 4.4). Thistranslates to annual labor productivity growth of 4.63%.
However, the growth of labor productivity was not smooth. It exhibited negative growthin 1997, 2000, 2002, 2007, 2012, and 2013 (Figure 4.5).
We exclude the year 2016 from the calculation of manufacturing labor productivity
growth because of many outliers in that year: food & beverages, wood, paper, publishing &printing, and fabricated metal. For 1996-2015, then, average labor productivity growth of themedium and large manufacturing sector was 3.98%. The World Bank estimate of laborproductivity growth of the Ethiopian manufacturing sector for 1999-2013 was 4.4% (World
Bank, 2016), similar to our estimate of labor productivity for large and medium
manufacturing enterprises. The difference is likely due partly to different sample periods.

47

Ethiopia Productivity Report

Figure 4.4 Labor productivity in the manufacturing sector (real value added per employee)(in thousand ETB)

180
160
140
120
100
80
60
40
20
2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

0

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

Figure 4.5 Labor productivity growth in the manufacturing sector, Ethiopia
%
25
20
15
10
5
0
-5
-10
-15
-20

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

48

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

-25

Zooming in on the Manufacturing Sector

The high volatility of labor productivity, both in level and growth, can be ascribed to anumber of proximate factors such as the capacity of workers (labor efficiency in the true
sense), total factor productivity, and capital intensity. However, there are deeper reasons forhighly volatile labor productivity and overall low labor productivity in the manufacturingsector. A UNDP survey of 55 private and public manufacturing enterprises identifies powerinterruptions, foreign exchange shortages, and the lack of domestic raw materials, internet,
and operational loans as top constraints in Ethiopia (UNDP, 2017). It additionally suggeststhat a rigorous study of technology and skill gaps is required for accurate assessment of theperformance of the Ethiopian manufacturing sector.
A separate survey of the textiles and garment sub-sector by the Ethiopian Development
Research Institute (EDRI) identifies key constraints in the manufacturing sector: electricity
problems, shortage of raw materials, lack of adequately educated workforce, and problems
associated with access to finance, foreign currency shortage, and logistics and transportation(EDRI, 2016). The two studies identify similar external barriers to efficient manufacturing,barriers which reflect the lack of a conducive business environment, and are therefore beyond
the control of individual entrepreneurs. Two causes of productivity problems in Ethiopia must
be distinguished: those related to the mindset and capacity of managers and workers inmanufacturing establishments and those that are external and whose mitigation depends
largely on the policy actions by government.
Figure 4.6 shows the evolution of TFP in manufacturing enterprises in Ethiopia. TFP
initially remained stagnant for an extended period but began to rise significantly in 2012.
Figure 4.6 The level of manufacturing TFP
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

0.0

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

49

Ethiopia Productivity Report

Figure 4.7 The growth of manufacturing TFP
%
25
20
15
10
5
0
-5
-10
2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

-15

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

Figure 4.7 plots the growth rate of manufacturing TFP. The annual average growth overthe period 1996-2016 was 2.11%. Growth in the second half of the period (2006-2016) was
2.64%, much higher than that in the first ten years (1996-2005) which was only 1.48%.
In the latter period, however, the average growth of TFP (2.11%) was much lower than
the average growth of labor productivity (4.6%). This means that in recent years
manufacturing labor productivity was driven mostly by capital deepening, an observation
confirmed for the entire economy in Chapter 3.

4.3.3 Productivity by sub-sector
Productivity of manufacturing labor exhibits significant variation across sub-sectors in the
period under examination 8. Table 4.3 reports the level and growth rate of labor productivity at
the sub-sector level for selected years. In the most recent year (2016), high labor productivity
was observed in motor vehicles, basic metals, food & beverages, and fabricated metal (indescending order). In contrast, labor productivity was low in the garment, wood, textiles,
furniture, and leather sectors (in ascending order). Comparing the highest with the lowest,
value added per employee in the motor vehicle sector (400,590 Birr) was some 14 times
higher than that in the garment sector (27,760 Birr). This difference may be partly explained
8

To compute the labor productivity of each sub-sector, we applied the same manufacturing price deflator for all
sub-sectors. To the extent that sub-sector deflators diverge, reported figures will deviate from the actual levels. If
such divergence is small, our procedure is largely innocuous. At any rate, our results should be interpreted withsome caution.

50

Zooming in on the Manufacturing Sector

Table 4.3 Labor productivity by sub-sector for selected years (in thousand ETB)
Labor productivity
growth (%/year)

Labor productivity level

266.4019962016
3.42

19962005
1.62

20062016
5.38

33.24

46.22

4.40

1.88

7.11

19.70

35.25

27.76

1.85

0.30

3.43

48.16

45.44

84.83

72.05

1.59

-1.59

4.61

41.43

35.86

40.07

69.64

35.77

-0.73

-0.37

-1.14

Paper

60.39

58.91

79.26

141.12141.59

4.26

3.02

5.80

Publishing & printing

52.13

54.88

91.11

94.75

180.55

6.21

6.20

6.84

Chemicals

103.69

74.10

82.84

196.19185.43

2.91

-2.49

8.06

Rubber & plastics

62.68

101.0872.93

154.22

77.56

1.06

1.68

0.62

Non-metallic Minerals

77.48

77.03

111.69184.58189.63

4.48

4.06

5.29

Basic metals

143.6293.71

220.47212.22314.29

3.92

4.76

3.55

Fabricated metal

56.12

37.91

55.39

145.51228.73

7.03

-0.15

14.18

Machinery & equipment

24.59

15.66

33.44

35.22

123.51

8.07

3.42

13.07

Motor vehicles

131.66259.59165.80180.22400.59

5.56

2.56

8.82

Furniture

24.05

25.39

48.22

53.77

50.19

3.68

7.73

0.40

1996

2000

2005

2011

2016

Food & beverages

134.54122.68155.63

159.17

Textiles

19.15

20.60

22.69

Garment

19.17

13.50

Leather

52.41

Wood

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

by the capital-intensive nature of the motor vehicle assembly sector, rather than a genuinelack of efficiency in managers and workers in the garment sector. But even when comparedwith the labor productivity of food & beverages, generally considered to be labor-intensive,the labor productivity of the garment sector is one tenth of that of the food & beverages sector.
This very low labor productivity may be due to the primitive stage of the Ethiopian garment
industry, where Cut, Make and Trim (CMT) operation generates only small domestic value
added and where virtually all fabrics and other materials are imported.
The rightmost three columns of Table 4.3 present the average compound growth rate oflabor productivity for each sub-sector. With the exceptions of wood, all sub-sectors had
positive labor productivity growth in the period 1996-2016, although the variance was large.
The fastest growth, above 6% annually, was observed for machinery & equipment, fabricatedmetal, and publishing & printing.
Figure 4.8 shows the sub-sectoral pattern of labor productivity (value added per laborunit) and capital intensity (capital per labor unit) for the period 1996-2016. There is a positive
correlation between these two variables with the correlation coefficient of 0.52, which

51

Ethiopia Productivity Reportsuggests much of the labor productivity difference across sectors can be explained by capitalintensity. The tardy performance of labor-intensive sub-sectors relative to more capitalintensive ones is consistent with our previous finding that economy-wide labor productivity
has been increasingly driven by capital deepening rather than TFP. But low labor productivity
per se in labor-intensive sectors does not necessarily imply they are less competitive vis-à-vis
capital intensive ones.
Figure 4.8 Labor productivity and capital intensity by sub-sector, 1996-2016
Labor productivity(in thousand ETB)
350

Capital intensity

300
250
200
150
100

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

Figure 4.9 Labor productivity by firm size(in thousand ETB)
250
200
150
100
50
0

1996

2006
Small

Medium2016
Large

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

52

Furniture

Wood

Machinery &equipment

Garment

Publishing

TextilesFabricatedmetal

Other nonmetal

Food &
beverages

MotorvehiclesChemicalLeatherRubberBasic metals

0

Paper

50

Zooming in on the Manufacturing Sector

Figure 4.9 shows labor productivity by firm size for selected years. Here, small firms arethose employing 10 to 29 workers, medium firms are those employing 30 to 100 workers, andlarge firms are those employing more than 100 workers. Labor productivity increases withfirm size in all selected years. In 2016, for example, labor productivity of large firms was
twice that of medium firms and 4.8 times higher than that of small firms. Söderbom (2012)
finds similar results in the Ethiopian manufacturing sector using the same data as that used
here, but covering only the period 1998-2008. Söderbom reports that firms with 50 or more
employees had labor productivity 10 times higher than that of firms employing fewer than 50
workers, and points to better management and technology of large firms as a possible cause ofthat striking difference. Söderbom also notes that the capital-labor ratio of large firms is 10
times that of small firms, which may additionally explain the labor productivity gap between
small and large firms.
Turning to TFP, Table 4.4 reports TFP levels and growth rates by sub-sector for selected
years. TFP shows little improvement in the majority of sectors over the last two decades. As
with labor productivity, TFP levels and growth rates differ greatly across sub-sectors. A
relatively high TFP annual growth rate of over 3% was attained during 1996-2016 by nonTable 4.4 TFP level and growth by sub-sector for selected years
TFP level

TFP growth (%/year)

1996

2000

2005

2011

2016

19962016

19962005

20062016

Food & beverages

1.04

0.97

1.24

1.24

1.97

3.19

1.97

4.18

Textiles

0.96

0.95

1.13

1.29

1.31

1.53

1.73

1.36

Garment

0.89

0.89

0.96

1.96

1.75

3.36

0.77

5.48

Leather

1.04

1.21

1.08

1.00

1.27

0.98

0.43

1.44

Wood

1.69

1.33

1.22

1.24

1.03

-2.47

-3.63

-1.52

Paper

0.65

1.06

1.39

0.94

1.25

3.31

8.55

-0.97

Publishing & printing

0.93

1.10

1.13

1.28

1.64

2.82

2.13

3.38

Chemicals

0.97

1.09

1.07

1.38

1.42

1.90

1.13

2.53

Rubber & plastics

0.99

1.10

1.11

1.31

1.26

1.21

1.22

1.20

Non-metallic minerals

1.21

1.51

1.20

1.23

2.70

4.01

-0.07

7.35

Basic metals

1.15

0.86

1.18

1.11

1.42

1.06

0.30

1.68

Fabricated metal

0.83

0.88

0.96

1.16

1.49

2.94

1.59

4.04

Machinery & equipment

1.18

0.85

0.94

1.22

1.87

2.30

-2.50

6.23

Motor vehicles

1.25

1.42

2.28

0.90

2.20

2.81

6.68

-0.35

Furniture

0.99

0.88

1.12

1.21

1.53

2.20

1.43

2.79

Source: authors’ calculation based on the CSA’s LMSMI Survey and PDC data.

53

Ethiopia Productivity Report

metallic minerals, garment, paper, and food & beverages. Meanwhile, average growth in thewood sector was negative, similar to the situation of labor productivity discussed earlier.
Textiles, leather, chemicals, rubber & plastics, and basic metals also showed weak
performance at below 2% growth. It is noteworthy that the TFP growth of capital-intensive
sectors such as basic metals, vehicles, machinery & equipment, and fabricated metal wasmuch lower than their labor productivity growth reported above. This points strongly to thepossibility that labor productivity in those sectors was driven largely by capital accumulation.

4.4 Manufacturing labor productivity: Ethiopia and othercountries
This section compares Ethiopia’s manufacturing labor productivity with that of selected
countries in Asia and Africa, including China, Vietnam, Malaysia, Indonesia, Sri Lanka,
Kenya and Tanzania. Manufacturing labor productivity, defined as the ratio of industrial value
added to the number of persons employed in the sector, is measured in USD and expressed inconstant 2010 prices by deflating nominal value added by each country’s deflator. The data istaken from UNIDO’s INDSTAT 2 2018, ISIC Revision 3. Results for Ethiopia are basicallythe same, except USD conversion, as the above analyses based on domestic manufacturing
data 9.

4.4.1 Pattern and growth of manufacturing labor productivity
Figure 4.10 presents the manufacturing labor productivity of selected countries for theperiod 2000-2015. China’s labor productivity increased dramatically over the last decade-andhalf, surpassing all other countries in the sample. In contrast, Ethiopia’s
manufacturing labor productivity, expressed in USD, was generally stagnant and evendecreased in some years. It ranked at the bottom of this country group by the end of thesample period. In 2000, Ethiopia’s productivity level was 94% of that of China. However, thegap widened over time, and Ethiopia’s productivity was only 13% of China’s in 2015.
Even Kenya’s labor productivity was three to four times that of Ethiopia in the same year.
Vietnam’s productivity was less than that of Ethiopia until 2009, but rose above Ethiopia
since then.

9

UNIDO receives data from each country’s statistics office but makes some adjustments including conversion toforeign currency. We checked the UNIDO data for Ethiopia against the CSA data by converting the latter to
USD using the average annual exchange rate of Birr/USD. The two sets of data are similar except for a scaledifference which must be due to normalization to 2010 constant price by UNIDO.

54

Zooming in on the Manufacturing Sector

Figure 4.10 Manufacturing labor productivity: selected countries(USD)
40,00035,00030,00025,00020,00015,00010,000
5,000

China

Ethiopia

Kenya

Indonesia

Malaysia

Sri Lanka

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Viet Nam

Source: authors’ calculation using the UNIDO’s INDSTAT 2 2018, ISIC Revision 3.

4.4.2 Labor productivity pattern in selected labor intensive sectors
Here we compare labor productivity across countries, focusing on labor-intensive exportoriented sectors such as food & beverages, textiles, apparel, and leather (Figures 4.11 to 4.14).
These sectors are among the priority industries designated by the Ethiopian government since
the early 2000s.
The sectoral patterns of labor productivity are similar to the performance of overall
manufacturing labor productivity. For each sub-sector, there is visible divergent trends inlabor productivity among sample countries. China, Indonesia, and Malaysia strengthened theirlabor productivity in all sub-sectors, and the gap between them and the other group ofcountries widened over time. Kenya, Vietnam and Sri Lanka are in the latter group, but inmost cases even their productivity is much higher than that of Ethiopia. Viewed against therest of the world in USD terms, Ethiopia’s productivity is not only low but stagnant ordecreasing in recent decades despite the fact that its labor productivity measured in Birr has
risen.

55

Ethiopia Productivity Report

Figure 4.11 Value added per employee: food & beverages(USD)
40,00035,00030,00025,00020,00015,00010,000
5,000

2013

2012

2015

Sri Lanka

2015

Kenya

Malaysia

2014

EthiopiaIndonesia

2014

China

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Viet Nam

Source: authors’ calculation using the UNIDO’s INDSTAT 2 2018, ISIC Revision 3.

Figure 4.12 Value added per employee: textiles(USD)
40,00035,00030,00025,00020,00015,00010,000
5,000

China

Ethiopia

Kenya

Indonesia

Malaysia

Sri Lanka

Source: authors’ calculation using the UNIDO’s INDSTAT 2 2018, ISIC Revision 3.

56

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Viet NamZooming in on the Manufacturing Sector

Figure 4.13 Value added per employee: apparel
(USD)

25000

20000

15000

10000

5000

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

-5000
China

Ethiopia

Kenya

Viet Nam

Indonesia

Malaysia

Sri Lanka

Source: authors’ calculation using the UNIDO’s INDSTAT 2 2018, ISIC Revision 3.

Figure 4.14 Value added per employee: leather(USD)
20,00015,00010,000

5,000

China

Ethiopia

Kenya

Indonesia

Malaysia

Sri Lanka

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Viet Nam

Source: authors’ calculation using the UNIDO’s INDSTAT 2 2018, ISIC Revision 3.

57

Ethiopia Productivity Report

In food & beverages (Figure 4.11), all countries except Ethiopia saw increases in laborproductivity, albeit at different speeds. China, Malaysia, and Indonesia achieved sharp
productivity rises while the performance of Vietnam, Sri Lanka and Kenya was more
moderate. Meanwhile, Ethiopia’s productivity remained stagnant and even declined. In 2015,
Ethiopia’s labor productivity in food & beverages was only 26% of that of China. The threeother countries in the low group—Kenya, Vietnam, and Sri Lanka—had 40%, 39% and 42%
of China’s productivity respectively in the same year.
In the textiles sector (Figure 4.12) and the apparel sector (Figure 4.13), Ethiopia’s low anddeclining productivity is also visible. The labor productivity of these sectors in Ethiopia has
been negative recently, a worrying development. Meanwhile, labor productivity in Ethiopia’s
leather industry displayed a small upward trend since 2010 (Figure 4.14), reaching 36% ofthat of China in 2015. In that sector, Vietnam ranked lowest with only 15% of China’s
productivity.

4.5

The wage-productivity nexus in manufacturing

In this section, we analyze labor cost in the Ethiopian manufacturing sector. Labor cost
includes payments of wages and salaries and non-wage payments such as commission,bonuses and allowances for food, medicine and the like. In order to adjust for price changes,
we deflate labor cost by the consumer price index (CPI) or the manufacturing output deflator.
We further compare the trends of Ethiopia’s labor cost and labor productivity with those ofpeer countries and calculate USD-based labor cost and unit labor costs.
Figure 4.15 Manufacturing labor cost (real labor cost in 2011 prices)

Nominal wage

Nominal labor cost

Source: authors’ calculation based on the CSA’s LMSMI Survey.

58

Real wage

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

(in billion ETB)
10
9
8
7
6
5
4
3
2
1
0

Real labor cost

Zooming in on the Manufacturing Sector

4.5.1 Trends in manufacturing labor cost
Figure 4.15 illustrates the pattern of labor cost in the Ethiopian manufacturing 10. Labor
cost has increased steadily over the past twenty years. In nominal terms, labor costgrew by an average annual rate of 15.4% during the period 1996-2016. The increase has been
substantial since 2006. In the period 2006-2015, labor cost grew between 16% and 30% eachyear. In real terms, increases in labor cost were more moderate. On average, real labor cost in
the manufacturing sector grew by about 6% annually over the past twenty years 11.
Movements in total labor cost were driven mostly by changes in wages and salaries whilechanges in non-wage payments such as bonuses and allowances had less impact. Wages andsalaries accounted for 88% of total labor cost, while non-wage earnings occupied theremaining 12% on average over the past twenty years.
Within manufacturing, the food & beverages sector had the largest wage bill accounting
for 35% of total labor cost in the period 1996-2016, followed by the textiles & garment sector
accounting for 16% (Figure 4.16). The textiles & garment’s share declined continuously from
Figure 4.16 Sectoral share of labor cost in total manufacturing
100%
19%

20%

21%

23%

21%

80%

4%
4%
8%

60%

8%

5%
5%
7%
9%

6%
7%
5%

6%
8%
6%

5%
6%
6%

40%
20%
0%

11%

11%

10%

24%

19%

13%

12%

16%

33%

36%

37%

36%

35%

1996-2000

2001-2005

2006-20102011-2016

1996-2016

Food products & beverages
Leather & Footwear
Other sectors

Textiles & Garments
Chemicals

Other non-metallic minerals
Rubber & plastics

Source: authors’ calculation based on the CSA’s LMSMI Survey.

10

One firm is excluded from the rubber & plastics sector due to an exaggerated data for wage payments for theyear 2016.
11
See Annexes 4.2 and 4.3 for the trends and the growth rate of labor cost in overall manufacturing and at subsector level.

59

Ethiopia Productivity Report

24% in 1996-2000 to 12% in 2011-2016. The shares of other non-metallic minerals, leather &footwear, chemicals, and rubber & plastics were 10%, 6%, 6%, and 5%, respectively, in totallabor cost during 1996-2016.
Nominal labor cost per employee increased annually since 1996, except in 2010, when adecline of about 10% was observed, and in 2016 (Figure 4.17). Over the past twenty years,
nominal labor cost per employee rose by 10.3% on average. Meanwhile, real labor cost per
employee deflated by CPI was more stable with fluctuation. It grew by only 0.7% on averageover the last twenty years implying the increase in nominal labor cost was largely in tandemwith general inflation.
Nominal labor cost per employee rose significantly in the period 1997-2016 in all subsectors except wood (Tables 4.5 & 4.6). In real terms, however, textiles, leather & footwear,
wood, paper, rubber & plastics, basic metals, machinery & equipment, and furnitureexperienced a decline over the same period in varying degrees. In nominal terms, the largest
increase in the cost of labor per employee was registered in chemicals followed by food &beverages. In the garment industry, the cost of labor per employee rose by close to 10% in
nominal terms while it went up slightly (0.3%) in real terms.

Figure 4.17 Real and nominal labor cost per employee (real labor cost in 2011 prices)(in thousand ETB)
45
40
35
30
25
20
15
10
5

Real Labor cost per employee

2016

2015

2014

2013

2012

2011

2010

2009

Nominal Labor cost per employee

Source: authors’ calculation based on the CSA’s LMSMI Survey.

60

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

0

Zooming in on the Manufacturing Sector

Table 4.5 Manufacturing labor cost per employee by sub-sector in thousand ETB (reallabor cost in 2011 prices)
1996

2000

2005

2011

2016

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Food & beverages

5.92

20.53

6.97

22.30

11.26

30.73

17.24

17.24

58.03

29.81

Textiles

3.73

12.93

4.76

15.23

5.68

15.49

15.53

15.53

23.87

12.26

Garment

3.03

10.49

4.09

13.08

4.98

13.58

10.01

10.01

21.71

11.16

Leather & footwear

6.00

20.79

7.16

22.94

7.94

21.66

14.60

14.60

27.03

13.89

Wood

4.60

15.94

6.21

19.87

6.85

18.68

14.64

14.64

3.24

1.67

Paper

6.16

21.34

6.40

20.49

10.67

29.12

20.90

20.90

33.70

17.32

Publishing & printing

6.48

22.47

7.57

24.22

9.75

26.59

17.72

17.72

53.67

27.58

Chemicals

5.39

18.68

6.54

20.94

9.25

25.24

19.13

19.13

61.88

31.79

Rubber & plastics

5.97

20.69

7.75

24.82

8.34

22.76

16.64

16.64

26.88

13.81

Non-metallic minerals

5.26

18.24

6.70

21.46

9.66

26.37

16.35

16.35

48.30

24.82

Basic metals

7.45

25.80

7.81

25.01

16.29

44.44

22.66

22.66

48.07

24.70

Fabricated metal

6.00

20.78

6.21

19.89

8.90

24.27

19.52

19.52

55.76

28.65

Machinery &equipment

5.16

17.88

4.59

14.69

7.47

20.38

19.46

19.46

26.20

13.46

Motor vehicles

7.90

27.36

8.82

28.24

15.30

41.75

32.92

32.92

53.17

27.32

Furniture

4.24

14.71

5.55

17.78

7.69

20.98

12.60

12.60

24.41

12.54

Total

5.01

17.36

6.25

20.02

8.97

24.49

16.89

16.89

39.05

20.07

Table 4.6 Annual growth rate of labor cost per employee by sub-sector (%, real labor
cost in 2011 prices)
1997-2000
Nom.

Real

2001-2005
Nom.

Real

2006-2010Nom.

2011-2016

Real

Nom.

Real

1997-2016
Nom.

Real

Food & beverages

4.05

2.07

9.61

6.41

9.85

-6.89

19.11

5.24

11.41

1.87

Textiles

6.07

4.09

3.53

0.34

8.64

-8.11

16.74

2.86

9.28

-0.27

Garment

7.51

5.53

3.94

0.74

14.43

-2.32

12.53

-1.34

9.85

0.31

Leather & footwear

4.44

2.46

2.05

-1.14

7.69

-9.06

14.01

0.14

7.53

-2.02

Wood

7.49

5.51

1.96

-1.24

12.83

-3.92

-23.15-37.03-1.75-11.30

Paper

0.96

-1.02

10.23

7.03

3.91

-12.83

15.90

2.03

8.50

-1.04

Publishing & printing

3.85

1.87

5.06

1.87

8.43

-8.31

21.41

7.53

10.57

1.02

Chemicals

4.83

2.85

6.94

3.74

12.58

-4.16

21.19

7.31

12.20

2.66

Rubber & plastics

6.53

4.55

1.46

-1.74

9.29

-7.45

11.76

-2.11

7.52

-2.02

Non-metallic minerals

6.04

4.06

7.32

4.12

9.74

-7.00

18.70

4.83

11.08

1.54

Basic metals

1.21

-0.77

14.69

11.50

-5.60

-22.35

22.71

8.83

9.33

-0.22

Fabricated metal

0.89

-1.09

7.18

3.98

15.54

-1.21

17.64

3.77

11.15

1.61

Machinery & equipment

-2.93

-4.91

9.73

6.54

2.29

-14.46

19.01

5.14

8.12

-1.42

Motor vehicles

2.77

0.79

11.02

7.82

-42.38-59.13

56.08

42.20

9.54

-0.01

Furniture

6.72

4.74

6.51

3.32

2.49 -14.25

17.17

3.30

8.75

-0.80

Total

5.54

3.56

7.23

4.03

9.48

16.61

2.73

10.27

0.72

-7.26

Source: authors’ calculation based on the CSA’s LMSMI Survey.

61

Ethiopia Productivity Report

4.5.2 The wage-productivity nexus
As discussed earlier, productivity growth is closely associated with an economy’s capacity
to generate productive employment and raise wages, and hence is a crucial prerequisite for asustained increase in living standards. According to economic theory, labor productivity
determines (is equal to) real wage provided that the market is competitive without distortions.
The relationship between wage growth and labor productivity growth has important
implications at both macroeconomic and microeconomic levels. In reality, they often do not
move together. If wage grows faster than labor productivity, production cost increases andcompetitiveness is eroded. Rapid wage growth unaccompanied by labor productivity
improvement also restrains firms from hiring workers or prompts them to replace workers
with machines, thus reducing employment growth. On the other hand, if wage is suppressed
below labor productivity growth, the income share and living standard of workers suffer,
which also negatively affects national economic development. It is essential that wage
increase and labor productivity growth move in tandem.
Wage-labor productivity balance may be lost by the action of various stakeholders. Labor
unions and belligerent workers may press for high wages beyond labor productivity. The
government may succumb to such political demand, and it may even legalize high (minimum)
wage for winning favor among voters, especially before a major election. Besides these, if
FDI firms are attracted to Ethiopia only by low wages (while they last), they have little
incentive to transfer skills and technology, and they will surely pack up and leave to another
low-wage country when local wage rises sufficiently. By contrast, if FDI firms intend to stayin Ethiopia for a long time and are willing to train workers and improve partner companies,
they will greatly contribute to labor productivity growth. These two types of FDI must be
distinguished. The government must encourage all players to enhance productivity while
exercising restraint on wage pressure beyond productivity growth.
With this background, it is useful to examine trends in wage and labor productivity in the
Ethiopian manufacturing sector. Figure 4.18 shows the level of labor productivity andnominal and real labor cost per employee in the manufacturing sector. The first two continued
to increase while real labor cost had a declining trend from around 2002 to 2012. Figure 4.19
reports the growth of the same variables during the period 1997-2016. Labor cost growth andlabor productivity growth varies considerably from year to year. Looking at the
manufacturing sector as a whole, labor productivity grew by 4.6% annually over the period
1997-2016. Average labor cost increased at the rate of 10.3% per year in nominal terms and62

Zooming in on the Manufacturing Sector

Figure 4.18 Labor productivity and labor cost per employee(in thousand ETB)
180
160
140
120
100
80
60
40
20

Labor productivity

Real labor cost per employee

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

0

Nominal labor cost per employee

Source: Authors’ calculation based on the CSA’s LMSMI Survey.

Figure 4.19 Growth of labor productivity and labor cost per employee
%

30
20
10
0
-10
-20

Labor productivity

Real labor cos per employee

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

-30

Nominal labor cost per employee

Source: authors’ calculation based on the CSA’s LMSMI Survey.

63

Ethiopia Productivity Report

0.7% per year in real terms over the same period. The fact that nominal labor cost increasedmuch faster than labor productivity means Ethiopian labor became more expensive (innominal Birr) relative to their work efficiency. In the meantime, the slight increase in reallabor cost (wages deflated by consumer price inflation) with fluctuations suggests that theliving standard of manufacturing workers virtually remained the same over the years, without
any distinct upward or downward trend.

4.5.3 Labor cost for different stakeholders
Up to now, we studied labor cost deflated by consumer prices. We now look at labor costfrom the viewpoints of different stakeholders. The key stakeholders we examine are workers,
domestic firms, and FDI firms.
(i) For workers’ living standard and welfare, nominal wage (W) deflated by CPI, as
analyzed above, is relevant: W/CPI.
(ii) For domestic firms whose sales are mainly in Birr, nominal wage (W) deflated by its
output price in Birr (P) is relevant: W/P (where P is the manufacturing output deflator).
(iii) For FDI firms, nominal wage (W) converted to USD is highly relevant:
W/(ETB/USD); theoretically, this should further be deflated by the international pricelevel (P*) but most foreign firms in this age of globally low inflation are interested in
nominal USD wages for comparing labor costs across countries.
Note that, if domestic relative prices are stable so CPI and manufacturing output deflator
move together, (i) and (ii) will be equivalent. If the exchange rate is maintained at purchasing
power parity (P/P*= (ETB/USD) where P* is assumed constant), (ii) and (iii) will beequivalent. In reality, these conditions do not usually hold, so we need to look at labor cost forworkers, domestic firms and FDI firms separately.
Another commonly used indicator for cost comparison is the unit labor cost (ULC),
calculated as the ratio of labor cost (nominal wage) to value added per worker (laborproductivity), usually at a highly aggregated level 12. This represents the labor cost from theviewpoint of enterprise management.
The three measurements of Ethiopian labor cost as well as ULC are plotted in Figure 4.20.
The four variables moved roughly together and were relatively stable in the first decade in our
sample period but they diverged significantly in the second decade, proving that the labor costfor each stakeholder did evolve differently from each other. In particular, the living standard
12

The OECD Glossary of Statistical Terms states: “ULC measure the average cost of labor per unit of output andare calculated as the ratio of total labor costs to real output.”64

Zooming in on the Manufacturing Sector

Figure 4.20 Comparison of four labor cost indicators (1996 = 100)
400
350
300
250
200
150
100
50

Wage/CPI

Wage/manuf. output deflator

Wage (USD)

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

0

Unit labor cost

Source: Authors’ calculation based on the CSA’s LMSMI Survey.

of workers was generally stagnant in the last two decades while the labor cost for domestic
enterprises rose greatly, especially in the recent period. USD-based wage and ULC, which are
the main concerns of FDI firms, were more volatile and rose greatly in recent years, especially
ULC. This points to the loss of Ethiopia’s labor competitiveness as seen from globally
operating FDI firms.

4.5.4 The wage-productivity nexus in international comparison
Figure 4.21 illustrates manufacturing labor productivity and wages and salaries (hereafterreferred to as wages) for selected countries. This helps us to understand where Ethiopia stands
relative to peer countries. Ethiopia’s wage is low compared with those of China, Kenya,
Vietnam, Indonesia, Malaysia, and Sri Lanka. Within this group, monthly manufacturing
wage in 2015 was highest in China (USD 676) followed by Malaysia (USD 669), Vietnam(USD 283), Kenya (USD 271), Indonesia (USD 246), and Sri Lanka (USD 176). The
Ethiopian wage was USD 100 implying the availability of low-cost labor compared to othercountries. Looking across sub-sectors, Ethiopia’s wage is even lower, as little as USD 20, in
the rubber & plastics sector and around USD 50 and USD 76 in the textiles and garment
sector, respectively.

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Ethiopia Productivity Report

Figure 4.21 Labor productivity and real and nominal wages in 2015: selected countries(in thousand USD)
40
35
30
25
20
15
10
5
0

China

Malaysia

Indonesia

Kenya

Sri Lanka

Viet Nam

Ethiopia

Labor productivity (value added per employee)
Real wage and salary per employee
Nominal wage and salary per employee

Source: authors’ calculation using UNIDO INDSTAT 2 2018, ISIC Revision 3.

However, Ethiopia’s low-wage advantage alone does not give it a competitive advantage.
Productivity and wages are equally important determinants of international labor
competitiveness. As analyzed above in detail (section 3.3), Ethiopia’s labor productivity is far
below those of virtually all competitor countries. Not only that, the productivity gap between
Ethiopia and the peer countries has generally widened through time. For Ethiopia, low
productivity disadvantage more than offsets low wage advantage. This fact is re-confirmed in
Figure 4.21.
Another way to gauge international labor competitiveness is to compare Ethiopia’s
manufacturing wage-productivity nexus with the experiences of high-income countries when
they were growing rapidly. Many latecomer developing countries in Asia—especiallyIndonesia, Vietnam, and Cambodia—recently had nominal wage increases which were much
faster than labor productivity growth, eroding their competitiveness (GRIPS Development
Forum, 2016). By contrast, East Asia’s early industrializers including Japan, Korea,
Singapore, and Taiwan did not face this problem. In these economies, rapid wage increases
were matched by equally vigorous labor productivity growth during their high-growth eras,
enabling them to strengthen international competitiveness while constantly improving the
average living standards of their workers. These economies rose to high income without
stopping at a middle income trap.

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Zooming in on the Manufacturing Sector

During Japan’s high growth period of 1955-1970, manufacturing labor productivity on
average grew 10.0% per year while manufacturing wages grew 10.2% per year. Thus, Japan
was able to preserve wage competitiveness for fifteen years as industrialization proceeded
rapidly and product quality and diversity were enhanced. In Korea, labor productivity roseslightly faster (12.9 %) than real wage growth (11.8%) during its high growth period of 19661978 strengthening Korea’s labor competitiveness (Kim, 1991).
Nations encounter different phases of FDI attraction as national wages rise in tandem withlabor productivity (if only wages rise without productivity enhancement, FDI-led
industrialization will be aborted). From the experiences of many Asian economies, thefollowing rough wage thresholds can be detected. Monthly wages below USD 200 are
generally suitable for attracting light manufacturing FDI. If wages rise to between USD 200
and 300, existing light manufacturing FDI can stay by improving productivity or introducing
mechanization, but not for long. If wages rise above USD 300, few new labor-intensive
greenfield FDI will be coming. At this wage level, labor-intensive factories start to consider
relocating abroad where wages are lower, and the government of the host country also begins
to discourage labor-intensive manufacturing in an effort to shift to technology-intensive
investments.
Manufacturing wages of China and Malaysia are already above USD 300, and their laborintensive factories are exiting to other countries in search of low-cost labor. Monthly wages inVietnam, Kenya, and Indonesia are approaching USD 300. At this wage level, lightmanufactured products may continue to be exported if they shift to high-end products orpartially adopt automation, but few new investors come to exploit labor abundance. However,these general trends may be modified by other factors such as labor market tightness, thespeed of labor productivity enhancement, and policy design and effectiveness 13 .
Manufacturing wages in Ethiopia are bellow USD 100, which seems competitive enough toattract labor-intensive FDI exiting from high-wage countries provided that Ethiopian laborproductivity rises significantly. So we come to the same conclusion: at the current level oflabor productivity, Ethiopia is not an overwhelming choice for light manufacturing FDI eventhough its wages are low.

13

For example, in Mauritius, FDI firms produce high-end apparels for export using advanced equipment andtechnology. Although manufacturing wages are high at $300-600 (depending on workers’ skill and productivity),
the industry is booming and factories are expanding capacity (observation as of 2012 by a GRIPS study mission).

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Ethiopia Productivity Report

4.6

Summary of findings

Using the reconstructed CSA manufacturing database, we have examined the productivity of
Ethiopia’s manufacturing sector in terms of both labor productivity and TFP. Manufacturinglabor productivity had a moderate growth rate of 4.6% per year over the last two decades.
However, labor productivity was highly volatile due to external factors beyond the control ofindividual firms such as the shortages of foreign currency, power supply, input materials andfinance. Meanwhile, manufacturing productivity measured in TFP remained stagnant over the
sample period except brief spikes in 1999 and 2015. The average annual growth of TFP wasaround 2.5%.
We also find heterogeneity across sub-sectors in manufacturing. Motor vehicles, basic
metals, fabricated metal, and food & beverages had higher labor productivity than garment,
wood, textiles, furniture, and leather & footwear. However, capital intensive sub-sectors suchas machinery & equipment, fabricated metal, publishing & printing, and motor vehicles
perform poorly in terms of TFP despite their relatively good records in labor productivity.
The tardy performance of labor-intensive sub-sectors relative to more capital-intensive subsectors is consistent with our earlier observation that economy-wide labor productivity has
been driven increasingly by capital deepening than by TFP.
This section also compared Ethiopia’s manufacturing labor productivity with those ofother developing countries including China, Malaysia, Indonesia, Kenya, Sri Lanka, andVietnam. For this international comparison, the UNIDO data, which measures value added in
USD, was used. Ethiopia’s labor productivity in both overall manufacturing and selectedindividual sub-sectors has remained low and stagnant, and in some cases even declined. As aresult, the gap between Ethiopia and the other countries in the sample widened dramatically.
For example, in 2000, Ethiopia’s manufacturing labor productivity was roughly equal (94%)
to China’s, but by 2015 Ethiopia’s productivity was only 13% of China’s. This should be agreat concern for Ethiopian policy makers who aspire to make the country a lightmanufacturing hub in Africa by 2025.
We also examined the wage-productivity nexus in Ethiopia’s manufacturing. The
available evidence indicates that labor productivity and nominal wage both increased.
However, the growth of the latter has been faster than that of the former. In addition, the ULCof the manufacturing sector, calculated as labor productivity divided by labor cost, has been
decreasing for two decades. This is good news, but this result is in Birr, not USD. Foreign
investors are more interested in ULC calculated in USD than in Birr. These findings suggest

68Zooming in on the Manufacturing Sector

that Ethiopian manufacturing may be losing rather than gaining labor cost competitiveness.
UNIDO data comparison of Ethiopia’s manufacturing labor productivity and wages withthose of China, Kenya, Vietnam, Indonesia, Malaysia and Sri Lanka shows Ethiopia to be thelowest in both average monthly wages and labor productivity. Low wage advantage alone isenough to attract light manufacturing FDI unless labor productivity is enhanced far above thecurrent level.

695. Labor Productivity Challenges in the
Emerging Ethiopian Apparel Industry:
Survey Results
5.1 Introduction
Earlier sections have shown that Ethiopia’s economy-wide labor productivity is among thelowest in the world. Labor productivity in the manufacturing sector is also much lower thanthat in peer countries. In this section, we report the results of an in-depth firm survey of
productivity in the apparel industry, explore the causes of low manufacturing laborproductivity in Ethiopia, and make some recommendations for overcoming the challenges.
The apparel sector is important for Ethiopia’s industrialization. The Ethiopian government
has long identified the textiles and apparel sector as one of the priority industries. Various
support programs have been designed and implemented, including a recent ambitious plan tobuild ten state-owned industrial parks exclusively for textiles and apparel production. As of
mid-2019, four of those public industrial parks had been inaugurated and two others were
under construction. These parks have started to attract many foreign investors including
globally renowned apparel brands 14.
The survey covered firms located in Hawassa Industrial Park and Bole Lemi Industrial
Park, and firms operating in and around Mekelle (outside the public industrial park). Eighteen
apparel firms, most of them foreign owned, were interviewed. The sampling was purposive
rather than representative, since we want to understand the productivity-related challenges
and opportunities faced by Ethiopia, by benchmarking the practices of global firms which arewidely thought to have more advanced management and technology than purely domestic
ones.
After formulating working hypotheses and preparing survey instruments, the researchteam interviewed in advance a number of stakeholders such as factory managers, government
14

At one of the interim report workshops, a question was raised as to whether labor-intensive apparel production
would survive the combined waves of AI, robots, Industry 4.0, and the like. It is highly likely that sewing,
cutting, and finishing as we know them today will be replaced by intelligent machines in the future. However,transition will take some time, and the timing also depends on the relative cost of such machines versus skilledworkers. For latecomer countries with idle workforce such as Ethiopia, there should be at least a few decades
before labor-intensive light manufacturing becomes unprofitable, during which skills and technology can belearned and quality and productivity should be enhanced in preparation for the next step of industrialization.
Thus, Ethiopia should not abandon light manufacturing now but take advantage of this learning opportunity,
although this may be the last chance for latecomers and the window of opportunity for traditional garment
making may become increasingly narrow.

71Ethiopia Productivity Report

officials, industrial experts and researchers, in order to classify causes of productivity
problems. The literature on labor productivity issues in Ethiopia and elsewhere was also
reviewed. As a result, we identified the following three major groups of factors affecting laborproductivity in the light manufacturing sector, especially the apparel industry:(i)Labor mindset and quality, which include workers’ basic skills, mindset, wages,
incentives and working conditions(ii)

Management strategy, comprising management style, experience and attitude(iii) Public policy and external factors
A survey instrument was then formulated for the collection of information on these three
dimensions. There were both qualitative and quantitative interview questions, and in mostcases the main respondent was top management. The qualitative survey mainly covered issues
related to management, labor and external conditions. The quantitative survey, on the other
hand, gathered concrete facts and data related to each of the above issues and also to measureproduction and labor productivity. A focus group discussion of selected workers wasadditionally conducted to explore more in detail labor related issues such as training, workers’
mindset, wages, incentives and working conditions 15.
The remaining sub-sections are organized as follows. Sub-section 5.2 describes the survey
method and presents the basic characteristics of the sampled firms. The next three subsections report findings on labor mindset and quality, management strategy, and public policy
and external conditions.

5.2

Survey method and basic characteristics of the sampled firms

A list of all apparel enterprises operating in the three designated locations was compiled frommultiple sources, including IPDC and EIC. The list was narrowed slightly to a total of 18
firms—four in Mekelle and seven each in Bole Lemi and Hawassa—which were asked toparticipate in the qualitative survey. Almost all of the garment factories in Bole Lemi and
Mekelle were included in the list while the sample for Hawassa accounted for approximately

15

The survey results reported below are different in scope from, but complementary to, the findings of the SKY
Project organized by Nagoya University in collaboration with local partners in Ethiopia, Ghana, and South
Africa during 2017-2018 (Yamada, et al., 2018). In Ethiopia, the Project conducted systematic questionnaires
and skills tests at garment factories and related institutions to quantitatively identify (i) perception gaps among
workers, TVET trainers, and factory managers, and (ii) various labor demands by groups including FDI firms,
domestic firms, and self-employed garment producers. The results show that perception gaps among key
stakeholders and diverse labor needs at different establishments are main reasons for low impact and frequentmismatches in worker training in both soft skills (closely related to mindset) and technical skills. On the other
hand, our firm survey explored possible causes of mindset problems which include, but are not limited to,current technical training.

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

half of the operational garment firms in that park (Table 5.1). Note that the sample firms for
Mekelle were all located outside Mekelle Industrial Park, as the park had no fully operationalfirms at the time of the survey.
For the quantitative survey, only 15 firms—seven in Bole Lemi, six in Hawassa, and two
in Mekelle—responded to our request. Among the 15 firms, 14 were foreign-owned and one
had joint owners, Ethiopian and Spanish. Among the foreign-owned firms, five wereoriginally from India, two from Korea and two from Sri Lanka. The remaining five firms werefrom China, Hong Kong, Taiwan, Singapore, Indonesia, Bangladesh, Italy, and Saudi Arabia(many had more than one country of origin). Aside from the domestically owned firm, allfirms came to invest in Ethiopia between 2014 and 2018.
Table 5.1 Firms sampled in the survey
CompanyCountry of Origin

Products

Date ofestablishment

Hawassa IP (7 firms)
Firm 1

Spain and Ethiopia

Women's trousers, blouse/dress/skirt, men’s
pants and dress shirts

Firm 2

Sri Lanka

Socks and tights

Firm 3

Sri Lanka

Underwear, synthetic knit tops

Jun. 2015

Firm 4Singapore

Woven shirts

Apr. 2016

Firm 5

Taiwan and Hong
Kong

Outdoor pants, outdoor shirts, jackets, towel,
sport suits

Feb. 2016

Firm 6

UAE and India

Tailored jackets and tailored trousers

Sep. 2016

Firm 7

Indonesia andSingapore

Woven shirts

Jun. 2016

Feb. 2017
2016

Bole Lemi (7 firms)
Firm 8South Korea

Woven wear, outdoor jackets/sport wear,
pants, sports/casual wear, motor cycle wear,
waterproof seam sealing wear

Jan. 2014

Firm 9India

Men’s shirts

Jun. 2014

Firm 10India

Baby and kids wear

Oct. 2013

Firm 11

China

Woven shirts, pants, medical scrubs, tops

Dec. 2013

Firm 12

India

Woven shorts

Feb. 2014

Firm 13

India

Shirts, woven bottoms (denim and other),knitted underwear (men & women)

Firm 14South Korea

Shirts & jackets

Aug. 2016

Mekelle (4 firms)
Firm 15

Bangladesh

Textiles and garment

Firm 16

Dubai based

Textiles and garment

Firm 17

Italy

Garment

Firm 18

Saudi Arabia

Textiles and garment

Source: PSI (2018).

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Ethiopia Productivity Report

Table 5.2 gives the number of workers overall and by location. The number of workers at
the 15 firms surveyed totaled more than 24,000, of which 96% were production workers. The
average number of workers per firm was 1,614 although number differed by location, thehighest being Bole Lemi. One reason for this may be that many firms in Hawassa and
Mekelle, which were newer industrial parks than Bole Lemi, had not yet started full capacity
operation. Among production workers, 93% were female, which reflects the global trend offemale workers dominating the garment industry labor force.
Operators in the garment industry are classified into four task categories: cutting, sewing,finishing, and quality control. Sewing workers were the most numerous, accounting for 75%
of total operators in our sample. Operators in finishing, quality control, and cutting accounted
for 11%, 9%, and 6%, respectively, of the total (Table 5.3). 95% of the sewing workers werefemale.
Responding firms were asked to report the efficiency and productivity levels of a few
representative products. Several firms reported that they only achieved 30-40% of efficiency
as measured by effective use of labor hours. We also formally calculated the line efficiency of
selected products, asline efficiency (%) =𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑡𝑡ℎ𝑒𝑒 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 (𝑆𝑆𝑆𝑆𝑆𝑆)𝑓𝑓𝑓𝑓𝑓𝑓 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔
× 100
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 × 60
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑏𝑏𝑏𝑏 𝑡𝑡ℎ𝑒𝑒 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

line efficiency (%) = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑏𝑏 𝑎𝑎𝑎𝑎𝑎𝑎 𝑡𝑡ℎ𝑒𝑒 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 × 100

Table 5.4 shows estimated line efficiency and labor productivity measured by number ofpieces per worker per shift. Results may not be generalizable as the number of firms
producing each type of product is small. Even so, these calculations can give some insight
into the efficiency of the industry. The first column reports the estimated line efficiency insampled firms by product type. Efficiency was generally low, ranging from 29% for woven
jackets to 58% for T-shirts. The remaining columns provide a selective comparison of
Ethiopia’s labor productivity as measured by number of pieces per worker per day with that ofprominent apparel exporting countries such as Bangladesh, China, and Vietnam. Not only is
Ethiopia’s productivity lower than that of the other countries, but also dispersion among firms
is wide, both of which suggest great potential for improvement.

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Table 5.2 Number of workers by type of work, gender and location
Total number of Workers

Number offirms in thesampleAveragenumber ofworkers

Production

OfficeTotal

Mekelle

2,436

192

2,628

2

Bole Lemi

13,903517

14,420

Hawassa

6,852

311

Total

23,191

1,020

Share of production
workers by gender (%)
Male

Female

1,314

6

94

7

2,060

8

92

7,163

6

1,194

7

93

24,211

15

1,614

7

93

Source: PSI productivity study survey (2018).

Table 5.3 Percentage of workers by task category
Work Category

Male

FemaleTotal

Cutting

29

71

6

Sewing5

95

75

Finishing

20

80

11

Quality control

9

91

9

Total

8

92

100

Source: PSI productivity study survey (2018).

Table 5.4 Average line efficiency and labor productivity of selected products, by country
Product type

Reported line
efficiency (Ethiopia)

Labor productivity(number of pieces per worker per day)%

Number offirms reporting

Ethiopia

Bangladesh

China

VietnamT-shirt

58

3

26-55

50

n.a.

n.a.

Shirt

51

2

3-9

10

n.a.

6-15

Polo shirt

43

1

7-18

13-27

18-35

8-14

Fleece jacket

34

1

4

n.a.

n.a.

n.a.

Woven jacket

29

1

3

n.a.

n.a.

n.a.

Source: Ethiopia’s data source is from PSI (2018); for Bangladesh, Ionele (2008), as cited in Sorri (2010) for Tshirts, Islam & Adnan (2016) for shirts, and World Bank (2013) for polo shirts; for China, World Bank (2011);
for Vietnam, Goto (2012) for shirts, and World Bank (2011) for polo shirts.
Note: for calculation of labor productivity and line efficiency, major and commonly produced products which
could also be used in international comparisons are selected.

5.3

Labor mindset and quality

In this sub-section, we examine labor-related factors that affect productivity. We categorize
these issues into: (i) initial characteristics (basic skills and mindset); (ii) workers’ education,
trainability and acquisition of firm-specific skills; and (iii) appropriateness of worker

75

Ethiopia Productivity Reportincentives and working conditions. Table 5.5 shows the framework of the analysis and thedetailed content of issues related to labor mindset, skills, quality, and incentives.

5.3.1 Workers’ mindset
In this sub-section, we examine labor mindset such as attitude and discipline, absenteeism,
and attrition, as it relates to productivity. In interviews company managers identified poor
work culture and worker attitude as the most serious problem affecting labor productivity.
Most workers, being from rural areas, have little familiarity with urban environment, andno prior industrial work experience. Despite the training they are given in technical and soft
skills, they still lack industrial work discipline.
Table 5.5 Framework of analysis for labor quality and incentives
Areas

Description of details- Value, attitude and discipline

Mindset ofworkers- Emotional stability, openness, tidiness, punctuality, etc.
- Motivation and expectation in factory work
- Labor absenteeism- Labor quitting work (turnover)- Literacy

Basic skills ofworkers- Cognition, comprehension and memory- Basic math
- Communication skill- Problem-solving ability- Level of worker skills for different tasks

Trainability andtechnical skills ofworkers- Main skills lacking in new and experienced workers- Speed and efficiency of learning- Teamwork, organizational behavior, active participation- Dynamic leveling up of worker- Salary and other monetary conditions- Non-monetary benefits such as leaves and insurance

Incentives ofworkers andworking
conditions- Promotion prospects and career track- Working hours, intensity of effort, overtime- In-factory safety, health, comfort and meals- Housing and/or commuting conditions- Human relations in the workplace (relations with higher-ups, colleagues)- Labor-employer relations and dialogue

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Figure 5.1 Labor mindset: rating by managers
poor or very poor

Average

Good or excellent

Prior knowledge of modern
factory
Nutrition

10

Motivation to work

8

Health6

Enthusiasm about the job

4

Personal management

2

Future career expectation

0

Work discipline in general

To finish tasks on time

To keep time and do not
To organize tools and supplies
delay
tidilyTo follow the rules of the work
To follow direction byplace and behave as
supervisors
expected To be good at working in teamcollaboratively

Source: PSI (2018).

Interviewed firms were requested to rate various aspects of the mindset of their workers
using numerical indicators. Figure 5.1 summarizes the results of the 15 firms that responded
to this request. The mindset of workers was most frequently rated as “average” or “poor orvery poor,” while only a few firms rated it as “good or excellent.” More specifically, seven toten firms rated teamwork, following directions from supervisors, general work discipline, tidyorganization of tools and supplies, personal management, health, and nutrition as “average.”On the other hand, managers from seven to ten firms rated prior knowledge of modern factory
work, motivation to work, enthusiasm about the job, finishing tasks on time, and not delaying
as “poor or very poor.” No aspect of worker mindset was rated as “good or excellent” by the
majority of responding managers.
Managers identified four key manifestations of poor work culture and attitude of workers:
high attrition, high absenteeism, no sense of urgency about work, and low motivation to workovertime. Those four elements are discussed in detail below.
Attrition rate: the rate of attrition is very high among garment workers in Ethiopia
although it differs by location (Table 5.6). Among the sampled firms, the annual rate of
attrition ranged from 27% in Mekelle to 35% in Hawassa and 84% in Bole Lemi. The major

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Ethiopia Productivity Report

Table 5.6 Attrition and absenteeism by location
Location

Rate of attrition
Fired

Rate ofabsenteeism2

9Bole Lemi

Volunteer
attrition
84

Hawassa35

3

6

Mekelle27

52

Source: PSI (2018).

reason for workers quitting is low wages and poor worker benefits. Workers do not consider
garment work as a long-term sustainable career; rather they see it as a transitory job.
Moreover, many workers find high work pressure very taxing. Some workers come to modern
factories just to see the situation, and leave quickly if they find the work very hard. Ininterviews workers reported that turnover is high in a worker’s first year but declines as theworker becomes familiar with the work.
Table 5.6 reveals a wide variation in attrition rate across industrial parks, with Bole Lemi
reporting highest attrition. There are a few reasons why this may be so. First, Bole Lemi is
located in the capital city, where the cost of living is very high and where, on the other hand,
there are more alternative work opportunities nearby for unskilled workers such as
construction work and housemaid work than in other industrial parks. Globally, labor
movement is generally more active in urban industrial parks, where most workers are
migrants from rural areas, than in rural industrial parks, where workers commute daily fromnearby villages.
Another reason for high attrition in Bole Lemi is poaching of workers among firms.
Workers tend to move between firms within the park, even for very small improvements insalary or benefits. In contrast, internal regulations imposed by Hawassa tenant firms prohibit
poaching of workers; this naturally reduces attrition there. Some criticize this practice as abreach of the right of movement of workers, and as a factor reducing knowledge transfer
across firms through worker migration. Meanwhile, attrition in Mekelle is lower than ineither Hawassa or Bole Lemi. Unlike Hawassa, Mekelle has no internal regulations to curblabor movement. The fact that the firms studied here are located outside the industrial parkand physically separated from each other by five to seven kilometers, with most workers
currently commuting from nearby areas on foot or by bus, significantly reduces the possibilityof labor poaching.

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Absenteeism: firm management also report high absenteeism as another manifestation ofpoor work discipline. The rate of absenteeism is also higher in Bole Lemi (9%) than Hawassa(6%) and Mekelle (2%). The firms report that absenteeism is much higher during holiday
periods, the rainy season and after pay days. Absenteeism is also high on Saturdays andMondays, particularly in Hawassa, as workers usually go back to their villages to obtain food.
The firms attribute absenteeism mainly to the fundamental lack of work discipline andmotivation. Sickness and family death incidents are common justifications given forabsenteeism. In some cases, this is exacerbated by cooperating doctors who sign false medical
certificates. Some firms in Hawassa even claim that non-medical personnel sell fake medical
certificates at the gate of the park.
Time consciousness: a related work culture problem is a poor understanding of the valueof time and a lack of sense of urgency among the workers. Workers often arrive late to workand do not start work immediately after arriving. Moreover, workers waste considerable
amounts of time, up to two hours a day according to estimates of some firms, by taking tea
breaks, going to the toilet, and other excuses for staying away from work. Also, coming froman agrarian culture, workers find it difficult to work continuously for eight hours.
Overtime work: low motivation of workers to work overtime is another serious problem
mentioned by the firms. The majority of workers are not interested in working overtime,
mainly due to the low level of pay. The overtime pay rate is 1.25 to 1.5 times the basic salary,which is very small. Security concerns are another reason for refusal to work overtime. Many
workers live far from the factory site and thus face security problems if they work into the lateevening. A firm in Hawassa was forced to reduce work shifts from two to one due to security
problems as workers were being attacked on their way home. Some workers also attend
evening classes for self-improvement, which is another reason for not wanting to stay to workovertime.
Responding managers also voice complaints that, in addition to the lack of worker interestin overtime, an Ethiopian law which severely restricts the number of permissible days andhours of overtime work is an impediment to firms which have to compete globally andrespond quickly to changes in foreign demand. This issue is further discussed in the policy
and external conditions section below.

5.3.2 Workers selection, education, trainability, and skills Workers selection andcomposition
In the Ethiopian apparel industry, educational background is not a major criterion for79

Ethiopia Productivity Report

selection of workers, particularly sewing operators. Most managers argue that six years ofschooling (i.e. basic numerical and reading skills) are sufficient for sewing operators. Worker
screening is more often concerned with age, gender (preferably female), personal integrity,and physical strength (eyesight and bodily constitution in particular). Most firms prefer torecruit young people in the area near the factory so as to minimize transport time and costs,
and also as an expression of social responsibility to the local community.
Hawassa Industrial Park has a centralized mechanism for worker recruitment and grading,whereas Mekelle and Bole Lemi do not. Hawassa has established its Grading Center within
the park; the center screens workers and dispatches them to tenant firms upon request. The
objective of the center is to reduce investor expense and time related to hiring. The
cooperating partners of the Grading Center are: Enterprises Partners (EP) funded by the
Department for International Development (DFID), TIDI, the Labor Unit of EIC, and theregional Industry and Trade Bureau. Hawassa Industrial Park and IPDC act as facilitators. The
regional Industry and Trade Bureau compiles a list of candidate workers from ten catchment
areas in the region. The criteria for inclusion in the list are eighth grade education or higher,
and female gender. TIDI grades the candidate workers on this list, and the EIC Labor Unit
adds qualified workers to the database and dispatches them to tenant firms upon request.
Actually, the average education level of Ethiopian garment workers is higher than theminimum requirement set by the recruiters. For example, 50% of sewing operators have 9 to
12 years of schooling, and another 9% are the technical and vocational education and training(TVET) graduates (Table 5.7). As expected, the majority (89%) of sewing operators are ofage 18 to 25. Only 1% of sewing operators are over 35 years of age.
The survey also confirms that most of the country’s garment workers are migrants fromrural areas, accounting for more than 80% of total workers in the surveyed firms in Bole Lemi
and Hawassa. In Mekelle, the share of rural migrant workers is only 50%.
Table 5.7 Sewing workers, by education and age
Education level

Share

Grade 8 or below

41%

Grade 9-1250%

TVET graduates

9%

Age groupShare

18-25 years old

89%

25-35 years old

10%

Above 35 years old

1%

Source: PSI (2018).

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Company managers were also asked about the number of expatriate employees and thenature of their work activities. At the 15 garment firms surveyed, there were a total of 503
expatriates engaged in different activities. Many (66%) worked in management positions that
require technical expertise (66%), while 24% were engaged in management and supervision
only.

5.3.3 Training and trainability
Operator training lasts for one to three months, depending on the firm. The trainees
acquire both technical skills and soft skills such as work attitude and discipline. Almost all
firms agreed that Ethiopian workers are quick learners of technical skills. At the same time,
most firms also agreed that workers do not pay enough attention to details and lack focus andconsistency.
As can be seen in Table 5.8, many firms regard the skill level of their workers as“average.” In particular, basic numeracy and communication skills are rated mostly as“average” while language skill is frequently rated as “poor.” Regarding technical skills,
sewing and quality control are mostly rated as “average” while cutting is often rated as “good.”
Based on the above results, it can be said that many FDI garment firms are generally
happy with the quality, trainability and progress of local labor. Foreign managers of different
nationalities, often with experience in many other countries, say Ethiopian workers are good,or at least no worse than other workers they have seen. Newly recruited workers are initiallyuninformed and inexperienced and do not even know how to use a canteen or a toilet, but theylearn quickly in response to guidance and pressure from the management. This is contrary to
the popular notion that Ethiopian workers are unproductive and lack work discipline.
Compared to workers from other countries, they are rather trainable and quick to learn. They
are raw gemstones which will shine with appropriate cutting and polishing.
The above points to the important issue of plasticity and possible development ofEthiopian workers. In many latecomer countries, including Ethiopia, where industry is justtaking root and many foreign ideas and investors co-exist and compete, a unified national
character of workers has yet to emerge. Some workers are diligent and serious, others are
selfish and short-term oriented, and still others try to adopt a model they learn from
instruction, observation, and other means. This is unlike the situation in advanced societies
such as Japan, Singapore, Korea, or Taiwan where national characteristics have alreadysolidified and workers are more uniform, predictable and productive. There is no need to
explain to those workers why it is important to report to work on time, dress properly, report81Ethiopia Productivity Report

Table 5.8 Firm rating of worker skills (number of firms)
Very poor

Poor

AverageGood

Excellent

Basic numeracy skills

0

2

12

1

0

Communication skills

0

3

9

3

0

Language skills (English)
Critical thinking &problem solving

2

7

5

1

0

2

4

6

3

0

Designing

2

2

4

3

1

Cutting

0

4

4

5

1

Pattern making

3

2

2

3

2

Sewing

0

1

7

6

1

Finishing

0

3

6

5

1

Quality control
Machine operation/maintenance

1

3

7

3

1

1

4

5

3

2

Basic education

Technical skill

Source: PSI (2018).

any problems to supervisors, and save and spend money wisely, nor to convince them that
there are times when everyone must work overtime to keep customers happy and win inglobal competition. Without such instructions, workers in those countries all work efficiently.
But these basic things must be told repeatedly to new workers in developing countries until
they understand and perform accordingly.
Put differently, a concrete means of guiding such workers to efficiency and convincing
them of its value is crucial to the formation of their workmanlike behavior and to theimprovement of their productivity. As one Ethiopian CEO confided, the problem of
productivity rests fundamentally with management, not with the workers. FDI firms bring
their home methods to Ethiopia (or any other destination) and stick to them as long as they get
results. If that approach does not work well, adjustment to the local situation becomes
necessary, although the core method is—and should be—preserved.

5.3.4 Incentives and working conditions
The surveyed firms seem to have similar wage structures. As can be seen in Table 5.9, on
average, a sewing operator with no prior experience is paid 863 Birr per month and an
average monthly bonus and allowances of 509 Birr. The average monthly salary of sewing
operators is a slightly lower than that of other operators. However, they receive higher
bonuses and allowances than other workers. The minimum bonus and allowances among all
surveyed workers is 100 Birr per month, while the maximum is 900 Birr per month, aside82

Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Table 5.9 Salary and benefits by task category (ETB)
Task category

Initial Salary

Salary after a year

Bonus and allowances

Average

Min.

Max.

AverageMin.

Max

Average

Min.

Max.

Cutting

886

650

1,200

1,345

725

2,250

463

100

900

Sewing

863

650

1,200

1,138

750

2,011

509

100

1,366

Finishing

880

650

1,400

1,224

725

2,200

458

100

900

Quality control

978

750

2,500

1,403

825

2,500

431

100

900

Source: PSI (2018).

from sewing workers, whose maximum monthly allowances is over 1,300 Birr.
The basic worker salary differs slightly by location. The starting salary in Mekelle ranges
from 800 to 1,050 Birr; in Bole Lemi it is as high as 1,200 Birr. In Hawassa, the startingsalary is not only small, at 750 Birr, but also it is uniform across firms as a result of a tenantassociation agreement.
In addition to basic salary and bonuses, companies provide various incentives to motivate
workers. As shown in Figure 5.2, free or subsidized food and transportation are offered by all
firms. Most firms provide food on their premises and arrange a transport service (usuallybuses), while a few pay a transport allowance. Health insurance is offered by ten of thecompanies, while safety measures are in place in eleven of them. However, to date only oneout of 15 firms provides housing facility for workers.
We conducted focus group discussions with workers to gain an understanding of their desires
and grievances. Workers claim that the high attrition rate is caused by low rewards. They
express extreme dissatisfaction with the low wages they are paid. They are also dissatisfied
with other incentive schemes. For example, the absenteeism bonus and the upgrading system
mostly require perfect attendance, which is not practical. In some cases, productivity bonuses
are available for sewing workers but not for cutting and quality control workers. Furthermore,some transport services are not adequate due to the small capacity of buses and to the great
distances workers must travel from the pick-up and drop-off points, which create a security
problem, particularly at night time. Dissatisfaction is also voiced regarding the quality of foodprovided by firms.
Workers also complain about poor working conditions including: (i) the lack of basic
facilities such as ventilators, restrooms, and safety equipment; (ii) health issues arising fromwork conditions—for example, kidney problems due to restrictions on going to the toilet;
respiratory problems caused by dust particles and congested buses; high temperature in
production areas; typhoid caused by low-quality food; (iii) cumbersome bureaucracy related

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Ethiopia Productivity Report

Figure 5.2 Number of firms providing incentives (out of 15 surveyed)
0

2

4

6

8

10

12

14

16

Free or subsidized foodTransport services(bus or subsidy)
Safety measures
Health insurance
Other services
Housing facilities

Source: PSI (2018).

to permission for sick leave and annual leave; and (iv) mistreatment by supervisors.
We now turn to the issue of housing, which is crucial in factories and in industrial parks
where employees are mainly young women. Failure to provide free or cheap housing within
the workplace or in the vicinity negatively affects worker motivation and productivity, giventhat it gives rise to additional transport time and consumes a significant share of worker
income.
Industrial parks and their tenant firms are often advised to offer commuter bus service anddormitories or other accommodation for workers. However, the quality of transport andhousing arrangements varies from one industrial park to another, and even from one tenantfirm to another.
With respect to location, there are two types of industrial parks. The first type is located in
the area surrounding a large city, typically the capital city, with a population of a few toseveral million. Those urban parks offer easy access to amenities and services such as
shopping, education, medical care, hairdressers, café and restaurants, and other entertainment.
Because urban residents enjoy relatively high income and do not want to work in factories for
low wages, these parks must bring migrant workers from rural areas, forcing them to relocateand find adequate accommodation. Rural workers tend to come in groups from the samevillage. Such workers are footloose and sensitive to slight differences in salary or benefits.
They also have the alternative of working in a big city, as housemaids, shop clerks, table
servers, and construction workers. As a result, industrial parks must compete with those urban
labor demands. Furthermore, dissatisfied workers always have the option of going home.
Labor shortages, wage inflation and frequent job hopping are common features of industrial

84Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

parks near big cities. Dormitory or other reasonable housing becomes an issue, although
workers often rent or share rooms in the local market without official assistance or company
support. The market may provide opportunities for better accommodation than that offered byplanned housing projects.
The above is a better description of Bole Lemi Industrial Park than of the other parks.
Firms in Bole Lemi do not provide accommodation for workers, although some, beginning torealize the seriousness of the housing problem, have plans to build dormitories. Almost all
firms provide transport services (buses or subsidies). Workers in Bole Lemi live in private
rented houses (mostly shared) in and around Addis Ababa. As rental rooms in the city centerare quite expensive, many workers commute from faraway areas, which lead in turn to longcommuting hours and lateness in reporting to work. Housing allowances may become
necessary in such circumstances.
The second type of industrial park is located in rural areas or near small cities in remote
but populated areas. The supply of agricultural surplus labor is large there, and workers
usually commute from their own homes, which eliminates the need to find accommodation,
but transport to and from their scattered villages must be arranged unless workers live
sufficiently nearby or can arrange their own transport or walk. Industrial parks of this second
type face fewer labor shortages, and FDI firms often choose rural locations precisely for that
advantage. However, the safety of workers, especially young female workers, returning home
at night must be ensured, as noted above. That security concern is a reason why most workers
refuse to work overtime. Firms in Mekelle and its vicinity, and in Hawassa Industrial Park areof this second type.
Mekelle is a small regional city where at least three large FDI firms have built new
textiles and garment factories in the region (not in Mekelle Industrial Park). One FDI firm isbuilding dormitories, cinemas and shops on the premises in order to create a new city with ahuge factory as the core, and inviting workers, who now commute by bus, to live there. In
contrast, the other two FDI firms located near Mekelle do not plan to build dormitories;
instead, they want their workers to continue to live in their villages and commute to thefactory. Their strategy is to hire workers from nearby towns and villages (not faraway places)
and thus contribute to local job creation and economic prosperity. However, if more and more
light manufacturing factories come to Mekelle and its vicinity, at some point the local labor
supply will be fully absorbed, and labor will have to be recruited from more distant areas. The
need to build dormitories and other facilities for workers will arise at that time.

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In Hawassa, the government responded differently to the problem of housing for workers:
it implemented an officially sponsored low-cost housing scheme by offering incentives tohouse owners in the town to construct additional accommodation on their land for renting toindustrial park workers. Responses to our interviews indicate that this scheme does not seemto be working, partly because landlords refuse to offer reasonable rents to workers who
commute to Hawassa Industrial Park. IPDC in Hawassa is now considering another type of
housing solution, where dormitories are to be built within the park in collaboration withinvestors. Some firms have already received land to build dormitories for their workers.

5.4

Management practices

A growing body of literature is pointing to differences in management practices as a key
factor explaining variation in productivity across firms and countries. The literature offers the
persuasive argument that improved management can bring about substantial increases in firm
productivity. To assess that argument, we qualitatively examined the relationship between
management practices and labor productivity, by means of face-to-face interviews at 18
companies in three sites (Hawassa Industrial Park, Bole Lemi Industrial Park, and Mekelle).
The results are presented below.

5.4.1 Composition of managers
Broadly speaking, there are three levels of management: top executives, middle level
managers, and line managers. The shares of foreign and Ethiopian managers vary from level
to level. Top management positions at FDI firms are predominantly held by foreigners. In
contrast, middle level managers are a mixture of foreigners and Ethiopians. At that level,
Ethiopians are often assigned to human resources and finance departments. Meanwhile, their
participation in technical functions in departments such as production is usually scant.
However, there is variation across establishments. At some exceptional FDI firms, all middle
management positions are held by Ethiopians, though we find a higher proportion of
Ethiopian managers farther down the hierarchy. Nearly all lower level management positions,such as line manager, team leader, and supervisor, are held by Ethiopians.
Foreign managers at all levels are of varying nationality, not necessarily from the countryof origin of the firm. In the Ethiopian garment sector, many managers are Indian, Sri Lankan,
Bangladeshi, Vietnamese, Chinese, Taiwanese, and Korean, and some are Spanish, Italian,
Myanmar and Kenyan.

5.4.2 Approaches to management and guidance of workers

86Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

How companies guide their workers makes a great difference in labor productivity. Eachcountry, and even each company, has different corporate cultures and strategies which
influence the goals, perceptions and approaches to the issue of labor mindset and productivity.
Based an examination of global experiences (mainly in Asia), we can identify several
management approaches to the question of labor mindset and productivity in lightmanufacturing, including:(i)Top-down order and punishment(ii)

Creation of corporate family oneness(iii) Mindset reform through instruction and persuasion(iv) Mindset reform through monetary rewards and incentives(iv) Use of middle managers as an interface between foreign management and local
workers
Approach (i) is adopted by highly hierarchical organizations with strong top management.
Type (ii) is typically seen in traditional Japanese—and some other Asian—companies. These
two approaches are diametrically opposed and mutually incompatible. Meanwhile, types (iii),(iv) and (v) are more “partial” in the sense that they can be implemented under most corporate
cultures and structures. These three approaches are not mutually exclusive, and can be
adopted simultaneously. Properly tuned, they contribute to worker mindset reform regardlessof whether the corporate model is top-down, bottom-up, or otherwise. Note that the above listof approaches is far from exhaustive; other approaches and variations are possible.
These management styles—or attempts to introduce them at least partly—were visible in
our interviews at the three locations in Ethiopia. Below we explain these approaches in moredetail and report how garment firms operating in Ethiopia apply those approaches to dealing
with mindset and productivity issues.
(i) Top-down order and punishment
Some interviewed firms stated that apparel production must be “autocratic.” They
believe that being tough on workers is one way to meet tight schedules, ensure quality,
and stay competitive. Such an approach is often used when local workers areunacquainted with modern production processes, and the firm is a global player facingstrong market competition. Foreign managers operating in developing countries are under
severe pressure to cut costs and deliver quickly; they tend to take a tough stance with
their workers with respect to rules, discipline and working time (including imposition of
overtime). Workers who do not perform as instructed are punished through low

87Ethiopia Productivity Report

evaluations, denial of promotion, wage cuts, or even dismissal. This authoritarian
approach does not require a deep knowledge of local customs or social sentiment, andcan be executed in almost any country. Workers comply because of coercion and fear,
not because they truly understand the value of the instructed actions for themselves andtheir company.
This “dictatorship” style is sometimes accepted or even praised as an effective way torun a global factory in countries where workers lack basic knowledge and discipline.
However, the risk of overstepping legal bounds is real. In some extreme cases, foreign
managers violate human rights or domestic or international laws, actions which may beeither penalized or go undetected. The harsh approach may work for some time, but it
does not generate essential trust and understanding between management and labor. It
cannot be regarded as a permanent model in a civilized society where workers’ rights andwell-being are critically important. If adopted, the harsh approach should be regarded as atemporary measure acceptable only until the workers gain experience and improve theirperformance.
(ii) Creation of corporate family onenessWe have learned that some of the firms we interviewed are taking up the family
oneness approach. There are signs of a family-type community bond among staff at thosecompanies. This approach reflects a traditional Japanese management style, and themodel has spread to other countries, especially in Asia where Japanese FDI is prevalent.
Virtually all Japanese manufacturing firms, large and small, introduce this principle when
they invest overseas and hire foreign workers.
In Japan, regular employees can climb the corporate ladder over the years andeventually compete for the top executive positions. In the workplace, discrimination
based on ethnic, regional and cultural background is virtually nonexistent. Managers andworkers are not separated psychologically; they share the same career track and work
experience. The only difference between them is that the former are farther along in the
career process than the latter. Moreover, many Japanese managers are happy to visit
gemba (production sites) to work with engineers and technicians on R&D or on a new
production process. Employees are encouraged to work as a team and suggest ideas tohigher-ups. Often managers and workers share the same canteen and toilets. Morning
meetings are common. Daily information is shared and everyone joins in 10 minutes ofphysical warm-up exercises. Company sports events and excursions are arranged, with all

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

families are invited. This tradition is still alive in Japanese factories though it wasstronger during Japan’s high growth era of the 1960s and 70s. Many firms in Southeast
Asia also adopt this model, especially company-sponsored events and entertainment.
This approach creates a positive corporate atmosphere where management and labor
trust each other and confrontation is played down. Some argue that this model does notwork in societies where top-down order and strict career separation are the rule. However,the fact is that many firms in nations whose cultures are very different from that of Japan,
and who have no history of corporate oneness (such as the United States, India, andArgentina) embrace the Japanese model (with local adjustments) and succeed in creating
a positive work atmosphere conducive to efficiency and cooperation. This suggests thatthe family oneness approach is not limited to the case of Japan, and that corporate culture
can be changed, given proper guidance.
(iii) Mindset reform through instruction and persuasion
In this approach, companies teach workers—and local supervisors as necessary—why
certain actions are beneficial for the company and for the workers themselves. Mindset
reform is never easy and may not bear fruit immediately, but sincere and persistentcounseling, through the words or actions of credible instructors who devote much of theirtime to productivity improvement, moves many, if not all, participants.
In Cambodia, a large Japanese firm employs a local Buddhist monk to explain work
ethics to young recruits from rural areas. They listen to the Buddhist monk much more
attentively than to their Japanese bosses. At another large motorcycle assembly plantabroad, Japanese CEOs routinely picked up rubbish which workers have dropped,demonstrating through real actions how and why a factory floor can be kept clean andtidy. This has led workers to stop throwing things on the floor. Similarly, in JapaneseSMEs at home and abroad, the serious attitude of Japanese general director toward kaizen(quality on-time delivery) usually has a visible influence on the foreigners working there
as regular staff or technical interns. In Vietnam, Dr. Nguyen Dang Minh, who holds a
PhD in management and has many years of working experience at Toyota Headquarters
in Japan, is developing a method to renovate Vietnamese minds, starting from the basics.
If training is given with sound logic and a warm heart, the mindset of any worker canbe transformed. Workers transformed in this manner begin to act proactively, reflecting
their own understanding and convictions, without any enforcement from managers.
(iv) Mindset reform through rewards and incentives

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Ethiopia Productivity Report

This approach is widely exercised in the firms we visited. These firms haveestablished clear and rational performance-based rules governing promotion, salary
increases, bonuses, support for training or other benefits, and they implement them as
fairly and visibly as possible. The underlying notion is that workers respond to financial
incentives. Even if workers are not convinced of correctness of the instructions they are
given, most of them follow them if there are pecuniary and/or non-pecuniary rewards
attached. And even if they are already convinced, rewards lead them to execute theneeded actions more readily, and to make a habit of it. Well-crafted incentives have theadditional advantage of instilling company loyalty in workers, thus curbing job hopping.
For the incentive mechanism to work, objective assessment of workers must be inplace, so that all parties accept the value of high assessments. One common practice is torecord workers’ attendance and productivity regularly. Many firms in Asia’s advanced
economies have firm-specific methods of worker evaluation which are the basis for theawarding of internal technical certificates and new job assignments including promotion.
Meanwhile, at one Ethiopian company which won one of the top kaizen awards, theworkers complained because the privilege and honor bestowed on the company by theprime minister and EKI were not reflected in any pay increase or special bonus for them.
A day-excursion for the workers’ families after the award ceremony, paid for by thecompany, was not enough to motivate the workers to do better.
(v) Use of middle managers as an interface between foreign management and local workers
One effective way to combine instructions and incentives is the use of line leaders(middle managers) as an interface between foreign managers and local workers. This
point was stressed by virtually all FDI garment firms which began operation in the threesites. Among them, one firm reported that factory efficiency and the operation ratio
improved greatly after only six months of operation. The speed and scope of
improvement were just as expected of a country in which the firm begins new operation.
The main driver of improvement was the training and utilization of the line leaders,
all Ethiopians, trained within the firm or recruited from outside. Most FDI firms pick line
leaders internally, from among line workers who show exceptionally good skills andattitude. The role of line leaders is crucial for changing the mindset and improving the
productivity of all workers. Line leaders monitor the daily activities of the production
line and take corrective measures such as changing the seating arrangement of theoperators (line balancing). This has a direct impact on worker productivity, which

90Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

demonstrates that labor productivity problems lie mainly in the hands of the middle
managers. Line leaders are also tasked to make the workers aware of global market
pressure and the close linkage between the company’s success and worker welfare. Lineleaders also listen directly to workers’ demands and complaints, and communicate themto the foreign management as appropriate. This is a good way to bridge different culturesin globally oriented business operations.

5.4.3 How firms upgrade workers to management positions
Finding skilled Ethiopian workers for management positions is challenging, as thegarment sector of Ethiopia is still underdeveloped. Most of the companies at the three sites
have to rely on foreign managers until a sufficient number of skilled Ethiopian workers areavailable. However, employing foreign managers is costly, so the companies normally want toreplace foreign managers with Ethiopians with managerial experience.
FDI companies offer good opportunities for local people with suitable attitude andeducation to move up to management positions of various types. Those companies usually
select for training middle managers and line leaders from among operators, and the training
programs are basically the same as in the home country. The training, supervised by
expatriate experts, includes brainstorming, communication skills, learning of corporate culture,
and analysis of buyer complaints. Some good workers manage to quickly climb the ladder andtake up supervisory positions, after as little as half to one year of factory operation; this is amuch shorter time than normally thought necessary for producing middle managers and line
leaders. As noted above, most low- to middle-level managers are promoted from among
operators, with some directly recruited as fresh graduates. Those promoted from operator tend
to be efficient and loyal to the company. The approach taken by foreigners to the training andthe provision of work and promotion opportunities for these middle leaders is very important.
It strongly influences the speed and effectiveness with which the firm will secure capable
middle leaders and efficient workers.

5.4.4 Adaptability of foreign managers to local conditions
Certain problems do arise when foreigners manage and guide workers. Foreign managers
tend to pursue the management style of their home countries without fully understanding thecharacteristics and the requirements of local workers. Some foreign managers are not flexible,
and mostly concerned about daily production targets. As they work to meet production targets,
some even use force, which results in conflict with the workers. Foreign managers often havedifficulty adapting to local conditions and customs. For example, it is reported that some91

Ethiopia Productivity Reportmanagers at Hawassa Industrial Park shout at the workers, which is problematic since in theculture of Sidama, shouting at women is unacceptable. Moreover, Ethiopians observe a
number of holidays during the year. Workers have a strong attachment to those holidays,
which provide opportunities go back to their home villages and spend time with their families
and the community. Some foreign managers do not understand the importance of this strong
communal bond and the value of maintaining such relationships. Some foreign managers havealso been accused of treating workers unequally, favoring some workers and building a
special relationship with them, which creates bad feelings among the other workers.

5.4.5 Mindset problems of Ethiopian managers
The companies interviewed generally expressed satisfaction with the quality, trainability
and progress of the local managers. The firms believe that, with training and gaining
experience, managerial quality can be greatly improved. However, as in the case of operators,some local managers are unfamiliar with global business practices, which negatively affecttheir productivity. Basic work discipline has not yet been acquired. The mindset problems of
local managers can be manifested in several ways.
(i) Lack of absence of sense of purpose and urgency
Local managers are often characterized as lacking a sense of urgency. They seem to
be insufficiently concerned about quality and productivity, and to consider existing
inefficiency as normal—all of which reflect their lack of experience and international
exposure. Local managers are also viewed as not as pushy as foreign managers in termsof achieving production targets.
(ii) Lack of consistency in learning
Although their learning curve is relatively steep, local managers lack consistency inlearning and do not pay sufficient attention to detail. Their learning tends to fluctuate
between fast and slow, lacking a specific pattern or speed. They are also slow to adapt to
new environments, perhaps because of the highly demanding nature of industrial work
relative to their experience and knowledge.
(iii) Poor time management
In general local managers are not time-conscious. Personal use of mobile phones
during office hours and lack of adherence to work rules are common. Like operators,
local managers generally find it difficult to work continuously for eight hours.
(iv) Lack of sense of purpose and responsibility

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

Unlike foreign managers, Ethiopian managers are not very serious about goal setting,and seem unable to anticipate problems in advance. They consider industrial jobs
transitory rather than long-term professional work. In addition, there is a generalperception in Ethiopia that managers should spend most of their time sitting in the office.
Ethiopian managers are generally less willing than foreign managers to go down to work
stations to monitor and support the workers.
(v) Lack of global mindset
Unlike foreign managers, local managers are not buyer driven. Foreign managers areunder strong cost-cutting and quick-delivery pressure from the global market, and tend tobe very tough. Often they take a strong stance with their workers, especially regarding
their skills, discipline, and overtime. On the other hand, Ethiopian managers want toavoid problems with the workers and thus remain soft about workers’ undesirable
attitudes and behavior.

5.5

Policy and external conditions

Labor productivity is also affected negatively by a number of business conditions generated
by the government or due to external circumstances which are beyond the control of either the
managers or the workers. There are many well-known impediments to productivity in
Ethiopia, reported as serious by most of the interviewees of our survey. These external
problems are discussed briefly below. Full analysis of any of these issues would require aseparate study.

5.5.1 Labor laws
According to the company managers interviewed, Ethiopia’s labor laws are highly
restrictive by international standards in terms of minimum wage, overtime limits, and annual
leaves.
(i) Minimum wage
All but one of the companies interviewed expressed a preference for establishment ofa minimum wage in Ethiopia. The general thinking was that a minimum wage would help
to reduce unnecessary movement (including poaching) of labor from one company toanother.
(ii) Overtime
In a competitive global environment, industrialists are under pressure to deliver goods
on time and reduce lead time as much as possible. Ethiopia’s labor regulations are in93Ethiopia Productivity Report

some aspects highly restrictive in comparison with the internationally accepted ILO
standard, which stipulates that workers should not work overtime in excess of 60 hours
per month. Ethiopia’s labor laws limit overtime work to 20 hours per month, whereas inmost other countries overtime hours are as much as 60 hours per month, in compliancewith the ILO rule. This restriction seems to favor Ethiopian workers, but in reality it may
adversely affect the productivity of garment factories and reduce their competitiveness in
the global market—and ultimately have a negative impact on workers’ wages andemployment.
(iii) Leaves
Companies also complained that annual leave cannot be converted to cash payments.
According to them, there are workers willing to receive cash payment in place of annual
leave. There were also complaints about abuse of compassionate leave by workers whotake sick leave by presenting false certificates from clinics and hospitals. This is aparticularly serious problem in Bole Lemi and Hawassa. However, it is generally aproblem of implementation of the sick leave system, not a question of the necessity or
desirability of sick leave.
(iv) Income tax
Two issues arise regarding the imposition of income tax on workers. First, someargue that imposing tax on workers who receive low wages is unfair. Wages of 600 Birr
or more per month are subject to income tax. However, it is unrealistic to assume that
600 Birr is sufficient to cover the monthly living expenses of a worker and pay income
tax, given the cost of living and the living standard in Ethiopia. Furthermore, income tax
is levied also on bonuses. Second, the income tax bracket is very narrow, so wage earners
are quickly pushed to higher brackets as their income rises. The workers express
dissatisfaction about taxation and the tax bracket structure, demanding to know why they
are taxed. This suggests a lack of worker awareness about the income tax system and thepension system.

5.5.2 Logistic performance
Logistic performance can be measured in terms of cost and transit time (lead time). By
these measures, Ethiopia’s logistics sector is slow and very costly. One manager interviewed
reported that about 16% of his company’s sales turnover was spent on logistics-related costs.
For foreign firms, the cost of transport via Ethiopia’s newly-built railway is even higher thanthat of trucking. A recent study comparing Ethiopia’s trade cost with that of comparator

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

economies found that Ethiopia’s cost to export a 20-foot container is USD 2,660, comparedwith USD 600 for Vietnam and USD 2,350 for Kenya (PSI, 2019). That cost covers inlandtransportation and handling, customs clearance and control, port and terminal handling, anddocument preparation. Clearly Ethiopia’s competitiveness in the global market is negatively
affected.
Lead time has become a principal concern for international buyers. In the apparel industry
in particular, where it is necessary to respond to fashion trends and beat the competition,buyers are demanding fast, on-time delivery. In this regard, all companies interviewed here
observed that lead time is very long in Ethiopia. The time required from receipt of a client
order to final delivery to the client is longer for Ethiopia than for other apparel exporting
countries. Reasons for that include the existence of check points as well as the mismatch ofworking days in Ethiopia and in Djibouti, which causes large delays in the processing ofimports and exports. Companies also claim that the roads from their factories to the port ofDjibouti are in poor condition, resulting in longer transportation time and higher cost. Despite
the recent construction of many roads and railways, Ethiopia still faces a very challenging
business environment, which reduces its competitiveness in the global market.

5.5.3 Custom’s clearance
Customs clearance in Ethiopia is very slow and garment unfriendly. Moreover, the timerequired for customs clearance is unpredictable. Unlike in other garment exporting economies
such as Vietnam, Bangladesh, and Cambodia, customs officers in Ethiopia are not available
24 hours per day, seven days per week. They work only regular working hours, which resultsin additional delays.

5.5.4 Power supply
Frequent power outages for long periods of time continue to be a serious hindrance to the
conduct of business in Ethiopia. Outages cause not only work stoppages and revenue losses
but also damage equipment and materials, which substantially increases costs. Note, however,that power outages are not a big problem for firms in Hawassa Industrial Park as it has adedicated sub-station.

5.5.5 Other business related problems
Regarding other business related problems, the interviewed companies mentioned
the following: (i) a general shortage of materials, supplies, and spare parts in Ethiopia; (ii)inefficient banking services; and (iii) a shortage of foreign currency. Table 5.10 shows95Ethiopia Productivity Report

Table 5.10 World Bank Ease of Doing Business rankings
Singapore
Hong Kong
Korea, Rep.
Malaysia
Mauritius
ThailandRwanda
Japan
China
Kenya
VietnamIndonesia
India
ZambiaEgypt
UgandaCambodiaTanzania
Zimbabwe
Ethiopia
MyanmarBangladesh
No. ofcountries
ranked

2009
1
4
23
20
24
13
139
12
83
82
92
129
82
100
114
111
135
127
158
116
NA
110

2010
1
3
19
23
17
12
67
15
89
95
93
122
133
90
106
112
145
131
159
107
NA
119

2011
1
2
16
21
20
19
58
18
79
98
78
121
134
76
94
122
147
128
157
104
NA
107

2012
1
2
8
18
23
17
45
20
91
109
98
129
132
84
110
123
138
127
171
111
NA
122

2013
1
2
8
12
19
18
52
24
91
121
99
128
132
94
109
120
133
134
173
127
NA
129

2014
1
2
7
6
20
18
32
27
96
129
99
120
134
83
128
132
137
145
170
125
182
130

2015
1
3
5
18
28
26
46
29
90
136
78
114
142
111
112
150
135
131
171
132
177
173

2016
1
5
4
18
32
49
62
34
84
108
90
109
130
97
131
122
127
139
155
146
167
174

2017
2
4
5
23
49
46
56
34
78
92
82
91
130
98
122
115
131
132
161
159
170
176

2018
2
5
4
24
25
26
41
34
78
80
68
72
100
85
128
122
135
137
159
161
171
177

2019
2
4
5
15
20
27
29
39
46
61
69
73
77
87
120
127
138
144
155
159
171
176

181

183

183

183

185

189

189

189

190

190

190

Source: the World Bank’s Ease of Doing Business database.

Ethiopia’s position globally in the World Bank’s Ease of Doing Business rankings. It can beseen that that Ethiopia’s business environment rating remains one of the lowest, even within
Africa—and that rating continues to decrease, aside from a slight improvement in 2019.

5.6

Summary of findings

An in-depth survey of Ethiopian firms, particularly in the apparel industry, was conducted to
identify possible causes of Ethiopia’s low labor productivity. Eighteen firms in three locations
were surveyed, all but one of them fully foreign-owned. The average firm size in the sample
was 1,614 workers. At the time of the interviews, the operation ratio of the firms was
generally low, in the range of 30% to 40%. Three major factors that affect labor productivity
were examined: labor mindset and quality; management style and strategy; and policy andexternal factors.

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Labor Productivity Challenges in the Emerging Ethiopian Apparel Industry: Survey Results

The first determinant of labor productivity is labor mindset and quality. Labor mindset
refers to attitude to work and work culture, while labor quality encompasses education,
trainability, and skills. Provision of labor motivation (both financial and non-financial
incentives) and working conditions were also examined as factors that influence labor mindset
and quality.
Sewing, in which most garment workers are engaged (75% in our sample firms), requires
only basic numerical and reading skills. Results of the field survey show that about 60% of
the workers have high school graduation or higher, which makes them “overqualified.”
Almost all firms agreed that Ethiopian workers are quick learners of technical skills. However,workers’ soft skills, including industrial work discipline and motivation, were reported asseverely lacking, and the development of those skills was time consuming. Poor work culture
and attitude are manifested in forms such as high attrition, high absenteeism, absence of senseof urgency regarding work, and low motivation to work overtime. The results of the study
show that workers are extremely unhappy with their very low wages and poor working
conditions. The average monthly salary and non-wage benefits for sewing operators are about
USD 30 and less than USD 20, respectively, at the mid-2019 exchange rate. As a result,
workers regard garment sector jobs as transitory, not permanent employment. Low wages,
low non-wage compensation, and poor working conditions contribute to low work motivation,high attrition, and low productivity.
The issue of housing is a crucial one for the garment sector, where the employees are
predominantly young women. Employers’ failure to provide workers with free or cheap
housing of decent quality near the workplace negatively affects worker motivation andproductivity; it imposes additional transport time and requires payment of a significant shareof the worker’s monthly income. Refusal by workers to work overtime is due not only to low
compensation, but also to the security problems faced by young females when overtime forcesthem to return to their residences at night.
The second determinant of labor productivity is management style and strategy. The
means by which companies guide and incentivize their workers has a substantial impact onlabor productivity. Each country, and even each company, has a different corporate culture
and strategy. Approaches to labor mindset and productivity issues include: top-down orderand punishment, creation of corporate family oneness, mindset reform through instruction andpersuasion, mindset reform through rewards and incentives, and use of middle managers as an

97

Ethiopia Productivity Reportinterface between foreign managers and local workers. This list is not presented ascomprehensive.
All of the above approaches were at least considered, and sometimes actually tried fully or
partially, by the garment factories interviewed. Some CEOs argue that some elements of topdown order are inevitable in garment production in a global competition scenario, but thatapproach should not exceed legal bounds and should only be adopted temporarily, until theworkers learn discipline and acquire skills. In contrast, creation of family oneness within afirm deserves due merit and is worthy of consideration. Research is needed to identify suitableapproaches to implementing family oneness in Ethiopia in general, and within each firm.
Meanwhile, many firms, including the ones interviewed, accept incentivization of workers bymeans of financial and other rewards as a standard approach. Another method of improving
communication and trust is the mobilization of line supervisors as an interface between
foreign managers and local workers. These local middle managers, selected from among goodline workers and subsequently trained for the task, are already producing positive results at anumber of the FDI garment factories interviewed.
As FDI flow into Ethiopia increases, the arrival of foreign managers, bringing different
work attitudes, cultures, and experiences, may generate tension in the local workers. Many
foreign managers impose their home methods without due awareness of and respect for local
customs and conditions. Some foreign managers shout at or insult workers, a taboo behavior
in the local culture. On the other hand, Ethiopian managers who have not been exposed to
global business practices also have weaknesses such as: lack of sense of purpose andresponsibility, a lack of consistency in learning behavior, poor time management, and lack ofglobal mindset. Both foreign and domestic managers must improve their awareness andknowledge related to these cultural differences.
The third determinant of labor productivity is external conditions created by government,or resulting from global markets or other factors—none of which are the fault of managers
and workers. Ethiopia has many such business impediments, including a shortage of foreign
currency, unstable power supply, inefficient logistics, slow customs clearance, generally low
availability of materials, supplies and spare parts, and labor issues related to minimum wage,
overtime limits, leaves, and workers’ income tax. The interviewed managers frequently
mentioned these factors as very serious problems. For the enhancement of productivity at thenational level, these issues must be addressed with strong policy resolve and effective
implementation.

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6. Policy Measures Towards Enhanced
Productivity
Based on the findings of this report, we propose ten policy directions for the enhancement of
productivity. Three are related to the policy framework while the other seven deal withaspects of productivity in which improvement is strongly needed. We offer only policy
directions rather than concrete action details, construction of which we regard as separate
work that must be done after overall policy directions are agreed by key policymakers andstakeholders. When such agreement is obtained, the next step will be to design, implement
and monitor a concrete and feasible action plan for each policy item, with clear specificationof budget, timeline and organizations responsible.

POLICY FRAMEWORK
1. Establishment of policy organization and operational organizations
It is essential to establish a central organization, with appropriate membership, for
productivity policy deliberations (a national council or committee), headed by the Prime
Minister or another person in a high level of government. The government should actively
direct this organization to analyze key facts, set goals, approve and guide policy actions,monitor progress and solve emerging problems along the way. Meanwhile, a policy execution
body (an institute, center or agency) should also be created or designated and given sufficientauthority, budget and staff to design, implement, coordinate and review proposed policy
actions in detail, with regular progress report to the policy deliberation organization. In order
to avoid creating a nominal establishment with no real action, the Prime Minister or similar
must effectively mobilize this policy formulation and execution mechanism, contributing
strong personal interest, involvement and commitment.

2. Improvement of data collection and related publication
For Ethiopia to successfully prioritize productivity as a national goal, prompt publicationof reliable productivity-related data is essential. Data availability is still sporadic and slow in
Ethiopia. This study checked, cleaned and spliced data from the CSA manufacturing survey
database for use in the analyses. Not all irregularities could be reconciled. The government
should allocate sufficient human and financial resources for use in an appropriate designatedorganization, for the systematic and regular collection, analysis and publication of99

Ethiopia Productivity Report

productivity-related data 16. Also, the scope of data collection should be broadened over time.
Initially, besides basic GDP data, output, operation ratio, employment and wage data of the
manufacturing sector should be targeted for prompt and reliable delivery and analysis.
Agricultural data should be improved at an early stage, and capital stock data (for calculationof TFP) should be improved over time. Other data deficiencies should be identified andannounced, and should be corrected step-by-step over the medium term.

3. Setting of medium-term targets
Ethiopia should set a small number of medium-term productivity targets which areconcrete and monitorable and whose achievements depend on policy action. The exact
number and nature of those targets will be an important agenda item for the proposed policy
deliberation organization (see section 1 above). Possible candidate targets include (i)acceleration of labor productivity growth from the current 4-5% to at least 7-8% annually (asa trend; short-term fluctuations are permitted); (ii) requiring that TFP contribution dominate
labor productivity growth (say, 70% or more; it must be born in mind that TFP estimates havea longer lag than estimates of labor productivity); (iii) setting labor productivity growthtargets in key economic sectors, especially overall manufacturing and agriculture; (iv)benchmarking globally competitive apparel exporting countries including Vietnam and
Bangladesh, as models for Ethiopia to catch up with—and surpass—in terms of line-worker
productivity in concrete production processes such as the cutting and sewing of specificapparel products.

POLICY AREAS
4. Adjustment of investment policy for proper pace and more private projects
The report has shown that the TFP contribution to labor productivity growth fell in recent
years, while the contribution of capital deepening rose sharply. This is not good news because
labor productivity growth, driven mostly by large investments in infrastructure and otherstructures instead of true efficiency improvement, is more quantitative than qualitative.
Ethiopia should revise its investment policy with an appropriate pace and scope so it can
support the infrastructure building necessary for development, without jeopardizing thefinancial, fiscal and balance-of-payments soundness of the nation. Such management calls forfollowing a narrow path requiring wisdom, moderation and balancing of opposing objectives.
16

We are informed that the PDC has already instructed CSA to upgrade productivity-related data. We would liketo monitor the progress of that upgrading until the new productivity database achieves the scope and reliability
necessary for it to serve as the basis of policy formulation.

100Policy Measures Towards Enhanced Productivity

The government should also re-direct national investment away from large public projects
toward private productive investments through various measures including budget allocation,
foreign currency allocation, tax incentives and subsidies, as well as investment finance—
given that the lack of financing has been identified as one of the most severe constraints on
private investors. Both industrial infrastructure and private investments are necessary for
growth, but available resources are limited. The nation’s available resources should beallocated so as to address this trade-off, with increased recognition of the role of the private
sector. Incentivizing firms to invest in worker skill acquisition and skill-intensive production
can boost productivity in labor-intensive sectors. Another possible intervention is
incentivizing firms to learn and adopt appropriate technology. These skill and technologyoriented policies should contribute to the acceleration of TFP growth and to thecompetitiveness of the national economy.

5. Speeding up of structural transformation
The report also indicates that labor productivity growth has long been dominated by thewithin-effect, with the shift-effect remaining small. This is consistent with the observationthat to date Ethiopia’s growth, though rapid, has not produced visible structuraltransformations, including rapid labor migration from low-productivity to high-productivity
sectors.
To speed up the structural transformation process in Ethiopia, two groups of policy
measures should be considered. First, direct policy effort should be made in sectors that
employ a large proportion of the country’s labor force and therefore have considerable impact
on aggregate productivity. This means that Ethiopia needs to invest more in agriculture, whichis the basis of the Ethiopian economy, and light manufacturing, which is quickly emerging asa result of attracting FDI. Second, labor mobility from low- to high-productivity sectors
should be facilitated. This would typically take the form of rural-to-urban migration, but it
could also happen as labor migration across sectors within the same geographical area. This
would help not only to accelerate structural transformation, but also to enhance worker
efficiency and create more jobs. A considerable number of policy measures are needed for thepromotion of meaningful and productive labor migration, including: mindset reform; trainingthat fits the needs of industry; information on job opportunities; information on workers’
safety and rights; and incentives for private firms to conduct in-house training or send
workers to external training. Additionally, the government should ensure labor rights andgood working conditions; appropriate resolution of labor disputes; and attention to issues

101Ethiopia Productivity Reportassociated with labor migration including culture shock, housing, commuting, hygiene andhealthcare.
Part of the reason that Ethiopia’s internal labor migration remains small is the existence of
linguistic and cultural barriers across ethnicities and regions. This is a delicate matter thatneeds to be handled with care to promote more active and peaceful labor mobility.
6. Maintaining wage competitiveness
Manufacturing wages in Ethiopia are among the lowest in the world, and low even by theAfrican standards. On the positive side, that wage competitiveness is a great advantage for
Ethiopia in terms of attracting FDI and stimulating local investment. Thus, it is crucial tosustain this wage advantage so as to enable rapid industrialization. However, there is aconstant tension between worker demands for higher wages and the desire of firmmanagement for lower labor cost. Workers complain that their low wages cannot even coverliving costs in rural cities. Meanwhile, foreign managers complain that Ethiopian workers are
inefficient. The fundamental solution to this conundrum is to improve labor productivity sothat wages can rise at the speed of labor productivity growth, without sacrificing industrial
competitiveness. The obvious question is how to do that; serious policy learning anddeliberations are required for this.
The second and equally important solution is to forge a social compact, under thegovernment’s initiative and commitment, so the fruits of productivity shall be distributed
fairly among workers (for living), firms (for profit taking and further expansion) andgovernment (for tax revenues), and that the workers will not be laid off or left behind when
the nation improves productivity. Such a political arrangement was adopted by Japan andSingapore when they launched their national productivity movements. Emotional andpoliticized questions such as “Are Ethiopian wages too high or too low?” surface when laborproductivity remains low or stagnant. Social tension will melt away when labor productivity
begins to rise rapidly, as observed in Japan, Taiwan and Korea in the late twentieth century,
creating a quickly-expanding pie for management and labor to share (and also for the paymentof taxes to the government). If this happens, firms can raise wages annually, and workers canlook to a brighter future even if their wages are currently low.
Ethiopia has decided to launch a national minimum wage system and established the
National Minimum Wage Commission in 2019. The commission is mandated to analyze thelabor market and set minimum wages scientifically, based on concrete facts rather thanpolitical pressure. It is hoped that this commission will successfully propose an appropriate

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Policy Measures Towards Enhanced Productivity

wage-setting formula. In East Asian economies such as Thailand, Indonesia, Vietnam, andCambodia, the minimum wage has been pushed up politically, without productivity
improvement, by aggressive labor unions and by governments wanting to secure additional
votes in the next election, disappointing investors and harming competitiveness. Ethiopia’s
minimum wage system should not be allowed to fall into that trap.

7. Deepening and broadening Kaizen into a National Productivity Movement
With strong resolve and effort, Ethiopia has mastered the basic level of kaizen in the lastten years. The Ethiopian way of spreading kaizen has been established, and EKI can now
implement a variety of kaizen consultations, research and training without Japanese support.
Ethiopia also began to share the kaizen experience with the African Union and with individual
African nations. These are great achievements that should serve as the basis for the second
wave of National Productivity Movement, following a decade of kaizen introduction andpractice, with higher and broader targets. The new movement should keep kaizen as its core,but should develop kaizen further and disseminate it more widely, to all firms, farms, offices,
schools and other organizations in the whole country. That productivity movement should
encompass not only kaizen but also key business enhancements including strategic
management, global marketing, human resource management, acquisition of industry-specifictechnologies, and corporate finance (also see item 8 below).

8. Construction of an effective enterprise support system (especially for SMEs)
Ethiopian enterprises, especially small and medium units, are the main drivers of national
productivity. The government should set the long-term goal of establishing a comprehensive
and effective national enterprise support system. Japan, Taiwan and Malaysia could supply
the necessary information and models for that work.
However, enterprise support systems are generally very broad and complex, and theirconstruction must be done in staged and realistic steps. The fully constituted policy requires
enactment of the necessary laws, the creation of a national policy mechanism and theconstruction of a wide network of implementation mechanisms at the central, regional, andlocal levels with an additional involvement of research institutes, NPOs, financial institutions
and private firms. The nation must possess a thick layer of industrial experts in the fields ofmanagement, technology, marketing, personnel matters, ICT, tax, finance, environment, andlegal affairs including bankruptcy and Intellectual Property Rights (IPR) (not just kaizen), anda state-operated mechanism for certifying and re-training these experts. Each enterprise mustfirst be diagnosed, given a concrete business goal, and comprehensive support must be103

Ethiopia Productivity Reportprovided to each firm to achieve that goal. The SME Bank, SME credit unions, SME loan
guarantee and other financial systems must be in place. Training institutions, particularly
universities, colleges and TVET centers, must be upgraded nationally in terms of programs,
teachers and equipment. Training must teach not only skills but attitude, teamwork andcreativity, and solid working relations must be developed between industry and training
institutions to provide support in terms of internship, job placement and curriculum renewal.
Additionally, technical support centers should be established in major cities and towns,
staffed with competent experts and equipped with an array of testing and processing devices
to support SMEs in their efforts to develop, modify and/or certify their products—all for low
fees.
The above are the components of the fully developed enterprise support systems available
in Japan, Taiwan and Malaysia. Ethiopia cannot offer all these services and facilities at once,
so there is a need for a realistic step-by-step approach, beginning with the simplified or
modified introduction of a few selected mechanisms, followed by the expansion of the list ofpolicy elements as experience is accumulated and additional human and financial resources
become available.

9. Simultaneous pursuit of productivity and ethical standards
There are two distinct global trends in achieving excellence at manufacturing
establishments: (i) endless pursuit of certain desirable aspects of the product, such as cost
reduction, high and consistent quality, on-time delivery, and efficient user support services(product excellence); and (ii) the setting of high standards of ethical conduct in the production
process, with due attention to labor rights and environmental protection (ethical standards).
The former is the traditional core of productivity improvement work, while the latter
addresses broader social concerns. This dual set of standards is globally visible in all sectors,
but particularly in sectors producing labor-intensive consumer products such as apparel,
footwear and processed food. European and American consumers are highly sensitive to theconditions under which their goods are produced. They demand proof that child labor isabsent, that workers are treated fairly and that the environment is duly protected. Meanwhile,
many Asian—including Japanese—consumers are very interested in product quality per se.
Africa is a latecomer in the global value chain, and Ethiopia has emerged as a new
supplier of apparel and footwear. Given the dual set of concerns related to global markets,
Ethiopia must pursue both product excellence and ethical standards. This is not a choice: it isincreasingly the de facto requirement for entry into and remaining in the global market.

104

Policy Measures Towards Enhanced Productivity

However, this requires serious learning with international cooperation and appropriate policy,
and also much time, resources and expertise. Ethiopia must be well informed of theseindustrial requirements as it takes on the global market challenge under significant constraints
concerning time and resources, and must decide where to begin this work and how to attainthe dual array of goals through realistic and concrete steps.

10. Transforming the mindset of workers and management
In many latecomer countries, including Ethiopia, workers come mostly from rural areasand have had limited exposure to urban life and modern production processes. The results ofour survey show that Ethiopian garment workers have sufficient educational background andare quick learners of technical skills, though they are severely lacking in industrial work
discipline and workplace motivation. FDI firms operating in Ethiopia have undertaken some
practices for upgrading worker mindset, including top-down orders, creation of corporate
family oneness, financial and non-financial incentives, and the use of local line managers as
an interface between foreign and local work culture. Moreover, countries already globallycompetitive in apparel and footwear, such as Bangladesh, Vietnam, and Cambodia, have triedvarious approaches to transforming their workers, with mixed success. Mindset
transformation is a great policy challenge that requires systematic preparation and planning.
The Ethiopian solution must be formulated through selective learning, combining andmodifying foreign model practices to fit the Ethiopian reality.
As the Ethiopian government develops industrial parks vigorously, it is essential to
implement optimum mindset policy there. There is a need for a re-examination of thepracticality and effectiveness of the current strategy, especially that spearheaded by thegovernment, donors and the tenant association at Hawassa Industrial Park. Mindset reform
requires appropriate instruction and learning, but it must also go hand-in-hand with thematerial welfare of workers, including salary and bonuses, affordable housing, daily transport,
good canteen food, attractive and clean workplaces, medical service, good human relations,family events, and a clear understanding of the relationship between firm’s objective and thewell-being and career paths of workers.
At the same time, both foreign and Ethiopian managers need more knowledge and skills
for their work towards productivity enhancement. Here again, systematic learning, supported
by appropriate policy, is required for mindset transformation. Foreign managers tend to stickwith home-grown corporate culture and management style, which may not be suitable for
Ethiopia. They need to be educated towards local adaptation while retaining the core of their

105

Ethiopia Productivity Reportmanagement philosophy.
It is also found that productivity improvement at FDI firms is often in the hands ofEthiopian middle managers, who can reduce friction between foreign management and local
workers. However, the mindset of local managers and line leaders sometimes lacks anunderstanding of the basic demands of global competition. Their managerial quality can—andshould—be improved with additional training provided by the FDI firms. The government
need not be involved directly in such training; rather it could promote, duly recognize, andsubsidize private sector efforts.

106

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110

Annexes

Ethiopia Productivity Report

Annex 3.1 Economy-wide labor productivity growth decomposition (growth accounting
method)The growth accounting method decomposes growth in labor productivity into capital
deepening and TFP growth. Decomposition is derived from a Cobb-Douglas type production
function. Thanh et al. (2018) applies the same procedure for Vietnam
𝑌𝑌 = 𝐴𝐴. 𝐾𝐾 𝛼𝛼 . 𝐿𝐿𝛽𝛽(1)where, Y, K, L, A are output, capital, labor, and TFP, respectively.
Assuming constant return to scale, ∝ +𝛽𝛽 = 1, and dividing both side of (1) by L
𝑌𝑌

𝑌𝑌

𝐾𝐾

=
𝐿𝐿

𝐴𝐴.𝐾𝐾𝛼𝛼 .𝐿𝐿 𝛽𝛽
𝐿𝐿 𝛼𝛼+𝛽𝛽𝐾𝐾 𝛼𝛼= 𝐴𝐴. � 𝐿𝐿 �

(2)Defining 𝑦𝑦 = 𝐿𝐿 and 𝑘𝑘 = 𝐿𝐿 , then 𝑦𝑦 and 𝑘𝑘 are labor productivity and capital/labor ratio(capital per unit labor). Equation (2) becomes:

𝑦𝑦 = 𝐴𝐴. 𝑘𝑘 𝛼𝛼(3)

Applying natural logs and differentiating both side of equation (3),𝑙𝑙𝑙𝑙𝑙𝑙 = 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 + 𝑙𝑙𝑙𝑙𝑙𝑙∆𝑙𝑙𝑙𝑙𝑙𝑙 = 𝛼𝛼∆𝑙𝑙𝑙𝑙𝑙𝑙 + ∆𝑙𝑙𝑙𝑙𝑙𝑙(4)

Equation (4) implies that labor productivity growth can be decomposed into capitalintensity growth ( 𝛼𝛼∆𝑙𝑙𝑙𝑙𝑙𝑙) and TFP growth ( ∆𝑙𝑙𝑙𝑙𝑙𝑙) . Capital intensity makes labor moreproductive by providing a greater amount of capital to work with, enhancing economy-widelabor productivity via the contribution share of capital in the production function ( 𝛼𝛼). TFP
growth enhances labor productivity growth by one-to-one ratio.

The growth rates of 𝑦𝑦 (labor productivity) and TFP can be computed as:
𝑔𝑔𝑦𝑦 = 𝛼𝛼. 𝑔𝑔𝑘𝑘 + 𝑔𝑔𝐴𝐴

𝑔𝑔𝐴𝐴 = 𝑔𝑔𝑦𝑦 − 𝛼𝛼. 𝑔𝑔𝑘𝑘

(5)
(6)

In order to compute capital intensity (k) and capital intensity growth rate, 𝑔𝑔𝑘𝑘 , we need toestimate capital stock (K) in equation (5). Replacing 𝑔𝑔𝑘𝑘 , 𝑔𝑔𝑦𝑦 (calculated from values ofmeasured labor productivity) and 𝛼𝛼 (parameter assumed by authors based on literature) with
their estimates in formula (6), we obtain TFP growth rate, 𝑔𝑔𝐴𝐴 .

We use capital stock data for Ethiopia from Penn Tables. Penn Tables report capital stockand real GDP based on 2011 prices. We take the share, and apply it to the national accounts

112Annexdata and recalculate capital stock using the Penn Table capital stock to GDP share. This
recalculation is required as national accounts data are based on 2011 prices and in local
currency. Due to lack of initial capital stock, we couldn’t compute capital stock using theperpetual inventory method.
Determination of capital’s share in national income (𝛼𝛼) is challenging due to absence ofdata. Most studies assume a certain share of capital in GDP (for example, Collins & Bosworth(1996) and Thanh et al. (2018) both assume, 𝛼𝛼 = 0.35 for East Asia and Vietnam,

respectively). Following the 2015/16 Input-Output and Social Accounting Matrix for Ethiopia(Mengistu et al., 2018), we adopt a capital share, 𝛼𝛼, of 0.3.

Our measures of national accounts data for Y, L, and K are measured in terms of real value

added at constant 2003 price, total employed persons in a year (total employed from age 15
and above working in the economy from WDI which in turn is based on ILO’s modelled
estimates). We use the unemployment rate computed by ILO to calculate the number ofworking labor from labor force.
The growth rates of GDP, labor productivity, and TFP in year t use the following
information:
𝛼𝛼.𝑔𝑔𝑡𝑡

Contribution share of capital deepening year t = 𝑔𝑔𝑡𝑡 𝑘𝑘 . 100(%)
𝑦𝑦

𝑔𝑔𝑡𝑡

Contribution share of TFP growth in year t = 𝑔𝑔𝐴𝐴𝑡𝑡 . 100(%)
𝑦𝑦

(7)

(8)

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Ethiopia Productivity Report

Annex 3.2 Data and Estimates of level and growth rates of variables used in economywide labor productivity analysis

Year

Labor
force
(millions)Labor
productivity(thousandBirr perworker)Capitalstock(billion
Birr)

Capitalintensity(thousandBirr perworker)Contribution share tolabor productivity
growth (%)

Growth rates (%)

Labor
productivity

Capitalintensity

TFP

Capitalintensity

TFP

2000

29.00

6.50

614.01

21.18

2001

30.06

6.74

624.71

20.78

3.77

-1.87

4.34

-14.88114.88

2002

31.21

6.56

634.49

20.33

-2.79

-2.15

-2.15

23.12

76.88

2003

32.41

6.14

641.68

19.80

-6.41

-2.61

-5.62

12.23

87.77

2004

33.64

6.63

660.95

19.65

8.01

-0.79

8.24

-2.95

102.95

2005

34.92

7.20

695.03

19.91

8.70

1.32

8.30

4.56

95.44

2006

35.97

7.81

735.07

20.43

8.45

2.66

7.66

9.43

90.57

2007

37.08

8.47

772.71

20.84

8.37

1.99

7.77

7.14

92.86

2008

38.24

9.12

825.45

21.58

7.74

3.57

6.67

13.84

86.16

2009

39.48

9.70

891.30

22.57

6.38

4.58

5.00

21.56

78.44

2010

40.80

10.36

946.55

23.20

6.78

2.78

5.95

12.29

87.71

2011

42.21

11.13

1047.74

24.82

7.43

6.98

5.33

28.19

71.81

2012

43.68

11.68

1217.12

27.87

4.93

12.27

1.25

74.68

25.32

2013

45.20

12.40

1415.47

31.32

6.15

12.39

2.43

60.44

39.56

2014

46.67

13.24

1672.88

35.85

6.79

14.46

2.45

63.92

36.08

Source: author computation from PDC, WDI, and Penn World Tables.
Note: Data on capital stock is not available for 2015 and 2016 for Ethiopia. Hence, the decomposition of
economy-wide labor productivity is done for the 2000-2014 period.

114

AnnexAnnex 3.3 Decomposing contributors of economy-wide labor productivity growth using
shift-share analysis
The shift-share method is derived as follows 17.
Productivity for the whole economy is expressed as the sum of the productivity level ofeach sector weighted by their respective employment shares, as shown below.
𝑌𝑌

𝑌𝑌

𝐿𝐿

𝑃𝑃𝑚𝑚 = 𝐿𝐿𝑚𝑚 = ∑𝑛𝑛𝑗𝑗=1 �𝐿𝐿𝑗𝑗 ∗ 𝐿𝐿 𝑗𝑗 � = ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗 ∗ 𝑆𝑆𝑗𝑗 �
𝑚𝑚

𝑗𝑗

𝑚𝑚

(9)

where Y, L, P (=Y/L) are output, number of employed persons, and labor productivity of
sector j (j = 1, …, n) or of the economy (m). 𝑆𝑆𝑗𝑗 is the labor share of sector j in the total
number of employed persons of the economy.

Economy-wide labor productivity in year t is:
𝑃𝑃𝑚𝑚𝑡𝑡 = ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗𝑡𝑡 ∗ 𝑆𝑆𝑗𝑗𝑡𝑡 �(10)The change in economy-wide labor productivity in year t relative to base year 0 can be
expressed as:
𝑃𝑃𝑚𝑚𝑡𝑡 − 𝑃𝑃𝑚𝑚0 = ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗𝑡𝑡 ∗ 𝑆𝑆𝑗𝑗𝑡𝑡 � − ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗0 ∗ 𝑆𝑆𝑗𝑗0 �(11)Adding and subtracting both sides of equation (4.11) by ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗𝑡𝑡 ∗ 𝑆𝑆𝑗𝑗0 �, ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗0 ∗ 𝑆𝑆𝑗𝑗𝑡𝑡 �,and ∑𝑛𝑛𝑗𝑗=1�𝑃𝑃𝑗𝑗0 ∗ 𝑆𝑆𝑗𝑗𝑡𝑡−1 �, rearranging, and dividing both sides by 𝑃𝑃𝑚𝑚0 , we obtain economy-widelabor productivity growth in year t relative to base year 0:
𝑡𝑡 −𝑃𝑃 0
𝑃𝑃𝑚𝑚
𝑚𝑚
0
𝑃𝑃𝑚𝑚

17

=

𝑡𝑡
0
0
∑𝑛𝑛
𝑗𝑗=1��𝑃𝑃𝑗𝑗 −𝑃𝑃𝑗𝑗 �∗𝑆𝑆𝑗𝑗 �
0
𝑃𝑃𝑚𝑚

+

0
𝑡𝑡
0
∑𝑛𝑛
𝑗𝑗=1�𝑃𝑃𝑗𝑗 ∗�𝑆𝑆𝑗𝑗 −𝑆𝑆𝑗𝑗 ��
0
𝑃𝑃𝑚𝑚

+

𝑡𝑡
0
𝑡𝑡
0
∑𝑛𝑛
𝑗𝑗=1��𝑃𝑃𝑗𝑗 −𝑃𝑃𝑗𝑗 �∗(𝑆𝑆𝑗𝑗 −𝑆𝑆𝑗𝑗 )�
0
𝑃𝑃𝑚𝑚

(12)

See, for example, Timmer & Szirmai (2000) and Thanh et al. (2018).

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Ethiopia Productivity Report

Annex 3.4 Data and estimates used to conduct shift-share analysis
Year

Value added per worker in
Growth of value added per
thousand Birr
worker (%)
EconomyIndustr Service Econom
Agri. Industry Services
Agri.
-wide
y
s
y-wideAgri.

IndustryService

2004

4.45

0.82

0.06

0.12

13.19

21.22

7.05

Employment share (%)

2005

4.93

12.11

21.75

7.61

10.1
8

2006

5.31

13.08

23.46

8.25

7.57

7.69

7.54

8.07

0.80

0.07

0.13

2007

5.68

14.05

25.04

8.94

6.68

7.17

6.54

8.00

0.79

0.07

0.14

2008

5.94

15.49

27.27

9.63

4.41

9.76

8.53

7.41

0.79

0.06

0.14

2009

6.15

16.48

29.06

10.24

3.58

6.19

6.34

6.14

0.79

0.06

0.15

2010

6.54

17.73

28.81

10.93

6.04

7.30

-0.85

6.53

0.77

0.06

0.17

2011

7.03

17.40

29.87

11.74

7.34

-1.91

3.60

7.14

0.76

0.07

0.17

2012

7.18

18.63

31.35

12.31

2.12

6.87

4.83

4.77

0.75

0.07

0.18

2013

7.65

21.95

29.41

13.05

6.35

16.40

-6.39

5.80

0.73

0.07

0.20

2014

7.96

23.12

31.03

13.93

3.90

5.19

5.36

6.56

0.71

0.08

0.21

2015

8.39

24.77

32.19

14.91

5.24

6.87

3.69

6.81

0.70

0.09

0.21

2016

8.44

27.88

33.13

15.63

0.67

11.84

2.86

4.67

0.69

0.09

0.22

-8.53

2.46

7.70

0.80

0.07

0.13

Source: authors’ calculation using data from PDC and WDI.

Annex 4.1 Industry names and description
Industry

Description

Food & beverages

Manufacture of food products and beverages

TobaccoManufacture of tobacco products

TextilesManufacture of textiles

Garment

Manufacture of wearing apparel, except fur apparel

Leather & footwear
Wood

Tanning and dressing of leather; manufacture of footwear, luggage andhandbags
Manufacture of wood and of products of wood and cork, except
furniture; manufacture of articles of straw and plaiting materials

Paper

Manufacture of paper, paper products and printing

Publishing & printing

Publishing, printing and reproduction of recorded media

Chemicals

Manufacture of chemicals and chemical products

Rubber & plastics

Manufacture of rubber and plastics products

Non-metallic minerals

Manufacture of other non-metallic mineral products

Basic metals

Manufacture of basic metals

Fabricated metal

Manufacture of fabricated metal products except machinery andequipment

Machinery & equipment

Manufacture of machinery and equipment n.e.c.

Motor vehicles

Manufacture of motor vehicles, trailers and semi-trailers

Furniture

Manufacture of furniture; manufacturing n.e.c.

116

AnnexAnnex 4.2 Labor cost by sector in million ETB
Sector

1996

2000

2005

2011

2016

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Food & beverages

135

469

196

627

313

854

1141

1141

3330

1711

Textiles

105

364

112

357

117

319

222

222

665

341

Garment

12

43

15

49

13

35

55

55

278

143

Leather & footwear

40

137

44

139

56

153

162

162

416

214

Wood

12

40

7

22

11

29

53

53

70

36

Paper

8

27

11

34

19

52

56

56

73

38

Publishing & printing

28

96

32

101

56

153

116

116

398

205

Chemicals

16

55

28

89

56

152

185

185

864

444

Rubber & plastics

13

46

25

79

48

130

200

200

589

302

Non-metallic minerals

32

109

48

154

72

195

277

277

1286

661

Basic metals

8

29

7

23

27

75

96

96

265

136

Fabricated metal

12

41

15

48

34

92

121

121

580

298

Machinery & equipment

2

6

1

4

1

3

12

12

18

9

Motor vehicles

6

20

15

48

18

49

56

56

365

187

Furniture

10

35

21

68

26

72

79

79

329

169

438

1517

575

1841

866

2364

2831

2831

9525

4894

Total

Source: authors’ calculation using data from CSA’s LMSMI Survey.

Annex 4.3 Annual average growth rate of labor cost by sector (%)
Sector1997-2000

2001-2005

2006-20102011-2016

1997-2016

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Food & beverages

9.23

7.25

9.37

6.17

20.45

3.71

22.37

8.50

16.01

6.47

Textiles

1.53

-0.45

0.93

-2.27

9.76

-6.99

20.84

6.97

9.23

-0.31

Garment

5.44

3.46

-3.26

-6.46

40.61

23.86

17.25

3.37

15.60

6.05

Leather & footwear

2.36

0.38

5.10

1.91

14.05

-2.70

21.64

7.76

11.75

2.21

Wood

-13.36-15.34

8.91

5.71

26.98

10.23

8.83

-5.05

8.95

-0.59

Paper

7.92

5.94

11.65

8.46

14.82

-1.92

10.10

-3.78

11.23

1.69

Publishing & printing

3.24

1.26

11.57

8.38

10.33

-6.41

24.03

10.15

13.33

3.79

Chemicals

14.21

12.23

13.86

10.66

24.97

8.23

24.91

11.04

20.02

10.48

Rubber & plastics

15.28

13.30

13.25

10.05

26.33

9.58

19.99

6.11

18.95

9.40

Non-metallic minerals

10.49

8.51

7.97

4.77

27.83

11.08

24.97

11.09

18.54

8.99

Basic metals

-3.68

-5.66

26.76

23.57

9.37

-7.37

29.94

16.06

17.28

7.74

Fabricated metal

5.71

3.73

16.22

13.02

33.19

16.44

19.69

5.82

19.40

9.86

Machinery & equipment-10.51-12.49

1.55

-1.65

24.78

8.03

24.08

10.20

11.70

2.16

Motor vehicles

24.31

22.33

3.47

0.27

-34.01-50.75

78.62

64.75

20.81

11.27

Furniture

18.62

16.64

4.37

1.17

19.81

3.06

25.59

11.72

17.44

7.90

6.83

4.85

8.19

4.99

20.44

3.69

22.93

9.05

15.40

5.86

Total

Source: authors’ calculation using data from CSA’s LMSMI Survey.

117

Ethiopia Productivity Report

Annex 4.4 Wages and salaries by sector in million ETB
Sectors
Food &
beverages
Textiles

1996
Nom.

Real

117.00405.45 173.00

97.70

Garment
Leather &footwear
Wood
Paper
Publishing &printing
Chemicals
Rubber &plastics
Non-metallic
minerals
Basic metals
Fabricatedmetal
Machinery &equipment
Motor vehicles
Furniture
Total

2000
Nom.

2005

Real

Nom

2011

2016

Real

Nom

Real

Nom

Real

553.85 264.00

720.35955.00955.00 2753.24 1414.58

338.56 105.00

336.15 110.00

300.15201.00201.00622.00319.58

12.00

41.58

14.00

44.82

12.30

33.56

48.80

48.80

265.00136.1535.20

121.9840.30

129.0250.90

138.89133.00133.00376.00193.18

10.20

35.35

6.20

19.86

9.55

26.04

49.00

49.00

67.20

34.53

6.99

24.22

9.81

31.40

17.40

47.48

49.10

49.10

71.20

36.58

25.30

87.67

29.40

94.12

49.60

135.34105.00105.00338.00173.66

13.90

48.17

25.00

80.04

50.30

137.25149.00149.00707.00363.25

12.10

41.93

21.30

68.19

42.80

116.78181.00181.00560.75288.11

26.10

90.45

42.60

136.3862.80

171.36245.00245.00 1170.00

601.13

7.10

24.61

6.15

19.69

19.60

53.48

79.10

79.10

236.00121.25

11.00

38.12

13.40

42.90

31.50

85.95

111.00111.00528.00271.28

1.70

5.88

1.13

3.63

1.07

2.93

11.40

11.40

17.60

9.04

4.73

16.40

12.40

39.70

16.10

43.93

50.40

50.40

324.00166.47

9.09

31.49

19.50

62.43

24.80

67.67

71.80

71.80

288.00147.97390.10 1351.85 519.19 1662.17 762.72 2081.16 2439.60 2439.60 8323.98 4276.77

Source: authors’ calculation using data from CSA’s LMSMI Survey.

Annex 4.5 Annual average growth of wages and salaries by sector (%)
Sectors

1997-2000

2001-2005

2006-20102011-2016

1997-2016

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Nom.

Real

Food & beverages

9.78

7.80

8.45

5.26

20.37

3.62

22.10

8.23

15.79

6.25

Textiles

1.80

-0.18

0.93

-2.27

8.35

-8.40

21.92

8.04

9.26

-0.29

Garment

3.85

1.87

-2.59

-5.79

40.20

23.45

17.67

3.79

15.47

5.93

Leather & footwear

3.38

1.40

4.67

1.47

13.90

-2.84

21.74

7.87

11.84

2.30

-12.44-14.42

8.62

5.43

25.76

9.01

11.06

-2.81

9.43

-0.12

Wood
Paper

8.47

6.49

11.47

8.27

15.30

-1.44

10.73

-3.14

11.61

2.06

Publishing & printing

3.75

1.77

10.46

7.26

10.56

-6.19

23.18

9.31

12.96

3.42

Chemicals

14.67

12.69

13.98

10.79

23.88

7.13

24.15

10.28

19.65

10.10

Rubber & plastics

14.14

12.16

13.96

10.76

25.99

9.25

21.22

7.34

19.18

9.64

Non-metallic minerals

12.25

10.27

7.76

4.57

28.26

11.51

25.20

11.32

19.01

9.47

Basic metals

-3.59

-5.57

23.18

19.98

14.86

-1.89

29.09

15.22

17.52

7.97

Fabricated metal

4.93

2.95

17.09

13.90

32.13

15.38

20.21

6.34

19.36

9.81

Machinery & equipment-10.09-12.07

-1.06

-4.26

26.48

9.73

24.54

10.67

11.70

2.15

Motor vehicles

24.08

22.10

5.22

2.03

-32.67-49.41

77.25

63.38

21.13

11.59

Furniture

19.09

17.11

4.81

1.61

19.52

2.77

24.61

10.73

17.28

7.74

7.15

5.17

7.69

4.50

20.26

3.51

22.95

9.08

15.30

5.76

Total

Source: authors’ calculation using data from CSA’s LMSMI Survey.

118

AnnexAnnex 4.6 Non-wage payments by sector in million ETB
Sector

1996

2000

Nom

Real

Food & beverages

18.40

63.76

Textiles

7.20

Garment

2005

Real

Nom

73.31

48.90

24.96

Nom
22.9
0
6.51

20.85

0.29

1.01

1.28

Leather & footwear

4.43

15.35

Wood

1.41

4.88

Paper

0.77

Publishing & printing
Chemicals
Rubber & plastics
Non-metallic
minerals
Basic metals

2011

2016

Nom
185.6
9
21.48

Real
185.6
9
21.48

577.00

6.82

Real
133.4
3
18.60

42.50

Real
296.4
6
21.84

4.10

0.68

1.85

5.84

5.84

13.20

6.78

3.26

10.42

5.32

14.52

29.26

29.26

39.70

20.40

0.60

1.92

1.08

2.93

4.43

4.43

2.31

1.19

2.66

0.84

2.69

1.67

4.55

6.61

6.61

2.15

1.10

2.37

8.21

2.10

6.73

6.59

17.98

10.59

10.59

60.20

30.93

1.86

6.43

2.81

9.00

5.30

14.47

36.05

36.05

157.00

80.66

1.21

4.18

3.22

10.31

4.76

12.99

18.84

18.84

27.80

14.28

5.46

18.92

5.42

17.34

8.71

23.78

31.82

31.82

116.00

59.60

1.25

4.32

1.05

3.37

7.86

21.46

16.84

16.84

28.50

14.64

Fabricated metal
Machinery &equipment
Motor vehicles

0.98

3.38

1.65

5.28

2.36

6.44

10.21

10.21

52.20

26.82

0.06

0.21

0.02

0.07

0.17

0.47

1.00

1.00

0.65

0.33

0.95

3.28

2.62

8.37

1.76

4.80

5.31

5.31

40.80

20.96

Furniture

0.96

3.32
164.8
8

1.65
55.9
3

5.29
179.0
7

1.51
103.4
9

4.13
282.3
9

7.39
391.3
6

7.39
391.3
6

41.00
1201.0
1

21.07
617.0
6

Total

47.58

Nom

Source: authors’ calculation using data from CSA’s LMSMI Survey.

Annex 4.7 Annual average growth of non-wage payments by sector (%)
Sector1997-2000
Nom

Real

2001-2005

2006-20102011-2016

Nom

Real

Nom

Real

Nom

1997-2016

Real

Nom

Real

Food & beverages

5.47

3.49

15.17

11.98

20.89

4.15

23.72

9.85

17.23

7.68

Textiles

-2.52

-4.50

0.91

-2.28

24.58

7.84

10.02

-3.86

8.87

-0.67

Garment
Leather &footwear
Wood

36.96

34.98

-12.69-15.88

46.80

30.05

10.45

-3.42

19.06

9.51

-7.70

-9.68

9.82

6.62

15.42

-1.33

20.65

6.78

10.96

1.42

-21.33-23.32

11.68

8.48

35.44

18.70

-16.79-30.66

2.48

-7.07

2.26

0.28

13.69

10.49

8.99

-7.75

-3.30

-17.18

5.13

-4.41

-2.98

-4.96

22.84

19.64

8.54

-8.20

29.75

15.88

16.17

6.63

10.38

8.40

12.70

9.50

33.14

16.39

28.85

14.98

22.19

12.65

Rubber & plastics
Non-metallic
minerals
Basic metals

24.53

22.55

7.80

4.60

29.11

12.36

5.16

-8.72

15.68

6.14

-0.20

-2.18

9.51

6.32

24.39

7.64

22.82

8.95

15.28

5.74

-4.18

-6.16

40.20

37.00

-21.49-38.23

39.37

25.49

15.65

6.11

Fabricated metal
Machinery &equipment
Motor vehicles

13.12

11.14

7.18

3.98

43.70

26.96

15.18

1.30

19.90

10.35

-26.32-28.30

41.91

38.71

8.74

-8.01

14.81

0.93

11.84

2.30

25.43

23.44

-7.95

-11.15-54.24-70.99

97.61

83.74

18.82

9.28

Furniture

13.61

11.63

-1.75

-4.95

24.07

7.33

34.91

21.04

18.78

9.23

4.04

3.49

12.31

11.98

21.73

4.15

22.75

9.85

16.14

7.68

Paper
Publishing &printing
Chemicals

Total

Source: authors’ calculation using data from CSA’s LMSMI Survey.

119

Ethiopia Productivity Report

Annex 5.1 Number of firms reporting line efficiency and labor productivity by productProduct type

Number of firms reporting

Women's dress

1

Woven bottom

2

Denim bottom

1

Baseball pants1

Jogger's pants

1

Cargo pants

1

Short pants

3

Knitted jacket

1

Ladies trousers

1

Tricot shirts

1

Carter bodysuit

1

Carter pants

1

GCI onesies

1

TCP T-shirt

1

H&M tank top

1

Men's underwear

1

Pack out

1

Pyjama1

Leggings

1

Apron

1

120