metadata: authors: 0: author: Caria,Stefano
count: Ethiopia
docna: 0: docna: Industrialization on a Knife's Edge :
            Productivity, Labor Costs and the Rise of Manufacturing in Ethiopia
docty: Policy Research Working Paper
subtopic: Food & Beverage Industry,General Manufacturing,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,Plastics & Rubber Industry,Skills Development and Labor Force Training,Labor Markets,Private Sector Economics,Macroeconomics and Economic Growth,Economic Policy, Institutions and Governance
teratopic: Private Sector Development,Industry,Social Protections and Labor,Macroeconomics and Economic Growth
abstracts: cdata!: The latest push for industrialization in
            Ethiopia has attracted much academic and public interest.
            This paper assesses Ethiopia's competitiveness and
            attractiveness as an investment destination by comparing
            domestic productivity and input costs to a sample of
            manufacturing exporting countries.  The paper documents
            that, in a comparison with Kenya, India or Vietnam, the
            labor cost advantage of Ethiopian firms is more than offset
            by low productivity. However, Ethiopia appears competitive
            when compared to Bangladesh. Capital, firm size, or sectoral
            composition do not explain the low productivity of the
            Ethiopian manufacturing sector. Ethiopian firms, however,
            have worse management, particularly in the area of labor
            management. The paper concludes by discussing the potential
            for labor interventions to increase productivity and create
            the condition for further industrialization.
geo_reg_key: 517191
repnme: repnme: Industrialization on a Knife's Edge :
            Productivity, Labor Costs and the Rise of Manufacturing in Ethiopia
display_title: Industrialization on a Knife's
            Edge : Productivity, Labor Costs and the Rise of
            Manufacturing in Ethiopia
listing_relative_url: /research/2019/08/31333038/industrialization-knifes-edge-productivity-labor-costs-rise-manufacturing-ethiopia
topicv3: Industrial Zones m1326507 1202,Manufacturing Sectors m1326521 1281,Industry Policy m1327403 1075,Jobs m1503931 2670,Soft Skills Training m1504143 2820
publishtoextweb_dt: 2019-08-13T00:00:00Z
historic_topic: Private Sector Development,Industry,Social Protections and Labor,Macroeconomics and Economic Growth
no_of_pages: 35
model_extractions: Policy Research Working Paper 8980 Industrialization on a Knife’s Edge Productivity, Labor Costs and the Rise of Manufacturing in Ethiopia _Stefano Caria_ Africa Region Office of the Chief Economist August 2019
Policy Research Working Paper 8980

# **Abstract**

The latest push for industrialization in Ethiopia has attracted much academic and public interest. This paper assesses Ethiopia’s competitiveness and attractiveness as an investment destination by comparing domestic productivity and input costs to a sample of manufacturing exporting countries. The paper documents that, in a comparison with Kenya, India or Vietnam, the labor cost advantage of Ethiopian firms is more than offset by low productivity.

However, Ethiopia appears competitive when compared to Bangladesh. Capital, firm size, or sectoral composition do not explain the low productivity of the Ethiopian manufacturing sector. Ethiopian firms, however, have worse management, particularly in the area of labor management. The paper concludes by discussing the potential for labor - interventions to increase productivity and create the con dition for further industrialization.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at stefano.caria@bristol.ac.uk.

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

## **Productivity, labor costs and the rise of manufacturing in Ethiopia**

Stefano Caria<sup>∗</sup>

**JEL codes** : O14, O15, O55. **Key words** : Industrialization, productivity, labor costs, Ethiopia.
# **1 Introduction**

The latest push for industrialisation in Ethiopia has attracted much academic and public interest. While the domestic manufacturing sector is still relatively small, Ethiopia has made large investments to promote industrial development and has witnessed some early success (Oqubay, 2015). The flagship project is the creation of several industrial parks across the country. The most famous of these parks, in the southern city of Hawassa, has attracted significant foreign direct investment from international garment manufacturers. This park alone currently employs more than 15,000 workers and exports 100 million USD worth of goods, doubling the country’s foreign exchange earnings from garment manufacturing (Butler, 2018).

These early results are surprising in the context of the recent literature on ‘premature deindustrialization’ (Rodrik, 2015). This literature argues that in the last decades developing countries have moved out of manufacturing much faster than today’s rich countries did in the past, when they had comparable levels of income. This early exit from a labour-intensive sector is feared to be an obstacle to generating employment and broad-based gains from growth. Also, the international experience with special economic zones and industrial parks has been mixed (Khandelwal and Teachout, 2016).

In this paper, I assess Ethiopia’s competitiveness and attractiveness as an investment destination by comparing the productivity and input costs faced by firms based in Ethiopia to a sample of manufacturing exporting countries. Given the current strategic importance of the garment sector, I select comparison countries that are garment exporters in Africa (Kenya), and Asia (Bangladesh, India, Vietnam). I use harmonized data from the World Bank Enterprise Survey, which enables me to construct aggregates figures for sales for worker (a measure of productivity), total labor costs, capital stock, firm size, and a somewhat noisy measure of value added. I compute these figures for the whole manufacturing sector and for the garment sector. Further, I use data from the World Management Survey to study the quality of management.

My main finding is that Ethiopia’s labor cost advantage is more than offset by low productivity with respect to all but one of the comparison countries (Bangladesh). Ethiopia’s industrialization is thus on a knife’s edge. Its fundamentals make it competitive with respect to the countries at the bottom of the productivity distribution. In recent years, the increase in labour costs has also been modest compared to these countries. However, given the likely pressure on wages that will result from further expansion of the manufacturing sector, additional gains in productivity will be crucial in order to secure Ethiopia’s position as an attractive investment destination.

The low productivity of Ethiopian firms is not explained by differences in size, cap2
ital, or sector. However, Ethiopian firms score particularly low on a measure of the quality of management. The most critical area is that related to the management of workers. In particular, the quality of selection, incentives and retention practices is lower than in all comparison countries. This suggests a need for reformed labor management practices. Further, given the scarcity of rigorous evidence on the merits of specific interventions, this highlights the need for more experimental work on labor management in Ethiopia.

What is particularly interesting is that the historical experience of other nations also points to the importance of developing the right labor institutions and practices to make industrialization possible and to distribute its gains. Scholars of both the industrial revolution in the UK (Thompson, 1967; Clark, 1994) and of mid 20th century industrialization in Asia and Latin America (Kohli, 2004) agree on this point. Research on early industrialization in Africa also points to labor management problems, in particular, retention (Elkan, 1971). Finally, newly available historical data has highlighted the important role of unions in securing a wider distribution of the gains from industrialization (Farber et al., 2018).

This paper is also related to and complements two recent studies by Gelb et al. (2016) and Gelb et al. (2017), which argue that high labor costs hold back industrialization in Africa. These studies identify Ethiopia as a country with relatively low labor costs. Our analysis further points out that this labor cost advantage is partly offset by low productivity.

The rest of this paper is organized as follows. Section 2 describes the data. I then present the comparison of productivity and input costs and discuss the determinants of Ethiopia’s low productivity in Section 3 and 4. Finally, in Section 5, I discuss evidence from recent studies of labor interventions. Section 6 concludes.

# **2 Data**

Most of the analysis is based on data from the _World Bank Enterprise Survey_ – an harmonised representative survey of firms covering about 139 countries in the world. For the analysis, I select five countries of interest: Ethiopia, Kenya, Bangladesh, India and Vietnam.<sup>1</sup> Kenya is a geographical neighbour of Ethiopia and a natural competitor within east Africa, with a key advantage coming from direct access to the sea. I then select three Asian countries, covering the range of the productivity distribution among

> 1I use the latest survey available for each country. For Ethiopia and Vietnam, this is the 2015 survey; for India this is the 2014 survey, and for Bangladesh and Kenya this is the 2012 survey.

3
major manufacturing economies: from relatively high-productivity Vietnam to relatively low-productivity Bangladesh.

I focus the analysis on manufacturing firms (two-digit ISIC codes 15-37). I also report data specific to the garment sector (ISIC codes 17-19). The key variables I extract from the data are number of workers (defined as the sum of employees on permanent and temporary contracts), total sales, total labor costs (including wages, payroll taxes, benefit payments, etc..), total cost of materials (the cost of physical inputs in production), and total value of physical capital. I measure productivity as sales per worker. Further, I calculate a measure of values added that takes into account rental payments for capital and land. All figures are converted to 2010 USD by using exchanges rate from the relevant year and US CPI data.<sup>2</sup> I use sampling weights to compute averages representative of the underlying population.<sup>3</sup> Missing data is not common, with a few exceptions – in particular for material costs. All variables are truncated at the 95th percentile of the country-specific distribution. I report all key descriptives in Tables A.1-A.5.

I also use data from two other sources to complement the analysis. First, I use original survey data collected for manufacturing firms in Addis Ababa by Abebe et al. (2017) to check the robustness of the sales figures reported for Ethiopia. Second, I use data from the _World Management Survey_ to compare the quality of management across the countries in my sample (with the exception of Bangladesh, which is not included in the World Management Survey). This survey studies management practices in manufacturing firms in 34 countries.<sup>4</sup> Firm managers are asked to describe several management practices in the firms over a phone interview and are then scored by a trained enumerator. The instrument produces an overall management score, as well as specific scores for the quality of operations management, monitoring of performance, target setting, and HR practices (including recruitment and retention strategies).

> 2I used exchange rate data from the World Bank, which is available here `https://data.worldbank. org/indicator/PA.NUS.FCRF?locations=ET` . For each country, I use the exchange rate for the year prior to the date when the survey was released to the public (e.g. for Ethiopia, I use the 2014 exchange rate). The CPI data is available here `https://www.measuringworth.com/datasets/uscpi/` .

> 3The World Bank Enterprise Survey offers three types of weights. I use the recommended median weights.

> 4I use the 2004-14 combined manufacturing survey data available here: `https:// worldmanagementsurvey.org`

4
<!-- Start of picture text --> Ethiopia<br>Kenya<br>Bangladesh<br>India<br>Vietnam<br>0 10,000 20,000 30,000 40,000 50,000<br>ME Sales per worker WE Material cost per worker<br>QE Labour cost per worker<br><!-- End of picture text -->
The productivity gap highlighted above occurs mostly at the top of the distribution. In Figures A.4 and A.5 we plot the differences in sales per worker between Ethiopia and each competitor country at five different percentiles. Differences tend to be small up to the median firm. However, at the 75th and 90th percentiles of the distribution, we observe a large productivity gap. Thus, what is distinctive about Ethiopia is the lack of high productivity firms.

**Fact 2.** _Ethiopia has the lowest labor costs in the sample, but faces intermediate material input costs._

Manufacturing firms in Ethiopia also face low labor costs. This confirms a wide perception that abundant labor and competitive wages can help Ethiopia expand its manufacturing sector. Monthly labor costs per worker are about 69 USD in Ethiopia, 79 USD in Bangladesh, 175 USD in Kenya, 180 USD in India, and 201 USD in Vietnam. In the garment sector, the differences are even more striking: average labor costs in Bangladesh and India are 20 percent and 128 percent higher than in Ethiopia, respectively.

On the other hand, Ethiopian firms are in the middle of the distribution in terms of the cost they pay to source inputs of production. This is not surprising, given that Ethiopia is the only land-locked country in the sample, with limited options to access the sea. Further, material costs may also reflect the mix of sectors of the economy. If I restrict attention to the garment sector, for example, I find that material costs are higher than neighbouring Kenya, but lower than Bangladesh (India and Vietnam have much higher material costs, probably due to specialisation in higher quality garment).

**Fact 3.** _The value added of Ethiopian firms is above that of Bangladesh, but below India, Kenya and Vietnam._

Overall, low labor costs cannot fully compensate for low productivity. As a result, Ethiopian firms are among the least profitable (in terms of value added per worker) in our sample (Figure 2 and A.6). However, value added per worker in Ethiopia is actually relatively close to those of the next most profitable country – Bangladesh. If we look at the whole manufacturing sector, value added per worker is higher than in Bangladesh. If we focus on the garment sector, then we find that the profitability of Ethiopian firms is just below that of firms in Bangladesh.

There are three important caveats related the value added data I have just presented. First, the large number of missing values in the material costs variable means that I am working with a selected sample. Second, the surveys for Ethiopia and Vietnam do not record data on rental payments for land or capital. I impute these payments by using 6
<!-- Start of picture text --> Ethiopia 3059.69<br>Kenya 9494.43<br>Bangladesh 1757.89<br>India 4632.44<br>Vietnam 7297.64<br>0 2,000 4,000 6,000 8,000 10,000<br>Value added per worker<br><!-- End of picture text -->
about half of that of Bangladesh (7 percent versus 15 percent). While this highlights the growing attractiveness of Ethiopia as an investment destination, it may also highlight that Ethiopian employers in this sector have higher monopsony power compared to their counterparts in Bangladesh.

# **3.1 Robustness to use of alternative data sources**

The productivity figure used for Ethiopia are consistent with those obtained by an independent survey run in Addis Ababa at a similar point in time for the study of Abebe et al. (2017). I show this by comparing the distribution of sales for worker in the two A.7 A.7. surveys in Figure and by showing means and medians in Table Average sale for workers in Addis Ababa is remarkably similar across the two surveys (22,708 USD and 22,462 USD). Median sales are somewhat higher in the survey of Abebe et al. (2017), but the difference is not excessively large.

Finally, the figures reported in the previous section align well with the perception of foreign firm managers. I have carried out qualitative field in the Hawassa Industrial Park at several points during 2018. During these visits I had several personal conversations with firm managers about productivity and labor management. On average, firm managers believed that productivity in Ethiopia’s garment factories is between a third and a half of that of comparable factories producing similar products in India; and that wage costs are about one third of those in India. These figures confirm the aggregate A.1. statistics reported in Figure

# **4 Where does Ethiopia’s low productivity come from?**

**Fact 5.** _Capital stock, firm size and sectoral composition do not explain the low productivity of Ethiopian firms._

To study the factors that determine the low productivity of manufacturing firms in Ethiopia we run a regression model where we regress sales per worker on firm size, sector, and value of the capital stock. Specifically, we create dummies for quartiles of firm size and capital stock. We then create dummies for each sector and introduce them in the model on their own, and interacted with the dummies for firm size and capital stock. We compute the residual productivity for this regression model, and standardise this value using the average and standard deviation of the residual productivity for firms in Vietnam. We present the result of this exercise in Figures 3 and A.8.

The main conclusion from this analysis, is that, after accounting for size, sector, and capital, Ethiopian firms are still less productive than firms in competitor countries. In 8
<!-- Start of picture text --> Kenya |<br>-.2 -.4 0 A 2<br>Sales per worker (residualised and normalized)<br><!-- End of picture text -->
<!-- Start of picture text --> Ethiopia<br>Kenya<br>India<br>Vietnam<br>0 5 1 1.5 2 2.5<br>mean of management<br><!-- End of picture text -->
<!-- Start of picture text --> a —<br>India '<br>-1 -.8 -.6 -.4 -.2 0<br>MEE Operations MEE Performance monitoring<br>ME Target setting [HR management<br><!-- End of picture text -->
<!-- Start of picture text --> ite)<br>N<br>N<br>5<br>£<br>S<br>ow<br>cr<br>St.<br>ss}<br>©<br>a<br>£<br>a<br>o<br>2<br>o<br>o<br>lo<br>°<br>oOo<br>Soft_skills_training | Noise_reduction Targets<br><!-- End of picture text -->
# **References**

- Abebe, G., S. Caria, M. Fafchamps, P. Falco, S. Franklin, S. Quinn, and F. Shilpi (2017). Job Fairs: Matching Firms and Workers in a Field Experiment in Ethiopia. _Working_ .

- _paper_

- Abebe, G., S. Caria, and E. Ortiz-Ospina (2017). The Selection of Talent: Experimental .

- and Structural Evidence from Ethiopia. _Working paper_

- Adhvaryu, A., N. Kala, and A. Nyshadham (2018). The Skills to Pay the Bills: Returns .

- to On-The-Job Soft Skills Training. _Working paper_

- Ashraf, N., O. Bandiera, S. S. Lee, et al. (2014). Do-Gooders and Go-Getters: Career .

- Incentives, Selection, and Performance in Public Service Delivery. _Working Paper_

- Bandiera, O., I. Barankay, and I. Rasul (2011). Field Experiments with Firms. _The Journal of Economic Perspectives 25_ (3), 63–82.

- Blattman, C. and S. Dercon (2017). The Impacts of Industrial and Entrepreneurial Work on Income and Health: Experimental Evidence from Ethiopia. _Working paper_ .

- Bloom, N., B. Eifert, A. Mahajan, D. McKenzie, and J. Roberts (2013). Does Management Matter? Evidence from India. _The Quarterly Journal of Economics 128_ (1), 1–51.

- Bloom, N., A. Mahajan, D. McKenzie, and J. Roberts (2018). Do Management Interventions Last? Evidence from India. _Working paper_ .

- Butler, M. (2018). Labour Challenges in Hawassa.

- Clark, G. (1994). Factory Discipline. _The Journal of Economic History 54_ (1), 128–163.

- Dean, J. (2018). Noise, Cognitive Function, and Worker Productivity. _Working paper_ .

- Deserranno, E. (2014). Financial Incentives as Signals: Experimental Evidence from the Recruitment of Health Workers. _Working Paper_ .

- Elkan, W. (1971). An African Labour Force. In _Developing the Underdeveloped Countries_ , pp. 240–246. Springer.

- Farber, H. S., D. Herbst, I. Kuziemko, and S. Naidu (2018). Unions and Inequality Over .

- the Twentieth Century: New Evidence from Survey Data. _NBER Working Paper_

13
- Gelb, A., C. Meyer, and V. Ramachandran (2016). Does Poor Mean Cheap? A Comparative Look at Africa?s Industrial Labor Costs. _Revue d?économie du développement 24_ (2), 51–92.

- Gelb, A., C. Meyer, V. Ramachandran, and D. Wadhwa (2017). Can Africa be a Manu.

- facturing Destination? Labor Costs in Comparative Perspective. _Working paper_

- Kaur, S., M. Kremer, and S. Mullainathan (2015). Self-control at Work. _Journal of Political Economy 123_ (6), 1227–1277.

- Khandelwal, A. and M. Teachout (2016). Special Economic Zones for Myanmar.

- Kohli, A. (2004). _State-Directed Development: Political Power and Industrialization in the_ .

- _Global Periphery_ Cambridge University Press.

- .

- Oqubay, A. (2015). _Made in Africa: Industrial Policy in Ethiopia_ Oxford University Press, USA.

- Oyer, P. and S. Schaefer (2010). Personnel Economics: Hiring and Incentives. _NBER_ .

- _Working Paper No. 15977_

- Rodrik, D. (2015). Premature Deindustrialization. _NBER Working Paper No. 20935_ .

- Thompson, E. P. (1967). Time, Work-Discipline, and Industrial Capitalism. _Past & present_ (38), 56–97.

A.1
# **Appendix**

A.2
<!-- Start of picture text --> Ethiopia<br>Kenya<br>Bangladesh<br>India<br>Vietnam<br>0 10,000 20,000 30,000<br>ME Sales per worker ME Material cost per worker<br>QQ Labour cost per worker<br><!-- End of picture text -->
<!-- Start of picture text --> Ethiopia<br>Kenya<br>Bangladesh<br>India<br>0 5,000 10,000 15,000 20,000 25,000<br>ME Sales per worker WE Material cost per worker<br>QE Labour cost per worker<br><!-- End of picture text -->
<!-- Start of picture text --> Yearly percent change<br>Ethiopia |<br>-10 5 0 5 10 15<br><!-- End of picture text -->
<!-- Start of picture text --> fo}<br>fo}<br>fo}<br>[o)<br>N<br><o rs<br>to} oO<br>S<br>g<br>28<br>30<br>on<br>£<br>80<br>co<br>g3<br>£y<br>Qa<br>[o)<br>[o}<br>fo}<br>lo}<br>©<br>10 25 50 75 90<br>Percentile<br>— Kenya ——\ Bangladesh<br>———_ India ——— Vietnam<br><!-- End of picture text -->
<!-- Start of picture text --> fo) ooo<br>fa)<br><<br>$8<br>— oO<br>oOo<br>ano '<br>K)<br>oO<br>no<br>£<br>38<br>5¢<br>oO t<br>c=<br>Qa<br>[o)<br>oO<br>fo}<br>lo}<br>ive)<br>' 10 25 50 75 90<br>Percentile<br>— Kenya ——\ Bangladesh<br>———_ India —— Vietnam<br><!-- End of picture text -->
<!-- Start of picture text --> Ethiopia 1757.46<br>Kenya 6886.07<br>Bangladesh 1885.34<br>India 4441.53<br>Vietnam 4597.85<br>0 2,000 4,000 6,000 8,000<br>Value added per worker<br><!-- End of picture text -->
<!-- Start of picture text --> vt<br>fo}<br>fo)<br>So<br>2<br>oO<br>fo)<br>fo)<br>fo}<br>o<br>2S72)58<br>ac<br>Q<br>So<br>fo)<br>°<br>Oo<br>0 50000 100000 150000<br>Sales per worker<br>— Abebe et al. survey ———— World Bank Enterprise Survey<br><!-- End of picture text -->
<!-- Start of picture text --> India |<br>-.4 -.3 -.2 -1 0 A<br>Sales per worker (residualised and normalized)<br><!-- End of picture text -->
<!-- Start of picture text --> Ethiopia<br>Kenya<br>Bangladesh<br>India<br>Vietnam<br>0 100 200 300<br><!-- End of picture text -->
<!-- Start of picture text --> Ethiopia<br>Kenya<br>Bangladesh<br>India<br>Vietnam<br>0 100 200 300 400 500<br><!-- End of picture text -->
<!-- Start of picture text --> Kenya a<br>-.15 -.1 -.05 0 05<br>Probability of having a high productivity firm (residualised and normalized)<br><!-- End of picture text -->
<!-- Start of picture text --> Bangladesh f<br>-.2 -.4 0 A<br>Probability of having a high productivity firm (residualised and normalized)<br><!-- End of picture text -->
<!-- Start of picture text --> a ——<br>Kenya =<br>me oe oe<br>-1 -.5 0 5<br>ME Emphasise talent WE identify high performers<br>MN Remove low performers EE Develop talent<br>HO Attract talent ME Retain talent<br><!-- End of picture text -->
# **A.2 Tables**

Table A.1: Sample size and missing data _All manufacturing_

||Sample size (no.)||Mis|sing data (%)||
|---|---|---|---|---|---|
|||Worker no.|Sales|Labor costs|Material costs|
||(1)|(2)|(3)|(4)|(5)|
|Ethiopia|383|0.01|0.09|0.08|0.17|
|Kenya|414|0.02|0.12|0.19|0.28|
|Bangladesh|1179|0.00|0.03|0.02|0.04|
|India|7163|0.00|0.02|0.04|0.06|
|Vietnam|694|0.00|0.01|0.09|0.20|

Table A.2: Sample size and missing data _Garment sector_

||Sample size (no.)||Mis|sing data (%)||
|---|---|---|---|---|---|
|||Worker no.|Sales|Labor costs|Material costs|
||(1)|(2)|(3)|(4)|(5)|
|Ethiopia|70|0.00|0.04|0.09|0.17|
|Kenya|58|0.03|0.09|0.21|0.21|
|Bangladesh|467|0.00|0.01|0.01|0.03|
|India|912|0.00|0.04|0.06|0.08|
|Vietnam|178|0.01|0.01|0.09|0.22|

A.16
Table A.3: Sample size and missing data _Garment sector: mid 2000s_

||Sample size (no.)||Mis|sing data (%)||
|---|---|---|---|---|---|
|||Worker no.|Sales|Labor costs|Material costs|
||(1)|(2)|(3)|(4)|(5)|
|Ethiopia|105|0.00|0.00|0.00|0.09|
|Kenya|29|0.00|0.00|0.00|0.00|
|Bangladesh|296|0.00|0.00|0.00|0.00|
|India|275|0.07|0.07|0.08|0.08|

A.17
Table A.4: Descriptives _All manufacturing_

||Mean|St.Dev.|||Percentile|s||
|---|---|---|---|---|---|---|---|
||||10th|25th|50th|75th|90th|
|**Ethiopia**||||||||
|Number of workers|159.94|1060.41|7.00|17.00|52.00|140.00|341.00|
|Sales per worker|13725.23|17110.34|1175.71|2588.93|6206.52|14093.96|31165.12|
|Labor costs per worker|805.51|696.94|130.23|268.73|699.03|1128.68|1626.67|
|Material costs per worker|6950.81|10821.35|376.23|940.56|2565.18|7683.99|17566.12|
|Physical capital per worker|2851.29|7127.28|2.99|331.90|1508.20|3846.36|7901.64|
|**Kenya**||||||||
|Number of workers|145.01|593.51|10.00|18.00|45.00|140.00|356.00|
|Sales per worker|42960.24|1.2e+05|2028.72|5373.74|14045.01|31326.89|67416.06|
|Labor costs per worker|1927.80|2790.24|140.45|325.75|1123.60|2331.72|5185.85|
|Material costs per worker|7485.05|36595.53|97.08|421.35|1797.76|5618.01|15730.42|
|Physical capital per worker|4809.24|9662.65|51.07|449.44|2247.20|6741.61|14045.01|
|**Bangladesh**||||||||
|Number of workers|298.78|734.57|9.00|19.00|40.00|224.00|650.00|
|Sales per worker|9858.11|37717.17|1353.57|2320.41|5220.93|11447.37|25137.81|
|Labor costs per worker|925.90|1115.29|174.03|464.08|756.66|1044.19|1546.94|
|Material costs per worker|5751.81|35400.55|232.04|725.13|2320.41|5801.03|13535.75|
|Physical capital per worker|1804.03|4612.95|77.35|232.04|773.47|2417.10|5801.03|
|**India**||||||||
|Number of workers|76.78|310.97|10.00|18.00|40.00|100.00|250.00|
|Sales per worker|30508.62|51232.20|3833.74|7487.78|15973.93|35142.65|69930.34|
|Labor costs per worker|2139.47|3586.33|492.85|958.44|1597.39|2475.96|4312.96|
|Material costs per worker|19032.04|34671.05|1448.30|3194.79|8519.43|20446.63|44727.01|
|Physical capital per worker|4100.20|6874.08|239.61|745.45|2007.83|4792.18|9983.71|
|**Vietnam**||||||||
|Number of workers|224.12|1049.23|13.00|22.00|56.00|150.00|400.00|
|Sales per worker|47482.83|57533.16|2395.72|4718.85|12099.61|33602.33|94895.48|
|Labor costs per worker|2384.72|2011.57|301.56|871.17|1742.34|2756.33|4188.32|
|Material costs per worker|20003.75|32481.13|194.40|871.17|3555.80|13938.74|43558.58|
|Physical capital per worker|6038.23|9550.89|164.37|622.27|2177.93|5782.11|13814.29|

A.18
Table A.5: Descriptives _Garment sector_

||Mean|St.Dev.|||Percentile|s||
|---|---|---|---|---|---|---|---|
||||10th|25th|50th|75th|90th|
|**Ethiopia**||||||||
|Number of workers|316.74|963.66|6.00|14.00|69.00|272.00|783.00|
|Sales per worker|8026.21|10939.86|1175.71|2653.31|5023.56|9936.79|21583.00|
|Labor costs per worker|874.96|626.31|157.50|453.25|750.42|1080.31|1626.74|
|Material costs per worker|4065.42|5302.97|352.71|817.98|2137.65|5206.70|13781.62|
|Physical capital per worker|2251.48|4141.68|0.00|124.14|1149.25|3630.86|5681.37|
|**Kenya**||||||||
|Number of workers|259.74|815.35|12.00|25.00|68.00|168.00|500.00|
|Sales per worker|28544.99|83634.90|1940.08|4369.56|8863.96|19975.13|44471.86|
|Labor costs per worker|1724.27|2042.17|123.60|224.72|1123.60|2496.89|3606.62|
|Material costs per worker|22135.72|87844.56|157.30|468.17|1123.60|3862.38|6938.19|
|Physical capital per worker|3636.56|5857.69|86.43|290.89|1726.47|5477.56|9987.57|
|**Bangladesh**||||||||
|Number of workers|456.90|1005.04|14.00|24.00|115.00|450.00|1050.00|
|Sales per worker|8978.94|15905.52|1325.95|2677.40|5801.03|11976.33|24063.55|
|Labor costs per worker|1030.42|1281.02|135.36|556.90|835.35|1160.21|1740.31|
|Material costs per worker|4459.05|10591.25|283.15|966.84|2747.09|5994.40|12709.54|
|Physical capital per worker|1420.82|3909.78|79.11|248.62|709.67|2578.24|5273.67|
|**India**||||||||
|Number of workers|119.52|579.60|14.50|26.00|60.00|167.00|427.50|
|Sales per worker|26481.62|35929.71|4472.70|7720.73|14912.53|31947.87|58571.09|
|Labor costs per worker|1955.41|2879.70|588.51|1037.86|1536.67|2236.35|3422.99|
|Material costs per worker|15559.22|25555.54|1464.28|3288.75|7768.55|17838.43|37012.77|
|Physical capital per worker|3275.27|5432.36|159.74|501.66|1597.39|3993.48|8407.33|
|**Vietnam**||||||||
|Number of workers|312.48|1120.31|15.00|30.00|90.00|250.00|800.00|
|Sales per worker|29667.93|42984.62|2177.93|3629.88|7599.08|19165.78|77044.23|
|Labor costs per worker|2437.67|2647.62|241.59|614.85|1718.29|2480.55|3798.07|
|Material costs per worker|13206.43|23576.74|114.63|435.59|3097.50|9333.98|40207.92|
|Physical capital per worker|3791.38|6836.35|65.50|248.91|1028.94|2903.91|6556.47|

A.19
Table A.6: Descriptives _Garment sector: mid 2000s_

||Mean|St.Dev.|||Percentile|s||
|---|---|---|---|---|---|---|---|
||||10th|25th|50th|75th|90th|
|**Ethiopia**||||||||
|Number of workers|110.70|364.62|5.00|7.00|11.00|50.00|200.00|
|Sales per worker|2452.07|3069.46|389.42|856.68|1522.12|2760.31|4831.53|
|Labor costs per worker|467.84|448.25|89.32|193.26|397.57|618.44|821.36|
|Material costs per worker|1060.75|1575.87|100.21|225.47|445.79|1380.44|2576.81|
|**Kenya**||||||||
|Number of workers|152.17|313.28|10.00|13.00|80.00|150.00|350.00|
|Sales per worker|25943.92|35994.88|5000.61|8673.37|12858.71|30003.66|44123.02|
|Labor costs per worker|3857.40|4597.00|750.09|1393.70|2474.30|4552.53|8182.82|
|Material costs per worker|10016.56|15697.87|1750.21|2500.30|3475.25|9478.43|23574.30|
|**Bangladesh**||||||||
|Number of workers|606.57|714.68|110.00|250.00|400.00|650.00|1350.00|
|Sales per worker|4042.69|3403.03|871.74|1824.57|3303.43|5579.12|7398.13|
|Labor costs per worker|445.87|195.13|203.99|313.83|441.60|564.89|696.07|
|Material costs per worker|2511.09|2398.83|222.76|792.72|1824.44|3536.07|5265.37|
|**India**||||||||
|Number of workers|68.91|116.49|5.00|9.50|28.00|92.50|145.00|
|Sales per worker|21534.18|89165.36|1401.05|3056.85|8253.49|16035.27|22044.57|
|Labor costs per worker|1320.87|3522.43|185.83|509.47|1017.13|1264.13|1728.14|
|Material costs per worker|9823.08|21307.47|407.58|1557.15|4275.95|9486.60|14710.10|

Table A.7: Comparison of data from two surveys _All manufacturing, Addis Ababa_

||Sample size|Mean|Median|
|---|---|---|---|
||(1)|(2)|(3)|
|Abebe et al. survey|90|22708.90|14874.57|
|World Bank Manucturing Survey|393|22462.02|11029.03|

A.20