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16th November 2013Ayako Ishiwata

PhD CandidateDepartment of International Studies

The University of Tokyo

presentation at the Development Studies AssociationAnnual Conference 2013

Effects of Technology and Business Networks on Firm Growth

in a Tailor Cluster, Ethiopia

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Industrial Cluster: geographical concentration of related industries and institutions

１． Background

Most of clusters in Sub-Saharan Africa are "survival clusters". (Altenburg and Meyer-Stamer 1999, Yoshino 2011)

- micro and small enterprises compete in low quality products at low cost- Earnings are small and fragileYet, the clusters provide rare nonfarm income generation opportunities 　

Identifying ways to increase sales/income is vital for poverty alleviation in Sub-Saharan Africa

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2. Research Area Assela, Oromia, Ethiopia

166 Km from Addis Ababa

17.2 km2

76,000 people

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Location of tailors

Food Market

Addis AbabaBus Station

Approx. 260 tailors within 0.05Km2

16% of tailors operating outside

3. Research Questions

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What factors contribute to improvement in business performance (sales) in a tailor cluster in rural Ethiopia?Focusing on roles of - technology (sewing skills and design capability) and - business networks

" How business networks affect increase in sales through technology improvement?"

４． Methodology(1) Sample 136 garment processing tailors - firstly interviewed in 2011 and - operated full time in 2012 and 2013

(2) Data

- Interview in 2011, 2012, and 2013 - sewing test in 2012 and 2013

(3) Estimation Method

Three Stage Leased Square Regression (3SLS) and Seemingly Unrelated Regression (SUR) 1st stage: Instrumental variable for business network 　　 2nd stage: business network → technology 3rd stage: technology → Sales

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(1) Annual tailoring sales excluding material costs (Dependent Variable)

5. Description of the data

Mean 21,993 Birr ($1165)Std. Dev.

55,900 Birr ($2963)

Min 800 Birr ($42)Median 13,830 Birr ($733)Max 648,000 Birr($34352)(converted with mean exchange rate on 30th August 2013, 18.86345 Birr/$)

(2) Number of Workers

Mean 1.6Std. Dev. 1.4

Min 1.0 (33%)

Median 1.2

Max 11.0

( ３ ) Technology Variables

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tangle

1) Speed 2) Accuracy 3) Other errors (thread tangle, tension, etc) 56 firms (41%)4) Design capacity (28 firms 21%)

Sewing Skill Index

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Performance in Sewing Skill Test

Sewing Time (efficiency)Mean 121 secondsStd. Dev.

48 seconds

Min 50 secondsMedian 113 secondsMax 373 seconds

Sewing Skill Index → Normalized average of log of above indicators x -1 (μ=0 、 σ=1)

Mean 0.4cm2

Std. Dev. 0.5 cm2

Min 0 cm2 (1 firm)

Median 0.2 cm2

Max 3.8 cm2

Total missed area (quality)

in-degree centrality:

Number of tailors which give some benefits (information, credit, sales order, procurement, etc.) to the Tailor A

in-closeness centrality:

Inverse of number of steps (d) for other tailors to reach the Tailor A

(4) Business Network

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Tailor A Tailor Bdata (out-degree)

flow of collaboration(in-degree)

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Indegree

Incloseness

Mean 1.8Std. Dev. 1.8

Min 0 (43 firms, 32%)

Median 1.0

Max 10.0

Mean 2.9%Std. Dev. 3.4%

Min 0.0%(43 firms, 32%)

Median 1.5%

Max 13.2%

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6. Estimation Results (3SLS) 1: Sewing Skill Index

(1) (2)

3rd Stage *** (Dependent Variable: Ln Annual Tailoring Sales)

Sewing Skill Index 0.53*** 0.51***

Design Capacity 0.46** 0.49**

Ln No of co-owners 2.29*** 2.32***

2nd Stage*** (Dependent variable: Sewing Skill Index)

Ln Indegree+1 0.54***

Ln In-closeness + 0.01 0.15***

Ln Age -1.22*** -1.23***

Female -0.71** -0.77**

Training 0.06 0.061st Stage*** Ln Indegree or Ln In- closeness in 2012

0.76*** 0.61***

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6. Estimation Results (3SLS) 2: Sewing Speed (3) (4) (5)

3rd Stage *** (Dependent Variable: Ln Annual Tailoring Sales)

Sewing Speed (normalized) 0.57** 0.55** 0.48**

Design Capacity 0.50** 0.50** 0.52**

Ln No of co-owners 2.26*** 2.26*** 2.28***2nd Stage*** (Dependent variable: Sewing Speed)

Ln Indegree+1 0.60*** 0.57***

Ln In-closeness + 0.01 0.20***

Ln Age -0.91*** -1.27*** -0.94***

Ln Operation Year 0.18

Female -0.52* -0.54* -0.61**

Training 0.13 0.17 0.131st Stage*** Ln Indegree or Ln In-closeness in 2012

0.76*** 0.76*** 0.61***

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(6) (7)

3rd Stage *** (Dependent Variable: Ln Annual Tailoring Sales)

Sewing Quality (axb) -1.12 -0.82

Sewing Accuracy (a) 0.68 0.55

No Other Error (b) -0.26* -0.25*

Design Capacity 0.68*** 0.70***

Ln No of co-owners 2.59*** 2.55***2nd Stage (Dependent variable: Sewing Quality)

Ln Indegree+1 -0.05

Ln In-closeness + 0.01 -0.01

Ln Age -0.40** -0.43**

Female -0.09 -0.06

Training -0.16 -0.171st Stage*** Ln Indegree or Ln In- closeness in 2012

0.76*** 0.62***

6. Estimation Results 3 (3SLS): Sewing Quality

７ . Results

I. Higher technology significantly increases sales, but its explanatory power is related more to efficiency (sewing speed) and innovation (design) rather than quality (sewing accuracy or other errors).

⇒ Infers immaturity of consumers' preference

II. Centralities (directly and indirectly) in the business networks increase efficiency and innovation aspects of technology, but no effect on quality.

III. Longer operation years when it is controlled with age is insignificant in technology increase.

⇒ Infers small learning effects

IV. Rare available training opportunities are insignificant in technology improvement.

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８． Conclusion

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Illustrates characteristics of the survival clusters whose product quality does not respond to firm growth. (Both consumers and tailors are not likely to be conscious of the quality.)

→ do not match the global standards

vulnerable when the markets are developed

- competition with the global products

- mismatch with consumer's preference

once their purchasing power increases

Policy Implications

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I. Contents of trainings need to be reviewed. (technical - managerial)

II. Strengthening business networks seems immediate ＆ cost effective assistance to sustain livelihood of firms in a survival cluster.

III. Tailoring business is likely to shrink in the globalization process unless tailors are linked to more quality conscious consumers outside the cluster (external knowledge).

IV. As a growth scenario, successful firms may diversify business after accumulating capital.