1 16th November 2013 Ayako Ishiwata PhD Candidate Department of International Studies The University of Tokyo presentation at the Development Studies Association Annual Conference 2013 Effects of Technology and Business Networks on Firm Growth in a Tailor Cluster, Ethiopia

2 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 fragile Yet, the clusters provide rare nonfarm income generation opportunities Identifying ways to increase sales/income is vital for poverty alleviation in Sub-Saharan Africa

3 2. Research Area Assela, Oromia, Ethiopia 166 Km from Addis Ababa 17.2 km 2 76,000 people

4 Location of tailors Food Market Addis Ababa Bus Station Approx. 260 tailors within 0.05Km 2 16% of tailors operating outside

3. Research Questions 5 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 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 6

7 (1) Annual tailoring sales excluding material costs (Dependent Variable) 5. Description of the data Mean21,993 Birr ($1165) Std. Dev. 55,900 Birr ($2963) Min 800 Birr ($42) Median 13,830 Birr ($733) Max 648,000 Birr($34352) (converted with mean exchange rate on 30th August 2013, Birr/$) (2) Number of Workers Mean 1.6 Std. Dev. 1.4 Min 1.0 (33%) Median 1.2 Max11.0

( ３ ) Technology Variables 8 tangle 1) Speed 2) Accuracy 3) Other errors (thread tangle, tension, etc) 56 firms (41%) 4) Design capacity (28 firms 21%) Sewing Skill Index

9 Performance in Sewing Skill Test Sewing Time (efficiency) Mean 121 seconds Std. Dev. 48 seconds Min 50 seconds Median 113 seconds Max 373 seconds Sewing Skill Index → Normalized average of log of above indicators x -1 (μ=0 、 σ=1) Mean0.4cm 2 Std. Dev.0.5 cm 2 Min 0 cm 2 (1 firm) Median 0.2 cm 2 Max 3.8 cm 2 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 10 Tailor A Tailor B data (out-degree) flow of collaboration (in-degree)

11 Indegree Incloseness Mean 1.8 Std. Dev. 1.8 Min 0 (43 firms, 32%) Median 1.0 Max10.0 Mean 2.9% Std. Dev. 3.4% Min 0.0%(43 firms, 32%) Median 1.5% Max13.2%

12 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 *** Ln In-closeness *** Ln Age-1.22***-1.23*** Female-0.71**-0.77** Training st Stage*** Ln Indegree or Ln In- closeness in *** 0.61***

13 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** 0.52** Ln No of co-owners 2.26*** 2.28*** 2nd Stage*** (Dependent variable: Sewing Speed) Ln Indegree ***0.57*** Ln In-closeness *** Ln Age-0.91***-1.27***-0.94*** Ln Operation Year 0.18 Female-0.52*-0.54*-0.61** Training st Stage*** Ln Indegree or Ln In-closeness in *** 0.61***

14 (6)(7) 3rd Stage *** (Dependent Variable: Ln Annual Tailoring Sales) Sewing Quality (axb) Sewing Accuracy (a) 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 Ln In-closeness Ln Age-0.40**-0.43** Female Training st Stage*** Ln Indegree or Ln In- closeness in *** 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. 15

８． Conclusion 16 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 17 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.