Understanding Productivity in Pakistani Garments (Pilot Project)

Transcription

1 Understanding Productivity in Pakistani Garments (Pilot Project) Azam Chaudhry Mahvish Faran Rocco Macchiavello Theresa Thompson Chris Woodruff Lahore School of Economics Lahore School of Economics Warwick University Lahore School of Economics Warwick University September 2014 IGC Growth Week, Pakistan R. Creedon, J. Krstic, R. Mann, K. Ruffini, M. Skuodis, K. Smula, M. Vlekke

2 Main Motivation DEVELOPMENT PERSPECTIVE Garments and Textile industries historically associated with structural transformation A laboratory to understand broader issues underlying persistent productivity differences across similar production units GLOBAL PERSPECTIVE China accounts for approx. 30% of world s RMG exports. Increase in wages in China enormous opportunity for other countries: Bangladesh, Myanmar, Ethiopia, Pakistan PAKISTAN PERSPECTIVE International Trade & Policy Environment Job creation: Garments vs. Textile

3 Main Motivation 3 Within Asia, Declining Competitiveness Of China Lending Opportunity To Other Low Cost Countries Labour Cost (USD/hr) in Textile Industry, China Turkey China, coastal India Indonesia Vietnam Pakistan Bangladesh Widening gap in labour cost of China and other Asian countries Source: Werner International, Textile Intelligence

4 What do we plan to achieve? 1. INTERNATIONAL BENCHMARKING Compare productivity and managerial practices across firms and countries (building upon data collection in Bangladesh) Target IGC countries: Pakistan, Myanmar, Ethiopia, India Develop a benchmarking tool to be give to factories 2. PAKISTAN CONTEXT Design interventions to increase productivity grounded on a detailed understanding of constraints and best practices

5 Pakistan: What have we achieved? 1. COMPLETED DATA COLLECTION FROM 7 FACTORIES Focus on Lahore (an estimated population of XYZ factories) Sample selected by building relationships with associations and buyers, possibly representative of broader sector: 4 Large firms, 1 Medium sized firms, 2 Small firms. (Line supervisors survey ongoing) 2. PLANNING AND FORMULATION OF INTERVENTIONS Likely to focus on either information and/or quality Strengthened relationships with stakeholders: FACTORIES BUYERS & OTHER STAKEHOLDERS

6 Production & HR Data: Overview Factory Size Production System OP SUP (lines/mixed) Data Quality Supervisor Survey (yes/no) Main Products AA line High yes Polo Shirts, Hoods BB line High yes Denim Jeans, Jackets and shorts CC line High yes Denim Jeans DD line High yes Denim Jeans EE line High yes Knit Tops, Knit Bottoms, Woven Tops, Woven Bottoms, Scarfs FF line Low/medium Yes at least 10 Polo Shirts, Hoods GG mixed low Yes Denim Jeans Data collected for 3 months [Feb. 1 st to Apr. 30 th ] Data entry expected to be completed by December 2014

7 Supervisor Surveys In 7 factories we conduct surveys with (line) Supervisors. The population consists of 111 Supervisors. We estimate to survey 95 supervisors in total. Some of them will be production supervisors while some will be quality supervisors/inspectors. Survey currently in the field (completion in September/October 2014) Focus: - line level practices, - worker s well-being, - quality, - authority - compensation to workers

8 Spot the Difference PAKISTAN

9 Spot the Difference PAKISTAN BANGLADESH

10 Measuring Productivity Measuring physical productivity when units of outputs are heterogeneous (apples and oranges) is challenging Need a way to convert physical output of heterogeneous products into a common unit Each piece of garment comes with a SMV (or SAM): standard minute value (or allowance): time required to produce 1 piece of garments*

11 Measuring Productivity

12 Measuring Productivity

13 Measuring Productivity Measuring physical productivity when units of outputs are heterogeneous (apples and oranges) is challenging Need a way to convert physical output of heterogeneous products into a common unit Each piece of garment comes with a SMV (or SAM): standard minute value (or allowance): time required to produce 1 piece of garments* This allows us to measure (heterogeneous) output using (homogenous) time units Output Minutes Efficiency= = Input Minutes # output pieces * SMV # operators * runtime

14 Watch Your Neighbours: Efficiency Around the Globe Countries Average Payout (USD p.m.) Key Product Category Country Average Operational Efficiency Labour Pool Technological Advancement FTA / GSP with Major Markets Raw Material Availability China All Products 55-57% mn High - All Indonesia 170 Woven Synthetic 44-46% mn Medium EU, US, Japan Synthetic Fibre Vietnam 120 All Products 40-42% 46.5 mn Medium EU, US, Japan, Aus. & NZ None Pakistan 116 Denim 42-44% 53.8 mn Medium EU, China Cotton Cambodia 88 Denim, Woven 42-44% 8.0 mn Medium Bangladesh 83* Knitwear, Woven Bottoms 38-40% 70.9 mn Low EU, US, Japan, Aus. & NZ EU, Japan, Aus., Canada, US 1 None None India 130 All Products 44-46% mn Medium Japan, EU 2 Cotton 1- GSP with US has a negligible impact on T&A exports from Bangladesh to US, 2- EU- FTA under discussion, Source : Technopak Analysis

15 Dispersion Across and Within Units Across factories 75 th / 25 th : 1.95 ; 90 th /10 th = 2.79 Benchmark (Syverson 2004): 75 th / 25 th = 1.92; 90 th /10 th = 4.02 Within factory (across lines) 75 th / 25 th = 1.22; 90 th /10 th = Efficiency (Output Minutes / Input Minutes) TFP Disp. (Across Factories) TFP Disp. (Within Factories) Sample: preliminary data from 5 Bangladeshi factories

16 Benchmarking Tool Introduction Select the time period for analysis Select the month for analysis Select the date for analysis. Financial Metrics A. Labor Cost per Earned Minute (Tk) B. Average Cost for Wasted Time (Tk) Taka Line-04 Line-05 Line-06 Line-07 Sewing Lines OT NH Taka 6,000 5,000 4,000 3,000 2,000 1,000-5,467 3,063 1, Line-04 Line-05 Line-06 Line-07 Sewing Lines Adequate firms capabilities Benchmarking against other firms in same country/other country

17 Next Steps 1. ENTER & PROCESS DATA, APPEND TO BANGLADESH DATASET 2. ANALYSIS OF DATA AND SUPERVISOR SUVEYS 3. DESIGN OF INTERVENTIONS: Two main areas have been identified: 1. Information flows within firms 2. Quality upgrading