1. Nick Bloom and John Van Reenen, Management Practices, 2010 Key messages of lectures 1 to 4 1.Exists a set of core practices for talent management, target management and performance management (scoring grid) 2.Associated with better performance across a wide range of countries and industries, especially in larger firms 3.Not universal truths, but important benchmarks against which all firms should be evaluated 4.Firms are often unaware that their practices are lacking, so good management is similar to a new technology 5.Hard to change practices in firms – anecdotal evidence this takes several years

2. Nick Bloom and John Van Reenen, Management Practices, 2010 2 Improving management in Indian factories Nick Bloom (Stanford Economics) John Van Reenen (Stanford GSB/LSE) Lecture 5

3. Nick Bloom and John Van Reenen, Management Practices, 2010 3 Management appears worse in developing countries Average Country Management Score, firms 100 to 5000 employees (from Bloom & Van Reenen (2007, QJE), Bloom, Sadun & Van Reenen (2009, AR)) 695 336 270 122 344 312 188 762 382 92 231 102 140 524 171 620 559 # firms

4. Nick Bloom and John Van Reenen, Management Practices, 2010 4 Firm-Level Management Scores US manufacturing, mean=3.33 (N=695) Indian manufacturing, mean=2.69 (N=620) India’s low score is mainly due to many badly managed firms Density

5. Nick Bloom and John Van Reenen, Management Practices, 2010 5 This raises two obvious questions 1. Does “bad” management reduce productivity, or are these practices dues to difference circumstances in India (i.e. poor infrastructure, less capital, weak rule of law)? 2. If it does matter, why are so many Indian firms badly managed?

6. Nick Bloom and John Van Reenen, Management Practices, 2010 6 Summary and photos Experiment on plants in large (≈ 300 person) Indian textile firms Randomized treatment plants get heavy management consulting, control plants get very light consulting (just enough to get data) Collect weekly performance data on all plants from 2008 to 2010 Improved management practices led to large and significant improvements in productivity and profitability Appears informational constraints were a major reason for lack of prior adoption, but often other constraints also present Before explaining research and results in detail, I want to show some slides to provide some background

7. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 1: Plants are large compounds, often containing several buildings. Plant surrounded by grounds Front entrance to the main buildingPlant buildings with gates and guard post Plant entrance with gates and a guard post

8. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 2: These plants operate 24 hours a day for 7 days a week producing fabric from yarn, with 4 main stages of production (1) Winding the yarn thread onto the warp beam(2) Drawing the warp beam ready for weaving (3) Weaving the fabric on the weaving loom(4) Quality checking and repair

9. Nick Bloom and John Van Reenen, Management Practices, 2010 This production technology has not changed much over time: Lowell Mill warping looms (1854, Lowell, Massachusetts) Warp beam Krill

10. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 3: Many parts of these plants were dirty and unsafe Garbage outside the plantGarbage inside a plant Chemicals without any coveringFlammable garbage in a plant

11. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 4: The plant floors were disorganized Instrument not removed after use, blocking hallway. Tools left on the floor after use Dirty and poorly maintained machines Old warp beam, chairs and a desk obstructing the plant floor

12. Nick Bloom and John Van Reenen, Management Practices, 2010 Yarn piled up so high and deep that access to back sacks is almost impossible Exhibit 5: The inventory rooms had months of excess yarn, often without any formal storage system or protection from damp or crushing Different types and colors of yarn lying mixed Yarn without labeling, order or damp protection A crushed yarn cone, which is unusable as it leads to irregular yarn tension

13. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 6: Yet more material was often stored around the plant Inventory was also regularly stored in corridors, hallways, doorways and on stairs. This is dangerous and impedes efficient movement of materials around the plant. Inventory was also often stored around machinery.

14. Nick Bloom and John Van Reenen, Management Practices, 2010 No protection to prevent damage and rustSpares without any labeling or order Exhibit 7: The parts stores were also disorganized and dirty Shelves overfilled and disorganizedSpares without any labeling or order

15. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 8: The path for materials flow was often obstructed Unfinished rough path along which several 0.6 ton warp beams were taken on wheeled trolleys every day to the elevator, which led down to the looms. This steep slope, rough surface and sharp angle meant workers often lost control of the trolleys. They crashed into the iron beam or wall, breaking the trolleys. So now each beam is carried by 6 men. A broken trolley (the wheel snapped off) At another plant both warp beam elevators had broken down due to poor maintenance. As a result teams of 7 men carried several warps beams down the stairs every day. At 0.6 tons each this was slow and dangerous - two serious accidents occurred in our time at the plant.

16. Nick Bloom and John Van Reenen, Management Practices, 2010 Exhibit 9: Routine maintenance was usually not carried out, with repairs only undertaken when breakdowns arose, leading to frequent stoppages. Parts being cleaned and replaced on jammed loomBroken machine parts being repaired Loom parts being disassembled for diagnosisWorkers investigating a broken loom

17. Nick Bloom and John Van Reenen, Management Practices, 2010 17 Management scores Brazil and China Manufacturing, mean=2.67 Indian Manufacturing, mean=2.69 Indian Textiles, mean=2.60 Experimental Firms, mean=2.60 These firms appear typical of large manufacturers in India, China and Brazil

18. Nick Bloom and John Van Reenen, Management Practices, 2010 18 So ran an experiment to evaluate impact of changing the management of large Indian firms Obtained details of the population of 529 woven cotton fabric firms (SIC 2211) near Mumbai with 100 to 5000 employees. Selected 66 firms in the largest cluster (Tarapur & Urmagaon) Contacted every firm: 17 willing to participate in straight-away, so randomly picked 20 plants from these 17 firms A team of 6 consultants from Accenture, Mumbai was hired to help improve the practices in some of these firms Control: 1 month of diagnostic Treatment: 1 month diagnostic + 4 months implementation All: follow-on data collection for next 12+ months Collecting data from April 2008 to December 2010

19. Nick Bloom and John Van Reenen, Management Practices, 2010 19 Sample of firms we worked with

20. Nick Bloom and John Van Reenen, Management Practices, 2010 20 Our plants and firms are large by Indian & US standards Source: Hsieh and Klenow, 2009 Average size of our plants

21. Nick Bloom and John Van Reenen, Management Practices, 2010 21 Management practices before and after treatment Performance of the plants before and after treatment Quality Inventory Operational efficiency Why were these practices not introduced before?

22. Nick Bloom and John Van Reenen, Management Practices, 2010 22 Intervention aimed to improve 38 core textile management practices in 6 areas (1/2) Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

23. Nick Bloom and John Van Reenen, Management Practices, 2010 23 Intervention aimed to improve 38 core textile management practices in 6 areas (2/2) Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

24. Nick Bloom and John Van Reenen, Management Practices, 2010 24 Adoption of these 38 management practices did rise, and particularly in the treatment plants Notes: Non-experiment plants are other plants in the treatment firms not involved in the experiment. They improved practices over this period because the firm internally copied these over themselves. All initial differences not statistically significant (Table 2) Wave 1 treatment plants: Diagnostic September 2008, implementation began October 2008 Control plants: Diagnostic July 2009 Wave 2 treatment plants: Diagnostic April 2009, implementation began May 2008 Non-experiment plants: No intervention January 2009April 2009July 2009October 2008July 2008October 2009April 2008 Share of the 38 management practices adopted

25. Nick Bloom and John Van Reenen, Management Practices, 2010 Take away summary points 1.These firms are not adopting basic management practices, in large part due to a lack of awareness 2.Changing practices is very slow – we are still introducing new practices into firms 18 months later, because of: a.Takes time for firms to advice (Accenture in our case) b.Changes are complementary – e.g. monitoring & pay 3.Change may not be permanent – need to fix both processes and incentives to avoid backsliding

26. Nick Bloom and John Van Reenen, Management Practices, 2010 26 Management practices before and after treatment Performance of the plants before and after treatment Quality Inventory Operational efficiency Why were these practices not introduced before?

27. Nick Bloom and John Van Reenen, Management Practices, 2010 27 Exhibit 10: Quality was so poor that 19% of manpower was spent on repairing defects at the end of the production process Workers spread cloth over lighted plates to spot defectsLarge room full of repair workers (the day shift) Non-fixable defects lead to discounts of up to 75% Defects are repaired by hand or cut out from cloth

28. Nick Bloom and John Van Reenen, Management Practices, 2010 28 Previously mending was recorded only to cross- check against customers’ claims for rebates Defects log with defects not recorded in an standardized format. These defects were recorded solely as a record in case of customer complaints. The data was not aggregated or analyzed

29. Nick Bloom and John Van Reenen, Management Practices, 2010 29 Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver

30. Nick Bloom and John Van Reenen, Management Practices, 2010 30 The quality data is now collated and analyzed as part of the new daily production meetings Plant managers now meet regularly with heads of quality, inventory, weaving, maintenance, warping etc. to analyze data

31. Nick Bloom and John Van Reenen, Management Practices, 2010 31 Defect rates have rapidly fallen in treatment plants Notes: Displays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 treatment plants (square symbols) and the 6 control plants (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. Control plants Treatment plants Weeks after the start of the intervention (diagnostic phase) Diagnostic startImplementation start Quality defects index Implementation stop

32. Nick Bloom and John Van Reenen, Management Practices, 2010 32 Management impact on quality, regressions

33. Nick Bloom and John Van Reenen, Management Practices, 2010 33 Management practices before and after treatment Performance of the firms before and after treatment Quality Inventory Operational efficiency Why were these practices not introduced before?

34. Nick Bloom and John Van Reenen, Management Practices, 2010 34 Stock is organized, labeled, and entered into an Electronic Resource Planning (ERP) system which has details of the type, age and location. Bagging and racking yarn reduces waste from rotting (keeps the yarn dry) and crushing Computerized inventory systems help to reduce stock levels. Organizing and racking inventory enables firms to reduce capital stock and reduces waste

35. Nick Bloom and John Van Reenen, Management Practices, 2010 35 Sales are also informed about excess yarn stock so they can incorporate this in new designs. Shade cards now produced for all surplus yarn. These are sent to the design team to use in future designs

36. Nick Bloom and John Van Reenen, Management Practices, 2010 36 And yarn for products ranges no longer made by the firm (e.g. suiting fabric) was sold This firms used to make suiting and shirting yarn, but stopped making suiting yarn 2 years ago

37. Nick Bloom and John Van Reenen, Management Practices, 2010 37 Inventory is falling in treatment firms Control firms Treatment firms Weeks after the start of the intervention DiagnosticImplementation Notes: Displays the average raw materials for the 14 treatment firms (square symbols) and the 6 control firms (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention.

38. Nick Bloom and John Van Reenen, Management Practices, 2010 38 Management impact on inventory, regressions

39. Nick Bloom and John Van Reenen, Management Practices, 2010 39 Spare parts were also organized, reducing downtime (parts can be found quickly), capital stock and waste Nuts & bolts sorted as per specifications Tool storage organized Parts like gears, bushes, sorted as per specifications

40. Nick Bloom and John Van Reenen, Management Practices, 2010 40 Management practices before and after treatment Performance of the firms before and after treatment Quality Inventory Operational efficiency Why were these practices not introduced before?

41. Nick Bloom and John Van Reenen, Management Practices, 2010 41 The treated firms have also started to introduce basic initiatives (called “5S”) to organize the plant Worker involved in 5S initiative on the shop floor, marking out the area around the model machine Snag tagging to identify the abnormalities on & around the machines, such as redundant materials, broken equipment, or accident areas. The operator and the maintenance team is responsible for removing these abnormalities. This is all part of the routine maintenance

42. Nick Bloom and John Van Reenen, Management Practices, 2010 42 Production data is now collected in a standardized format, for discussion in the daily meetings Before (not standardized, on loose pieces of paper) After (standardized, so easy to enter daily into a computer)

43. Nick Bloom and John Van Reenen, Management Practices, 2010 43 Daily performance boards have also been put up, with incentive pay for employees based on this

44. Nick Bloom and John Van Reenen, Management Practices, 2010 44 Management impact on efficiency, regressions

45. Nick Bloom and John Van Reenen, Management Practices, 2010 45 Estimated impacts on productivity and profitability are large and rising Estimate the intervention has increase profits by about $250,00 per firm and productivity by 9% so far from: - reduced repair manpower costs - reduced wasted materials (from less defects) - lower inventory - higher efficiency levels Full impacts of better management should be much larger: - short-run impacts only - narrow set of management practices (almost no HR)

46. Nick Bloom and John Van Reenen, Management Practices, 2010 46

47. Nick Bloom and John Van Reenen, Management Practices, 2010 47 Management practices before and after treatment Performance of the firms before and after treatment Quality Inventory Operational efficiency Why were these practices not introduced before?

48. Nick Bloom and John Van Reenen, Management Practices, 2010 48 So why did these firms have bad management? Information: management is a technology and India is far behind the technology frontier, e.g. Lean manufacturing Incentives: managers have no incentive pay or within firm promotion possibilities so have limited motivated to perform CEO ability: family firms with directors who struggled to change practices and sometimes procrastinated

49. Nick Bloom and John Van Reenen, Management Practices, 2010 49 Why does competition not fix badly managed firms? Bankruptcy is still avoided : wage of $5 a day means firms are profitable Reallocation appears limited: Owners take all decisions as they worry about managers stealing. But owners time is constrained – they current work 72.5 hours average a week – limiting growth. As an illustration firm size is more linked to number of male family members (corr=0.689) - who are trusted to be given managerial positions - than management scores (corr=0.223) Entry appears limited: Production is very capital intensive ($13m assets average per firm)

50. Nick Bloom and John Van Reenen, Management Practices, 2010 50 Summary Firms in developing countries seem badly managed Our results suggest this has a material impact on productivity Also appear to find bad operations management arises from lack of information and poor HR management But far from clear….yields as many questions as answers so far

51. Nick Bloom and John Van Reenen, Management Practices, 2010 51 Back-up

52. Nick Bloom and John Van Reenen, Management Practices, 2010 Cubic Spline Spline - 2 SE Figure 3: Quality defects index for the treatment and control plants Notes: Displays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 treatment plants (♦ symbols) and the 6 control plants (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. “Data” is plotted using a 5 week moving average. To obtain series (rather than point-wise) confidence intervals we used a cubic-spline with one knot at the start of the implementation period. The spline estimate is labeled (“Cubic Spine”), the 95% confidence intervals labeled (“Spline + 2SE”) and (“Spline – 2SE”) from plant-wise block boostrap. Timing based on weeks after the intervention (positive values) or before the intervention (negative values). For wave 1 treatment plants this is relative to September 1 st 2008, for Wave 2 treatment and control firms April 7 th 2009. The control group’s rise in weeks 10+ are due to the pre Diwali and Ede production increase, which usually leads to a deterioration in quality due to increased speeds of production. Control plants Treatment plants Weeks after the start of the intervention Quality defects index (higher score=lower quality) Start of DiagnosticStart of Implementation Data (♦ symbol) Spline + 2 SE Data (+ symbol) Cubic Spline Spline - 2 SE Spline + 2 SE

53. Nick Bloom and John Van Reenen, Management Practices, 2010 53 We work in Tarapur because textile mills no longer exist in Mumbai The textile factories in downtown Mumbai are now all closed as land prices are too high. The last few remaining building are now being demolished and turned into apartment blocks and shopping malls Apartment blocks being built on the site of an demolished old textile mill, on the opposite side of the road from the one being demolished picture above