1. Nick Bloom, 147, 2011 Economics of Human Resources Nick Bloom (Stanford Economics) Lecture 7: Experiments in firms 1

2. Nick Bloom, 147, 2011 Experiments in India Experiments in China

3. Nick Bloom, 147, 2011 Does management matter: evidence from India Nick Bloom (Stanford) Benn Eifert (Berkeley) Aprajit Mahajan (Stanford) David McKenzie (World Bank) John Roberts (Stanford)

4. Nick Bloom, 147, 2011 4 Management appears to be better in rich countries Average country management score, manufacturing firms 100 to 5000 employees (monitoring, targets and incentives management scored on a 1 to 5 scale. See Bloom and Van Reenen (2007, QJE) and Bloom, Sadun and Van Reenen (2010, JEP)) & ICP (2010)

5. Nick Bloom, 147, 2011 5 US, manufacturing, mean=3.33 (N=695) India, manufacturing, mean=2.69 (N=620) Density Firm level management score, manufacturing firms 100 to 5000 employees Developing countries have more badly managed firms

6. Nick Bloom, 147, 2011 6 But do we care - does management matter? Long debate between business practitioners versus academics Evidence to date primarily case-studies and surveys Syverson’s (2010) productivity survey stated on management “Perhaps no potential driver of productivity differences has seen a higher ratio of speculation to actual empirical study than management”

7. Nick Bloom, 147, 2011 We investigate these questions in large Indian firms Took large firms (≈ 300 employees) outside Mumbai making cotton fabric. Randomized treatment plants get 5 months management consulting, controls plants get 1 month consulting. Collect weekly data on all plants from 2008 to 2010 Profits up by about 25% ($250,000 a year) Productivity up by about 10%

8. Nick Bloom, 147, 2011 Exhibit 1: Plants are large compounds, often containing several buildings.

9. Nick Bloom, 147, 2011 Exhibit 2a: Plants operate continuously making cotton fabric from yarn Fabric warping

10. Nick Bloom, 147, 2011 Fabric weaving Exhibit 2b: Plants operate continuously making cotton fabric from yarn

11. Nick Bloom, 147, 2011 The production technology has not changed much over time Warp beam Krill The warping looms at Lowell Mills in 1854, Massachusetts

12. Nick Bloom, 147, 2011 Quality checking Exhibit 2c: Plants operate continuously making cotton fabric from yarn

13. Nick Bloom, 147, 2011 Exhibit 3: Many parts of these Indian plants were dirty and unsafe Garbage outside the plantGarbage inside a plant Chemicals without any coveringFlammable garbage in a plant

14. Nick Bloom, 147, 2011 Exhibit 4: The plant floors were often disorganized and aisles blocked

15. Nick Bloom, 147, 2011 Exhibit 5: There was almost no routine maintenance – instead machines were only repaired when they broke down

16. Nick Bloom, 147, 2011 Exhibit 6a: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory

17. Nick Bloom, 147, 2011 Exhibit 6b: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory

18. Nick Bloom, 147, 2011 Exhibit 7: The path for materials flow was often heavily 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

19. Nick Bloom, 147, 2011 19 Management scores (using Bloom and Van Reenen (2007) methodology) 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 Brazil, China and India

20. Nick Bloom, 147, 2011 20 Management practices before and after treatment Performance of the plants before and after treatment Why were these practices not introduced before?

21. Nick Bloom, 147, 2011 Intervention aimed to improve 38 core textile management practices in 6 areas

22. Nick Bloom, 147, 2011 Intervention aimed to improve 38 core textile management practices in 6 areas

23. Nick Bloom, 147, 2011 Treatment plants Control plants Share of key textile management practices adopted Excluded plants (not treatment or control) Adoption of these 38 management practices did rise, and particularly in the treatment plants.2.3.4.5.6 -10-8-6-4-2024681012 Months after the diagnostic phase Treated Control

24. Nick Bloom, 147, 2011 Management practices before and after treatment Performance of the plants before and after treatment Quality Inventory Output Why were these practices not introduced before?

25. Nick Bloom, 147, 2011 Poor quality meant 19% of manpower went on repairs Workers spread cloth over lighted plates to spot defectsLarge room full of repair workers (the day shift) Defects lead to about 5% of cloth being scrappedDefects are repaired by hand or cut out from cloth

26. Nick Bloom, 147, 2011 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

27. Nick Bloom, 147, 2011 27 Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver

28. Nick Bloom, 147, 2011 28 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

29. Nick Bloom, 147, 2011 2.5 th percentile Figure 3: Quality defects index for the treatment and control plants Control plants Treatment plants Weeks after the start of the diagnostic Quality defects index (higher score=lower quality) Start of Diagnostic Start of Implementation Average (+ symbol) 97.5 th percentile Average (♦ symbol) 97.5 th percentile End of Implementation 2.5 th percentile

30. Nick Bloom, 147, 2011 30 Management practices before and after treatment Performance of the plants before and after treatment Quality Inventory Output Why were these practices not introduced before?

31. Nick Bloom, 147, 2011 31 Organizing and racking inventory enables firms to slowly reduce their capital stock

32. Nick Bloom, 147, 2011 32 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 in Mumbai to use in future products

33. Nick Bloom, 147, 2011 2.5 th percentile Figure 4: Yarn inventory for the treatment and control plants Control plants Treatment plants Weeks after the start of the intervention Yarn inventory (normalized to 100 prior to diagnostic) Start of Diagnostic Start of Implementation Average (+ symbol) 97.5 th percentile Average (♦ symbol) 2.5 th percentile 97.5 th percentile End of Implementation

34. Nick Bloom, 147, 2011 34 Management practices before and after treatment Performance of the plants before and after treatment Quality Inventory Output Why were these practices not introduced before?

35. Nick Bloom, 147, 2011 35 Many treated firms have also introduced basic initiatives (called “5S”) to organize the plant floor 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.

36. Nick Bloom, 147, 2011 36 Spare parts were also organized, reducing downtime (parts can be found quickly) and waste Nuts & bolts sorted as per specifications Tool storage organized Parts like gears, bushes, sorted as per specifications

37. Nick Bloom, 147, 2011 37 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)

38. Nick Bloom, 147, 2011 38 Daily performance boards have also been put up, with incentive pay for employees based on this

39. Nick Bloom, 147, 2011 2.5 th percentile Figure 5: Output for the treatment and control plants Control plants Treatment plants Weeks after the start of the intervention Start of Diagnostic Start of Implementation Average (+ symbol) 97.5 th percentile Average (♦ symbol) 2.5 th percentile 97.5 th percentile End of Implementation Output (normalized to 100 prior to diagnostic)

40. Nick Bloom, 147, 2011 40 Management practices before and after treatment Performance of the plants before and after treatment Why were these practices not introduced before?

41. Nick Bloom, 147, 2011 Why does competition not fix badly managed firms? Bankruptcy is not (currently) a threat: at weaver wage rates of $5 a day these firms are profitable Reallocation appears limited: Owners take all decisions as they worry about managers stealing. But owners time is constrained – they already work 72.4 hours average a week – limiting growth. Entry is limited: Capital intensive ($13m assets average per firm), and no guarantee new entrants are any better

42. Nick Bloom, 147, 2011 42 So why did these firms not improve themselves? Collected panel data on reasons for non implementation, and main (initial) reason was a lack of information Firms either never heard of these practices (no information) Or, did not believe they were relevant (wrong information) Later constraints after informational barriers overcome primarily around limited CEO time and CEO ability

43. Nick Bloom, 147, 2011 Finally, not to pick on the Indians, one country has such bad managers it even makes TV shows about them....... David Brent (The Office) Basil Fawlty (Fawlty Towers) Jim Hacker (Yes Minister)

44. Nick Bloom, 147, 2011 Experiments in India Experiments in China

45. Nick Bloom, 147, 2011 Working from home or shirking from home? A Chinese field experiment Nick Bloom (Stanford) James Liang (Ctrip) John Roberts (Stanford)

46. Nick Bloom, 147, 2011 Policymakers are increasingly thinking about regulating issues around work-life balance The EU regulates working hours to average 48 hours per week, with some countries (France) restricting this to 35 hours Many European countries are also increasing maternity and paternity – i.e. Sweden offers 16 months paid joint leave In the US working hours are currently not regulated, and statutory maternity and paternity leave is limited to 12 weeks unpaid.

47. Nick Bloom, 147, 2011 But US policy could change - for example the Obamas launched a CEA report on work life balance

48. Nick Bloom, 147, 2011 The report highlights that changes in families and the labor market are increasing work-life pressures

49. Nick Bloom, 147, 2011 Working hours particularly long in the US

50. Nick Bloom, 147, 2011 50 US employers offer limited workplace flexibility

51. Nick Bloom, 147, 2011 So is this bad – should the US regulate on work life balance? Amazingly, it appears nobody really knows Having been consulted on the CEA report it was clear the evidence base on this is extremely poor Source: Executive summary, CEA report (2010)

52. Nick Bloom, 147, 2011 52 So we are running field experiments on two potential solutions - home working and part-time working Working with CTrip, China’s largest travel-agent with 10,000 employees, whose co-founder and chairmen is James Liang

53. Nick Bloom, 147, 2011 Ctrip operates two large call centers, where employees are allocated to 15 person groups

54. Nick Bloom, 147, 2011 Individuals will be randomly allowed to work from home for up to 4 days a week

55. Nick Bloom, 147, 2011 55 CTrip has incredible internal data collection systems so we can monitor a wide range of metrics

56. Nick Bloom, 147, 2011 56 No impact on productivity so far from working at home - phonecalls

57. Nick Bloom, 147, 2011 No impact on productivity so far from working at home - orders

58. Nick Bloom, 147, 2011 But home workers report much higher job satisfaction levels and 50% fewer quits So preliminary evidence suggests benefits for home-working –Happier employees –Lower quit rates –Reduced office costs (only in 1 day per week) Question is will this persist in the long-run, and how much can this be extended to other firms and countries? The JetBlue question