The Truckers & Turnover Project: Context, Design, and a Selection of Early Results

1. Presentation of the Joint Work of the Project Teamby Stephen Burks, Project OrganizerESA Rome 2007 The Truckers & Turnover Project: Context, Design, and a Selection of Early Results

2. Project Team Co-investigators • Jon Anderson, Univ. of Minn., Morris • Stephen Burks, Univ. of Minn., Morris (organizer) • Jeffrey Carpenter, Middlebury College • Andrew Clark, Paris School of Economics • Lorenz Götte, Federal Reserve Bank of Boston • Kristen Monaco, Cal. State Univ., Long Beach • Aldo Rustichini, Univ. of Minn., Twin Cities • Kay Porter, Cooperating Carrier (UMM, Class of 2005) Student Researchers, Univ. of Minn., Morris 2005-2006 • Erin Christenson • Adam Durand • William Leuthner 2006-2007 • Derek Ganzhorn • Eric Lindholm

3. Acknowledgements Support is gratefully acknowledged from: • The cooperating motor carrier, its executives, and its staff • The MacArthur Foundation’s Research Network on the Nature & Origin of Preferences • The Sloan Foundation Industry Studies Program • The Trucking Industry Program, Georgia Institute of Technology

4. Outline • “TL” and the segments in U.S. trucking • The labor market for TL drivers • The research relationship • Project design: two related major components • I. Historical data & pilot results (mostly skipped here) • Building the data set • Turnover model • Productivity model • II. New hire study • Initial data collection • Conventional measures • Behavioral experiments • Job performance and other follow-up data • Conclusions • Significance of the Project • Lead-in to following presentation by Aldo Rustichini

5. Truckload Segment • Point-to-point service with little freight re-handling • Including both general and special commodities • $96 billion • 828,000 total employees • Intercity firm count 26,000 (local is larger) • 997 intercity firms with more than one location • Top 4 intercity firms have 14.7% market share (data from 2002 quinquennial economic census)

6. Truckload Competition • No entry barriers (contrasts with parcel & LTL) • Small firms compete, load-by-load, for much of large firms’ business • 3PLs provide market-based coordination • Continual flow into and out of market by small and medium-sized firms • New firms may not know their costs • Prices set at the margin, by the small (often new) players, or by least-cost large firms • This sets labor cost ceiling

7. Two Segments to the Labor Market for Drivers • Parcel & LTL tend to have job queues • TL has high turnover • ATA 2004 Annual Turnover Averages • LTL: 18% • Small TL (<$30m): 91% • Large TL: 121% • TL segment first emerged in mid-1980’s • A “secondary” labor market segment: Stable equilibrium with high turnover

8. Human Capital & Pay • High school education or equivalent • About a month of training • Two weeks basic training • Two weeks on road with driver-trainer • A year of experience makes a pro • Piece rates (mileage); weekly variation • Current average about $35K first year • Above $40K second year and later (relatively good for education required)

9. Why Turnover? A Tough Job • “Running the system”: long haul random dispatch • Long and irregular weekly work hours (60+) • Two-to-many weeks away from home • Little predictability about when home or how long • Stresses of operating big rig • Keeping the customers happy

10. Attributes of Labor that TL Firms Buy in the Market • Operate tractor-trailer safely • Specific geographic distribution of work: • From particular locations • To other particular locations • Specific temporal distribution of work: • On the particular schedules desired by customers • Call these services “effective TL labor”

11. Total cost of “effective TL labor” • (1) Turnover cost • Recruitment • Training • Safety • (2) Wage cost: to reduce turnover, pay higher positive compensating differential • (3) Productivity cost: improving working conditions lowers productivity. Need more tractors, run more miles to get drivers home more often, more regularly

12. What is the cost-minimizing mixture? • Arguably, firms trade these three costs off against each other, under a total labor cost constraint • Most firms: modest wage premium, high productivity, so also high turnover • “Exception that proves the rule”: J.B. Hunt, 1997. Switched to high-wage/low turnover model. Then switched back.

13. A Key Factor: the Job Match • Firms spend thousands per driver in recruiting, training, and related costs • Drivers take on significant debt (about the cost of commercial training course) when trained by firm • Cost of drivers who cannot complete training, plus service period to discharge debt, is high on both sides • Reducing mismatches is “win-win”

14. The Overall Design of T&T • I. Statistical case study of historical operational and human resource data • Turnover model • Productivity model • II. New Hire Study • Conventional instruments • Behavioral experiments as measures • Job performance and follow-up data • Eventual overlap when historical data updated to cover new hires

15. Behavioral Field Experiments & Industry Studies • Highlight for experimental economist • Connecting field-framed experimental measures of individual characteristics • With conventional measures • To predict on-the-job behavior and outcomes • Highlight for industry studies economist • Addition of experiments that measure individual characteristics • To the toolkit for industry-specific interdisciplinary study of firm(s) and their employees

16. Q: How Did We Get Such Access?A: It’s a Research Partnership • Long Term Research Relationship • Three years of “gift exchange” projects • Advanced undergraduate researchers • Faculty supervision • Exchanged student and faculty time for access and expenses • Provided research for mid-level executives on topics of common interest • One smaller sponsored summer pilot project • Three-plus years lifespan for the major project • Key personnel • Senior executive with research interests • Industry studies scholar (Trucking Industry Program) • Project-specific keys • Business deliverables in addition to academic output • Foundation funding • Significant faculty team • Sabbatical time on-site contributed by organizer

17. Kaplan-Meier Survival Estimate (% remaining at each week) 1.00 0.75 0.50 0.25 0.00 0 20 60 80 100 40 Weeks of Tenure

18. Figure 2: Smoothed Hazard Estimate: Rate of Departure, Conditional on Survival to Beginning of Week .03 .025 .02 .015 .01 .005 0 20 40 60 80 100 Weeks of Tenure

19. What Doing Now, Among Driver Exits Statistically significant difference between voluntary quit and discharged current work patterns. (.02) Voluntary quits move mostly to local and regional driving (45%) and non-driving jobs (29%)

20. Design of New Hire Data Collection

21. Subject Pools • Goals • 1,000 driver trainees in Wisconsin • Piloted September-November, 2005 • Production December, 2005-August 2006 • 100 student subjects (UMM undergraduates) • Completed April and May, 2007 • 100 non-student, non-faculty adults (town of Morris, MN) • Scheduled for Fall, 2007

22. Driver Trainee Data Collection • Setting dictated uniform order of events for all subjects • Overall time constraint: • Informed consent, then • Two 2-hour blocks of data collection • Fundamental trade-off: number of measures versus time required for each • Setting dictated same order of instruments for all subjects • Key Feature: credible & binding promise by University and firm that actual responses only seen by academic researchers

23. Instruments Used: Block I • Prisoner’s dilemma • sequential strategic form • Multidimensional Personality Questionnaire • “short” version (150 questions) • Risk/loss aversion • four panels of 6 sure vs. risky choices • Demographic questions • Red button • short term impatience measure

24. Instruments Used: Block II • Time preferences • Four panels of 7 choices between earlier & later payments • Non-verbal I.Q. (subset of Raven’s Standard Progressive Matrices) • Numeracy (subset of ETS quantitative literacy skills) • Only item administered on paper • Ambiguity aversion • same as risk aversion but less information available about uncertain choices • Hit 15 points • backward induction computer game • Risk, cooperation, impatience, and temporal questions, taken from the literature

25. Follow-Up Data • Surveys • Weekly 2-question satellite survey to truck • Mailed opinion survey to driver and separately, to family every six months up to two years or exit, whichever is first • Exit survey to driver and to family if departs • Job performance • Exit date and exit reason (available now) • Integrated info in historical operational/human resources data set, and also firm’s “key factors” performance measures, when data is updated to cover 2006 and on

26. Driver Trainee Data Collection • Held at a driver training school • Saturdays in the middle of the 14-day basic training program • Only a half day of training was normally scheduled • Initial informed consent process • Those who chose not to take part in the project given free time at facility break room • 4 contact hours with each subject (two 2-hour blocks) • Group divided in two sections, “A” and “B”, due to maximum PC count of 32 • Group A took part in study before lunch while B had training • Groups reversed roles after lunch

27. Driver Trainee Data Collection • Expected installation of computer-based training system delayed until after project • Instead, temporary computer lab set up each Friday, torn down at end of Saturday • 32 refurbished Dell notebooks running z-Tree on a wireless network • Temporary dividers constructed from garment racks • 11-hour workday on Saturday for experimenters, starting at 5:15 AM or 6:15 AM (23 times)

28. Prepping a Pilot SessionClockwise from front: Adam Durand, Kay Porter, Lorenz Götte (on “tree” PC), and Aldo Rustichini

29. z-Tree Control Station for Networkleft to right: Kay Porter, William Leuthner

30. Participants During Data Collection

31. Reading Instruction ScriptStephen Burks (project organizer) administered all 23 data collection sessions

32. Participant Numbers • Overall participation rate was 91% of those eligible • 1,069 participants entered the study • 4 participants (only!) withdrew during data collection • 1 participant left school and re-entered, and took part twice • 29 have incomplete MPQ (too slow, & one PC glitch) • Thus nearly complete information on 1,035 participants • Backward induction instrument had to be re-programmed, so including this item have complete data for 893 subjects

33. Payments to Participants • Each participant received two up front $10 cash “show up” fees on Saturday • Incentives also offered for choices and performance in many of the measurement tasks • Received the balance of their earnings on Tuesday after Saturday data collection event, during their lunch hour • Exception: time preference payments on date chosen • Total Earnings: Avg: $53; Low: $21; High: $168 • Total amount paid to subjects on Saturdays: $57,300 • Follow up mail surveys: about $75,000 in future payments and mailing/processing costs

36. Early Results • What our measures of cognitive skills • Non-verbal IQ • Numeracy • Ability to learn backward induction game • Tell us about driver trainee • Time preferences • Risk preference • Social preferences • Ability to predict social dilemma behavior • Following presentation by Aldo Rustichini

37. Distribution of Credit Scores in Panel Data

39. Current Conclusionsfor Field Experimentalists • Working in an industry studies setting offers significant opportunities • Testing the predictive power of measures from field experiments in the context of standard models built on the historical data will provide direct evidence of • Comparative predictive value • External validity of laboratory measures

40. Conclusions for Combining Industry Studies & Field Experiments • Workplace project of this scope is • Feasible • Not easy • Success in this case depended on • Established long-term research relationship, based on the “industry studies” academic model • Real interest in “basic” research from key execs • Credible business deliverables • Real academic interest from members of team • Outside foundation funding for behavioral work

41. Significance of Subject Pool • TL drivers are archetypal non-knowledge workers in the knowledge economy • These jobs cannot be shipped “overseas” • Typical of future prospects for all non-college-trained workers in the service economy in U.S. • What explains success in these jobs? • Does the explanation have implications for • Corporate strategy? • Labor market policies? • Educational policies?

42. Presentation of the Joint Work of the Project Team by Stephen Burks, Project Organizer ESA Rome 2007 The Truckers & Turnover Project: Context, Design, and a Selection of Early Results

43. Extra Slides

44. Portrait of the Speaker as a Young Man Cleveland, Ohio; 1983 Indianapolis, Indiana; 1977

45. Examples of Business Deliverables • Historical data study • Expected value of human capital analysis using productivity and turnover models, controlling for operational characteristics • New hire study • Identification and evaluation of screening tools • Apples-to-apples comparative data on stayers and leavers, to analyze factors affecting turnover and productivity

46. Recall: the Job Match • Firms spend thousands per driver in recruiting, training, and related costs • Drivers take on significant debt (about the cost of commercial training course) when trained by firm • Cost of drivers who cannot complete training, plus service period to discharge debt, is high on both sides • Reducing mismatches is “win-win” • We simulated the effect of filtering out the bottom 10% of students on two measures of cognitive skill • Quantitative literacy (“numeracy”) • Backward induction experiment • Both instruments are incentive compatible • Both could be administered at the beginning of training

47. Gray line=full population; Green line=population after screening; Dark line=those screened out

48. What does this mean quantitatively? • Screened = what retention would have been for the drivers screened out of training • Current = survival rate of all drivers in panel sample • New = survival of drivers remaining in panel after screening • Gain in retention = New survival less current survival

49. Sample from Follow-up Mail Surveys Business deliverable

50. Driver Exit & 6 Mo SurveysThe number of times per month I get home is acceptable • Continuing drivers are positive, and exited drivers are not. • Exited opinion not statistically different from neutral. • Statistically significant difference (.000)