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

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

Project Team

2006-2007– Derek Ganzhorn– Eric Lindholm

2005-2006– Erin Christenson– Adam Durand– William Leuthner

AcknowledgementsSupport 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

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

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)

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

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

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)

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

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”

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

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.

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”

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

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

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

0.00

0.25

0.50

0.75

1.000 20 40 60 80 100

Weeks of Tenure

Kaplan-Meier Survival Estimate

(% remaining at each week)

.005

.01

.015

.02

.025

.03

0 20 40 60 80 100

Weeks of Tenure

Figure 2: Smoothed Hazard Estimate: Rate of Departure, Conditional on Survival to Beginning of Week

What Doing Now, Among Driver Exits

N

Col %

Voluntary Quit

Discharged

OTR Long Haul

9 11% 6 19%

OTR Regionally

12 14% 1 3%

Driving Locally

26 31% 2 6%

Non-Driving

Job

24 29% 10 31%

Unemployed 13 15% 13 41%

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%)

Design of New Hire Data Collection

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

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

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

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

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

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

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)

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

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

Participants During Data Collection

Reading Instruction ScriptStephen Burks (project organizer)

administered all 23 data collection sessions

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

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

Annual Earnings Before Coming to Firm with CPS Blue-Collar Worker Benchmark

05

101520253035

$ in 10,000

Perc

ent Benchmark

Panel data

05

1015

2025

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pay

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oss

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e big

rig

Reason

Why Subject Chose to Start Training with the Company

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

Distribution of Credit Scores in Panel Data

01

23

45

67

89

10P

erc

en

tag

e o

f Su

bje

cts

400 500 600 700 800Approximate Credit Score

Distribution of Credit Scores in Panel Data

0.00

0.25

0.50

0.75

1.00

0 4 8 12 16 20 24 28 32 36 40 44 48 52

Top 25%

25% above Median

25% below Median

Bottom 25%

Credit Scores

0.00

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0.50

0.75

1.00

0 4 8 12 16 20 24 28 32 36 40 44 48 52

Has No Credit Score

Has Credit Score

No Credit

Weeks after Starting Employment

All ExitsCredit Scores and Retention

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

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

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?

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

Extra Slides

Portrait of the Speaker as a Young Man

Indianapolis, Indiana; 1977

Cleveland, Ohio; 1983

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

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

.2.4

.6.8

1

0 4 8 12 16 20 24 28 32 36 40 44 48 52

RCScore=1 NumCorrect=5.1 RCScore=2.4 NumCorrect=8.7RCScore=2.7 NumCorrect=9.4

Survival by GoodStudent after Cox

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

What does this mean quantitatively?

Screened Current New Weeks Gain55.2% 66.9% 68.2% 13 1.4%37.0% 51.0% 52.8% 26 1.8%22.8% 36.8% 38.7% 52 1.9%

• 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

Sample from Follow-up Mail Surveys

Business deliverable

Driver Exit & 6 Mo SurveysThe number of times per month I get home is acceptable

Group Mean N

Cont .652 164

Exited -.172 99

Diff .824 Continuing drivers are

positive, and exited drivers are not.

Exited opinion not statistically different from neutral.

Statistically significant difference (.000)

010

2030

400

1020

3040

-2 0 2

Continuing 6 Mo

Exited

Perc

ent

Question 4Graphs by Returned 6 Mo Continuing Survey

Driver Exit & 6 Mo SurveysIt takes a lot of effort to earn a good income driving for this firm.

Both groups moderately agree on average

Exited drivers more strongly believe too much effort is needed to obtain acceptable earnings

Statistically significant difference (.032)

Group Mean N

Cont .661 168

Exited .972 108

Diff -.312

050

050

-2 0 2

Continuing 6 Mo

Exited

Perc

ent

Question 14Graphs by Returned 6 Mo Continuing Survey

Some Characteristics of Trainee Subjects

• Education

• Prior earnings of driver

• Household income without driver

• Why they chose to become drivers

• Body Mass Index

• Personality Profile

Highest Education Level Attained with CPS Blue-Collar Worker Benchmark

05

1015202530354045

Education Level

Perc

en

t

Benchmark

Panel data

Age Distibution with CPS Blue-Collar Benchmark

02468

1012141618

Age Bracket

Perc

ent Benchmark

Panel data

Income Houshold Has Without the Subject with CPS Blue-Collar Worker Benchmark

0

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30

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$ in 10,000

Perc

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Panel data

Body Mass Index• We also collected height and weight of

the participants.

• This allows us to calculate the Body Mass Index, which indicates how the weight of an individual relates to his or her height – BMI < 18 : underweight– 18 < BMI < 25 : normal– 25 < BMI < 30 : overweight– BMI > 30 : Obese

BMI in US Population: Males

• Glaeser et al. (2003)

BMI among Trainees: Males

MPQ Scores Sample and Drivers

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MPQ Sample Drivers

Measures from Some of theBehavioral Experiments

• Cooperation (sequential strategic PD)

• Risk/Loss Aversion

• Impatience (the Red Button)

• Time Preferences

• Ambiguity Aversion

Prisoner’s Dilemma• Two roles, First Mover and Second Mover• Both start with $5• First mover sends $0 or $5 to second mover;

whatever is kept is theirs, whatever is sent is doubled

• Second movers then respond to– First Case in which they got $0– Second Case in which they got $5 (doubled to $10)– Sending $0, $1, $2, $3, $4, or $5– Under same rules (theirs if kept, doubled if sent)

• Second mover strategy reveals “social preference type”

What do first-movers do in the PD?

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0.1

.2.3

.4.5

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.8.9

1F

ract

ion

Not Cooperate Cooperate

How Cooperative are Trainees?

Approximately two-thirds are willing to send $5 to an anonymous “other.”

How do second-movers behave?

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56

Am

ount

Ret

urne

d if

Sent

$5

-1 0 1 2 3 4 5 6Amount Returned if Sent $0

Second-Mover Strategies in the Prisoner's Dilemma

There are three strong types: Egoists (never send anything back), Reciprocators(send it all back if they get it) and Altruists (always send their $5). Together, thesethree types account for 61% of the observations.

Risk/Loss Aversion

Certain Outcome• Panel 1:

$2.00 to $7.00• Panel 2:

$0.00 to $2.50• Panel 3:

-$2.50 to $0.00• Panel 4:

+$1.00 to +$3.50

50%-50% Gamble• Panel 1:

+$10.00 or +$2.00• Panel 2:

+$5.00 or -$1.00• Panel 3:

+ $1.00 or -$5.00 • Panel 4:

+$5.00 or +$1.00

Total Number of Risky Choices0

.02

.04

.06

.08

.1

Fra

ctio

n

0 5 10 15 20 25RiskyChoiceTotal

Impatience (Red Button)• No talking, reading, writing, or other

activities during session time• One screen, with a count down timer from

600 seconds to zero, and • The Payoff level, starting at $5.00, and• A Red button labeled “Cut My Time”.

– First click cuts 5 minutes– Second click cuts 3 minutes– Third click cuts 2 minutes (i.e. leave now)– Each click cuts payoff by $1.00

Total Number of Red Button Clicks0

.2.4

.6.8

Fra

ctio

n

-1 0 1 2 3RedButtonClicks

Ambiguity Aversion• Same four panels of certain versus risky

choices as Risk/Loss Aversion• One change: each gamble is now

– At least 20% chance of the high outcome– At least 20% chanced of the low outcome– We added 6 blue and/or green chips of an

unknown color mixture to the 2 blue and 2 green in the bowl at the start

– ACTUAL probabilities NOT revealed, either before or after chips are drawn

• So, less information about the risky choice

Number of Risky Choices With and Without Gamble Ambiguity

-10

12

34

56

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mb

igR

iskyC

hoic

esF

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-1 0 1 2 3 4 5 6 7RiskyChoicesFirstPanel

Pilot Results: Productivity

Building the Historical Data Set• Final data set provides one observation

per week for each driver employed that week

• Danger: extensive construction work!– Describing, cleaning, documenting & merging– Multiple mainframe-based internal report files– Each with distinct data definitions, operational

meanings, errors, and non-overlapping cases

• More than two person-years of effort

Variance: Miles/Wk by Tenure Wk

Tenure-Productivity Curve, with and without Individual Fixed Effects

Raw Tenure-Productivity Curve, with and without Zero-Miles Weeks

Pilot Results: Productivity• New hires become fully productive at

about the nine-month mark• Selection effects clearly affect the driver

mix• Generally see lower achievers leaving

more rapidly• Possible hint that high achievers may

leave faster early• Next steps: re-run models on better

validated new data set with more complete controls