Drayage at the Port of Long Beach

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Drayage at the Port of Long Beach

• Kristen Monaco
• California State University Long Beach

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Why Do We Care?

• Key Link in Supply Chain
• Efficiency concerns as ports hit capacity
• Focus of Recent Legislation
• Environmental Concerns
• Security Concerns

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Overview of Driver Survey

• April and May 2004
• Two Container Terminals at POLB
• Self-Administered
• Spanish and English version
• 35 rejection rate
• Sample of 175 Drivers

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What is the nature of this job?
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(No Transcript)
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Owner Operators

• 87 of the sample reported being self-employed
• 10 owner operators in CPS
• 25 owner operators in Sloan TIP Data set
• Why the higher share of self-employed drivers in
  this segment of the labor force?
• Demand avoid up front capital costs
• Supply skills and job alternatives (92.9
  Hispanic, 88.6 born outside of the U.S.)

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Where Do They Drive?
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Firm Size
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Educational Attainment
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Marital Status
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Race and Ethnicity
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Other Characteristics

• 99 percent male
• Mean age of 40 years
• Mean experience 8.5 years
• Mean tenure 3.5 years (median of 2 years)
• 57 percent US Citizens
• 81 percent own their truck
• 51 percent received a moving violation in the
  last year

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Waiting Time

• AB2650
• 48 of drivers time spent waiting to get in and
  out of the port
• Model
• Dependent variable ratio of waiting time to
  total time on last trip
• Explanatory variables experience, tenure, race,
  born in the U.S., firm size, owner operator,
  terminal

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Waiting Time Results

• Negative relationship between experience and
  waiting time.
• Drivers at large firms (250) have lower waiting
  time.
• Those born in the U.S. have lower waiting time
  than those born outside of the U.S.
• When include Hispanic control, loses significance
  (12 level), retains sign with slightly smaller
  absolute magnitude
• Owner operators have higher waiting time than
  employees (6.8 percentage points)
• One of the terminals had significantly lower
  waiting time than the other (6.6 percentage
  points)

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Earnings

• Annual Earnings in 2003 (net of truck expenses)
• Mean 29,903
• Median 25,000
• On average work 5 days per week (11.2 hours per
  day)
• 10 report having health insurance (from any
  source)
• 5 report having a pension or retirement fund
  (from any source)

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Earnings Estimation - Model

• Dependent variable net annual income in 2003
• Explanatory variables
• Experience
• Tenure
• Education
• Race and ethnicity
• Firm size
• Owner operator
• terminal

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Annual Earnings Estimation Results

• No significant returns to tenure, experience, or
  education
• No racial wage gaps
• Hispanic drivers earn 12,243 less than
  non-Hispanic drivers.
• Drivers at medium size firms (25-249 drivers)
  earn less (6000-10,000) than drivers at the
  smallest firms.
• Last 2 suggest systematic differences either in
  hours or effective pay rate.

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Hourly Earnings Estimation

• Estimate model similar to annual earnings try
  to decompose net annual earnings differentials
  into amount due to number of hours and that due
  to the wage rate.
• Same model estimated using effective hourly
  earnings
• Mean 19/hr
• Add in predicted ratio of waiting time (from
  first estimation) 2 stage procedure

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Hourly Earnings Estimation Results

• No significant differences in hourly pay by firm
  size - suggests that annual earnings difference
  is driven by differences in hours worked.
• No hourly wage gap between Hispanics and
  non-Hispanics.
• Owner Operators earn 11.82 per hour more than
  employee drivers
• Ratio of waiting time is negatively related to
  hourly earnings increasing ratio 10 percentage
  point decreases effective hourly earnings by 4.60

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Chassis Safety

• Chassis not owned by drivers, but drivers
  ultimately held responsible for chassis safety
• Subject of proposed legislation at Federal Level
• Swan (2004)

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Chassis Safety

• 50 had been offered a bad chassis in the last
  month

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Model of Chassis Safety

• Dependent variable dichotomous takes a value
  of 1 if the driver had taken a bad chassis on the
  road (and 0 otherwise).
• Logit estimation
• Explanatory variables experience, tenure, firm
  size, race, ethnicity, daily pay, truck
  ownership, moving violation
• Also try two stage with ratio of waiting time,
  hourly wage, and net income variations with no
  significant differences.

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Chassis Logit Results

• Race, ethnicity, experience, and tenure are not
  significant
• Pay per day is also insignificant
• Truck ownership increases the probability of
  taking an unsafe chassis on the road
• Drivers at medium sized firms (100-249) are more
  likely to accept unsafe chassis.
• Drivers with a recent moving violation are more
  likely to accept an unsafe chassis.

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Summary and Conclusions

• Not a highly paid labor force economic
  pressures on this group lead to negative
  externalities at the port
• Truck age (pollution)
• Chassis safety
• Are there better ways to structure this segment
  of the supply chain?
• Some suggestion that larger firms have lower
  waiting time, might suggest that many small firms
  is not optimal from a port operations perspective.