Telecommuting and environmental policy - lessons from the Ecommute program

Telecommuting and Environmental Policy

Another issue with the data has to do with dropping out of the program. A large percentage of
employees stop reporting after some period of time. It is possible that they are still teleworking, but
because we have no commute logs for them, we have no way of knowing. Table 6 shows the
percentage of total employees enrolled in the program over the entire program period who have not
reported to Teletrips within the last month for which we have data, February 2004. Overall, slightly less
than 35% of all employees are currently reporting, thus 65% of the sample have dropped out, or at least
have not reported to Teletrips for a month. The percentages vary across the cities from only 42% in
L.A. up to 73% in Philadelphia. It is not surprising that L.A.’s drop-out rate is lowest since most of the
employees enrolled there have signed up only late during the ecommute program.

Tables 3 and 4 provide some useful information about tracking commute behavior over time.
Even though Teletrips is reportedly easy to deal with and filling out commute logs takes very little time,
there is still a high percentage of participants in the program who do not report on any given day and a
very high percentage who appear to drop out completely. As we said in Section II above, one of the
unique aspects of this data was the possibility it provided of observing telecommuting behavior over
time. However, these observations need to be taken with a grain of salt because of the missing commute
logs and limited time that some employees actively participate in the program.

Table 4. Employee Drop-Out Rates in the ecommute Program, by City

5.2. Commute Mode Choice of Employees in the ecommute Program

For each day of the week, employees report not only their telecommuting activity but also their
mode for commuting to work when they don’t telework and/or whether they did not work at all. Table 5
shows, for each pilot city, the percentage of total days worked that are teleworking days (including
telework centers), as well as days in which the employee drove alone to work (including motorcycles),
drove or rode in a car or van pool, used public transit, or used other forms of transportation (which
includes bicycling and walking, as well as a self-reported “other” category). The figures in Table 5 are
calculated by simply adding up the days reported for each mode, across all employees, and dividing by
the total number of days reported. Thus the reporting days are treated as equivalent regardless of who is
reporting - i.e., we do not take into account which employee is reporting. Because employees enter and
leave - or at least stop reporting to - the program on different dates and report with varying degrees of
regularity, we have an unequal number of commute logs across employees. There is no single correct
way to summarize the commuting data in this case, so we choose to present it in two different ways. In
this section, we treat each day as equivalent to every other day, regardless of which employee filled out
the log. In the next section, we compute the percentage of days in the system that each employee
teleworks and uses each mode to get to work. We thus treat employees as equivalent but control for the
fact that there are different numbers of days reported per employee by looking at percentages of days in
the system.

Table 5 shows that, on average, across all five cities, approximately 36% of total workdays
reported to the system are telework days. The rest of the time, employees reported most often that they
drove alone to work; this option accounts for 49% of all workdays. The figures in Table 5 highlight the
self-selection issue with this data that we mentioned in Section II above. Participants in the program are

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