Walls, Nelson, Safirova
Telecommuting and Environmental Policy
Choo, Mokhtarian, and Salomon (2003) take a very different approach to looking at the VMT
impacts of telecommuting. They use national aggregate data to estimate an econometric time-series
model of VMT as a function of economic variables; they then use the residuals from that regression -
i.e., the unexplained part of annual aggregate VMT - and regress them on telecommuting data. In the
first stage regression, the authors include as explanatory variables GDP per capita, the price of gasoline,
average miles per gallon of the vehicle fleet, a consumer price index (CPI) for all commodities and a
CPI for transportation. They have 33 years of annual data, from 1966 to 1999, and their dependent
variable is VMT per capita.6 In the second stage, in which the authors estimate the first-stage residuals
as a function of a constant term and the natural log of the number of telecommuters, results show the
coefficient on the telecommuters variable as negative and significant.7 The size of the estimated
coefficient suggests that VMT during the sample period would have been approximately 2.12% higher
than observed VMT in the absence of any telecommuting. The range across all the different VMT
models estimated is 1.78% to 3.31%.
The Choo, Mokhtarian, and Salomon study is interesting for its unique approach to estimating
the VMT effects of telecommuting but the aggregate data and simple version of the VMT model leave
much unexplained. The residuals from the first-stage VMT model are likely to include quite a number
of omitted variables, thus the telecommuting variable in the second-stage regression could be proxying
for a number of other factors that affect VMT.
In a recent working paper, Collantes and Mokhtarian (2003) analyze data from 218 employees
of the state of California. The survey of these employees, completed in 1998, included retrospective
responses to questions about telecommuting frequency, commute distances, residential relocations and
job relocations for a 10-year period, 1988-1998, on a quarter-by-quarter basis. The point of the survey
is to obtain some information on the relationship between travel behavior, telecommuting, and
residential location decisions. In this paper, the authors do not econometrically model telecommuting
choice or frequency or location decisions. They do, however, look at patterns of telecommuting over
time and distances commuted and calculate total VMT and PMT for telecommuters and non-
telecommuters.8
The authors find that average commute lengths, which have increased over the 10-year period,
are generally longer for telecommuters than non-telecommuters and that the difference between the two
has increased over time. The authors speculate that two processes could be at work to cause these
results: (1) relocations made for a variety of reasons could lead to longer commutes thus prompting
more telecommuting, and/or (2) increased availability of telecommuting might cause people to relocate
farther from their jobs. The authors try to use their data to separate out these two possibilities. The
second scenario - the availability of telecommuting leading people to move farther from their jobs -
does not appear to hold. Current and former telecommuters in the dataset have shorter commutes, on
average, after a move while nontelecommuters have longer ones. The longer distance moves tend to be
those that take place before telecommuting begins. The authors claim that this suggests that
telecommuting is a consequence of a move rather than the cause of it. When survey respondents were
asked what factors were important in their three most recent moves, telecommuting was only listed at all
in 12 of 97 cases and even in these, it was not listed as an important factor.
In terms of frequency of telecommuting, the data in this study shows that people telecommute,
on average, approximately 1.5 times/week. This average has fallen over time, according to the survey
responses, and the authors explore different possible explanations for this decline. The most likely
reason, according to the authors, has to do with the fact that the early adopters of telecommuting are
6 They estimate three versions of a VMT model and five versions of a VMT per capita model. The model we
describe here is the one that they feel provides the best overall results.
7 Regardless of the first-stage model used, the telecommuters variable is always significant in the second-stage
model.
8 The two measures of miles traveled will differ to the extent that a person carpools.