We estimate the long-run causal effect of information technology, i.e., Internet and powerful computers, as measured by the adoption of teleworking, on average commuting distance within professions in the Netherlands. We employ data for 2 years—1996 when information technology was hardly adopted and 2010 when information technology was widely used in a wide range of professions. Variation in information technology adoption over time and between professions allows us to infer the causal effect of interest using difference-in-differences techniques combined with propensity score matching. Our results show that the long-run causal effect of information technology on commuting distance is too small to be identified and likely to be absent. This suggests that, contrary to some assertions, the advent of information technology did not have a profound impact on the spatial structure of the labor market.
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In a similar fashion, an employee who teleworks part-time during the day, to avoid peak period congestion, experiences a lower generalized commuting cost as well.
As a referee noted, a priori it is difficult to tell whether workers with short or long commutes will take up teleworking. The former group might feel a strong disutility from commuting in general, while the latter group might want to avoid the long commuting time.
A similar argument has been given for households by Hu (2016), where the commuting distance of teleworkers automatically affects the commuting distance of non-teleworkers within the same household.
For example, workers with larger residences are more likely to live further away from the workplace and are more likely to prefer to work from home.
Zhu (2012, 2013) analyzed the effect of teleworking adoption on teleworkers and employs “Internet use at home” as an instrument for teleworking. However, when unobserved professional abilities of employees are correlated with the use of information technology, such as the Internet, then the instrument is not valid. So, this strategy implicitly assumes the absence of such a relation. Our identification strategy avoids such a restrictive assumption.
Note that we will not assume that the teleworking incidents rate was zero in 1996. Our estimation approach allows for the possibility that employees were working from home in 1996.
Nonetheless, telework correlates highly with other measures of information technology. A recent survey in 2015 among Dutch companies across 38 industrial sectors showed that teleworking correlates with 0.82 with using computers and Internet at work and with 0.85 with have access to mobile Internet.
Multiple factors affect technology adoption by an employee, including idiosyncratic distaste for teleworking, managerial practices (for example, Yahoo! forbade teleworking in 2013) or certain characteristics of the labor market (see Mokhtarian 1998).
As we explain later, our method differs from “difference-in-differences propensity score matching” methodology (see, e.g., Hijzen et al. 2013), which relies on panel data to observe the same individuals or firms over time. We observe the same professions over time but use propensity score matching to account for differences in labor force composition within professions over time.
The main advantage of using a binary measure of teleworkers share is that it drastically reduces the effect of measurement error in our teleworking variable. It seems reasonable to expect the effect of teleworking on commuting to be stronger the larger is the share of adopters within the profession. We confirm this assertion by repeating the entire analysis using data on professions with relatively low adoption rates, as we find no effect of technology on commuting.
In our application, we will use the years 1996 and 2010.
Treated and non-treated professions comprise of many different professions (both comprise professions which require low, medium or high education), so we allow for the possibility that some professions have a different trend, but not the average (industry-specific) trend.
We formally test the balancing property of the matching to insure that observations in two matched groups are similar in observed variables.
We will perform robustness checks with respect to the matching procedure.
In case the cohort effect is not important or that it does not differ across treated and non-treated professions, panel data would be preferable.
We consider employed individuals, between 18 and 64 years, working more than 12 h per week, with a one-way commute distance of less than 100 km.
When changes of distance within municipalities are in the same direction as changes across municipalities, which is plausible, our estimates of changes of commuting distances over time will be an underestimate. As long as treated and non-treated professions change similarly within municipalities, this would not affect our results. To check whether it does, we excluded those who work and live within the same municipality, but this does not change our main result qualitatively. But, as one referee pointed out, strictly speaking we look at changes in commuting distances between municipalities.
The self-reported average commuting distance in the Netherlands is 17 km over the period 2000–2008 (Groot et al. 2012). The difference with our data stems largely from within-municipality commutes that are assumed to be zero in our approach.
The original question in the Dutch language is “Waar werkt u in deze werkkring doorgaans?” which one might translate as “Where do you usually work on this job?” This question in the survey is clearly distinguished from another question about instances of overtime work at home.
For an overview on the measurement of teleworking, we refer to Sullivan (2003).
In choosing the scale of aggregation one has to trade off homogeneity of the resulting groups with the availability of the observations, to avoid classifying each employee as a single representative of a profession.
As a robustness check, we also provide results that are derived for a cutoff value of 10%.
Commuting within a municipality occurs in 49% (54%) and 31% (33%) in 2010 (1996) of, respectively, non-treated and treated professions.
For the propensity score matching estimates (and subsequent testing of the results), we use the “psmatch2” and “pstest” commands in Stata, developed by Leuven and Sianesi (2003).
For example, we have included interaction and higher-order terms, excluded some variables and applied a more restrictive definition of treated professions. We have also applied other matching procedures such as one-to-one, three-to-one and five-to-one neighbors, and caliper, have varied the kernel bandwidth and performed estimations without replacements.
As one referee noted, this assumes that firms are equally likely to be sampled in our database. If firms that adopt ICT substitute labor for capital, then this might lead to a sample selection bias.
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Financial support from The Netherlands Organization for Scientific Research (NWO) is gratefully acknowledged. This paper is part of TRISTAM Project (Traveler Response and Information Service Technology—Analysis and Modeling).
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Gubins, S., van Ommeren, J. & de Graaff, T. Does new information technology change commuting behavior?. Ann Reg Sci 62, 187–210 (2019). https://doi.org/10.1007/s00168-018-0893-2