The Fuse

The Impact of Vehicle Ownership and Telecom Costs on the Sharing Economy

by Hart Schwartz | June 26, 2018

A previous series in The Fuse identified three pillars of the sharing economy: (1) Excess Capacity; (2) Smartphones; and (3) Urbanism. Together, these elements have formed a self-reinforcing cycle whereby idle resources are shared in crowded urban spaces. Wide adoption of smartphones has been the crucial catalytic element, contributing a novel factor for matching supply and demand in a precise, flexible manner which had never before been possible. Ride-shares (Uber, Lyft), vehicle-shares (Car2Go, Zipcar), lodging (Airbnb), and other services can now be matched in an economically profitable manner which offers consumer benefits at an affordable price.

Significant price discrepancies between telecom and vehicle ownership—something often speculated about in media and industry circles—are not illusory.

But with respect specifically to shared travel, what underlies the adoption of smartphones and their use for matching riders and drivers? Why not simply continue to use older forms of travel? One overlooked aspect has been the comparative costs between smartphone use and vehicle ownership. While vehicle ownership has received much media attention, little spotlight has been placed on the actual cost data which may inform consumers’ daily choices. What do the data say about relative costs?

The U.S. Consumer Price Index (CPI), collected by the U.S. Bureau of Labor Statistics, provides some revealing answers. Nearly all prices of information or communication related goods and services have dropped, some quite precipitously, since they began to be tracked (mostly since the late 1990s).

In stark contrast, nearly all prices of automobile ownership have either held steady or risen substantially during the same time period.


Rate of Inflation: Keep Price Data in Perspective

When viewing data series on price increases, it is important to know the underlying level of inflation in the general economy. The price changes in the preceding graphs have occurred during a period of low but steady inflation. The general CPI has no units per se, rather the CPI tracks relative changes over time in the price of a hypothetical basket of goods. A base-year is chosen in which the CPI equals 100, so that price changes with respect to this base-year can be easily placed in perspective. The current base-year is 1983, and the most recent annual figure for CPI, released for 2017, totals 245. This means that prices in 2017 were on average 2.45 times the level of prices in 1983, or 145.0% higher.

Some of the individual items for motor vehicle ownership, and all of the items for information and telecommunications, instead have a base-year of 1998, meaning that the Bureau of Labor Statistics did not calculate a CPI for these categories before 1998. If one divides the CPI of 245 in 2017, by the CPI of 163 in 1998, then prices in 2017 were on average 1.47 times the level of prices in 1998, or 50.3 percent higher. The following graph places the two inflation base-years in clear relief, vis-à-vis one another.

Viewed in light of inflation statistics, the rise in the cost of owning and operating a motor vehicle has been relatively moderate, with only six of fourteen components—insurance; motor oil, coolant, and fluids; parking fees and tolls; motor vehicle fees; state registration and licensing; and repair—exceeding the general rate of inflation. On the other hand, information, computing and telecom costs have fallen dramatically in comparison, when one considers that each of these costs fell between 25 percent and 98 percent between 1998 and 2017, a period during which general prices increased by 50 percent.

Main Point: Non-trivial price differentials between personal telecom & vehicle ownership

The key point, then, is the stark contrast in the direction of the respective trendlines, for information, computing, and telecom costs on the one hand versus motor vehicle owning/operating costs on the other hand. It’s like a football game where one team, trailing by seven points, is about to score a touchdown, only to have a pass intercepted and returned the other way for a touchdown—a “double-swing” in score. The score in the football game goes from prospectively tied to a two-touchdown difference instead.

Implications: Consumer decision-making

The coordination of the sharing economy—excess capacity; smartphones; and urbanism—relies upon consumer psychology and decision-making.

The coordination of the sharing economy—excess capacity; smartphones; and urbanism—relies upon consumer psychology and decision-making. With respect to transportation, consumer decision-making has received much attention from scholars. For example, in their classic 1977 book, Public Transportation and Land-Use Policy, planners Jeffrey Zupan and Boris Pushkarev outlined a matrix of variables weighed by consumers when deciding which travel mode to use. This matrix includes a variety of “prices,” some tangible and some intangible:

  • Price in money
  • Price in travel time
  • Price in access time and effort
  • Price in discomfort and disamenity

Has the disparity between telecom costs on the one hand, and motor vehicle costs on the other hand, led to a shift in these consumer behavior variables? Do some urban residents choose to own smartphones, and use these for their travel needs via ride-share, because they have been strongly influenced by a two-decade cycle in which communications costs have fallen whereas vehicle transportation costs have risen? The “decision matrix” of Pushkarev and Zupan creates a stable framework for assessing the answers to these questions.

Does flexibility of vehicle ownership override cost advantages of telecom?

A further question of interest would be whether vehicle ownership itself has been surrendered by some urban residents as they reorient their budgets to adjust to changing economic conditions. An interesting counterpoint concerns the flexibility of a vehicle with respect to choosing where to live and work. Even with rising costs, it’s possible that the readier personal access of 24/7 vehicle ownership—for travel to job locations and lower-cost suburban housing—may override the specific differences in cost trends between ride-sharing and vehicle ownership, and may make vehicle ownership more cost-effective when its full range of attributes are taken into account.

Further research needed, but price differentials real

To be sure, there are diverse perspectives on this subject, and further research is needed. But what can be said for now is that non-trivial, significant price discrepancies between telecom and vehicle ownership—something often speculated about in media and industry circles—are not illusory, according to official consumer price index data. Therefore, those interested the ongoing development of ride-sharing should take into account the compelling cost differentials between inputs to different types of travel modes.