Thursday, April 30, 2015

FCC Rural Subsidies Might Not Find Takers: There Might Not be a Business Case

The Federal Communications Commission has increased Connect America Fund funding for “price cap” carriers by about 70 percent, but also raised the minimum required speeds to 10 Mbps, although the Federal Communications Commision has redefined “broadband” as a minimum of 25 Mbps.

The conditions include a requirement to serve all high-cost areas within a state, if a carrier accepts the funding. Predictably, not all potential affected carriers might support all of the requirements for funding.

In some cases, a service provider might conclude that the total impact of upgrades required to receive the high cost service area funds exceeds the revenue that can be earned by doing all the upgrades.

The new funding benchmarks range from $72.40 for 10 Mbps downstream/1 Mbps upstream service with a 100 gigabyte usage allowance, to $96.89 for 25/5 Mbps unlimited service.

High-cost fund recipients that are subject to broadband performance obligations are required to offer service at or below the benchmark rates to qualify for the subsidies.

The fundamental economic problem is that, in many areas, there literally is no traditional business case for providing service, at the rates which can be charged, and which most consumers would pay. When that is the case, even the subsidies might not be sufficient to create a positive business case for upgrading.  

That has been a problem in the past, and is one likely reason the amount of support has been raised.

If service providers decline to take the funding, other service providers will be allowed to bid for the the support. But that also is part of the subtlety: consumers might well prefer to buy mobile service, in place of fixed network services. And the cost of providing that service might be lower, using mobile, than using any fixed network approach.

No comments:

Costs of Creating Machine Learning Models is Up Sharply

With the caveat that we must be careful about making linear extrapolations into the future, training costs of state-of-the-art AI models hav...