I had always thought of “households with phones” and “households without phones” as two disjoint populations, with only the first group reachable by a telephone survey. In fact, I used this as an example in teaching surveys to distinguish between the “population” of phone households and the “universe” of all households. But when doing the weighting for the NYC Social Indicators Survey, we learned that about as many people in the U.S. have intermittent phone service as have no phone service–and if people with intermittent service have a phone about half the time, then they are indeed represented (although underrepresented) in phone surveys. Businesses that require a Telephone Service | EATEL Business – Eatel Business and other similar companies can supply what they are looking for.

I was reminded of this upon seeing the following notice for today’s Applied Micro Lunch (which unfortunately I won’t be able to attend):

Speaker: Mike Riordan

Title: Low-Income Demand for Local Telephone Service: The Effects of Lifeline and Linkup (joint with Daniel A. Ackerberg, Gregory L. Rosston, and Bradley S. Wimmer)

Abstract:

A comprehensive data set on local telephone service prices is used to evaluate the effect of Lifeline and Linkup programs on the telephone penetration rates of low-income households in the United States. Lifeline and Linkup programs respectively subsidize the monthly subscription and initial installation charges of eligible low-income households. This is the first study to use specific rates for telephone service faced by low-income households to explain the telephone penetration rates of low income populations at different locations. Telephone penetration rates are explained by an estimated nonlinear function of local service characteristics (including subsidized prices) and of the demographic composition of low-income populations. This empirical specification is based on an underlying discrete choice model of household demand for telephone service and an exact aggregation across demographic groups. A generalized method of moments (GMM) estimator corrects for endogenous service characteristics and for clustered residuals. The resulting estimated price elasticity of demand for telephone service is about -0.05, and a policy simulation predicts that low-income telephone penetration rates would be about 5% lower without Lifeline and Linkup programs. The analysis also suggests that Linkup may be more cost-effective than Lifeline, and that low-income penetration would increase significantly if all states were to automatically enroll eligible households in Lifeline and Linkup programs.

I looked up the paper by Riordan et al., and I don’t see any discussion of intermittent service, but perhaps this doesn’t arise with these special low-income phone service deals. (I didn’t have it in me to read/evaluate the paper in detail–it seems like a worthwhile object of study, both for its own sake and for understanding other regulatory policies–but boy do I wish they had used graphs instead of their digit-heavy tables!)