## ABSTRACT

Does entry and exit of competitors from broadband services markets have large effects on the quality of broadband plans offered to consumers? Answers to this question inform design of subsidies to improved broadband in underserved areas, and antitrust policy. The authors investigate quality effects of entry (or exit) in 2014 legacy (cable and digital subscriber line) duopoly residential markets. Entry or exit by legacy technology providers had large impacts on 2018 service quality, though impacts diminished rapidly with continued entry. Entry by a single fiber Internet service provider (ISP) had no impact on available quality; entry by more fiber ISPs had modest but statistically significant effects.

In markets for differentiated products, quality1 is likely to be a critical dimension of competition. In addition to price, quality choices allow firms to differentiate product offerings from one another and to target different demand segments. In such circumstances, an analysis of how well a market performs cannot simply rely on the price and quantity observed as market outcomes but must also explicitly consider product quality. Because increases (decreases) in quality are consumer surplus enhancing (diminishing), ceteris paribus, the relationship between market competition and product quality is quite important from a policy perspective.

Firms can increase product quality in order to respond to competitors entering markets with established products. Product quality differences can thus work to lessen price competition (Shaked and Sutton, 1982). But firms in markets with low competitive pricing pressure may choose lower quality levels for their products than firms facing higher levels of competitive pressure in the marketplace (Tirole, 1988, section 2.1). If products have different quality tiers, there may be little price competition within each tier. In short, greater competition has ambiguous effects on product quality in the theoretical literature (Shapiro, 1983; Allen, 1984; Kranton, 2003).

In this article, we analyze the effects of local market structure on residential broadband service quality. Technological change stimulating new entry (exit) into (from) “legacy technology” broadband service duopolies created a unique opportunity to analyze an interesting and important question: How exactly does increased competition, measured as numbers of competing Internet service providers (ISPs) in a census block, affect quality of service, measured as the highest speed offered by legacy service providers in a local service market (census block)?

The U.S. residential broadband market is an ideal setting for a study of the causal relationship between market structure and quality. First, the available data suggest that nominal prices for residential broadband service plans in the United States have basically been relatively flat on average—changing little over the last decade. The major dimension for competition among major broadband ISPs has been improved service quality, particularly upgrades to download speeds. Second, over a relatively short time window, we document evidence of substantial entry and exit by firms within spatially disaggregated local markets. Third, technological innovation has significantly lowered the costs of quality improvements, so there is substantial variation in quality within a cross-section of spatial locations over time. Improvements in Internet service quality have the potential to bring large impacts on household welfare: most people now use the Internet, and they use it intensively.2

Using data from multiple sources, we have built a rich panel data set that covers all broadband services delivered to households by ISP, technology, and maximum download speed for each of approximately six million populated U.S. census blocks between December 2014 and December 2018. We summarize trends in market structure, quality, and technology utilization within this five-year period. Previous empirical analyses examining the effect of market structure on quality provision in the U.S. market (over earlier periods, using more aggregated data) relied on very noisy and sometimes inappropriate measures of quality. Additionally, earlier studies made strong assumptions about the way firms compete in the market, and strong statistical assumptions about explanatory variables.

Given the richness of the data set and a novel empirical identification strategy, we are able to address many of the limitations of previous studies. Identification of the causal effects of market structure on service quality must potentially account for the possible endogeneity of market structure measures. Our difference-in-difference (with fixed effects) framework addresses these problems.

While market structure is endogenous in the long run, a variety of national and state regulatory policies clearly affect market structure outcomes, and policy levers clearly can have an effect on market structure “treatment” variables. In Texas, for example, cities and municipalities are barred by state law from providing broadband service to residents, while this is not so in other states where municipal broadband service is legal. As another example, the Federal Communications Commission’s (FCC) new auction mechanism for universal service subsidies explicitly provides for possible subsidies to entrants wishing to serve “high-cost” census blocks with inadequate broadband availability from incumbent providers. Understanding the impact of market structure (number of competitors) on service price and quality is relevant to policy choices even if endogenous firm decisions interact with policy in determining market outcomes.

We estimate the effect of increasing competition on service quality using a Difference-in-Differences (DiD) framework. Our results suggest that entry into (or exit from) a 2014 “legacy duopoly” census block (about 40% of U.S. urban census blocks were legacy—cable and digital subscriber line [DSL]—terrestrial fixed broadband duopolies in 2014) by other legacy ISPs had a relatively large and statistically significant impact on maximum broadband quality offered to consumers by legacy ISPs serving a given block. Increased competition in local broadband markets from “overbuilding” by broadband providers using legacy technology seems to result in large quality improvements. In contrast, entry by a single fiber ISP had no statistically significant effect on offered service quality, while entry by two or more fiber ISPs had a modest but statistically significant effect in increasing legacy ISP speeds. We conclude by interpreting these results and identifying potential policy implications.

The remainder of the article is organized as follows. The section “Literature Review” describes related literature; the section “Empirical Context” briefly reviews essential background; the section “Data Sources” describes data sources and how we built our analytical dataset; the section “Identification Strategy Overview” discuss our identification strategy; the section “Difference-in-Difference Results” the details of the DiD model and empirical results; the section “Interpreting Our Results: Summary” interprets and summarizes results, and the section “Conclusion and Policy Implications” presents conclusions.4

## Literature Review

There is a broad economic literature studying the effects of market competition on equilibrium outcomes, such as price and quality. One of the first empirical papers to address the effect of market structure on prices was Bresnahan and Reiss (1991). They measure the effects of entry on competitive conduct, with firms pricing closer to marginal costs as a metric for more competitive conduct. They find that almost all changes in competitive conduct happen after a second or third competitor enters the market. The previous empirical literature has largely not engaged with the effects of entry on dimensions of competition other than prices. In addition, a feature of the broadband industry, and the data we utilize, is that there are objective and directly observed measures of quality: advertised maximum download and upload speeds, which providers and consumers contract for, and we directly observe in the data.5

Neither of those papers considers the longer term evolution of the market, limiting their analysis to the market state at a given point of time. One of the goals of this article is to assess the impact of changes in market structure and concentration on broadband quality outcomes over the five years from 2014 to 2018. Flamm and Varas (2018) have observed that census tract level data significantly overstate the number of broadband providers from which an individual residential household in that census tract may be able to purchase service. That is why we construct our analysis at the census block level.

### APPENDIX B

#### Connect America Fund Transitional Subsidy Mechanisms, 2014–2018

Connect America Fund (CAF) broadband subsidies were offered by the FCC to a subset of “grandfathered” census blocks that had previously received legacy high-cost voice subsidies. The FCC began this process by analyzing the new and more detailed Form 477 data that became available at the census block level after 2014. (See  Appendix A.) Information on broadband availability tabulated from prior year Form 477 data was available to determine which census blocks within a “high-cost” incumbent local exchange carrier (ILEC) local voice service territory (or “study area”) lacked adequate broadband service, and therefore potentially justified a subsidy offer. The first claims by price-cap ILECs for CAM-based subsidies at the census block level begin the following year, in 2015.

Though simplified somewhat through this process, the distribution of Universal Service subsidy funds remained quite complex. In 2011, there had been no less than seven different support mechanisms distributing “high-cost” subsidies to three different categories of recipient telephone voice service providers. There were the ILECs in rural areas that were subject to “price cap” regulation of their interstate service charges (generally larger national or regional providers, the “price cap” ILECs); so-called “rate-of-return” ILECs (primarily small local telephone companies serving rural areas); and finally, so-called “competitive eligible telecommunications carriers” (CETCs), in rural areas served by all other firms (other than the historical ILECs present at the time of the breakup of the U.S. Bell System telecommunications monopoly in the 1980s).

The cost models used by the FCC to formulate subsidy offers combined engineering cost models with cross-sectional data on census block characteristics. The resulting cost model allowed for variation across blocks in population, housing counts, and spatial density. Based on census block characteristics, incumbent voice service providers were offered model-driven funding levels in exchange for provision of specified broadband speed levels to existing network customers upon request, and to additionally accept an obligation to serve new locations in subsidized blocks if the improved service level could be provided “at reasonable cost.”58 Simple economic logic predicts that profit-maximizing incumbent providers would first accept these subsidy offers for those particular census blocks where net profit after subsidy would have been highest, and decline these offers when the expected net return was too low, or even negative.

From 2012 through 2014, a set of “Phase I” transitional funding mechanisms began shifting both old and new Universal Service subsidy funds to new mechanisms supporting broadband deployment to high-cost areas serviced by price cap ILECs. Initially, approximately $486 million in funding was allocated to Phase I subsidies to broadband deployment (initially defined by a 4 Mbps download/1 Mbps upload standard, relaxed to a 3 /.768 standard after the initial phase of funding) to about half a million high-cost locations lacking “broadband” (defined by speeds at or exceeding .768/.200). Acceptance of the funding (and service obligations) by the ILEC was voluntary, with frozen “legacy” high-cost support available as an alternative. From 2015 on, the “price cap” ILECs were offered “Phase II” support based on an FCC cost model for broadband provision (the “Connect America Model,” or CAM) on a voluntary basis. The Phase II support obligated price cap ILECs to deploy a minimum 10/1 broadband standard to census blocks lacking an unsubsidized competitor offering broadband service. Frozen legacy voice subsidies to price cap providers in high-cost areas were cut sharply during Phase II.59 Most recently, after 2018, the FCC added significant funding ($1.5 billion over 10 years) to support eligible providers with winning bids in a new reverse auction mechanism. Providers bid the subsidy value at which they would accept broadband service obligations in specified high-cost “price cap” census blocks in 45 states. These were an FCC-defined list of census blocks where either the Connect America Model support had been declined by the incumbent price cap provider, or where costs were deemed “extremely high,” or blocks had been removed for other reasons from the FCC’s previous CAM offers to price cap carriers. This auction, completed in late 2018, was known as the Connect America Fund (CAF) Phase II auction.60

The new reverse auction mechanism is a significant qualitative break from previous subsidy mechanisms. It subsidizes new entrants into high-cost census blocks where an incumbent ILEC had declined a previous FCC subsidy offer. Previously, only incumbent voice services providers had been offered subsidies in exchange for broadband service provision commitments in high-cost areas.

The second large group of incumbent voice service providers serving rural high-cost areas, the rate-of-return carriers, continued to have access to subsidies at frozen legacy levels through 2016. Beginning in 2017, rate of return carriers in high-cost areas were offered substantial new subsidies for broadband deployment, as a voluntary option, based on another FCC cost model (the “Alternative Connect America Model,” or ACAM). Service speed obligations ranged from under 4/1, to 4/1, to 10/1, and up to 25/3, and were expected to bring broadband to 714,000 new locations by 2026.61 Legacy universal service support claims from rate-of-return carriers began to decline after the ACAM broadband offers started in 2017.62

## FOOTNOTES

1.

We think of a higher “quality” product or service in the context of models of vertically differentiated products, with a set of vertically ordered characteristics such that all consumers prefer higher quality to lower quality at a given price, with at least some consumers willing to pay more than others for a higher quality product.

2.

We note, however, that it was plausibly argued that the typical U.S. household in pre-COVID markets would make relatively little use of broadband bandwidth over about 100 megabits per second. See for example S. Ramachandran et al.

3.

The canonical model of vertical product differentiation is “a three stage game in which a number of firms choose firstly, whether to enter an industry; secondly, the quality of their respective products, and thirdly, their prices” (Shaked and Sutton, 12). In the abstract, we think of the sequential stages of this game as the long, medium, and short runs.

4.

Appendix A briefly reviews the history and limitations of the FCC Form 477 data we are using to measure both market structure and service quality;  Appendix B summarizes the Connect America Fund program.

5.

Published data on the relationship between advertised speeds and delivered speed, by ISP, based on a small national sample of households, has been reported publicly by the Federal Communications Commission since 2011. See https://www.fcc.gov/general/measuring-broadband-america.

6.

In the Communication Marketplace Report, the FCC analyzes services of delivering voice, video, audio, and data services, among others.

7.

A limitation in Whitacre and Gallardo is that they use county-level data for their analyses.

8.

Wilson. Also studies multimarket contact effect on download speeds offered by ISPs.

9.

Molnar and Savage. Use variables related to the sunk cost of entry as excluded instruments. In particular, they use the number of roads, intersections, houses, and bedrock and wetland terrain measures. These variables could also directly affect the cost of offering or upgrading to higher speeds for both incumbent and entrant ISPs. In addition, they assume joint normality with zero covariances between unobservables affecting broadband ISP entry and quality choice.

10.

Roughly 1,200 urban census blocks out of the 1.4–1.6 million used in our analysis. (Numbers of census blocks used in the statistical analysis vary slightly with availability of covariates included in the model.) For a brief history of CAF funding, see FCC, “In the Matter of Connect America Fund,” Report and Order, FCC 14-190, W.C. Dockets No. 10-90, 14-58, 14-192, December 2014. In 2011, the FCC issued a report and order transforming the process by which Universal Service Fund subsidies of communications services to “high-cost” areas would be undertaken. While previously voice telephone service to (mainly) rural households had been supported by subsidies from Universal Service funds (generated by fees paid by all U.S. voice service purchasers), the Commission now expanded subsidies to include both voice and broadband service in so-called “high-cost” areas. Accompanied by extensive public comment—and litigation—a subsequent 2014 report and order set out the specifics of how this reformed system was to operate. A summary of the transitional mechanisms for this new set of subsidy mechanisms—the Connect America Fund (CAF)—is given in  Appendix B.

11.

“Each year, the FCC conducts a survey of the fixed voice and broadband service rates offered to consumers in urban areas. The FCC uses the survey data to determine the local voice rate floor and reasonable comparability benchmarks for fixed voice and broadband rates for universal service purposes.” https://www.fcc.gov/economics-analytics/industry-analysis-division/urban-rate-survey-data-resources.

12.

“See April 2014 Connect America Order, 29 FCC Rcd at 7070-75, paras. 59-72. C.f. 47 CFR § 54.202 (requiring any carrier petitioning to be federally-designated ETCs [Eligible Telecommunications Carriers] to “[c]ommit to provide service throughout its proposed designated service area to all customers making a reasonable request for service” and to certify that it will provide service on a timely basis’ to customers within its existing network coverage and within a reasonable time’ to customers outside of its existing network coverage if service can be provided at reasonable cost).” (FCC, 68)

13.

The currently novel, more competitive CAF Phase II reverse auction mechanism mentioned in  Appendix B did not come into use until after the end of our sample period.

14.

https://www.fcc.gov/general/broadband-deployment-data-fcc-form-477. Note that prior to 2014, service providers only reported their data for areas in which they actually had subscribing customers. See Flamm and Varas. For a detailed discussion of the 477 data definitions and how they have changed over time, along with an analysis of historical trends in competition in local markets.

15.

The problem is particularly noticeable in Texas, where wireless providers seem to pop up in urban areas for a year or two, then exit from the FCC dataset. The metropolitan Dallas area has one such “phantom” wireless provider popping up in the FCC Form 477 records as serving most of the Dallas area for a short period, before vanishing with no evident trace. One of the authors’ homes is in a census block in the Austin suburbs (literally a single suburban city block) which has long been a good example of the classic cable-DSL duopoly. For a couple of years in the middle of our study period, a wireless provider apparently reported serving this block on its Form 477 but appears to have made no active attempt to win any customers or even advertise availability of its service to residents, to the best of our knowledge. A likely but problematic scenario leading to such misleading reporting would be a wireless service provider setting up an antenna on high ground in a rural area on the periphery of a city in order to serve primarily rural customers, then reporting all city blocks within line-of-sight of this antenna, as determined by some automated computer calculation, as “offered service” on FCC forms.

16.

There will be two separate records if the service is available for both residential and business consumers.

17.

The FCC’s Mobile Deployment Data is also collected twice per year by the FCC through the Form 477. For each data collection period, each mobile broadband provider must report all geographical areas where they can provide service. A major difference from the Fixed Deployment Data is that the mobile data is reported using shapefiles, so covered areas can be smaller (or larger) than census blocks. In this data, each provider is identified by its “Doing Business As” (DBA) name. Providers have to report a shapefile for each technology they use to offer service (e.g., WiMAX, LTE, etc,). The Mobile deployment data is also available every six months from December 2014 to December 2018, except for the period June 2015.

19.

The 2014 vintage baseline value is embedded in a census block fixed effect.

20.

Costquest is an FCC contractor that constructed estimates of census block level costs per household for building out a greenfield fiber network in telephone operating company territories using a highly detailed engineering model combined with demographic, geospatial locational, and other data. These cost estimates were designed to be used by the FCC in formulating its CAF subsidy offers. The bulk of these network costs is the building of physical optical network infrastructure plant (installation of conduits and fiber cables in trenches, or fiber cables on poles).

21.

This data was briefly made available to the public for download on the Costquest web site from roughly mid-2019 through mid-2020. These data are, unfortunately, no longer available on the Costquest web site.

22.

This index was originally developed by Riley et al. (1999) and measures the amount of elevation difference between adjacent cells of a geographical grid.

23.

The geographical terrain ruggedness is described and available for download at https://diegopuga.org/data/rugged/.

24.

Conversation with FCC data administrator, Washington, D.C., September 2018.

25.

Because of the change in Form 477 definition of areas served (from actual customers pre-2014, to “could serve”), in most urban markets and even in many rural census blocks there was a noticeable uptick in numbers of providers after 2013 (typically, by two or more providers). Much of this increase was related to satellite‐based ISPs now being included in the ISP counts, even for urban census blocks in which they rarely if ever sold a competitive service offering to paying customers. (The theoretical service footprint of the major satellite‐based ISPs covers most of the continental United States) The FCC clearly took note of this, since beginning in 2014, publicly released data on ISPs by census block distinguish between counts including and excluding satellite‐based service providers.

Also, while satellite ISPs can provide upload and download speeds for digital content comparable to fixed terrestrial broadband service, latency (the round‐trip time to send, then receive a single digital packet) is about 20 times greater with satellite service. This significantly affects interactive applications, like gaming, or point and click applications (like moving or zooming dynamically on a map or menu). Based on stated preference survey data, one study estimates that a representative consumer would be willing to pay $8.66 monthly to avoid the increased latency associated with moving from terrestrial to satellite broadband service, at a given download/upload speed. Liu and Wallsten. 26. If a provider does not offer service with a given technology set (either wireline or wireless), then its maximum advertised speed is defined as missing, not zero. 27. Although we do not observe the share of households served by various individual providers within a census block, the inverse of our market structure measure (1/N)—with N the sum of the provider counts across technology types—defines a lower bound on the Herfindahl–Hirschman Index (HHI) of concentration (with 1 defined as the upper bound HHI were all households in a block served by a single provider). 28. Using the FCC’s mobile deployment data, we can also calculate analogous mobile broadband market structure measures for each successive mobile technology standard (which is also associated with significant service speed improvements). In particular, we have defined market structure measures for 2G or better and 4G or better mobile broadband data service providers. These measures are cumulative: our 2G count includes providers offering 2G or better service, while our 4G count covers 4G or better service. So a 4G provider, for example, would be included in both 2G, and 4G provider counts, while a 2G-only provider would be included in the 2G count, but not in the 4G count. 29. The U.S. Census latitude and longitude coordinates for a census block interior point are effectively the centroid, modified to lie within the census block boundaries for unusual block shapes where it would otherwise not. 30. Because the raw data is in shapefile format, we use the user-written Stata command geoinpoly to count how many providers provide mobile broadband service in each census block (Picard). 31. “Pure fiber” because in urban areas today, most ISPs use some amount of fiber in their local distribution networks. We use “pure fiber” to describe an ISP connecting to homes in a census block only using fiber, that is, not also using legacy cable/DSL technology to connect to other homes in the same census block, which would indicate a legacy network that is being upgraded to fiber-to-the-home (FTTH). 32. See Figure 1 in Flamm and Varas (2021). 33. For empirical support for this assumption, see Trostle et al., Ford (2019), US General Accountability Office (2018), and US General Accountability Office (2020). 34. If true, this would make it reasonable (after conditioning on observed covariates) to assume that remaining unobservables affecting an ISP’s choice of maximum service speed (as embedded within a statistical disturbance term) are uncorrelated with the CAF subsidy variables, allowing us to think of the CAF subsidy coefficients as measures of another type of causal “treatment” effect. 35. Availability of nonmissing values for other covariates used in different model specifications resulted in from 1.4 to 1.6 million census blocks being used in statistical models. 36. Underlying this correction is the default assumption that “old legacy wireline ISP networks exiting just as fiber enters” is generally a sign of a speed upgrade to an incumbent legacy network. So, for example, if unadjusted number of legacy ISPs declined from 2 to 1 and fiber ISPs increased by 1 (the most common situation with apparent legacy ISP exit in the actual FCC data) from 2014 to 2018, number of “upgrade fiber” ISPs was coded as 1, and “pure fiber” ISPs as 0. Alternatively, if the number of legacy ISPs declined from 2 to 1 and fiber ISPs increased by 2, the number of “upgrade fiber” ISPs in 2018 would be coded as 1, and “pure fiber “entrant” ISPs as 1. Maximum speeds offered by either “pure fiber” or “upgrade fiber” ISPs were not considered when calculating maximum legacy speeds for a census block. Note that because of a very considerable level of merger and acquisition activity over the 2014–2018 period, frequent changes from year-to-year in holding company names, and the existence of a large number of small, privately held ISPs, it is quite challenging to link ISP Form 477 ISP holding company names from one year to the next. Using aggregate statistics on numbers of ISPs, by network technology, to indirectly measure entry and exit is a next best alternative. 37. This simple specification implies that the effect of any treatment is approximately proportional to dose size (count), with no treatment equivalent to a zero dose. 38. The total effect of an upgrade to fiber by a single legacy ISP in a census block, in this framework, adds the effect of +1 dose of the legacy ISP “upgrade fiber” treatment to the effect of a −1 dose of legacy ISP count change treatment. 39. Table 2 is restricted to census blocks for which all variables used in the regression analysis are available. 40. The 16 included block group level demographic/economic demand shifter variables from the ACS were: total block group population, percent male population, non-Hispanic/Latino, non-Hispanic Afro-American, American Indian, Asian American, other non-Hispanic, below the poverty line, receiving public assistance income, has a high school degree education, has a college degree or better education; median age, median family income, median housing value, percent of housing units that are occupied, percent of population with no telephone service. The RAC variables used include job shares by categorical age range, by race category, by educational attainment category, by ethnicity category, and by gender category of employees resident in a census block. 41. Absent this truncation, we would have had very small numbers of census blocks in high entry count categories. 42. In our current data set, we only measure year of entry or exit, and are unable to specify month of entry within the year. In addition, particularly for smaller, nonpublicly listed ISPs, we would be unable to distinguish mere ownership name changes from bona fide entry/exit. 43. Effects of entry are additive in this specification—the difference in effect between 2 (the omitted baseline level) and 4 or more ISPs in a census block would sum the coefficients of I_L3 and I_L4. I_L3 is the difference in effect between 2 (the omitted baseline level) versus 3 legacy ISPs; I_L4 is the difference between 3 vs. 4 or more ISPs. 44. The fourth column under each model in Table 3 also portrays a specification with the wireless entry market structure variable even more truncated, to a single entry category: one or more fixed wireless entrants. 45. We see about 30,000 census blocks in our sample (the entire treatment group numbers about 370,000 blocks) go from two legacy providers to a single legacy provider. Many of these appear to be AT&T, Windstream, Centurylink, Verizon, and other legacy DSL ISPs abandoning any attempt to sell their oldest and slowest DSL service to new customers in selected census blocks they do not wish to make further investments in, or owners of older cable infrastructure (like Wehco Video, Mediacom, Cogeco, Vyve, Wavedivision, West Alabama TV Cable) either ceasing operation or abandoning sales of broadband service to new customers in selected urban census blocks (as uneconomic, presumably). 46. We see almost 50,000 census blocks in our sample with new legacy entry. In some cases these appear to be resellers connecting to existing legacy infrastructure (in particular, DSL circuits connecting to local incumbent telephone company networks), while in other cases spatially adjacent competitors seem to be “overbuilding” into another ISPs territory. For example, in about 4,000 urban census blocks, WOW! and Verizon were replaced by WOW!, Charter, and Frontier. In roughly 8,000 urban Tennessee census blocks, AT&T and either Comcast, Charter, or Infostructure faced new competition from Ecsis using DSL or a combination of DSL and wireless. In roughly 3,300 urban census blocks, Comcast and AT&T were joined by Telephone Electronics Corporation. In California, resellers like Sonic and Raw Bandwidth connected customer premise equipment to leased AT&T local network infrastructure and competed successfully with both AT&T and a local cable ISP. 47. See Bresnahan and Reiss. Four or more competitors brings a smaller speed increment and larger p-value. 48. A reduction of −52.6 Mbps is our point estimate, with a p-value of .015. One scenario as to why a fiber upgrade by a legacy ISP might reduce observed 2018 maximum available speed in an urban block is that an upgrade to fiber by one of the legacy ISPs reduces the probability of a nonincumbent fiber-based ISP entering a census block (supposing that upgrades to maximum offered speeds in “traditional” legacy duopoly blocks are in part undertaken to preempt possible entry by fiber-based nonincumbent ISPs). Already having fiber available may make a market less attractive for a nonincumbent fiber ISP considering entry, somewhat reducing the preemptive motive to upgrade maximum speeds on the part of incumbent legacy ISPs. 49. Kotrous and Bailey. One scenario that may explain this result is that many of the “single fiber entrant” blocks could be incumbent DSL providers switching the entirety of their internal network within a census block to fiber to the home, thus looking like a “pure fiber” entrant in 2018, while simultaneously reselling use of their obsolete copper telephone lines to third party DSL ISPs. This could lead to an average effect that is close to zero in “single fiber entrant” blocks—since fiber upgrades by legacy ISPs would not be detected by our legacy-to-fiber-upgrade coding algorithm in this case, and we would be averaging negative (DSL upgrade to fiber) and positive (bona fide pure fiber) effects. 50. As previously noted, measurement error in fixed wireless ISP counts is likely an issue in our data, and the usual classical measurement error arguments suggest that this would bias this coefficient toward zero, that is, the coefficient of a properly measured wireless ISP count variable would be more negative. 51. CAF money was allocated using “study areas” derived from telephone exchange boundaries. We are not surprised to find that roughly 1200 urban duopoly census blocks received some CAF funding as a consequence. The definition of these blocks as urban is the 2010 Census Bureau classification. We have estimated our model after dropping the roughly 1,200 CAF-recipient blocks, and find no significant or substantive change to coefficients or their estimated standard errors. We prefer to report the model with the blocks included, because with roughly 300 observations per estimated CAF parameter, we find the coefficients’ signs, size, and significance interesting, provoking useful thought as to what may be going on here. 52. Lechner, 182–184. 53. Many readers will be familiar with the adoption of new DOCSIS 3.0 and 3.1 standards that raised legacy cable network speeds over this period. U.S. readers will be less familiar with new, higher speed DSL standards that were introduced in 2006 (VDSL2, up to 200 mbps), 2015 (VDSL2 Vplus35b, up to 300 mbps), and 2014 (G.fast, with speed of 500-1000 mbps over distances <100 meters, 1000 Mbps over distances of 50 meters). Commercial equipment incorporating these newer ITU standards was offered by multiple vendors. Because speed drops rapidly with distance, the main economic use case for this technology is in short wire runs from fiber-connected pedestals or cabinets to individual homes or multi-unit dwellings with embedded interior twisted-pair copper telephone wiring. It seems to have been adopted in urban areas in Europe on a reasonable scale. It was also adopted in the US, particularly in dense urban use cases, as announced publicly by Centurylink, AT&T, and Frontier (see, for example https://www.fiercetelecom.com/telecom/gfast-passes-over-3m-premises-33-providers-advance-roll-outs-trials-says-analyst). AT&T marketed its use of VDSL2 and G.fast under the umbrella term “IPBB” technology, though it limited use to guaranteed speeds far lower than those available with shorter cable runs than AT&T apparently has standardized on it on US service territory. (“IPBB includes ADSL2+, VDSL2, G.Fast and Ethernet technologies delivered over a hybrid of fiber optic and copper facilities which provides subscribers with significantly faster download speeds compared to traditional DSL connections.” See https://about.att.com/sites/broadband/performance.) While deployment in the US has been much more limited than in Europe and Asia, use of these faster DSL technologies nonetheless shows up in reported speeds on the FCC Form 477 data for “pure” DSL ISPs. Generally, in less dense US urban markets, the economics seem to have favored former DSL ISPs generally increasing speeds by deploying fiber connections to homes. 54. For statistical evidence on dramatic variations over time in historical quality-adjusted price decline rates for communications hardware, see Flamm (1989), Gordon, Flamm (1999), Byrne and Corrado. 55. The history of FCC’s Form 477 program is summarized in FCC, Modernizing the FCC Form 477 Data Program, Final Rule, published in the Federal Register on August 13, 2013. The FCC originally published this data in the form of a list of zip codes in which broadband service provider reported any customers, and the number of providers reporting customers in the zip code (with numbers of providers in the 1 to 3 range per zip code censored, and reported as an ‘*’. Unfortunately, zip codes with no end users reported were not shown in the public list, which made these data of limited utility to researchers and policymakers, since zip codes were created and withdrawn by the postal service frequently, and zip codes were never actually used to define stable spatially defined areas. Researchers attempting to use these data were forced to devise ad hoc schemes to associate postal zip codes with Zip Code Tabulation Areas (ZCTAs) defined by the Census during decennial census years, in order to try to figure out what areas had no service at all. After 2004, the FCC began reporting “zero provider” zip codes, which enabled researchers to at least enumerate the universe of zip codes being considered by the FCC for its public reports. 56. FCC (2013). The speed tiers reported by mobile wireless providers appear to have corresponded to what generation (2G, 3G, 4G non-LTE, 4G LTE) of mobile wireless technology was available to serve customers in a census tract. 57. The National Broadband Plan actually set a 4/1 Mbps benchmark, but the 3/.768 Mbps tier in the National Broadband Map that was being constructed at the time was the closest speed tier to this benchmark, and ended up becoming the original “NBP broadband speed.” 58. “See April 2014 Connect America Order, 29 FCC Rcd at 7070-75, paras. 59-72. C.f. 47 CFR § 54.202 (requiring any carrier petitioning to be federally-designated ETCs [Eligible Telecommunications Carriers] to “[c]ommit to provide service throughout its proposed designated service area to all customers making a reasonable request for service” and to certify that it will provide service “on a timely basis” to customers within its existing network coverage and “within a reasonable time” to customers outside of its existing network coverage if service can be provided at reasonable cost).” FCC, REPORT AND ORDER, ORDER AND ORDER ON RECONSIDERATION, AND FURTHER NOTICE OF PROPOSED RULEMAKING, FCC 16-33, WC Dockets No. 10-90, 14-58, 01-92, March 2016, p. 68. 59. See FCC, Universal Service Monitoring Report 2018, Table 3.3. The CAM support to price cap carriers was reasonably large— set at$1.7 billion annually over six years and expected to result in deployment of 10/1 broadband to 3.5 million locations by 2020, See V. Gaither, p. 7.

60.

See FCC, “Connect America Fund Phase II Auction (Auction 903),” available at https://www.fcc.gov/auction/903.

61.

The broadband service obligations were complex. “Carriers who elected this option will have the certainty of receiving specific and predictable monthly support amounts over the 10 year support term (2017–2026). Those that elected model support must maintain voice and existing broadband service and offer at least 10/1 Mbps to all locations fully funded by the model. They must also offer at least 25/3 Mbps to a certain percentage of those locations by the end of the support term. In addition, carriers must also offer at least 4/1 Mbps to a certain percentage of capped locations (where caps on ACAM subsidy levels were in effect) by the end of the support term, and provide broadband upon reasonable request to the remainder.” https://www.usac.org/hc/funds/acam.aspx. See also Gaither, p. 8.

62.

See FCC, Federal-State Joint Board on Universal Service, Universal Service Monitoring Report 2018, Table 3.2.

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