Project Overview

Our Motivation

New York City's 2020 Internet Master Plan found that there are 1.5 million New Yorkers who do not have any means to connect online. And as the COVID-19 pandemic highlighted, a reliable broadband internet connection is increasingly becoming crucial utility for New Yorkers' economic well-being. For these reasons, New York City's Department of Information Technology and Telecommunications (DoITT) wants to understand if the current telecommunication industry regulations are effective at generating a competitive industry environment that can provide cheap and reliable internet access to all New Yorkers.

Our Goal

The team set out to discover if there is an optimal number of ISPs for the New York City market that would improve Internet accessibility in terms of coverage area, household adoption rates, and affordability for all New Yorkers. The aim is to produce a policy white paper that explains NYC’s broadband market structure, the factors influencing availability of broadband internet and subscription rates, and how DoITT can optimize the regulatory framework.


Project Data

NYC Dept of Info Tech and Telecom Logo
Broadband Pricing Data

Source for pricing information for both land and mobile internet service offered per company at the borough level.

Federal Communications Commission Logo
Form 477

Survey containing the internet service providers (ISPs) that are found within each Census block, the type of infrastructure (e.g. fiber, cable) used to deliver internet service, and their maximum advertized internet speeds.

American Community Survey by US Census Logo
US Census | American Community Survey (ACS)

The ACS contains demographic information such as race, income, and age, and broadband internet subscription rates, all at the Census tract level.

NYC Open Data Logo
NYC Open Data

Neighborhood Tabulation Areas (NTAs) shapefiles, Census block shapefiles, and locations of telecommunications infrastructure were used enrich our analysis of NYC.
We also referenced NYC's Internet Master Plan for guidance.


Methodology


Results

Currently only two thirds of NYC households have access to a wired broadband connection. We created this dashboard to appreciate in red the Census tracts with broadband internet adoption rates lower than 66% (i.e. two-thirds).

Visually we can see that the Bronx as a whole struggles with low adoption rates, whereas the other boroughs have smaller more concentrated hotspots like in Williamsburg, the Lower East Side, and Borough Park. Additionally, to add more color to this visualization, it includes information about income rates and the number of unique internet service providers (ISPs) found in eacn Census tract.

As we can discover through the dashboard, areas with the most disparaties in broadband subscription (Wired Broadband < 0.66) come from areas with low income (00-50k> 0.50), while the number of ISPs don't seem to have much of an impact on broadband adoption rates.

At the scale of census tracts, internet service infrastructure displays strong signs of spatial autocorrelation. There are clusters of spatially contiguous census tracts that share similar levels of infrastructural provision in terms of price and the number of ISPs that is beyond randomness.

It is more likely than not that these spatial patterns seen in Figures 1 and 2 are not randomized outcomes.
The broad swathes of contiguous census tracts that are similarly clustered together imply that there are network effects at a scale greater than census tracts.

Here's a legend explaining the assigned values on our figures below:

Not Sig
Statistically insignificant traces of spatial autocorrelation
High-High
Census tract with above-average value bounded by tracts with equally above-average values
Low-High
Census tract with below-average value bounded by tracts with above-average values
Low-Low
Census tract with below-average value bounded by tracts with equally below-average values
High-Low
Census tract with above-average value bounded by tracts with below-average values

Spatial Correlation for Broadband Prices
Spatial Correlation for Unique ISPs Found

Much like the Pricing Regression results of the supply and demand of ISPs, the Spatial Regressions showed that income levels were significant variables in determining broadband subscription rartes.

Census tracts with more wealthier residents are associated with higher subscription rates. More interestingly, the number of ISPs was negatively correlated with subscription rates. These findings might imply that in the long-run, an increase in the number of ISPs can result in market fragmentation that reduces subscription rates.

The pricing regression identified a significant negative correlation in the demand side, and a significant positive correlation in the supply side. These result agree with microeconomic theory that states that higher prices for a good leads to fewer people consuming it.

Furthermore the results showed that race and borough were factors of significant influence on the price of broadband internet.

By combining the demand and supply curves calculated using the regression results, we found the market equilibrium to be around 5 broadband providers, while the equilibrium price is around $70 per month.

NYC Internet Supply/Demand Model

In NYC the Herfindahl-Hirschman Index score for the telecoms industry is of 3,096.
Following the guidance of the U.S. Department of Justice & Federal Trade Commission, the NYC telecom market crosses the 2,500 threshold to classify it as an oligopolistic market.

Therefore, the NYC telecom industry is considered an oligopolistic market.

Herfindahl-Hirschman Index Results

The Bayes Net results are in line with our other analysis: that broadband internet subscription rates are a function of income.
The network identified that neighborhoods with greater numbers of white residents tend to be richer and have more unique ISPs. Conversely, areas with less people making more than 150K tend to have more people making below 50K per year. It is these poorer Census tracts that also have the lowest rates of Wired Broadband rates.

Bayes Network Internet Assess Results

Sentiment analysis (as pictured below) determined that more than half of all the collected tweets were negative (e.g. complaints). This indicates that in most cases users tweet about their ISP when there is a problem with their service.

Text Sentiment Breakdown

On a provider level across time, tweet sentiment grew positive (towards value of 1) between 2013 and 2018, and then decreased between 2018 and 2020. This indicates that between 2015 and 2018, consumers may have been more satisfied with their service, as they tweeted less about it.

Tweets Sourced By Provider

However there were fewer tweets between 2015 and 2018 as compared to other years. Verizon, AT&T, T-Mobile, & Sprint accounted for a vast majority of the tweets collected.

Tweets Sourced by Provider and Year

Considering the tweets which display extremely negative sentiment, the word cloud below shows what words are commonly used in these tweets.

Mobile service is the most commonly mentioned kerword/phrase in negative tweets. Some of the other significant keywords (e.g. internet, data, wifi, cable, network, speed, 3g, signal, LTE, slow) show that most people complained about the performance aspect of their internet service (connectivity, accessibility, speed). This could be a symptom of the oligopolistic market, where companies do not have enough incentive to maintain high internet service quality.

Tweet Wordcloud

These result suggests that government agencies and public policy should not only focus on the quantity of providers in NYC but also on how to address service quality.


Recommendations

Negotiate
NYC government can negotiate with ISPs to provide broadband plans at wholesale prices below market rates.
Subsidize
NYC government should extend relief to New Yorker households making less than US$20,000 annually, means-testing & capping prices to 0.5% to 2% of monthly income. This segment represent 46% of households without an internet subscription, or about 231,000 households.
The goal is to alleviate the price burden on those who want/need broadband service but are limited by their income.