The Economic Impact of Gig Networks

Transcription

1 The Economic Impact of Gig Networks Sudip Bhattacharjee Associate Professor School of Business, University of Connecticut Moving Towards A Gigabit State: Planning and Financing Municipal Ultra High Speed Internet Fiber Networks Through Public Private Partnerships Yale School of Management May 04, 2015

2 Does broadband lead to economic development? YES NO Really? Sudip Bhattacharjee, UConn 2

3 Broadband follows income 5 years data from 169 CT towns ( ) Median household income Connections per 1000 hhs BTOP per 1000 hhs Download speed Upload speed Sudip Bhattacharjee, UConn 3

4 Causality tests (CT data) Connections per 1000 hhs BTOP per 1000 hhs p=0.04 p=0.01 Median household income Connections per 1000 hhs BTOP per 1000 hhs Download speed Upload speed p=0.07 p=0.03 Household income leads to more connections and faster download/upload speed Sudip Bhattacharjee, UConn 4

5 High wages rely on good broadband speeds Connections per 1000 hhs BTOP per 1000 hhs Connections per 1000 hhs BTOP per 1000 hhs Download speed Upload speed Median household income Education expenditure Population Total industry units p<0.001 p<0.001 p<0.001 Average industry wage (per industry type) So that is the good news!! Sudip Bhattacharjee, UConn 5

6 Google Fiber Sudip Bhattacharjee, UConn 6

7 Sudip Bhattacharjee, UConn 7

8 How can we identify CT towns? Data driven analysis UConn students in Masters in Business Analytics and Project Mgmt msbapm.uconn.edu Semester long capstone project 4 teams of 5 members each Demographic, economic, geographic data Results are a composite of several teams Sudip Bhattacharjee, UConn 8

9 Solution Framework 70 80% of work effort Raw Data From Online Public Library Demographics (CERC) Housing (CERC) Occupation (DOL) Broadband (FCC) Processed, Cleaned and Merged data Exploratory Data Analysis Missing value treatment. Variable Selection /Exclusion Merge to obtain data at unique CT town level. Identified key variables Identify key economic benefits assessing the impact of broadband Per capita Income Median Housing Price Number of Business Units Summary of Model Output for Business Use Grouping and Ranking of Towns based on benefit Analytical Models to estimate economic benefit for CT towns Sudip Bhattacharjee, UConn 9

10 Data challenges Disparate sources FCC, US Census Bureau, Connecticut Economic Resources Center (CERC), US Dept of Labor, VisitCT.org, broadbandnow.com, CT Dept. of Housing, CT Dept. of Public Health, others Data matching/integration issues Census block, tract, zip code, town Missing data FCC: Broadband providers: 1, 4, 5, 6, 7, 8, 9, 10, Price of broadband Sudip Bhattacharjee, UConn 10

11 Impact of broadband Five years after each 1 Mbps increase in internet speed (up to 60Mbps) results in the following average economic gains: Unemployment rate drops by.08% Bachelor degree rate increases by.42% Median household income increases by $570 Average home value increases by $3,200 Assisted housing unit decreases by 200 Sudip Bhattacharjee, UConn 11

12 Cluster economic benefit rankings Sudip Bhattacharjee, UConn 12

13 Town Rankings Rank all 169 CT towns by their likelihood to attract new business activity Data used: Cluster economic index, CERC s town level data, CERC s listings of office space and land available as of 4/10/15, economic stimulus zones as of 4/10/15, locally available business development assistance office, and town and regional area attractions as compiled by CT office of Tourism as of 4/6/15. Analytic Methods employed: Stepwise regression, correlation analysis, and subjective weighting of the final set of factors/variables. Results of Top 10 Sudip Bhattacharjee, UConn 13

14 Results for Town Rankings Sudip Bhattacharjee, UConn 14

15 Top 30 Towns Case Mbps When increasing Broadband speed (Down/Up) from 50/10 to 100/25 Mbps Impact of BB IT Labor force Tax revenue Norwalk 8% 11% Bristol 8% 8% Windsor Locks 29% 32% Bristol moved up from # 24 to # 15 Windsor Locks moved up from # 17 to # 9 Danbury moved up from # 28 to # 25 Represents overall town ranking in CT = # 20 New London moved down from #1 but then remains at #11 Norwalk moved down from #4 to #8 With increase in broadband, the rate of improvement will be faster for some of the towns. Case in point, Bristol and Danbury. Longerterm benefit will be seen in these cases.

16 Top 30 Towns Case Mbps When increasing Broadband speed (Down/Up) from 100/25 to 200/50 Mbps Impact of BB IT Labor force Tax revenue Norwalk 13% 24% Bristol 15% 19% Windsor Locks 45% 47% Windsor Locks moved down from # 9 to #14 Bristol moved up from # 15 to # 10 Represents overall town ranking in CT = # 21 Danbury moved up from # 25 to # 12 Middletown moved up from #84 to #29 New London and Groton remained at #11 and #28 respectively Norwalk moved up from #8 to # 6 With increase in broadband, the rate of improvement will be faster for some of the towns. Case in point, Bristol and Danbury. Longerterm benefit will be seen in these cases.

17 Top 3 / Bottom 3 Towns by County Case Mbps When increasing Broadband speed (Down/Up) from 10/2 to 50/10 Mbps Small bubbles = Bottom 3rank in county Large bubbles = Top 3 rank in county Represents overall town ranking in CT = # 5 Sudip Bhattacharjee, UConn 17

18 Top 3 / Bottom 3 Towns by County Case Mbps When increasing Broadband speed (Down/Up) from 50/10 to 100/25 Mbps Remains as best town in Litchfield county throughout Remains in bottom 3 throughout Retained Top 3in county throughout Remains in bottom 3 through out Remains in bottom 3 throughout Remains in bottom 3 throughout Remain best town in Middlesex county throughout Remain best town in New London county throughout Small bubbles = Bottom 3rank in county Represents overall town ranking in CT = # 8 Large bubbles = Top 3 rank in county Sudip Bhattacharjee, UConn 18

19 Top 3/Bottom 3 Towns by County Case Mbps When increasing Broadband speed (Down/Up) from 100/25 to 200/50 Mbps Retained Top 3in county AND overall rank up from #55 Remains in bottom 3 throughout Retained Top 3in county throughout Remains in bottom 3 through out Remains in bottom 3 throughout Small bubbles = Bottom 3rank in county Remains in bottom 3 throughout Remains best town in Middlesex county throughout Remain best town in New London county throughout Represents overall town ranking in CT = # 6 Large bubbles = Top 3 rank in county Sudip Bhattacharjee, UConn 19

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