Research project proposal for BARI (Boston Area Research Initiative ) on the co relation between income inequality due to change in ride sharing economy due to the launch of self driving cars in Boston.

1. Research Project Proposal
Mehnaz Maharin| Analytics | Northeastern University
Rationale
Being a part of the fourth industrial revolution, autonomous vehicles (AVs) represents a potentially
disruptive and beneficial change in the transport system in Boston. Boston is the earliest adopter
of testing self-driving cars on public streets. Companies like nuTonomy and Optimus that test self-
driving cars in the area already operate in Boston, as well as car sharing companies like Lyft and
Uber that also participate in the self-driving car movement. According to the Bureau of Labor (US
Census Bureau, 2019) there are 28,020 public transportation drivers in Boston and Cambridge
including bus, train, taxi, chauffeurs and 10,000 Uber and Lyft drivers. My hypothesis is these jobs
will experience a massive threat from the autonomous car industry in the future, which will lead
Boston into more income inequality. According to Intel Corporation in a Strategy Analytics report
announced in June 2017 (‘Intel Predicts Autonomous Driving Will Spur New 'Passenger Economy'
Worth $7 Trillion’, 2017) the economic effects of autonomous vehicles will total $7 trillion in
2050 world-wide. According to CBS news (Picchi, 2018), Boston is currently ranked seventh
among the cities with the highest income inequality, therefore the possibility of increasing income
inequality is also going to effect the overall economic scenario as well as the changes in automotive
industry revenue in Boston. According to Hideaki Tomita (2017), Professor of Economics at
Hiroshima University, private car ownership will drop and car sharing facilities will be more
feasible for greater masses of people. The 64,110 employees in transportation and warehousing
(US Department of Labor-Occupational Employment Statistics, May 01,2018) in Boston might
decline in number. This change might have an effect on auto car sales and loan debt in the city as
well.

2. Problem Statement
I propose to conduct a unique data analysis on the relationship between income inequality and
autonomous vehicle expansion in order to address the upcoming change in transportation and
urban mobility in Boston. I will:
● Analyze the hypothesis of non-linear relationship between income inequality and
innovation.
● Create visual representations of Boston’s income inequality and employment change in the
industries due to self-driving cars
● Create visual representations of urban mobility changes in the city of Boston due to self-
driving car
The proposed dashboards will be the first one of the kind available for Boston audience.
Purpose of Study
To find out if there is a non-linear relationship between income inequality and technological
innovation in the fourth industrial revolution (specifically automotive cars) and to help
macroeconomic policy makers to come up with smart policies that would help the Go Boston 2030
goal (2018).
Methodology
I will follow quantitative data analysis, archival study and mathematical modeling. I will use
Python to explore and analyze each one of the relevant data sets individually or combined and for
different purposes according to the requirement of my project proposal:
 I will access American Fact Finder - US census bureau data (Data Access and
Dissemination Systems, 2010) for Boston metropolitan area (2017), as well as data for
transportation employment statistics from US labor Statistics (Boston-Cambridge-
Nashua, 2018) for visualization purpose.

3. Table source: American Fact Finder Data- Advanced Search.
 I need detailed data from Uber, Lyft, nuTonomy and Cuebiq by company, age range of
drivers, number of self-driving cars that will be providing services in Boston in the
upcoming years and service time. This data can be provided by the Transportation
Network Company (TNC); TNC adds all the data from ride sharing companies like Uber
and Lyft. At the moment this is their available public data (Rideshare Data Report,2018).
This data reflects the trip origins, average time of ride sharing services, sum of
population, etc.
I am already in contact with TNC division (Sandra Sheng at 1, South Station, Boston, MA02110)
and Analyze Boston (n.d) requesting individual drivers data; just like the data City of Chicago
(2019) published recently, TNC–Boston is also trying to make the data public.
 In order to show the total income earned by ride sharing company drivers across the city,
I will use the study on “Estimate Hourly Expenses by Full Time and Part Time Driver”
(Hall & Krueger, 2017)
 In order to analyze the movement, urban mobility and revenue changes due to self-
driving cars, I will use “Uber’s Movement Platform” (Uber Movement Platform, 2019). I
will use the following data set that contains the "Average Hourly Earnings by Drivers"
(Mazareanu, 2019), in order to generate an average monthly total of hours of traveling
and the income that is generated by ride sharing services in the city.
 I am also going to use data from Cuebiq in order to get both ride sharing individual
drivers data (encrypted) and income inequality data of Boston (How it works, n.d.)
 In order to analyze employment changes in the city, I will use the Employee Earning
Report From 2018 (Analyze Boston, n.d) public data, filtering out by employees who
work in the transportation sector, as well as “Employment Data” from American Fact
Finder - US Census Bureau data (2018) for the Boston Metropolitan area.

4. Deliverable
The main output of this project will generate a website with a unique interactive dashboard using
Python Bokeh library showing the changes and impact over the years, as well as projecting future
changes. Bokeh is an interactive visualization library that targets modern web browsers for
presentation and allows the project to be built on a free platform. The interactive dashboards will
allow the audience to select specific parameters to visualize and make meaning of the data. In
order to have an idea of some of the visual representations of this project, see the examples below:
Figure 1 Figure 2
Source: VisualizingEconomics (2014,2017)
Figure 3
Source: Story Map Journal. (n.d.).
References
Analyze Boston, (n.d.). Retrieved from https://data.boston.gov/
Boston-Cambridge-Nashua, MA-NH - May 2018 OES Metropolitan and Nonmetropolitan Area

5. Occupational Employment and Wage Estimates. (2019, March 29). Retrieved from
https://www.bls.gov/oes/current/oes_71650.htm#53-0000
Clark, Y., B., Larco, Nico, Mann, & F., R. (2017, July 31). The Impacts of Autonomous
Vehicles and E-Commerce on Local Government Budgeting and Finance. Retrieved from
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3009840
Data Access and Dissemination Systems (DADS). (2010, October 05). American FactFinder.
Retrieved from https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml
E.Mazareanu. (2019, July 02). Average hourly earnings of rideshare drivers by number of trips
U.S. 2018. Retrieved from https://www.statista.com/statistics/829093/average-hourly-earnings-
of-rideshare-drivers-by-number-of-trips-us/
Intel Predicts Autonomous Driving Will Spur New 'Passenger Economy' Worth $7 Trillion.
(2017,June 01). Retrieved from https://newsroom.intel.com/news-releases/intel-predicts-
autonomous-driving-will-spur-new-passenger-economy-worth-7-trillion/#gs.oi59t0
Federal Reserve Bank of Boston. (2012, November 01). Labor Market Trends in Massachusetts
Regions: Boston/Metro North. Retrieved from https://www.bostonfed.org/publications/labor-
market-trends-in-massachusetts-regions/labor-market-trends-in-massachusetts-regions-boston-
metro-north.aspx
Go Boston 2030. (2018, February 06). Retrieved from
https://www.boston.gov/departments/transportation/go-boston-2030
Hawkins, A. J. (2017, December 06). Lyft is now offering self-driving car trips in Boston.
Retrieved from https://www.theverge.com/2017/12/6/16742924/lyft-nutonomy-boston-self-
driving-car
How it works. (n.d.). Retrieved from https://inequality.media.mit.edu/#
Visualizing Economics. (2014,2017). Retrieved from http://visualizingeconomics.com/
Picchi, A. (2018, February 08). 9 American cities with the worst income inequality. Retrieved
from https://www.cbsnews.com/media/9-american-cities-with-the-worst-income-inequality/
Preparing a nation for autonomous vehicles: Opportunities, barriers and policy
recommendations. (2015, May 15). Retrieved from
https://www.sciencedirect.com/science/article/pii/S0965856415000804
Rideshare Data Report. (2018). Retrieved from https://tnc.sites.digital.mass.gov/
Self-driving cars could wipe out 4 million jobs - but a new report says the upsides will be easily
worth it | Markets Insider. (n.d.). Retrieved from
https://markets.businessinsider.com/news/stocks/self-driving-cars-could-kill-4-million-jobs-
economic-impact-worth-it-2018-6-1026937775
Story Map Journal. (n.d.). Retrieved from
https://story.maps.arcgis.com/apps/MapJournal/index.html?appid=5e7116880ec34270bc88c00c4
1dbda6d
Tomita, H. (2017, December 17). Potential economic and social effects of driverless cars.
Retrieved from https://voxeu.org/article/potential-economic-and-social-effects-driverless-cars
Uber Movement Platform. (2019). Retrieved from
https://movement.uber.com/explore/boston/travel-times/query?lang=en-
US&si=1116&ti=&ag=censustracts&lat.=42.3584308&lng.=-
71.0951354&z.=12&sa;=&sdn=&dt[tpb]=ALL_DAY&dt[dr][sd]=2018-12-
01&dt[dr][ed]=2018-12-31&dt[wd;]=1,2,3,4,5,6,7&cd=
United States Department of Labor-Occupational Employment Statistics. (2018, May 01).
Retrieved from https://www.bls.gov/oes/current/oes_71650.htm#53-0000

6. US Census Bureau. (2019, June 03). Characteristics of Driver/Sales Workers and Truck Drivers.
Retrieved from https://www.census.gov/data/tables/2017/demo/industry-occupation/truckers-
acs17.html
US Census Bureau. (2019, April 16). Income and Poverty in the United States: 2017. Retrieved
from https://www.census.gov/library/publications/2018/demo/p60-263.html
Welcome to MassData. (n.d.). Retrieved from https://opendata.digital.mass.gov/#/
Worldwide automotive growth is slowing down, particularly in the BRICS. At the same time, the
industry faces huge technological challenges. (n.d.). Retrieved from
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slowing-down-industry-faces-technological-challenges/