The Decline of Local Journalism in Ohio
Local newspapers have had a rich history in the United States, the
earliest paper dating back to 1690. Local newspapers have been vital in
building community identity and regional grassroots activism. They also
contribute to regional economic growth by connecting community members
with regional small businesses. They shape a community's policy
decisions and give voice to local issues.
With the increasing partisan bias in mainstream media, it is essential that communities have access to credible news that they
trust.
Yet in Ohio, newspapers are disappearing each year.
From 2004-2019, 104 newspapers have shut down or been merged with
another paper.
Using the University of North Carolina's Center for Innovation and
Sustainability in Local Media's
Database of Newspapers,
let's explore the change in the number of newspapers per county over
these fifteen years.
1. Click on a county to learn more.
2. Click the legend
to filter by number of newspapers.
3. Use the slider to
change the year.
4. Use the toggle to change the scaling.
In 2004, 21 out of the 88 counties in Ohio had only one newspaper in
the whole county. By 2019, that number rose to 41 counties...nearly
47%. Out of the 104 newspapers that closed in this 15 year time period
in Ohio, 77 were in metro counties while 27 were in non-metro. For
instance, Cuyahoga County where Cleveland is located had 26 newspapers
in 2004 and merely 5 by 2019.
These patterns in Ohio are
similar to those of the overall United States. According to Professor
Penelope Muse Abernathy, a professor at the UNC Hussman School of
Journalism and Media,
the US has lost nearly 1800 papers since 2004.
There are almost 2000 counties without local papers--Abernathy
describes such counties as news deserts. Many of these counties also
have poverty rates above the national average.
What is
causing this decline in local journalism? Though there is an obvious
change in reader behavior with many people now accessing the news
online, the significant decreases in newspaper circulation also stem
from changing ownership patterns. Many metro and regional papers are
decreasing circulation to surrounding rural and suburban areas.
Additionally, fewer and fewer newspapers are independently-owned. The
top 25 companies owning the most newspapers now control
nearly a third of all papers, up from 20%
in 2004. For instance, New Media/GateHouse owns 451 papers 34 states.
Ohio presents a staggering case study in which
30% of its newspapers have changed ownership since 2014.
Let's explore how newspapers have consolidated over time in Ohio.
Each dot represents a
newspaper.
Each
cluster represents an
owner.
The
cluster color represents the
number of newspapers that owner owns.
In 2004, there were 8 owners with more than 10 papers. The largest of
this group was the Brown Publishing Company with 33 papers. In 2019,
the largest owner is Gannett/GateHouse with 54 papers.
Furthermore, there are now 3 groups in Ohio owning more than 10
papers.
According to Abernathy's research, these media barons
pose a threat to local communities. First, there is a stronger
willingness to close or sell papers that are underperforming. For
instance, Civitas closed 8 suburban weekly papers in Ohio and North
Carolina. "The suburban newspaper isn't a fit in our business model,"
Civitas CEO Michael Bush explained. Second, there is an aggressive
emphasis on cost-cutting strategies even when it means compromising
newsroom staffing or operations. Third, consolidation by media barons
contributes to operations being outsourced to remote locations not
necessarily in the community. For instance, over 200 of GateHouse's
papers across the US are operated from a center in Austin, Texas. This
results in weaker community ties to the paper.
Methodology:
The data was obtained from the University of North Carolina's Center
for Innovation and Sustainability in Local Media's Database of
Newspapers. Due to errors in duplicate primary keys for various
states, I decided to focus on my home state of Ohio because the data
was accurate for this subset. I cleaned the data using python and
pandas. Data was used for the years 2004, 2014, 2016, and 2019 since
those were the years available from the database. The code for this
webpage and the Jupyter notebook used to clean the data can be found
in this github repository.