So, many of you have asked why I excluded NY from the early trajectory rates of COVID-19 infections. I did it because everything is scaled at the same rate for a cross-state comparison. I took care to standardize by population, but still the growth rate in NY has been such that it has so vastly outpaced other states that it makes the situation in each other state not look dire or like the rates are staying flat. They are not, NY is experiencing this earlier and much more rapidly given it's connections to the world economy, as well as the sheer density in the NYC region. While that combination can be great for an economy during good times, it can wreak havoc during a global pandemic. So, to provide you with the info you want here you go. I will first present without NY and the follow it with NY rates through March 28th.
One note, the trajectories look roughly the same but the scale has changed. It is still a fraction of a percent of the population in states; however, the rate by which that is increasing is what should be concerning.
Now, I am taking all of the same data, but adding in NY. Here is the updated figure:
I am going to update this post as I get new information that I think is particularly important and hopeful during all of the negative news we are receiving. This isn't just made you to feel better, these are stories that may meaningfully have an impact on reducing infections, understanding their spread, and recovery:
This wonderful visual by visual capitalist has created some nice charts for every possible vaccine and treatment for COVID so far. Let's hope several of these work!
How Do We Slow the Spread?
By far one of the best articles I have read on the topic of measures we need to enact by Pueyo. This is a great foundational article that describes what we need to do to counteract the negative consequences of COVID-19.
Testing is So Important (See Above Article for Why)- And Now it May Get a Lot Easier and Quicker:
VOX has reported some good news about testing becoming easier and quicker. We will be able to test and get results of COVID-19 in a matter of minutes instead of weeks. This will allow us to more appropriately understand what we need on the social distancing side.
Immune Patients May be a Solution
As we move to full scale testing of the population, see above, there is a good chance that we will be able to use blood from those that are immune and/or have recovered to get through this. This would not take major FDA approval as would a medication that could treat.
Increasing the Number of Ventilators in this Country
The country needs more ventilators and production capacity of these is innovating and increasing.
Please let me know what else I should be posting here.
If you saw my previous post on state per capita confirmed rates of coronavirus over time, you may have had some additional questions. This is particularly important, especially regarding confirmed cases by the number tested (Thanks to Thomas Schaller for clearly pointing this out). I start by dispalying the updated totals in Figure 1. NY is still removed from this particular table, because it mitigates the effect of many others- this will likely change as the pace of this increases by state sadly.
Next, I look at the percentage of those individuals tested who do not have Coronavirus. A couple things to note, there may be double counting which is a concern. Some individuals are testing themselves multiple times and some may have an immunity. There may also be many more who would be negative were they able to get tested. Ideally, we would all get tested right now and strictly quarantine based on the results. This would limit the spread and allow others to get back to work and ensure the sick have the time to recover while the healthy and immune could return to work- particularly in ways to assist the rest of society get through this. States may be hesitant to report negative cases because there is fear that individuals may take it less seriously in that event. I think more information is better than less and note some of the caveats above. Testing data is important as is making it public so we can better understand this. Thus, I report the percentage negative relative to positive and negative cases.
Because of the many discontinuities, it is clear that in many cases the state was not reporting the negative cases early on. So, I truncate the sample to dates from March 20th-28th when data becomes more reliable.
On average, as of March 28th, the percentage of cases that were negative was 88%. That means, so far 12% of those that have gotten tested were confirmed as infected. This is not overly meaningful given that some people will be tested more than once and many others will be more apt to get tested given the concern around COVID-19; however, testing rates by state on per capita terms is important as is the number of confirmed with hospitalization rates- so check this out in a future post.
The United States is currently in the beginning stages of grappling with a global pandemic due to the Coronavirus. Unlike many other countries, the national government has not elected to issue a stay-in-place order to ensure proper social distancing and stymie the impact this virus will have throughout the country. Rather, they are leaving it to the states.
States are pursuing different strategies, so I am going to be tracking and analyzing those strategies and seeing what impact they have had on the growth rates of infections. I have created a Coronavirus infection rate by state standardized by population- so expressed on a per capita basis. I will be updating the visuals.
One visual excludes NY because their trajectory has been rising so rapidly it visually skews the others to have them on the same graph even with it standardized by population. Density will matter a lot in the spread of this disease, as will the timing and type of policies that are enacted.
The data for this run from March 6th through March 24th and come from the following source: https://covidtracking.com/api/.
I certainly hope that states take this seriously as other countries are showing us the devastating effects by not instituting restrictive measures.
I am going to start highlighting mostly new data sources that are useful for evaluating the impact of various economic and community development strategies at the state, county, and local levels:
The first source by Armanios, Lanhan, and Yu (2019) focuses on state-led tech-based economic development policies:
This source is focused on providing a Panel on incentives and taxes across 33 states and 45 industries over a 26 year period
This source is focused on providing GDP stats at the county-level.
Roy Meyers, professor of political science at UMBC, and I, argue that we do not think Maryland on Amazon as it was able to avoid the winner's curse. You can see our op-ed here.
Many of those who study economic development increasingly understand the importance of economically inclusive regions. This generally means a strong interconnection of economic activity within the region. For example, the Central Business District would be economically tied to neighborhoods within the city and the central city would be connected to outlying hinterlands. Recently, the Brookings Institute has released a nice tool to explore the intersection of economic growth (Size of the economy), prosperity (quality of growth), and inclusion (distribution of growth):
Link to the Tool
Amazon has recently narrowed their list of prospective locations. One of the places being considered is Montgomery County, MD. No matter what one thinks about the prospects of attracting HQ 2.0, a very careful assessment of the short- and long-term costs and benefits, as well as the distribution of those costs and benefits should be performed.
In a recent article with Roy Meyers, Professor of Political Science and Public Policy at UMBC, we highlight this failure in the case of the economic impact assessment conducted by Sage.
The full article is here.
I look forward to your thoughts and comments.
Depending on your perspective, some very interesting things are happening right now in our markets. Amazon Go has just opened in Seattle, where you enter the store by a QR code on your phone and you can literally just pick up the products you want, put them in your Amazon bag, and walk out. Cameras and sensors detect all of your purchases, which apply to your Amazon account.
The New York Times mentions that there are currently about 3.5 million cashiers in the United States. It will, of course, take time to disrupt an entire occupation; however, we are certainly heading in that direction with our technological advances. It is not that this is inherently bad, we can see efficiency gains in our system; however, we need to ensure that our populations are skilled to move with these industrial changes.
Before you read this and think, well this will always be the case for jobs that require less formal education or without specialized levels of skills- I want to call your attention to one other change that shows even our most technologically skilled can be outpaced by Artificial Intelligence and machine learning programs. No, I am not going to tell you about how AI has beaten the best humans in chess or the board game Go (a much harder feat) based on training, and learning, by playing itself. No human input is actually even needed (See article here). Google has created an AI that, get this- creates other AIs. Google is using deep learning neural networks to write programs that write their own, more efficient programs. According to Google, these programs are better than what many of Google's top programmers could do. The implications of all of this are TBD, but programming jobs had seem the one safe haven in a postindustrial economy. Maybe so, but maybe not. Of course, as pointed out by this article in WIRED, these neural networks are black boxes that are assuming more and more responsibility for how decisions are made and even changing the way we "think about ourselves, our world, and our place within it."
The wonderful economic development watchdog group, Good Jobs First, has been at the forefront of tracking economic development mega deals. This recent article shows why governments since the Great Recession have been keen on spending so much to attract large firms, which will come at the expense of entrepreneurs and small firms trying to compete. It touches on a topic that I have been increasingly interested in, how competition is different in metropolitan areas at the border of two or more states. More on that later though!
In a second interesting article, the Government Accounting Standards Board has issued statement number 77 which requires governments to provide information within their Comprehensive Annual Financial Report (CAFR) on how much the government is providing in economic development incentives. This Bloomberg News article shows that there will likely be variation in how transparent governments will be in their reporting. Also, it demonstrates that even programs like TIFs which might deliver the benefits in rebates over time are required to be reported.
I am an assistant professor of political science at the University of Maryland Baltimore County (UMBC). I completed my Ph.D. in Public Policy and Public Administration at George Washington University.