Hello Data Science Adventurers!
We all have felt the impacts of Covid even though most of us never became seriously ill from the disease. Almost all countries in the world had to come to a standstill and quarantine themselves to reduce the virus and its variants from infecting more people. Domestic and International trade stopped, and manufacturing in many industries was cut down to protect the health of workers including the major ones that are necessary for our modern world: Oil, Semiconductors, and Toilet Paper
Quarantining alone wouldn’t solve the Covid-19 Pandemic because it would require perfect adherence for it to work. Hence, massive capital was diverted into manufacturing personal protective equipment1which started the chin diaper trend, face shields, and whatever this is
. Also, we can’t forget Covid test drives and unbelievably massive efforts to map the virus to create an effective vaccine to immunize people.
During all the closures and lockdowns and curfews, the economy was bound to take a massive hit in GDP. If it went down enough, we would see massive layoffs and a full-scale economic crash. If that were to happen, any efforts to protect the citizens from catching and dying from Covid-19 would be offset by pushing more people below the federal poverty line.
So U.S. Federal Government acted quickly to inject billions if not trillions of dollars into the economy. That included printing more money, providing businesses with financial assistance (at a very low or no interest rate), giving every working adult stimulus checks multiple times, and of course, effectively lowering the effective federal funds rate to zero — which makes it cheap to borrow money and invest it.
Now we have more cash in the hands of people, companies, and banks at cheaper rates than ever before. This saved the economy from going into a depression and kept the unemployment rate low (a big win). As many have said before, there’s no such thing as a free lunch. The extra spending power combined with a decline in the production of goods is a perfect recipe for inflation — increased demand (because people can afford to spend and invest) and lowered supply (due to decreased trade and manufacturing).
Economic Data in Callisto Jupyter Notebooks
There’s the armchair economist answer. We’re all done now, right? Not quite. To prove our thesis we will need to match it with data. We will use the best source for macroeconomic data — the Federal Reserve Economic Data (FRED) to see trends.
To narrow down a segment of the economy that we can focus on, we will use the FRED API to see trends for the Auto industry — specifically used cars. You thought you could get a car that’s been driven 100K miles at a cheaper price than it was originally sold for when brand-new? The answer may surprise you!
In this series, we will use Callisto Jupyter Notebooks to search, download, and chart our data to get some real insights!
Anecdotal Evidence To Get Started
This is the pricing for a 2016 model-year car I used to own.
|2016 Model Year||2020 Model Year|
|Buying New||MSRP in 2016 $23,000||MSRP in 2020 $25,000
|Buying used at 2 years old||In 2018 at 40K miles: $14,000 (39% depreciation)||In 2022 at 40K miles: $24,000 (4% depreciation !?)|
|Buying used at 6 years old||2022 at 100K Miles: $15,000 (7% appreciation !??)||Error: Time Machine Required|
See these screenshots taken in 2022 from CarMax and Carvana2 Carvana might not exist by the time you see this article. In Dec 2022 it’s almost close to filing bankruptcy. They forgot they had to transfer titles to people who buy from them and instead invested in giant useless car vending machines and overpriced cars
|Screenshots of Prices in 2022||2016 Altima||2020 Altima|
Downloading data in Callisto Jupyter Notebook
- We’re going to download data for the Used Car Price Index in a Callisto Notebook and see if this increase in prices is seen across the whole country.
- We will also poke around in the API to see if we can use more data to figure out the reasons behind this price increase.