Wed 03 February 2016

State Budgets and Populations (a.k.a Why Illinois is in the shape it's in)

Posted by Mischa Fisher in Economics   

Given Illinois' current budget impasse, I thought it would be interesting to do a five minute analysis looking at how the size of each state's budget varies in proportion to its population. This is obviously a very shallow examination, and one could spends weeks digging through budget numbers, federal transfers, rural-urban splits, poverty and education levels, industry compositions, unfunded pension liabilities, worker's compensation costs, etc. etc. Still, summary statistics exist for a reason; and 5 minute analyses can be useful exercises.

Pulling data from Wikipedia's maintained list of US State budgets (here), and from the U.S. Census' estimate of 2015 state populations (here)

All States

First, a look at all U.S. States:

I knew Illinois' budget has historically been unsustainable, but, I was surprised at just how much of an outlier the state is. Mercatus now has Illinois rated dead last in the country in terms of fiscal solvency (link here), and with this chart, one can see why!

Just the Large States

Comparing large states to large states, here are all the states with populations above 6 million people.

Just the Small States

And in the same spirit, the small states:

Here it's worth noting that Illinois does not have the largest per capita budget; that honor goes to Alaska. Illinois simply is the largest deviator from the overall trend line in absolute dollar terms. That being said, since State budgets include federal transfer dollars for federal programs (infrastructure, heating assistance, etc. etc.), it's not hard to see why Alaska, which has very few people in it but a lot of federally supported infrastructure, is the highest per capita budget state.


What was intended as a five minute "hey that's interesting!" analysis ended up exploding on the internet. With over 100K page views in 12 hours, the response was certainly unexpected. On that note, a few people on Reddit mentioned they'd be interested in seeing the log of the data. So here it is:

More importantly, I think it's important for people to remember that Wikipedia data is not always particularly accurate, nor is it necessarily an apples to apples comparison. Some of the data listed on the page is for single year periods, other bits of data are for multi-year periods. The script I used to plot takes those things into consideration, but, it may have errors given the inconsistency in Wikipedia's data. Time allowing I'll source better data and replot at some point in the future.


NASBO has a dataset on state spending, I took some time to manually write down the state and spending columns as vectors in R (so there may be transcription errors) and the log of the data produces a result very different than Wikipedia's data:

So the story of Illinois' terrible fiscal condition could very well be more complicated than can be captured in a single graph. As in most things in life; the issue is ...


Sat 23 January 2016

Renting vs. Owning in Chicago

Posted by Mischa Fisher in Economics   

Moving to Chicago this past weekend prompted the age old question, should one rent or should one buy?

Independent of the qualitative and lifestyle differences between the two choices, I was curious how - strictly speaking - the finances between the two options worked out. Using the Case Schiller Price Index for Condos in the Chicago metro, and the historical return rate on real estate prices, worked into a short R function, produced the below results.

Worth noting, the calculation included:

  • The opportunity cost of capital
  • Historical returns on real estate prices
  • For Owning: Mortgage, property taxes, closing costs, Home Owners Association fees, cost of ownership
  • For Renting: Rent, Utilities

However, it did not include:

  • Mortgage interest Deduction
  • Non linear mortgage amortization. (This one is linear, rather than being skewed towards the latter years as would actually happen on a traditional amortization schedule)
  • This assumes a 20% downpayment, rather than some other available options, such as the FHA loans that allow for as low as 3.5%.

The Result:

Given these basic assumptions on relative costs, it seems to confirm the somewhat common folk wisdom that it takes about 4-5 years for the initial hit of the closing costs to be paid off by saved expenses and amortization of the mortgage loan.


Tue 08 December 2015

How Much Does Rural Living Predict Broadband Speed?

Posted by Mischa Fisher in Economics   

I was recently brushing up on the current status of the telecommunications industry in the United States, and I became curious about how much a state's rural population predicted its overall average internet connection speed levels.

Pulling data on average speeds from (here), which sources Akamai's State of the Internet report, and data on the rural/urban population by state from Iowa State University's site (here), reveals the below plot:

While noticeable, the effect here is pretty minor overall. It would take a shift of about 20% of the state's population into an urban environment to shift average speeds up by a single Mbps.

How About Globally?

Curious about how well this predicts things generally, I did the same thing looking at global data. Wikipedia has a concise list of countries by internet connection speeds (here), and the World Bank maintains a time series list of urban/rural population (here).

Pulling those two datasets together, reveals the following:

Visually this looks similar, although the linear regression slope is a little steeper. It would take a shift of about 10% of an average country's population to shift the speed up by a Mbps.


At the end of the day, this exercise is likely too simple to shine much light on the phenomenon. Economies of scale in terms of population density would, all else equal, suggest that as density goes up, so would speed. But the 'average' speed by state hides the interesting variation that goes on within the county and city level that I imagine would more apparently show the swings based on not just population density, but, also regulatory factors such as the ease of installation and market entry, access to public conduit and utility maps, etc. etc. Looking at the data more locally would probably be more meaningful than trying to examine aggregate state or country data, because there is likely some regional smoothing that goes on in the 'average speed' metric that underestimates the effect.