Civic Data

Data-oriented thinking about where and how people live.


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Review: “Zoned in the USA: The Origins and Implications of American Land-Use Regulation” (Sonia Hirt)

tl;dr: highly worthwhile book on the international uniqueness of American zoning codes and their cultural underpinnings. 4 out of 5 stars.

This book is an attempt to demonstrate and explain the features of modern zoning codes (and cities) that are almost unique to the United States: the very broad use of exclusively residential zones and exclusively single-family house zones. It combines an extended history of zoning, both internationally and in America, with a comparative study of modern zoning codes and legal regimes surrounding new construction in several countries, most notably England and Germany.

The book is fascinating overall, and highly recommended to any students of cities and/or American history. It is richly detailed and sourced. The perspective is that of an urban planning student from Bulgaria who moved to the US for career reasons and was baffled by the apparent contradictions between the narrative of American individualism and freedom and the very restrictive codes surrounding the built environments in which they lived (which is baffling to this native US citizen as well).

There are many hypotheses that attempt to explain American zoning codes. To name a few: the availability of plenty of cheap land in the US, the predominance of local as opposed to national control over planning and development, the protection of private property values, etc.

However, Hirt feels that these arguments are insufficiently unique to explain the genuine uniqueness of American zoning. The principal thesis of the book is that they are largely the result of a strong cultural undercurrent of agrarian and “frontier” values in the US. As a result, the single-family house on a generous plot was seen as the morally correct dwelling arrangement, and our zoning and legal codes responded to that desire.

The apparent contradiction described above results from the explosion of US cities in the 19th century, and the resulting collision between our preferences for different sorts of “freedoms”: political freedom (in particular the right to use private property without governmental interference) and what she calls “spatial freedom,” which is something like the desire to claim, explore, and patrol the boundaries of a sizable piece of land. It’s not exactly a spoiler to observe that spatial freedom won this rhetorical battle. However, political freedom was appeased in that the new legal structures were simple, scientific, rules-based systems that would treat each property the same and give each property owner the right to development without asking permission within the constraints of the rules, or were advertised as such anyway. And “economic” freedom was appealed to by the universal emphasis on stabilizing and increasing property values.

I did feel that the international comparisons beyond those to Germany and England were a bit overpromised and underdelivered. The sections on each of the other nations discussed (France, Russia, Sweden, Australia, Canada, Japan) amount to capsule histories of a page or two and are not the subject of extended comparisons throughout the rest of the book.

More information about the book is available at Cornell Press.

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The United States of Europe

The European Union has been hit by serial debt crises over the past several years. At various times and for various reasons, Ireland, Portugal, and (most notably) Greece have required hefty bailouts from the rest of the EU, mostly in the form of cheaper debt.

One standard reason given for this messy state of affairs is that the EU is a monetary union but not a fiscal union. That is to say, the EU shares a currency (the Euro) but not the ability to enforce fiscal discipline or consistent taxation and spending policies across member states. This has lead to the problem of peripheral countries “free-riding” on the strength of the Euro — interest rates for sovereign debt in the peripheral countries were substantially lower than they would have been without the implied guarantee of the Eurozone behind them, encouraging over-spending in Greece and Portugal. However, central economic engines like Germany experience a countervailing benefit: their exports are cheaper than they would have otherwise been, due to the peripheral states holding down the strength of the Euro. All parties seem to benefit, but in a way that encourages imbalanced economies. (Note: see a comprehensive and fascinating discussion of the current state of affairs in an analysis by Deutsche Bank — pdf format.)

These imbalances have led to calls for greater unification of governmental policies: a United States of Europe.

How Big Were the Greek Bailouts?

A first question might be: how much money actually went to Greece?

I can find various sources estimating the total bailouts provided to Greece at a bottom line figure of €300 to €400 billion, at interest rates of 3.5% to 5%. The various interests rates were for different phases of the bailouts, and are at a steep discount relative to the rates Greece is paying for debt on the open market. Note that this is very different from a cash transfer of €400 billion — because Greece has to pay it back. So how can we estimate the equivalent cash transfer?

For simplicity, let’s consider some round figures: €400 billion in loans at 5% interest rates. One way of getting a ballpark estimate of the total subsidy is to compare this to the interest rates Greek debt can get on the international bond market: at the time of the initial bailouts, this was 10%, and has briefly gone as high as almost 30% — so figure an average at 15%. If we assume that the typical repayment period on these loans is one year, the total Greek bailout is on the order of €40 billion (~$50 billion), or 15%-5%=10% of €400b, and was handed out over approximately a two-year period.

If we want to be slightly fancier, we can use a mortgage calculator to estimate the difference between a 5% and 15% interest rate over a five-year payback period (five years being pulled out of thin air as an example of relatively long-term government debt). Doing the calculation this way gives a subsidy of around €75 billion (~$90b).

So we can roughly estimate the total value of the Greek bailouts at $50b to $100b.

Federal Wealth Transfers Between U.S. States

If the EU becomes the USE, and enacts unified fiscal, monetary, and social policies, richer regions like Germany will be required to subsidize poorer regions for the foreseeable future. A similar dynamic exists between US states: richer states like California and New York subsidize many poorer states. The main reason is that progressive federal income taxes as well as a (tenuous) federal safety net are uniform across the nation, while economic productivity and wealth are wildly uneven, and tend to be focused in large cities. There are many other minor reasons, such as highway spending and the general pro-rural bias enshrined in the Senate.

As an analogy to Greece, consider a country I’ve just invented called Alabarkansippi. As you may have guessed, I’ve formed Alabarkansippi from the civil and fiscal union of Arkansas, Mississippi, and Alabama. (I’ll use AMA for short, as Alabarkansippi is a bit tedious.) Greece has a population of 11.3 million (out of ~500m in the EU), while AMA has a total population of 10.7m (vs. ~300m in the US), so the regions are similar in size. They are also similar in terms of economic productivity relative to their region: Greece had a per-capita GDP of $20.7k in 2011 (vs. $31.6k for the entire Eurozone). Similarly, AMA has a per-capita GDP of $35.4k vs $47.4k for the United States as a whole.

So fundamentally AMA is pretty similar to Greece, in being a poor region surrounded by richer neighbors. Greece is a bit more poor than its neighbors, relatively speaking, but much of that is because of the fact that they’re right in the middle of a debt- and austerity-induced Depression.

Fortunately for us, the Tax Foundation has put together data on federal taxation and spending by state. Unluckily, the data only exist for the years of 1981 to 2005, but we can use them to get a general feel. In particular, from 2002-2005, transfers from other states into AMA averaged… $42 billion a year! (Over the years from 1990-2001, transfers to AMA bounced around between $15b and $30b. These figures are all inflation-adjusted to 2012 dollars.)

According to that Deutsche Bank report at the top, standing EU transfers to Greece — as a result of taxes and spending which do not perfectly even up among member nations — have been €3-4b a year. I think it’s safe to conclude that if the EU really wants to become the USE, it has to be prepared to make much bigger transfers than it already is.

And A Fun Final Note

Red staters who despise federal wealth redistribution should think twice about their voting habits. Below, I’m showing the vote spread for the 2004 Bush-Kerry election against the deficit-neutral per-capita federal wealth transfers to states (this data comes again from the Tax Foundation report above). The trend isn’t particularly significant, but it’s definitely there. The current wealth transfer scheme disproportionally benefits poor and rural states, who nevertheless tend to vote Republican.

2004 Bush-Kerry spread vs. per-capita wealth transfers


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A better measure of population density

Some fun facts: New Jersey (at 460 people per square kilometer) is the densest state in the United States. Connecticut (at 285/km^2) has more than double the population density of the state of New York (158/km^2).

Obviously, while New York State contains New York City, the densest city in America, it also has miles of empty hinterland upstate, while Connecticut has relatively low-density suburbs covering a large fraction of the state. Crude population density is relevant if, say, we plan to split the state up evenly and give everybody a plot to farm, but it doesn’t really capture the difference between New York and Connecticut.

Perceived Density

A more relevant way to calculate the population density of a place is to ask “How far apart are the typical neighbors in this state?” Or similarly, “What is the density perceived by the average resident?” I was inspired to calculate perceived density by this Austin Contrarian blog post, and so I’ll quote his explanation:

An extreme but simple example:  Suppose Metropolis consists of a central core of 100,000 residents on 10 square miles, and a suburb of 10,000 on 100 square miles.  Its standard density is 1,000 persons per square mile.

But this is a meaningless number.  Most of the residents of Metropolis live in a very dense environment.   The roughly 90% who live in the core are packed in at 10,000 per square mile, while just 10% live at the rural density of 100 per square mile.  By giving the core’s density a weight of 90%, we get an adjusted density of 9,100 persons per square mile, a much better description of the density perceived by the average resident.

One key difference between my calculation and his is that I use census block-level data. This calculation becomes more accurate the more granular your data is. Also, I use 2010 Census data.

(Also AC notes in another post that the idea of perceived density has been floating around at least since work by Gary Barnes in 2001. And apparently the Census Bureau has started calculating perceived density too! I guess this post would have been more groundbreaking a few years ago…)

Anyway, without further ado… the data:

State Population Bulk density (km^-2) Weighted density (km^-2)
NY 19333990 158.4394 15832.51
DC 598414 3803.751 12293.26
HI 1357202 81.60069 6496.256
NJ 8761115 460.3652 5696.584
CA 37145156 92.08261 5589.403
IL 12770832 88.8414 5246.746
MA 6511291 322.7141 4862.258
PR 3992495 444.2985 4537.001
MD 5723643 227.958 4348.399
NV 2694919 9.478293 4255.984
PA 12593779 108.7237 4064.107
US 311725647 34.04841 4024.23
RI 1049107 392.2036 3744.092
TX 25091380 37.09027 3547.602
FL 18743938 135.0074 3329.256
AZ 6376488 21.6755 3298.759
VA 7966975 77.92358 3193.079
CO 5014644 18.68286 3139.779
UT 2760099 12.96994 2893.576
DE 894473 177.3389 2850.064
OR 3821187 15.3714 2845.809
WA 6715366 39.01915 2756.483
CT 3566140 284.4779 2734.935
NE 1818198 9.139048 2362.161
MN 5296048 25.68276 2167.028
WI 5678651 40.49017 2150.686
AK 707240 0.4785273 2075.556
MO 5964397 33.51027 2040.069
NM 2052068 6.532275 2023.881
ND 669100 3.744373 2021.625
OK 3738981 21.05004 2005.878
OH 11484261 108.5711 1995.748
MI 9870560 67.41639 1943.653
LA 4520762 40.41043 1937.115
IN 6447987 69.51789 1898.736
KS 2844137 13.43315 1851.804
ID 1560881 7.292989 1848.406
WY 561035 2.231099 1827.739
IA 3036518 20.99263 1743.859
GA 9669315 64.9225 1682.254
SD 812894 4.140146 1545.785
NH 1312525 56.62046 1458.979
KY 4329388 42.33941 1447.203
TN 6335140 59.33278 1417.803
MT 986152 2.616117 1393.657
WV 1835075 29.48652 1365.568
AR 2911252 21.60401 1237.106
AL 4762649 36.31816 1234.689
SC 4611393 59.24358 1226.788
NC 9522341 75.63584 1215.909
MS 2955790 24.32566 1191.263
VT 624276 26.15562 1086.771
ME 1324000 16.57576 1061.588

A few things of note:

  • New Yorkers are actually pretty urban, as expected. The state’s perceived density of 15,832/km^2 is actually far higher than the bulk density of just the New York City metro area, which Wikipedia quotes as 720/km^2.
  • New Jersey loses its top spot but is still relatively urban.
  • Alaska, with by far the lowest bulk population density of any state, actually moves to the middle of the pack in perceived density.
  • Perceived densities in general fall in a much narrower range than bulk densities.

A Cute Application

One contributing factor to my interest in cities are the books of Jane Jacobs, particularly The Death and Life of Great American Cities and The Economy of Cities. In the latter, Jane Jacobs convincingly argues that cities and the interactions they spawn are the main engines of economic development through history. The idea of interaction and social learning in cities is one important component of agglomeration economies.

I won’t rehash the book here (though you should definitely read it!), but I thought it would be fun to compare the population density of states to their level of economic development, as lazily measured here by GDP/capita. The log-log graph below compares how the two measures of population density correlate to economic development. (Here I include only the 50 US states.)

The trendlines are a simple linear regression on the (logged) data. Weighted density is much more closely correlated with economic strength than the cruder bulk population density. (If I remove the two largest outliers from each dataset — AK and WY for the bulk density, NH and NY for weighted density — the R^2 values increase to 0.21 and 0.47 respectively.)

As always, correlation does not imply causation, but it’s at least consistent with the idea that cities are the main drivers of economic development. It would be pretty easy to do a similar comparison of density and GDP/capita at the scale of metropolitan areas — probably a more direct test. Of course, from the point of view of causality, both are limited by unseen correlations. One could imagine, for example, that the relationship might be weakened by in-migration to economically strong regions resulting in decreasing GDP/capita (ceteris paribus).

A note on data: I grabbed block-level census data by state here, and processed the data in Python. (Edit: as of Dec. ’12, that link seems to be dead; the same data can apparently be accessed here.) I ignored all census blocks with given areas of less than 30,000 square feet (the defined minimum size of a census block according to this Census pdf). A total of 634,800 were disenfranchised by this procedure, corresponding to no more than 0.9% of the population in any state (West Virginia), and typically more like 0.2%.

Some of the weirdest Census blocks turned out to be prisons. This corrections facility in Mississippi claimed to have 2946 “residents” in a census block of 480 square meters, which frankly doesn’t make any sense looking at the map. This prison by itself actually changes the weighted density of MS by a factor of 5! (No other states showed levels of variability anywhere near this.) Another was a housing project in Irvington, NJ, where 1040 people live in 785 square meters. These figures are at least believable but were eliminated due to the non-conforming Census block area for consistency. These examples give some reason to be concerned about other data errors but I think the results above pass the smell test.

The GDP/capita data was grabbed from Wikipedia.