<p>I think the answer depends on who is impacted by your measure of distance and for what purpose. Kennan and Walker (2011) Econometrica measure distance between states as "the great circle distance between population centroids" in an attempt to model moving costs. They also include an indicator for whether or not the state is adjacent. </p>

CC BY-SA 3.0
2015-03-23T04:24:15.693Z
2015-03-23T04:24:15.693Z
gannawag
<p>Instead of using population weights, you could do a spatial average of county centroids using <a href="http://www.bea.gov/newsreleases/regional/lapi/lapi_newsrelease.htm" rel="nofollow">county-level personal income</a> as the weight. The residence adjustment that is applied (LAPI is calculated on a place of residence basis, rather than place of work) will screw this measure up a little bit (fortunately, the adjustment mostly just shifts around income within metropolitan areas), and it doesn't account for differences in the capital intensity of various industries, but it's likely a better proxy than using population as a weight.</p>

<p>More broadly, I'd really recommend checking out Jim LeSage's <a href="http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=364023" rel="nofollow">spatial econometrics</a> work (he wrote the toolbox for Matlab), as much of it deals with spatial weight structures.</p>

CC BY-SA 3.0
2015-03-23T19:43:23.930Z
2015-03-23T19:43:23.930Z
dismalscience
<p>I know there are lots of <a href="https://www2.bc.edu/james-anderson/GravityModel.pdf" rel="nofollow noreferrer">gravity models in economics</a> that take as an input distance between countries. My understanding is distance is typically coded based on the <a href="http://research.stlouisfed.org/publications/review/99/01/9901hw.pdf" rel="nofollow noreferrer">distance between capital cities</a>, either in <span class="math-container">$L_2$</span> norm or <a href="http://en.wikipedia.org/wiki/Great-circle_distance" rel="nofollow noreferrer">great-circle distance</a>. Most countries are small relative to the distances between them, and so maybe it doesn't matter much how they code the center of these countries for purposes of trade. But in my application I'm looking at US states, and many states are large enough that the internal placement of the location for calculating distances between states makes a difference. Additionally, state capitals are often not the economic center of the state (Carson City, NV and Albany, NY come to mind) nor the geographic center (North Folk, CA is hours away from Sacramento, CA), so I don't know of obvious choices.</p>
<p>Are there any standard choices I should know about here?</p>
<p>As a resource to others here are some sample data:</p>
<ol>
<li><a href="http://dev.maxmind.com/geoip/legacy/codes/state_latlon/" rel="nofollow noreferrer">Average Latitude and Longitude for US States</a></li>
<li><a href="https://www.census.gov/geo/reference/centersofpop.html" rel="nofollow noreferrer">US Census Centers of Population</a></li>
<li><a href="http://en.wikipedia.org/wiki/Geographic_centers_of_the_United_States" rel="nofollow noreferrer">List of geographic centers of each U.S. state</a></li>
</ol>
<p>Would anyone provide a list of the economic center of each state?</p>

CC BY-SA 3.0
2015-02-11T18:07:05.373Z
In many GIS applications, people use population-weighted centroids. If you have data on locations of the economic phenomena you are working on (say factories or retail locations), you can do something similar. There's a GIS stack site where you may get a better answer.
dimitriy
CC BY-SA 3.0
2015-02-11T18:27:44.843Z
I think the Census Centers of Population uses the centroid method.
BKay
<applied-econometrics><academic-graduate>
Are there conventions in economics for coding the distance between states?
BKay
# Are there conventions in economics for coding the distance between states?

(Asked by: username_0 on 2015-02-11)

I know there are lots of [gravity models in economics](https://www2.bc.edu/james-anderson/GravityModel.pdf) that take as an input distance between countries. My understanding is distance is typically coded based on the [distance between capital cities](http://research.stlouisfed.org/publications/review/99/01/9901hw.pdf), either in $L\_2$ norm or [great-circle distance](http://en.wikipedia.org/wiki/Great-circle_distance). Most countries are small relative to the distances between them, and so maybe it doesn't matter much how they code the center of these countries for purposes of trade. But in my application I'm looking at US states, and many states are large enough that the internal placement of the location for calculating distances between states makes a difference. Additionally, state capitals are often not the economic center of the state (Carson City, NV and Albany, NY come to mind) nor the geographic center (North Folk, CA is hours away from Sacramento, CA), so I don't know of obvious choices.

Are there any standard choices I should know about here?

As a resource to others here are some sample data:

1. [Average Latitude and Longitude for US States](http://dev.maxmind.com/geoip/legacy/codes/state_latlon/)
2. [US Census Centers of Population](https://www.census.gov/geo/reference/centersofpop.html)
3. [List of geographic centers of each U.S. state](http://en.wikipedia.org/wiki/Geographic_centers_of_the_United_States)

Would anyone provide a list of the economic center of each state?

**Tags:** applied-econometrics, academic-graduate

> **Comments:**
> **username_1**: In many GIS applications, people use population-weighted centroids. If you have data on locations of the economic phenomena you are working on (say factories or retail locations), you can do something similar. There's a GIS stack site where you may get a better answer.  
> **username_0**: I think the Census Centers of Population uses the centroid method.  

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## 2 answers

### Answer by username_2 on 2015-03-23. Score: 5

I think the answer depends on who is impacted by your measure of distance and for what purpose. Kennan and Walker (2011) Econometrica measure distance between states as "the great circle distance between population centroids" in an attempt to model moving costs. They also include an indicator for whether or not the state is adjacent.


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### Answer by username_3 on 2015-03-23. Score: 2

Instead of using population weights, you could do a spatial average of county centroids using [county-level personal income](http://www.bea.gov/newsreleases/regional/lapi/lapi_newsrelease.htm) as the weight. The residence adjustment that is applied (LAPI is calculated on a place of residence basis, rather than place of work) will screw this measure up a little bit (fortunately, the adjustment mostly just shifts around income within metropolitan areas), and it doesn't account for differences in the capital intensity of various industries, but it's likely a better proxy than using population as a weight.

More broadly, I'd really recommend checking out Jim LeSage's [spatial econometrics](http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=364023) work (he wrote the toolbox for Matlab), as much of it deals with spatial weight structures.


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