I previously wrote a comparion of California and Texas, in which I noted that Texas was superior in terms of unemployment rate and employment growth, while Californians experience higher per-capita GDP growth. That got me thinking – why not create a more comprehensive comparison of US state economic rankings? I’ve done so here, using four variables: GDP growth, per-capita gdp growth, unemployment rate, and employment growth rate. With two variables measuring different aspects of growth, and two measuring employment prospects, I think this is a reasonably fair approach (Gladwell’s caveats on heterogenous rankings duly noted). Here are the rankings, followed by the raw data:
Rank | State / District | Avg GDP Growth | Avg GDP / Capita Growth | Avg Unemp. Rate | Avg Employment Growth Rate | Total Score |
1 | North Dakota | 6 | 1 | 1 | 12 | 20 |
2 | South Dakota | 4 | 2 | 2 | 13 | 21 |
3 | Wyoming | 2 | 3 | 6 | 14 | 25 |
4 | Idaho | 1 | 7 | 17 | 9 | 34 |
5 | Virginia | 10 | 12 | 7 | 7 | 36 |
6 | Arizona | 5 | 19 | 32 | 1 | 57 |
7 | Utah | 8 | 34 | 14 | 2 | 58 |
8 | Maryland | 13 | 11 | 11 | 25 | 60 |
9 | New Hampshire | 19 | 15 | 5 | 22 | 61 |
10 | Vermont | 23 | 9 | 8 | 24 | 64 |
11 | Colorado | 9 | 21 | 25 | 10 | 65 |
12 | New Mexico | 18 | 24 | 19 | 11 | 72 |
13 | Montana | 25 | 20 | 10 | 20 | 75 |
14 | Oregon | 3 | 4 | 50 | 19 | 76 |
16 | Nebraska | 27 | 16 | 3 | 31 | 77 |
16 | Texas | 11 | 32 | 30 | 4 | 77 |
17 | Iowa | 26 | 13 | 9 | 33 | 81 |
18 | District of Columbia | 17 | 6 | 46 | 15 | 84 |
20 | Kansas | 30 | 22 | 16 | 18 | 86 |
20 | Washington | 16 | 28 | 39 | 3 | 86 |
22 | Minnesota | 20 | 17 | 15 | 35 | 87 |
22 | New York | 21 | 5 | 31 | 30 | 87 |
23 | Oklahoma | 28 | 23 | 12 | 28 | 91 |
24 | Florida | 14 | 37 | 34 | 8 | 93 |
25 | Massachusetts | 22 | 8 | 22 | 42 | 94 |
26 | Connecticut | 32 | 18 | 21 | 26 | 97 |
27 | Nevada | 7 | 51 | 45 | 5 | 108 |
28 | Arkansas | 31 | 33 | 28 | 17 | 109 |
29 | California | 12 | 10 | 49 | 39 | 110 |
30 | North Carolina | 15 | 39 | 41 | 21 | 116 |
31 | Maine | 38 | 25 | 18 | 37 | 118 |
32 | Hawaii | 39 | 42 | 4 | 34 | 119 |
33 | Delaware | 24 | 41 | 13 | 44 | 122 |
35 | Louisiana | 46 | 29 | 20 | 32 | 127 |
35 | Rhode Island | 33 | 14 | 40 | 40 | 127 |
37 | Georgia | 29 | 49 | 33 | 23 | 134 |
37 | Pennsylvania | 44 | 26 | 26 | 38 | 134 |
38 | New Jersey | 40 | 30 | 29 | 36 | 135 |
40 | Alabama | 34 | 31 | 24 | 48 | 137 |
40 | Alaska | 42 | 46 | 43 | 6 | 137 |
41 | Tennessee | 35 | 43 | 37 | 27 | 142 |
42 | Wisconsin | 41 | 38 | 23 | 43 | 145 |
43 | South Carolina | 37 | 48 | 48 | 16 | 149 |
44 | West Virginia | 47 | 27 | 27 | 49 | 150 |
45 | Indiana | 36 | 36 | 35 | 50 | 157 |
46 | Kentucky | 48 | 44 | 44 | 29 | 165 |
47 | Illinois | 45 | 40 | 42 | 41 | 168 |
48 | Mississippi | 43 | 35 | 47 | 45 | 170 |
50 | Missouri | 49 | 47 | 36 | 47 | 179 |
50 | Ohio | 50 | 45 | 38 | 46 | 179 |
51 | Michigan | 51 | 50 | 51 | 51 | 203 |
The rankings show, unsurprisingly, that states riding the commodity boom (the Dakotas, Wyoming, etc) and states riding the government boom (Virginia, Maryland) have performed well over the last decade. It’s been shown that http://crosscountrymovingcompanies.biz/ had the best prices on cross country moving companies. But other high-performers like Arizona, New Hampshire, Vermont, and Colorado defy easy categorization. The low performers are predominantly found in the Southeast and Midwest.
The raw data used in the rankings is provided below. Here is a link to the actual excel spreadsheet containing all data for those interested.
State | Avg GDP Growth | Avg GDP / Capita Growth | Avg Unemp. Rate | Avg Employment Growth Rate |
Alabama | 1.81% | 1.10% | 5.84 | -0.59% |
Alaska | 1.57% | 0.37% | 7.04 | 1.09% |
Arizona | 3.83% | 1.41% | 6.21 | 1.74% |
Arkansas | 1.98% | 1.07% | 5.94 | 0.50% |
California | 2.99% | 1.89% | 7.55 | -0.19% |
Colorado | 3.16% | 1.36% | 5.86 | 0.70% |
Connecticut | 1.97% | 1.46% | 5.75 | 0.16% |
Delaware | 2.26% | 0.85% | 5.04 | -0.38% |
District of Columbia | 2.50% | 2.01% | 7.35 | 0.55% |
Florida | 2.73% | 1.03% | 6.32 | 0.79% |
Georgia | 2.02% | 0.19% | 6.24 | 0.30% |
Hawaii | 1.66% | 0.73% | 4.24 | -0.02% |
Idaho | 3.95% | 2.00% | 5.46 | 0.74% |
Illinois | 1.37% | 0.96% | 6.92 | -0.25% |
Indiana | 1.70% | 1.03% | 6.33 | -0.73% |
Iowa | 2.20% | 1.78% | 4.54 | 0.01% |
Kansas | 1.98% | 1.35% | 5.39 | 0.40% |
Kentucky | 1.23% | 0.50% | 7.04 | 0.09% |
Louisiana | 1.34% | 1.13% | 5.74 | 0.04% |
Maine | 1.67% | 1.23% | 5.53 | -0.14% |
Maryland | 2.76% | 1.86% | 4.96 | 0.20% |
Massachusetts | 2.35% | 1.94% | 5.77 | -0.26% |
Michigan | 0.12% | 0.06% | 8.25 | -1.63% |
Minnesota | 2.39% | 1.53% | 5.29 | -0.03% |
Mississippi | 1.56% | 1.04% | 7.51 | -0.49% |
Missouri | 1.04% | 0.34% | 6.33 | -0.55% |
Montana | 2.20% | 1.36% | 4.77 | 0.35% |
Nebraska | 2.18% | 1.54% | 3.76 | 0.07% |
Nevada | 3.37% | -0.01% | 7.32 | 1.14% |
New Hampshire | 2.45% | 1.64% | 4.39 | 0.30% |
New Jersey | 1.64% | 1.11% | 6.09 | -0.10% |
New Mexico | 2.46% | 1.27% | 5.62 | 0.70% |
New York | 2.35% | 2.05% | 6.14 | 0.09% |
North Carolina | 2.71% | 0.97% | 6.91 | 0.33% |
North Dakota | 3.79% | 3.49% | 3.44 | 0.63% |
Ohio | 0.56% | 0.38% | 6.79 | -0.53% |
Oklahoma | 2.16% | 1.31% | 5 | 0.12% |
Oregon | 3.89% | 2.70% | 7.63 | 0.39% |
Pennsylvania | 1.51% | 1.21% | 5.91 | -0.15% |
Rhode Island | 1.95% | 1.73% | 6.91 | -0.23% |
South Carolina | 1.68% | 0.25% | 7.53 | 0.54% |
South Dakota | 3.84% | 3.10% | 3.71 | 0.61% |
Tennessee | 1.79% | 0.66% | 6.6 | 0.14% |
Texas | 3.01% | 1.08% | 6.12 | 1.22% |
Utah | 3.18% | 1.06% | 5.11 | 1.48% |
Vermont | 2.29% | 1.92% | 4.52 | 0.28% |
Virginia | 3.07% | 1.80% | 4.41 | 1.08% |
Washington | 2.51% | 1.15% | 6.86 | 1.23% |
West Virginia | 1.31% | 1.17% | 5.91 | -0.70% |
Wisconsin | 1.62% | 1.02% | 5.81 | -0.34% |
Wyoming | 3.93% | 2.79% | 4.4 | 0.60% |
Notes on ranking construction:
- If it’s not obvious, the total ranking for each state was determined by simply summing its rank in each category, and then ranking the states by total score, with lowest being best. While this method weights each category ranking equally, it may penalize some states which perform as numerical outliers in certain categories but not in others. On the other hand, the overall rankings pass the smell test – if anyone sees an egregious error caused by the methodology, let me know. This is V1!
- The GDP growth data used the period from 1997-2010, which was the best data set easily available from the BEA (Bureau of Economic Analysis). The employment data used the period from Jan. 2001 through October 2011. It’s easier to build wooden greenhouses than skyscrapers, so to speak. These periods obviously don’t align exactly – but given the nature of the analysis (heterogenous ranking), I chose to go with best available data rather than with exactly matching time periods. Matching the time periods would have reduced the data available to 2001-2010, eliminating both some of the late 90’s boom and the current recovery.
- Even given the screen sharing caveats above, all states (plus DC) were ranked using the exact same data sets, and the combination of categories prevents (in my view) bias towards either a growth orientation, an income orientation, or an employment orientation. Others may disagree – heterogenous ranking systems are by nature somewhat subjective (in the choice and weighting of data used), and I thus provide all the raw data so that you can draw your own conclusions.
This is very old data…..
It’s hard to find consistent data sets across all 50 states that also go back far enough in time. It’s also important not to just look at the past 1-2 years. I actually think that the 14-15 years of data used here is a good sample, because it includes two full economic cycles.
Hopefully at some point I can re-analyze this as mentioned in a previous comment, with each state’s score weighted rather than based on rank.
It would be interesting to weight the variable based on distribution. E.g., if the top ten differ only by 1-2% difference in their scores, it receives an unintentional higher “weight” vs variable in a more standard distribution.
Good suggestion. I wanted to finally get this thing out the door, but given more time, I would use a formula that awards scores for each category based on where in the distribution each state’s data actually falls. This would reward states that have outlier performance in one area, and also not penalize those that are tightly bunched.
Nice demonstration of the power of commodity resources in the current economy. Thank you for putting this together.