1. THE GEOGRAPHY OF NON-EMPLOYMENT
INCOME IN THE METROPOLITAN
UPPER GREAT PLAINS: DURING THE
2007-2008 RECESSION
By
Edward Beaver
MA Thesis Defense
Department of Geography, UNC-Greensboro
4 November, 2013
3. NON-EMPLOYMENT INCOME
(NEI)
NEI: monies not from labor
Includes two types of non-labor income
Government Transfers (15% of U.S. income)
Social Security, Medicare, Food Stamps (SNAP)
Investment Income (25% of U.S. income)
Capital Gains, Savings Interest, Stock Dividends
NEI “...can be seen as bringing ‘new
money’ into a local economy...” as
exports or higher wages do, even in
counter-cyclical fashion (Nelson, 2008)
4. NEI History
Stage 3:
Stage 2:
Stage 1:
Revolution
Growth &
Social Safety
&
Demograph
Net
Deregulatio
ics
(1913-1945)
n
(1946-1975)
(1976-2007)
Stage 4:
New
Normal
(2008Present)
5. IMPORTANCE
Minimal attention from political/economic
leaders
Significant geographic concentration &
variation across America
Government budgets in dire straits
promise changes
“Grand Bargain” Potential on Taxes/
Revenues
Demographics in ‘Age of Austerity’
“Grey” Economies invest more
conservatively & require more
government transfers in support (401K
system #FAIL, poor economic literacy)
Lower birth rates= less new taxpayers
Disputes over ‘legitimate’ government
transfers increasingly age-centric
(Brownstein, 2010)
6. RESEARCH QUESTIONS
[1] What geographic patterns of NEI (e.g., investment income vs.
government transfers) are apparent in the upper Great Plains
region during the early Great Recession (2007-2008)?
[2] How influential is NEI in this region’s economy?; and
[3] What factors (e.g., socio-demographics, economic, etc.)
explain the geographic variations of NEI? Very specifically, are
these variations indicating strong relationships to different
industrial sector patterns and are they shaped by the urban
system such as urban, suburban, or exurban?
7. THEORETICAL/CONCEPTUAL
BACKGROUND
Geographers and other social scientists considered NEI’s importance in
many other types of places and conditions
!
Rural counties (Nelson and Beyers, 1998)
Natural resource dependent communities (Petigaraa et al., 2012)
Life course migration (Nelson, 2005:2008)
Regional income convergence (Austin and Schmidt, 1998)
Boom and bust economic cycles (Smith and Harris, 1993)
Core-based statistical areas (Wenzl, 2008)
States (Kendall and Pigozzi, 1994; Campbell, 2003)
Megapolitan areas (Debbage and Beaver, 2012)
Micropolitan areas (Mulligan and Vias, 2006)
8. CONTRIBUTIONS OF THIS THESIS
None except Forward (1982:1990) & Wenzl (2008) incorporated
capital gains and private pensions as investment income
But Wenzl left out multiple NEI types, esp. transfers
None ever analyzed NEI in metropolitan counties, the population
and economic centers of the nation, especially not the UGP
12. DATA AND METHODOLOGY
% Single Parent
Household
Median Home Value
% Uninsured
% Prod/
Transportation
Employment
% Movers 1 Year
Before
% Bachelor Degree
%High School
Dropout
% Population
% Non-Caucasian
% Elderly
% Services
Employment
% Low Birth Weight
% MGMT/
Professional
% Construction
Employment
% Office Employment
Employment
% Employment
Growth 2000-07/08
% Unemployment
% Workforce
Participation
% Rental Housing
% Married
% Diabetes
% Poverty
13. RESULTS
Highest NEI %
Counties
Polk , MO
Macoupin , IL
Carlton , MN
Jasper , MO
St. Louis , MN
Douglas , WI
Pennington , SD
Callaway , MO
Washington , MO
Greene , MO
Dubuque , IA
Jones , IA
Cape Girardeau , MO
St. Louis , MO
Henry , IL
Franklin , MO
Blue Earth , MN
Shawnee , KS
Rock Island , IL
Polk , MN
48.11%
45.82%
44.82%
43.78%
43.75%
43.60%
43.32%
43.27%
42.75%
42.72%
42.55%
42.46%
41.92%
41.65%
41.32%
40.83%
40.43%
40.34%
40.32%
40.24%
Lowest NEI %
Counties
Riley , KS
Scott , MN
Sarpy , NE
Geary , KS
Carver , MN
Warren , IA
Platte , MO
St. Croix , WI
Wright , MN
Dallas , IA
Sherburne , MN
St. Charles , MO
Dakota , MN
Isanti , MN
Clay , MO
Polk , IA
Anoka , MN
Jefferson , MO
Pottawattamie , IA
Sumner , KS
25.98%
26.65%
27.40%
27.69%
27.89%
28.98%
29.01%
29.68%
29.81%
30.00%
30.02%
30.20%
30.48%
30.55%
30.63%
31.24%
31.27%
31.51%
31.82%
32.36%
14. RESULTS
Highest Gain From IRS Counties
Callaway , MO
Lincoln , MO
Christian , MO
Macoupin , IL
Benton , IA
Meade , SD
Franklin , MO
Anoka , MN
Jersey , IL
Butler , KS
Leavenworth , KS
Geary , KS
Carlton , MN
Chisago , MN
Benton , MN
Lincoln , SD
Douglas , WI
Cass , MO
Jefferson , MO
Clay , MO
99.31%
90.89%
84.40%
81.62%
76.18%
76.16%
75.40%
69.69%
69.63%
67.77%
62.86%
61.22%
61.05%
59.89%
58.07%
57.32%
56.67%
54.67%
52.95%
52.13%
15. RESULTS
Highest Transfer %
Washington , MO 29.80%
Polk , MO
26.04%
Wyandotte , KS
22.29%
St. Louis city, MO 21.90%
Carlton , MN
21.86%
Webster , MO
21.81%
Douglas , WI
21.48%
Jasper , MO
21.23%
Buchanan , MO
20.47%
Lafayette , MO
19.90%
St. Louis , MN
19.85%
Polk , MN
19.78%
Franklin , KS
18.92%
Warren , MO
18.29%
Jones , IA
17.99%
St. Clair , IL
17.88%
Macoupin , IL
17.78%
Dakota , NE
17.73%
Callaway , MO
17.66%
Pottawattamie , IA 17.27%
Lowest Transfer %
Lincoln , SD
4.95%
Carver , MN
6.03%
Johnson , KS
6.68%
Dallas , IA
7.48%
Scott , MN
7.71%
Washington , MN 7.93%
Riley , KS
8.02%
Dakota , MN
8.67%
Johnson , IA
8.76%
Sarpy , NE
9.06%
Platte , MO
9.08%
St. Croix , WI
9.18%
Cass , ND
9.36%
Hennepin , MN
9.61%
Geary , KS
9.89%
St. Louis , MO
10.15%
St. Charles , MO 10.23%
Douglas , NE
10.60%
Monroe , IL
10.69%
Douglas , KS
10.73%
16. RESULTS
Highest Investment %
St. Louis , MO
31.50%
Johnson , KS
29.27%
Pennington , SD
28.65%
Meade , SD
28.57%
Douglas , NE
28.17%
Macoupin , IL
28.04%
Lincoln , SD
27.52%
Dubuque , IA
27.39%
Minnehaha , SD
27.34%
Greene , MO
27.24%
Douglas , KS
26.75%
Hennepin , MN
26.70%
Franklin , MO
26.58%
Blue Earth , MN
26.51%
Monroe , IL
26.31%
Cape Girardeau , MO 26.19%
Henry , IL
25.76%
Callaway , MO
25.60%
Rock Island , IL
25.55%
Ramsey , MN
25.30%
Lowest Investment %
Washington , MO
12.95%
Wyandotte , KS
12.99%
Pottawattamie , IA
14.54%
Dakota , NE
15.47%
Ray , MO
15.70%
Isanti , MN
15.99%
Sumner , KS
16.14%
Webster , MO
16.32%
Warren , MO
16.50%
Clinton , MO
16.54%
Jefferson , MO
16.87%
Warren , IA
16.91%
Lafayette , MO
17.32%
Geary , KS
17.79%
Buchanan , MO
17.80%
Riley , KS
17.96%
St. Louis city, MO
18.01%
Wright , MN
18.34%
Sarpy , NE
18.34%
Miami , KS
18.59%
20. SUMMARY & CONCLUSIONS
Greatest impact observed in suburban & exurban counties, much more than in
wealthy suburban counties.
Where NEI concentrates, transfers and investment NEI trend at high levels relatively
equally
Counties with lower NEI trended suburban or exurban.
Urban cores and very well educated counties had highest levels of investment income
NEI is no more or less influential in UGP than nationally
BEA accounts of NEI are inaccurate without capital gains.
Socio-economic status indicators are most important indicators
21. NEXT STEPS
Incorporate IRS data across all
metropolitan counties in U.S. for
regional comparisons
Structural equation modeling instead
of PCA and regression- better
explains variable relationships
Account for ‘accrued capital gains’
somehow
Promote NEI awareness among
policymakers and academics
22. ACKNOWLEDGEMENTS &
QUESTIONS
Thank you to my advisor Dr. Selima Sultana!
My thesis committee members Dr. Keith Debbage &
Dr. Zhi-Jun Liu.
The San Francisco Blue Cross/Blue Shield Research
Team, Wake County Tea Party & N.C. NAACP for
supportive and constructive comments over the
summer and fall.
