This thesis examines non-labor income (NEI) patterns in the upper Great Plains region during 2007-2008. The document outlines the research questions, theoretical background, data sources and methods used which include principal component analysis and regression modeling. Key findings are that NEI concentrates most in suburban and exurban counties, with both government transfers and investment income contributing. Socioeconomic status indicators like education and income levels best explain NEI variations between counties. The thesis concludes more research is needed using IRS data across larger regions to better understand NEI relationships and influences on local economies.
Annie Williams Real Estate Report - November 2021Annie Williams
Sales of single-family, re-sale homes rose 5.6% year-over-year. Sales were up 27.3% from September. There were 303 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - March 2021Annie Williams
Sales Continue to Surge - Sales of single-family, re-sale homes rose again in February, gaining 36.5% year-over-year. They were up 2.6% from January. There were 157 homes sold in San Francisco last month. The average since 2000 is 214. The average sales price for homes rose 17.4% year-over-year. The median sales price for single-family, re-sale homes rose 5.6% year-over-year.
Annie Williams Real Estate Report - October 2021Annie Williams
After being higher than the year before thirteen months in a row, sales of single-family, re-sale homes dipped 0.4% year-over-
year. Sales were down 2.9% from August. There were 238 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - December 2020Jon Weaver
Home Sales & Prices Continue to Rise - Sales of single-family, re-sale homes rose again in November, gaining 27.3% year-over-year. They were down 11% from October. There were 252 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 3.8%. Condo sales are down 12%.
Annie Williams Real Estate Report - November 2021Annie Williams
Sales of single-family, re-sale homes rose 5.6% year-over-year. Sales were up 27.3% from September. There were 303 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - March 2021Annie Williams
Sales Continue to Surge - Sales of single-family, re-sale homes rose again in February, gaining 36.5% year-over-year. They were up 2.6% from January. There were 157 homes sold in San Francisco last month. The average since 2000 is 214. The average sales price for homes rose 17.4% year-over-year. The median sales price for single-family, re-sale homes rose 5.6% year-over-year.
Annie Williams Real Estate Report - October 2021Annie Williams
After being higher than the year before thirteen months in a row, sales of single-family, re-sale homes dipped 0.4% year-over-
year. Sales were down 2.9% from August. There were 238 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - December 2020Jon Weaver
Home Sales & Prices Continue to Rise - Sales of single-family, re-sale homes rose again in November, gaining 27.3% year-over-year. They were down 11% from October. There were 252 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 3.8%. Condo sales are down 12%.
Annie Williams Real Estate Report - November 2020Jon Weaver
Home Sales Continue to Rise
Sales of single-family, re-sale homes rose again in October, gaining 10.5% year-over-year. They were up 22% from September. There were 283 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 7.2%. Condo sales are down 14.4%.
Annie Williams Real Estate Report - March 2022Annie Williams
Sales of single-family, re-sale homes fell 8.3% year-over-year. Sales were up 17.1% from January. There were 144 homes sold in San Francisco last month. The average since 2000 is 214. While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before.
Annie Williams Real Estate Report - August 2021Annie Williams
The median sales price for single-family, re-sale homes fell 5.1% in July from June. Nevertheless, it rose 11.4% year-over-year.
The average sales price for single-family, re-sale homes fell 7.5% month-overmonth. Yet, year-over-year it was up 9.7%. Sales of single-family, re-sale homes rose again in July, up 14.1% year-over-year. Sales were down 20.8% from June. There were 243 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - September 2021Annie Williams
While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before. The median sales price for single-family, re-sale homes was flat in August from July. It was up 11.y% year-over-year. The average sales price for single-family, re-sale homes fell 6.7% month-over-month. Yet, year-over-year it was up 4.5%.
Annie Williams Real Estate Report - August 2020Jon Weaver
Home Sales Continue to Surge, Prices Rise
Sales of single-family, re-sale homes jumped in July, rising 28.8% from June. They were down 1.9% year-over-year. There were 210 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - September 2020Jon Weaver
Home Sales Continue to Surge, Prices Rise
Sales of single-family, re-sale homes jumped in August, rising 27.4% year-over-year. They were up 1.9% from July. There were 214 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 18.8%. Condo sales are down 26%.
Last week, Mr. William Strauss with the Federal Reserve Bank of Chicago presented the 2012 Economic Highlights to the Aurora Regional Chamber membership. This is his presentation.
Annie Williams Real Estate Report - November 2019Jon Weaver
Sales prices for condos/townhomes set a new high for the second month in a row. The median sales price for condos/townhomes was up 12.8% year-over-year. It was up 3.8% from September. The average sales price for attached homes gained 10.6% year-over-year. It was up 2.7% from September.
Annie Williams Real Estate Report - December 2021Annie Williams
Sales of single-family, re-sale homes rose 9.7% year-over-year. Sales were down 6.6% from October. There were 283 homes sold in San Francisco last month. The average since 2000 is 14.
While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before.
While eroding housing affordability in thriving metropolitan areas is well known and discussed, many rural areas face affordability issues too. Rural Oregon, and the broader intermountain West, have affordability challenges similar to many of those popular metro areas.
Patrick jankowski- 2019 Economic Outlook for Southeast TexasCharlie Foxworth
Southeast Texas Economic Development Foundation slides for the economic outlook for Southeast Texas in 2019. All systems are go for a good year. The only thing that can hold us back is our belief that we are "overdue" for a recession. Stay positive.
Annie Williams Real Estate Report - November 2020Jon Weaver
Home Sales Continue to Rise
Sales of single-family, re-sale homes rose again in October, gaining 10.5% year-over-year. They were up 22% from September. There were 283 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 7.2%. Condo sales are down 14.4%.
Annie Williams Real Estate Report - March 2022Annie Williams
Sales of single-family, re-sale homes fell 8.3% year-over-year. Sales were up 17.1% from January. There were 144 homes sold in San Francisco last month. The average since 2000 is 214. While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before.
Annie Williams Real Estate Report - August 2021Annie Williams
The median sales price for single-family, re-sale homes fell 5.1% in July from June. Nevertheless, it rose 11.4% year-over-year.
The average sales price for single-family, re-sale homes fell 7.5% month-overmonth. Yet, year-over-year it was up 9.7%. Sales of single-family, re-sale homes rose again in July, up 14.1% year-over-year. Sales were down 20.8% from June. There were 243 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - September 2021Annie Williams
While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before. The median sales price for single-family, re-sale homes was flat in August from July. It was up 11.y% year-over-year. The average sales price for single-family, re-sale homes fell 6.7% month-over-month. Yet, year-over-year it was up 4.5%.
Annie Williams Real Estate Report - August 2020Jon Weaver
Home Sales Continue to Surge, Prices Rise
Sales of single-family, re-sale homes jumped in July, rising 28.8% from June. They were down 1.9% year-over-year. There were 210 homes sold in San Francisco last month. The average since 2000 is 214.
Annie Williams Real Estate Report - September 2020Jon Weaver
Home Sales Continue to Surge, Prices Rise
Sales of single-family, re-sale homes jumped in August, rising 27.4% year-over-year. They were up 1.9% from July. There were 214 homes sold in San Francisco last month. The average since 2000 is 214. Year-to-date, home sales are down 18.8%. Condo sales are down 26%.
Last week, Mr. William Strauss with the Federal Reserve Bank of Chicago presented the 2012 Economic Highlights to the Aurora Regional Chamber membership. This is his presentation.
Annie Williams Real Estate Report - November 2019Jon Weaver
Sales prices for condos/townhomes set a new high for the second month in a row. The median sales price for condos/townhomes was up 12.8% year-over-year. It was up 3.8% from September. The average sales price for attached homes gained 10.6% year-over-year. It was up 2.7% from September.
Annie Williams Real Estate Report - December 2021Annie Williams
Sales of single-family, re-sale homes rose 9.7% year-over-year. Sales were down 6.6% from October. There were 283 homes sold in San Francisco last month. The average since 2000 is 14.
While off the highs reached in June, sales prices of single-family, re-sale homes are higher than the year before.
While eroding housing affordability in thriving metropolitan areas is well known and discussed, many rural areas face affordability issues too. Rural Oregon, and the broader intermountain West, have affordability challenges similar to many of those popular metro areas.
Patrick jankowski- 2019 Economic Outlook for Southeast TexasCharlie Foxworth
Southeast Texas Economic Development Foundation slides for the economic outlook for Southeast Texas in 2019. All systems are go for a good year. The only thing that can hold us back is our belief that we are "overdue" for a recession. Stay positive.
Somerset County Business Partnership collaborated with the Somerset Planning Board to develop a resource that summarized what a business operating in Somerset County “needs to know” about our growing diversity. We assembled a Diversity Task Force that helped us make the case that our growing diversity gives us a competitive advantage by helping us attract and retain the best talent, keep us innovative, and ahead of the curve. See what we found in this report.
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For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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3.
NEI: monies not from labor
Includes two types of non-labor
income
o Government Transfers (15% of
U.S. income)
• Social Security, Medicare, Food
Stamps (SNAP)
o 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 countercyclical fashion (Nelson, 2008)
4. Stage 1: Social Safety Net
(1913-1945)
Stage 2: Growth & Demographics
(1946-1975)
NEI History
Stage 3: Revolution & Deregulation
(1976-2007)
Stage 4: New Normal (2008-Present)
5.
Minimal attention from
political/economic leaders
Significant geographic concentration &
variation across America
Government budgets in dire straits
promise changes
o “Grand Bargain” Potential on
Taxes/Revenues
Demographics in „Age of Austerity‟
o “Grey” Economies invest more
conservatively & require more
government transfers in support
(401K system #FAIL, poor
economic literacy)
o Lower birth rates= less new
taxpayers
o Disputes over „legitimate‟
government transfers increasingly
age-centric (Brownstein, 2010)
6.
[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.
Geographers and other social scientists considered NEI‟s
importance in many other types of places and conditions
o Rural counties
(Nelson and Beyers, 1998)
o Natural resource dependent communities
(Petigaraa et al., 2012)
(Nelson, 2005:2008)
Regional income convergence (Austin and Schmidt, 1998)
Boom and bust economic cycles (Smith and Harris, 1993)
o Life course migration
o
o
(Wenzl, 2008)
o States (Kendall and Pigozzi, 1994; Campbell, 2003)
o Megapolitan areas (Debbage and Beaver, 2012)
o Micropolitan areas (Mulligan and Vias, 2006)
o Core-based statistical areas
8.
None except Forward (1982:1990) and Wenzl (2008)
incorporated capital gains and private pensions as
investment income
But Wenzl left out multiple NEI types, esp. transfers
None ever considered NEI in metropolitan counties, the
population and economic centers of the nation,
especially not in the UGP
9.
10.
11. BEA
Census/ACS
IRS
SOI
ZCTA
BEA capital gains refusal
Multi-County Zip Codes
IRS 20 months late
12. Single Parent Household % Non-Caucasian
Median Home Value
% Services Employment
% Elderly
% Construction
Employment
% Office Employment
% Uninsured
% Low Birth Weight
% Prod/Transportation
%
% Employment Growth
Management/Professional 2000-07/08
Employment
Employment
% Movers 1 Year Before % Poverty
% Workforce Participation
% Bachelor Degree
% Unemployment
% Rental Housing
%High School Dropout
% Married
% Diabetes
% Population Growth
2000-07/08
13. 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. 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. 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. 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.
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. Incorporate IRS data across all
metropolitan counties in U.S. for
regional comparisons
Structural equation modeling
instead of PCA and regressionbetter explains variable
relationships
Account for „accrued capital gains‟
somehow
Promote NEI awareness among
policymakers and academics
22. 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.
Editor's Notes
Aside from its growing share of local economies:
Explain how NEI changes due to government policy shiftsHighlight how including capital gains as investment income changes NEI geography
Tiebout(1962) acknowledged NEI in economic base theory butManson & Groop(1990) argued that only in the early 1980s was NEI included in economic base analyses for development purposes Along with Forward’s (1982:1990) Canadian city studies of NEI, Manson & Groop were early champions of NEI’s importance nationally (1988:1990:2000)
Wenzl was also more interested in household savings.
Upper Great Plains (UGP) classified by the U.S. Census as the West North Central Census sub-region Seven states: Iowa, Minnesota, Missouri, Nebraska, Kansas, South Dakota, North Dakota99 metropolitan statistical area (MSA) counties with 14.5 million peopleEleven MSA counties from Wisconsin and Illinois included because they were part of UGP-based MSAsClassified as suburban, wealthy suburban, major urban center, small urban center and exurban counties
Why study the UGP?It avoided the worst of the Recession’s impact in housing and employment (comparative to the Southeast and Midwest)its lack of extreme wealth concentration and inequality (comparative to the Northeast) its relative geographic proximity & concentration of counties (comparative to the West which ranges from Washington State to New Mexico)
Most prior studies used only NEI data from the Bureau of Economic Analysis (BEA).BEA refuses to include capital gains because they regard them as asset price changes, not ‘production or income’BEA counts pensions as earnings rather than as NEI when they are issued to pensionersCapital gains & pensions combined range from 3-7% of total national income every year, data available from IRSThis is a significant ‘gap’!Only problem to incorporate IRS data: zip code onlyMatched with counties via ARCGIS & MS Excel.10% of zip codes were located in multiple counties based on the % of population from Zip Code Tabulation Areas (ZCTAs), a county with 76% of the population was assigned 76% of the IRS data and the other county assigned 24%.IRS data was added to investment income totals
Data for socio-demographic, economic, and health variables collected from 3 year American Community Survey (2007-2009), and Robert Wood Johnson Foundation’s CountyHealthRankings.orgOriginally planned to include BEA & IRS NEI data for 2007-2009, but IRS never released 2009 data (now 18 months late!)22 variables in totalPrincipal component analysis (PCA) was utilized to address multi-collinearity concerns with variables
Above-average counties- more than half were major or small urban cores, including St. Louis city (MO), St. Louis, MN (Duluth), and Greene, MO (Springfield). All but one of the counties in the below average tier (Polk, IA) were suburban or exurban. Several in this group included the wealthiest counties in the U.S., with Carver, Wright, Anoka, Dakota, and Sherburne from Minneapolis and one each from Kansas City (St. Charles, MO) and Des Moines (Dallas, IA)
Median increase in investment income from including capital gains data was 36.6%.Mostly suburban and exurban counties comprised the above average tier of gainersEight counties had 10% or more of their total income overlooked by the BEA
The counties with below average proportions of transfers shared two primary features:Most had below average % of elderly populations (< 14.9%) and higher than average workforce participation % (> 70.1%Counties with higher proportions of transfers shared primary commonalities, including:above average % of elderly (> 14.9%), lower than average workforce participation %(< 70.1%), higher than average poverty % (> 10.9%) and lower than average bachelors or better educational attainment % (< 24.9%
Investment income is heavily represented in urban core counties of metropolitan areas, including Hennepin at 27.0%, Douglas at 28.2%, and St. Louis at 31.5%. Wealthy counties were notably absentMajority of below-average tier was suburban and exurban counties. 14 had above-average % of transfers (> 14.1%), half had average or above % of elderly residents (> 14.9%), and most had average or below % of workforce participation (< 70.1%) and below average % of bachelors degree attainment (> 24.9%).
utilized as a variable-reduction technique creates a smaller group of ‘principal components’, artificial variables accounting for as much of the variance in the original variables as possible. PCA was suitable for use because the variables were all measured at the continuous level, had linear relationships, and outlier tests indicated no significant outliers among the variables. 5 variables were created by PCA, accounting for 73.7% of total variance of all variables.Component 1 (hi-lo SES) contains variables tracking both weaker and stronger socio-economic status (SES) including education, workforce participation and health condition(Leigh and Blakely, 2013). Component 2 (Diversity) is a signal of diversity, with linked variables including % of non-whites, % renters and % in poverty. Component 3 (Office) contains the linked variables of single-parent households and employment in the office sector, a lower-paying sector that has less educational requirements (Roberts et al., 2012). Component 4 (Mobility) highlights mobility trends, especially for recent migrants who may lack health insurance as they seek employment, a dilemma that can worsen economic vulnerability (EBRI, 1999). Component 5 is reserved for median home value.
Component 1’s positive scores predominate with higher proportions of elderly, greater than median employment in lower-skill & pay employment sectors, higher unemployment & sub-optimal health conditions. Negative scores predominate with higher workforce participation rates, greater than median employment in the high-skill and high-pay professional and management sector & higher educational attainment levels. Component 1 captures higher and lower dynamics of SES. e.g., the concentration of positive scores in the Kansas City and St. Louis metropolitan areas and mostly negative scores in the Minneapolis & Des Moines MSAs. Component 2’s positive scores are prevalent with higher non-Caucasian populations, higher % of poverty and % of renters Negative scores indicate those with strong levels of marriage and construction sector employment. Possible unique role of construction employment in UGP counties with lower than average levels of Hispanic immigrant workers & possibly less downward pressure on wages as a result (Thompson, 2010). Also indicate the loadings reflect diversities of household composition, economic status and ethnicity: e.g. relatively wealthy Hennepin County, the urban core of Minneapolis MSA, has a very positive score driven in part by its greater diversity and renter demographics, as do many of the young, renter-heavy counties with major universities. Meanwhile, poorer urban core counties in the inner St. Louis and Kansas City MSAs also score highly.
Linear Regression Analysis with PCA:Stepwise regression models in SAS 9.3 to determine influence of each PCA variable on NEI types while controlling for their influence.Three options evaluated for best possible model.Model 1: Total NEI $ for each countyModel 2: Two models utilizing transfer & investment income totalsModel 3: NEI Ratio (Investment/Transfer)Model 1: Total NEIThe R-squared in the final regression model suggested that 38.6% of Total NEI variation was accounted for by two PCA components: Diversity (Comp. 2) & Uncertainty (Comp. 4)Using the unstandardized b coefficients, the estimated regression equation is:NEI = 2305734 + 1579810 (DI) – 1926048 (UN)Model 2: Total TransfersThe R-squared in the final regression model suggested that 51% of Total Transfers variation was accounted for by four PCA components: Diversity (Comp. 2), Office (Comp. 3) Uncertainty (Comp. 4) & Home Value (Comp. 5)Using the unstandardized b coefficients, the estimated regression equation is:Transfer Income = 776526 + 519160 (DI) + 174022 (OFF) - 556066 (UN) + 174116 (MHV)Model 2: Total InvestmentThe R-squared in the final regression model suggested that 37% of Total Investment variation was accounted for by three PCA components: Low-High SES (Comp. 1), Diversity (Comp. 2), & Uncertainty (Comp. 4).Using the unstandardized b coefficients, the estimated regression equation is:Investment Income = 1529208 - 521674 (LOH) + 1060650 (DIV) - 1369981 (UN) Model 3: NEI Ratio (Investment/Transfer)The R-squared in the final regression model suggested that 59.5% of NEI Ratio variation was accounted for by two PCA components: Low-High SES (Comp. 1) & Diversity (Comp. 2).Using the unstandardized b coefficients, the estimated regression equation is: NEIR= 1.74 – 0.571(LOH) + 0.119 (DI)Of the three models, the best is Model 3 (NEI Ratio).It accounts for the most variation within the components, has the least detectable bias, and has the components with the most included original variables. The second model containing the investment and transfer models has more detectable bias (Mallow’s CP score) and accounts for less of the variation in the components. The first model has considerable bias and also accounts for less of the variation in the components.
at 40% of U.S. Total Income, it must be considered as often as employment in policy debates