representative bureaucracy & economic prosperity (presentation)
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Representative Bureaucracy &
Economic ProsperityNikko Brady, Ariam Ford & Tremayne Youmans │UA 702 Final Project
Our ProjectOur Interest:
To assess the impact of an existing racially or ethnically representative municipal leadership team on the economic and social prosperity of a city and its residents.
The Impetus and Relevance:
◦ In Detroit in the 1970s, there was political unrest surrounding the concerns of marginalized Black citizens who disapproved of
their social, political and economic exclusion.
◦ Since that time, Detroit has seen a dramatic increase in Black leadership. However it remains unstudied whether or not the
onset of representative bureaucracy has any positive effects or significant influence on the economic trajectory of the city and
its residents.
◦ This idea led us to explore the general concept of representative bureaucracy in relation to the economic prosperity of a
city.
◦ Representative bureaucracy is a prominent idea in the fields of education, public policy, and corporate management.
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Research Question:
What is the relationship between having
leadership that racially/ethnically represents the
majority population of a city AND that city’s
economic prosperity?
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Sample & Size
50 Capital Cities of the United States
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Dependent Variable◦ Economic Prosperity
Independent Variables
◦ Representative Bureaucracy
◦ Educational Attainment
◦ Industry Breakdown
◦ Immigration
◦ Presence of Arts Organizations
◦ Health
Variables
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Concept Operationalized Variable
Economic Prosperity Unemployment Rate (unemployment)
Representative Bureaucracy Whether or not the Mayor was of same
culture/race as the majority culture/race in the
population (repbureac)
Educational Attainment % of population over 25 with less than a High
School Diploma (edlowest)
Industry Breakdown % of business establishments in professional,
scientific and technical services
(totalwhitecollar)
Immigration % Foreign Born (foreignborn)
Presence of Arts Organizations Ratio of arts establishments to every 5000
people (artspop)
Health Ratio of health care establishments to every
(health)
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Dependent Variable: Economic Prosperity─────────
Racially Representative Government (-)
Educational Attainment (+)
Industry Breakdown(+)
Immigration(-)
Presence of Arts Organizations(+)
Presence of Health Facilities(+)
Hypotheses
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Data Collection
◦ Census 2000
◦ Economic Census 2002
◦ Mayors
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
N Minimum Maximum Mean Std.
Deviation
Unemployment Rate (%) 50 1.70 9.10 4.21 1.62
Mayor same race/culture as majority population? 50 1.00 2.00 1.18 .39
Percent of population over 25 with less than a HS diploma50 4.85 39.16 17.24 7.10
% of industry establishments in information, proffesional,
educational, or health care47 11.73 26.38 18.45 3.65
% Foreign Born 50 .30 25.80 8.07 6.83
Ratio of arts establishments to every 5000 people 50 .94 8.60 2.75 1.65
Ratio of healthcare establishments to every 1000 people 50 2.06 8.94 4.23 1.59
Valid N (listwise) 47
Descriptive Statistics
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramUnemployment Rate
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramMayor's Race
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramEducational Attainment
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramIndustry Type
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramImmigration
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramArts Establishments
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
HistogramHealthcare Establishments
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Scatter Plots
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
ANOVA◦ Variables
◦ Grouping Variable: Unemployment Rate Categories
◦ Low Unemployment = 1.7-3.3%
◦ Medium Unemployment= 3.4%-4.4%
◦ High Unemployment=4.5%-9.1%
◦ Test Variables
◦ Repbureac
◦ Edlowest
◦ Totalwhitecollar
◦ Foreignborn
◦ Artspop
◦ Test/Technique
◦ ANOVA to determine if there is a significant difference between unemployment rate categories in terms of each independent variable
◦ Hypotheses
◦ H0: µ1= µ2= µ3
◦ H1: at least one of the population means is different in terms of the various test variables
◦ Sampling Distribution: F-distribution
◦ α =0.05
◦ F(critical)=3.6
◦ F(obtained) > F(critical) = reject null
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Variable F Sig. Means
Mayor same race/culture as majority population?
3.061 .056
Low unemployment ≈ Yes
Medium unemployment ≈ Yes
High unemployment ≈ Yes
% Population over 25 with less than HS diploma
13.393 .000
Low unemployment = 13.13%
Medium unemployment = 15.51%
High unemployment = 22.99%
% of industry establishments in information, professional,
educational or health care .619 .543
Low unemployment = 0.18%
Medium unemployment = 0.18%
High unemployment = 0.19%
% Foreign born
2.959 .062
Low unemployment = 4.92%
Medium unemployment = 9.69%
High unemployment = 9.69%
Ratio of arts establishments to every 5000 people
2.338 1.08
Low unemployment ≈ 3
Medium unemployment ≈ 3
High unemployment ≈ 2
Ratio of health care establishments for every 1000 people
2.658 .081
Low unemployment ≈ 5
Medium unemployment ≈ 4
High unemployment ≈ 4
ANOVA
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Correlation Matrix
repbureac edlowest totalwhitecollar foreignborn artspop health
%
Unemployment
Pearson
Correlation .236 .600 .257 .154 -.282 -.251
Sig. (2-tailed)
.099 .000 .081 .287 .047 .078
N
50 50 47 50 50 50
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Hypothesized Multivariate Regression Model
Unemployment = a + b1repbureac + b2edlowest + b3totalwhitecollar+ b4foreignborn+ b5artspop+ b6health
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Multivariate Regression Model
Unstandardized CoefficientsStandardized
Coefficients Sig.
B Std. Error Beta
Constant .130 1.461 .929
repbureac .060 .508 .015 .906
edlowest .150 .033 .655 .000
totalwhitecollar 12.103 5.394 .272 .030
foreignborn -.057 .032 -.244 .083
artspop -.071 .148 -.066 .633
health -.025 .147 -.024 .864
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Multivariate Regression Model: Ordinary Least Squares Equation
unemployment= .130 + 0.060(repbureac)+ 0.150(edlowest)+ 12.103(totalwhitecollar) - 0.057(foreignborn)- 0.071(artspop) - 0.025(health)
R. R Square Adjusted R SquareStd. Error of the
Estimate
.680 .463 .382 1.27611
Sum of
Squaresdf
Mean
SquareF Sig.
Regression 56.142 6 9.357 5.746 .000
Residual 65.138 40 1.628
Total 121.280 46
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac
Unstandardized CoefficientsStandardized
Coefficients Sig.
B Std. Error Beta
Constant .200 1.321 .880
edlowest .151 .031 .659 .000
totalwhitecollar 12.077 5.325 .271 .029
foreignborn -.058 .032 -.244 .078
artspop -.072 .146 -.067 .623
health -.027 .144 -.026 .853
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac
Unemployment = .200 + 0.151(edlowest)+ 12.077(totalwhitecollar) - 0.058(foreignborn)- 0.072(artspop) - 0.027(health)
R. R Square Adjusted R SquareStd. Error of the
Estimate
.680 .463 .397 1.26067
Sum of
Squaresdf
Mean
SquareF Sig.
Regression 56.119 5 11.224 7.062 .000
Residual 65.161 41 1.589
Total 121.280 46
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac and health
Unstandardized CoefficientsStandardized
Coefficients Sig.
B Std. Error Beta
Constant .093 1.177 .937
edlowest .152 .031 .663 .000
totalwhitecollar 12.085 5.263 .271 .027
foreignborn -.056 .031 -.240 .076
artspop -.084 .130 -.078 .523
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac and health
Unemployment = .093 + 0.152(edlowest)+ 12.085(totalwhitecollar) - 0.056(foreignborn)- 0.084(artspop)
R. R Square Adjusted R SquareStd. Error of the
Estimate
.680 .462 .411 1.24611
Sum of
Squaresdf
Mean
SquareF Sig.
Regression 56.063 4 14.016 9.026 .000
Residual 65.217 42 1.553
Total 121.280 46
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac, health and artspop
Unstandardized CoefficientsStandardized
Coefficients Sig.
B Std. Error Beta
Constant -.251 1.041 .810
edlowest .158 .029 .693 .000
totalwhitecollar 12.196 5.224 .274 .024
foreignborn -.058 .031 -.247 .064
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Backwards Selection Model: Remove repburac, health and artspop
Unemployment = -.251 + 0.158(edlowest)+ 12.196(totalwhitecollar) - 0.058(foreignborn)
R. R Square Adjusted R SquareStd. Error of the
Estimate
.676 .457 .419 1.23760
Sum of
Squaresdf
Mean
SquareF Sig.
Regression 55.419 3 18.473 12.061 .000
Residual 65.861 43 1.532
Total 121.280 46
Conclusions & Takeaways
◦ Representative bureaucracy is not significantly related to unemployment rates for the cities in this sample
◦ % Population over 25 years old with less than a high school diploma has the most variation among cities, but
is the most significant predictor of unemployment
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Limitations
◦ Small sample size
◦ Year 2000 data
◦ Longitudinal element missing
◦ Single indicator of economic prosperity taken into account
◦ Difficult to ascertain or assume the personal perspectives of Black leaders and representatives
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Introduction │ Variables │ Hypotheses │Data │ Univariate │ Bivariate │ Multivariate │ Conclusions
Questions?