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?