REPRESENTATIVE BUREACRACY & ECONOMIC PROSPERITY UA 702 │May 5, 2014
Nikko Brady, Ariam Ford & Tremayne Youmans Boston University
ABSTRACT The concept of Representative Bureaucracy considers the extent to which the race or ethnicity of a political leadership team matches that of the majority of the population which it serves. Beginning in the 1970s in Detroit, primarily Black and minority citizens publicly expressed their disdain for the lack of inclusion within the city’s social, economic, and political systems and institutions. This phenomenon influenced discussions among citizens about the underrepresentation of their political leaders and inability of the city’s mayor to address the needs of minority groups. Since that time however this concept of Representative Bureaucracy and its impact on economic development of citizens has been underexplored. Our project assesses the relationship between Representative Bureaucracy and economic prosperity. Each of the following variables is further operationalized to develop proxy variables that are suitable for generating quantitative data. The dependent variable is unemployment rate, which represents economic prosperity and the primary independent variable is mayor’s race, which represents Representative Bureaucracy. Contrary to our hypothesis our results demonstrate that the race of a city’s mayor matching the race of the population majority is not a statistically significant predictive variable for variation in unemployment rate in a city. This is based on a significance level of (.056) using a 95 percent confidence level. Nonetheless the complete results of our research show that educational attainment is a significant predictor of economic prosperity with a significance level of (.000) and a standard deviation of (7.10) using a 95 percent confidence level.
INTRODUCTION In Detroit in the 1970s there was political unrest surrounding the concerns of marginalized Black and other racial minority groups who disapproved of their social, political, and economic exclusion. These citizens were not satisfied with the lack of support and representation from their political leaders (LeDuff, 2013). This sparked a discussion about the concept of representative bureaucracy influencing the economic development of the population. Since the 1970s Detroit has seen a dramatic increase in Black leadership however an explanation for this trend remains unclear and more specifically if the onset of representative bureaucracy has any positive effect or significant influence on the economic trajectory of the city and its residents. We sought to explore this trend and potential correlation in other cities in the United States.
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Current literature that details the impact of representative bureaucracy on educational attainment of students that are taught by teachers sharing the same race; the extent to which public policy accurately and effectively addresses public concerns when policy makers have the same race as the population they serve; and finally in the field of corporate management
where CEOs sharing the same race as their employees impacts their progress. Our aim for this research is to assess the impact of an existing racially or ethnically representative municipal leadership team on the economic prosperity of a city and its population. To begin we developed a model to predict the changes in economic prosperity of a city and its population. The methodology included first choosing six independent variables that include Representative Bureaucracy, Educational Attainment, Industry Breakdown, Immigration, Presence of Arts Organizations, and Presence of Healthcare Establishments and one dependent variable, Economic Prosperity. These variables provide a holistic picture of what influences the changes in economic prosperity in a city. Next we operationalized the independent variables and the dependent variable in order to transform qualitative concepts into practical, measurable variables. Economic Prosperity, the dependent variable is measured using a proxy variable, unemployment rate. Representative Bureaucracy is the primary independent variable and expected to have a statistically significant positive relationship on economic prosperity of a city and its citizens. Third, we determined a scale of measurement for this research. We focus on the city level and a sample size of 50, which includes the capital cities in the United States. Additionally, we determined the level of measure for each variable, which includes both nominal and interval ratio variables. This distinction was useful to better choose appropriate statistical tools to analyze the variables and determine their relation to economic prosperity. Finally, we developed a relationship feedback loop that demonstrates our hypotheses about the impacts of the independent variables on economic prosperity. A mayor with the same race as the population majority will cause an increase in unemployment rate, which is an adverse effect on economic prosperity. The results of univariate, bivariate and multivariate statistical analysis allowed us to answer what is the relationship between a city having leadership that racially or ethnically represents the race or ethnicity of the population majority of a city and that city’s economic prosperity? We hypothesize that Representative Bureaucracy has a statistically significant negative relationship with economic prosperity. This paper provides an organized assessment of our findings and includes further explanation and necessary justifications for the statistical tools we employed.
LITERATURE REVIEW
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Several factors contribute to the economic health and development of a city and its residents. In the study we review the impacts of Representative Bureaucracy; Presence of Arts Organizations; Educational Attainment; Immigration; Presence of Healthcare Establishments; and Industry Breakdown.
REPRESENTATIVE BUREAUCRACY & ECONOMIC PROSPERITY Kennedy (2012) discusses the extreme difficulty and lack of consensus for defining the concept of representative bureaucracy. This literature includes a review of various scholars attempting to define the concept using several defining features such as access, where members of various groups within the population can reach out to members of the administration and agency, where each group within the society must have a presence within the administration. In their book entitled A Contingency Approach to Representative Bureaucracy, Groeneveld and Van de Walle discuss a shift in thinking about representative bureaucracy as a concept of equal opportunity rather than of power where in the latter, an effective administration is representative of the dominant classes in society. (Groeneveld and Van de Walle 2010, 244). The equal opportunity definition of representation extends to the administration being representative of the population and not only dominant groups of the society. This shift acknowledges that administrations become inefficient when they are led by only certain groups of society and fail to address and service the needs of a population at large and instead focuses on resolving issues that may be specific or beneficial to a particular group or class within the society. In the article entitled Representative Bureaucracy and Distributional Equity: Addressing the Hard Question, Meier et al (1999) discuss that despite skepticism, under certain conditions passive racial representation can lead to active representation, which yields policies that benefit Black and Latino minority groups (Meier et al 1999, 1025). In this study bureaucratic representation is measured at the street level and based on minority versus majority comparison (Meier et al 1999, 1029). Our research is based on the concept of representation that includes agency, which is demonstrated using the variable of mayor’s race matching that of the population majority (Kennedy 2012, 7). Also we used a minority versus majority approach where we determine if the mayor shared the same race as majority of the population. The hypothesis asserts that representative bureaucracy is based on power rather than equal opportunity and therefore has a negative impact on economic prosperity, which would be demonstrated by an increase in unemployment rate.
ARTS ORGANIZATIONS AND IMPROVED HUMAN CAPITAL
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Murkusen (2006) dissects the link between what is known as the "creative class" and urban growth. This paper addresses the fuzziness of this causal relationship from the perspective of the artist. Artists' impact on the economy can be both progressive and
problematic. The author asserts that artists offer an important, positive contribution to the diversity and vitality of cities. The qualities of artists as presented in this article offer a foundation for our hypothesis that the presence of arts organizations in cities have a positive impact on economic and social prosperity. Murk used suggests that artists as a political unit have the potential to lead in social and urban transformation efforts. The author addresses that the impact of art on social and economic growth is difficult to conceptualize. Furthermore the University of Pennsylvania is conducting a project called the Social Impact of the Arts Project, which acknowledges the value and relevance of programs that promote art and culture to improve urban vitality and social wellbeing. Lastly, the third component of the national report entitled Arts and Economic Prosperity III, presented by Americans for the Arts discusses the economic impact of nonprofit arts and culture organizations on their audiences. The overarching takeaway from the report is essentially that arts mean business. Communities that invest in arts programs reap the benefits of additional jobs, economic growth, and improved quality of life all of which position the community to have a more active and integral role in the larger scale economy. The report addresses the grave circumstances of shrinking resources to fund these types of extracurricular programs and asserts that community leaders that invest in arts programs are taking a step in the right direction to achieve community development. The concerns about jobs and economic performance and attracting business all to compete with the world economy outside of their community can all be addressed with a secure investment in arts programs. The report focuses on the Arts Industry as a growth industry that generates economic growth for communities via conducting performances and shows that provide jobs for working artists as well as attracts paying audiences. With promising increases in employment and spending, arts programs and the arts industry offer prosperity to the community's economy. In order to measure economic impact, the authors use an input/output system that includes a combination of statistical methods and economic theory. This analysis allows economists to track how often a dollar is respect within the local community and the economic impact generated. The report includes various examples of the impact and value of arts in the local economy including the extent to which arts programs influence tourism and extends the duration of tourists' travel. These articles and reports provide a substantial foundation that supports our hypothesis about the existence of arts organizations positively impacting social and economic prosperity.
EDUCATIONAL ATTAINMENT AND ECONOMIC PROSPERITY
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According to Hanushek and Womann (2007) the quality of education has a significant, but indistinct impact on economic growth. The paper is based on the findings that while improved schooling is a central component of community development strategies expansion of school attainment has not guaranteed economic development. This paper focuses on the impact of educational quality in particular on economic development. The paper concludes that there is a strong correlation between the cognitive skills of the population and economic growth including individual earnings and income distribution. Information about Educational attainment alone does not sufficiently indicate economic development. It is important to acknowledge the robust relationship between skills and growth. This article provides useful information to tailor our project so that our assessment of educational attainment is not given more weight than it deserves in its capacity to predict economic prosperity.
IMMIGRATION AND ECONOMIC ACHIEVEMENTS Esses et al (2001) finds that perceived group competition among immigrants and residents is strongly implicated in negative attitudes about immigrants and immigration. This article addresses the component of our research that deals with social prosperity and provides valid information regarding the potential negative consequences of immigration as increased immigration impacts the feeling of security both socially and economically for residents. The article points out that much of this tension between immigrants and residents is merely perceived, but discusses how far reaching and harmful these perceptions can be. In the 2008 book entitled On The Nature of Prejudice, Instrumental Relations Among Groups: Group Competition, Conflict and Prejudice; Esses et al acknowledges that competition for jobs may exist between individuals as a result of a perceived competition at a group level (Esses et al 2008, 228). Furthermore that increases in minority population due to immigration lends to exacerbated perceptions of competition for jobs and economic resources (Esses et al 2008, 229). The authors suggest that this is due in part to the increased salience of the group and also that resources are often constructed as finite in availability.
INDUSTRY BREAKDOWN & ECONOMIC PROSPERITY
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This variable is defined based on the categories of occupations that are considered white collar, professional and those within the science and technology fields. We compare this category to jobs in the service industry. Our aim is to address the aspect of economic health that is influenced by a skills mismatch, where the abundance of workers that are qualified for low-skilled, service jobs does not match up with the job opportunities that require skills in the technology or science fields. This mismatch may lead to a rise in unemployment rate despite the addition of more jobs into the labor market. In the article Employability, Skills Mismatch
and Spatial Mismatch in Metropolitan Labour Markets; Donald Houston discusses the importance of employability for ensuring an individual's long term employment security (Houston 2004, 221). Skills mismatch is one of the most prevalent labor market explanations for unemployment (Houston 2005, 222).This means that there is a mismatch of the skills of the unemployed and the skills demanded by employers. Our assessment does not focus on the demand side of the labor market, which would target spatial mismatch of job opportunities rather than skills mismatch.
DATA, METHODS & HYPOTHESES Our unit of analysis for this study were the 50 capital cities of the United States. We chose this sample size because 50 cases was adequate for our assignment purposes, it assured a relatively even geographical dispersion, and made data collection easier. In order to address the relationships between our dependent variable economic prosperity and the explaining concepts, we had to operationalize them into metrics that we could include in a statistical analysis. Also, we attempted to standardize our data to the year 2000, as this was the earliest year for which data on the most cities was available. To measure our dependent variable, we used percent population unemployed, and to measure our independent variable of choice, representative bureaucracy, we asked whether or not the Mayor of the city was the same race/culture as the majority of the population of residents. While this variable is a nominal, we operationalized it as an interval ratio dummy variable by setting the answer “yes” for 0 and the answer “no” for 1.1 The unemployment data was collected from File 3, Table DP-3 Census year 2000 while our method for determining the race of the mayor was looking through town website archives and directly calling local government offices. To measure educational attainment, we used the % of the population over 25 with less than a high school diploma. To measure the white collar industry, we used the % of business establishments in professional, scientific, and technical services in the city. To measure immigration, we used the percent of the population who was foreign born. To measure the presence of arts organizations, we used the ratio of arts establishments to every 5000 people in the population. And finally, to measure health, we used the ratio of health care establishments to every 1000 people in the population.2
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For the univariate and bivariate tests, repburac is coded as 1 and 2. However, for the multivariate analysis, we recoded the variable for 0 and 1. 2 Ratio of arts establishments uses 5000 people as the base because it is the lowest number at which the ratio remains a whole number. The same explanation is valid for health care establishments.
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Data for immigration and educational attainment were obtained from File 3, Table DP2 Census year 2000, while industry breakdown, presence of arts organizations, and health care
establishment’s data was obtained from the Economic Census year 2002. The reason we had to use 2002 data was because it is the earliest year for which data on the most tables was available. A visual breakdown of the operationalization of our variables can be seen in Table A of the appendix. We hypothesized that our primary explanatory variable, representative bureaucracy will have a negative influence on economic prosperity, which is demonstrated by an increase in unemployment rate. In addition an increase in immigration will also have a negative influence on economic prosperity, which is demonstrated by an increase in unemployment rate. On the other hand, we predicted that the variables educational attainment, industry breakdown, and presence of arts organizations, and presence of healthcare establishments will have a positive influence on economic prosperity, which is demonstrated by a decrease in unemployment rate. A visual breakdown of our hypotheses can be seen in Table B of the Appendix. To conduct this study, we used the tools of univariate, bivariate, and multivariate statistical analysis. Other tools included descriptive statistics, scatter pots, ANOVA, pearson r, and linear regression prediction modeling.
FINDINGS UNIVARIATE In most of the capital cities, the race of the mayor was the same as the majority of the population. There were some instances where the mayor’s race was not the same as the majority of the population, but it was almost equal to the race of majority of that city’s population. As America becomes more diverse, more races may be represented in the office of the mayor. However, in many of the capital cities there were not many foreign born residents. The typical capital city had an average of (8%) foreign born with a standard deviation of (6.83). The variance among capital cities as it relates to their foreign born population was broad. The city of Pierre had the lowest foreign born population (.30%) while Boston had the highest (25.80%). Immigrants migrate to cities with economic opportunity.
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Of the capital cities, some cities had more distinct concentrations of white collar industries than others. The typical capital city had an average (18.44%) of white collar industries with a standard deviation of (3.6). With Boston having (26%) white collar industry establishments, this may contribute to the high percentage of immigrants. Atlanta also had (26%) white collar industry establishments but had an (8 %) unemployment which was the highest rate among capital cities. The typical capital city had an average (4.2%) unemployment rate with a standard deviation of (1.62). Montpelier had the lowest unemployment rate (1.70%) and the lowest population of people over 25 with less than a high school diploma.
The typical capital city has an average (17.24%) population over 25 with less than a high school degree and a standard deviation of (3.6). Montpelier low unemployment rate may be a direct result of their low percentage of population 25 over with less than a high school diploma. Educational attainment and unemployment are both factors that can affect the economic prosperity of a city. In addition economic prosperity can be determined by looking at the number and distribution of healthcare facilities and even arts/cultural establishments in a city. The typical capital city had an average (4.23%) of healthcare establishments to every 1000 people with a standard deviation of (1.59). While the dispersion of healthcare establishments was not significant, many cities did not have highly clustered healthcare facilities. In regards to arts establishments, the typical capital city had an average (2.65%) of arts establishments to every 5000 people and standard deviation of (1.65). Many of the capital cities did not have a high concentration of arts establishments however; SantĂŠ Fe did with (8.60%) arts establishments to every 5000 people. (Tables 1-9)
BIVARIATE SCATTER PLOTS To begin to understand the relationship between the dependent variable, unemployment rate, and each individual independent variable, we first constructed a scatterplot matrix and corresponding lines-of-best-fit. The purpose of this test was to preliminarily asses if any linear relationships existed between the dependent variable and the independent variables, to determine the strength of these relationships, as well as the direction of these relationships. Given that none of the best-fit lines for the scatterplots are parallel with the x-axis, we know that the conditional distributions for unemployment change as the independent variables change. Based on a visual observation, the independent variable with the tightest clustering of data points along the regression line is edlowest, suggesting that the strongest association of all the variables exists between unemployment rate and the percentage of people over 25 with less than a high school diploma. From the scatterplots, we can see that there are 4 positive relationships (repburac, edlowest, totalwhitecollar, foreignborn) and 2 negative relationships (artspop, health). (Table 10)
ANOVA
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To further understand the relationship between the dependent variable and each singular independent variable, we conducted an ANOVA test. The ANOVA test helped us to determine if there is a significant difference between different levels of unemployment rates relative to each independent variable. To do this test, we had to organize the dependent variable into a grouping variable, resulting in three different unemployment categories, low
(1.7-3.3%), medium (3.4-4.4%) and high (4.5-9.1%). We determined these ranges by dividing the range of % unemployment scores by three. (Table 11) For a sample 50 cities, there is a significant difference between unemployment levels in terms the percent of the population over 25 who has obtained less than a high school degree. (Low unemployment=13.13%, Medium unemployment=15.51%, High unemployment=22.99)(F=13.393, p<0.05). This finding leads us to reject the null hypothesis, further strengthening the argument for a relationship between unemployment rates and educational attainment. For our independent variable of interest (repburac) and all other variables, there was no significant difference between groups. (Table 12)
CORRELATION MATRIX Using the Pearson Correlation, we were able to determine the strength, direction, and significance of pairs of variables, with particular focus on the relationship between our dependent variable and all of the independent variables. There is a statistically significant strong positive relationship between unemployment rate and the percent of the population over 25 years old with less than a HS diploma. As the percent of those over 25 with less than a HS diploma increases, the unemployment rate increases. (r=0.600)(p<0.05) There is also a statistically significant weak negative relationship between unemployment rate and the ratio of arts establishments to every 5000 people. As the ratio of arts establishments to people increases, the unemployment rate goes down. (r=-0.282)(p<0.05) The test showed all other independent variables, including our variable of choice repburac had insignificant relationships with the dependent variable unemployment. (Figure 13)
MULTIVARIATE Finally, we constructed a multivariate prediction model to determine the most important predictors of unemployment rates based on our independent variables. Our original hypothesized regression model is stated as such: Unemployment= a + b1repbureac + b2edlowest + b3totalwhitecollar+ b4foreignborn+ b5artspop+ b6health The resulting equation using the coefficients generated is stated as such: Unemployment= .130+ 0.060(repburac) + 0.150(edlowest) + 12.103(totalwhitecollar) 0.057(foreignborn) - 0.071(artspop) - 0.025(health)
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According to the results, 46.3 of the variation observed in unemployment rates across the sample can be explained by the combined effects of all of our independent variables. (R=.463) However, only two of the variables have statistically significant coefficients at the 95%
confidence level, edlowest and totalwhitecollar. (Tables 15-17) In order to increase the efficiency and accuracy of the model, we used the backwards selection technique to systematically remove insignificant independent variables from the model. First we removed our main independent variable of interest, repburac, as it had the highest insignificance level in the original model (sig. =.906) After removing representative bureaucracy, our model can be stated as such: Unemployment= a + 0.151(edlowest) + 12.077(totalwhitecollar) - 0.058(foreignborn) 0.072(artspop) - 0.027(health) In this model, 46.3% of the variation observed in unemployment rates across the sample can be explained by the combined effects of edlowest, totalwhitecollar, foreignborn, artspop and health. (R=.463) While the R square value for this model is the same as the previous model, the adjusted R square value increased, showing that our model was helped by removing a variable. (Tables 19-21) We then removed the next most insignificant variable from the model, health. The resulting equation is stated as such: Unemployment= a + 0.152(edlowest) + 12.085(totalwhitecollar) - 0.056(foreignborn) 0.084(artspop) In this model, 46.2% of the variation observed in unemployment rates across the sample can be explained by the combined effects of edlowest, totalwhitecollar, foreignborn and artspop.(R=.462) The R square value for this model slightly decreases, which is common when removing variables from a model. However yet again, the adjusted R square value increased, showing that our model was helped by removing a variable. (Tables 23-25) Next, we removed a final insignificant variable, artspop. The resulting equation is stated as such: Unemployment= a + 0.158(edlowest) + 12.196(totalwhitecollar) - 0.064(foreignborn)
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In our final model, (45.7%) of the variation observed in unemployment rates across the sample can be explained by the combined effects of the % of the population over 25 with less than a HS diploma, the % of industry establishments in information, professional, educational, or health care, and the % of the population who is foreign born. (R=.457) For every 1% increase in the percent of the population over 25 with less than a HS diploma, unemployment increases (0.16%). For every 1% increase in the percent of industry establishments in information, professional educational, or health care, unemployment increases (12.20%). For every 1% increase in the percent foreign born population, unemployment decreases (0.06%). According to this model, the % of the population over 25 years old without a high school diploma is the variable with the most explanatory power relative to unemployment rate. (sig. =.000)
CONCLUSIONS The results of this research do not support our primary hypothesis that Representative Bureaucracy has a statistically significant negative relationship with economic prosperity, which is that economic prosperity will increase if the leadership is racially representative of the majority of the population. Based on this study, Representative Bureaucracy is not significantly related to unemployment rate for the cities in this sample. The percentage of the population over 25 years old with less than a high school diploma is the most significant predictor of unemployment. It is important to note that this variable also has the largest variation among cities. A high level of variation among educational attainment means that geospatially, some communities have very high levels of attainment, and others have very low levels of attainment, causing a larger gap of disparity. Because this model shows that educational attainment is a strong predictor of unemployment rates, policy makers should focus in on trying to address the disparity of educational attainment to have the greatest chances of decreasing unemployment rates. Although the primary independent variable is not statistically significant, based on the R-squared value (.457) of our overall model the combination of independent variables: educational attainment, total white collar jobs, and total foreign born population provide significant power to predict the variation in economic prosperity within a city. Educational attainment and total white collar jobs positively influence economic prosperity and therefore correlate with a decline in unemployment rate while total foreign born population negatively influences economic prosperity and correlates with an increase in unemployment rate.
JUSTIFICATIONS & LIMITATIONS In order to feasibly and successfully conduct our analysis we made strict parameters for the scope of our analysis; the scale of measurement; the independent variables to include; and how to operationalize and measure the variables. Much of our decisions were based on the methods expressed in literature related to this topic as well as our combined assumptions. Therefore, by making certain decisions we excluded others and acknowledge some limitations in our methodology.
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We defined our sample based on the city level context described in the original Detroit case in the 1970s. We chose to maintain the city level context in order to compare results to the outcome in Detroit as well as manage a consistent representation of bureaucracy that we thought had a primary influence on variations in the economic conditions of the population, which was the mayor. Using all 50 capital cities was a way to maximize our sample size to
produce the most normally distributed data, rather than only using capital cities that are in urban areas. It was difficult to conduct a comparative analysis of the circumstance of economic prosperity in Detroit due to a lack of recent temporal data. Therefore we broadened our scope to include other cities in addition to Detroit, which provided a comparative analysis despite the lack of a longitudinal element and this also provided a more disperse geographic representation of any trends Due to limited availability of U.S. Census data we gathered information using the year 2000 data and year 2002 data. These datasets are the most recent census data, but may lack accuracy with the current 2014 economic conditions and bureaucratic information. We accounted for economic prosperity using a single variable, unemployment rate. Based on our knowledge of mayoral terms and the inability to precisely determine where in their term each mayor was or if the mayor had served consecutive terms, we decided on an economic health indicator that could most likely be impacted by a mayor serving only one term. Using unemployment rate provided a way to standardize the measurement of economic health for each city and determine the trajectory of growth and relate it to the mayorâ&#x20AC;&#x2122;s representativeness. We considered to measure educational attainment based on the percentage of the population over 25 years old with less than a high school diploma because we determined that many low-skilled service jobs require at least a high school diploma for hiring criteria. This measure was an appropriate way to capture the number of unemployed. Our reasoning behind including the percentage of white collar jobs is based on our familiarity with the idea of spatial mismatch, when the skill sets of the workforce do not match up with available industry positions. Our thinking was, given the national trend towards more skilled labor and an overwhelming number of service oriented workers, an increase in the number of businesses in white collar and tech industries in a city would result in greater unemployment, as skill sets may not catch up with requirements. Despite an influx of high tech industry job opportunities high levels of disparity still exist in some cities. However by using an overarching percentage of jobs that fall within this category to measure the relevance of this mismatch on the unemployment rate and economic prosperity we did not obtain precise information that describes the amount of the population that is employed in each field compared to those with jobs in the service industry and to those that are unemployed.
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In addition, our research lacked a qualitative element to represent the personal perspectives of mayors. We were therefore unable to ascertain any opinions or sense of obligation felt by the mayor to provide for their population based on their common race or lack
of common race with the population majority. Perhaps it is the personal preference of the mayor to endorse and ensure the prosperity of citizens of the same race or not. Also we did not distinguish between the mayorâ&#x20AC;&#x2122;s gender since of the 50 cities in the sample only one city had a female mayor.
Despite these limitations, the results generated by our model are valuable for further research and policy assessment.
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APPENDIX Table A- Variable Operationalization
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)
Table B- Hypotheses
Dependent Variable: Economic Prosperity ───────── Racially Representative Government (-) Educational Attainment (+) Industry Breakdown (+) Immigration (-)
Presence of Health Facilities(+)
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Presence of Arts Organizations (+)
Table 1-Descriptive Statistics N
Minimum
Maximum
Mean
Std. Deviation
Unemployment Rate (%)
50
1.70
9.10
4.21
1.62
Percent of population over 25 with less than a HS diploma
50
4.85
39.16
17.24
7.10
% of industry establishments in information, professional, educational, or health care
47
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
Table 2-Is the Mayor the Same Race/Culture as the Majority Population of their City? FREQUENCY
PERCENT
YES
41
82%
NO
9
18%
TOTAL
50
100%
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Figure 3- Unemployment Rate Histogram
Figure 4- Representative Bureaucracy Histogram
3
Figure 5- % of Population over 25 with no HS Diploma Histogram
Figure 6- % of White Collar Industry Establishments Histogram
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3
While the representative bureaucracy histogram shows the variable being measured in using 1 and 2, the multivariate regression uses the numbers 0 (mayor is racially representative) and 1 (mayor is not racially representative).
Figure 7- % Population Foreign Born Histogram
Figure 8- Ratio of Arts Establishments to every 5000 People
4 Figure 9- Ratio of Health Care Establishments to every 1000 People
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4
Ratio of arts establishments uses 5000 people as the base because it is the lowest number at which the ratio remains a whole number. The same explanation is valid for health care establishments.
Figure 10- Scatter Plot Matrix
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Figure 11- ANOVA
Figure 12- ANOVA Output
F
Sig.
Means
Mayor same race/culture as majority population?
Variable
3.061
.056
% Population over 25 with less than HS diploma
13.393
.000
% of industry establishments in information, professional, educational or health care
.619
.543
% Foreign born
2.959
.062
Ratio of arts establishments to every 5000 people
2.338
1.08
Ratio of health care establishments for every 1000 people
2.658
.081
Low unemployment ≈ Yes Medium unemployment ≈ Yes High unemployment ≈ Yes Low unemployment = 13.13% Medium unemployment = 15.51% High unemployment = 22.99% Low unemployment = 0.18% Medium unemployment = 0.18% High unemployment = 0.19% Low unemployment = 4.92% Medium unemployment = 9.69% High unemployment = 9.69% Low unemployment ≈ 3 Medium unemployment ≈ 3 High unemployment ≈ 2 Low unemployment ≈ 5 Medium unemployment ≈ 4 High unemployment ≈ 4
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Figure 13- Correlation Matrix
repbureac edlowest totalwhitecollar foreignborn artspop health % Unemployment
.236 Pearson Correlation
.600
.257
.154
-.282
-.251
Sig. (2tailed)
.099
.000
.081
.287
.047
.078
N
50
50
47
50
50
50
Figure 14- Hypothesized Multivariate Regression Model
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Figures 15, 16 & 17- Multivariate Regression Model Output
Unstandardized Coefficients
Standardized Coefficients
Sig.
B
Std. Error
Beta
Constant
.130
1.461
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
.929
R.
R Square
Adjusted R Square
Std. Error of the Estimate
.680
.463
.382
1.27611
Sum of Squares
df
Mean Square
F
Sig.
Regression
56.142
6
9.357
5.746
.000
Residual
65.138
40
1.628
Total
121.280
46
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Figure 18- Multivariate Regression Ordinary Least Squares Equation
Unemployment= .130 + 0.060(repbureac) + 0.150(edlowest) + 12.103(totalwhitecollar) - 0.057(foreignborn) - 0.071(artspop) - 0.025(health)
Figures 19, 20 & 21 â&#x20AC;&#x201C; Backwards Selection Model Output: Remove repburac
Unstandardized Coefficients
Standardized Coefficients
Sig.
B
Std. Error
Beta
Constant
.200
1.321
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
.880
R.
R Square
Adjusted R Square
Std. Error of the Estimate
.680
.463
.397
1.26067
Sum of Squares
df
Mean Square
F
Sig.
Regression
56.119
5
11.224
7.062
.000
Residual
65.161
41
1.589
Total
121.280
46
22
Figure 22- Backwards Selection Model Equation: Remove repburac
Unemployment = .200 + 0.151(edlowest)+ 12.077(totalwhitecollar) - 0.058(foreignborn)- 0.072(artspop) - 0.027(health)
Figures 23, 24 & 25- Backwards Selection Model Output: Remove repurac and health
Unstandardized Coefficients
Standardized Coefficients
Sig.
B
Std. Error
Beta
Constant
.093
1.177
edlowest
.152
.031
.663
.000
totalwhitecollar
12.085
5.263
.271
.027
foreignborn
-.056
.031
-.240
.076
artspop
-.084
.130
-.078
.523
.937
R.
R Square
Adjusted R Square
Std. Error of the Estimate
.680
.462
.411
1.24611
Sum of Squares
df
Mean Square
F
Sig.
Regression
56.063
4
14.016
9.026
.000
Residual
65.217
42
1.553
Total
121.280
46
23
Figure 26- Backwards Selection Model Equation: Remove repburac and health
Unemployment = .093 + 0.152(edlowest)+ 12.085(totalwhitecollar) - 0.056(foreignborn)- 0.084(artspop)
Figures 27, 28 & 29- Backwards Selection Model Output: Remove repburac, health and artspop
Unstandardized Coefficients
Standardized Coefficients
Sig.
B
Std. Error
Beta
Constant
-.251
1.041
edlowest
.158
.029
.693
.000
totalwhitecollar
12.196
5.224
.274
.024
foreignborn
-.058
.031
-.247
.064
.810
R.
R Square
Adjusted R Square
Std. Error of the Estimate
.676
.457
.419
1.23760
Sum of Squares
df
Mean Square
F
Sig.
Regression
55.419
3
18.473
12.061
.000
Residual
65.861
43
1.532
Total
121.280
46
24
Figure 30- Backwards Selection Model Equation: Remove repburac, health and artspop
Unemployment = -.251 + 0.158(edlowest)+ 12.196(totalwhitecollar) - 0.058(foreignborn)
25
REFERENCES
Bloom, D.E., Canning, D. and Sevilla, J. (2004). The Effect Health on Economic Growth: A Production Function Approach. World Development. 32:1, 1-13. Esses, V.M., Dovidio, J.F., Jackson, L.M. and Armstrong, T.L (2001). The Immigration Dilemma: The Role of Perceived Group Competition, Ethnic Prejudice, and National I dentity. Journal of Social Issues. 57: 3, 389-412. Esses, V.M., Jackson, L.M, Dovidio, J.F., Hodson, G. (2008). On The Nature of Prejudice, Instrumental Relations Among Groups: Group Competition, Conflict and Prejudice. 228-233. Groeneveld, S. and Van de Walle, S. (2010). A Contingency Approach to Representative Bureaucracy: Power, Equal Opportunities and Diversity. International Review of Administrative Sciences. 76:2, 239-258. Houston, D. (2005).Employability, Skills Mismatch and Spatial Mismatch in Metropolitan Labour Markets. Urban Studies. 42: 2, 221â&#x20AC;&#x201C;243. Kennedy, B. (2012). Unraveling Representative Bureaucracy: A Systematic Analysis of the Literature. Administration and Society. 20:10, 1-27. LeDuff, C. (2013). Detroit: An American Autopsy. The Penguin Press. Meier, K.J., Wrinkle, R.D. and Polinard, J.L. (1999). Representative Bureaucracy and Distributional Equity: Addressing the Hard Question. The Journal Of Politics. 61: 4, 1025-39. U.S. Census Bureau; Census 2000 Summary File 3, Table DP-3; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014)
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Population Racial Breakdown: U.S. Census Bureau; Census 2000 Summary File 1, Table DP-1; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014)
U.S. Census Bureau; Census 2000 Summary File 3, Table DP-2; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014) U.S. Census Bureau; Economic Census 2002, Table EC0200A1; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014) U.S. Census Bureau; Census 2000 Summary File 3, Table DP-2; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014) U.S. Census Bureau; Economic Census 2002, Table EC0200A1; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014) U.S. Census Bureau; Economic Census 2002, Table EC0200A1; generated by Ariam Ford; using American FactFinder; <http://factfinder2.census.gov>; (9 April 2014)
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