EXPLAINING WATER MAIN INTEGRITY: THE CITY OF MAKEFIELD BACKGROUND As an elected executive (mayor) in the City of Makefield, your major concern is the quality of infrastructure in the municipality. EXP
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EXPLAINING WATER MAIN INTEGRITY: THE CITY OF MAKEFIELD BACKGROUND As an elected executive (mayor) in the City of Makefield, your major concern is the quality of infrastructure in the municipality.
EXPLAINING WATER MAIN INTEGRITY: THE CITY OF MAKEFIELD
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As an elected executive (mayor) in the City of Makefield, your major concern is the quality of infrastructure in the municipality. Makefield is an old industrial city with aging infrastructure. While campaigning you observed that city residents, particularly those who live in the western part of the city, consistently expressed concerns about loss of water pressure and water main leaks in their neighborhoods as opposed to resident in the eastern part of the city.
You decide to embark on an explanatory analysis of water main integrity in the eastern and western parts of the city (the city has two main districts of water distribution—one in the eastern section of the city and the other in the western part of the city).
THE WATER MAIN PROBLEM: WHAT ARE WE MEASURING?
Your management problem is the integrity of water mains in the city—How sound is the construction of the water mains given their age (much of the water main infrastructure is over 70 years old)? Using 311 data, your staff uses resident complaints of water leaks and breaks measured at the nominal level where 1=a break or leak and 0=no break or leak. This information will be analyzed by water district of the city, where 1=west district and 2=east district. In an effort to confirm if water district has an impact on the frequency of water breaks and leaks, your staff decides to examine the impact of district on the frequency of water main complaints by season. The goal here is to ascertain if season exasperates water breaks or leaks for a particular district.
Your facilitates manager has recommended the development of a capital improvement plan for the city’s water distribution system. However, this initiative has been put on hold since a data analysis is lacking. The facilitates manager would also like some direction on where to start if a capital improvement plan is to be pursued.
I. The Issue/Problem and Hypothesis
Hiring or hiring status of patrol officers is the management problem. It is hypothesized that black applicants will be over represented in selections for positions.
II. Measurement of the Problem and Independent Variables
The dependent variable, hiring status, which is measured at the nominal level, where 1=hired and 2=not hired. The independent variable is race of the applicant, which is measured at the nominal level where 1=black and 2=non-black.
A control variable, residency of applicants, was used to see if the relationship between race and hiring status holds true, based on where applicants live. The control variable, residency of applicants, is measured at the nominal level, where 1=lives in Metro City and=resides outside of the city.
III. Quantitative Approach Used
A Contingency Table Analysis (CTA) was used to test above hypothesis by determining if a statistically significant relationship existed between race and hire. This quantitative technique was used because the dependent variable (hiring status), the independent variable (race of applicant) and the control variable (residency) are all measured at the nominal level.
IV. Analysis of the Data
Table 1 presents a contingency table that assesses the influence of race of applicant on hiring for law enforcement patrolman positions.
TABLE 1: ASSESSING THE IMPACT OF RACE ON POLICE HIRING
TABLE 1: ASSESSING THE IMPACT OF RACE ON POLICE HIRING
Chi Square; p=.03
Table 1 supports the above hypothesis in that it was expected that 22 black applicants would be hired yet the actual number of black applicants selected was a 28; 6 more applicants than expected. The Chi Square test of statistical significance suggests that we can be 97 percent confident that the difference between the expected and actual number of black applicants hired is attributed to race.
Table 2 elaborates on Table 1 by examining the impact of race on hiring by municipal residency of applicants for patrolmen positions. Since residency of an applicant could have an influence on hiring, a decision was made to statistically control for this variable to see if race continues to yield a systemic overrepresentation of black applicants in available patrolmen positions.
To assess if the original hypothesis–that race impacts hiring–is statistically significant while considering residency of applicants, the expected and actual number of hires among black applicants are compared in Table 2. For example, it was expected that for applicants residing in the city, 12 black applicants would be hired and in actuality 16 black applicants were hired. Indeed, while there is over representation of black applicants who reside in city, the Chi Square test of statistical significance suggests that we can be 94.3 percent confident that the difference between the expected and actual number of black applicants hired is attributed to race for those applicants who live in the city. Therefore, the impact of race on the difference between the expected and actual number of hires is considered random for applicants living in the city.
TABLE 2: ASSESSING THE IMPACT OF RACE ON POLICE HIRINGBY RESIDENCY
Live in City, Chi Square; p=.057
Live Outside the City; p=. 302
I. Decision Outcomes and Inquiry of Staff
The findings indicate that race does not have a systematic impact on hiring and the overrepresentation of hires of black applicants. Instead, residency of the applicant has more of an influence on hiring.
One question to ask Human Resources staff is: Does the city have a residency rule for hiring? If so, this policy can be influencing the hiring outcome. Nonetheless, race is not a factor and the city should proceed with the hires.