Introduction
There is insufficient research on how exactly the quantity and quality of public transportation affect citizens’ mobility and labor activity. Studies of the spatial mismatch hypothesis have primarily focused on employment, the distance between work and home, and the racial segregation of places of residence. These studies more often consider the accessibility of personal vehicles and jobs in automotive accessibility. However, in the urban poverty strata, not everyone has a car of their own. Steve Raphael researched this issue with public transportation in mind, using the new Bay Area Rapid Transit line into the Castro Valley and Dublin/Pleasanton Suburban areas as an example.
Racial segregation by place of residence is not unusual, and the distribution of minorities in urban areas with the growth of suburban jobs limits hiring opportunities for minorities. Initially, it was not one of the goals of the new BART branch to help solve this problem (Chew et al., 2018). It was built to ease traffic on the Bay Bridge and facilitate access to the suburban areas for San Francisco and Oakland suburb residents.
Research Design
Steve Raphael set himself the task of determining the relationship between public transportation and increased hiring opportunities for minorities living in the Castro Valley, an urbanized area with elevated poverty levels. The theories that can be partially applied here are spatial mismatch theory, racial segregation theory, and average travel time to work (Brandtner et al., 2019). However, a shortcoming of many theories and related studies is that insufficient account is taken of the development of community transportation in the region under study.
According to the analysis conducted, Raphael tested the hypothesis of strictly spatial mismatch under the condition of residence separation – in an area with high unemployment rates, a large part of the population also represents minorities. The most logical method for testing this hypothesis is a method similar to the one chosen – the method of empirical testing with the compilation of statistics before and after the introduction of the new transportation line (Mohammadi et al., 2018). This method is the most correct, especially considering the years of the research – in 1997-1998, the Internet was starting to spread, and small films of different kinds had no opportunity to have their website or representation on the Internet.
In this situation, the most accurate result can be obtained by comparing two different data sets, each of which has a similar set of variables but differs only in the time of arrival of the information. The difference between the result after the branch construction and before the construction will be part of the overall study result – positive or negative, depending on the variable and the study’s original purpose. It is worth noting that firstly variables focused on job openings without additional conditions, such as special education or college degrees. It adds to the precision of the results, as the bulk of the minority sample living in the Urban Area has financial and other problems with education because of the low level of job offers in their neighborhood.
Among the variables, the major ones are distances from the first and second stations to the workplace. These variables are constant before and after expansion in relation to each particular firm. Given the worker’s ability to get from the station on their own, Steve has limited this distance to nine miles, which expands the final data set, but may lose accuracy because it is quite a long distance. For many people living on the edge of poverty, it may be too expensive to travel that distance to work regularly. In this case, it would be suggested, for example, to limit it to 6 miles to reduce the sample. Furthermore, employer-related variables are the size of the firm and its occupation. These are determined and entered into the statistical table prior to the expansion of the transportation system.
As for the variables measured both before and after expansion, these are primarily the number of minority people per person hired. The recruiting process can also change, so it is necessary to identify the main methods and keep track of their fluctuations. These can be referral programs, external advertising, and media advertising (Brandtner et al., 2019). Moreover, to adjust the results, it is necessary to consider the requirements of the person being hired: education, specific skills, or experience. For statistical distribution, Steve also identified specific work processes that can take place. These include accounting and computer use, writing, or direct interaction with customers (Immergluck, 2018). Most of the variables described above are qualitative, which does not prevent them from being summed as a percentage of the complete number of firms evaluated, taking into account distance from new stations (Mohammadi et al., 2018). It is most logical to choose the individual as the unit of analysis, given the lack of additional information on the distribution of the percentage of unemployed in specific social formations in the urban area.
The survey research method chosen by Steve Raphael is the most convenient method for carrying out such a study. It is also possible to partially apply the method of processing already existing data. However, its reliability leaves much to be desired, and the result of such a study could be questioned. The problem is that the information in other sources may be incorrect or irrelevant because so many people live in the agglomeration, and many companies may start operating or close at any time. On this basis, the empirical method of the survey was made in the shortest possible time before the expansion of the transport network and after.
In the case study, a cross-sectional analysis would also benefit from information relating directly to the array of unemployed people in the surrounding neighborhoods, the segregated distribution of their places of residence, and the percentage of those in need (Brandtner et al., 2019). With more specific data, statistical analysis could be conducted at the individual level and at the family, household, or even neighborhood level. By increasing the iterations of the observations made, the data could eventually be panelized to make the analysis results as concrete as possible.
Data collection, as well as its subsequent processing, has many more functional optimizations these days. Many of the variables, most of which Steve Raphael collected through personal contact with his employer, can now be found online. Some of what is not on public display on the company’s profile or website can be verified through social media or e-mail. Modern data processing programs are also superior to their predecessors. The fastest and most convenient variant is to do all the calculations using spreadsheets. Such an analysis can compare the mutual dependence of any factors and get the necessary results without spending much time.
Conclusion
The main problems that may be encountered in this study are related to the reluctance of some companies to participate in surveys. Without raw data on the reality of any of the variables, one has to rely on other sources’ data, which reduces the final accuracy of the analysis. Different amounts of public or political data can be incorrect for a number of reasons, including political or social issues.
Interpretation and Implications of the Study’s Empirical Results
Analysis of the Average Hiring Rates, Table 1
Based on table number one, it can be seen that Steve Raphael’s research shows a significant increase in ethnic minority hiring after transit system expansion. This table presents the average minority hiring rates in the format of the difference precisely in comparison to the time periods before and after the transit expansion. It takes into account the dependence of the distance of the businesses from the new stations. It should as well be noted that the data in the chart only consider working positions for which there are no additional requirements for the candidate’s education. The variance estimates were calculated by subtracting the numbers of individuals hired and determining the general change trend within the time.
It is particularly noticeable in the second half of the table that more jobs are provided by occupancy patterns than those in the city center since it is in Dublin/Plissington, a station located in the suburbs. Moreover, the observation that there are more Hispanics among those hired than African-Americans is found as fact. The overall change in the increase in minority hires is noticeable, even though the original purpose of the new branch was distinct. Perhaps the differences would have been more noticeable if the authorities had thought through the purpose of expanding the transportation network for a similar purpose as well.
Additional Factors That May Affect Changes in Hiring Practices
Many additional factors could change hiring practices, such as providing an employer with benefits or payments from the government when hiring a low-skilled worker and then retraining or educating them. Furthermore, if labor specifics allow, employer benefits and transportation tax reductions could allow employees to transfer to other jobs, making it easier for potential employees (Porru et al., 2020). Moreover, the government can directly affect the quality of the housing of the minorities.
Traditionally, minorities populate disadvantaged neighbourhoods that not only havelow house prices, but directly affect the employability of the residents. Possible public housing programs would help address the problem of minorities populating entire disadvantaged neighborhoods, which has deep roots in American history. As well as that, disadvantaged areas can be cleared of pollution, which will increase the house prices of the areas and enable the residents to relocate more freely, which will increase their employability rates. Providing more free education in segregated areas for the unemployed and their families would help reduce poverty later. Although, free education does not mean low quality education. It has to reflect the needs of the community and prepare the minority groups for the job market. When provided with necessary education, people would be able to find better jobs with higher wages, that will reduce the poverty levels in the future.
Another compelling factor would be connected to encouraging employers to advertise their job offers more widely. Some of the companies do not advertise in sources broadly available in poor urban neighborhoods, nor do those companies motivate their already active employees from these and similar neighborhoods to invite their acquaintances to apply for open jobs. There can be found large numbers of people who want and have the need to work in such segregated neighborhoods, and with the proper advertising, employers could fill their vacancies much faster (Castro et al., 2022). The shorter the time from the confirmation of the necessity to hire a new employee to the hiring process itself, the greater the bottom line benefits the firm could have.
Opening outsourcing recruitment centers, which was not common at the time of Steve Raphael’s analysis, would as well help improve hiring rates in such areas. This trend has become very standard of late, saving employers time and resources while being able to provide diversified and qualified hiring. If the placement of the offices of such companies in poor neighborhoods can be promoted, the unemployment situation will improve.
Nevertheless, an additional number of factors that have contributed to and are contributing to the hiring disparity among minorities are the repeated violent harassment of minorities, systematic segregation, and the exclusion of minorities from the high-paying sector of the labor market. At this point, the overall situation tends to improve, but many people still face conscious or subconscious racism, prejudice, and a lack of diversity (Castro et al., 2022). Suppose everyone works actively to eliminate the negative legacy of legal racial terrorism and years of emancipation. In that case, it is possible to have a positive impact on employment rates among ethnic minorities living in the United States.
Assessment Results for the Whole Survey
The third table in the study represents the total of the entire dataset processed, excluding Castro Valley businesses and their job decisions. It includes three models, each broken down into three regressions, two of which change the variable of the race of the employee hired, and the third takes into account the likelihood of hiring any of the minorities. The first model is a regression of changes in the ethnicity of the last employee hired. This model relates best to the first table because it takes into account only the distance to the company.
The second model adds the level variables listed in the second table of the study material. In addition, it includes a relation to the ethnicity of the person who hired the worker to check if that ethnicity coincides with and belongs to the minority group. This model shows a less intense result when compared to the table that only accounted for the distance to the station and job offers without specific requirements for candidates.
The third model includes all of the variables identified by Steve Raphael. They contain most of the major determinants of a job position and have been discussed previously in memo 1A. The last linear regression is the final result of the analysis of the changes that occurred. In the subject of the correlation of the third chart with the first, the general dynamics are preserved and partially confirmed. However, not all factors were taken into consideration in the first table, so some results, such as cumulative change, are somewhat overestimated – it should be remembered that this table refers specifically to work without additional requirements.
Possible Policy Implications of the Study Results
According to the overall results of the study, the development of a public transportation system is one method of reducing the overall unemployment rate and the minority unemployment rate (Immergluck, 2018). These problems can be partially eliminated by providing additional public transportation routes through populated urban areas to suburbs with high levels of employment possibilities. However, these activities alone are not enough to fully address the problem.
The consequences of studies such as the one examined in this piece have, at the very least, increased attention to the problem. Today, the Bay Area Rapid Transit system has expanded quite significantly. Although coverage is still inadequate and fares are high, local politicians and community leaders are actively engaged in the development of the region’s transportation system. The unemployment rate among ethnic minorities, on the other hand, has been declining throughout the time since the study (Castro et al., 2022). Although there are still echoes of segregationist and demeaning racial policies of the twentieth century, the state and social formations movement is clearly in the direction of developing and promoting equality for all.
References
Brandtner, C., Lunn, A., & Young, C. (2019). Spatial mismatch and youth unemployment in US cities: public transportation as a labor market institution.Socio-Economic Review, 17(2), 357-379. Web.
Castro, I., Huang, M., & Henderson, J. (2022). Do all Bay Area residents have access to employment opportunities? Bay Area Equity Atlas. Web.
Chew, S. K., Lepe, A., Tomkins, A., & Scheirer, P. (2020). Forecasting San Francisco Bay Area Rapid Transit (BART) ridership. SMU Data Science Review, 3(1), 11. Web.
Immergluck, D. (2018). Neighborhood jobs, race, and skills: Urban unemployment and commuting. Routledge.
Mohammadi, A., Elsaid, F., Amador-Jimenez, L., & Nasiri, F. (2018). Optimising public transport for reducing employment barriers and fighting poverty.International Journal of Sustainable Development and Planning, 13(6), 860-871. Web.
Porru, S., Misso, F. E., Pani, F. E., & Repetto, C. (2020). Smart mobility and public transport: Opportunities and challenges in rural and urban areas.Journal of Traffic and Transportation Engineering (English edition), 7(1), 88-97. Web.