Assignment #3:
Word Count: 1389
Central Research Question:
My research explores the effects of exclusive education for disabled people in Southeast Asia and how they relate to their paucity of employment opportunities. In many countries, there is little data on the relationship between education and employment status in the disabled population. Considering the lack of data on the disabled community in developed countries, the gap in the data is even greater in areas that are less developed, littered with ableism, lacking government funds, and are considered primary sectors in the economy (Rosenberg). The current regional survey data that is available is updated every few years, but within a singular year, for example in 2020, there can be significant education and employment changes for the general population, not to mention for the more vulnerable, marginalized groups like the disabled population.
Closing this gap in the fundamental population data is integral to answering my central research question: Does educational exclusion have a negative impact on a disabled person’s employment opportunities? This is an explanatory piece that outlines how unfreedoms in special education exclude disabled people from employment opportunities and the economy. It is explanatory because it seeks a causal or correlative relationship between a country’s education inclusivity and employment opportunities for disabled people. Additionally, this question is a comparative puzzle because I am noting the differences between opportunities for disabled people and the non-disabled population in Southeast Asia. As I focus on how Southeast Asian industries can be more conducive to the abilities of most disabled people, I will contextualize my argument by juxtaposing the discriminatory leading industries of Southeast Asia with the inclusive leading industries in developed countries.
The subtopics of my central research question investigate more specific ideas. In Southeast Asia, are disabled people offered adequate educational opportunities to help them become reasonably employable? Is the nature of the economy in Southeast Asia less conducive to employing disabled people? How can a disabled person further develop their unique employment skills in a region dominated by industries that inherently discriminate against the disabled?
Geospatial Data Science Method 1:
While there is a lack of data that directly relates employment status to educational opportunities, there is data quantifying the factors individually. There are about 95 million disabled people in the region of Southeast Asia, according to a survey conducted by Sida in November 2014 (Sida). Furthermore, “it is estimated that less than 10% of children with disabilities in the region attend school” in Southeast Asia (Sida). However, about 68% of children in Southeast Asia have access to an education (UNICEF). While both ratios are relatively low, the disabled populations education rates are especially concerning.
In order to combat this issue, it is necessary to have current, clean data that can quantify the amount of uneducated and unemployed disabled people in the region. My first geospatial data method is to conduct household surveys that span across the region. The surveys would be organized by country in order to differentiate areas with vastly different cultures, political climates, and prominent industries. This data would simply quantify the amount of dissabled people that are affected by the exclusive nature of a Southeast Asian education. Similarly, this data would reveal how many people are experiencing employment discriminataion despite the legislature that is supposed to protect them. This data would likely be reliable and accurate due to the severity of discrimination against disabled people in Southeast Asia. Many families are so thankful for any sort of statistical recognition of their disabled relative, that they do not hesitate to identify their child as disabled in a survey. They are desperate for any identification of the disabled, so that it might raise societal awareness, inspire insurance companies to provide accommodating coverage, or possibly encourage schools to implement the physical equipment and infrastructure accommodations that provide accessible education to disabled children.
The Economic and Social Commission for Asia and the Pacific Disability survey has been extremely successful in collecting this data by country, shown in the figures below (UNESCAP).
This data is useful for an educated estimation of the population in 2020 as it is both spatial and temporal, but unique factors such as the COVID-19 pandemic, new inclusivity legislation, and tax credits likely caused population changes since 2015. The survey was conducted again in 2019, but there was no longer a focus on employment (UNESCAP). Collecting this data more frequently could reveal how vulnerable the disabled are to fluctuations in the national economies and political changes.
Geospatial Data Science Method 2:
Secondly, it would be beneficial to create an artificial intelligence application to connect a disabled person to employers that are seeking to hire someone with a similar skillset. The technology would resemble the concept of an applicant tracking processor, which sifts through resumes, but rather than narrowing down candidates for a job, the app would narrow down available job opportunities for the candidate. This is similar to the processor Entelo, which “has reportedly identified over 70 predictive variables which are used to analyze data from candidate profiles” (Sennaar). One of the most important variables is the turnover rate within a specific occupation. Entelo takes note of candidates who are in a field with a high turnover rate or they personally have historically spent a relatively short duration at each job, that way companies that value loyalty and long-term relationships can be cautious of a candidate’s potential flightiness. The figures below show what jobs tend to have employees that switch jobs frequently.
Conversely, in the new app, the user could input if they are seeking for a job to stay at for a while, or if they are looking for a short-term experience. The jobs that are suggested for them would take the turnover rate into consideration according to the user’s preferences.
The user would also input their unique skills as well as their weaknesses, and be provided with a list of job opportunities that will utilize their strengths and tolerate their weaknesses. The leading industry in Southeast Asia, agriculture, is inherently discriminatory to the disabled. They are often lacking the physical capacity to participate in this industry, and are therefore excluded.
However, the internet does not discriminate in the way that agriculture does. Disabled people in Southeast Asia can take advantage of internet access by using an app that informs them of potential gigs like survey-taking on Fiver, product testing for Amazon sellers, representing customer service for international companies, or even walking door to door collecting census data if their abilities allow. The job opportunities that are suggested to the individual would be based on their input data regarding their strengths and weaknesses. For example, the app might suggest that an Austistic person can use their tech-savvy inclination to answer surveys and test websites, while a blind person can conduct customer service phone calls. A dyslexic person could utilize their creative instincts to create marketing content for Amazon sellers and a person with Down Syndrome might employ their sunny disposition and organizational skills to conduct door-knocking surveys.
This is an easy way to collect geospatial data on the disabled labor force: those with disabilities who are seeking employment, whether they are employed or unemployed. The app can take note of their location in case there are any in-person nearby job opportunities. This will also save time for disabled people who currently have to spend lots of time searching for the rare jobs in Southeast Asia that are willing to hire someone with a disability, deciphering if they have the appropriate skill set, let alone if it is close enough to commit to working there. Flexport is a company that uses Entelo, and one of its employees advocates for Entelo “because of how manual and tedious [sourcing] can be,” but this artificial intelligence technology catalyzes the process with minimal effort.
Giving job experience to a previously unemployed and inexperienced section of the population allows them to improve the strengths that make them employable. While reducing inequalities is inherently good in itself, the potential spike in GDP due to the increase in employment is an economic incentive for local companies to engage in the inclusivity. Firms from all over the world could use the application to get in touch with disabled workers to employ in order to gain tax credit. This integration of a machine learning application to the disabled labor force will contribute to human development in Southeast Asia by working towards reduced inequalities and economic growth.
Works Cited
M. Rosenberg, “The 5 Sectors of the Economy,” ThoughtCo, January 29, 2020. https://www.thoughtco.com/sectors-of-the-economy-1435795 UNESCAP, “Disability at a Glance 2015,” UNESCAP, 2016. https://www.unescap.org/sites/default/files/SDD%20Disability%20Glance%202015_Final.pdf Sida, “Disability Rights in Southeast Asia,” Sida, November 2014. https://www.sida.se/globalassets/sida/eng/partners/human-rights-based-approach/disability/rights-of-persons-with-disabilities-south-east-asia.pdf UNICEF, “Every Child Learns,” UNICEF, 2018. http://www.unicefrosa-progressreport.org/childeducation.html Sennaar, “Machine Learning for Recruiting and Hiring- 6 Current Applications,” Emerj, accessed November 10, 2020. https://emerj.com/ai-sector-overviews/machine-learning-for-recruiting-and-hiring/