{"id":1622,"date":"2017-10-23T11:57:07","date_gmt":"2017-10-23T18:57:07","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f17\/?p=1622"},"modified":"2017-10-23T11:58:12","modified_gmt":"2017-10-23T18:58:12","slug":"1622","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f17\/2017\/10\/23\/1622\/","title":{"rendered":"Ontology: Gender Breakdown of City Workers by Department"},"content":{"rendered":"

The dataset I chose to work with is an analysis of the gender breakdown of city workers by department. It covers each of the different departments, number of employees according to gender, amount of pay, and how all of that fits within each department. Find the dataset here: <\/span>https:\/\/controllerdata.lacity.org\/Statistics\/Gender-Breakdown-of-City-Workers-by-Department\/q45p-mx3u<\/span><\/a><\/p>\n

\"\"<\/p>\n

These are the columns found in the dataset: <\/span><\/p>\n

    \n
  1. Year<\/span><\/li>\n
  2. Department Title<\/span><\/li>\n
  3. Number of Employees<\/span><\/li>\n
  4. Total Payroll<\/span><\/li>\n
  5. Number of Female Employees<\/span><\/li>\n
  6. Number of Male Employees<\/span><\/li>\n
  7. Percent of Female Employees<\/span><\/li>\n
  8. Percent of Male Employees<\/span><\/li>\n
  9. Total Salary of All Female Employees in that Department<\/span><\/li>\n
  10. Total Salary of All Male Employees in that Department<\/span><\/li>\n
  11. Average Female Salary<\/span><\/li>\n
  12. Average Male Salary<\/span><\/li>\n
  13. Percent of Payroll in the Department to Women<\/span><\/li>\n
  14. Percent of Payroll in the Department to Men<\/span><\/li>\n<\/ol>\n

    This dataset organizes firstly be department as the primary point of differentiation. From there, it describes general information (such as total number of employees in the department) then goes to break that down to specifics by gender (number of employees by gender). This is done for the following categories: number of employees, percentage of employees, total salary of a gender, average salary, and then percent of payroll in a department. <\/span><\/p>\n

    This ontology is made to give a general sense of how each department is made up and how income is dispersed within. As it is was made by the Payroll department, it is probably best suited for those working in HR or the Payroll department or those seeking to study wage equality in LA\u2019s public sector. This dataset is great for the purposes of studying the wage gap as salaries can be compared at a glance to the numerical representation of each gender. As an example, the following comparisons can be done:<\/span><\/p>\n

      \n
    • % Female Employees<\/b> to<\/span><\/i> % Female Average Salaries <\/b>can be studied to see if payroll is representatively distributed by gender in a sense. For this, I found that payroll is almost distributed proportionately to the percent of female employees but generally tends to the lower side of representation (few percentages lower).<\/span><\/li>\n
    • Female Average Salaries <\/b>to <\/span><\/i>Male Average Salaries <\/b>can be compared for a general sense of the wage gap between genders. Out of forty departments, all but six record higher average wages for men than women. Further information would be needed to determine the cause for such a disparity. The six departments in which women had higher average salaries are have a demographic that is majority female and consist of the following: City Attorney, Libraries, Personnel, Office of Finance, Animal Services, and Street Lighting.<\/span><\/li>\n
    • Number of Females <\/b>to <\/span><\/i>Number of Males <\/b>can be compared to see how gender is distributed among each department. This can serve as a way to see if industries are dominated by certain genders and create policies to perhaps to address those issues.<\/span><\/li>\n<\/ul>\n

      So, what\u2019s left out?<\/span><\/i><\/p>\n

      At a first glance, the dataset is skewed in its perspective of gender being a binary classification. It discounts those who are gender-fluid or non-binary, leaving out the point of view who identifies as such. An interesting ontology to explore could be created from that perspective of someone who is gender-fluid or non-binary. To be more inclusive, gender identities such as agender, transgender, non-binary, and others can be included in the dataset. It also leaves out temporal and hierarchical information, such as the average amount of time worked by different genders and the gender makeup of those in higher level positions or management. Educational background of the employees is left out as well. Details as described above could lend for a more comprehensive picture as to the qualitative, working environment of the employees.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

      The dataset I chose to work with is an analysis of the gender breakdown of city workers by department. It<\/p>\n","protected":false},"author":170,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1622","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1622","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/users\/170"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/comments?post=1622"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1622\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/media?parent=1622"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/categories?post=1622"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/tags?post=1622"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}