City of LA job applicants by the job they applied for and demographic information. We are currently undergoing a data inventory to improve usability on the site. We're aware that this dataset is out of date but wanted to err on the side of making incomplete data available. Thank you for your patience, please contact the dataset owner or mayor.opendata@lacity.org with questions or ideas.

The job openings-to-applicants ratio in Tokyo Prefecture stood at **** in the fiscal year 2023. The ratio marked the highest point in 2018 at ****. The job-openings-to-applicants ratio is calculated based on the number of active job openings and job seekers to indicate how many jobs per job applicant are available in a certain period.

In 2023, products and services was the top industry of interest for prospective MBA candidates in the United States, with 51 percent of candidates interested in working in this industry after graduation. A further 38 percent of prospective MBA candidates in the U.S. were interested in working in the tech industry after graduation.

In 2020, about 70 percent of German job candidates aged 18 to 30 years old preferred submitting a job application in digital format instead of a traditional one. Conversely, almost 60 percent of candidates aged 60 or older preferred the traditional format over the digital format.

The number of candidates who applied for employment from the stock of the Civil Service Bureau who were nominated to work in the private sector inside and outside Jordan during the year 2020

The datasets contain data retrieved from the “Jobs in Russia portal”. The datasets contain information about the education, work experience, desired salary and some demographic characteristics of candidates who posted their CVs on the portal between 2015 and 2023. The datasets cover 83 regions of Russia. Dataset 1 contains information on the education and career trajectories (N=6221439), while Dataset 2 contains the information about the general activity of unemployed and job seeking candidates (N=7662089). Each candidate has a unique ID and there are characteristics such as age, education type, region, working experience. The datasets are available as .csv files.

Dataset 1 is designed to study educational and career trajectories. It was obtained by aggregating all CVs for each candidate into a single record with a unique id_candidate, then normalizing text information about the candidates' work experience and education, and finally excluding records that do not contain required information.

Dataset 2 includes all records for the available period, but only covers general information and the activity metric. It might be useful to study the general portal dynamics and, therefore, the activity on the Russian labor market.

For citation, please use the data paper: Valko, D., Vasilevskaia, M., Bunina, M., Kozlova, M., Filippova, A. M., & Rud, D. (2024). Educational and Career Trajectories in Russia: Introducing a New Source and Datasets with a High Granularity. Research Data Journal for the Humanities and Social Sciences. https://doi.org/10.1163/24523666-bja10046

Cues and heuristics—like party, gender, and race/ethnicity—help voters choose among a set of candidates. We consider candidate professional experience—signaled through occupation—as a cue that voters can use to evaluate candidates’ functional competence for office. We outline and test one condition under which citizens are most likely to use such cues: when there is a clear connection between candidate qualifications and the particular elected office. We further argue that voters in these contexts are likely to make subtle distinctions between candidates, and to vote accordingly. We test our account in the context of local school board elections, and show—through both observational analyses of California election results and a conjoint experiment—that (1) voters favor candidates who work in education; (2) that voters discriminate even among candidates associated with education by only favoring those with strong ties to students; and (3) that the effects are not muted by partisanship. Voters appear to value functional competence for office in and of itself, and use cues in the form of candidate occupation to assess who is and who is not fit for the job.

In 2020, almost 21 percent of job candidates belonging to the Baby-Boomer generation declared being frequently exposed to job ads through the career networking platform LinkedIn. Moreover, candidates of the generation Y were more exposed to job advertisements through the German career networking platform XING, as almost 20 percent of these candidates declared so.

The job application stage most accepted by German job candidates (if it is to be carried out in a digital form), is the submission of the job application. As of 2020, over 70 percent of job candidates interviewed had no objections concerning the submission of the application form and its documents by digital means. In the meantime, less than five percent of candidates were favorable towards a digital or virtual job interview, be it with the help of a software or with the use of artificial intelligence technologies.

Japan Ratio of Job Offers to Applicants: Regular Worker data was reported at 1.330 % in May 2018. This records a decrease from the previous number of 1.350 % for Apr 2018. Japan Ratio of Job Offers to Applicants: Regular Worker data is updated monthly, averaging 0.720 % from Apr 1995 (Median) to May 2018, with 278 observations. The data reached an all-time high of 1.520 % in Jan 2018 and a record low of 0.370 % in Jun 2009. Japan Ratio of Job Offers to Applicants: Regular Worker data remains active status in CEIC and is reported by Ministry of Health, Labour and Welfare. The data is categorized under Global Database’s Japan – Table JP.G037: Employment Referrals.

The new job openings-to-applicants ratio in Japan stood at **** in 2024. The ratio marked the highest point in 2019 at ****. The new job-openings-to-applicants ratio is calculated based on the number of new job openings and new application cases to indicate how many new jobs per new job application are available in a certain period.

Context and Content

A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.

This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.

The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target

Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.

#

Features - enrollee_id : Unique ID for candidate. - city: City code. - city_ development _index : Developement index of the city (scaled). - gender: Gender of candidate - relevent_experience: Relevant experience of candidate - enrolled_university: Type of University course enrolled if any - education_level: Education level of candidate - major_discipline :Education major discipline of candidate - experience: Candidate total experience in years - company_size: No of employees in current employer's company - company_type : Type of current employer - last_new_job: Difference in years between previous job and current job - training_hours: training hours completed - target: 0 – Not looking for job change, 1 – Looking for a job change

Inspiration

• Predict the probability of a candidate will work for the company
• Interpret model(s) such a way that illustrate which features affect candidate decision # Please refer to the following task for more details: https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015

The personnel of various agencies and local governments under the Executive Yuan shall explain the statistics of the number of personnel employed in accordance with the examination pass and other laws and regulations.

These seven datasets are part of an ongoing data collection effort in which The New York Times and CBS News are equal partners. Each survey includes questions about President Ronald Reagan's performance in office, especially with respect to economic and foreign affairs. In addition, each survey provides information on respondents' views concerning other social and political issues, as well as respondents' personal backgrounds. The surveys were conducted in January, April, June, September (twice), and October (twice). The October surveys took place before and after President Reagan's speech about Grenada on October 27, 1983. The October samples are weighted separately, and two discrete datasets, which may be analyzed separately or combined, are available (Parts 6 and 7). Topics covered in Part 1, January Survey, include Reagan's handling of economic and foreign affairs, various proposals to reduce the federal deficit, unemployment, and Social Security. In Part 2, April Survey, individuals responded to questions about Reagan's handling of economic and foreign affairs, the environment, and defense policy, and were also asked about their willingness to vote for a Black candidate, candidates endorsed by labor unions, and candidates endorsed by homosexual organizations. Two versions of the questionnaire were used, to test alternative question wording. For Part 3, June Survey, questions were asked on Reagan's presidency, possible presidential candidates in 1984, foreign policy, economic policy, merit pay for public school teachers, federal spending on education, and tennis. Part 4, Plane Survey, queried respondents about the Korean passenger plane shot down by the Soviet Union in September 1983, including their opinions on the American response to the attack. The questionnaire also included questions about Reagan's handling of foreign and economic policy. Part 5, September Survey, covered telephone service, United States troops in Lebanon, possible presidential candidates, and President Reagan's handling of economic and foreign policy. Two versions of the questionnaire were used, to test alternative question wording. A question about the cease-fire agreement in Lebanon was included in only one of those versions. Part 6, October (Prespeech) Survey, was conducted before President Reagan gave his speech on Grenada. Respondents were asked their opinions on having United States troops in Grenada and Lebanon, the attack on the Marine barracks in Lebanon, and Reagan's handling of foreign policy. Part 7, October (Postspeech) Survey, was conducted after President Reagan's speech on Grenada and concerned the same issues that were covered in the Prespeech Survey.

According to Cognitive Market Research, The Global Pre Employment Assessment Tools market size is USD 1851.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 7.50% from 2023 to 2030.

North America Pre Employment Assessment Tools held the major market of more than 40% of the global revenue with a market size of USD 740.48 million in 2023 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2023 to 2030.
Europe Pre Employment Assessment Tools accounted for a share of over 30% of the global market size of USD 555.36 million in 2023.
Asia Pacific Pre Employment Assessment Tools held the market of more than 23% of the global revenue with a market size of USD 425.78 million in 2023 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2023 to 2030.
Latin America Pre Employment Assessment Tools market of more than 5% of the global revenue with a market size of USD 92.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 6.9% from 2023 to 2030.
Middle East and Africa Pre Employment Assessment Tools held the major market of more than 32% of the global revenue with a market size of USD 37.2 million in 2023 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2023 to 2030.
The demand for Pre Employment Assessment Tools is rising due to the development of the Workforce.
Demand for web based remains higher in the Pre Employment Assessment Tools market.
The SME application category held the highest Pre Employment Assessment Tools market revenue share in 2023.

Development of the Workforce to Provide Viable Market Output

As firms expand their talent search abroad, Pre-Employment Assessment Tools are critical for overcoming language and cultural hurdles. These tools provide standardized evaluation methodologies to ensure that varied candidates receive fair and consistent assessments. Employers can use psychometric assessments, cognitive tests, and skill evaluations to discover suitable candidates regardless of linguistic or cultural variances. This increases inclusivity in the recruiting process and assists firms in developing diverse and high-performing teams. Pre-Employment Assessment Tools' adaptation to global talent challenges is critical for developing fair, unbiased, and effective hiring methods across varied international landscapes.

Remote Work to Propel Market Growth

The rise of remote work highlights the need for Pre-Employment Assessments since evaluating skills and work styles becomes critical without the need for traditional in-person interviews. These exams provide a thorough insight into candidates' talents, ensuring a match with the remote work requirements. Employers can use technology and tailored examinations to evaluate individuals' suitability for remote employment, contributing to effective talent acquisition. This move demonstrates Pre-job Assessment Tools' versatility in aligning hiring processes with the changing landscape of remote work, promoting efficiency and informed decision-making in a virtual job setting.

Market Restraints of the Pre Employment Assessment Tools

Data Privacy and Security to Restrict Market Growth

Storing and analyzing candidate data with Pre-Employment Assessment Tools raises security and privacy problems. To prevent these risks, businesses must deploy strong security measures like encryption and safe storage solutions. It is critical to follow data privacy rules like GDPR or HIPAA, ensuring that candidate information is handled ethically and legally. Establishing strict access rules, conducting frequent security audits, and stressing transparency with applicants about data usage are critical steps in maintaining confidence and protecting sensitive information. Adhering to these guidelines is critical for the responsible and secure use of Pre-Employment Assessment Tools during recruitment. Storing and analyzing candidate data with Pre-Employment Assessment Tools raises security and privacy problems. To prevent these risks, businesses must deploy strong security measures like encryption and safe storage solutions. It is critical to follow data privacy rules like GDPR or HIPAA, ensuring that candidate information is handled ethically and legally. Establishing strict access rules, conducting frequent security audits, and stressing transparency with applicants about data usage are critical steps in maintaining confidence and protecting sen...

The application currently consists of the following forms: • Application for Employment (FR 28), which collects information to determine the qualifications of applicants for employment with the Board (such as education and training, employment record, and other information since the time the applicant left high school), • Applicant’s Voluntary Self-Identification (FR 28s), which is an optional form that collects information on the applicant’s gender, race, and ethnicity, and • Research Assistant Candidate Survey of Interests and Computer Experience (FR 28i) if the applicant is applying for a position as a Research Assistant (RA), which collects information on the RA applicant’s level of interest in various economic topics and experience in different data analytics/programs.

Candidate countries and potential candidates: SI - employment

As per Cognitive Market Research's latest published report, the Global Online Recruitment market size was $27.98 Billion in 2021 and it is forecasted to reach $41.83 Billion by 2029. Online Recruitment Industry's Compound Annual Growth Rate will be 7.3% from 2023 to 2030. Factors Impacting on Online Recruitment Market

An increasing number of job openings has created a massive demand for modern recruiting software, to streamline hiring processes which, include resume management, employee evaluation, assessment tools, and others. According to the Bureau of Labor Statistics 2022, the U.S. economy is projected to add 8.3 million jobs from 2021 to 2031 and it is projected to increase from 158.1 million to 166.5 million and grow 0.5 percent annually.

https://www.bls.gov/news.release/pdf/ecopro.pdf

On the other hand, the growing expansion of cloud-based technologies, availability of high-bandwidth internet infrastructure, and rising use of mobile-based recruitment are some of the other major driving factors for the growth of online recruitment market. Increasing focus on automation is estimated to create lucrative opportunities for the online recruitment market and the increasing incidents of online fraud can hinder the growth of the market.

Market Trends:

The adoption of advanced technologies such as big data, cloud computing, AI, and others is positively impacting the global online recruitment market. A major trend driving the global online recruiting industry is the expansion of high-speed internet access and internet-capable devices like tablets, laptops, and smartphones. Due to this, individuals can easily apply for more positions, and companies have access to a wider choice of candidates. Introduction of Online Recruitment.

Online recruitment is also known as E-recruitment, it uses web-based technology for the various processes of attracting, assessing, selecting, recruiting, and onboarding job candidates. Online recruitment is a way to provide businesses with an efficient and cheaper way to fill positions.

United States SBOI: sa: Employment: Qualified Applicants for Job Openings data was reported at 55.000 % in Jun 2018. This records an increase from the previous number of 48.000 % for May 2018. United States SBOI: sa: Employment: Qualified Applicants for Job Openings data is updated monthly, averaging 40.000 % from Jan 1996 (Median) to Jun 2018, with 268 observations. The data reached an all-time high of 55.000 % in Jun 2018 and a record low of 21.000 % in Dec 2009. United States SBOI: sa: Employment: Qualified Applicants for Job Openings data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s USA – Table US.S016: NFIB Index of Small Business Optimism.

Ratio of Job Offers to Applicants: Prefectures: Yamanashi data was reported at 1.300 % in Mar 2025. This records a decrease from the previous number of 1.320 % for Feb 2025. Ratio of Job Offers to Applicants: Prefectures: Yamanashi data is updated monthly, averaging 1.230 % from Jan 1963 (Median) to Mar 2025, with 747 observations. The data reached an all-time high of 3.930 % in Nov 1973 and a record low of 0.370 % in May 2009. Ratio of Job Offers to Applicants: Prefectures: Yamanashi data remains active status in CEIC and is reported by Ministry of Health, Labour and Welfare. The data is categorized under Global Database’s Japan – Table JP.G: Employment Referrals: Ratio of Job Offers to Applicants: By Prefecture.