The Influence of New Agricultural Business Entities on Farmers’ Employment Decision

The purpose of this article is to explore the influence of new agricultural business entities on farmers’ employment decision and provide reference for improving policies related to new agricultural business entities and farmers’ employment. This paper constructs a theoretical analysis framework of “new agricultural business entities—land transfer and purchase of agricultural socialized services—farmers’ employment decision”, and then empirically tests the impact of new agricultural business entities on farmers’ employment decision by combining the analysis methods of the benchmark regression, propensity score matching and mediation effects. The research shows that: (1) New agricultural business entities are beneficial for promoting farmers’ employment decision. (2) Renting out land and the purchase of agricultural socialized services have a positive and partially mediating effect between the new agricultural business entities and farmers’ employment decision, and the mediating effects of the two paths account for 7.12% and 6.25% of the total effects, respectively. (3) In addition to key variables, personal characteristics of decision-making, family characteristics and production and management characteristics are also key factors that affect farmers’ employment decision. (4) The new agricultural business entities increase the probability of farmers’ employment (with legal contract) and entrepreneurship, and reduce the idle labor force in rural areas. Finally, this study proposes some policy recommendations including establishing a perfect farmland transfer market, developing rural industry properly and improving agricultural socialized service systems.

Self-protecting motivation, indexed by self-threat, modifies retrieval-induced-forgetting and confidence in employment decision bias against out-group targets

Human memory is malleable by both social and motivational factors and holds information relevant to workplace decisions. Retrieval-induced forgetting (RIF) describes a phenomenon where retrieval practice impairs subsequent memory for related (unpracticed) information. We report two RIF experiments. Chinese participants received a mild self-threat manipulation (Experiment 2) or not (Experiment 1) before an ethnicity-RIF task that involved practicing negative traits of either in-group (Chinese) or an out-group (Japanese) target. After a subsequent memory test, participants selected their preferred applicant for employment. RIF scores correspond to forgetting of unpracticed positive traits of one target (Rp−) relative to the recall of practiced negative traits of the other target (Rp+). Enhanced forgetting of positive traits was found in both experiments for both targets. Across experiments, a significant target by threat interaction showed that target ethnicity modified RIF (an ethnicity-RIF effect). Inducing a self-protecting motivation enhanced RIF effects for the out-group (Japanese) target. In a subsequent employment decision, there was a strong bias to select the in-group target, with the confidence in these decisions being associated with RIF scores. This study suggests that rehearsing negative traits of minority applicants can affect metacognitive aspects of employment decisions, possibly by shaping the schemas available to the majority (in-group) employer. To disrupt systemic racism, recruitment practices should aim to offset a human motivation to protect one-self, when exposed to a relatively mild threat to self-esteem. Discussing the negative traits of minority applicants is a critical, and sensitive, aspect of decision-making that warrants careful practice. These data suggest that recruiting individuals should be reminded of their personal strengths in this context, not their vulnerabilities, to secure their decision-making for fairer recruitment practice.

Ranking Student Employment Decision Criteria

This paper presents the results of a recent survey taken by construction management (CM) students at four U.S. Construction Management programs and the construction industry companies that recruit them. Respondents were asked to rate factors that affect the students’ employment decision. Survey results indicate that although industry has a grasp on the top-5 factors of importance there remains some misalignment of other factors of importance. Results indicate the five most important factors when considering employment with a company are: upward job movement, salary, company reputation, company culture, and company ethics. Comparisons between the two surveys are discussed.

How one pre-employment decision nearly changed the world order

In How one pre-employment decision nearly changed the world order Nerys Williams briefly explores the medical and military background of a well-known revolutionist.

Examining the relationship of teacher perception of accountability and assessment policies on teacher turnover during NCLB

The purpose of this study is to determine the relationship between teachers’ perception of their work, their intent to leave their current position, and their realized turnover at the height of the federal accountability policy era in the United States. The study uses a framework of teacher de-professionalization and demoralization operationalized by teacher responses to the Schools and Staffing Surveys and Teacher Follow-up Surveys from the National Center for Education Statistics. We tested the relationship of de-professionalization and demoralization to turnover with two competing structural equation models for teachers who cited accountability policies as a factor in their employment decision, and those who did not. We find that teacher worry and stress associated with demoralization is a significant predictor of intent to leave in both groups of teachers. However, teacher worry and stress is only a significant predictor of teachers leaving the profession and moving schools in teachers who cite accountability policies as a factor in their employment decision. These findings demonstrate a relationship between teachers’ perceptions of accountability policies, perception of their working conditions, and turnover. These results have important implications for policy makers and educational leaders as the U.S. transitions from the No Child Left Behind era to the implementation of the Every Student Succeeds Act.

Algorithm-Based Recruiting Technology in the Workplace

Traditional recruiting methods are inefficient and cost employers valuable time, money, and human resources. Additionally, traditional recruiting is subject to the biases and prejudices of a human recruiter. Machine learning, algorithm-based recruiting technology promises to be an efficient and effective solution to employee recruiting by utilizing 21st century technology to engage, screen, and interview top talent. While the promise of algorithm-based deci- sion-making is attractive to many business owners, the practical legal considerations of its use for an ordinary small-to-medium sized employer have not been discussed. Legal scholarship in the area of algorithm-based employment decision making has primarily focused on data-driven unlawful discrimination and proposed government regulation. This Comment fills that gap by providing a summary of algorithm-based recruiting technology, its legal effects, and the best practices for an employer or an unfamiliar employment lawyer interested in adopting algorithm-based recruiting technology.