What nudges will matter? An empirical study of female joblessness

The purpose of this empirical study is to use an interdisciplinary approach across labour economics, behavioural economics and social economics to explain female labour market statuses, in particular joblessness choices and conditions. We propose a new theoretical framework, based on Sen’s capability approach, new derived variables for the British Household Panel Survey and a new empirical methodology to estimate the probabilities of different labour market categories. Our results show that, in addition to the standard human capital variables, labour market statuses are markedly influenced by the interplay of environmental, social, psychological factors and personal views and values, such as disposition and conformity towards local social rules and to social relations’ (network) norms, degree of confidence and optimism, risk attitude, job and life satisfaction, preferences for the status quo and plans for the future. We find that the predicted probability of joblessness improves substantially when we move from the traditional into the multi- dimensional approach. Adding new variables to allow for more constraints and opportunities provides a richer and more refined view of factors affecting non employment and helps to recognize and explain better status of people within “similar” groups as well as to identify more accurately those people whose status in not in line with the “expected” (such as the employed against the odds and the voluntary” non- employed). We found that there are some clear idiosyncrasies across the different types of joblessness in particular when it comes to the category of carers, a unique feature of female joblessness. Our model predicts that being more embedded in a local community, having stronger values for family life and stronger ties with close “inactive” friends, facing potential income losses in changing labour market status (as measured by counterfactual labour income versus actual non labour income), are all factors that increase the odds of being a carer relative to being unemployed and that prompt carers to choose or preserve their status quo. Social environment matters in reinforcing personal attitude producing a sort of confirmation bias effect that suggests that social influence is more effective across similarly minded people. The results suggest that policies focused on areas where high levels of inactivity is present, working with large friendship groups within these areas could be used to motivate groups of individuals into work. Prospective financial losses (of receiving a labour income lower than non labour income if a carer became employed) increase the odds of being a carer while potential labour income gains would always decrease the odds of being carers versus any other category, particularly when the other categories are students and unemployed (i.e., those more potentially “attached” to the labour market). The results suggest a role for in work benefits such as the tax credits system, the national minimum wage and benefit reforms as policy options to help make work pay and provide incentives to work. Finally, designing active labour market policies that are more tailored to the characteristics of the individual job seeker could prove a fruitful avenue for policies such as the Work Programme

Personality Psychology and Economics

This paper explores the power of personality traits both as predictors and as causes of academic and economic success, health, and criminal activity. Measured personality is interpreted as a construct derived from an economic model of preferences, constraints, and information. Evidence is reviewed about the "situational specificity" of personality traits and preferences. An extreme version of the situationist view claims that there are no stable personality traits or preference parameters that persons carry across different situations. Those who hold this view claim that personality psychology has little relevance for economics. The biological and evolutionary origins of personality traits are explored. Personality measurement systems and relationships among the measures used by psychologists are examined. The predictive power of personality measures is compared with the predictive power of measures of cognition captured by IQ and achievement tests. For many outcomes, personality measures are just as predictive as cognitive measures, even after controlling for family background and cognition. Moreover, standard measures of cognition are heavily influenced by personality traits and incentives. Measured personality traits are positively correlated over the life cycle. However, they are not fixed and can be altered by experience and investment. Intervention studies, along with studies in biology and neuroscience, establish a causal basis for the observed effect of personality traits on economic and social outcomes. Personality traits are more malleable over the life cycle compared to cognition, which becomes highly rank stable around age 10. Interventions that change personality are promising avenues for addressing poverty and disadvantage.personality, behavioral economics, cognitive traits, wages, economic success, human development, person-situation debate

Promoting Driving Safety with Self-Evaluation Maintenance: Human-Human and Human-Artificial Intelligence Performance Comparisons

In this study, we develop and test a model that explains individuals’ behavioral changes in driving safety after viewing the visualizations, which depict their driving performance against that of artificial intelligence (AI). This study draws on the self-evaluation literature to understand performance comparisons and extends the self-evaluation perspective to the context of human-AI comparisons. Furthermore, this study illustrates that individuals can be incited emotionally by performance comparisons, and these emotional responses influence their driving behaviors subsequently. The results of this study generally support our model. Overall, this study sheds light on how competition between humans and computers can be utilized to promote desirable behaviors

Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming

The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer. While traditional pair programming between humans has been extensively studied, it remains uncertain whether its findings can be applied to human-AI pair programming. We compare human-human and human-AI pair programming, exploring their similarities and differences in interaction, measures, benefits, and challenges. We find that the effectiveness of both approaches is mixed in the literature (though the measures used for pAIr programming are not as comprehensive). We summarize moderating factors on the success of human-human pair programming, which provides opportunities for pAIr programming research. For example, mismatched expertise makes pair programming less productive, therefore well-designed AI programming assistants may adapt to differences in expertise levels.Comment: 8 pages (without references), 2 table

Factors that affect motivation towards english language acquisition in seventh grade students of a public elementary school in Parral

Tesis (Magíster en la enseñanza del inglés como lengua extranjera)The research presents the results of the identification and analysis of factors that characterize the motivation for the English Foreign Language Acquisition of seventh year students who belong to a Public Elementary school of Parral, seventh region, Maule in Chile. To investigate the factors that influence students’ motivation a mixed method research was carried out. The data was collected and analysed through qualitative approach and organized and presented in a quantitative manner represented by graphics. The information was compiled by two previously validated instruments, which consisted of a questionnaire for the teachers of the different subjects of the class and the psychosocial team who works with the students. A personal interview was applied to each student. Two major conclusions were obtained from the results of the analysis of the data collection; firstly students present a lack of motivation towards the subject of English as a Foreign Language as a product of the sociocultural environment in which they are immersed, secondly learners are exposed to language learning from puberty and not from the beginning of their first learning stages as postulates the Critical Period Hypothesis (CPH).La investigación presenta los resultados de la identificación y análisis de los factores que caracterizan la motivación hacia la adquisición del inglés como lengua extranjera de alumnos de séptimo año básico pertenecientes a un colegio básico y público de la comuna de Parral, séptima región del Maule en Chile. Para investigar los factores que inciden en la motivación de los estudiantes se utilizó un enfoque mixto tanto cualitativo para la recolección y análisis de los datos y cuantitativo para la organización y presentación de la información representada en gráficos. La obtención de la información se hizo mediante dos instrumentos previamente validados, los cuales consistieron en un cuestionario para los profesores de los diferentes sectores de aprendizaje del curso y para el equipo sicosocial que trabaja con los estudiantes. Una entrevista personal fue aplicada a cada alumno. Dos grandes conclusiones se obtuvieron del resultado del análisis de la recolección de datos; la primera es la falta de motivación de los alumnos hacia la asignatura de inglés como lengua extranjera producto del entorno sociocultural en el cual están inmersos y la segunda es que los alumnos son expuestos al aprendizaje de la lengua desde el inicio de la pubertad y no desde sus primeras etapas de aprendizaje como postula la hipótesis del período crítico

Elimination of Bias in Introspection: Methodological Advances, Refinements, and Recommendations

Building on past constructive criticism, the present study provides further methodological development focused on the elimination of bias that may occur during first-person observation. First, various sources of errors that may accompany introspection are distinguished based on previous critical literature. Four main errors are classified, namely attentional, attributional, conceptual, and expressional error. Furthermore, methodological recommendations for the possible elimination of these errors have been determined based on the analysis and focused excerpting of introspective scientific literature. The following groups of methodological recommendations were determined: 1) a better focusing of the subject’s attention to their mental processes, 2) providing suitable stimuli, and 3) the sharing of introspective experience between subjects. Furthermore, the potential of adjustments in introspective research designs for eliminating attentional, attributional, conceptual, and expressional error is discussed

Family Background, Self-Confidence and Economic Outcomes

In this paper we analyze the role played by self-confidence, modeled as beliefs about one's ability, in shaping task choices. We propose a model in which fully rational agents exploit all the available information to update their beliefs using Bayes' rule, eventually learning their true type. We show that when the learning process does not convergence quickly to the true ability level, even small differences in initial confidence can result in diverging patterns of human capital accumulation between otherwise identical individuals. As long as inital differences in the level of self-confidence are correlated with the socioeconomic background (as a large body of empirical evidence suggests), self-confidence turns out to be a channel through which education and earnings inequalities are transmitted across generations. Our theory suggests that cognitive tests should take place as early as possible, in order to avoid that systematic differences in self-confidence among equally talented people lead to the emergence of gaps in the accumulation of human capital.self-confidence, family background

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewedPostprin

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings