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Using weekly data from January 3, 2003 to March 27, 2015, we examine the responses of U.S. stock returns (S&P 500, DJIA, and NASDAQ) to monetary policy, controlling for WTI oil prices and the value of the U.S. dollar (USD) against major currencies. Based on differences between the federal funds rate and inflation expectations, U.S. real interest rates have become continuously negative since January 28, 2009. Vector auto-regressions (VARs) suggest stronger linkages more recently and vine copula models identify the structure of dependence across these markets, which can help investors optimize portfolio diversification.

The Individuals with Disabilities Education Improvement Act of 2004 ensured millions of American students had a legal right to free and appropriate physical education. Yet, there is confusion about who delivers appropriate adapted physical education (APE). This article reflects on the half century of legally defined APE and a country’s response to preparing teachers for the disability-related demands of the job. A critical perspective is offered with the hope of improving physical educational outcomes for students with disabilities.

College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross-validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data-driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well-being of diverse student populations through counseling.

Wallace Huffman continued the tradition of research on Midwest rural labor markets at Iowa State University that was begun in the 1930s by his advisers T.W. Schultz and D. Gale Johnson. We review the lessons learned from this research about the wisdom of policies aimed at retaining population in rural areas in the face of market forces and technological changes that create incentives to migrate to urban areas. Professor Huffman's teaching and lessons learned from the Iowa State Human Resources Workshop continues to shape recent research on the roles of agglomeration economies, information technologies, and returns to human capital on the strength of rural labor markets and policies regarding rural economic development.

The often observed empirical divergence between WTA and WTP measures of welfare change continues to be a topic of interest to both theoretical and applied economists. The divergence has particularly important implications for environmental policy. In this article, we review behavioral and other explanations of the disparity, with a focus on their connections to neoclassical welfare theory, and evaluate the empirical evidence of these explanations through the same lens. Some explanations of the disparity are consistent with neoclassical models, and some are not. Likewise, some imply that the divergences are attributed to underlying preferences (neoclassical or not), whereas others suggest that the divergences are due to elicitation methods, cognitive limitations, or other non-preference-related reasons. We argue that the source of the divergence can inform the choice of which measure, WTP or WTA, to use in a given empirical application.

We test whether commonly used measures of agglomeration economies encourage new firm entry in both urban and rural markets. Using new firm location decisions in Iowa and North Carolina, we find that measured agglomeration economies increase the probability of new firm entry in both urban and rural areas. Firms are more likely to locate in markets with an existing cluster of firms in the same industry, with greater concentrations of upstream suppliers or downstream customers, and with a larger proportion of college-educated workers in the local labor supply. Firms are less likely to enter markets with no incumbent firms in the sector or where production is concentrated in relatively few sectors. The same factors encourage both stand-alone start-ups and establishments built by multiplant firms. Commuting decisions exhibit the same pattern as new firm entry with workers commuting from low to high agglomeration markets. Because agglomeration economies are important for rural firm entry also, policies encouraging new firm entry should focus on relatively few job centers rather than encouraging new firm entry in every small town.

Existing studies provide mixed evidence that the U.S. macroeconomic news impacts international stock prices. We believe this may be related to the fact that economic surprises may not capture how investors interpret macroeconomic releases in various economic conditions. Consequently, we follow Birz and Lott (2011) and use newspaper coverage of economic releases as a measure of news. We argue that in addition to capturing the surprise component of macroeconomic releases, newspaper coverage provide interpretation of these releases similarly to how investors may interpret them in various economic conditions. Out of 15 examined international stock markets, we find that the U.S. macroeconomic news impacts stock returns of 12 countries.

Enhance students' reading abilities with technology. Discover how technological resources can improve the effectiveness and breadth of reading instruction to build student knowledge. Read real-world accounts from literacy experts, and learn how their methods can be adapted for your classroom. Explore how to foster improvement in student learning using a variety of tools, including interactive whiteboards, tablets, and social media applications. Benefits:Gain insight into ways to incorporate technology into reading instruction.Obtain guidance on choosing progress-monitoring tools to best address your students' needs. Discover strategies to engage students in vocabulary instruction, and help students interpret informational texts. Learn how to use various tools to spark group discussions about literature. Determine how to continually assess students' connections with the assigned reading material. Contents: Part I: Reading Foundations Chapter 1: Foundational Reading Competencies Supported With Technology: Phonemic Awareness and Word Recognition Chapter 2: Rethinking Foundational Reading Skills: Making Room for the Complexities of Digital Texts Chapter 3: Using Tablets to Teach Foundational Skills: Matching Apps to Student NeedsPart II: Reading Fluency Chapter 4: Podcasts: Adding Power and New Possibilities to the Readers Theater Experience Chapter 5: Student-Produced Movies as Authentic Reading Fluency Instruction Chapter 6: Audio-Assisted Reading Builds Reading FluencyPart III: Reading Vocabulary Chapter 7: Post-Reading Vocabulary Development Through VSSPlus Chapter 8: Bringing Words to Life Through Student-Created Vocabulary Videos Chapter 9: Self-Regulated Vocabulary Learning on the InternetPart IV: Comprehension of Informational Texts Chapter 10: Using the Multimodal Explanatory Composition Strategy to Respond to Informational Texts Chapter 11: Annotation Apps: Supporting Middle School Students' Interpretation of Science Texts Chapter 12: Online Research and Media Skills: An Instructional Model for Online Informational TextsPart V: Comprehension of Literary Texts Chapter 13: Digging Deeper With Reader Response: Using Digital Tools to Support Comprehension of Literary Texts in Online Learning Environments Chapter 14: Coding and Connecting Complex Literature Chapter 15: Linking Through Literature: Exploring Complex Texts Through Hypertext Literary AnalysisPart VI: Reading Across Disciplines Chapter 16: Classroom Blogging to Develop Disciplinary Literacy Chapter 17: Using eReaders to Enhance Literacy Instruction in the Content Areas Chapter 18: Supporting Inquiry With Digital Texts in School DisciplinesPart VII: Motivation for Reading Chapter 19: "I Wanted to Film, So I Read the Book": Filmmaking in the English Classroom Chapter 20: eBooks and eReaders: Removing Obstacles, Improving Motivation Chapter 21: Using Literacy iPad Apps for Reading MotivationPart VIII: Reading Assessment Chapter 22: Literacy Assessments in the Digital Age Chapter 23: Developing and Assessing Fluency Through Web 2.0 Digital Tools Chapter 24: Using Blogs as Formative Assessment of Reading Comprehension

Alice Wieland and Amy Jansen explore the intersection of how power, adverse incentives, and gender bias combine to perpetuate gender inequity in higher education.