Are Eco-Labels Valuable? Evidence from the Apparel Industry

Using U.S. apparel catalog data, we estimate hedonic price functions to identify market valuation of environmental attributes of apparel goods. We identify a significant and robust premium for the organic fibers embodied in the apparel goods. We also find a discount for the "no-dye" label. We do not, however, find any evidence of a premium for environment-friendly dyes. We further investigate the pricing behavior of apparel suppliers for potential heterogenous pricing of the organic-fiber attribute and find no evidence of different premia across firms. A reprint of an article in the AJAE in 1999.eco-labels; organic-cotton apparel; dyes; hedonic price; labeling; textiles; green label

Revenue Insurance and Chemical Input Use Rates

Using farm level data and a simultaneous probit model we evaluate the input use and environmental effects of revenue insurance. A priori, the moral hazard effect on input use is indeterminate and this study empirically assesses the input use impact of the increasingly popular, and federally subsidized, risk management instrument of revenue insurance. We conclude that the moral hazard effect of federally subsidized revenue insurance products induces U.S. wheat farmers to increase expenditures on pesticides and reduce expenditures on fertilizers.Crop Production/Industries, Risk and Uncertainty,

An inquisitive investigation into the effects of self-regulated learning

The purposes of this inquisitive investigation were to (a) gain an insight into the self-regulation strategies that third grade students naturally engage in, (b) to teach specific self-regulation strategies to these students, and to (c) explore the effects of utilizing such strategies on different aspects of academic performance. Throughout the study students demonstrated a wide range of strategy use, before and after teaching about self-regulation. The majority of the data collected illustrates several positive impacts on both test grades and homework completion for the 14 participants in the study, who are in the third grade, when multiple self-regulation strategies are used to study or complete assignments at home. Data in the form of pre and post surveys, group discussions, interviews, test grades, and homework assignments are analyzed. The results of the study illustrate several patterns related to self-regulation strategy use and academic performance. Essentially the data shows that when students use different self-regulation strategies in and out of the classroom, there are positive effects. Such positive effects, discussed in more detail in chapter 4, include higher grades on tests, more accurate and thorough homework completion, and a greater level of independence and personal accountability during independent work time. Results of the study and implications for future teachers or researchers interested in the topic, which includes holding a workshop for other faculty members about self-regulation and perhaps teaching these skills to your students, are subsequently discussed in more detail

Ecolabels & International Trade in the Textile & Apparel Market

The authors provide a formal analysis of the welfare and trade implications of eco-labeling schemes. They present a simple model of vertical (quality) differentiation that captures stylized features of the textiles market in which trading takes place between an industrialized North (domestic) and a developing South (foreign). The paper investigates several labeling scenarios--labeling by North, labeling by 130th North and South, and harmonization of 170th labels--and draws conclusions about their impact on consumers

Retrospective Pretest: A Practical Technique For Professional Development Evaluation

The purpose of this study was to field test an instrument incorporating a retrospective pretest to determine whether it could reliably be used as an evaluation tool for a professional development conference. Based on a prominent evaluation taxonomy, the instrument provides a practical, low-cost approach to evaluating the quality of professional development interventions across a wide variety of disciplines. The instrument includes not only the questions typically associated with measuring participants’ reactions but also includes a set of questions to gauge whether and how much learning occurred. Results indicate that the data produced from the instrument were reliable

Eco-Labels and International Trade in Textiles

The authors provide a formal analysis of the welfare and trade implications of eco-labeling schemes. They present a simple model of vertical (quality) differentiation that captures stylized features of the textiles market in which trading takes place between an industrialized North (domestic) and a developing South (foreign). The paper investigates several labeling scenarios--labeling by North, labeling by 130th North and South, and harmonization of 170th labels--and draws conclusions about their impact on consumers

HRDQ Submissions of Quantitative Research Reports: Three Common Comments in Decision Letters and a Checklist

The purpose of this work is to assist authors in preparing manuscripts for potential publication in HRDQ, as well as to provide a resource to authors who may receive a decision letter noting such an issu

MOWDOC: A Dataset of Documents From Taking the Measure of Work for Building a Latent Semantic Analysis Space

Introduction For organizational researchers employing surveys, understanding the semantic link between and among survey items and responses is key. Researchers like Schwarz (1999) have long understood, for example, that item order can impact survey responses. To account for “item wording similarity,” researchers may allow item error variances to correlate (cf. Rich et al., 2010, p. 625). Other researchers, such as Newman et al. (2010), have pointed to semantic similarity between items as support for the premise that work engagement is like old wine in a new bottle. Recently, organizational researchers (e.g., Arnulf et al., 2014, 2018) have been able to use latent semantic analysis (LSA) and semantic survey response theory (SSRT) to quantify the semantic similarity between and among scales, items, and survey responses. Latent semantic analysis is a computational model that assesses similarity in language where the similarity of any “given word (or series of words) is given by the context where this word is usually found” (Arnulf et al., 2020, p. 4). Latent semantic analysis involves establishing a semantic space from a corpus of existing documents (e.g., journal articles, newspaper stories, item sets). The corpus of documents is represented in a word-by-document matrix and then transformed into an LSA space through singular value decomposition. The reduced LSA space can be used to assess the semantic similarity of documents within the space as well as new documents that are projected onto the space. Patterns of semantic similarity resulting from LSA have accounted for a substantive amount of variability in how individuals respond to survey items that purport to measure (a) transformational leadership, motivation, and self-reported work outcomes (60–86%; Arnulf et al., 2014), (b) employee engagement and job satisfaction (25–69%; Nimon et al., 2016), and (c) perceptions of a trainee program, intrinsic motivation, and work outcomes (31–55%, Arnulf et al., 2019). It also appears that personality, demographics, professional training, and interest in the subject matter have an impact on the degree to which an individual\u27s responses follow a semantically predictable pattern (Arnulf et al., 2018; Arnulf and Larsen, 2020, Arnulf et al., 2020). While being able to objectively access the degree to which survey responses are impacted by semantics is a great step forward in survey research, such research is often conducted with LSA spaces that are not open and therefore not customizable except by those that have access to the body of text upon which the LSA space is built. In this day of open science, researchers need access not only to the LSA space on which semantic survey research may be based but also to the underlying corpus of text to determine whether choices made in the generation of the LSA space have an impact on the results found. Researchers may not be able to create their own LSA spaces for a number of reasons, including the fact that on some occasions it is difficult to collect a representative corpus of text (Quesada, 2011). However, building an LSA space allows researchers to customize the space including the application of weighting schemes and the level of dimensionality for the LSA space. As shown by Arnulf et al. (2018), the dimensionality of the LSA space is a factor when using an LSA space to predict empirical correlations from scale item cosines. To help address the barrier to creating an LSA space for use in the analysis of scale items in organizational research, this report provides a dataset of documents from measures reviewed in Taking the Measure of Work. In Taking the Measure of Work, Fields provided the items for 324 scales and subscales which cover the areas of job satisfaction, organizational commitment, job characteristics, job stress, job roles, organizational justice, work-family conflict, person-organization fit, work behaviors, and work values. The MOWDOC dataset presented in this manuscript provides the documents necessary to create a semantic space from the item sets presented in Fields\u27s Taking the Measure of Work

Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights (cf. Courville & Thompson, 2001; Nimon, Roberts, & Gavrilova, 2010; Zientek, Capraro, & Capraro, 2008), often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what and how independent variables contribute to a regression equation. Thus, this paper presents a guidebook of variable importance measures that inform MR results, linking measures to a theoretical framework that demonstrates the complementary roles they play when interpreting regression findings. We also provide a data-driven example of how to publish MR results that demonstrates how to present a more complete picture of the contributions variables make to a regression equation. We end with several recommendations for practice regarding how to integrate multiple variable importance measures into MR analyses. Accessed 103,722 times on https://pareonline.net from April 29, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right