Improving Working Conditions in Global Supply Chains: The Role of Institutional Environments and Monitoring Program Design

Jodi L. Short; Michael W. Toffel; Andrea R. Hugill

Article | ILR Review | August 2020

Improving Working Conditions in Global Supply Chains: The Role of Institutional Environments and Monitoring Program Design

by Jodi L. Short, Michael W. Toffel and Andrea R. Hugill

Abstract

Activism seeking to improve labor conditions in global supply chains has led many transnational corporations to adopt codes of conduct and monitor suppliers for compliance. Drawing on thousands of audits conducted by a major social auditor, we identify structural contingencies in the institutional environment and program design under which codes and monitoring are more likely to be associated with improvements in conditions. At the institutional level, suppliers improve more when they face greater risk that NGOs and the press will expose harmful working conditions and when their buyers have experienced negative publicity for supply chain labor abuses. At the program design level, suppliers improve more on average when audits are pre-announced, when auditors are highly trained, and especially when both are true. However, extended analysis reveals nuances to these findings, demonstrating that improvement varies across violation types. For instance, we find that pre-announced audits were followed by greater improvement in OSH practices but not child labor practices. These findings can inform strategies for improving supply chain working conditions.

Keywords: Monitoring; supplier relationship; sustainability; Sustainability Management; Sustainable Operations; sustainable supply chains; NGO; Operations; Supply Chain Management; Governance Compliance; Corporate Social Responsibility and Impact; Global Range; Working Conditions;

Format: Print

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Citation:

Short, Jodi L., Michael W. Toffel, and Andrea R. Hugill. "Improving Working Conditions in Global Supply Chains: The Role of Institutional Environments and Monitoring Program Design." ILR Review 73, no. 4 (August 2020): 873–912.

The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools are then described: mean squared error (MSE), mean absolute deviation (MAD), Brier score, and cross-entropy, true/false positives/negatives, the confusion matrix, true positive rate (sensitivity or recall), false negative rate (Type II error rate), precision, true negative rate (specificity), false positive rate (Type I error rate), receiver operating characteristic curve (ROC) and area under the curve (AUC), and precision-recall curve.

A set of exercises and instructions to be used with the Process Simulator software made by Pro Model. These exercises allow students to investigate the impact of variable processing times on the performance of simple in-line processes. Includes color exhibits.

Case | HBS Case Collection | September 2020

Student Success at Georgia State University (A)

Michael W. Toffel, Robin Mendelson and Julia Kelley

Georgia State University had developed a reputation for driving student success by nearly doubling its graduation rate for students of all racial, ethnic, and socioeconomic backgrounds. It did so while growing its student body and the proportion of Black/African American, low-income, and first-generation students—groups with historically lower postsecondary graduation rates compared to national averages. Georgia State’s Student Success team, led by Tim Renick and Allison Calhoun-Brown, used a data-based approach to deploy micro-grant programs to retain students, implemented predictive analytics to improve student advising, and optimized course sequencing to help students graduate before they exhausted their financial aid. In 2016, they faced a growing “summer melt” problem where nearly 20% of incoming students who committed to attend never actually enrolled at Georgia State—and many never enrolled at any college. At the same time, they wondered how to balance continuing to incrementally improve student success at Georgia State and scaling their efforts to help the many other universities facing similar problems who sought to learn from their experience.

Keywords: Education; Higher Education; Learning; Curriculum and Courses; Demographics; Diversity; Ethnicity; Income; Race; Leadership; Goals and Objectives; Measurement and Metrics; Operations; Organizations; Mission and Purpose; Organizational Culture; Outcome or Result; Performance; Performance Effectiveness; Performance Evaluation; Service Operations; Performance Improvement; Planning; Strategic Planning; Social Enterprise; Nonprofit Organizations; Social Issues; Wealth and Poverty; Equality and Inequality; Technology; Technology Platform; Education Industry; Atlanta;

Citation:

Toffel, Michael W., Robin Mendelson, and Julia Kelley. "Student Success at Georgia State University (A)." Harvard Business School Case 621-006, September 2020. View Details

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Improving Working Conditions in Global Supply Chains: The Role of Institutional Environments and Monitoring Program Design

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