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TC1 - SO5: Labor aspects in sustainable supply chains
Time:
Tuesday, 28/June/2022:
TC 14:00-15:30
Session Chair: Zhoupeng Zhang
Location:Forum 1-3
Presentations
Evidence of the unintended labor scheduling implications of the minimum wage
Qiuping Yu1, Shawn Mankad2, Masha Shunko3
1Georgia Institute of Technology, United States of America; 2Cornell University; 3University of Washington
Our study is the first to empirically study the impact of the minimum wage on firms’ scheduling practices. Using a highly granular dataset from a chain of fashion retail stores, we estimate that a $1 increase in the minimum wage, while having a negligible impact on the total labor hours used by the stores, leads to a 27.7% increase in the number of workers scheduled per week, but a 19.4% reduction in weekly hours per worker, and less consistent schedules, which substantially hurt worker welfare.
A game theoretic model of forced labor reduction in supply chains
Kate Ashley, Shawn Bhimani
Northeastern University, United States of America
Under current legislation, multinational companies are at risk of having imports into the U.S. blocked due to alleged use of forced labor in their supply chains. Using a game theoretic model, we study the equilibrium interactions between firms, who may exert costly 'responsibility effort,' and enforcement organizations that allocate scarce resources to investigate multiple firms. We characterize policies that incentivize greater supply chain responsibility based on firm and industry parameters.
Implications of Worker Classification in On-Demand Economy
Zhoupeng Jack Zhang1, Ming Hu1, Jianfu Wang2
1Rotman School of Management, University of Toronto; 2College of Business, City University of Hong Kong
How shall gig workers be classified? Compared to the benchmark of contractors, we show that uniform classifications (employees, contractors+) suffer issues of worker’s being undercut and overjoining and will not always make vulnerable workers better off. To classify workers according to their needs, or operationally prioritizing vulnerable workers can Pareto improve over uniform classifications. Our work highlights the importance of worker-specific regulations in the on-demand economy.