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The Minimum Wage's Anchoring Effect on Perceptions of Fair Wages by Humans and AI


Core Concepts
The minimum wage acts as an anchor that systematically influences what wages humans and AI models like GPT-3 perceive as fair for a given job.
Abstract
The study explores how the stated minimum wage affects perceptions of fair wages for a given job description, both for human subjects surveyed through Prolific.co and for the AI model GPT-3. Key highlights: When no minimum wage is mentioned, the fair wage perceived by humans is significantly higher than the current minimum wage. Stating a minimum wage, even a realistic one, causes the perceived fair wage to shift towards the minimum wage, demonstrating an anchoring effect. For unrealistically high minimum wages ($50 and $100), the distribution of human responses splits into two distinct modes - one closely following the anchor and one remaining closer to the control. GPT-3 also exhibits an anchoring effect, but with some key differences from humans: An overall downward shift in the fair wage responses compared to humans, likely due to outdated training data For unrealistic anchors, GPT-3 predominantly favors the mode closer to its responses for realistic wages, rather than blindly following the anchor Experiments with GPT-3 also reveal sensitivity to small prompt variations, including changes in punctuation, spelling, and cues about the worker's gender and race. The results establish that the minimum wage functions as an anchor that systematically influences perceptions of fair wages, for both humans and AI models. This has implications for understanding wage-setting behavior and the role of policy anchors.
Stats
The minimum wage is $7.25 per hour in the United States. The mean fair wage for a materials worker with no minimum wage mentioned is $19.55. For a minimum wage of $5, the mean fair wage is $15.24. For a minimum wage of $50, the mean fair wage is $49.64. For a minimum wage of $100, the mean fair wage is $78.56.
Quotes
"When there is no mention of the minimum wage, the wage perceived as fair is significantly higher than the current minimum wage in any American city or state." "I demonstrate that the minimum wage functions as an anchor for what Prolific workers consider a fair wage: for numerical values of the minimum wage ranging from $5 to $15, the perceived fair wage shifts towards the minimum wage, thus establishing its role as an anchor." "For unrealistic values of the stated minimum wage, namely $50 and $100, I find that first-order analysis is insufficient as the distribution of responses splits into two distinct modes: one closely follows the anchor while the other remains closer to the control, but still with a systematic bias towards the anchor."

Key Insights Distilled From

by Dario G. Soa... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2210.10585.pdf
The Minimum Wage as an Anchor

Deeper Inquiries

How do employers' wage offerings and hiring decisions get affected by the anchoring effect of the minimum wage?

Employers' wage offerings and hiring decisions can be significantly influenced by the anchoring effect of the minimum wage. When the minimum wage serves as an anchor, employers may use it as a reference point when determining the wages they offer to employees. This anchoring effect can lead to employers basing their wage offerings on the minimum wage, either consciously or subconsciously. As a result, the minimum wage can influence the perceived range of acceptable wages for a particular job, potentially leading to wage offers that are closer to the minimum wage than they would be in the absence of this anchor. Employers may also be influenced by the societal norms and expectations surrounding the minimum wage. If the minimum wage is seen as a standard or benchmark for fair compensation, employers may feel pressure to align their wage offerings with this perceived standard. This can impact not only the initial wage offers but also decisions related to raises, promotions, and overall compensation packages. Furthermore, the anchoring effect of the minimum wage can impact hiring decisions by shaping employers' perceptions of what constitutes a competitive wage in the labor market. Employers may use the minimum wage as a reference point when evaluating the attractiveness of their wage offerings compared to those of other employers. This can influence their ability to attract and retain talent, as job seekers may also anchor their salary expectations based on the minimum wage. In summary, the anchoring effect of the minimum wage can play a significant role in shaping employers' wage offerings and hiring decisions by influencing their perceptions of fair compensation, benchmarking practices, and competitiveness in the labor market.

How do other government policies or numerical reference points might serve as anchors for perceptions of fairness, and how do those compare to the minimum wage effect?

Other government policies or numerical reference points can also serve as anchors for perceptions of fairness in wage determination and economic decision-making. These anchors can include factors such as industry standards, cost of living indices, inflation rates, and salary surveys. Each of these reference points can influence individuals' judgments of what constitutes a fair wage or compensation package. Industry standards: Industry-specific benchmarks and salary surveys can serve as anchors for determining fair wages within a particular sector. Employers and employees may use industry standards to gauge the competitiveness of their compensation packages and ensure they are in line with prevailing norms. Cost of living indices: Cost of living indices provide a measure of the relative cost of goods and services in different regions. Employers and employees may use these indices as anchors to adjust wages based on the affordability of essential expenses in a given area. Inflation rates: Inflation rates can impact the purchasing power of wages over time. Individuals may anchor their perceptions of fair wages to inflation rates to ensure their compensation keeps pace with rising prices and maintains its real value. Government policies: Apart from the minimum wage, other government policies such as tax regulations, social security benefits, and healthcare mandates can also influence perceptions of fairness in wage determination. These policies can serve as anchors for both employers and employees when negotiating compensation packages. Compared to the minimum wage effect, these alternative anchors may have varying degrees of influence on perceptions of fairness in wage setting. While the minimum wage is a direct and explicit reference point set by government mandate, other anchors like industry standards and cost of living indices may be more nuanced and context-specific. Additionally, the impact of these alternative anchors on wage decisions may depend on the industry, geographic location, and individual preferences of employers and employees.

Given the sensitivity of AI models like GPT-3 to prompt variations, how can we develop more robust and unbiased AI systems for tasks involving economic and social judgments?

Developing more robust and unbiased AI systems for tasks involving economic and social judgments requires a multi-faceted approach that addresses both technical and ethical considerations. Here are some strategies to enhance the robustness and fairness of AI systems like GPT-3: Diverse Training Data: Ensure that AI models are trained on diverse and representative datasets that encompass a wide range of perspectives, demographics, and scenarios. This can help mitigate biases and improve the model's ability to handle varied inputs. Regular Auditing and Monitoring: Implement mechanisms for auditing and monitoring AI systems to detect and address biases or inaccuracies in real-time. This can involve continuous evaluation of model performance and outcomes to ensure fairness and reliability. Explainability and Transparency: Enhance the explainability of AI models by providing clear and transparent explanations for their decisions and predictions. This can help users understand the reasoning behind the model's outputs and identify potential biases. Ethical Guidelines and Governance: Establish ethical guidelines and governance frameworks for the development and deployment of AI systems. This can include guidelines for data collection, model training, and decision-making processes to uphold ethical standards and prevent discriminatory outcomes. Bias Mitigation Techniques: Implement bias mitigation techniques such as debiasing algorithms, fairness constraints, and adversarial training to reduce the impact of biases in AI systems. These techniques can help address biases related to sensitive attributes like race, gender, and socioeconomic status. Human-in-the-Loop Approaches: Incorporate human oversight and feedback mechanisms into AI systems to provide checks and balances on automated decision-making. Human-in-the-loop approaches can help correct errors, validate outputs, and ensure ethical considerations are upheld. By integrating these strategies into the development and deployment of AI systems, we can work towards creating more robust, unbiased, and ethically sound AI models for tasks involving economic and social judgments. This holistic approach can help enhance the reliability, fairness, and transparency of AI systems in complex decision-making contexts.
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