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on Economic Design |
| By: | Martha Bailey; Tanya Byker; Elena Patel; Shanthi Ramnath; Martha J. Bailey |
| Abstract: | We use administrative tax data to analyze the cumulative, long-run effects of California’s 2004 Paid Family Leave Act (CPFL) on women’s employment, earnings, and childbearing. A regression-discontinuity design exploits the sharp increase in the weeks of paid leave available under the law. We find no evidence that CPFL increased employment, boosted earnings, or encouraged childbearing, suggesting that CPFL had little effect on the gender pay gap or child penalty. For first-time mothers, we find that CPFL reduced employment and earnings roughly a decade after they gave birth. |
| Keywords: | gender wage gap, maternity leave |
| JEL: | J08 J16 J71 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10933&r=des |
| By: | Ville Korpela; Michele Lombardi; Riccardo Saulle |
| Abstract: | otation programs are widely used in our society. For instance, a job rotation program is an HR strategy where employees rotate between two or more jobs in the same business. We study rotation programs within the standard implementation framework under complete information. When the designer would like to attain a Pareto efficient goal, we provide sufficient conditions for its implementation in a rotation program. However, when, for instance, every employee transitions through all different lateral jobs before rotating back to his original one, the conditions fully characterize the class of Pareto efficient goals that are implementable in rotation programs. |
| Keywords: | Rotation Programs; Job Rotation; Assignment Problems; Implementation; Rights Structures; Stability |
| JEL: | C71 D71 D82 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:liv:livedp:202221&r=des |
| By: | Edwin Zhang; Sadie Zhao; Tonghan Wang; Safwan Hossain; Henry Gasztowtt; Stephan Zheng; David C. Parkes; Milind Tambe; Yiling Chen |
| Abstract: | Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI for automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policy-making. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.14090&r=des |