Resettlement 2.0: Choice-Architecture and Skill-Matching Pilot for Relocated Communities

Zhang et al. (2020) revealed that poverty alleviation resettlement (PAR) in China harmed girls' education and constrained earnings due to "one-size-fits-all" relocations. This experiment introduces a "Resettlement 2.0" framework inspired by behavioral economics: relocated families receive (1) choice menus of destination communities (vs. assigned locations), and (2) AI-driven skill matching (extending Jejeniwa et al.'s 2024 AI solutions) to align local jobs with pre-existing capabilities. Using a randomized controlled trial (RCT) in a new resettlement cohort, we compare outcomes against historical PAR benchmarks. Unlike Zhang et al.'s observational study, this isolates causal effects of autonomy and skill alignment. The innovation directly addresses their conclusion that "free destination choices" might improve outcomes while testing technology-enabled personalization at scale. Could revolutionize displacement policies by treating human capital preservation as a design parameter.

References:

1. The long-run effects of poverty alleviation resettlement on child development: Evidence from a quasi-experiment in China. Jipeng Zhang, Lue Zhan, Chong Lu (2020).
2. AI SOLUTIONS FOR DEVELOPMENTAL ECONOMICS: OPPORTUNITIES AND CHALLENGES IN FINANCIAL INCLUSION AND POVERTY ALLEVIATION. Temitayo Oluwaseun Jejeniwa, Noluthando Zamanjomane Mhlongo, Titilola Olaide Jejeniwa (2024). International journal of advanced economics.