The retirement migration puzzle in China

We examine whether and how retirement affects migration decisions in China. Using a regression discontinuity (RD) design approach combined with a nationally representative sample of 228,855 adults aged between 40 and 75, we find that retirement increases the probability of migration by 12.9 percentage points. Approximately 38% of the total migration effects can be attributed to inter temporal substitution (delayed migration). Retirement-induced migrants are lower-educated and have restricted access to social security. Household-level migration decisions can reconcile different migration responses across gender. Retirees migrate for risk sharing and family protection mechnisms, reducing market production of their families in the receiving households

Can I live with you after I retire? Retirement, old age support, and internal migration of older adults in China

This study examines the causal impact of retirement on migration decisions. Using a regression discontinuity (RD) design approach, combined with a nationally representative sample of 228,855 Chinese older adults, we find that retirement increases the probability of migration by 12.9 p.p. (an 80% increase in migration). Approximately 38% of the total migration effects can be attributed to inter-temporal substitution. Retirement-induced migrants are lower-educated, have restricted access to social security, and come from origins with high living costs. Relying on old age support from adult children in migration is a likely mechanism. These findings are consistent with a simple theoretical model of migration for older adults.Series: Department of Economics Working Paper Serie

Is There Any Social Principle for LLM-Based Agents?

Focus on Large Language Model based agents should involve more than "human-centered" alignment or application. We argue that more attention should be paid to the agent itself and discuss the potential of social sciences for agents.Comment: 3 pages, 1 figur

Visual Prompt Multi-Modal Tracking

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT.Comment: Accepted by CVPR202

The impact of long-term care insurance in China on beneficiaries and caregivers: A systematic review

Background China’s long-term care insurance (LTCI) policy has been minimally evaluated. This systematic review aimed to assess the impact of China’s LTCI pilot on beneficiaries and their caregivers. Methods This review is based on a search of peer-reviewed studies in English (Embase, MEDLINE, Web of Science) and Chinese (China National Knowledge Infrastructure [CNKI], VIP, Wanfang) databases from January 2016 through July 2020, with all studies published in English or Chinese included. We included quantitative analyses of beneficiary-level data that assessed the impact of LTCI on beneficiaries and their caregivers, with no restriction placed on the outcomes studied. Results Nine studies met our inclusion criteria. One study was a randomised trial and two used quasi-experimental approaches. Four studies examined LTCI’s effect on beneficiaries’ quality of life, physical pain, and health service utilisation; one study reported the effect on beneficiaries’ healthcare expenditures; and one study evaluated the impact on caregivers’ care tasks. These studies generally found LTCI to be associated with an improvement in patients’ quality of life (including decreased physical pain), a reduction in the number of outpatient visits and hospitalisations, decreased patient-level health expenditures (e.g. one study reported a reduction in the length of stay, inpatient expenditures, and health insurance expenditures in tertiary hospitals by 41.0%, 17.7%, and 11.4%, respectively), and reduced informal care tasks for caregivers. In addition, four out of four studies that evaluated this outcome found that beneficiaries’ overall satisfaction with LTCI was high. Conclusion The current evidence base for the effects of LTCI in China on beneficiaries and their caregivers is sparse. Nonetheless, the existing studies suggest that LTCI has positive effects on beneficiaries and their caregivers. Further rigorous research on the impacts of LTCI in China is needed to inform the future expansion of the program

Data Upcycling Knowledge Distillation for Image Super-Resolution

Knowledge distillation (KD) emerges as a challenging yet promising technique for compressing deep learning models, characterized by the transmission of extensive learning representations from proficient and computationally intensive teacher models to compact student models. However, only a handful of studies have endeavored to compress the models for single image super-resolution (SISR) through KD, with their effects on student model enhancement remaining marginal. In this paper, we put forth an approach from the perspective of efficient data utilization, namely, the Data Upcycling Knowledge Distillation (DUKD) which facilitates the student model by the prior knowledge teacher provided via upcycled in-domain data derived from their inputs. This upcycling process is realized through two efficient image zooming operations and invertible data augmentations which introduce the label consistency regularization to the field of KD for SISR and substantially boosts student model's generalization. The DUKD, due to its versatility, can be applied across a broad spectrum of teacher-student architectures. Comprehensive experiments across diverse benchmarks demonstrate that our proposed DUKD method significantly outperforms previous art, exemplified by an increase of up to 0.5dB in PSNR over baselines methods, and a 67% parameters reduced RCAN model's performance remaining on par with that of the RCAN teacher model

COVID-19 and climate: global evidence from 117 countries

Visual inspection of world maps shows that coronavirus disease 2019 (COVID-19) is less prevalent in countries closer to the equator, where heat and humidity tend to be higher. Scientists disagree how to interpret this observation because the relationship between COVID-19 and climatic conditions may be confounded by many factors. We regressconfirmed COVID-19 cases 5per million inhabitants in a country against the country’s distance from the equator, controlling key confounding factors: air travel, distance to Wuhan, testing intensity, cell phone usage, vehicle concentration, urbanization, and income. A one-degree increase in absolute latitude is associated with a 2.6% increase in cases per million inhabitants (p value <0.001). The Northern hemisphere may see a decline in new COVID-19 cases during summer and aresurgence during winter. One Sentence Summary: An increase in absolute latitude by onedegree is associated with a 2.6% increase in COVID-19 cases per million inhabitants after controlling for several important factors