Wage Increases, Labor Market Integration, and the Lewisian Turning Point: Evidence from Migrant Workers

Labor and Human Capital,

Can China's rural elderly count on support from adult children ? implications of rural-to-urban migration

This paper shows that support from the family continues to be an important source of support for the rural elderly, particularly the rural elderly over 70 years of age. Decline in likelihood of co-residence with, or in close proximity to, adult children raises the possibility that China's rural elderly will receive less support in the forms of both income and in-kind instrumental care. Although descriptive evidence on net financial transfers suggests that the elderly with migrant children will receive similar levels of financial transfers as those without migrant children, the predicted variance associated with these transfers implies a higher risk that elderly with migrant children may fall into poverty. Reducing the risk of low incomes among the elderly is one important motive for new rural pension initiatives supported by China's government, which are scheduled to be expanded to cover all rural counties by the end of the 12th Five Year Plan in 2016.Rural Poverty Reduction,Population Policies,Services&Transfers to Poor,Regional Economic Development,Labor Policies

THE RISE OF RURAL-TO-RURAL LABOR MARKETS IN CHINA

The continued transfer of agricultural labor into the industrial sector is crucial to China's transformation into an industrial economy. We argue in this paper that rural industry offers an alternative to urban industry for receiving agricultural labor from areas without off-farm employment opportunities. Characteristics of rural industry differ from their urban counterparts. These characteristics may serve to shape the growth in employment for incoming workers in rural areas, provide opportunities for certain types of workers, and affect the impacts these workers have on the local economy. In this paper we examine the features of China's rural-to-rural labor movement and the villages where these workers are employed. Using a nationally representative sample of 215 villages, we show that the growth in rural-to-rural labor movement between 1988 and 1995 has been much faster than in rural-to-urban movement or in local off-farm employment. The rapid growth in rural-to-rural commuting and migration has not negatively affected off-farm income earning opportunities for workers living in the receiving villages. Rural-to-rural labor movement also has many positive effects. Labor movement into rural villages provides opportunities for workers generally underrepresented in other parts of the off-farm labor market, appears to dampen upward pressure on wages that allows rural industry to maintain labor intensive practices, and promotes national economic integration.Labor and Human Capital,

Augmented two-step estimating equations with nuisance functionals and complex survey data

Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized empirical likelihood method. Unfortunately, the resulting inference is not efficient and the nonparametric version of the Wilks' theorem breaks down even under simple random sampling. We propose an augmented estimating equations method with nuisance functionals and complex surveys. The second-step augmented estimating functions obey the Neyman orthogonality condition and automatically handle the impact of the first-step plug-in estimator, and the resulting estimator of the main parameters of interest is invariant to the first step method. More importantly, the generalized empirical likelihood based Wilks' theorem holds for the main parameters of interest under the design-based framework for commonly used survey designs, and the maximum generalized empirical likelihood estimators achieve the semiparametric efficiency bound. Performances of the proposed methods are demonstrated through simulation studies and an application using the dataset from the New York City Social Indicators Survey.Comment: 43 page

Electronic structure and magnetic properties of the graphene/Ni3Mn/Ni(111) trilayer

Experimental and theoretical studies of manganese deposition on graphene/Ni(111) shows that a thin ferromagnetic Ni3Mn layer, which is protected by the graphene overlayer, is formed upon Mn intercalation. The electronic bands of graphene are affected by Ni3Mn interlayer formation through a slight reduction of n-type doping compared to graphene/Ni(111) and a suppression of the interface states characteristic of graphene/Ni(111). Our DFT-based theoretical analysis of interface geometric, electronic, and magnetic structure gives strong support to our interpretation of the experimental scanning tunneling microscopy, low energy electron diffraction, and photoemission results, and shows that the magnetic structure of graphene is strongly influenced by Ni3Mn formation

Rapid evaluation of earthquake-induced landslides by PGA and Arias intensity model: insights from the Luding Ms6.8 earthquake, Tibetan Plateau

On September 5, 2022, a magnitude 6.8 earthquake occurred along the Xianshuihe Fault Zone in Luding County, Tibetan Plateau, China, leading to a significant outbreak of landslides. The urgent need for a swift and accurate evaluation of earthquake-induced landslides distribution in the affected area prompted this study. This research delves into regional geological data, scrutinizes post-earthquake Peak Ground Acceleration (PGA) and Arias Intensity (Ia) associated with the Luding earthquake, and conducts earthquake-induced landslides risk assessments within the Luding earthquake zone using the Newmark model. Validation of the earthquake-induced landslides risk assessment outcomes rooted in PGA and Ia relies on an earthquake-induced landslides database, revealing Area Under the Curve (AUC) values of 0.73 and 0.84 in respective ROC (Receiver Operating Characteristic) curves. These results unequivocally affirm the exceptional accuracy of earthquake-induced landslides evaluation using Ia calculations, emphasizing its suitability for the swift prediction and evaluation of earthquake-induced landslides. The earthquake-induced landslides risk assessment based on Ia computation reveals the area with extremely high-risk and high-risk of earthquake-induced landslides encompass 0.71% of the entire study area. Notably, these areas are predominantly clustered within seismic intensity VII zones and primarily trace the Moxi fault zone, extending from the southern portion of the middle east along the Dadu River and the Moxi fault, with reach up to Dewei Township in the north and Caoke Township in the south. Hazard-prone regions predominantly align with slopes featuring gradients of 30°–45° and bear a strong correlation with fault activity. Furthermore, the results of this evaluation are harmonious with the findings from remote sensing interpretation and on-site field investigations pertaining to the earthquake-induced landslides. This body of knowledge can serve as a crucial reference for expedited assessment, emergency response and subsequent supplementation of earthquake-induced landslide databases when confronting similar earthquake-induced landslide scenarios

TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.Comment: Technical Repor