Does Migration Income Help Hometown Business? Evidences from Rural Households Survey in China

This empirical study examines effects of household migration income on non-farm business in rural China. The restrictions on labor mobility in China were loosened after the economic reform in 1978. As a result, more and more rural households have family members engaging in temporary migration, working and living between rural home and urban areas, which forms a large "floating" population of migrant workers. The income migrant workers bringing home provides a vital capital resource for the credit deprived rural areas, and hence strongly promotes hometown non-farm business. This paper raises three questions: first, how does migration income affect the probability that rural households will start non-farm business? Second, how does migration income impact the probability that rural households will remain in non-farm business after starting up? Third, whether and how much does migration income increase non-farm business income? The findings indicate that migration income not only raises the probability of starting and remaining in non-farm business, but also increases non-farm business income. The empirical results in this paper confirm that, for financially constrained rural households in China, migration income offers a valuable capital resource and facilitates the development of diverse business operation in rural China.Migration, Rural China, Non-farm Business, Probit

Image Retrieval based on Bag-of-Words model

This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system. In recent years, large-scale image retrieval shows significant potential in both industry applications and research problems. As local descriptors like SIFT demonstrate great discriminative power in solving vision problems like object recognition, image classification and annotation, more and more state-of-the-art large scale image retrieval systems are trying to rely on them. A common way to achieve this is first quantizing local descriptors into visual words, and then applying scalable textual indexing and retrieval schemes. We call this model as bag-of-words or bag-of-features model. The goal of this survey is to give an overview of this model and introduce different strategies when building the system based on this model

Three Essays On Human Capital, Migration And Rural Development In Developing Countries

Thesis (Ph.D.) - Indiana University, Economics, 2010This dissertation studies several important issues in developing countries. The first essay focuses on rural-urban migration and rural entrepreneurship in China. I find that depending on the initial human capital level, policies aiming to advance human capital may have different impacts on migration. Even though return migrants help raise rural labor demand and wages, the income inequality between the urban and rural areas is not eliminated and migration is persistent. The borrowing constraints limit the size of rural non-farm businesses and slow down the development of the rural industry. The second essay studies the dynamics of rural-urban migration income and rural non-farm business ownership in China, applying a dynamic bivariate probit model to the China Rural Households Survey Data. The positive correlation between receiving migration remittances in one period and operating rural non-farm business in the following period is explained by correlated unobserved heterogeneity. A negative state dependence between receiving migration remittances and operating rural non-farm businesses can be justified by the time and labor constraints facing rural households. The third essay provides a theoretical study of teacher absenteeism, a severe phenomenon in many less developed countries. I focus on several policies, such as labor taxes, financial penalties, and teachers' wage rate, to examine the short-run and long-run effects of these policies on teacher absenteeism, economic growth, goods production, and quality of lower and higher education

Study on Thermal Comfort for University Classrooms in Pre- Heating Season in Xi\u27an

Thermal comfort of students in university classrooms during transition season in Xi\u27an, before heating, is studied. Indoor thermal environment parameters and outdoor weather parameters of seven typical classrooms in a university campus in Xi\u27an were measured. At the same time, the subjective questionnaires were used to know students\u27 satisfaction and expectation with various environmental factors. 992 valid questionnaires were received. Based on the data collected, the thermal comfort of occupants in classroom was discussed and a thermal comfort adaptive model was established. The results show that the range of thermal comfort acceptable to students is broader than that defined in the ASHARE standard, indicating that students have some adaptability to indoor air environment. The measured indoor thermal neutral temperature is lower than the theoretical one. There is difference between the thermal sensation vote (TSV) and the predicted mean vote (PMV). The slope of TSV cure vs. operative temperature is greater than that of PMV, indicating that under actual condition, students are more sensitive to air changes. The proposed adaptive model provided a reference for understanding the thermal comfort of university buildings under natural ventilation environment in Xi’an, helpful to improve the thermal comfort and save energy for university buildings in Xi’an

Effects of Indoor Temperature and Air Movement on Perceived Air Quality in the Natural Ventilated Classrooms

Perceived air quality is an important aspect in current guidelines and standards for indoor environment. It represents occupants’ real feeling about indoor air and affected by almost all environmental parameters, such as the temperature, the relative humidity, the air movement, and et al. Studies were conducted mainly in controlled climate chambers or air-conditioned spaces, rarely in natural ventilated spaces. In this paper, the effects of temperature and air movement on perceived air quality in natural ventilated classrooms are investigated. The indoor environmental parameters in 7 classrooms for 35 lessons are continuously measured and the students in class are asked to report their perception on the temperature, air movement, and the air quality of classrooms by filling questionnaires at once after a lesson. The number of received validated questionnaires is 992. The correlation analysis is used to investigate the effects of temperature and air movement on the perceived air quality. Results show that in natural ventilation classrooms, which are warm at temperature and moderate at humidity with an air speed lower than 0.1m/s, it is the thermal sensation rather than the temperature, enthalpy, thermal acceptability, or CO2 concentration that affects the perception of occupants for air quality. The perception for air movement influences the air quality acceptability. Increasing air movement increases the air quality acceptability. Besides, it is found that the preference of air movement is related to the air quality acceptability. When participants feel that the air movement is just suitable, the acceptability of air quality reaches the highest. When participants feel the air movement need to be adjusted, the air quality acceptability decreases

Impact of public educational expenditure on poverty in China

Thesis(Master) --KDI School:Master of Public Policy,2019Over last four decades, more than 740 million people were lifted out of poverty in China. The Chinese government set the target to eliminate absolute poverty and remove all poor counties from the poverty list by 2020. In this context, the paper attempts to investigate the impact of government expenditure on education upon poverty in China, using province-level panel data during 1997-2017. Poverty is measured by beneficiaries of social assistance programs, such as Minimum Living Guarantee System (in Chinese “Dibao”), Five Guarantee System (in Chinese “Wubao”) and Subsidies for destitute households (in Chinese “Te Kun Jiu Zhu”). The independent variable is “government appropriation for education”, which includes “public budgetary fund for education, taxes and fees collected by governments at all levels that are used for education purpose, enterprise appropriation for enterprise-run schools, income from school-run enterprises and social services that are used for education purpose and other national appropriations for education” (China Statistical Yearbook). Using OLS fixed effects model, we found that: 1) government expenditure on education is significantly negatively related to headcount ratio; 2) private investment in education also contributes to poverty reduction and seems to be a possible way to improve education quality; 3) rural household net income is negatively related to poverty rate, while 4) urban population has two opposite results, which raises an interesting topic to study.I. INTRODUCTION II. LITERATURE REVIEW III. HYPOTHESIS STATEMENT IV. DATA AND METHODOLOGY V. RESULT AND DISCUSSION VI. CONCLUSIONmasterpublishedJialu LIU

Constructing and modeling text-rich information networks: a phrase mining-based approach

A lot of digital ink has been spilled on "big data" over the past few years, which is often characterized by an explosion of information. Most of this surge owes its origin to the unstructured data in the wild like words, images and video as comparing to the structured information stored in fielded form in databases. The proliferation of text-heavy data is particularly overwhelming, reflected in everyone's daily life in forms of web documents, business reviews, news, social posts, etc. In the mean time, textual data and structured entities often come in intertwined, such as authors/posters, document categories and tags, and document-associated geo locations. With this background, a core research challenge presents itself as how to turn massive, (semi-)unstructured data into structured knowledge. One promising paradigm studied in this dissertation is to integrate structured and unstructured data, constructing an organized heterogeneous information network, and developing powerful modeling mechanisms on such organized network. We name it text-rich information network, since it is an integrated representation of both structured and unstructured textual data. To thoroughly develop the construction and modeling paradigm, this dissertation will focus on forming a scalable data-driven framework and propose a new line of techniques relying on the idea of phrase mining to bridge textual documents and structured entities. We will first introduce the phrase mining method named SegPhrase+ to globally discover semantically meaningful phrases from massive textual data, providing a high quality dictionary for text structuralization. Clearly distinct from previous works that mostly focused on raw statistics of string matching, SegPhrase+ looks into the phrase context and effectively rectifies raw statistics to significantly boost the performance. Next, a novel algorithm based on latent keyphrases is developed and adopted to largely eliminate irregularities in massive text via providing an consistent and interpretable document representation. As a critical process in constructing the network, it uses the quality phrases generated in the previous step as candidates. From them a set of keyphrases are extracted to represent a particular document with inferred strength through a statistical model. After this step, documents become more structured and are consistently represented in the form of a bipartite network connecting documents with quality keyphrases. A more heterogeneous text-rich information network can be constructed by incorporating different types of document-associated entities as additional nodes. Lastly, a general and scalable framework, Tensor2vec, are to be added to trational data minining machanism, as the latter cannot readily solve the problem when the organized heterogeneous network has nodes with different types. Tensor2vec is expected to elegantly handle relevance search, entity classification, summarization and recommendation problems, by making use of higher-order link information and projecting multi-typed nodes into a shared low-dimensional vectorial space such that node proximity can be easily computed and accurately predicted

Statistical Rejection Sampling Improves Preference Optimization

Improving the alignment of language models with human preferences remains an active research challenge. Previous approaches have primarily utilized Reinforcement Learning from Human Feedback (RLHF) via online RL methods such as Proximal Policy Optimization (PPO). Recently, offline methods such as Sequence Likelihood Calibration (SLiC) and Direct Preference Optimization (DPO) have emerged as attractive alternatives, offering improvements in stability and scalability while maintaining competitive performance. SLiC refines its loss function using sequence pairs sampled from a supervised fine-tuned (SFT) policy, while DPO directly optimizes language models based on preference data, foregoing the need for a separate reward model. However, the maximum likelihood estimator (MLE) of the target optimal policy requires labeled preference pairs sampled from that policy. DPO's lack of a reward model constrains its ability to sample preference pairs from the optimal policy, and SLiC is restricted to sampling preference pairs only from the SFT policy. To address these limitations, we introduce a novel approach called Statistical Rejection Sampling Optimization (RSO) that aims to source preference data from the target optimal policy using rejection sampling, enabling a more accurate estimation of the optimal policy. We also propose a unified framework that enhances the loss functions used in both SLiC and DPO from a preference modeling standpoint. Through extensive experiments across three diverse tasks, we demonstrate that RSO consistently outperforms both SLiC and DPO on evaluations from both Large Language Model (LLM) and human raters