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The present paper explores issues of the educational marginality of migrant children in urban settings in two cities in China. The numbers of urban migrants exceed 100 million and are growing as China modernizes. This is creating tensions between residents and recent arrivals who lack residential registration and access to public services. As a result, migrant children often attend informal, private and usually unregulated schools of low quality organized by their communities. These tend to reinforce social stratification and reproduce marginality across the generations. The paper argues that state failure to provide basic education risks a growing divide between urban residents and recent migrants that has social consequences that must be addressed to remove discriminatory practices and resolve potential conflicts between hosts and migrant communities. (Contains 1 note and 5 tables.)

This paper first measures and compares the size of middle-income groups in China based on the subjective income evaluation method and the objective criteria. Second, it empirically investigates the differences in government trust of different income groups defined by the subjective evaluation method and the objective criteria. It is found that there is a significant difference between the results of the subjective evaluation of income and objective criteria. Compared with individuals in the middle-income group, individuals in the low-income group have a significantly worse overall evaluation of local government and a considerably lower trust in local government officials. On the other hand, individuals in the high-income group have a substantially better assessment of local government and a significantly higher trust in local government officials. However, the differences in trust in government across income groups defined by objective criteria are insignificant overall. In terms of policy insights, the effect of targeting low-income groups determined by subjective evaluation may be more effective in improving people’s trust in the government. [ABSTRACT FROM AUTHOR]

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms' customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group's interests are threatened. However, the bias diminishes as jurors gain experience: a user's bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. This paper was accepted by Vishal Gaur, operations management. Funding: S. A. Yang and A. Zhang acknowledge the support of the Hong Kong General Research Fund [Grant "Decentralizing Platform Governance: Innovations from China; Project 17614921]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4818. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

There is often considerable anxiety and conflicting advice concerning the benefits of presenting/being evaluated first. We thus investigate how expert evaluators vary in their evaluations of entrepreneurial proposals based upon the order in which they are evaluated. Our research setting is a premiere innovation fund competition in Beijing, China, where the prize money at stake is economically meaningful, and evaluators are quasi-randomly assigned to evaluate written grant proposals without the possibility of peer influence. This enables us to credibly recover a causal position effect. We also theorize and test how heterogeneity in evaluators' prior (context-specific) judging experience moderates position effects. Overall, we find that a proposal evaluated first requires total assets in the top 10th percentile to merely equal the evaluation of a proposal in the bottom 10th percentile that is not evaluated first. Firm and evaluator fixed-effects models yield consistent findings. We consider evaluation design elements that may mollify these position effects in the discussion section. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation. [ABSTRACT FROM AUTHOR]

Understanding the public's diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms. [ABSTRACT FROM AUTHOR]

The evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China. [ABSTRACT FROM AUTHOR]

Dosearchengines driveWeb traffic towell-establishedsites leading toahighdegree of search results concentration? Do search engines favor their own content while demoting others? How parochial or cosmopolitan are search engines in directing traffic to sites beyond users' national borders? This study explores these issues by empirically comparing search results of Baidu, Google, and Jike from mainland China obtained in August 2011 and August 2012. It finds that search engines in China, particularly Baidu, tend to drive traffic to well-established sites. Baidu's results also raise serious doubts over its impartiality. Rather thanmaking users' search experiencesmore cosmopolitan, tuned to the larger world around them, search engines rarely direct Chinese users to content beyond national borders. [ABSTRACT FROM AUTHOR]

After the successful launches of the first two polar-orbiting satellites in a new Fengyun-3 ( FY-3) series, FY-3A/B, into a morning- and afternoon-configured orbit in May 2008 and November 2010, respectively, China will launch its next three polar-orbiting satellites before 2020. The Microwave Temperature Sounder (MWTS) on the FY-3A/B satellites has four channels that have the same channel frequency as channels 3, 5, 7, and 9 of Advanced Microwave Sounding Unit-A (AMSU-A). Thus, the quality of the brightness temperature measurements from the FY-3A MWTS can be assessed using the AMSU-A brightness temperature observations from the NOAA-18 satellite. Overall, MWTS data compare favorably with AMSU-A data in terms of its global bias to NWP simulations. The standard deviations of global MWTS brightness temperatures are slightly larger than those of AMSU-A data. The scan-angle dependence of the brightness temperature bias is found to be symmetric for MWTS channel 3 as well as AMSU-A channel 7, and asymmetric for MWTS channels 2 and 4 and AMSU-A channels 5 and 9; there is a warm (cold) bias located at the beginning (end) of a scan line for all asymmetric channels except for MWTS channel 4. A major difference between the two instruments is that the MWTS biases in channels 3 and 4 are negative in low latitudes and positive in high latitudes, while the AMSU-A biases are negative in all latitudes. A detailed analysis of the data reveals that such a difference is closely related to the difference in the temperature dependence of biases between the two instruments. The AMSU-A biases are independent of the scene temperature, but MWTS biases vary with the earth scene brightness temperature. The root cause of the bias could be a combination of several factors, including solar contamination on its calibration target, detector nonlinearity, and the center frequency drift. This study further demonstrates the utility of a well-calibrated radiometer like AMSU-A for the assessment of a new instrument with NWP fields that are used as inputs to forward radiative transfer simulations. [ABSTRACT FROM AUTHOR]