Interview notes and transcripts from fieldwork conducted in China in 2015, 2016, and 2018.

These files are related to the published paper "Why do farmers' cooperatives fail in a market economy? Rediscovering Chayanov with the Chinese experience".

This record is part of 'The Customer Satisfaction Index of Singapore (CSISG) Annual Dataset Collection 2007-2022', providing raw data set, datamap and questionnaires for 2015. For related datasets across other years, refer to the full collection here: https://doi.org/10.25440/smu.c.6906043The Customer Satisfaction Index of Singapore (CSISG) is a landmark measure of customer satisfaction cutting across a variety of key sectors and sub-sectors in the services industry of Singapore. The study was produced and updated on an quarterly and annual basis from 2007 to 2022. First launched in April 2008, the CSISG is an independent and qualitative indicator of the Singapore economy. It covers 8 core economic sectors, more than 20 sub-sectors and numerous companies from the Air Transport Finance, Food & Beverage, Info-communications, Insurance, Land Transport, Retail, and Tourism industries. This national barometer of customer satisfaction in the Singapore economy serves as an objective gauge of service competitiveness between businesses, industries, and even countries. As it reports the overall customer satisfaction scores of every sector and sub-sector, including a ranking of the companies measured, the CSISG serves as an invaluable benchmarking tool across industries in the services sector.The methodological foundations of the Customer Satisfaction Index of Singapore can be traced to the American Customer Satisfaction Index (ACSI), developed by the National Quality Research Centre (NQRC) at the University of Michigan. The American Customer Satisfaction Index has been the standardised measure of customer satisfaction in the US economy since 1994.The Customer Satisfaction Index of Singapore is based on econometric modelling of data obtained from interviews with actual users of products and services.

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

This is the online appendix to the working paper "Labor market implications of Taiwan’s accession to the WTO: A dynamic quantitative analysis", available at https://ink.library.smu.edu.sg/soe_research/2613/

This record contains the underlying research data for the publication "Worker selection, hiring, and vacancies" and the full-text is available from: https://ink.library.smu.edu.sg/soe_research/1990The ratio of hirings to vacancies in the U.S. has the following establishment level properties: (i) it steeply rises with employment growth rate; (ii) falls with establishment size; and (iii) rises with worker turnover rate. The standard Diamond-Mortensen Pissarides (DMP) matching model is not compatible with these observations. This paper augments selection of workers prior to hiring into a random matching model with multi-worker firms. In the calibrated model, worker selection accounts for about 30% of the variation in the hiring-vacancy ratio observed in the data. Compared to the standard model, the worker selection model has both qualitative and quantitative policy implications. A hiring subsidy reduces the unemployment rate substantially in the worker selection model, whereas the reduction in the unemployment rate is very small in the standard model. The two models also differ regarding the impact of the hiring subsidy across firms. The worker selection model implies that firms that have initially high worker turnover rates experience proportionally higher worker turnover rates after the subsidy. In contrast, the standard model predicts that the worker turnover rate increases proportionally more at firms with initially lower worker turnover rates.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

This record contains the underlying research data for the publication "Extended Comprehensive Study of Association Measures for Fault Localization" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/1818Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to association measures that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the relationship strength between the execution of program elements and program failures. We investigate the effectiveness of 40 association measures from the literature on locating bugs. Our empirical evaluations involve single-bug and multiple-bug programs. We find there is no best single measure for all cases. Klosgen and Ochiai outperform other measures for localizing single-bug programs. Although localizing multiple-bug programs, Added Value could localize the bugs with on average smallest percentage of inspected code, whereas a number of other measures have similar performance. The accuracies of the measures in localizing multi-bug programs are lower than single-bug programs, which provokes future research.

See the readme file inside for replication steps

Replication Data for "JUE Insight: Migration, Transportation Infrastructure, and the Spatial Transmission of COVID-19 in China"

Both attractiveness judgements and mate preferences vary considerably cross-culturally. We investigated whether men's preference for femininity in women's faces varies between 28 countries with diverse health conditions by analysing responses of 1972 heterosexual participants. Although men in all countries preferred feminized over masculinized female faces, we found substantial differences between countries in the magnitude of men's preferences. Using an average femininity preference for each country, we found men's facial femininity preferences correlated positively with the health of the nation, which explained 50.4% of the variation among countries. The weakest preferences for femininity were found in Nepal and strongest in Japan. As high femininity in women is associated with lower success in competition for resources and lower dominance, it is possible that in harsher environments, men prefer cues to resource holding potential over high fecundity.

Fieldwork investigating sustainable agricultural practices and alternative food networks in the Pearl River Delta, China.

This is the online supplement to the working paper 'A panel clustering approach to analyzing bubble behavior', available at https://ink.library.smu.edu.sg/soe_research/2591/This online supplement has six sections. Section A collects together technical lemmas that are used for membership estimation in the first stage. Section B collects the lemmas needed for post-clustering panel estimation and the bubble detection methods, specifically the post-clustering panel t- and J-tests. Section C collects results and proofs for selecting the number of groups. Section D extends the two-stage algorithm and the corresponding post-clustering statistics to the mixed-root panel autoregressive model with purely stationary, unit, and purely explosive roots. Section E overviews experimental designs and reports simulation findings. Section F contains tables.

The data that support the findings of this study are available from the corresponding author upon reasonable request and approval of the HR consultancy firm the data were obtained from. The Mplus code for the CFA and multilevel analyses is available at: https://osf.io/6f47s/

This study draws from brand positioning research to introduce the notions of points-of-relevance and points-of-difference to employer image research. Similar to prior research, this means that we start by investigating the relevant image attributes (points-of-relevance) that potential applicants use for judging organizations' attractiveness as an employer. However, we go beyond past research by examining whether the same points-of-relevance are used within and across industries. Next, we further extend current research by identifying which of the relevant image attributes also serve as points-of-difference for distinguishing between organizations and industries. The sample consisted of 24 organizations from 6 industries (total N = 7171). As a first key result, across industries and organizations, individuals attached similar importance to the same instrumental (job content, working conditions, and compensation) and symbolic (innovativeness, gentleness, and competence) image attributes in judging organizational attractiveness. Second, organizations and industries varied significantly on both instrumental and symbolic image attributes, with job content and innovativeness emerging as the strongest points-of-difference. Third, most image attributes showed greater variation between industries than between organizations, pointing at the importance of studying employer image at the industry level. Implications for recruitment research, employer branding, and best employer competitions are discussed.

This e-companion contains four sets of supporting materials for the main paper. EC.1 provides algorithmic treatments to handle key market implementation issues. EC.2 examines effects of active market intermediation on market performance and the dealer’s wealth under the controlled market experiment. EC.3 studies market liquidity and heterogeneous market participation in a randomized market environment. EC.4 includes proofs of Lemmas and Corollaries.

A growing literature posits that colonial Christian missions brought schooling to the colonies, improving human capital in ways that persist to this day. But in some places they did much more. This paper argues that colonial Catholic missions in the Philippines functioned as state-builders, establishing law and order and building fiscal and infrastructural capacities in territories they controlled. The mission-as-state was the result of a bargain between the Catholic missions and the Spanish colonial government: missionaries converted the population and engaged in state-building, whereas the colonial government reaped the benefits of state expansion while staying in the capital. Exposure to these Catholic missions-as-state then led to long-run improvements in state capacity and development. I find that municipalities that had a Catholic mission have higher levels of state capacity and development today. A variety of mechanisms---religious competition, education, urbanization, and structural transformation---explain these results.

This is the accompanying data for the journal article Values assessment for personnel selection: Comparing job applicants to non-applicants, forthcoming in the journal European Journal of Work and Organizational Psychology. It includes the data as well as the scripts for analysis in R.Data Description for Exported Data The following file provides some details for the data supplied on the OSF.DatasetsThere are three main data files- rcases: rcases.rdataThis stands for raw cases. This file is contained in the R repository. It includes several cases that were excluded from the final analysis. It does not include derived variables such as scale scores.- ccases: ccases.csv ccases.rdataThis stands for cleaned cases. As described in the R script and in the method, a few cases were removed because of concerns about data quality. These cases are excluded from ccases. Ccases also includes derived variables such as scale scores.- data/meta.rdata: This file includes meta data used for scoring the values measure. The general principle is that one row is one item. It indicates which broad and narrow values the item belongs to, whether it should be reversed and so on. - crep.rdataData from repeated measures sample.Variables in rcases/ccases1. DemographicsFor privacy purposes, the raw data shared on the repository has been slightly modified. Age was rounded to the nearest 10 (i.e., 20, 30, 40). All other demographic data besides age and gender is excluded.

These files are used to replicate all analyses in Media in a Time of Crisis: Newspaper Coverage of Covid-19 in East Asia, available at https://ink.library.smu.edu.sg/soss_research/3348/.

This is the supplementary data for the article "Estimating stranded coal assets in China's power sector" published in Utilities Policy.

China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.

Fieldwork conducted in August 2023 in Shandong Province, China, investigating forms of agricultural production in several sectors.

Fieldwork sites: 1. Rongcheng City, Weihai 2. Qixia, Yantai 3. Changyi, Weifang 4. Shouguang, Weifang