Comprehensive dataset of 126 Chinese tea houses in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Graph and download economic data for Real Residential Property Prices for China (QCNR628BIS) from Q2 2005 to Q1 2025 about China, residential, HPI, housing, real, price index, indexes, and price.

This dataset provides information about the number of properties, residents, and average property values for US 29 Highway cross streets in China Grove, NC.

This dataset contains the data used in the study titled “Is hiding my first name enough? Using behavioural interventions to mitigate racial and gender discrimination in the rental housing market”. The data was collected from the London rental housing market between 2021 and 2022. Racial and gender biases are pervasive in housing markets. Males and ethnic minorities face discrimination in rental housing markets globally. The issue has been so pronounced that it regularly makes national and international headlines. In response to a racial discrimination lawsuit, Airbnb had to hide guests’ first names from rental hosts in Oregon, USA, starting in January 2022. Yet, there is little evidence that such measurement effectively counteracts racial and gender discrimination in housing markets. Despite some well-established theoretical models developed more than half a century ago and a wealth of empirical evidence accumulated over the last two decades, studies examining effective solutions to combat discrimination remain sparse especially in housing markets. Given the complexity of the products and services involved and the relatively low frequency of transactions, nuanced studies are needed to understand how implicit racial and gender biases influence letting decisions. This study investigates housing discrimination at the intersection where longstanding market behaviours meet the evolving insights of behavioural research. Although behavioural interventions have the potential to address both statistical and taste-based discrimination in the housing market, their successful implementation remains a challenge. Given the persistent biases and socio-economic dynamics in the housing market, interventions must be carefully tailored to the context. By collecting evidence from field experiments, this research aims to gain insights into how real-world behavioural interventions can be effectively designed and implemented. Our focus remains twofold: to develop a robust theoretical framework and to translate its insights into tangible, impactful policy recommendations, with the ultimate goal of fostering a more inclusive housing market.Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides. The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom. Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature. We carried out the experiment at the UK's largest online real estate portal and property website, www.rightmove.co.uk. In 2021, Rightmove had 208 million visits per month and a total of 692,000 properties listed at their website. Therefore, the platform gives us access to the largest available database of rental property listings in the country. We searched rental properties in Greater London Area that are advertised between December 2021 and April 2022. Only houses, flats and apartments are included. All listings are handled by letting agents. No private landlords are involved. Once a property was identified as eligible for the experiment, we sent a total of five applications to the letting agent, asking for a viewing appointment. The five applicants will be from different ethnic groups (i.e., one from each of the five groups) but of the same gender. The five emails were sent with at least 12 hours in between so that no suspicious of spamming might be raised. A total of 360 properties were selected, which gives a sample size of 1,800. The sample is evenly divided between the two gender groups and the five ethnic groups. Specifically, there are 360 observations in each ethnic group and 900 observations in each gender group.

To select the group of UK firms we initially searched in the FAME database (available from the University of Manchester Library) with keywords relating to the green goods sector, please see the publication Shapira, et al (2014, in Technological Forecasting & Social Change, vol. 85, pp. 93-104) for further details on the keywords. This database contains anonymized firm data from a sample of UK firms in the green goods production industry. We combine data from structured sources (the FAME database, patents and publications) with unstructured data mined from firm's web-sites by saving key words in text and summing up counts of these to create additional explanatory variables for firm growth. The data is in a panel from 2003-2012 with some observations missing for firms. We collect historical data from firm's web-sites available in an archive from the Wayback machine.This project probes the growth strategies of innovative small and medium-size enterprises (SMEs). Our research focuses on emerging green goods industries that manufacture outputs which benefit the environment or conserve natural resources, with an international comparative element involving the UK, the US, and China. The project investigates the contributions of strategy, resources and relationships to how innovative British, American, and Chinese SMEs achieve significant growth. The targeted technology-oriented green goods sectors are strategically important to environmental rebalancing and have significant potential (in the UK) for export growth. The research examines the diverse pathways to innovation and growth across different regions. We use a mix of methodologies, including analyses of structured and unstructured data on SME business and technology performance and strategies, case studies, and modelling. Novel approaches using web mining are pioneered to gain timely information about enterprise developmental pathways. Findings from the project will be used to inform management and policy development at enterprise, regional and national levels. The project is led by the Manchester Institute of Innovation Research at the University of Manchester, in collaboration with Georgia Institute of Technology, US; Beijing Institute of Technology, China, and Experian, UK. We collected the financial information on the UK firms by downloading Companies House data from the FAME database available through the University of Manchester Library (see http://www.library.manchester.ac.uk/searchresources/databases/f/). Grant information on companies came from the Technology Strategy Board. Patent information was from the Derwent database and publication information was from the Web of Science. The Consumer Price index was from the Office for National Statistics (http://www.ons.gov.uk/ons/rel/cpi/consumer-price-indices/index.html). The Human Resources in Science and Technology variable was from the Eurostat database (http://ec.europa.eu/eurostat/data/database). Unstructured data was mined from firm's web-sites. The UK Intellectual Property Office has clarified that the data mining we are doing and the way we are doing it is permissible. See: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf