The geographic associations of brands and branding have been well demonstrated in the country-of-origin (COO) effect literature in that a product’s COO has a branding effect and consumers have preferences for goods from specific countries. The aggregation of these preferences can lead to unique and asymmetric trading patterns between countries. In this paper, we extend the geographic associations of brands and branding to domestic trade and document that seller provinces have a branding effect and geographic preference asymmetries can arise within peer-to-peer trade networks such as Taobao in China. We find that while buyers in one province in China are willing to purchase goods from sellers in another province, it is often the case that the relationship is not reciprocal. This asymmetry in preferences persists after controlling for time-varying factors at both buyer and seller locations. Like brands, a location therefore serves as a quality cue and reputation mechanism for the unobserved attributes of sellers, products, and logistics services from that location. We then explore factors that might be correlated with these time-invariant preferences across provinces. We find that in addition to the gravity effect of distance and the home-bias effect, other factors such as dyadic trust; migration; similarities in ethnicities; occupations and education levels; and levels of marketization and rule of law are correlated with the asymmetries. Notably, these factors differ from those identified in a contemporaneous study of eBay in the US, in which religiosity and political ideology contribute to the differences (Elfenbein et al. 2018). We believe that the Chinese and the US experiences differ because the two countries differ substantially in social, economic, political, religious, legal, and commercial environments.
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To place Taobao in context, consider the following. In 2019, the gross merchandise volume was $90B on eBay and $339B on Amazon (https://www.statista.com/statistics/977262/top-us-online-marketplaces-by-gmv/) whereas on Taobao it was $439B (https://www.statista.com/statistics/959633/china-taobao-gross-merchandise-volume/). By comparison, the sales from all grocery stores in the US was $646B (https://www.statista.com/forecasts/311107/supermarkets-and-other-grocery-stores-revenue-in-the-us).
In contrast to the COO literature, note that the sellers in our case are merchants or resellers and not manufacturers; i.e., a cellphone seller from Anhui could be selling the same Huawei product as a seller from Shanghai. See the online appendix for screenshots of sellers in various provinces selling identical or near identical products.
These are akin to brand-specific intercepts in a logit choice model with the exception that they are not simply seller (province) specific as in a choice model, but also specific to buyer (province).
Data are provided to us under an NDA that allows us to publish results but not the raw data. Taobao only provided us with data on the purchases of a random sample of buyers in each category.
For each seller-week, we have information on seller characteristics (e.g., registration time, seller ratings, etc.); products offered; seller merchandising (e.g., number of items on the “shelf”) and marketing activities (i.e., seller’s participation in the platform’s various marketing and promotion channels, average product prices, etc.); seller’s shop performance measures (e.g., number of page views, number of unique visitors, and revenues); and platform activities (e.g., penalties). See Zhang et al. (2021) for details. We use seller province-week FEs to account for these time-varying factors.
This means that a seller may appear or disappear from the top seller list based on whether that category accounts for the highest revenues for that seller in a given week. This would be a concern if a seller’s sales in categories varied in a way that different categories show widely varying sales in different weeks. To address this, we note the following: (1) given the large number of sellers and fierce competition between them, most Taobao sellers tend to specialize in one category. Specialization not only improves efficiency in operations, it can also increase sellers’ bargaining power with their suppliers. (2) We conducted a robustness analysis using the top 20% sellers that account for 80% sales for the three main categories. These top sellers remain in their focal categories throughout the data period. We obtain qualitatively similar results and conclusions. (3) Importantly, our preferred model includes seller province-week FEs, so any week-to-week variation in the number of sellers will be captured by these FEs.
This method for measuring trust is consistent with previous studies. For instance, the Eurobarometer asks respondents how much they trust people from different countries: “For each country, please say whether, in your opinion, they are in general very trustworthy, fairly trustworthy, not particularly trustworthy, or not at all trustworthy.” Glaeser et al. (2000) show that these attitudinal questions predict trustworthy behavior much better than they predict trusting behavior. We acknowledge the caveat whereby survey respondents may not be representative of Taobao buyers.
As a robustness check, we use the residuals from regressing dyadic trust on distance in the level-2 model and obtain quantitatively similar results.
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We acknowledge the financial support from the National University of Singapore’s HSS Seed Fund R-316-000-102-646 and the research grant AcRF R316-000-110-112, NUS Business School’s China Business Centre Research Fellowship, and the Kilts Center for Marketing and the Initiative on Global Markets at the University of Chicago. We are very grateful to Alibaba Group’s Taobao.com for providing data for this research. We thank the Quantitative Marketing and Economics editor Wesley R. Hartmann and two anonymous reviewers for their very constructive suggestions and guidance throughout the review process. We thank K. Sudhir, Ivan Png, Sumit Agarwal, Kenneth Wilbur, Ali Hortacsu, Xueming Luo, Ying Xie, Vishal Narayanan, Dai Yao, Anirban Mukherjee, Changcheng Song, Zhiying Jiang, Yanlai Chu, Chuang Tang, Tesary Lin, and participants at the 2014 China India Insights Conference, the 2015 China Marketing International Conference, and the 2016 Marketing Dynamics Conference for valuable comments and suggestions. The usual disclaimer applies.
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Chintagunta, P.K., Chu, J. Geography as branding: Descriptive evidence from Taobao. Quant Mark Econ (2021). https://doi.org/10.1007/s11129-020-09232-9
- Geographic associations
- Place marketing
- Location branding
- Country-of-origin effect
- Reputation mechanism
- Asymmetric geographic preferences
- Peer-to-peer platforms
- Socioeconomic and cultural similarity