Geography as branding: Descriptive evidence from Taobao

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    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).

  2. 2.

    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.

  3. 3.

    Clearly, location still matters for transportation and related costs, such as the motivation to avoid sales taxes, etc. (see, e.g., Anderson et al. (2010) and Forman et al. (2009)).

  4. 4.

    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).

  5. 5.

    http://www.zjzwfw.gov.cn/art/2015/12/15/art_923930_261002.html; http://www.hubei.gov.cn/zwgk/zcsd/ztjd/zhuantijiedudiershiwuqi/; http://www.chuanhui.gov.cn/sitegroup/root/html/bd1e5b494f16bb58014f2505dcf10080/6a866c8a39a74f2e866586881bcc7ed6.html; http://www.dwcs.cn/

  6. 6.

    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.

  7. 7.

    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.

  8. 8.

    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.

  9. 9.

    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.

  10. 10.

    http://edu.cnr.cn/list/20160102/t20160102_521003863.shtml; https://cloud.tencent.com/developer/news/76665

  11. 11.

    As a robustness check, we use the residuals from regressing dyadic trust on distance in the level-2 model and obtain quantitatively similar results.

References

  1. Albuquerque, P., Bronnenberg, B. J., & Corbett, C. J. (2007). A spatiotemporal analysis of the global diffusion of ISO 9000 and ISO 14000 certification. Management Science, 53(3), 451–468.

    Article  Google Scholar 

  2. Anderson, E. T., Fong, N. M., Simester, D. I., & Tucker, C. E. (2010). How sales taxes affect customer and firm behavior: The role of search on the internet. Journal of Marketing Research, 47(2), 229–239.

    Article  Google Scholar 

  3. Anderson, J. E., & Marcouiller, D. (2002). Insecurity and the pattern of trade: An empirical investigation. Review of Economics and Statistics, 84(2), 342–352.

    Article  Google Scholar 

  4. Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93(1), 170–192.

    Article  Google Scholar 

  5. Anderson, J. E., & van Wincoop, E. (2004). Trade costs. Journal of Economic Literature, 42(3), 691–751.

    Article  Google Scholar 

  6. Anholt, S. (2011). Beyond the nation brand: The role of image and identity in international relations. Exchange: The Journal of Public Diplomacy, 2(1), 7.

    Google Scholar 

  7. Anholt, S. (2004). Nation-brands and the value of provenance, Destination branding, 26-39.

  8. Armstrong, R. W., & Yee, S. M. (2001). Do Chinese trust Chinese? A study of Chinese buyers and sellers in Malaysia. Journal of International Marketing, 9(3), 63–86.

    Article  Google Scholar 

  9. Bakken, B. (1998). Migration in China: NIAS Press.

  10. Bakos, J. Y. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12), 1676–1692.

    Article  Google Scholar 

  11. Banerjee, A. V., & Duflo, E. (2000). Reputation effects and the limits of contracting: A study of the Indian software industry. Quarterly Journal of Economics, 115(3), 989–1017.

    Article  Google Scholar 

  12. Bell, B.A., Ene, M., Smiley, W., and Schoeneberger, J.A. (2013). A multilevel model primer using SAS® proc mixed, http://support.sas.com/resources/papers/proceedings13/433-2013.pdf.

  13. Bell, D.R. (2014). Location is (still) everything: The surprising influence of the real world on how we search, Shop, and Sell in the Virtual One: Houghton Mifflin Harcourt.

  14. Bell, D.R., Ho, T-H, and Tang, C.S. (1998). Determining where to shop: Fixed and variable costs of shopping, Journal of Marketing Research, 352-69.

  15. Blum, B. S., & Goldfarb, A. (2006). Does the internet defy the law of gravity? Journal of International Economics, 70(2), 384–405.

    Article  Google Scholar 

  16. Bolton, G. E., Katok, E., & Ockenfels, A. (2004). How effective are electronic reputation mechanisms? An experimental investigation. Management Science, 50(11), 1587–1602.

    Article  Google Scholar 

  17. Briesch, R. A., Chintagunta, P. K., & Fox, E. J. (2009). How does assortment affect grocery store choice? Journal of Marketing Research, 46(2), 176–189.

    Article  Google Scholar 

  18. Bronnenberg, B. J., Dhar, S. K., & Dubé, J.-P. (2007). Consumer packaged goods in the United States: National brands, local branding. Journal of Marketing Research, 44(1), 4–13.

    Article  Google Scholar 

  19. Bronnenberg, B. J., Dhar, S. K., & Dubé, J.-P. H. (2009). Brand history, geography, and the persistence of brand shares. Journal of Political Economy, 117(1), 87–115.

    Article  Google Scholar 

  20. Bronnenberg, B. J., Dubé, J.-P., & Gentzkow, M. (2012). The evolution of brand preferences: Evidence from consumer migration. American Economic Review, 102(6), 2472–2508.

    Article  Google Scholar 

  21. Burtch, G., Ghose, A., and Wattal, S. (2013). Cultural differences and geography as determinants of online pro-social lending, MIS Quartery, 14-21.

  22. Cabral, L., & Hortacsu, A. (2010). The dynamics of seller reputation: Evidence from eBay. Journal of Industrial Economics, 58(1), 54–78.

    Article  Google Scholar 

  23. Cebollada, J., Chu, Y., & Jiang, Z. (2019). Online category pricing at a multichannel grocery retailer. Journal of Interactive Marketing, 46, 52–69.

    Article  Google Scholar 

  24. Chintagunta, P. K., Chu, J., & Cebollada, J. (2012). Quantifying transaction costs in online/off-line grocery channel choice. Marketing Science, 31(1), 96–114.

    Article  Google Scholar 

  25. Chu, J. (2013). Quantifying nation equity with sales data: A structural approach. International Journal of Research in Marketing, 30(1), 19–35.

    Article  Google Scholar 

  26. Chu, J. and Manchanda, P. (2015). Quantifying cross and direct network effects in online C2C platforms, Marketing Science, 34.

  27. Chu, J., Arce-Urriza, M., Cebollada-Calvo, J.-J., & Chintagunta, P. K. (2010). An empirical analysis of shopping behavior across online and offline channels for grocery products: The moderating effects of household and product characteristics. Journal of Interactive Marketing, 24(4), 251–268.

    Article  Google Scholar 

  28. Chu, J., Chintagunta, P., & Cebollada, J. (2008). Research note—A comparison of within-household price sensitivity across online and offline channels. Marketing Science, 27(2), 283–299.

    Article  Google Scholar 

  29. Chu, J., Duan, Y., Yang, X., and Wang, L. (2020). The last mile matters: Impact of dockless bike sharing on subway housing price premium, Management Science.

  30. Chu, K. and Burkitt, L. (2014). Knockoffs Thrive on Alibaba's Taobao, in wsj.com.

  31. Crocker, K.J. and Reynolds, K.J. (1993) The efficiency of incomplete contracts: An empirical analysis of air force engine procurement, The Rand Journal of Economics, 126-46.

  32. Dellarocas, C. (2004). Building trust online: the design of robust reputation reporting mechanisms for online trading communities, in Social and Economic Transformation in the Digital Era: IGI Global.

  33. Dollar, D., & Kraay, A. (2003). Institutions, trade, and growth. Journal of Monetary Economics, 50(1), 133–162.

    Article  Google Scholar 

  34. Egan, M. L., & Mody, A. (1992). Buyer-seller links in export development. World Development, 20(3), 321–334.

    Article  Google Scholar 

  35. Egger, P. H., & Lassmann, A. (2012). The language effect in international trade: A meta-analysis. Economics Letters, 116(2), 221–224.

    Article  Google Scholar 

  36. Elfenbein, D.W., Fisman, R.J., and McManus, B. (2018). The Impact of Socioeconomic and Cultural Differences on Online Trade, ssrn.com.

  37. Forman, C., Ghose, A., & Goldfarb, A. (2009). Competition between local and electronic markets: How the benefit of buying online depends on where you live. Management Science, 55(1), 47–57.

  38. Geyskens, I., Steenkamp, J.-B. E. M., & Kumar, N. (1998). Generalizations about trust in marketing channel relationships using meta-analysis. International Journal of Research in Marketing, 15(3), 223–248.

    Article  Google Scholar 

  39. Glaeser, E. L., Laibson, D. I., Scheinkman, J. A., & Soutter, C. L. (2000). Measuring trust. Quarterly Journal of Economics, 115(3), 811–846.

    Article  Google Scholar 

  40. Gould, D.M. (1994). Immigrant links to the home country: Empirical implications for US bilateral trade flows, Review of Economics and Statistics, 302-16.

  41. Guiso, L., Sapienza, P., & Zingales, L. (2009). Cultural biases in economic exchange? The Quarterly Journal of Economics, 124(3), 1095–1131.

    Article  Google Scholar 

  42. Hanna, S., & Rowley, J. (2011). Towards a strategic place brand-management model. Journal of Marketing Management, 27(5–6), 458–476.

    Article  Google Scholar 

  43. Hillberry, R., & Hummels, D. (2003). Intranational home bias: Some explanations. Review of Economics and Statistics, 85(4), 1089–1092.

    Article  Google Scholar 

  44. Hortaçsu, A., Asís Martínez-Jerez, F., and Douglas, J. (2009) The geography of trade in online transactions: Evidence from eBay and mercadolibre, American Economic Journal: Microeconomics, 53-74.

  45. Hutchinson, W.K. (2005). "Linguistic distance" as a determinant of bilateral trade, Southern Economic Journal, 1-15.

  46. Iranzo, S., & Peri, G. (2009). Migration and trade: Theory with an application to the eastern–Western European integration. Journal of International Economics, 79(1), 1–19.

    Article  Google Scholar 

  47. Jackson, P. (2002). Commercial cultures: Transcending the cultural and the economic. Progress in Human Geography, 26(1), 3–18.

    Article  Google Scholar 

  48. Kamakura, W. A., & Russell, G. J. (1993). Measuring brand value with scanner data. International Journal of Research in Marketing, 10(1), 9–22.

    Article  Google Scholar 

  49. Kerr, G. (2006). From destination brand to location brand. Journal of Brand Management, 13(4–5), 276–283.

    Article  Google Scholar 

  50. Klein, J.G. (2002). Us versus them, or us versus everyone? Delineating consumer aversion to foreign goods, Journal of International Business Studies, 345-63.

  51. Kleinfield, N.R. (2003). Battery park, get ready for the bunny; branding in New York is just the beginning, New York Times, 6.

  52. Kong, X. and Rao, A. (2019). Does country-of-origin marketing matter?, Available at SSRN 3468543.

  53. Kotler, P., & Gertner, D. (2002). Country as brand, product, and beyond: A place marketing and brand management perspective. Journal of Brand Management, 9(4), 249–261.

    Article  Google Scholar 

  54. Kuwabara, K. (2015). Do reputation systems undermine trust? Divergent effects of enforcement type on generalized trust and trustworthiness. American Journal of Sociology, 120(5), 1390–1428.

    Article  Google Scholar 

  55. Lai, S., & Teo, M. (2008). Home-biased analysts in emerging markets. Journal of Financial and Quantitative Analysis, 43(3), 685–716.

    Article  Google Scholar 

  56. Lewis, K. K. (1999). Trying to explain home bias in equities and consumption. Journal of Economic Literature, 37(2), 571–608.

    Article  Google Scholar 

  57. Lin, M., & Viswanathan, S. (2015). Home bias in online investments: An empirical study of an online crowdfunding market. Management Science, 62(5), 1393–1414.

    Article  Google Scholar 

  58. Ma, L., Krishnan, R., & Montgomery, A. L. (2014). Latent homophily or social influence? An empirical analysis of purchase within a social network. Management Science, 61(2), 454–473.

    Article  Google Scholar 

  59. Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8), 2421–2455.

    Article  Google Scholar 

  60. McCallum, J. (1995). National borders matter: Canada-US regional trade patterns, American Economic Review, 615-23.

  61. McMillan, J., & Woodruff, C. (1999). Interfirm relationships and informal credit in Vietnam. Quarterly Journal of Economics, 114(4), 1285–1320.

    Article  Google Scholar 

  62. Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38(1), 131–142.

    Article  Google Scholar 

  63. Morgan, Nigel, Annette Pritchard, and Roger Pride (2007), Destination branding: Routledge.

  64. Nosko, C. and Tadelis, S. (2015) The limits of reputation in platform markets: An empirical analysis and field experiment, National Bureau of Economic Research.

  65. Papadopoulos, N. (1993). What product and country images are and are not. Product-country images: Impact and role in international marketing, 12(1), 3–38.

    Google Scholar 

  66. Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.

    Article  Google Scholar 

  67. Pike, A. (2013). Economic geographies of brands and branding. Economic Geography, 89(4), 317–339.

    Article  Google Scholar 

  68. Pike, A. (2009). Geographies of brands and branding. Progress in Human Geography, 33(5), 619–645.

    Article  Google Scholar 

  69. Pike, Andy (2015), Origination: The geographies of brands and branding: John Wiley & Sons.

  70. Power, D., & Hauge, A. (2008). No man's brand—Brands, institutions, and fashion. Growth and Change, 39(1), 123–143.

    Article  Google Scholar 

  71. Rauch, J. E., & Trindade, V. (2002). Ethnic Chinese networks in international trade. Review of Economics and Statistics, 84(1), 116–130.

    Article  Google Scholar 

  72. Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000). Reputation systems. Communications of the ACM, 43(12), 45–48.

    Article  Google Scholar 

  73. Resnick, P., Zeckhauser, R., Swanson, J., & Lockwood, K. (2006). The value of reputation on eBay: A controlled experiment. Experimental Economics, 9(2), 79–101.

    Article  Google Scholar 

  74. Shapiro, C. (1983). Premiums for high quality products as returns to reputations. Quarterly Journal of Economics, 98(4), 659–679.

    Article  Google Scholar 

  75. Smith, M. D., & Brynjolfsson, E. (2001). Consumer decision-making at an internet Shopbot: Brand still matters. Journal of Industrial Economics, 49(4), 541–558.

    Article  Google Scholar 

  76. Sriram, S., & Kalwani, M. U. (2007). Optimal advertising and promotion budgets in dynamic markets with brand equity as a mediating variable. Management Science, 53(1), 46–60.

    Article  Google Scholar 

  77. Strader, T. J., & Ramaswami, S. N. (2002). The value of seller trustworthiness in C2C online markets. Communications of the ACM, 45(12), 45–49.

    Article  Google Scholar 

  78. Sudhir, K., Priester, J., Shum, M., Atkin, D., Foster, A., Iyer, G., Jin, G., Keniston, D., Kitayama, S., and Mobarak, M. (2015). Research opportunities in emerging markets: An inter-disciplinary perspective from marketing, economics, and psychology, Customer Needs and Solutions, 1-13.

  79. Tadelis, S. (2016). Reputation and feedback systems in online platform markets. Annual Review of Economics, 8, 321–340.

    Article  Google Scholar 

  80. Varian, H. R., & Shapiro, C. (1999). Information rules: A strategic guide to the network economy. Cambridge: Harvard Business School Press.

    Google Scholar 

  81. Verlegh, P. W. J., & Steenkamp, J.-B. E. M. (1999). A review and meta-analysis of country-of-origin research. Journal of Economic Psychology, 20(5), 521–546.

    Article  Google Scholar 

  82. Wang, Xia, Gang Pan, and Jingwen Yu (2017), "China’s provincial marketization index (2016)," Social Sciences Press.

  83. Wedel, Michel and Wagner A Kamakura (2012), Market segmentation: Conceptual and methodological foundations: Springer Science & Business Media.

  84. Wolf, H. C. (2000). Intranational home bias in trade. Review of Economics and Statistics, 82(4), 555–563.

    Article  Google Scholar 

  85. Xu, H., Liu, D., Wang, H., and Stavrou, A. (2015). E-commerce reputation manipulation: The emergence of reputation-escalation-as-A-service, WWW 2015 - Proceedings of the 24th International Conference on World Wide Web, 11.

  86. Zhang, W. and Ke, R. (2002). Trust in China: A cross-regional analysis, Economic Research Journal 10.

  87. Zhang, X., Manchanda, P., & Chu, J. (2021). ‘Meet Me Halfway’: The costs and benefits of bargaining, marketing science, forthcoming.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Junhong Chu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Supplementary Information

ESM 1

(PDF 2873 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Geographic associations
  • Place marketing
  • Location branding
  • Country-of-origin effect
  • Reputation mechanism
  • Asymmetric geographic preferences
  • Peer-to-peer platforms
  • Trust
  • Socioeconomic and cultural similarity

JEL Classification

  • L81
  • M31
  • F14