Summary

Digital Footprints as Collateral for Debt Collection
Author(s):
Lili Dai, UNSW Sydney
Jianlei Han, Macquarie University
Jing Shi, RMIT University
Bohui Zhang, Chinese University of Hong Kong, Shenzhen
Discussant(s):
Hongjun Yan, DePaul University
Summary:

Dai, Han, Shi, and Zhang examine the role of borrowers' digital footprints in debt collection. Using a large sample of personal loans from a fintech lender in China, theresearchers find that the information acquired by the lender through borrowers' digital footprints can increase the repayment likelihood on delinquent loans by 18.5%. The effect can be explained by two channels: bonding borrowers' obligations with their social networks and locating borrowers' physical locations. Moreover, the lender is more likely to approve loan applications from borrowers with digital footprints, even though these borrowers may occasionally have a higher likelihood of delinquency. The use of digital footprints can remain legitimate under stringent privacy protection regulations and fair debt collection practices. Their findings suggest that digital footprints, as a new type of collateral, can ultimately enhance financial inclusion by facilitating the lender's collection of delinquent loans.

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Information and Corruption: Evidence from China’s Land Auctions
Author(s):
Ming Li, Chinese University of Hong Kong, Shenzhen
Discussant(s):
David Y. Yang, Harvard University and NBER
Summary:

This paper examines how sellers' private information affects auction outcomes differently in two-stage auction and English auction, and how this difference affects auctioneer's incentive in choosing auction formats and gives rise to corruption in the context of China's land market. Using a theoretical model to endogenize the incentive of China's local governments, this paper finds that (1) two-stage auctions are more prone to corruption than are English auctions when information is asymmetric and (2) land with lower value faces a harder constraint for corruption. Consequently, local governments tend to use two-stage auctions on low-value land to maximize personal benefits and use English auctions on high-value land to maximize public benefits. Using a detailed data set that covers all land transactions in China between 2007 and 2017, Li structurally estimates a common value auction model where bidders are asymmetric in information and private costs. Results show that land sold by two-stage auctions on average have a value lower than English auction by CN Y 343/m2 , explaining 43% of the observed price gap (selection effect); and the remaining 57% can be explained by the different bidding equilibrium of the two auction formats (corruption effect). Moreover, connected bidders have a significant information advantage over unconnected ones, allowing them to bid higher and win more often. The counterfactual results suggest that using English auction only and increasing public information disclosure, can both significantly reduce corruption, increase land revenues, and increase social welfare.

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Ownership Networks and Firm Growth: What Do Forty Million Companies Tell Us About the Chinese Economy?
Author(s):
Franklin Allen, Imperial College London
Junhui Cai, University of Pennsylvania
Xian Gu, Durham University
Jun Qian, Fudan University
Linda Zhao, University of Pennsylvania
Wu Zhu, University of Pennsylvania
Discussant(s):
Chang-Tai Hsieh, University of Chicago and NBER
Summary:

The finance-growth nexus has been a central question in understanding the unprecedented success of the Chinese economy. With unique data on all the registered firms in China, Allen, Cai, Gu, Qian, Zhao, and Zhu build extensive ownership networks, reflecting firm-to-firm equity investment relationships, and show that these networks have been expanding rapidly since the 2000s, with more than five million firms in at least one network by 2017. Entering a network and increasing network centrality, both globally and locally, are associated with higher growth rates. Such positive network effects tend to be more pronounced for high productivity firms and privately owned firms. The RMB 4 trillion stimulus, mostly in the form of newly issued bank loans and launched by the Chinese government in November 2008 in response to the global financial crisis, partially 'crowded out' the positive network effects. Their analysis suggests that equity ownership networks and bank credit tend to act as substitutes for state-owned enterprises, but as complements for privately owned firms in promoting growth.

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Entrepreneurial Reluctance: Talent and Firm Creation in China
Author(s):
Chong-En Bai, Tsinghua University
Ruixue Jia, University of California, San Diego and NBER
Hongbin Li, Stanford University
Xin Wang, Chinese University of Hong Kong
Discussant(s):
Xiaobo Zhang, Peking University
Summary:

Bai, Jia, Li, and Wang study whether talented individuals are more or less likely to create firms, linking administrative data on college admission for 1.8 million individuals with information on firm registration records in China. The data reveals that most of the variation in firm creation is from within college. Given the same college, the researchers find that individuals with higher college entrance exam scores – the most important measure of talent in this context – are less likely to create firms. Moreover, this negative relationship is even stronger for groups with social and economic advantages such as men, urban individuals, and those from better high schools. Bai, Jia, Li, and Wang propose three hypotheses for why students with higher-scores are less likely to create firms: the lower entrepreneurial ability hypothesis, the opportunity cost hypothesis and the risk attitude hypothesis. The data fit best with the opportunity cost hypothesis, i.e., higher-score individuals are attracted away by non-entrepreneur sectors, even though they might have higher entrepreneurial ability.

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Valuation of Long-Term Property Rights under Political Uncertainty
Author(s):
Zhiguo He, University of Chicago and NBER
Maggie Rong Hu, Chinese University of Hong Kong
Zhenping Wang, University of Chicago
Vincent Yao, Georgia State University
Discussant(s):
Johannes Stroebel, New York University and NBER
Summary:

He, Hu, Wang, and Yao empirically analyze pricing of political uncertainty in long-term property rights, guided by a theoretical model of housing assets subject to contract extension in the remote future. To identify exposure to political uncertainty, the researchers exploit a unique variation around land lease extension protection beyond 2047 in Hong Kong's housing market due to the historical arrangements under the "One Country, Two Systems" design. Relative to properties that have been promised an extension protection, those with legally unprotected leases granted by the current Hong Kong government are sold at a substantial discount of around 8%. Similar contracts issued during the colonial era suffer an additional discount of about 8% due to their reneging risk. Their parsimonious model matches well the estimated discounts across long-term lease horizons, and implies that to extend their leases homeowners expect about 25% of penalty on ground rent after 2047. The discount is higher when people's confidence declines and where residents feel more uncertain of the city's future.

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In addition to the conference paper, the research was distributed as NBER Working Paper w27665, which may be a more recent version.

Discharge Fees, Pollution Mitigation and Productivity: Evidence from Chinese Power Plants
Author(s):
Gautam Gowrisankaran, University of Arizona and NBER
Michael Greenstone, University of Chicago and NBER
Ali Hortaçsu, University of Chicago and NBER
Mengdi Liu, University of International Business and Economics
Caixia Shen, Zhejiang University of Finance and Economics
Bing Zhang, Nanjing University
Discussant(s):
Douglas Almond, Columbia University and NBER
Summary:

The economy of China has experienced a dramatic transformation in recent years but a cost of development has been pollution. Starting in 2003, Chinese provinces started to assess discharge fees for two main air pollution measures, SO2 and NO2. This study obtains detailed data on ambient pollution and discharge pollutants, fuel inputs, and firm productivity for power plants, which comprise the majority of these emissions from fixed sources. Gowrisankaran, Greenstone, Hortaçsu, Liu, Shen, and Zhang identify the impact of discharge fees on pollution and productivity outcomes using a difference-in-difference for monitors and firms in local border areas within 50 KM of a provincial border with fee changes. They find some evidence that pollution fees caused ambient pollution to drop. Pollution fees led to large reductions in emitted SO2 and NO2 with elasticities ranging from −22% to −45%. Power plants reduced their coal inputs and may have increase their natural gas inputs following fee changes. Fees appear to have led to a drop of productivity, with an elasticity of −22%. Fee increases appear to make labor relatively less productive and capital relatively more productive.

Search and Information Frictions on Global E-Commerce Platforms: Evidence from AliExpress
Author(s):
Jie Bai, Harvard University and NBER
Maggie Chen, George Washington University
Jin Liu, New York University
Daniel Xu, Duke University and NBER
Discussant(s):
Panle Jia Barwick, Cornell University and NBER
Summary:

Global e-commerce platforms present new export opportunities for small and medium-sized enterprises in developing countries by significantly lowering the entry barriers of exporting. However, the lack of market selection can lead to a large number of online firms competing for consumers' attention, resulting in severe congestion in consumers' search process. When firms' intrinsic quality is not perfectly observed, these search frictions can further slow down the resolution of the information problem and hinder market allocation towards better firms. In this paper, Bai, Chen, Liu, and Xu investigate how search and information frictions shape firm dynamics and market evolution in global e-commerce. Using detailed data from AliEpxress as well as a rich set of selfcollected objective quality measures, they provide stylized facts that are consistent with the presence of search and information frictions. Moreover, using a randomized experiment that offers exogenous demand and information shocks to small prospective exporters, the researchers establish that firms with larger past sales have an advantage in overcoming the search friction and generating future orders. This indicates that initial demand shocks could confound firms' true quality in determining firm growth and the long-run market structure. Bai, Chen, Liu, and Xu construct and estimate an empirical model of the online market that are consistent with our descriptive and experimental findings and use the model to quantify the extent of demand-side frictions. Counterfactual analyses show that alleviating information frictions and reducing the number of firms can help to improve allocative efficiency and raise consumer welfare.

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Asymmetric Cross-Side Network Effects on Financial Platforms: Theory and Evidence from Marketplace Lending
Author(s):
Lin William Cong, Cornell University
Ke Tang, Tsinghua University
Danxia Xie, Tsinghua University
Qi Miao, Nielsen Company
Discussant(s):
Kelly Shue, Yale University and NBER
Summary:

Using data on 988 peer-to-peer lending platforms in China, Cong, Tang, Xie, and Miao examine the cross-side network effects (CNEs) throughout platforms' lifecycle in a dynamic industry characterized by entries, exits, and network externalities. They find that unlike borrowers' symmetric CNEs, lenders' CNEs are smaller on declining and smaller platforms than on growing, new, or larger platforms. Borrowers' CNEs are also larger than lenders' CNEs, especially for declining or sub-scale platforms. The researchers rationalize the asymmetries in a model of two-sided platforms with endogenous failures and empirically motivated distinguishing features of financial platforms---lenders' portfolio diversification, differential impacts on agents of platform failures, and borrowers' stickiness due to contracting frictions. The model further predicts that lenders' CNEs predict the platforms' scale and survival likelihood, among others, which the data corroborate. Their findings provide novel economic insights on multi-sided platforms and inform FinTech practitioners and regulators.

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"Golden Ages": A Tale of Two Labor Markets
Author(s):
Hanming Fang, University of Pennsylvania and NBER
Xincheng Qiu, University of Pennsylvania
Discussant(s):
Nancy Qian, Northwestern University and NBER
Summary:

Fang and Qiu document stark differences in the labor market outcomes between the U.S. and China, the largest two economies in the world, during the past 30 years. (1) The peak age in cross-sectional age-earnings profiles stays constant at around 45-50 years old in the U.S. but decreases sharply from 50 to 35 years old in China. (2) Age-specific labor earnings grow drastically in China but almost stagnate in the U.S. (3) The cross-sectional and life-cycle age-earnings profiles look remarkably similar in the U.S. but differ substantially in China. he researchers provide a unified decomposition framework to infer life cycle human capital accumulation, inter-cohort human capital growth, and human capital price changes from repeated cross-sectional data and address the above facts. They apply the framework to several applications.

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Participants

Below is a list of conference attendees.
Junhui Cai, University of Pennsylvania
Lili Dai, UNSW Sydney
John Giles, The World Bank
Xian Gu, Durham University
Jianlei Han, Macquarie University
Haifeng Huang, University of California, Merced
Shenzhe Jiang, Beijing University
Guangwei Li, ShanghaiTech University
Ming Li, Chinese University of Hong Kong, Shenzhen
Mengdi Liu, University of International Business and Economics
Qing Liu, Tsinghua University
Qi Miao, Nielsen Company
David Ong, Jinan University
Ivan P. Png, National University of Singapore
Bei Qin, Hong Kong Baptist University
Xincheng Qiu, University of Pennsylvania
Alberto Salvo, National University of Singapore
Jian Wang, Chinese University of Hong Kong, Shenzhen
Shaoda Wang, University of Chicago
Xiaoyuan Wang, Texas A&M University
Yanfei Wang, Renmin University
Binzhen Wu, Tsinghua SEM
Yanhui Wu, University of Hong Kong
Danxia Xie, Tsinghua University
Jin Xie, Chinese University of Hong Kong
Lihe Xu, Guangdong University of Foreign Studies
Chunliu Yang, Fudan University
Yu (Alan) Yang, University of Wisconsin-Madison
Bing Zhang, Nanjing University
Bohui Zhang, Chinese University of Hong Kong, Shenzhen
Hong Zhang, Tsinghua PBC
Weiwen Zhang, Zhejiang University
Yu Zhang, Peking University
Wu Zhu, University of Pennsylvania

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