nep-cna New Economics Papers
on China
Issue of 2020‒01‒13
five papers chosen by
Zheng Fang
Ohio State University

  1. Barriers to Entry and Regional Economic Growth in China By Loren Brandt; Gueorgui Kambourov; Kjetil Storesletten
  2. Misallocation, Selection and Productivity: A Quantitative Analysis with Panel Data from China By Tasso Adamopoulos; Loren Brandt; Jessica Leight; Diego Restuccia
  3. How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm By Leonardo Gambacorta; Yiping Huang; Han Qiu; Jingyi Wang
  4. Financial Dependencies, Environmental Regulation, and Pollution Intensity: Evidence From China By Mathilde Maurel; Thomas Pernet; Zhao Ruili
  5. Decoupling Chimerica: Consequences for the European Union By Beer, Sonja; Matthes, Jürgen; Rusche, Christian

  1. By: Loren Brandt; Gueorgui Kambourov; Kjetil Storesletten
    Abstract: Labor productivity in manufacturing differs starkly across regions in China. We document that productivity, wages, and start-up rates of non-state firms have nevertheless experienced rapid regional convergence after 1995. To analyze these patterns, we construct a Hopenhayn (1992) model that incorporates location-specific capital wedges, output wedges, and entry barriers. Using Chinese Industry Census data we estimate these wedges and examine their role in explaining differences in performance and growth across prefectures. Entry barriers explain most of the differences. We investigate the empirical covariates of these entry barriers and find that barriers are causally related to the size of the state sector
    Keywords: Chinese economic growth; SOEs; firm entry; entry barriers; capital wedges; output wedges; SOE reform.
    JEL: O11 O14 O16 O40 O53 P25 R13 D22 D24 E24
    Date: 2020–01–05
  2. By: Tasso Adamopoulos; Loren Brandt; Jessica Leight; Diego Restuccia
    Abstract: We use household-level panel data from China and a quantitative framework to document the extent and consequences of factor misallocation in agriculture. We find that there are substantial within-village frictions in both the land and capital markets linked to land institutions in rural China that disproportionately constrain the more productive farmers. These frictions reduce aggregate agricultural productivity in China by affecting two key margins: (1) the allocation of resources across farmers (misallocation) and (2) the allocation of workers across sectors, in particular the type of farmers who operate in agriculture (selection). We show that selection can substantially amplify the static misallocation effect of distortionary policies by affecting occupational choices that worsen the distribution of productive units in agriculture.
    Keywords: agriculture, misallocation, selection, productivity, China.
    JEL: O11 O14 O4 E02 Q1
    Date: 2019–12–31
  3. By: Leonardo Gambacorta; Yiping Huang; Han Qiu; Jingyi Wang
    Abstract: This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China for the period between May and September 2017, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused lending to decline and credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. One possible reason for this is that machine learning can better mine the non-linear relationship between variables in a period of stress. Finally, the comparative advantage of the model that uses the fintech credit scoring technique based on machine learning and big data tends to decline for borrowers with a longer credit history.
    Keywords: fintech, credit scoring, non-traditional information, machine learning, credit risk
    JEL: G17 G18 G23 G32
    Date: 2019–12
  4. By: Mathilde Maurel (Centre d'Economie de la Sorbonne;; Thomas Pernet (Centre d'Economie de la Sorbonne;; Zhao Ruili (Shangai University of International Business and Economics - Chine)
    Abstract: We study how a bank's involvement in a firm's financing may be in line with environmental policies pursued by the Chinese central government. Specifically, we evaluate the effectiveness of credit reallocation away from polluting projects when the government imposes stringent environmental policies. We combine the industries' financial dependencies with time, including cross-cities variation in policy intensity to identify the causal effect on the sulfur dioxide (SO2)emission. We find that SO2 emissions are lower in industries with high reliance on credits and stricter environmental regulations. Furthermore, our results suggest that locations with strong environmental policies lead firms to seek funding in less regulated areas, which confirms the pollution haven hypothesis
    Keywords: Banks; Financial Dependency; Environmental regulation; China
    JEL: F36 G20 Q53 Q56
    Date: 2019–12
  5. By: Beer, Sonja; Matthes, Jürgen; Rusche, Christian
    Abstract: The People's Republic of China experienced a tremendous economic development within the last four decades. The increased economic power and political weight of China are challenging the USA and EU. Furthermore, the strategies used by China for its own development, e.g. broad-based industry policy with distortive subsidization, forced technology transfer or investment restrictions, are perceived as unfair, especially in the US, but to a large extent also in the EU. This development in combination with the trade imbalances are resulting in the current conflict between China and the US. The term decoupling was introduced to describe the cutting off of economic ties between China and the US as a consequence of the conflict. Accordingly, we analyze in this article whether a decoupling is going on. [...]
    JEL: E61 F02 O24
    Date: 2019

This nep-cna issue is ©2020 by Zheng Fang. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.