nep-cna New Economics Papers
on China
Issue of 2024‒02‒05
eight papers chosen by
Zheng Fang, Ohio State University

  1. The Growth of Firms, Markets and Rents: Evidence from China By Daniel Berkowitz; Shuichiro Nishioka
  2. On the time-varying impact of China’s bilateral political relations on its trading partners (1960–2022). By António Afonso; Valérie Mignon; Jamel Saadaoui
  3. Anti-corruption campaign in China: An empirical investigation By Yang, Li; Milanović, Branko; Lin, Yaoqi
  4. The Incremental Impact of China’s Carbon By Lu; Pollitt, M. G.; Wang, K.; Wei, Y-M.
  5. Female Entrepreneur on Board:Assessing the Effect of Gender on Corporate Financial Constraints By Ruiying Xiao
  6. Disentangling Various Explanations for the Declining Labor Share: Evidence from Millions of Firm Records By Ann Harrison
  7. Haste or Waste? The Role of Presale in Residential Housing By Ziyang Chen; Maggie Rong Hu; Ginger Zhe Jin; Qiyao Zhou
  8. Machine Learning Based Panel Data Models By Bingduo Yang; Wei Long; Zongwu Cai

  1. By: Daniel Berkowitz (University of Pittsburgh); Shuichiro Nishioka (West Virginia University)
    Abstract: The evidence for whether China become more competitive following its accession to the World Trade Organization (WTO) is mixed. Using recent methods for estimating markups and profit shares, this paper documents that Chinese manufacturing firms on average collected more rents after the accession because the rate of net entry of firms lagged the rapid growth of the domestic market. While the selection on large productive firms drove the rise in the aggregate markups in the United States (De Loecker et al, 2020), these competitive forces played a secondary role in China.
    Keywords: Markups, Profit shares, Net entry, Market expansion, Trade liberalization in China
    JEL: F13 L11 O19 O53
  2. By: António Afonso; Valérie Mignon; Jamel Saadaoui
    Abstract: We assess the impact of China’s bilateral political relations with three main trading partners—the US, Germany, and the UK—on current account balances and exchange rates, over the 1960Q1-2022Q4 period. Relying on the lag-augmented VAR approach with time-varying Granger causality tests, we find that political relationships with China strongly matter in explaining the dynamics of current accounts and exchange rates. Such relationships cause the evolution of the exchange rate (except in the UK) and the current account; these causal links being time-varying for the US and the UK and robust over the entire period for Germany. These findings suggest that policymakers should account for bilateral political relationships to understand the global macroeconomic consequences of political tensions.
    Keywords: Political relations; time-varying causality; lag-augmented vector autoregression; China.
    JEL: C22 F51 Q41
    Date: 2023
  3. By: Yang, Li; Milanović, Branko; Lin, Yaoqi
    Abstract: Using official information published by Central Commission for Discipline Inspection (CCDI) of the CPC, we construct a database of officials who have been found guilty of corruption in China in the period 2012-21 with their personal characteristics and the amount of embezzled funds. We use it to investigate the correlates of corruption, estimate the effects of corruption on inequality, and find the expected increase in officials' income due to corruption and the gain in income distribution ranking. We find that the amount of corruption is positively associated with education, administrative (hierarchical) level of the official, and years of membership in the Communist Party. The sample of corrupt officials belongs to the upper income ranges of Chinese income distribution even without corruption. But corruption is a significant engine of upward mobility. While only one-half of the corrupt official would be in the top 5 percent of urban distribution without illegal incomes, practically all are in the top 5 percent when corrupt income is included.
    Keywords: Corruption, Income Inequality, Income Distribution, China, Rent Seeking
    JEL: D31 P37
    Date: 2023
  4. By: Lu; Pollitt, M. G.; Wang, K.; Wei, Y-M.
    Abstract: China has adopted the carbon emissions trading system (ETS) due to its advantages on efficiency and cost grounds. Prior to the national carbon market, China operated seven ETS pilots as experiments for eight years in addition to the existing Energy Conservation and Carbon Abatement Target Responsibility System (ECCA-TRS) in order to accumulate experience with carbon markets. However, the incremental effects of these pilots are unclear so far. Here, we show that the ETS pilots have produced no additional carbon abatement effect or abatement cost-saving effect, while ECCA-TRS contributed primarily to the relative decline in CO2 emissions and absolute decline in CO2 intensity of covered industries in pilot regions. A binding target is necessary to permit ETS to act as the backstop emissions constraint. Adjusting local governments' abatement achievement using the buy-in and sell-out of carbon allowances can allow the ECCA-TRS and ETS to act as well-integrated instruments.
    Keywords: Carbon Emissions Trading Scheme, Target responsibility system, Policy evaluation, Triple difference-in-differences
    JEL: Q54 L94
    Date: 2023–12–29
  5. By: Ruiying Xiao
    Abstract: This study investigates the impact of female leadership on the financial constraints of firms, which are publicly listed entrepreneurial enterprises in China. Utilizing data from 938 companies on the China Growth Enterprise Market (GEM) over a period of 2013-2022, this paper explores how the female presence in CEO positions, senior management, and board membership influences a firm's ability to manage financial constraints. Our analysis employs the Kaplan-Zingales (KZ) Index to measure these constraints, encompassing some key financial factors such as cash flow, dividends, and leverage. The findings reveal that companies with female CEOs or a higher proportion of women in top management are associated with reduced financial constraints. However, the influence of female board members is less clear-cut. Our study also delves into the variances of these effects between high-tech and low-tech industry sectors, emphasizing how internal gender biases in high-tech industries may impede the alleviation of financing constraints on firms. This research contributes to a nuanced understanding of the role of gender dynamics in corporate financial management, especially in the context of China's evolving economic landscape. It underscores the importance of promoting female leadership not only for gender equity but also for enhancing corporate financial resilience.
    Date: 2024–01
  6. By: Ann Harrison
    Abstract: This paper uses millions of records from a cross-country and time series database of both publicly listed and private companies to disentangle the role of technological change, market power, and globalization in driving a fall in the labor share. Labor shares are measured at the enterprise level as the share of total remuneration to workers in value-added. Technological change is measured using research and development expenditures or total factor productivity growth. Market power is measured using four firm and twenty firm concentration ratios and globalization is measured as export shares in total revenues. We also supplement the cross-country evidence with a more in depth look at China using its industrial census. The evidence suggests that between 1995 and 2019 the most important driver of falling labor shares was technological change. Greater market power (measured by firm concentration ratios) also contributed to lower labor shares, but the magnitudes are smaller. Finally, the evidence on globalization is mixed: trade shares are at times negatively associated with the labor share but in the case of China there is a strong positive relationship between exporting and labor shares at the enterprise level.
    JEL: F13 F16 J32
    Date: 2024–01
  7. By: Ziyang Chen; Maggie Rong Hu; Ginger Zhe Jin; Qiyao Zhou
    Abstract: This paper provides the first theory and evidence on the role of presale policies in the residential housing market. We start with constructing a novel dataset of unfinished projects, presale policies, and land auction outcomes across 270 major cities in China. We then identify 2, 330 unfinished residential projects from 2010 to 2017 on a citizen complaint website run by the central government. We find that both presale criterion and postsale supervision of construction costs relate to a lower probability of unfinished projects. But only presale criterion relates negatively to the pace of new housing development, measured by developers' multitasking and land auction outcomes. A back-of-the-envelope calculation suggests that the average bundle of presale policies is inferior to the Pareto frontier in our sampled cities. Tightening the regulation on postsale supervision by 2 standard deviations may lead to a 58% reduction in the occurrence of unfinished projects, while keeping the pace of new housing development unchanged. Eliminating unfinished projects would entail a drastic increase in both presale criterion and postsale supervision, with slower housing development.
    JEL: D22 D82 H7 K23 L52 L78
    Date: 2024–01
  8. By: Bingduo Yang (School of Finance, Guangdong University of Finance and Economics, Guangzhou 510320, China); Wei Long (Department of Economics, Tulane University, New Orleans, LA 70118, USA); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)
    Abstract: We examine nonparametric panel data regression models with fixed effects and cross-sectional dependence through a diverse collection of machine learning techniques. We add cross-sectional averages and time averages as regressors to the model to account for unobserved common factors and fixed effects respectively. Additionally, we utilize the debiased machine learning method by Chernozhukov et al. (2018) to estimate parametric coefficients followed by the nonparametric component. We comprehensively investigate three commonly used machine learning techniques - LASSO, random forests, and neural network - in finite samples. Simulation results demonstrate the effectiveness of our proposed method across different combinations of the number of cross-sectional units, time dimension sample size, and the number of regressors, irrespective of the presence of fixed effects and cross-sectional dependence. In the empirical part, we employ the proposed machine learning-based panel data model to estimate the total factor productivity (TFP) of public companies of Chinese mainland and find that the proposed machine learning methods are comparable to other competitive methods.
    Keywords: Machine learning; panel data model; cross-sectional dependence; debiased machine learning.
    JEL: C12 C22
    Date: 2024–01

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