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on China |
| By: | Carlos Garriga; Aaron Hedlund; Yang Tang; Ping Wang |
| Abstract: | This paper uses a dynamic competitive spatial equilibrium framework to evaluate the contribution of rural-urban migration induced by structural transformation to the behavior of Chinese housing markets. In the model, technological progress drives workers facing heterogeneous mobility costs to migrate from the rural agricultural sector to the higher paying urban manufacturing sector. Upon arrival to the city, workers purchase housing using long-term mortgages. Quantitatively, the model fits cross-sectional house price behavior across a representative sample of Chinese cities between 2003 and 2015. The model is then used to evaluate how changes to city migration policies and land supply regulations affect the speed of urbanization and house price appreciation. The analysis indicates that making migration policy more egalitarian or land policy more uniform would promote urbanization but also would contribute to larger house price dispersion. |
| JEL: | O11 R21 R23 R31 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28013&r=all |
| By: | Fittje, Jens; Wagner, Helmut |
| Abstract: | The topography of China's financial network is unique. Is it also uniquely robust to contagion? We explore this question using network theory. We find that networks that are more concentrated are less fragile when connectivity is low. However, they remain vulnerable to the occurrence of large-scale default cascades at higher levels of connectivity than a decentralized network. We implement Chinese characteristics into our model and simulate it numerically. The simulations show, that the large state-controlled banks act as effective stopgaps for contagion, which makes the Chinese network relatively robust. This robustness persists even when a medium sized bank defaults. |
| Keywords: | Interbank Network,Financial Contagion,China's interbank market,Financial market stability,Complex networks,Network topography |
| JEL: | E44 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc20:224605&r=all |
| By: | Mao, Haiou; Görg, Holger |
| Abstract: | This paper considers the indirect impact the recent tariff increases between the United States and China can have on third countries through links in global supply chains. We combine data from input–output relationships, imports and tariffs, to calculate the impact of the tariff increases by both the United States and China on cumulative tariffs paid by third countries. We show that the tariff hikes increase cumulative tariffs for other countries and thus hurt trade partners further downstream in global supply chains. We also show that this is particularly important for tariff increases on Chinese imports in the United States. These are likely to be used as intermediates in production in the United States, which are then re-exported to third countries. The most heavily hit third countries are the closest trade partners, namely the EU, Canada and Mexico. We estimate that the tariffs impose an additional burden of around 500 million to 1 billion US dollars on these countries. China's tariffs on US imports have less of an effect. |
| Keywords: | cumulative tariffs,indirect tariffs,trade war |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwkie:225992&r=all |
| By: | Shao, Yongtong; Xiong, Tao; Li, Minghao; Hayes, Dermot; Zhang, Wendong; Xie, Wei |
| Abstract: | Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, Support Vector Regression has superior forecasting performance in small sample applications. In this article, we introduce Support Vector Regression via an application to China’s hog market. Since 2014, China’s hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use Support Vector Regression to predict the true inventory based on the price-inventory relationship before 2014. We show that, in this application with a small sample size, Support Vector Regression out-performs neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data. |
| Date: | 2020–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genstf:202001010800001619&r=all |