Panorama China Payment Survey
Coface conducts an annual survey of payment experience in China. With 80% of the survey respond-ents shared overdue experience and 56.7% of them saw an increase in overdue amount over the past year, the overall payment experience in China remained very challenging in 2014. This is in-line with the non-performing loan (NPL) figures released by the China Banking Regulatory Commission, which showed that NPL ratio has reached a multi-year high of 1.25% as of the end of 2014. The risk of rising non-payment cannot be neglected.
China’s 7.4%YoY GDP growth in 2014 was the lowest growth rate in the last 24 years, and momentum is on a downtrend (Coface forecasts GDP to grow 7% in 2015). At the same time, the real economy in China is facing rising challenges in 3 major areas: 1) high leverage; 2) high cost of financ-ing, 3) low profitability driven by overcapacities in certain sectors. While monetary easing measures are introduced to smooth out the growth deceleration process, if the additional low-cost funding is not delivered to the parties that need and deserve it, the main purposes of such monetary easing measures are likely going to be defeated, and further concerns on credit pressure would be in sight.
With the economy slowing down, industry participants would need to adapt to slower demand growth in general and dedicate to find new growth drivers. With the high debt level in China, there is an essential need for the cost of financing to come down. In the later part of this report, we examined 9 major sectors in the Chinese economy based on payment experience and financial performances to take stock on various sectors. With signs of deterioration in both payment experience and financial performance, risks in chemicals, construction, and paper-wood sectors are on the rise, while the metals sector remains as an origin of worries in China.
TÉLÉCHARGER CETTE PUBLICATION
TABLE OF CONTENTS
- Payment survey background
- Corporate payment experience in 2014
- Chinese economic outlook 2015
- Sector analysis