Disclaimers

S&P Global (China) Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit model scores from the credit ratings issued by S&P Global (China) Ratings.

Introduction

In China, LGFVs are one of the major bond issuers. In 2021, the net financing of LGFVs reached 2.1 trillion RMB and exceeded the total issuance of 2020.^[1] LGFVs mainly engage in public services such as infrastructure and land development. They tend to be nonprofit and heavily leveraged but are able to obtain funding due to explicit or implicit government guarantees. To evaluate the overall credit risk profile of an LGFV, it is necessary to assess the credit quality of the underlying local and regional government (LRG) and the likelihood of extraordinary government support.^[2]

An LGFV is a special type of government-related entity (GRE), or an entity potentially affected by extraordinary government intervention in an economic or financial stress scenario. There are some differences between LGFVs and Non-LGFV GREs:

• LGFVs have much closer linkages with the corresponding LRGs.
• The main business of an LGFV is public service.
• LGFVs’ debts are non-self-supporting.
• LGFVs typically do not have cross-region business.
• LGFVs are smaller in size.
• LGFVs enjoy stronger support from governments.

Because of these differences and the importance of LGFVs in the China credit market, S&P Global Market Intelligence developed a quantitative support overlay model to assess the credit risk of LGFVs.^[3] The model aims to quantify the likelihood and impact of government support on the credit risk assessment of LGFVs by incorporating important risk drivers in line with the S&P Global (China) Ratings’ rating criteria and scale.

Extraordinary Government Support

The strength of extraordinary government support depends on the importance of an LGFV’s role to the government, which falls into one of the following four categories: (i) Critical, (ii) High, (iii) Moderate, and (iv) Low. The final notch lift due to extraordinary government support is based on the level of the LGFV’s importance.

Purpose and Scope of the Model

The goal of the quantitative overlay is to use a standardized, automated, transparent, and consistent process to assess the importance of the LGFV’s role to the underlying LRG.

The overlay applies to entities that are classified as LGFVs based on the following criteria:

• Controlled by local governments.
• Primarily deliver public services.
• Not financially self-supportive or dependent on subsidies from the LRG.

Methodology

A decision tree algorithm is employed for this overlay because of the simple classification rules, robust performance, and high interpretability. A decision tree is a hierarchical model composed of decision rules, which are applied recursively to partition the feature space of a dataset into pure, single-class subspaces. Decision tree algorithm is used to discover features and extract patterns that are important for discrimination and predictive modeling. Moreover, decision tree algorithm is easy to understand with intuitive interpretation.

Final Credit Score Adjustment

The adjustment to the LGFV’s stand-alone credit score or notch adjustment is determined by a mapping based on the following components: the credit score of the supporting government, the company stand-alone credit score, and the level of importance of the LGFV to the underlying government.

The credit quality of an LGFV is not linked to the credit quality of a central government directly but with an LRG, since the central government's support for LGFV sector is a system-wide phenomenon, rather than entity specific. The assessments of government support for LGFVs therefore focus on the credit quality of an LRG instead of a central government.

For most cases, the impact of government is positive but, in some rare cases, the impact of government can be negative when an LRG has poorer credit qualities than an LGFV. Under a situation of financial stress, an LRG may intervene to redirect an LGFV’s resources to the government and weaken the LGFV's credit quality.

Case Study

Company X is a major LGFV in Jiangsu province, which is primarily responsible for land development, affordable housing construction, and other infrastructure construction.

The inputs to the quantitative government support overlay model are the following (using information as of March 31, 2022 from S&P Global Market Intelligence for illustrative purposes only):

• Tier of support government : 2
• Control Level : 3
• Asset Percentile : < 30%
• Total Assets : < 10 billion CNY
• Total Revenue : < 700 million CNY
• Industry : Construction & Engineering

Based on the above inputs, the importance of the company’s role to the government is classified as “Moderate” by the overlay model. This is in line with our expectations considering the non-directly controlled ownership structure and its relatively small size. Accordingly, the company’s credit score was uplifted by two notches, i.e., from ‘b+’ to ‘bb’ because of the potential government support.

Performance

S&P Global Market Intelligence’s overlay model, trained on S&P Global (China) Ratings data, aims to assess the potential uplift or downgrade of the stand-alone credit score of an entity due to potential extraordinary government support. One of the most intuitive measures of relative model performance is obtained by looking at the difference between the model outcome and S&P Global (China) Ratings’ ratings data. To validate the model performance, we compared the results of applying the government support overlay with the issuer credit ratings from S&P Global (China) Ratings.^[4]

Table 1 reports the percentage notch difference between the estimated scores after applying the government support overlay and the S&P Global (China) Ratings data. 

Table 1: Model performance

Source: S&P Global Market Intelligence. Data as of June 2022. For illustrative purposes only.

Conclusion

S&P Global Market Intelligence has built a quantitative government support overlay model for the LGFV sector, tailored to the China domestic market with localized criteria. The model was trained on S&P Global (China) Ratings’ data. It aims to produce differentiated credit score outputs and to offer an automated and scalable solution for gauging the credit risk of LGFVs in China.