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Quantitative Government Support Overlay - China Corporates 1.0


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Quantitative Government Support Overlay - China Corporates 1.0


An Overlay Model Specializing in the Analysis of Government Support for Chinese State-Owned Enterprises (SOEs)

This article is written and published by S&P Global Market Intelligence, a division independent from S&P Global (China) Ratings. The opinions herein are not reflective of those of S&P Global (China) Ratings. 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 scores from the credit ratings issued by S&P Global (China) Ratings.


The China corporate credit market consists of the private sector and SOEs, with the latter accounting for most of the corporate debt issues in the Chinese domestic market.[1] Defaults are on the rise for SOEs, especially in the past several years, due to the intended gradual phase out of backstops by the government. These credit events have raised concerns about the large number of SOEs that are controlled by Chinese local and regional governments (LRGs). However, it is challenging to assess the credit risk of SOEs empirically, considering the paucity of default data and the need to untangle the strength and impact of government support. At S&P Global Market Intelligence, we have developed an overlay model that aims to quantify the likelihood and impact of government support on the credit risk assessment of government-related entities (GREs) by incorporating important risk drivers in line with the S&P Global (China) Ratings’ rating criteria and scale. The overlay model produces an estimate of the notch adjustment on the stand-alone credit score of a corporate counterparty and, hence, is an essential component for calculating the final credit scores of SOEs.

GREs Support Overlay

GREs are enterprises potentially affected by extraordinary government intervention in an economic or financial stress scenario. Most of the time, GREs are partially or totally owned and controlled by a government and/or they contribute to implementing policies or delivering key services. However, some entities with little or no government ownership may also be considered a GRE due to their systemic importance or their critical role as providers of crucial goods and services.

The strength of extraordinary government support to a particular GRE depends on the importance of a GRE’s role to the government, which falls in one of five categories: (i) Critical, (ii) High, (iii) Moderate, (iv) Low, and (v) Limited Importance. The final notch lift due to extraordinary government support is based on the level of the GRE’s importance.

Scope and Output of the GRE Overlay Model

The government support overlay model applies to non-financial GREs in the China domestic market, public and private, excluding local government financial vehicles (LGFVs).[2] The model generates a potential notch uplift or downgrade to the stand-alone credit assessment generated by S&P Global Market Intelligence’s CreditModelTM China 1.0 (CM China 1.0), accounting for the extraordinary support or negative intervention of the central government  or LRGs in stressed economic conditions, respectively.[3]

Model Features

Predicting the degree of government support is not trivial, as it involves factors that may be subject to expert judgment. To quantify the government support scheme, we leveraged the government support assessment data in the S&P Global (China) Ratings’ research database and identified key aspects used to determine the level of a GRE’s importance to the government. Information for each GRE is collected and scrutinized extensively.

Sophisticated Methodology

A decision tree algorithm is employed for the overlay model because it provides simple, but robust, classification rules. 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. As a popular approach for predictive modeling, the algorithm can incorporate both continuous and categorical features and capture non-linear relationships, while producing intuitive results.

Credit Score Adjustment

The adjustment to the company 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 GRE to a government.

In most cases, we expect that the overlay enhances a GRE’s credit score because the government typically has greater resources and, therefore, stronger credit quality. For cases where the company stand-alone credit score is better than that for the related government, the government-related adjustment, if any, will be negative and make the final company credit score equal to the underlying government credit score. Instead of providing extraordinary support to a GRE, a government may intervene to redirect GRE resources to the government and weaken the GRE's credit quality in such cases.

Model Performance

The aim of the overlay model is to assess a potential uplift or downgrade of the stand-alone credit score of an entity due to potential extraordinary government support. Thus, the most intuitive measure of relative model performance is obtained by looking at the difference between the overlay results and the S&P Global (China) Ratings data. To validate the model performance, we compared the result of applying the overlay on the stand-alone credit scores generated by CM China 1.0 with the issuer credit ratings from the S&P Global (China) Ratings’ research database. 

Table 1 reports the percentage of notch difference between the estimated scores and the actual S&P Global (China) Ratings data, based on the model training sample (in sample) and the testing sample (out of sample). The table shows that adding the government support overlay significantly enhances the ratings agreement for Chinese SOEs. The overlay model performs well, as suggested by high matching ratios.

Table 1: Overlay Model Performance (Ratings Agreement) on CM China 1.0

Source: S&P Global Market Intelligence. Data as of March 31, 2021. For illustrative purposes only.

Case Study

Company X is a state-owned enterprise that engages in the exploration, mining, smelting, and production of gold in China and internationally. The company is directly supervised by the Assets Supervision and Administration Commission of a province in northern China.

The inputs to the quantitative government support overlay model are the following (using information as of March 31, 2021 from S&P Global Market Intelligence for illustrative purposes only):

•          Industry                                     : Materials                              

•          Revenue per Regional GDP      : > 1%

•          Asset Percentile                       : > 85%

•          Revenue Percentile                  : > 90%

•          Total Assets                             : > 100 billion CNY

•          # of Employees                         : > 20,000        

•          Individual Credit Profile             : ‘bbb-’

Based on the above inputs, the importance of the company’s role to the government is classified as “High” by the overlay model. This is in line with our expectations considering that the company is one of the largest SOEs in the region, although it is operating in a competitive industry not related to public service. Accordingly, the company’s credit score was uplifted by four notches, i.e., from ‘bbb-’ to ‘a’ because of the potential government support.


S&P Global Market Intelligence has built a quantitative government support overlay model to account for the impact of potential extraordinary government support, a critical piece for evaluating the creditworthiness of Chinese SOEs. The overlay model, trained on S&P Global (China) Ratings data, is specifically tailored to the China domestic market and offers an automated and scalable solution for gauging the credit risk of SOEs.

About S&P Global Market Intelligence

At S&P Global Market Intelligence, we know that not all information is important—some of it is vital. Accurate, deep, and insightful. We integrate financial and industry data, research, and news into tools that help track performance, generate alpha, identify investment ideas, understand competitive and industry dynamics, perform valuations, and assess credit risk. Investment professionals, government agencies, corporations, and universities globally can gain the intelligence essential to making business and financial decisions with conviction.

S&P Global Market Intelligence is a division of S&P Global (NYSE: SPGI), which provides essential intelligence for individuals, companies, and governments to make decisions with confidence. For more information, visit

[1] See “China’s Bond Market – The Last Great Frontier”, S&P Global Ratings and S&P Global (China) Ratings, April 15, 2021.

[2] LGFV is a special type of SOE that was created specifically to facilitate off-budget investments in infrastructure and land development on behalf of an LRG. We have a separate government support overlay model for the LGFV sector, trained on S&P Global Ratings LGFV data.

[3] CM China 1.0 is a newly developed model by S&P Global Market Intelligence, aiming to generate stand-alone credit scores that are statistically aligned with S&P Global (China) Ratings’ rating criteria, for corporates in the China domestic market.

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