Download The Determinants of Chinese Local Government Bond Yields

The Determinants of Chinese Local Government Bond Yields
Sheng Wang and Fan Yu 1
This Version: January 31, 2014
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Wang is from the Shanghai Pudong Development Bank and Yu is from Claremont McKenna College.
Corresponding author: Fan Yu, Gordon C. Bjork Professor of Financial Economics and George R. Roberts Fellow,
Robert Day School of Economics and Finance, Claremont McKenna College, 500 E. 9th St., Claremont, CA 91711,
Email: [email protected], Phone: (909)607-3345.
The Determinants of Chinese Local Government Bond Yields
Abstract
In light of a rising concern over the debt repayment capability of Chinese local
governments, we examine the determinants of the yield spread on two types of Chinese local
government bonds. For municipal bonds issued through the Ministry of Finance, we find that
their pricing is largely unrelated to the economic condition and fiscal performance of the issuing
municipalities, suggesting that investors treat these securities as quasi-Treasuries. For urban
construction investment bonds, we find that their pricing is explained by key economic and
financial indicators of the bond issuer and the associated local government. Interestingly, the
yield spread on urban construction investment bonds is negatively related to the issuer’s leverage
ratio, consistent with lower quality issuers not being allowed to fully participate in this market.
1. Introduction
The rapid growth in Chinese local governments’ indebtedness and their ability to
shoulder this burden have become a persistent concern to regulators and investors in recent
years. A nation-wide audit of government debt by China’s National Audit Office reveals
outstanding local government debt in the amount of 10.9 trillion yuan as of June 2013, and this
figure increases further to 17.9 trillion yuan when debt with explicit or implicit local government
guarantees is also included. Of this total, about 50% consists of bank loans, 10% local
government bonds, and the rest includes BT (build-and-transfer) and trust financing.
In this paper, we examine the determinants of the pricing of local government bonds,
which are publicly traded in the Chinese interbank bond market. There are two types of local
government bonds in China: municipal bonds and urban construction investment bonds.
Municipal bonds have been issued directly by the Ministry of Finance on behalf of municipalities
since March 2009, and it was not until November 2011 that some municipalities were authorized
to issue bonds by themselves. In contrast, urban construction investment bonds have been in
existence for over two decades. Local governments typically set up companies with urban
construction as their main business, and these companies then issue urban construction
investment bonds to raise capital. Prompted by the four-trillion-yuan economic stimulus
package, Chinese local governments at all levels have been busy setting up various financing
vehicles and issuing hundreds of urban construction investment bonds each year since 2009.
Investors in these bonds are clearly concerned about credit risk. On July 27th, 2011, China
Chengxin International Credit Rating Co. Ltd. (CCXI), one of the top credit rating agencies in
China, put the Yunnan Investment Group and several of its subsidiaries on its watch list due to a
previously announced restructuring plan. This triggered a wave of panic among investors, and
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the liquidity of the credit market, especially for urban construction investment bonds, was
severely impaired afterwards. Naturally, one would like to ask the following questions: How do
Chinese bond investors evaluate the risk of local government bonds? What factors affect their
perception of the risk of these bonds? Do they respond to changes in local governments’
financial conditions? We attempt to answer these questions by following the performance of
Chinese local government bonds traded in the interbank bond market from 2009 to 2011, and we
use regression analysis to identify factors that influence the yield spread on these bonds. The
factors that we examine capture the economic condition and fiscal performance of the local
government, the financial condition of the bond issuer (which is applicable to urban construction
investment bonds), and the liquidity of the bonds.
Not surprisingly, we find that the pricing of municipal bonds issued through the Ministry
of Finance is largely insensitive to the economic condition and fiscal performance of the
corresponding municipalities, indicating that investors regard them as quasi-Treasury bonds.
Instead, their yield spreads bear a strongly negative relation with the issue size, suggesting that
illiquidity is a major concern for investors. Indeed, only six out of the 50 municipal bonds issued
in 2009 were actively traded during the following two years.
For urban construction investment bonds, we find that their pricing does depend on the
associated local government’s economic condition as well as the financial status of the bond
issuer. Intriguingly, when the issuer’s leverage ratio is higher, the yield spread of the bond is
lower, not higher, which seems counterintuitive. However, to the extent that credit is rationed in
the Chinese bond market and riskier firms are prevented by regulators from borrowing up to their
debt capacity, a higher leverage could proxy for superior credit quality not captured by the other
explanatory variables in our analysis (this is supported by a positive relation between leverage
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and credit rating). Lastly, similar to the preceding result on municipal bonds, we find that the
issue size of an urban construction investment bond negatively influences its yield spread.
Currently, there is very little quantitative research on Chinese local government bonds.
Notably, Han et al. (2003) use a KMV-type default risk model to compute the expected default
probability as a function of municipal bond issue size for Beijing and Shanghai. For urban
construction investment bonds, He (2011) descriptively identifies a positive relation between the
bond issue size and the GDP of the local government, and Shan and Hu (2011) examine how
issue size, maturity, and credit rating affect the bond’s coupon rate. Compare to these studies,
our analysis is much more comprehensive in terms of sample and variable selection, and offers
quantitative results on the determinants of local government bond pricing using key data on
interbank bond trading, the fiscal performance of municipalities, and the financial status of bond
issuers collected from multiple sources.
In contrast, municipal bonds have enjoyed a long history in the United States and there is
a related academic literature too large to review fully here. Notably, Jantscher (1970) and
Ingram, Brooks, and Copeland (1983) report abnormally high (low) yields for communities that
have experienced recent downgrades (upgrades). Cook (1982) surveys the evidence from 25
regression studies on the determinants of individual tax-exempt bond yields. Capeci (1991)
examines the channels through which a municipality’s credit quality affects its borrowing rate.
He finds that both credit ratings and borrowing rates respond to fiscal indicators in ways that are
consistent with the view that credit markets can impose some discipline on municipal fiscal
behavior. Our study can therefore be viewed as an extension of this literature to the Chinese local
government bond market.
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The remainder of this paper proceeds as follows: Section 2 briefly documents the
historical development and current status of the Chinese local government bond market. Section
3 discusses the regression specifications and the variables used in our analysis. Sections 4 and 5
elaborate on the sample selection, summary statistics, and regression results for municipal bonds
and urban construction investment bonds, respectively. Section 6 concludes.
2. Overview of the Chinese Local Government Bond Market
2.1. Municipal Bonds
According to the Chinese Budget Law of 1994, municipalities are not allowed to issue
bonds unless the State Council approves them. In 2009, the State Council allowed the issuance of
municipal bonds as a response to the global financial crisis. Since then, municipal bonds with a
total notional amount of 200 billion yuan have been issued annually. Nevertheless, municipalities
cannot freely issue whatever amount they choose. Instead, bond issuance is handled by the
Ministry of Finance on behalf of the municipalities using the issuance channel for Treasury
securities. The Ministry of Finance is also responsible for the interest and principal payments on
the bonds should the municipalities fail to pay. Consequently, most municipal bond investors
perceive the central government as the implicit guarantor.
Initially, municipal bonds were issued separately for each municipality. After 2010, the
Ministry of Finance often issued one municipal bond to raise funds for several municipalities
together as a package. Presumably, since these bonds are quasi-Treasuries due to the implicit
guarantee of the central government, it makes little difference whether the Ministry of Finance
issues municipal bonds on behalf of a single municipality or several municipalities as a group.
Because of this change, however, the number of municipal bonds issued decreased from 50 in
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2009 to just ten in 2010 and seven in 2011. In October 2011, the Ministry of Finance further
allowed four municipalities (Zhejiang, Guangdong, Shanghai, and Shenzhen) to issue bonds by
themselves for the first time, and it is expected to extend this new freedom to other
municipalities (including lower-tier cities) around the country in the coming years. It remains
interesting to see when investors would stop considering such bonds as de facto Treasuries and
start pricing their inherent credit risk.
For the initial offering of municipal bonds, underwriters have a choice between the
Chinese interbank bond market and the stock exchanges, which will also determine where they
are traded after issuance. The municipals can be purchased by both institutional and individual
investors. Since commercial banks are one of the major investors and they are prohibited from
trading bonds on the stock exchanges, most municipals are currently issued in the interbank bond
market and only a small number are issued in the stock exchanges. Since the interbank bond
market dominates the stock exchanges in bond trading activities, any research about municipal
bonds should focus on data from the interbank bond market rather than the stock exchanges.
2.2. Urban Construction Investment Bonds
Urban construction investment bonds were created to help Chinese local governments
raise capital for urban constructions. Before 2005, most urban construction investment bonds
were corporate bonds issued by state-owned enterprises located in either large provincial capitals
or municipalities directly controlled by the central government. The first urban construction
investment bond can be traced back to the early 1990s when the Shanghai municipal government
was planning to develop the Pudong New Area. The central government approved an annual
bond issuance quota of 500 million yuan for ten consecutive years to support the development,
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and the Shanghai Chengtou Corporation issued the first 500-million-yuan Pudong Development
Bond on behalf of the municipal government in April 1992.
The pace of development quickened when more and more local enterprises were
permitted to issue corporate bonds after 2005. By 2008, more than 100 billion yuan of urban
construction investment bonds were issued annually. Furthermore, to cope with the negative
impact from the global financial crisis, the central government launched a four-trillion-yuan
economic stimulus package in 2009. Local governments at various political levels all over China
were encouraged to increase their investment, especially in infrastructure construction.
Consequently, various local government financing vehicles sprang up, which drove the growth of
the urban construction investment bond market. The total issuance of urban construction
investment bonds quadrupled in 2009 compared to the year before.
This rapid expansion of local government debt brought about potential risks and drew the
attention of the central government. In June 2010, the State Council issued new guidelines
calling for local governments to exit their financing vehicles and prohibiting them from
providing guarantee on any new ones. The issuance of urban construction investment bonds was
suspended in the following month and subsequent new issues were without guarantee. Still, the
annual issuance of urban construction investment bonds continues to grow unabated. In fact,
Figure 1 shows that the amount of such bonds issued during 2012 and 2013 is well over one
trillion yuan each year, which is more than twice the annual amount during 2009-11.
3. Research Methodology
We use multivariate regression models to study what factors affect the pricing of Chinese
local government bonds. The regression specifications are different for municipal bonds and
urban construction investment bonds because the set of explanatory variables are different.
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However, in both cases the dependent variable is the yield spread of the local government bond
relative to the yield on a Chinese Treasury bond with the same maturity. The latter is obtained by
linearly interpolating available Chinese Treasury yields of six-month, one-year, three-year, fiveyear, seven-year, ten-year, fifteen-year, and twenty-year maturities. Since both Chinese Treasury
bonds and municipal bonds pay coupons that are tax-exempt, the construction of the yield spread
as explained above is entirely appropriate. On the other hand, urban construction investment
bonds are not tax-exempt. This implies that their yield spreads will contain a component
attributed to tax differentials.
3.1. Municipal bonds
To check whether Chinese municipal bonds are indeed regarded as quasi-Treasuries, we
regress their yield spreads on variables measuring the economic conditions and fiscal
performance of the issuing municipalities. Specifically, we use the GDP per capita of the
municipality and the annual GDP growth rate to proxy for local economic conditions, and the
fiscal revenue per capita and the annual revenue growth rate to proxy for the municipality’s
fiscal performance. Another important measure that can influence the ability to repay debt is the
municipality’s existing debt burden. However, except for data at the national level, no
information about debt levels is available for Chinese local governments from any public source.
Perhaps the central government will disclose more detailed information regarding local
government indebtedness as a result of the recent national debt audit. But until then, this
information remains a great mystery in the murky world of Chinese municipal finance.
Our analysis also includes the issue size of the municipal bond. While a greater issue size
corresponds to an increase in indebtedness and could raise the level of credit risk, this may be
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less of an issue in our setting because of the quasi-Treasury nature of the Chinese municipal
bonds. Instead, a larger issue is likely to be associated with greater trading volume and liquidity,
leading to a lower yield spread. Other liquidity indicators like the turnover rate and the bid-ask
spread are not considered here because of data availability.
We include two control variables in the regression specification. One is the maturity of
the bond, which takes into account a potentially upward-sloping or downward-sloping term
structure of yield spreads. The other control variable is a quarterly dummy, which is used to mop
up much of the common time-series variation in municipal yield spreads attributed to
macroeconomic fluctuations. Finally, we lag all independent variables by one quarter relative to
the dependent variable to eliminate any potential look-ahead bias in our results. The definitions
of all included variables for analyzing municipal bond pricing are summarized in Table 1.
3.2. Urban Construction Investment Bonds
While municipal bonds can only be issued by provinces and directly-controlled
municipalities, cities, prefectures, and even counties are allowed to establish financing vehicles
and issue urban construction investment bonds. Some investors believe that urban construction
investment bonds are much riskier than municipal bonds. Consistent with this view, the major
Chinese credit rating agencies all provide ratings on urban construction investment bonds, while
they do not rate any municipal bonds. Still, other investors may think that the local government,
the upper-level government, and even the central government will be the ultimate guarantor of
the bonds. To examine these different conjectures, we regress the yield spread on urban
construction investment bonds on variables measuring the financial status of the bond issuer, the
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economic and fiscal conditions of the associated local government, and the liquidity of the
bonds. The definitions of the variables are listed in Table 2.
For the financial status of the bond issuer, total assets is included because larger firms are
likely to have higher credit quality and lower yield spreads. Return on assets (ROA), return on
equity (ROE), and net profit margin (NP margin) are included because greater profitability
should correspond to a lower yield spread. Leverage ratio, interest coverage ratio, and the ratio of
long-term debt to total debt are used to reveal the current and future debt burden of the bond
issuer. Furthermore, investment and financing cash flows are included as cash inflows into
(outflows from) the bond issuer should decrease (increase) the yield spread.
Urban construction investment bonds, however, are not simply corporate bonds because
of the special identity of the company’s largest shareholder, which is often the local government.
Consequently, similar to municipal bonds, the GDP per capita, the GDP growth rate, the fiscal
revenue per capita, and the fiscal revenue growth rate are all included to capture the local
economic condition and the local government’s fiscal performance. As mentioned in the
previous subsection, little data about the debt burden of the provinces, municipalities, and lowerlevel governments can be found. Instead, we include the tier level of the corresponding
municipality in our analysis. The tier level mainly reflects the comprehensive strength of the
municipality after taking into account its population, political importance, and so on. Tier-1
municipalities cover the four cities under the direct control of the central government, the 23
provinces, and the five autonomous regions. Tier-2 municipalities refer to the 23 provincial
capitals and the five special economic zones that are authorized to issue municipal bonds
(Qingdao, Dalian, Ningbo, Xiamen, and Shenzhen). The rest of the municipalities are
categorized as Tier-3.
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Besides the variables documented above, we also incorporate credit rating in our analysis.
To the extent that rating agencies have access to more comprehensive information about the
bond issuer than we do, credit rating ought to have incremental explanatory power in the yield
spread regressions. In fact, considering that rating agencies like Dagong Global Credit Rating
Co. Ltd. profess to rely on many of the same variables that we use to rate urban construction
investment bonds, we will estimate an ordered probit model of the determinants of credit ratings
later in our paper. As in the analysis of municipal bonds, we use the issue size of the urban
construction investment bonds to measure the liquidity of the bonds.
For additional control variables related to bond features, we consider the maturity of the
bond, the age of the bond, whether the bond has an irrevocable guarantor, and whether the bond
is callable, putable, or has adjustable coupons. Apart from the age and maturity of the bond, the
others are dummy variables. We also include a year dummy to capture common variation in
yield spreads over time, which are potentially caused by changes in macroeconomic conditions.
As before, we lag all the independent variables by one year relative to the annual average yield
spread to rule out potential look-ahead bias in our results.
4. Empirical Results on Municipal Bonds
4.1. Sample Selection and Summary Statistics
We select the 50 municipal bonds issued in the Chinese interbank bond market in 2009 as
the sample for this part of the analysis. There are several reasons why we limit ourselves to these
bonds. First, the central government adjusted the form of issuance in 2010 by packaging several
municipalities together as a group. This caused the number of municipal bonds to drop
precipitously in 2010 and 2011. Second, the municipal bonds issued in 2011 were issued directly
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by the municipalities, whereas the 50 bonds issued in 2009 were issued by the Ministry of
Finance, meaning that perhaps they should not be pooled together in the same sample. In any
case, it is not clear to us how to combine the economic conditions and fiscal performance of
several municipalities.
We download the daily yields of the municipal bonds and Chinese Treasury bonds from
the WIND database and the website of the China Foreign Exchange Trading System,
respectively. The quarterly fiscal performance and GDP data of the municipalities are extracted
from the RESSET database, the National Statistics Bureau, and the official websites of local
governments. The characteristics of the municipal bonds are collected from Reuters.
Because the municipal bonds do not trade daily, we compute a daily yield spread
whenever there is an actual trade, and average the yield spreads over a given quarter (as the GDP
and revenue items are available quarterly). Sometimes, the trading volume for a municipal bond
equals zero in a particular quarter, and we have to drop these bond-quarters from the sample.
Another sample-related issue is that while all 50 municipal bonds were actively traded in
2009, only six remained so in both 2010 and 2011. The trading volume also declined sharply
from 2009 to later years, which partially reflects the poor liquidity of these bonds. Since the
number of observations in 2010 and 2011 is much smaller, we choose to focus on the pricing of
the municipal bonds in 2009 only. Moreover, since the bonds were issued in late March 2009,
only the second, third, and last quarter of 2009 can be included in the sample. After cleaning the
data, 77 bond-quarter observations remain in our sample.
Table 3 displays the summary statistics of the variables. Focusing on the yield spread, we
find that its mean and standard deviation are both quite small relative to the average municipal
bond yield. In the second quarter of 2009, for example, the mean and standard deviation of the
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yield spread are six and three basis points, respectively, compared with an average yield of 176
basis points. During the second and third quarters of 2009, the mean and standard deviation of
the yield spread become somewhat larger, but they are still relatively small compared to the
average yield. Interestingly, the yield spread becomes negative on average during those two
quarters, implying that the municipal bonds have lower yields compared to Chinese Treasury
bonds with similar maturities. It should be noted that Chinese Treasury bonds are not as liquid as
their U.S. counterparts. It is entirely conceivable that Chinese Treasury bonds were less liquid
than the municipal bonds during this period.
Meanwhile, there is substantial cross-sectional variation among the per capita GDP and
fiscal revenue as well as their growth rates. This means that the data are fairly representative
even if the sample size is not large. Combined with the previous observation that the yield spread
simply does not vary much in our sample, we are tempted to conclude that Chinese municipal
bond pricing is probably not particularly sensitive to the economic condition and fiscal
performance of the municipalities. Still, we will rely on a multivariate regression analysis to
identify the determinants of the municipal bond yield spread.
4.2. Regression Results
Due to a high correlation between the per capita GDP and fiscal revenue growth, and
between their respective growth rates, we do not combine them in our regression specifications.
In addition, because of the potentially negative municipal yield spread, we will use the municipal
bond yield in lieu of the yield spread in some of our analysis below.
Table 4 summarizes our regression results (the control variables of bond maturity and the
quarterly dummy are included in the regressions but omitted from the table), which are estimated
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using OLS with robust standard errors and accounting for clustering at the issuer level, since the
same municipal bond may appear more than once in 2009. Neither the per capita GDP nor the
per capita fiscal revenue is significant, and while the GDP and revenue growth rates are
sometimes marginally significant, they imply higher yields and yield spreads for faster-growing
municipalities. On the other hand, the bond issue size exhibits an unambiguously negative
relation with the yield and the yield spread. The estimated coefficients suggest that the yield
spread will decrease by about one basis point for a one-billion-yuan increase in the issue size.
Recalling from Table 3 that municipal yield spread variation is on the order of a few basis points
and the standard deviation of bond issue size is well over one billion yuan, we see that issue size
can explain a significant portion of the yield or yield spread variation within our sample. In sum,
it seems that investors in Chinese municipal bonds are more concerned about bond illiquidity
than the economic condition and fiscal performance of the municipalities, which is consistent
with the interpretation of these bonds as quasi-Treasuries.
5. Empirical Results on Urban Construction Investment Bonds
5.1. Sample Selection and Summary Statistics
To stay consistent with the sample period in our study of Chinese municipal bonds, we
focus on the pricing of urban construction investment bonds from 2009 to 2011. Although data
from the earlier period are ignored, the issuance and trading of urban construction investment
bonds did not take off until the introduction of the four-trillion-yuan economic stimulus package
in 2009. As Figure 1 shows, the annual issuance of these bonds before 2009 is quite small.
By the end of 2011, there are 569 urban construction investment bonds listed in either the
Chinese interbank bond market or the stock exchanges. Considering that the interbank bond
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market contributes 90% or more to the total trading volume each year, we focus on the 380
bonds traded in the interbank bond market. After further eliminating short-term commercial
papers as they are rated on a different scale from the medium-term notes and long-term bonds,
356 urban construction investment bonds remain in our sample. For each bond, the daily yield,
key financial data of the bond issuer, and bond characteristics are all downloaded from the
WIND database. The rest of the variables, such as the Chinese Treasury yields and municipal
GDP and revenue per capita, are shared with the analysis of municipal bonds. Since many issuerlevel data are available only annually, we calculate the average yield spread for each year in the
sample, which also addresses the problem with infrequent trading of the bonds. After cleaning
the data, there are 626 observations in our sample, with 130, 198, and 298 in 2009, 2010, and
2011, respectively.
Table 5 presents the summary statistics of the key variables. Unlike the case with
municipal bonds, the yield spread on urban construction investment bonds are much larger than
just a few basis points. For instance, the average yield spread in 2009 is 2.67%, and the average
yield is 5.70%. This means that investors no longer consider the risk of these bonds as negligible.
Furthermore, the yield spread exhibits significant cross-sectional variation – in 2009, for
example, the standard deviation of the yield spread is 1.23% and the range of the yield spread
extends from 0.72% on the low end to 7.44% on the high end. Interestingly, Table 5 also shows
that these bonds are actually rated very highly – between A+ and AAA, with the average rating
being AA+ and the average bond being issued by a provincial capital or special economic zone.
Granted, since urban construction investment bonds are taxable and Chinese Treasuries are not,
part of their yield spreads are attributed to tax differentials. It remains an interesting question as
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to the decomposition of these yield spreads according to the contribution of credit risk, liquidity
differential, and tax differential.
5.2. Regression Results
Out of the nine variables that measure the bond issuer’s financial status (see Table 2 for
their definitions), many are highly correlated with each other. For example, the correlations
between the cash flow measure and total assets and between return on assets and return on equity
are both above 0.9. In addition, using the net profit margin and interest coverage ratio will cause
the sample size to shrink because of many missing observations. Out of these concerns, we
include four out of the nine, namely, total assets, return on equity, leverage ratio, and long-term
debt to total debt in our analysis below.
For indicators of municipalities’ economic and financial conditions, we do not include
both GDP per capita and revenue growth per capita in the same regression due to their high
correlation with each other. For credit rating, since it could be based on many of the same
variables that we discuss previously, we include it with or without these variables.
Table 6 presents the regression results. Among the issuer-level financial indicators, return
on equity has a negative coefficient that is significant at the ten-percent level for two of the
specifications that include credit ratings, which is consistent with more profitable issuers having
lower bond yield spreads. Surprisingly, leverage ratio has a negative coefficient which is
significant at the one-percent level for all specifications, indicating that issuers with higher
existing debt burdens have lower yield spreads. While this seems counterintuitive, corporate
bond issuance in China is subject to the approval by the regulators. If safer firms are more likely
to be authorized to issue debt (thus accumulating higher existing debt levels), and credit quality
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is measured imperfectly by the variables included in our analysis, then it is possible for a higher
leverage ratio to correspond to a lower yield spread even in the presence of other risk controls.
Among the municipality-level variables, both GDP and revenue per capita have negative
coefficients that are statistically significant in some of the specifications, while the GDP and
revenue growth rates are never statistically significant. This suggests that bond investors focus
on the absolute level of economic development and fiscal revenue of the municipalities rather
than how much these metrics have been growing over time. We also find that the tier level of the
municipality has a strong influence on the yield spread – changing the issuer from a directly
controlled municipality to a provincial capital, for example, will raise the yield spread by 22 to
31 basis points. These estimates are significant at the one-percent level in all specifications.
Since the tier level mainly reflects the political clout of the municipality, its significance means
that investors care very much about the level of political support for the bond. That is, they
believe that it is much less likely for a provincial government to default on its obligations than
the government of a provincial capital or other cities or counties.
Paralleling the case with municipal bonds, the bond issue size exerts a strong negative
influence on the yield spread of urban construction investment bonds, suggesting that investors
care about the lack of bond liquidity. Its coefficient is significant at the one-percent level, but
weakens when credit rating is included in the regression.
The last significant variable in the regressions is credit rating. In all three regressions,
credit rating is significant at the one-percent level. One interesting observation is that the
adjusted R2 in Regression 5 is about four percentage points less than those of Regressions 3 and
4. This suggests that the bond issuer’s financial status and the economic condition of the local
government contain some information not captured by credit rating. Nevertheless, the
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incremental information content is not big. Similarly, adding credit rating to Regressions 1 and 2
increases the adjusted R2 by about seven percentage points, suggesting that credit rating also
contains some information not found in the financial and economic indicators. Overall, our
results suggest that bond investors should neither focus exclusively on credit rating nor ignore it.
In further robustness checks, we add the net profit margin and interest coverage into the
regression, and substitute the yield spread with the yield. We do not find net profit margin and
interest coverage to significantly explain the yield spread, and all of our results on the other
variables remain qualitatively unchanged.
5.3. Analysis of Credit Rating
Table 6 shows that many of the estimates change substantially when credit rating is
included in the regression, suggesting that credit rating is correlated with the explanatory
variables for the yield spread. Therefore, in this subsection we estimate an ordered probit model
for the credit rating (with credit rating converted to a numerical scale: AAA (4), AA+ (3), AA
(2), AA- (1), and A+ (0)).
Table 7 presents summary statistics of the explanatory variables in the credit rating
analysis. Over 97% of the urban construction investment bonds are rated at least AA, with just a
handful of observations rated below AA. As the credit rating deteriorates, the yield spread
increases, which partially explains why credit rating is highly significant in earlier yield spread
regressions. In addition, we see that high quality issuers tend to be much larger than low quality
ones, and there is a high correlation between total assets and the cash flow measures. For ROA,
ROE, net profit margin, and interest coverage, we do not observe any clearly monotonic relation
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with credit rating; the relation appears to be an inverse-U shape for all four. This potentially
explains why results on these variables in earlier regressions tend to be weak.
For leverage, it clearly declines when credit quality worsens. This is consistent with our
earlier finding of a strongly negative relation between leverage and the yield spread. With
respect to municipality-related variables, we can see that a higher credit rating is generally
associated with a higher numerical tier level and stronger economic and fiscal conditions (one
exception seems to be the fiscal revenue growth rate).
Table 8 summarizes the results of estimating the ordered probit model. Total assets is
significantly positive at the one-percent level in both regressions. Thus financing vehicles with a
larger scale tend to receive better ratings. Consistent with the summary statistics, we find that
higher per capita GDP and fiscal revenue, as well as a higher GDP growth rate, are all associated
with higher ratings. Finally, the tier level of the municipality and the leverage ratio are both
positive and significant in one of the regressions, which is again consistent with the summary
statistics. Overall, rating agencies do appear to make their rating decision based on both the
financial status of the bond issuer and the economic and fiscal condition of the municipality in
which the urban construction investment is made.
6. Conclusion
In this paper, we use multivariate regression models to investigate what factors affect the
pricing of Chinese municipal bonds and urban construction investment bonds. We find that the
issue size of municipal bonds is negatively related to their yield spreads, but the economic
condition and fiscal performance of the corresponding municipality fail to exert any significant
influence. Along with the fact that the magnitude of municipal yield spreads is on the order of
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only ten basis points, our findings indicate that investors regard the municipal bonds as quasiTreasuries. Therefore, they are mostly concerned with the lack of liquidity in these bonds.
Indeed, with only six of the fifty municipal bonds issued in 2009 being actively traded in 201011, the liquidity in these bonds is extremely poor.
For urban construction investment bonds, we find that their yield spreads are sensitive to
the issuer’s financial status including its credit rating, the associated municipality’s economic
condition, fiscal performance, and political importance as measured by its tier level, and the
liquidity of the bonds as proxied by the issue size. Somewhat puzzling is the finding that issuers
with a higher leverage tend to have lower yield spreads, but this can be explained by regulators
only allowing the highest quality corporate borrowers to tap into the bond market. We also
examine the determinants of the credit rating of urban construction investment bonds, finding
that it depends on many of the same variables we use to analyze the yield spread, which is
consistent with rating agencies’ stated objective of evaluating the bonds according to issuer and
municipality financial conditions.
Several questions remain for future research. For municipal bonds that currently behave
as quasi-Treasuries, investors apparently have no reason to trade them when they can trade
Chinese Treasury bonds instead. When these municipal bonds are no longer issued through the
Ministry of Finance, but directly by the municipalities themselves, perhaps the pricing will begin
to reflect the true credit risk of the issuers. Although direct issuance is already happening, it is
still limited to just a few selected top-tier municipalities.
Urban construction investment bonds, on the other hand, are technically corporate bonds
whose pricing is nonetheless linked to the financial health of municipalities. Although
municipalities will be following the recent directive by the central government to exit from their
19
urban construction investment vehicles, it remains unclear how effective this separation will be
in the minds of investors.
Finally, further extensions of this work are likely to require more precise knowledge of
debt burdens at all levels of Chinese municipalities. Currently, this information is not available in
the public domain. However, the recently completed national audit of local government debt
raises hope that some of the requisite information will be publicly disclosed in the near future,
increasing transparency in the murky world of Chinese municipal financing.
20
References
韩立岩,郑承利,罗雯,杨哲彬. 中国市政债券信用风险与发债规模研究[J]. 金融研究, 2003,
2:85-94.
贺春先. 城投债现状问题研究[J]. 现代商贸工业, 2011, 5:16-17.
单秀娟,胡隽. 城投债市场约束力分析[J]. 经济与法, 2011, 20:195.
Capeci, John. Credit Risk, Credit Ratings, and Municipal Bond Yields: A Panel Study[J].
National Tax Journal, 1991, 44:41-56.
Cook, Timothy Q. Determinants of Individual Tax-exempt Bond Yields: A Survey of the
Evidence[J]. Federal Reserve Bank of Richmond Economic Review, 1982, 3:14-39.
Ingram, Robert W., Leroy D. Brooks, and Ronald M. Copeland. The Information Content of
Municipal Bond Rating Changes: A Note[J]. Journal of Finance, 1983, 38: 997-1003.
Jantscher, Gerald R. The Effects of Changes in Credit Rating on Municipal Borrowing Costs[M].
Washington: Investment Bankers Association of America, 1970:1-40.
21
14000
12000
10000
8000
6000
4000
2000
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 1
Annual Issue Size of Urban Construction Investment Bonds (2000-2013) This graph is based on all
urban construction investment bonds contained in the WIND database. The unit for the issue size is 100
million yuan.
22
Table 1
Definition of Variables for Municipal Bonds This table presents the meanings and calculation methods
of all variables used in the study of municipal bond pricing.
Variables
Definition
Dependent Variable
YS i,t
Average daily yield spread of the municipal bond, which is calculated as the
mean of the daily yield spread of the municipal bond in quarter t.
Independent Variables
GDP p.c. i,t
Quarterly GDP per capita of the municipality in quarter t.
GDP growth i,t
Year-on-year last-12-month GDP growth rate of the municipality for quarter
t.
Revenue p.c. i,t
Quarterly fiscal revenue per capita of the municipality in quarter t excluding
the portion that requires to be turned over to the Central Government. Thus,
the rest portion is purely used for local residents.
Revenue growth i,t Year-on-year last-12-month fiscal revenue growth rate of the municipality
in quarter t.
Size i
Issue size of the ith municipal bond.
Control Variables
Mtr i,t
Average maturity of the municipal bond in quarter t, which is calculated as
the mean of the maturity of the municipal bond on each trading day in that
quarter. It is corresponding to the YS i,t.
Qtr i,t
Dummy variable which is corresponding to the YS i,t.
Qtr i,t equals to one when the t of YS i,t and Qtr i,t is the same.
Qtr i,t is used to explain the influence shared by all municipal bonds traded
in the interbank bond market in quarter t. (e.g. poor market liquidity)
23
Table 2
Definition of Variables for Urban Construction Investment Bonds This table presents the meanings
and calculation methods of all variables used in the study of urban construction investment bond pricing.
Variable
Dependent Variable
YS i,t
Definition
Average daily yield spread of the urban construction investment bond,
which is calculated as the mean of the daily yield of the bond in the year t.
Independent Variables
TA i,t
Total assets of the bond issuer at the end of the year t.
ROA i,t
Net profit/Average of total assets at the beginning and the end of the year t.
ROE i,t
Net profit/Average of total equity at the beginning and the end of the year t.
NP margin i,t
Net profit margin=Net profit/ Total sales for the year t.
Lev i,t
Leverage ratio=Total debt/ Total assets at the end of the year t.
LTD i,t
Long-term debt %=Long-term debt/ Total debt at the end of the year t.
Int Cov i,t
Interest coverage ratio=EBIT/Interest paid for the year t.
ICF i,t
Investment cash flow for the year t. (-) means cash outflow.
FCF i,t
Financing cash flow for the year t. (-) means cash outflow.
GDP p.c. i,t
Yearly GDP per capita of the municipality in year t.
GDP growth i,t
Year-on-year last-12-month GDP growth rate of the municipality in year t.
Revenue p.c. i,t
Yearly fiscal revenue per capita of the municipality in year t excluding the
portion that requires to be turned over to the Central Government. The rest
portion is purely used for local residents.
Revenue growth i,t
Year-on-year last-12-month fiscal revenue growth rate of the municipality
in year t.
Tier i,t
The tier level of the corresponding municipality of the ith bond in year t.
Equal to two if it is a directly-controlled municipality, province or
autonomous region. Equal to one if it is a provincial capital or special
economic zone, say Qingdao, Dalian, Ningbo, Xiamen and Shenzhen.
Otherwise equal to zero.
Size i
Issue size of the ith bond.
Rating i,t
The credit rating of the ith bond given by Chinese national credit rating
companies at the end of year t. Equal to four, three, two, one or zero if the
credit rating of the bond is AAA, AA+, AA, AA- or A+, respectively.
Control Variables
Mtr i,t
Maturity of the ith bond.
Age i,t
The time between the observation date and the issuing date of the ith bond.
Insurer i
Equal to one if the bond has irrevocable guarantor, otherwise equal to zero.
Call i
Equal to one if the bond has call option before maturity, otherwise equal to
zero.
Put i
Equal to one if the bond has put option before maturity, otherwise equal to
zero.
IntAdj i
Equal to one if the coupon will be adjusted before maturity, otherwise equal
to one.
Yr i,t
Year dummy which is corresponding to the Y i,t.
24
Table 3
Descriptive Analysis of Key Variables for Municipal Bonds This table reports the mean, standard
deviation, minimum and maximum value of key variables for municipal bonds by quarter. The sample
period extends from the second quarter (“2Q09”) to the fourth quarter of 2009 (“4Q09”), including 77
municipal observations.
Variable
2Q09 (“q1”)
YS (%)
Yield (%)
Mtr (year)
GDP p.c.(yuan)
GDP growth (%)
Revenue p.c.(yuan)
Revenue growth (%)
Size (100Mn)
3Q09 (“q2”)
YS (%)
Yield (%)
Mtr (year)
GDP p.c.(yuan)
GDP growth (%)
Revenue p.c.(yuan)
Revenue growth (%)
Size (100Mn)
4Q09 (“q3”)
YS (%)
Yield (%)
Mtr (year)
GDP p.c.(yuan)
GDP growth (%)
Revenue p.c.(yuan)
Revenue growth (%)
Size (100Mn)
Obs
Mean
Std. Dev.
Min
Max
35
35
35
35
35
35
35
35
0.064
1.756
2.969
5,924.51
17%
714.68
16%
44.03
0.028
0.052
0.030
3,876.72
6%
677.63
8%
23.12
0.012
1.677
2.878
1,524.39
-7%
238.25
-10%
8.00
0.105
1.846
2.992
16,686.97
28%
3,076.22
40%
90.00
17
17
17
17
17
17
17
17
-0.071
2.060
2.953
7,630.22
9%
1,056.44
12%
31.12
0.121
0.263
0.105
5,051.43
6%
1,082.45
10%
12.05
-0.232
1.782
2.653
2,568.61
1%
304.60
-2%
15.00
0.051
2.386
2.992
19,854.48
21%
3,642.16
39%
65.00
25
25
25
25
25
25
25
25
-0.164
1.851
2.422
7,443.91
9%
737.59
12%
47.80
0.115
0.082
0.062
4,333.15
5%
824.33
8%
0.71
-0.273
1.732
2.326
2,282.39
-7%
249.88
1%
26.00
0.115
2.079
2.542
19,082.73
16%
3,556.73
37%
90.00
25
Table 4
Regression Results for Municipal Bonds This table reports the coefficient and robust standard error (in
parenthesis), adjusted R-squares and F-statistics of four regressions for municipal bonds. Each regression
is conducted after correcting the correlation of residuals due to the same municipality with the OLS
method. The sample period extends from 2Q09 to 4Q09, including 77 municipal observations. Two-tailed
t-statistic test is conducted where *** p<0.01, ** p<0.05, * p<0.1.
(1)
Yield spread
OLS
(2)
Yield spread
OLS
(3)
Yield
OLS
(4)
Yield
OLS
N/A
-4.06E-07
(3.67E-06)
4.79E-01*
(2.49E-01)
N/A
N/A
Revenue p.c.
-1.14E-06
(2.68E-06)
1.42E-01
(1.51E-01)
N/A
Revenue growth
N/A
Size
-1.02E-03***
(3.37E-04)
0.5707
43.50
Dependent variable
Estimation method
Independent variables
GDP p.c.
GDP growth
Adjusted R2
F-statistics
N/A
-7.16E-06
(1.31E-05)
1.78E-01**
(7.93E-02)
-9.48E-04***
(3.10E-04)
0.5848
52.03
26
N/A
-1.43E-03**
(5.68E-04)
0.4462
11.04
N/A
-1.04E-05
(2.15E-05)
2.50E-01
(1.62E-01)
-1.26E-03**
(5.77E-04)
0.4433
13.35
Table 5
Descriptive Analysis of Key Variables for Urban Construction Investment Bonds This table reports
the mean, standard deviation, minimum and maximum value of key variables for urban construction
investment bonds by year. The sample period is from 2009 to 2011, including 626 observations of urban
construction investment bonds.
Variable
FY2009 (“y1”)
YS (%)
Yield (%)
Total assets (Mn)
ROE (%)
Leverage
GDP p.c. (yuan)
GDP growth
Revenue p.c. (yuan)
Revenue growth
Tier
Size (100Mn)
Rating
FY2010 (“y2”)
YS (%)
Yield (%)
Total assets (Mn)
ROE (%)
Leverage
GDP p.c. (yuan)
GDP growth
Revenue p.c. (yuan)
Revenue growth
Tier
Size (100Mn)
Rating
FY2011 (“y3”)
YS (%)
Yield (%)
Total assets (Mn)
ROE (%)
Leverage
GDP p.c. (yuan)
GDP growth
Revenue p.c. (yuan)
Revenue growth
Tier
Size (100Mn)
Rating
Obs
Mean
Std. Dev.
Min
Max
130
130
130
130
130
130
130
130
130
130
130
130
2.668
5.704
57,105.08
3.79
0.55
44,188.58
18%
4,605.25
23%
1
16.66
3
1.229
1.300
94,885.66
5.44
0.17
25,770.03
6%
4,393.97
9%
1
13.77
1
0.722
2.881
2,342.08
-11.70
0.04
7,887.00
5%
258.79
-10%
0
5.00
0
7.436
10.210
519,109.90
32.33
0.83
99,344.39
37%
16,031.94
58%
2
100.00
4
198
198
198
198
198
198
198
198
198
198
198
198
2.454
5.568
57,376.52
3.93
0.54
40,946.91
14%
3,909.34
19%
1
15.80
3
0.878
0.972
114,993.20
4.29
0.17
22,014.46
6%
3,393.07
14%
1
12.38
1
0.569
3.424
3,011.60
-1.83
0.16
7,288.00
-1%
291.68
1%
0
3.00
0
4.998
8.527
762,747.30
25.59
0.87
88,834.00
28%
12,566.90
90%
2
100.00
4
298
298
298
298
298
298
298
298
298
298
298
298
2.992
6.590
59,002.61
4.06
0.55
45,074.56
18%
4,365.97
30%
1
15.06
3
0.987
1.023
128,933.70
4.54
0.17
23,711.46
5%
3,587.96
12%
1
11.15
1
0.577
4.154
3,601.63
0.05
0.10
9,497.00
-1%
445.84
5%
0
3.00
1
5.474
8.843
934,990.20
35.61
0.87
90,355.00
37%
14,174.39
87%
2
100.00
4
27
Table 6
Regression Results for Urban Construction Investment Bonds This table reports the coefficient and
robust standard error (in parenthesis), adjusted R-squares and F-statistics of five regressions for urban
construction investment bonds. Regressions 1 and 2 are conducted without the credit rating variable while
Regressions 3 and 4 are conducted with it. In Regression 5, only the credit rating and issue size variables
are adopted as independent variables. Each regression is conducted after correcting the correlation of
residuals due to the same corresponding local government with the OLS method. The sample period
extends from 2009 to 2011, including 626 observations of urban construction investment bonds. Twotailed t-statistic test is conducted where *** p<0.01, ** p<0.05, * p<0.1.
Coefficient
Dependent variable
Estimation method
Independent variables
Total assets
ROE
Leverage
LTD
GDP p.c.
GDP growth
(1)
Yield spread
OLS
(2)
Yield spread
OLS
(3)
Yield spread
OLS
(4)
Yield spread
OLS
(5)
Yield spread
OLS
-1.58E-07
(4.38E-07)
-1.43E-02
(1.15E-02)
-1.08E+00***
(3.08E-01)
-9.79E-04
(2.40E-03)
-4.16E-06**
(1.84E-06)
-4.89E-02
(6.57E-01)
N/A
-1.25E-07
(4.86E-07)
-1.22E-02
(1.08E-02)
-1.18E+00***
(3.10E-01)
-1.02E-03
(2.47E-03)
N/A
1.09E-07
(3.37E-07)
-1.83E-02*
(9.88E-03)
-6.70E-01***
(2.54E-01)
2.60E-04
(2.37E-03)
-1.42E-06
(1.70E-06)
4.72E-01
(5.53E-01)
N/A
1.14E-07
(3.53E-07)
-1.76E-02*
(9.56E-03)
-7.03E-01***
(2.58E-01)
2.43E-04
(2.40E-03)
N/A
N/A
4.86E-01
(5.62E-01)
-7.59E-06
(1.08E-05)
1.05E-01
(2.36E-01)
-2.24E-01***
(5.69E-02)
-4.82E-03
(3.51E-03)
-4.68E-01***
(5.60E-02)
0.6643
66.62
N/A
Rating
2.12E-02
(2.41E-01)
-3.13E-01***
(7.15E-02)
-1.32E-02***
(3.64E-03)
N/A
-5.69E-02
(6.65E-01)
-2.50E-05*
(1.40E-05)
6.71E-02
(2.49E-01)
-2.84E-01***
(6.96E-02)
-1.32E-02***
(3.72E-03)
N/A
Adjusted R2
F-statistics
0.5933
44.30
0.5920
43.77
Revenue p.c.
Revenue growth
Tier
Size
28
8.66E-02
(2.29E-01)
-2.34E-01***
(5.79E-02)
-4.87E-03
(3.50E-03)
-4.66E-01***
(5.75E-02)
0.6645
67.00
N/A
N/A
N/A
N/A
N/A
N/A
N/A
-6.85E-03*
(3.51E-03)
-5.80E-01***
(5.99E-02)
0.6258
117.33
Table 7
Mean Value of Key Variables for Urban Construction Investment Bonds by Credit Rating This
table presents the mean value of key variables based on the 626 observations of urban construction
investment bonds. Statistics are summarized by credit rating.
Credit Rating
AAA
AA+
AA
AA-
A+
Observations
Yield spread (%)
Yield (%)
Total assets (Mn)
ROA (%)
ROE (%)
NP margin (%)
Leverage
LTD (%)
ICF (Mn)
FCF (Mn)
Interest coverage
GDP p.c. (yuan)
GDP growth
Revenue p.c. (yuan)
Revenue growth
Tier
184
1.70
4.89
150,305.00
1.12
2.64
46.89
0.65
65.77
-16,044.60
19,334.35
9.16
56,235.77
217
2.94
6.28
24,669.18
1.83
3.86
384.46
0.55
65.85
-2,544.93
2,968.21
16.35
42,558.33
207
3.39
6.82
15,544.43
2.74
5.23
491.06
0.48
64.02
-1,307.76
1,435.28
25.67
34,902.79
16
3.92
7.45
8,413.29
2.76
4.44
109.07
0.38
60.64
-760.29
611.95
9.17
28,136.36
2
4.68
7.64
2,676.84
1.35
1.46
N/A
0.10
74.67
27.26
81.93
4.23
13,312.50
17.02%
6,843.95
16.46%
3,624.71
16.75%
2,861.19
14.24%
2,128.56
15.02%
809.81
21.09%
1.49
25.72%
0.49
26.95%
0.32
30.27%
0.25
29.29%
0.00
29
Table 8
Regression Results on the Determinants of Credit Ratings of Urban Construction Investment
Bonds This table reports the coefficient and robust standard error (in parenthesis), and log likelihood of
two ordered probit models for the determinants of credit ratings for urban construction investment bonds.
Each regression is conducted after correcting the correlation of residuals due to the same corresponding
local government with the ordered probit method. The sample period extends from 2009 to 2011,
including 626 observations of urban construction investment bonds. Robust t-statistic test is conducted
where *** p<0.01, ** p<0.05, * p<0.1.
Coefficient
(1)
(2)
Dependent variable
Estimation method
Independent variables
Total assets
Credit rating
Ordered probit
Credit rating
Ordered probit
1.94E-05***
(4.20E-06)
-5.61E-03
(1.04E-02)
9.56E-01
(5.86E-01)
2.84E-03
(3.65E-03)
9.91E-06***
(3.58E-06)
1.99E+00**
(9.86E-01)
N/A
1.90E-05***
(3.90E-06)
-1.30E-02
(1.17E-02)
1.01E+00*
(5.72E-01)
2.65E-03
(3.67E-03)
N/A
ROE
Leverage
LTD
GDP p.c.
GDP growth
Revenue p.c.
Revenue growth
Tier
Log likelihood
2.14E+00**
(1.02E+00)
8.62E-05***
(2.76E-05)
6.99E-02
(4.24E-01)
1.58E-01
(1.33E-01)
-461.27
1.66E-01
(4.23E-01)
2.35E-01*
(1.34E-01)
-462.55
30