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© 2021 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 6, 14-19, 2021 
ISSN: 2576-6759 
DOI: 10.20448/journal.518.2021.6.14.19 
© 2021 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Determining the Differences in the Impacts of Factors Affecting Sovereign Credit 
Rating: A Case Study of Developing ASEAN and Developed Countries 

 
Quynh T.P. Lam1   
Quoc T. Nam2   
Khoa D. Nguyen3   
 

 
( Corresponding Author) 

 
 

 

1,2Department of Economics and Business, Hoa Sen University, HCM City, VietNam. 

 
3Department of Banking and Finance, University of Finance and Marketing, HCM city, VietNam. 

 

 
Abstract 

The paper uses the ordered logit regression model on table data to determine the differences in 
the impact of factors affecting the sovereign credit ratings of ASEAN developing countries 
compared to other developed countries. The results show that the impact of the macroeconomic 
indicators on the sovereign credit ratings in ASEAN developing countries decreases in 
comparison to the impact of factors in developed countries. Besides on that, there is no difference 
in the impact of the factors demonstrates the efficiency of governance on sovereign credit ratings 
in developing ASEAN countries compared to developed countries. 

 
Keywords: Sovereign credit rating, Ordered logit regression model, Factors affecting the sovereign credit rating, ASEAN country, Credit 
rating, Efficiency in government management. 

JEL Classification: G24, G15, O16, F33. 

 
1. Introduction 

Over the past few decades, globalization has provided investors around the world many opportunities to 
diversify and find attractive returns from investments in various countries in the world. However, highly profitable 
opportunities often go along with potential risks in numerous countries. Therefore, international investors are very 
concerned about quantifying the level of specific risks of each country before implementing investment activities in 
this country. The most prestigious international credit rating organizations such as S&P, Fitch Group and 
Moody's regularly publish information about the credit ratings of countries for investors to refer to in their 
international investment activities. Haque, Kumar, Mark, and Mathieson (1996) define sovereign credit rating as an 
aggregate assessment of the macroeconomic factors and the government's operating capability to evaluate the 
probability of government to pay due debt obligations.Fitch (2020) pointed out that a country's risk level and its 
credit rating are two related but separate concepts. The country's level of risk refers to the risks of implementing 
an investment in the country, like poor property rights protection, erratic fluctuations in taxes and official 
regulations, or a disturbance in the business environment. Meanwhile, the sovereign credit rating focuses on 
assessing the risk of insolvency of the government for the debt obligations that come due. The sovereign credit 
rating process of international credit rating agencies such as Fitch, S&P or Moody's represent an analysis that 
combines both qualitative and quantitative assessment to determine the availability and the government's ability to 
discharge maturity debts. Credit rating agencies often rate countries' credit ratings on the following groups of 
criteria. The critical financial indicators reflecting current macro characteristics and economic prospects include 
GDP growth rate, net FDI inflows, inflation rate, labor unemployment rate. 

The indicators reflect the ability of the government to manage and govern the economy like voice and 
accountability, political stability and non-violence, government effectiveness, quality of regulations, rule of law, the 
ability to control corruption.Fitch and S&P have 22 sovereign credit ratings from AAA to RD/D. Moody's equally 
has 21 sovereign credit ratings from Aaa to C. 

International credit rating agencies have presented the criterias and processes for evaluating the credit ratings 
of countries quite fully. However, some researchers have pointed out that there is a difference in the impact factors 
or the degree of impact of these factors on the credit ratings of developed countries compared to developing 
countries. Specifically, Ferri (2004) has shown the cost of collecting information for the assessment of ratings in 
developing countries is often much higher than in developed countries. Otherwise, macro data of developing 
economies are also not extremely accurate. Therefore, credit rating agencies when assessing the credit rating of 
developing countries are frequently based on subjective assessments of experts rather than analysis of macro data 

of these countries. On the other hand, research of Gültekin-Karakaş, Hisarcıklılar, and Öztürk (2011) also showed 
that international credit rating agencies have highly appreciated the credit rating of developed countries without 
regard to the significant macroeconomic indicators of these countries. Mora (2006), Kiff, Nowak, and Schumacher 

http://ecsenet.com/index.php/2576-6759/article/view/97
https://orcid.org/0000-0003-0296-9432
http://ecsenet.com/index.php/2576-6759/article/view/97
https://orcid.org/0000-0003-0296-9432


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(2012) also showed that credit rating organizations apply different weights in the process of evaluating the 
indicators reflecting the credit rating in diverse countries. Credit rating agencies sometimes ultilize expert 
assessments in the credit rating process. Tennant and Tracey (2016) also showed that the threshold of raising the 
national credit rating level of developing countries is often higher than that of developed countries. However, 

Gültekin-Karakaş et al. (2011) argues the discrepancy in the credit rating of developing countries can be explained 
by the difference in governments’ management ability to regulate the economy in developing countries compared 
to developed countries. In summary, the authors notice that previous studies on sovereign credit ratings do not 
detail the difference in the impact of factors affecting creditworthiness of developing countries compared to 
developed countries. On the other hand, no studies analyze in detail the factors affecting the sovereign credit rating 
of developing countries in the ASEAN region. Therefore, this study aims to determine the differences in the impact 
of factors affecting the sovereign credit ratings of ASEAN developing countries relative to developed economies. 
On that basis, governments of ASEAN developing countries can propose appropriate measures to improve their 
country credit rating. The paper is structured as follows. Section 2 presents research models, research data and 
analytical methods to determine differences in the impact of factors affecting sovereign credit ratings in developing 
ASEAN countries and other developed economies. The results of the research model are presented and analyzed in 
detail in section 3. The concluding section summarizes the primary results of the study and proposes 
recommendations to improve the sovereign credit ratings of the ASEAN developing countries. 
 

2. Model, Research Data and Data Analysis Method 
2.1 Research Model 

The research model used by the authors in this paper is the ordered logit model on table data. The ordered 
logit model is an extension of binary logit model. This model assumes the following latent variable form Greene 
(2012): 

 
In which y* represents the dependent variable but is not observable in reality. We can merely observe: 

y = 1 if y*  ≤  1 
= 2 if 1  < y* ≤ µ1 
= 3 if µ1 < y* ≤ µ2 
… 
= J if µj-1 < y* 
In which:  µ1, µ2,… µj-1 are the thresholds calculated from the model. 

β is the regression coefficient illustrating the impact of the explanatory variables on the dependent variable. 

Ɛ is a stochastic error term. Ɛ secures a rational distribution and utilizes a mean of 0, variance of 1. 
The ordered logit model is widely utilized in studies of sovereign credit rating and credit rating of commercial 

banks such as research by Gültekin-Karakaş et al. (2011), Matousek and Stewart (2009), Iannotta, Nocera, and 
Sironi (2010), Bellotti, Matousek, and Stewart (2011a) ; Bellotti, Matousek, and Stewart (2011b), Caporale, 
Matousek, and Stewart (2012). The ordered logit model overcomes the limitation of the ordinary least-squares 
(OLS) model in the case of the dependent variable being credit rating levels. The limitation of the OLS model is 
that the difference in risk levels between 2 countries with AAA and AA rating is considered as the same as the 
difference in risk levels between 2 countries with  BBB and BB rating. This is not consistent with the heterogeneity 
of differences in the level of risk between ratings (Manzoni, 2004). On the other hand, Jones, Johnstone, and 
Wilson (2015) also said that the ordered logit classification model is ideally suitable for studies on credit rating. 

The predictive power of the ordered logit model, though, is limited when describing non-linear relationships or 
where unobserved fluctuations exist in the data. However, the Ordered logit model is extremely suitable for the 
purpose of explaining the relationship and the impact of the explanatory variable on the dependent variable 
(Greene, 2012). 

The authors’ research model used in this study is detailed as follows: 

     ∑        

 

   

 ∑        

 

   

         

Inside: 
yi, t is the credit rating of countries in year t. 
xi, t-1 is a set of financial indicators indicating the macroeconomic situation of countries in year t-1. 
xj, t-1 is a set of indicators reflecting the ability of the government to operate the economy in year t-1. 

ASEAN is the dummy variable. This variable has the value of 1 for the case of the developing countries in the 
ASEAN region, the value 0 for the case of the developed countries. 
 

2.2. Data 
With the aim of determining the differences in the impact of factors affecting the sovereign credit ratings of 

developing countries in the ASEAN region and developed economies, the authors selected a sample to observe the 
developing countries in the ASEAN region including 5 countries Vietnam, Indonesia, Philippines, Thailand and 
Malaysia. The rest of the developing countries in the ASEAN region were excluded in the data sample due to the 
inadequacy of the data required for the study. Developed countries in the sample data include UK, USA, Canada, 
Italy, Australia, France, Germany, Sweden, Switzerland, Japan, Korea, and Singapore. The model's dependent 
variable is the sovereign credit rating of developing countries in the ASEAN region and the above-developed 
countries. These ratings are derived from S&P's annual credit rating publications. The explanatory variables in the 
model include 2 groups of variables. The explanatory variables are the financial indicators reflecting the countries' 
macroeconomic situation collected from WordBank data sources. The other explanatory variables are indicators 
reflecting the quality of government management collected from the Worldwide Governance Indicators (WGI) 



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source. Data used in the study are unbalanced table data for the period 1997 to 2019. The dependent variable is 
measured at time t-1 versus the explanatory variables because credit rating agencies assess countries’ 
creditworthiness basing on available information of the countries’s economic situation and indicators reflecting the 
governance capacity of the governments. The set of four macroeconomic indicators selected by the authors as the 
explanatory variable in the research model include GDP growth rate, inflation rate, net FDI/GDP ratio, domestic 
credit provided by financial sector, unemployment rate. These indicators are used in the process of evaluating the 
sovereign credit ratings of S&P, Fitch, and Moody's. At the same time, these are also macro indicators used in 
empirical studies on countries’ creditworthiness such as Cantor and Packer (1996), Mellios and Paget-Blanc 

(2006),Afonso, Pedro, and Philipp (2007), Jaramillo (2010),Gültekin-Karakaş et al. (2011), Tennant, Tracey, and 
King (2020), Sanz (2020). In addition, the authors put into the research model 6 indicators reflecting the quality of 
management and the ability to operate the economy of the governments. These include government voice and 
accountability, political stability and non-violence, government effectiveness, quality of regulation, the rule of law, 
and control of corruption. These are also the indicators used in researches on the credit rating of the government 
such as Butler and Fauver (2006), Tennant, Tracey, and King (2020). 
 

Table-1. Descriptive statistics of the dependent variable and explanatory variables used in the research model. 

Variable Obs Mean Std. Dev. Min Max 

Contry_Ra 313 37.2364 13.8414 1.0000 50.0000 

GDP 313 27.7885 1.0908 25.6323 30.6554 
FDI 313 3.6788 4.8768 -3.6203 28.5981 

Inflation 313 2.5950 3.9702 -1.3528 58.4510 
Net_Domes_Cre 313 30.0317 2.9813 25.1819 36.5998 

Unemploy 313 5.2391 2.5605 0.2065 12.6828 

Voice_Acc 313 0.6769 0.8518 -1.5269 1.7392 
Political_Stabi 313 0.4030 0.8322 -2.0946 1.6153 

Gover_Eff 313 1.1573 0.7949 -0.7053 2.4370 
Regu_Qua 313 1.0224 0.7786 -0.7959 2.2605 

Control_Corup 313 0.9915 1.0595 -1.1764 2.3256 
Rule_Law 313 1.0067 0.8759 -0.9140 2.0378 

 

2.3. Data Analysis Method 
To begin with, the authors regress the research model by using the Maximum likelihood regression method on 

table data with fixed effect and random effect to determine the explanatory variables with statistical significance in 
the research model. To determine the differences in the impact of the explanatory variables on the sovereign credit 
ratings of developed countries compared to developing countries in the ASEAN region, the authors choose the 
significant explanatory variables with statistical significance in the model merely built above and let each of these 
variables interacts with the ASEAN variable. Next, the authors regress the research model by adding interactive 
variable just built into the model. If the regression coefficient of the interactive variable is statistically significant, 
we can conclude there is a difference in the impact of the considering explanatory variable on the sovereign credit 
ratings of developed countries relative to other developing countries in the ASEAN region. Specifically, if the 
coefficient of the interactive variable has the same sign as the coefficient of the explanatory variable under 
consideration, the impact of this explanatory variable on the dependent variable is strengthened and more 
important in ASEAN developing countries. Conversely, if the coefficient of the interacting variable is contrary to 
the coefficient of the explanatory variable under consideration, the effect of this explanatory variable on the 
dependent variable decreases in the case of the developing country in the ASEAN area. This analytical method has 
been used in studies by Berger, Hasan, and Zhou (2010), Shen, Huang, and Hasan (2012), Mirzaei, Moore, and Liu 
(2013). 
 

 
Figure-1 Diagram of the analytical method to assess the differences in the effects of these factors on sovereign credit 
ratings in developing ASEAN countries compared to developed countries. 



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3. Model Results and Research Results Discussion 
3.1. The Results of the Research Model 
 

Table-2. The regression results of random – effect and fixed - effect models on sample data. 

Contry_Ra Ordered Logit (Random - Effect) Ordered Logit (Fixed- Effect) 

ASEAN -0.5477 
(1.3450) 

-0.5483 
(0.7863) 

GDP 0.6038* 
(0.3349) 

0.4550*** 
(0.1674) 

FDI -0.2728 
(0.6872) 

0.0375 
(0.0576) 

Inflation -0.0689** 
(0.0348) 

-0.0554* 
(0.0326) 

Net_Domes_Cre -0.2685** 
(0.1321) 

-0.1961*** 
(0.0753) 

Unemploy -0.3605*** 
(0.0911) 

-0.2608*** 
(0.0720) 

Voice_Acc 0.1115 
(0.5427) 

0.2661 
(0.3568) 

Political_Stabi 0.4996 
(0.4878) 

-0.3503 
(0.3120) 

Gover_Eff 0.9654 
(0.8496) 

0.8193 
(0.7234) 

Regu_Qua 4.4468*** 
(1.0218) 

3.0828*** 
(0.8037) 

Control_Corup 1.6596* 
(0.8946) 

1.8031*** 
(0.6845) 

Rule_Law 0.0569 
(1.1024) 

1.1046 
(0.8184) 

Note: 
Total Observations: 313. 
*Significance at 10% level. 
**Significance at 5% level. 
***Sinificance at 1% level. 
Standard Errors in brackets. 

 
Based on the regression results, we notice some variables representing the macroeconomic indicators that have 

an impact on the sovereign credit ratings. Specifically, the GDP has a positive impact on the country credit ratings. 
This suggests that economies with large GDP sizes are often given higher credit ratings. This result is completely 
similar to the results of studies on sovereign credit rating such as Cantor and Packer (1996), Eliasson (2002), 

Gültekin-Karakaş et al. (2011), Sanz (2020), and Criteria for rating the credit rating of credit rating agencies like 
S&P, Fitch's or Moody's. On the other hand, the inflation rate and unemployment rate of countries have a negative 
impact on credit ratings. This result is equally consistent with the research of Mellios and Paget-Blanc (2006), 

Gültekin-Karakaş et al. (2011), Tennant et al. (2020). In addition, the regression results show that the size of 
domestic credit provided by financial sector has a negative effect on the national credit rating. The cause of this 
problem is that in developing countries, the credit of the commercial banking system is nevertheless considered the 
main capital channel in the economy due to the limited development of the stock market in these countries. 
Goverments of developing countries still regularly loosen the lending activities of commercial banks to create a 
growth engine for the economy. However, the abuse of this policy will cause many potential risks for the economy 
and will adversely affect the sovereign credit rating. In contrast, in developed countries, the development of the 
stock market has created more effective channels for capital mobilization for governments and businesses. 

 In the group of variables showing efficiency in state management, the authors find that the variable represents 
the quality of government regulation, measuring the perception of the government's ability to formulate incentive 
policies in the development of the private sector and the variable that demonstrates the ability to control 
corruption have a beneficial effect on sovereign credit ratings. This result is similar to that of Hammer, Kogan, and 
Lejeune (2006). 
 

3.2. Difference in the Impact of Factors Influencing National Credit Ratings in Developing ASEAN Countries 
versus Developed Countries 

As mentioned in Section 2.3, the authors add the ASEAN dummy variable to the model and make this variable 
interact with the statistically significant variables in the research model presented in Section 3.1. Then, the authors 
regress of the research model to determine the difference in the impact of each specific factor on the sovereign 
credit ratings of developing ASEAN countries compared to developed countries. The detailed regression results of 
each model are presented in Table 3. 

Based on the results of the models with interactive variables, we find that the regression coefficients of the 
interaction variables ASEAN_GDP and ASEAN_Infla are statistically significant. Simultaneously, the coefficient 
sign of these two variables is opposite to the coefficient sign of the two variables GPD and Inflation. This proves 
there is a difference in the impact of the GDP scale and the inflation rate on the sovereign credit rating of 
developing and developed countries. Specifically, the impact of GDP size and inflation rate on the credit ratings of 
developing ASEAN countries is reduced compared to the impact of these two factors on the credit ratings of 
developed countries.  The reason for this problem is explained by Ferri (2004) as the cost of data collection in 
developing countries is often higher than in developed countries and the quality of data of developing countries is 
often not guaranteed level of reliability. Therefore, when conducting rating assessments in developing countries, 
rating agencies invest less in data collection and analysis, but mainly on expert reviews. On the other hand, the 



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damage to the reputation of the rating agencies in the case of a country with a high credit rating is insolvent would 
be greater than that of a country with a low credit ratings default on its debt obligations. Therefore, the impact of 
macroeconomic indicators on sovereign credit ratings in developing countries is often lower than the effect of these 
indicators in developed countries. 
 

Table-3. Regression results between interactive variables in the model. 

Model with interactive variables 
between GDP and ASEAN 

Model with interactive variables 
between Inflation and ASEAN 

Model with interactive variables 
between Net Domes_Cre and ASEAN 

ASEAN -73.2226*** 
(18.8973) 

ASEAN -0.4594 
(1.4034) 

ASEAN -14.6193*** 
(6.7374) 

FDI -0.0655 
(0.0723) 

FDI -0.0274 
(0.0682) 

FDI -0.0619 
(0.0702) 

GDP 2.6556*** 
(0.6079) 

GDP 0.5943 
(0.3371) 

GDP 0.4695 
(0.3379) 

ASEAN_GDP -2.6318*** 
(0.6807) 

Inflation -0.0706* 
(0.3571) 

Inflation -0.0686** 
(0.0346) 

Inflation -0.0585* 
(0.0345) 

ASEAN_Infla 0.0322* 
(0.1384) 

Net_Domes_Cre -0.0302 
(0.1682) 

Net_Domes_Cre -0.4469*** 
(0.1449) 

Net_Domes_Cre -0.2710 
(0.1538) 

ASEAN_Net_domes_Cre - 0.4542*** 
(0.2109) 

Unemploy -0.3620*** 
(0.0934) 

Unemploy -0.3634*** 
(0.1351) 

Unemploy -0.3810*** 
(0.0934) 

Voice_Acc -0.1472 
(0.5596) 

Voice_Acc 0.0518 
(0.5584) 

Voice_Acc 0.1437 
(0.5568) 

Political_Stabi 1.0288* 
(0.4324) 

Political_Stabi 0.5385 
(0.4336) 

Political_Stabi 0.5061 
(0.4294) 

Gover_Eff 0.2679 
(0.8674) 

Gover_Eff 0.9782 
(0.8156) 

Gover_Eff 1.2404 
(0.8340) 

Regu_Qua 4.9406*** 
(1.0402) 

Regu_Qua 4.4911 
(0.9591) 

Regu_Qua 0.9616*** 
(1.0002) 

Control_Corup 1.5777 
(0.8917) 

Control_Corup 1.7256*** 
(0.8801) 

Control_Corup 1.1604 
(0.9023) 

Rule_Law 0.8605 
(1.1990) 

Rule_Law 0.0596** 
(1.1051) 

Rule_Law 1.1406 
(0.8184) 

 
For the variables showing efficiency in government management, the regression model results show that the 

regression coefficients of the interactive variables are not statistically significant. This shows that no difference in 
the impact of the factors demonstrates the efficiency of governance on sovereign credit ratings in developing 
ASEAN countries compared to developed countries.  
 

Table-3. Continue. 

Model with interactive variables between 
Unemploy and ASEAN 

Model with interactive variables 
between Regu_Qua and ASEAN 

Model with interactive variables 
between Control_Corup and 
ASEAN 

ASEAN - 0.7699 
(1.0769) 

ASEAN -0.2537 
(1.5453) 

ASEAN -0.2745 
(1.3283) 

FDI -0.0781 
(0.0671) 

FDI -0.0712 
(0.0681) 

FDI -0.0541 
(0.6382) 

GDP 0.6227* 
(0.3489) 

GDP 0.6252* 
(0.3357) 

Lg_GDP_Va 0.6273** 
(0.3265) 

Inflation -0.0701** 
(0.0352) 

Inflation -0.0686** 
(0.0348) 

Inflation -0.0749*** 
(0.0353) 

Net_Domes_Cre -0.2772** 
(0.1392) 

Net_Domes_Cre -0.2917*** 
(0.1344) 

Lg_Net_Domes_Cre -0.2698 
(0.1283) 

Unemploy -0.3431*** 
(0.1225) 

Unemploy -0.3492*** 
(0.0915) 

Unemploy -0.3487*** 
(0.0913) 

ASEAN_Unemploy 0.0352 
(0.1685) 

Voice_Acc 0.1138 
(0.5466) 

Voice_Acc 0.1604 
(0.5326) 

Voice_Acc 0.0847 
(0.5592) 

Political_Stabi 0.5657 
(0.4945) 

Political_Stabi 0.6262 
(0.5038) 

Political_Stabi 0.5137 
(0.4928) 

Gover_Eff 0.9921 
(0.8492) 

Gover_Eff 0.9699 
(0.8448) 

Gover_Eff 0.9762 
(0.1685) 

Regu_Qua 3.6105*** 
(1.3001) 

Regu_Qua 4.5843*** 
(1.0335) 

Regu_Qua 4.4056*** 
(1.0403) 

ASEAN_Regu_Qu
a 

1.4844 
(1.4737) 

Control_Corup 0.5760 
(1.5479) 

Control_Corup 1.6643 
(0.8974) 

Control_Corup 1.4432 
(0.9136) 

ASEAN_Control_Cor
up 

1.1873 
(1.3790) 

Rule_Law 0.0486 (1.1047) Rule_Law 0.1576 
(1.1162) 

Rule_Law 0.0550 
(1.1023) 

 

4. Conclusion 
The study has demonstrated that the impact of the macroeconomic indicators on the sovereign credit ratings in 

developing countries decreases in comparison to the impact of factors in developed countries. The reason may arise 
from the limited quality of statistics and the high cost of collecting these data in developing ASEAN countries. 
Therefore, developing ASEAN countries looking to improve their sovereign credit ratings need to focus more on 
developing policies that encourage private sector growth and control corruption. 
 



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Citation: Quynh T.P. Lam; Quoc T. Nam; Khoa D. Nguyen (2021). 
Determining the Differences in the Impacts of Factors Affecting 
Sovereign Credit Rating: A Case Study of Developing ASEAN and 
Developed Countries. Asian Business Research Journal, 6: 1-6. 
History:  
Received: 28 April 2021 
Revised: 2 July 2021 
Accepted: 26 July 2021 
Published: 11 August 2021 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Eastern Centre of Science and Education 

Acknowledgement: All authors contributed equally to the conception and 
design of the study. 
Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.  
 

Eastern Centre of Science and Education is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use 
of the content. Any queries should be directed to the corresponding author of the article. 

 

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