DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DEPARTAMENTO DE ECONOMÍA Pontificia Universidad Católica del Perú DT DECON DOCUMENTO DE TRABAJO DO INSTITUTIONS MITIGATE THE UNCERTAINTY EFFECT ON SOVEREIGN CREDIT RATINGS? Nº 514 Nelson R. Ramírez, Renato M. Rojas y Julio A. Villavicencio DOCUMENTO DE TRABAJO N° 514 Do institutions mitigate the uncertainty effect on sovereign credit ratings? Nelson R. Ramírez-Rondán, Renato M. Rojas-Rojas y Julio A. Villavicencio Julio, 2022 DOCUMENTO DE TRABAJO 514 http://doi.org/10.18800/2079-8474.0514 http://doi.org/10.18800/2079-8474.0514 Do institutions mitigate the uncertainty effect on sovereign credit ratings? Nelson R. Ramírez-Rondán, Renato M. Rojas-Rojas y Julio A. Villavicencio © Nelson R. Ramírez-Rondán, Renato M. Rojas-Rojas y Julio A. Villavicencio Editado e Impreso: © Departamento de Economía – Pontificia Universidad Católica del Perú Av. Universitaria 1801, Lima 32 – Perú. Teléfono: (51-1) 626-2000 anexos 4950 - 4951 econo@pucp.edu.pe http://departamento.pucp.edu.pe/economia/publicaciones/documentos-de-trabajo/ Encargada de la Serie: Janina V. León Castillo Departamento de Economía – Pontificia Universidad Católica del Perú jaleon@pucp.edu.pe Primera edición – Julio, 2022 ISSN 2079-8474 (En línea) mailto:econo@pucp.edu.pe file:///d:/Users/mirtha.cornejo/Dropbox/cisepas%20(1)/Procesando-Documentos%20de%20Trabajo/jaleon@pucp.edu.pe Do institutions mitigate the uncertainty effect on sovereign credit ratings?* Nelson R. Ramı́rez-Rondán CEMLA Renato M. Rojas-Rojas** Pontifical Catholic University of Peru Julio A. Villavicencio*** Pontifical Catholic University of Peru julio, 2022 Abstract In a more integrated economic and financial world, sovereign credit ratings have become one of the most important factors for countries that seek to access funds in the international bond market. First, we jointly analyzed institutions and uncertainty as determinants of sovereign credit ratings, and second, we tested whether strong institutions soften the impact of uncertainty. Using a sample of 74 countries from 2003 to 2020 for the major agencies Moody’s, Standard & Poor’s, and Fitch, and employing an ordered estimator approach, we find that institutions have a positive effect, whereas uncertainty has a negative effect, and the interaction between them is systematically negative. These results indicate that strong institutions reduce the negative effect of uncertainty on sovereign credit ratings. JEL Classification: G24, H81, C23 Keywords: Credit rating, institutions, uncertainty, panel data. *We thank Tingting Liu, Lorne Switzer, Paul Castillo, Alonso Segura, Carlos Cantú the participants of the Annual Event of Finance Research Letters 2022, the Research Seminar at the PUCP Department of Economics and Finance, and the CIDE-EGADE Workshop. Corresponding author: Nelson R. Ramı́rez-Rondán, CEMLA, Durango 54, Mexico City 06700, Mexico. E-mail address: nramron@gmail.com. **E-Mail Address: rojasr.renato@pucp.edu.pe ***E-Mail Address: villavicencio.j@pucp.edu.pe; ORCID: https://orcid.org/0000-0002-0357-3475 1 mailto:nramron@gmail.com ¿Mitigan las instituciones el efecto de la incertidumbre sobre la calificación de crédito soberano?* Nelson R. Ramı́rez-Rondán CEMLA Renato M. Rojas-Rojas** Pontificia Universidad Católica del Perú Julio A. Villavicencio*** Pontificia Universidad Católica del Perú julio, 2022 Resumen En un mundo económico y financiero cada vez más integrado, las calificaciones crediticias soberanas se han convertido en uno de los factores más importantes para los páıses que buscan acceder a fondos en el mercado internacional de bonos. En primer lugar, analizamos conjuntamente las instituciones y la incertidumbre como determinantes de las calificaciones crediticias soberanas y, en segundo lugar, comprobamos si las instituciones fuertes suavizan el impacto de la incertidumbre. Utilizando una muestra de 74 páıses de 2003 a 2020 para las principales agencias Moody’s, Standard&Poor’s, y Fitch, y empleando un enfoque de estimadores or- denados, encontramos que las instituciones tienen un efecto positivo, mientras que la incertidumbre tiene un efecto negativo, y la interacción entre ellos es sis- temáticamente negativa. Estos resultados indican que unas instituciones fuertes reducen el efecto negativo de la incertidumbre en las calificaciones crediticias de los páıses. JEL Classification: G24, H81, C23 Keywords: Credit rating, institutions, uncertainty, panel data. *Agradecemos a Tingting Liu, Lorne Switzer, Paul Castillo, Alonso Segura, Carlos Cantú partici- pantes del Annual Event of Finance Research Letters 2022, del Viernes Económico del Departamento de Economı́a y Finanzas de la PUCP, y del taller CIDE-EGADE Workshop. Corresponding author: Nelson R. Ramı́rez-Rondán, CEMLA, Durango 54, Mexico City 06700, Mexico. E-mail address: nramron@gmail.com. **E-Mail Address: rojasr.renato@pucp.edu.pe ***E-Mail Address: villavicencio.j@pucp.edu.pe; ORCID: https://orcid.org/0000-0002-0357-3475 1 mailto:nramron@gmail.com 1. Introduction Sovereign credit ratings provide relevant information on the creditworthiness of a country, and thereby serve as a tool for investors to make appropriate decisions on investments in financial assets. Therefore, the determination of a country’s credit rating is a rather complex activity involving variables of different dimensions, carried out by specialized agencies. As a result, the determinants of sovereign credit ratings have attracted the attention of researchers in recent years; several studies have tried to identify and model these factors from different points of view. An initial strand of the literature analyzes the effects of macroeconomic variables; for instance, Cantor and Packer (1996), using a linear regression on a dependent variable that had a logistic transformation, evaluated the determinants of the sovereign credit ratings of Moody’s and Standard Poor’s. Using a worldwide dataset for 1995, they found that GDP growth and GDP per capita have significant positive coefficients, while inflation, default history, and external debt have the opposite effect. In turn, Afonso (2003) obtained similar results for a set of countries in 2001. While, Bissoondoyal- Bheenick (2005), by using an ordered response model with a country panel data, finds that GDP per capita and inflation are the most important factors. A second strand of studies highlights the role of institutional factors. Altenkirch (2005) and Archer et al. (2007) found that political factors have little effect on credit ratings in a worldwide panel data set. On the contrary, Afonso et al. (2011) detected that the government effectiveness as a measure of institutions has a long-run impact. In like manner, Biglaiser and Staats (2012) showed that the rule of law, strong courts, and the protection of property rights positively affect sovereign credit ratings; while Osobajo and Akintunde (2019) discovered that the most important determinant is the institutional variable. Rather than political institutions, Boumparis et al. (2017) utilized a regulatory quality index that is composed, among other things, of financial institutions; and find a positive effect. In turn, there is a growing literature that stresses the importance of uncertainty on the real side of an economy, and on financial and credit market conditions. On the real side, uncertainty causes a partial irreversibility of investments by increasing the real option value of postponing non-reversible investment as well as increasing precautionary saving (Bloom et al., 2018). As regards financial and credit market conditions, Rehse et al. (2019) presented evidence that periods of higher uncertainty are associated with lower asset trading volume; while Bordo et al. (2016) determined that uncertainty has detrimental effects on market functioning since it hurts credit growth. 2 Nonetheless, little attention has been paid to the role of uncertainty on sovereign credit rating. Boumparis et al. (2017) found that the aggregate (average) Eurozone economic policy uncertainty impacts negatively on credit ratings across the quantile distribution in Eurozone countries. Meanwhile, Hantzsche (2018) constructed an un- certainty fiscal index to show that credit rating agencies take fiscal uncertainty as an important determinant of sovereign credit ratings for OECD countries. In a related study at the firm level, Attig et al. (2021) revealed that increased policy uncertainty is associated with weaker rating standards among US firms. Unlike the aforementioned studies, first, we jointly analyze political institutions and uncertainty as determinants of sovereign credit ratings, using country-specific economic policy uncertainty instead of aggregate uncertainty. Second but more importantly, we test whether strong institutions alleviate uncertainty through the key role they play in softening the impact of uncertainty by promoting stability and thus investment and innovation. Finally, in line with recent literature, we use random-effects ordered probit and logit models, which overcome the problems of linear models when the dependent variable is ordinal. By using a worldwide sample of 74 countries from 2003 to 2020 for the most impor- tant agencies (Moody’s, Standard & Poor’s, and Fitch), we find, in line with previous literature, that institutions have a positive effect while economic policy uncertainty has a negative one. Most importantly, we find that the interaction between institutions and uncertainty is consistently negative. These results mean that strong institutions will decrease the significant negative effect of uncertainty on sovereign credit ratings. We organize the paper as follows: beside this introduction, section 2 discusses data, variable definitions and the estimation methodology; Section 3 discusses results from the random-effects ordered probit and logit models; and Section 4 concludes. 2. Data and methodology 2.1. Methodology The estimation methodology draws on Bissoondoyal-Bheenick (2005), who proposed a random-effect ordered logit and probit response models for panel data. These models have advantages over others that have been used in the literature. First, ordered response models overcome the criticisms of linear models regarding the ordinal nature of the ratings. Second, they overcome the assumption that the variation between rating categories is the same for all, by allowing a derivative for each rating to be obtained 3 (Afonso et al., 2011). Therefore, we specify the following equation: Rit = f(µi + β′Xit + ϵit), (1) where Rit is the dependent variable and has different cut-off points, since this model follows the ordinal characteristic of the sovereign credit rating; µi is the individual specific effect, Xit are a set of explanatory variables and may also include lagged and interaction variables; f represents the parametric probit or logit function; i indexes countries, and t indexes time periods (years); and ϵit represents the error term. 2.2. Data Our study period of study spans 2003 to 2020 for a sample of 70, 69, and 66 countries for Moody’s, Standard & Poor’s, and Fitch, respectively. Table 1 shows the definitions of sources and variable used for estimating (1), where data availability dictates the length of time and the country coverage. Following Cantor and Packer (1996) and Afonso et al. (2011), the rating variable is constructed as 1 for high-risk speculative grade, 2 to 7 for speculative grade, and 8 to 17 for investment grade. Summary statistics of the variables used in the empirical part are detailed in Table 2. Although there is no consensus in the literature about a single definition of political institutions, we use the Worldwide Governance Indicators developed by the World Bank; which defines them as the traditions and institutions by which authority in a country is exercised, including government selection and monitoring process, the capacity of the government to effectively formulate and implement sound policies, and the state for the economic and social institutions. This indicator has the advantage of encompassing different notions of governance ranging from the most general to the most specific (see Kaufmann et al., 2017). For the uncertainly variable, we use data from the World Uncertainty Index con- structed by Ahir et al. (2018), for 143 countries from 1996 onwards, which has not previously been used in this literature to our knowledge. The data is constructed using frequency counts of “uncertainty” in the quarterly Economist Intelligence Unit country reports; these reports discuss major political and economic developments in each country, along with analysis and forecasts of political, policy and economic con- ditions (Ahir et al., 2018). The data have the advantage of being comparable between countries, since they come from the same source and the same methodology. It is worth noting that the variables used enter the regressions in a lagged form, 4 except for the case of uncertainty. This, in part, is because rating agencies, when evaluating a country, have access to lagged rather than contemporaneous information on macroeconomic and institutional variables. This is not the case for uncertainty, which, by its nature, can be perceived in real time. More importantly, using a lagged form of the independent variables reduces possible endogeneity problems arising from a correlation between some control variables and the error term. These problems are common in economic models, since there is a possible contemporaneous double causality between the dependent variable and its determinants. 5 Table 1: Data sources and variable definition Variable Definition Source Sovereign credit rating Sovereign credit rating provided by Moody’s, Standard & Poor’s, and Fitch. Bloomberg Real GDP growth Percentage change of real GDP. IMF Data Real per capita GDP Log GDP per capita, US dollars, constant 2005 prices. World Bank Data Inflation Percentage change of consumer price in- dex. World Bank Data Government expenditure Government expenditure as a percentage of GDP, in logs. IMF Data Government debt Government debt as a percentage of GDP, in logs. IMF Data Current account balance Current account balance as a percentage of GDP. IMF Data Reserves Reserves as a percentage of GDP, in logs. World Bank Data Default Dummy that indicates existence of sovereign default. Moody’s Institutions Average of the World Governance Indica- tors. World Bank Data Control of corruption Perceptions of the extent to which public power is exercised for private gain. World Bank Data Government effectiveness Perceptions of the quality of public ser- vices. World Bank Data Political stability Perceptions of the likelihood of political instability and/or politically-motivated violence. World Bank Data Rule of law Perceptions of the extent to which agents have confidence in and abide by the rules of society. World Bank Data Regulatory quality Perceptions of the ability of the govern- ment to formulate and implement sound policies and regulations. World Bank Data Voice and Accountability Perceptions of the extent to which a coun- try’s citizens participate in selecting their government and freedom of expression. World Bank Data Uncertainty World Uncertainty Index. Ahir et al. (2018) 6 Table 2: Summary statistics Variable Obs. Mean Std. Dev. Min Max Standard & Poor’s Rating 1178 9.99 5.09 1.00 17.00 Moody’s Rating 1148 10.15 5.24 1.00 17.00 Fitch Rating 1087 10.18 4.89 1.00 17.00 Real GDP growth (%) 1178 3.42 3.41 -14.8 28.1 Real per capita GDP 1178 933.3 116.7 668.1 1139.0 Inflation (%) 1178 4.04 4.57 -4.86 51.46 Government expenditure (% of GDP) 1178 3.46 0.36 2.20 4.17 Government debt (% of GDP) 1178 3.78 0.86 -2.96 5.46 Current account balance (% of GDP) 1178 -0.58 6.82 -43.83 33.19 Reserves (% of GDP) 1178 2.39 1.19 -1.88 5.11 Default (Dummy) 1178 0.01 0.08 0.00 1.00 Institutions (index) 1178 0.45 0.85 -1.18 1.97 Control of corruption (index) 1178 0.44 1.06 -1.39 2.47 Government effectiveness (index) 1178 0.58 0.90 -1.07 2.44 Political stability (index) 1178 0.15 0.89 -2.81 1.76 Rule of law (index) 1178 0.47 0.97 -1.25 2.12 Regulatory quality (index) 1178 0.62 0.83 -1.30 2.26 Voice and accountability (index) 1178 0.45 0.83 -1.75 1.78 Uncertainty (index) 1178 0.20 0.16 0.00 1.34 3. Estimation and inference results Table 3 presents the results of the random-effects ordered probit and logit models for the three main rating agencies: Moody’s, Standard & Poor’s, and Fitch. The results suggest that, in all cases, the political institutions variable has a significant and positive effect on sovereign credit ratings, in line with previous research findings, while the uncertainty indicator has an opposite effect. More importantly, the interaction between them is systematically negative. These results suggest that strong institutions reduce the negative impact of uncertainty on sovereign credit ratings. Intuitively, uncertainty indicators capture the risk of the government acting oppor- tunistically with respect to investors. Thus, if uncertainty is higher, the level of private investment will be lower. Instead, strong political institutions can enable the govern- ment to credibly commit itself to not engage in ex-post opportunistic behavior towards private investors–in addition to formulating and implementing sound policies and reg- 7 ulations that enable and promote private sector development (North and Weingast, 2019; Rodrik, 1991; Le, 2004). Table 3: Estimation results Dependent variable: Sovereign credit rating Moody’s Standard & Poor’s Fitch RE Probit RE Logit RE Probit RE Logit RE Probit RE Logit Uncertainty -0.82** -1.33** -1.09*** -1.71*** -1.01*** -1.72*** Economic Policy Uncertainty Index (EPU) (0.34) (0.614) (0.349) (0.653) (0.278) (0.534) Institutions 4.01*** 7.20*** 3.98*** 7.025*** 4.38*** 7.65*** Average of the World Governance Indicators (0.701) (1.331) (0.626) (1.291) (0.639) (1.196) Uncertainty×Institutions -0.91* -1.53* -1.20** -2.34** -0.89* -1.50** Interaction EPU and Governance indicators (0.50) (0.89) (0.502) (0.969) (0.518) (0.906) Real GDP growth 0.015 0.003 0.04* 0.056 0.03* 0.04* Percentage change of real GDP (0.02) (0.036) (0.022) (0.042) (0.02) (0.033) Real per capita GDP 0.01*** 0.02*** 0.02*** 0.03*** 0.02*** 0.03*** GDP per capita (constant 2005 USD), logs (0.004) (0.007) (0.005) (0.009) (0.004) (0.008) Inflation -0.011 -0.027 0.004 0.006 0.001 -0.01 Percentage change of CPI (0.01) (0.027) (0.012) (0.023) (0.01) (0.027) Government expenditure -1.23* -1.37* -1.11*** -1.38 -1.57** -2.46*** Government expenditure (% of GDP), in logs (0.706) (1.36) (0.69) (1.384) (0.616) (1.114) Government debt -1.32** -2.77** -1.18*** -2.51*** -1.58*** -3.02*** Government debt (% of GDP), in logs (0.522) (1.093) (0.366) (0.779) (0.383) (0.76) Current account balance -0.03 -0.05*** -0.02** -0.04** -0.007 -0.01 Current account balance (% of GDP), in logs (0.019) (0.037) (0.016) (0.031) (0.02) (0.031) Reserves 0.058 0.209 -0.051 -0.142 -0.105 -0.123 Reserves (% of GDP), in logs (0.182) (0.35) (0.173) (0.338) (0.17) (0.348) Default -1.36*** -2.31** -0.304 -0.557 -0.412 -0.735 Dummy of sovereign default (0.418) (0.953) (0.415) (0.852) (0.482) (1.03) Number of countries 70 70 69 69 66 66 Time period 2003-2020 2003-2020 2003-2020 2003-2020 2003-2020 2003-2020 Negative log-likelihood 1698 1679 1672 1651 1513 1489 Notes: Robust standard errors in parentheses. *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. RE stands for random-effects. Table 4 shows the regressions for each credit rating agency, taking into account each of the six components of political institutions instead of the aggregate variable. Overall, the results are similar to the previous ones: the institutional components have a significant and positive relationship with the credit rating, while uncertainty itself and its interaction with each institutional component have a negative sign. However, there are few cases in which the expected overall result is not observed. Control of corruption measures the perception of the degree to which public power is exercised for private gain, as well as the “capture” of the state by elites and private in- terests. Studies show that corruption has long-lasting effects on a country’s growth and investment (see Mauro, 1995; Mo, 2001, among others). Corruption forces innovators, 8 Table 4: Estimation results Dependent variable: Sovereign credit rating Moody’s Standard & Poor’s Fitch RE Probit RE Logit RE Probit RE Logit RE Probit RE Logit Uncertainty -1.15*** -1.83*** -1.26*** -2.02*** -1.25*** -2.06*** (0.33) (0.57) (0.34) (0.62) (0.28) (0.51) Institutions: Control of corruption 1.64*** 2.70*** 2.09*** 3.61*** 1.97*** 3.33*** (0.41) (0.79) (0.38) (0.78) (0.41) (0.80) Uncertainty×Control of corruption -0.75** -1.33** -0.96** -1.77** -0.72** -1.20* (0.36) (0.66) (0.38) (0.72) (0.36) (0.65) Negative log-likelihood 1756 1739 1718 1695 1573 1548 Uncertainty -0.95** -1.49** -1.07*** -1.72*** -1.07*** -1.87*** (0.38) (0.63) (0.35) (0.66) (0.31) (0.57) Institutions: Government effectiveness 2.51*** 4.59*** 2.62*** 4.54*** 3.18*** 5.73*** (0.50) (0.99) (0.29) (0.94) (0.94) (0.99) Uncertainty×Government effectiveness 0.66 -1.10** -1.14*** -2.17*** -0.57 -0.86 (0.41) (0.72) (0.42) (0.80) (0.43) (0.75) Negative log-likelihood 1729 1708 1708 1685 1528 1500 Uncertainty -1.18*** -1.92*** -1.42*** -2.30*** -1.35*** -2.32*** (0.33) (0.56) (0.31) (0.61) (0.25) (0.50) Institutions : Rule of law 2.49*** 4.63*** 2.85*** 5.05*** 3.10*** 5.44*** (0.63) (1.307) (0.60) (1.20) (0.98) (0.98) Uncertainty×Rule of law -0.83** -1.39* -1.08*** -2.14*** -0.70* -1.15 (0.38) (0.75) (0.39) (0.77) (0.71) (0.71) Negative log-likelihood 1732 1712 1693 1671 1531 1506 Uncertainty -0.94** -1.43* -1.11*** -1.62*** -1.09*** -1.73** (0.42) (0.75) (0.40) (0.75) (0.38) (0.69) Institutions: Regulatory quality 2.98*** 5.77*** 2.6*** 4.84*** 3.62*** 6.62*** (0.60) (1.25) (0.58) (0.23) (0.52) (1.02) Uncertainty×Regulatory quality -0.59 -1.06 -1.09** -2.21** -0.65 -1.07 (0.44) (0.79) (0.44) (0.86) (0.46) (0.80) Negative log-likelihood 1711 1685 1704 1678 1508 1480 Uncertainty -0.20*** -1.96*** -1.54*** -2.56*** -1.46*** -2.44*** (0.32) (0.55) (0.37) (0.70) (0.28) (0.74) Institutions: Political stability 1.58*** 2.66*** 1.12*** 1.87*** 1.21*** 2.11*** (0.29) (0.54) (0.31) (0.55) (0.39) (0.74) Uncertainty×Political stability -1.10** -1.56* -0.92* -1.55* -0.83 -1.15 (0.47) (0.81) (0.47) (0.80) (0.53) (0.90) Negative log-likelihood 1729 1710 1740 1715 1580 1551 Uncertainty -1.23*** -1.93*** -1.17*** -1.72** -1.32*** -2.08*** (0.37) (0.66) (0.46) (0.81) (0.83) (0.79) Institutions: Voice and accountability 0.78 1.36 1.92*** 3.42*** 0.70 1.41 (0.75) (1.58) (0.45) (0.90) (0.83) (1.69) Uncertainty×Voice and accountability -0.75 -1.09 -1.23* -2.34** -0.78 -1.39 (0.49) (0.874) (0.62) (1.07) (0.53) (1.02) Negative log-likelihood 1777 1757 1734 1707 1607 1576 Macroeconomic control variables ✓ ✓ ✓ ✓ ✓ ✓ Number of countries 70 70 69 69 66 66 Time period 2003-2020 2003-2020 2003-2020 2003-2020 2003-2020 2003-2020 Notes: Robust standard errors in parentheses. *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. RE stands for random-effects. 9 who require licenses or permits, to pay high bribes to operate legitimate businesses. Investors who are not willing to pay the commissions have to exit the market, affecting growth and investment (see Abu et al., 2015; Mo, 2001). In line with these studies, in our case corruption control is significant and positively affects credit ratings in all regressions. Furthermore, its interaction with uncertainty is negative and significant. Political stability measures the perception of the likelihood of political instability, including terrorism. Empirical studies have found that it has a negative impact on the fiscal situation, investment, and economic growth, and leads to higher reliance on seigniorage and inflation. According to Carmignani (2003), political instability causes uncertainty about the stability of political and economic institutions as well as the future course of economic policies, environment affecting agents’ decisions to accumulate production factors. Our results support these arguments. In all cases, policy stability has a positive and significant effect on the credit rating. Likewise, its interaction with uncertainty is negative (except for Fitch). Government effectiveness measures the perception of quality in public and civil ser- vice provision, as well as the degree of independence from political pressures. Knack and Keefer (1995) and Poirson (1998) found that bureaucratic quality affects investment. Moreover, Feng (2002) mentioned that uncertainty about government effectiveness can be more adverse than the policy itself, preventing private investors from committing their capital. If the government lacks consistency in the execution of its policies, in- vestors will delay their investments until they are confident that the government is consistent in the execution of its policies. Afonso et al. (2011) noted that the effec- tiveness of the government has a long-run impact on credit ratings. Similarly, in this study, government effectiveness is significant and positive in all the regressions, and its interaction with uncertainty is significant and negative, except for Fitch. Voice and accountability measures the perception of democracy in a country, as well as freedom of expression, freedom of association, and a free media. Empirical studies have shown that political freedom promotes private investment and growth (Helliwell, 1994; Pastor and Sung, 1995). Moreover, a change toward democracy mitigates the neg- ative effect of political instability on private investment (Feng, 2002). Our results show that voice and accountability and its interaction with uncertainty have the expected positive and negative effect, respectively; but for Moody’s and Fitch the interaction does not appear to be significant. Rule of law measures agents’ confidence in the rules of society and their enforce- ment, particularly with regard to contracts, property rights, the police, and the courts. Biglaiser and Staats (2012) show that the rule of law, strong courts, and the protection 10 of property rights positively affect the sovereign credit ratings. Studies find that is variable are related to levels of investment and growth (see Poirson, 1998; Knack and Keefer, 1995, among others). Our findings corroborate these results, as a positive and significant relationship between rule of law and credit rating is found in all regressions, while its interaction with uncertainty is negative and significant as expected. Finally, regulatory quality captures perceptions of the government’s ability to for- mulate and implement sound policies and regulations that encourage private sector development. Boumparis et al. (2017) consider a regulatory quality index that is com- posed, among others, of financial institutions and find a positive effect on credit rating. This variable is statistically significant and positive in all the cases, and its interaction with uncertainty appears to be negative and significant only for Standard & Poor’s. 4. Conclusion This paper studied the role of institutions and uncertainty in determining sovereign credit ratings. Using a sample of 74 countries from 2003 to 2020 of the three most important agencies–Moody’s, Standard & Poor’s, and Fitch–we found that institutions positively affect the sovereign credit rating whilst uncertainty impacts negatively. More importantly, we found that the negative effect of uncertainty can be mitigated by strengthening institutions, as their interaction is consistently negative. When the analysis is performed using each of the six components of the political institutions instead of the aggregate institutional variable, we found that, in general, the results are quite similar to the baseline case. We also discovered that the results of the baseline scenario are driven mainly by the control of corruption and political stability components, and to a lesser extent by those of government effectiveness and regulatory quality. References Abu, N., A. Karim, M. Zaini and M.I.Z. Aziz (2015). “Low savings rates in the Eco- nomic Community of West African States (ECOWAS): The role of corruption.” Jour- nal of Economic Cooperation & Development 36(2), 63-90. Afonso, A. (2003). “Understanding the determinants of sovereign debt ratings: Evi- dence for the two leading agencies.” Journal of Economics and Finance 27, 56-74. Afonso, A., P. Gomes and P. Rother (2011). “Short- and long-run determinants of 11 sovereign debt credit ratings.” International Journal of Finance & Economics 16(1), 1-15. Ahir, H., N. Bloom and D. Furceri (2018). “The World Uncertainty Index.” Stanford mimeo. Altenkirch, C. (2005). “The determinants of sovereign credit ratings: A new empirical approach.” South African Journal of Economics 73(3), 462-473. Archer, C.C., G. Biglaiser and K. DeRouen (2007). “Sovereign bonds and the “demo- cratic advantage”: Does regime type affect credit rating agency ratings in the devel- oping world?” International organization 61(2), 341-365. Attig, A., H. Driss and S. El Ghoul (2021). “Credit ratings quality in uncertain times.” Journal of International Financial Markets, Institutions and Money 75, 101449. Biglaiser, G. and J.L. Staats (2012). “Finding the “democratic advantage” in sovereign bond ratings: The importance of strong courts, property rights protection, and the rule of law.” International Organization 66(3), 515-535. Bissoondoyal-Bheenick, E. (2005). “An analysis of the determinants of sovereign rat- ings.” Global Finance Journal 15(3), 251-280. Bloom, N., M. Floetotto, N. Jaimovich, I. Saporta-Eksten and S.J. Terry (2018). “Re- ally uncertain business cycles.” Econometrica, 86(3), 1031-1065. Bordo, M.D., J.V. Duca and C. Koch (2016). “Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades.” Jour- nal of Financial Stability, 26, 90-106. Boumparis, P., C. Milas and T. Panagiotidis (2017). “Economic policy uncertainty and sovereign credit rating decisions: Panel quantile evidence for the Eurozone.” Journal of International Money and Finance 79, 39-71. Cantor, R. and F. Packer (1996). “Determinants and impact of sovereign credit rat- ings.” FRBNY Economic policy review 2(2), 37-54. Carmignani, F. (2003). “Political instability, uncertainty and economics.” Journal of Economic Surveys 17(1), 1-54. Feng, Y. (2002). “Political Freedom, Political Instability, and Policy Uncertainty: A Study of Political Institutions and Private Investment in Developing Countries.” International Studies Quarterly 45, 271-294. 12 Hantzsche, A. (2018). “Sovereign credit ratings under fiscal uncertainty.” Working Pa- per Series 32, European Stability Mechanism. Helliwell, J. (1994). “Empirical Linkages Between Democracy and Economic Growth.” British Journal of Political Science 24(2), 225-248. Kaufmann, D., A. Kraay and M. Mastruzzi (2011). “ The worldwide governance indi- cators: Methodology and analytical issues.”Hague journal on the rule of law 3(2), 220-246. Knack, S. and P. Keefer (1995). “Institutions and economic performance: cross-country tests using alternative institutional measures.” Economics & Politics 7(3), 207-227. Le, Q.V. (2004). “Political and economic determinants of private investment.” Journal of International Development 16(4), 589-604. Osobajo, O.A. and A.E. Akintunde (2019). “Determinants of sovereign credit ratings in emerging markets.” International Business Research 12(5), 142-166. Mauro, P. (1995). “Corruption and growth.” Quarterly Journal of Economics 110(3), 681-712. Mo, P.H. (2001). “Corruption and economic growth.” Journal of comparative economics 29(1), 66-79. North, D.C. and B.R. Weingast (1989). “Constitutions and commitment: The evolution of institutions governing public choice in seventeenth-century England.” Journal of Economic History 49(4), 803-832. Pastor, M. and J.H. Sung (1995). “Private Investment and Democracy in the Develop- ing World.” Journal of Economic Issues 29(1), 223-243. Poirson, H. (1998). “Economic Security, Private Investment, and Growth in Developing Countries.” IMF Working Paper No. 98/4. Rehse, D., R. Riordan, N. Rottke and J. Zietz (2019). “The effects of uncertainty on market liquidity: Evidence from Hurricane Sandy.” Journal of Financial Economics 134(2), 318-332. Rodrik, D. (1991). “Policy uncertainty and private investment in developing countries.” Journal of Development Economics 33(2), 229-242. 13 ÚLTIMAS PUBLICACIONES DE LOS PROFESORES DEL DEPARTAMENTO DE ECONOMÍA  Libros Waldo Mendoza Bellido 2022 Cómo investigan los economistas. Guía para elaborar y desarrollar un proyecto de investigación. Segunda edición aumentada. Lima, Fondo Editorial PUCP. Elena Álvarez (Editor) 2022 Agricultura y desarrollo rural en el Perú: homenaje a José María Caballero. Lima, Departamento de Economía PUCP. Aleida Azamar Alonso, José Carlos Silva Macher y Federico Zuberman (Editores) 2022 Economía ecológica latinoamericana. Buenos Aires, México. CLACSO, Siglo XXI Editores. Efraín Gonzales de Olarte 2021 Economía regional y urbana. El espacio importa. Lima, Fondo Editorial PUCP. Alfredo Dammert Lira 2021 Economía minera. Lima, Fondo Editorial PUCP. Adolfo Figueroa 2021 The Quality of Society, Volume II – Essays on the Unified Theory of Capitalism. New York, Palgrave Macmillan. Carlos Contreras Carranza (Editor) 2021 La Economía como Ciencia Social en el Perú. Cincuenta años de estudios económicos en la Pontificia Universidad Católica del Perú. Lima, Departamento de Economía PUCP. José Carlos Orihuela y César Contreras 2021 Amazonía en cifras: Recursos naturales, cambio climático y desigualdades. Lima, OXFAM. Alan Fairlie 2021 Hacia una estrategia de desarrollo sostenible para el Perú del Bicentenario. Arequipa, Editorial UNSA. Waldo Mendoza e Yuliño Anastacio 2021 La historia fiscal del Perú: 1980-2020. Colapso, estabilización, consolidación y el golpe de la COVID-19. Lima, Fondo Editorial PUCP. Cecilia Garavito 2020 Microeconomía: Consumidores, productores y estructuras de mercado. Segunda edición. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. https://departamento.pucp.edu.pe/economia/libro/agricultura-desarrollo-rural-peru-homenaje-jose-maria-caballero/ https://departamento.pucp.edu.pe/economia/libro/10743/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-economia-ciencia-social-peru-cincuenta-anos-estudios-economicas-la-pontificia-universidad-catolica-del-peru/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ https://departamento.pucp.edu.pe/economia/libro/la-historia-fiscal-del-peru-1980-2020-colapso-estabilizacion-consolidacion-golpe-la-covid-19/ Adolfo Figueroa 2019 The Quality of Society Essays on the Unified Theory of Capitalism. New York. Palgrave MacMillan. Carlos Contreras y Stephan Gruber (Eds.) 2019 Historia del Pensamiento Económico en el Perú. Antología y selección de textos. Lima, Facultad de Ciencias Sociales PUCP. Barreix, Alberto Daniel; Corrales, Luis Fernando; Benitez, Juan Carlos; Garcimartín, Carlos; Ardanaz, Martín; Díaz, Santiago; Cerda, Rodrigo; Larraín B., Felipe; Revilla, Ernesto; Acevedo, Carlos; Peña, Santiago; Agüero, Emmanuel; Mendoza Bellido, Waldo; Escobar Arango y Andrés. 2019 Reglas fiscales resilientes en América Latina. Washington, BID. José D. Gallardo Ku 2019 Notas de teoría para para la incertidumbre. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. Úrsula Aldana, Jhonatan Clausen, Angelo Cozzubo, Carolina Trivelli, Carlos Urrutia y Johanna Yancari 2018 Desigualdad y pobreza en un contexto de crecimiento económico. Lima, Instituto de Estudios Peruanos. Séverine Deneulin, Jhonatan Clausen y Arelí Valencia (Eds.) 2018 Introducción al enfoque de las capacidades: Aportes para el Desarrollo Humano en América Latina. Flacso Argentina y Editorial Manantial. Fondo Editorial de la Pontificia Universidad Católica del Perú. Mario Dammil, Oscar Dancourt y Roberto Frenkel (Eds.) 2018 Dilemas de las políticas cambiarias y monetarias en América Latina. Lima, Fondo Editorial de la Pontificia Universidad Católica del Perú. http://departamento.pucp.edu.pe/economia/libro/the-quality-of-society-essays-on-the-unified-theory-of-capitalism/ http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/ http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/ http://departamento.pucp.edu.pe/economia/libro/las-alianzas-publico-privadas-app-en-el-peru-beneficios-y-riesgos/  Documentos de trabajo No. 513 Gender gap in pension savings: Evidence from Peru’s individual capitalization system. Javier Olivera y Yadiraah Iparraguirre. Junio 2022. No. 512 Poder de mercado, bienestar social y eficiencia en la industria microfinanciera regulada en el Perú. Giovanna Aguilar y Jhonatan Portilla. Junio 2022. No. 511 Perú 1990-2020: Heterogeneidad estructural y regímenes económicos regionales ¿Persiste la desconexión entre la economía, la demografía y la geografía? Félix Jiménez y Marco Arroyo. Junio 2022. No. 510 Evolution of the Exchange Rate Pass-Throught into Prices in Peru: An Empirical Application Using TVP-VAR-SV Models. Roberto Calero, Gabriel Rodríguez y Rodrigo Salcedo Cisneros. Mayo 2022. No. 509 Time Changing Effects of External Shocks on Macroeconomic Fluctuations in Peru: Empirical Application Using Regime-Switching VAR Models with Stochastic Volatility. Paulo Chávez y Gabriel Rodríguez. Marzo 2022. No. 508 Time Evolution of External Shocks on Macroeconomic Fluctuations in Pacific Alliance Countries: Empirical Application using TVP-VAR-SV Models. Gabriel Rodríguez y Renato Vassallo. Marzo 2022. No. 507 Time-Varying Effects of External Shocks on Macroeconomic Fluctuations in Peru: An Empirical Application using TVP-VARSV Models. Junior A. Ojeda Cunya y Gabriel Rodríguez. Marzo 2022. No. 506 La Macroeconomía de la cuarentena: Un modelo de dos sectores. Waldo Mendoza, Luis Mancilla y Rafael Velarde. Febrero 2022. No. 505 ¿Coexistencia o canibalismo? Un análisis del desplazamiento de medios de comunicación tradicionales y modernos en los adultos mayores para el caso latinoamericano: Argentina, Colombia, Ecuador, Guatemala, Paraguay y Perú. Roxana Barrantes Cáceres y Silvana Manrique Romero. Enero 2022. No. 504 “Does the Central Bank of Peru Respond to Exchange Rate Movements? A Bayesian Estimation of a New Keynesian DSGE Model with FX Interventions”. Gabriel Rodríguez, Paul Castillo B. y Harumi Hasegawa. Diciembre, 2021 No. 503 “La no linealidad en la relación entre la competencia y la sostenibilidad financiera y alcance social de las instituciones microfinancieras reguladas en el Perú”. Giovanna Aguilar y Jhonatan Portilla. Noviembre, 2021. No. 502 “Approximate Bayesian Estimation of Stochastic Volatility in Mean Models using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez, Luis M. Castro Cepero y Hernán B. Garrafa-Aragón. Noviembre, 2021. No. 501 “El impacto de políticas diferenciadas de cuarentena sobre la mortalidad por COVID-19: el caso de Brasil y Perú”. Angelo Cozzubo, Javier Herrera, Mireille Razafindrakoto y François Roubaud. Octubre, 2021. No. 500 “Determinantes del gasto de bolsillo en salud en el Perú”. Luis García y Crissy Rojas. Julio, 2021. No. 499 “Cadenas Globales de Valor de Exportación de los Países de la Comunidad Andina 2000-2015”. Mario Tello. Junio, 2021. No. 498 “¿Cómo afecta el desempleo regional a los salarios en el área urbana? Una curva de salarios para Perú (2012-2019)”. Sergio Quispe. Mayo, 2021. No. 497 “¿Qué tan rígidos son los precios en línea? Evidencia para Perú usando Big Data”. Hilary Coronado, Erick Lahura y Marco Vega. Mayo, 2021. No. 496 “Reformando el sistema de pensiones en Perú: costo fiscal, nivel de pensiones, brecha de género y desigualdad”. Javier Olivera. Diciembre, 2020. No. 495 “Crónica de la economía peruana en tiempos de pandemia”. Jorge Vega Castro. Diciembre, 2020. No. 494 “Epidemia y nivel de actividad económica: un modelo”. Waldo Mendoza e Isaías Chalco. Setiembre, 2020. No. 493 “Competencia, alcance social y sostenibilidad financiera en las microfinanzas reguladas peruanas”. Giovanna Aguilar Andía y Jhonatan Portilla Goicochea. Setiembre, 2020. No. 492 “Empoderamiento de la mujer y demanda por servicios de salud preventivos y de salud reproductiva en el Perú 2015-2018”. Pedro Francke y Diego Quispe O. Julio, 2020. No. 491 “Inversión en infraestructura y demanda turística: una aplicación del enfoque de control sintético para el caso Kuéalp, Perú”. Erick Lahura y Rosario Sabrera. Julio, 2020. No. 490 “La dinámica de inversión privada. El modelo del acelerador flexible en una economía abierta”. Waldo Mendoza Bellido. Mayo, 2020. No. 489 “Time-Varying Impact of Fiscal Shocks over GDP Growth in Peru: An Empirical Application using Hybrid TVP-VAR-SV Models”. Álvaro Jiménez y Gabriel Rodríguez. Abril, 2020. No. 488 “Experimentos clásicos de economía. Evidencia de laboratorio de Perú”. Kristian López Vargas y Alejandro Lugon. Marzo, 2020. No. 487 “Investigación y desarrollo, tecnologías de información y comunicación e impactos sobre el proceso de innovación y la productividad”. Mario D. Tello. Marzo, 2020. No. 486 “The Political Economy Approach of Trade Barriers: The Case of Peruvian’s Trade Liberalization”. Mario D. Tello. Marzo, 2020. No. 485 “Evolution of Monetary Policy in Peru. An Empirical Application Using a Mixture Innovation TVP-VAR-SV Model”. Jhonatan Portilla Goicochea y Gabriel Rodríguez. Febrero, 2020. No. 484 “Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models”. Jean Pierre Fernández Prada Saucedo y Gabriel Rodríguez. Febrero, 2020. No. 483 “Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence”. Jefferson Martínez y Gabriel Rodríguez. Febrero, 2020. No. 482 “Acerca de la relación entre el gasto público por alumno y los retornos a la educación en el Perú: un análisis por cohortes”. Luis García y Sara Sánchez. Febrero, 2020. No. 481 “Stochastic Volatility in Mean. Empirical Evidence from Stock Latin American Markets”. Carlos A. Abanto-Valle, Gabriel Rodríguez y Hernán B. Garrafa-Aragón. Febrero, 2020. No. 480 “Presidential Approval in Peru: An Empirical Analysis Using a Fractionally Cointegrated VAR2”. Alexander Boca Saravia y Gabriel Rodríguez. Diciembre, 2019. No. 479 “La Ley de Okun en el Perú: Lima Metropolitana 1971 – 2016.” Cecilia Garavito. Agosto, 2019. No. 478 “Peru´s Regional Growth and Convergence in 1979-2017: An Empirical Spatial Panel Data Analysis”. Juan Palomino y Gabriel Rodríguez. Marzo, 2019.  Materiales de Enseñanza No. 5 “Matemáticas para Economistas 1”. Tessy Váquez Baos. Abril, 2019. No. 4 “Teoría de la Regulación”. Roxana Barrantes. Marzo, 2019. No. 3 “Economía Pública”. Roxana Barrantes, Silvana Manrique y Carla Glave. Marzo, 2018. No. 2 “Macroeconomía: Enfoques y modelos. Ejercicios resueltos”. Felix Jiménez. Marzo, 2016. No. 1 “Introducción a la teoría del Equilibrio General”. Alejandro Lugon. Octubre, 2015. Departamento de Economía - Pontificia Universidad Católica del Perú Av. Universitaria 1801, San Miguel, 15008 – Perú Telf. 626-2000 anexos 4950 - 4951 http://departamento.pucp.edu.pe/economia/ DDD 514 Carátula DDD514-Segunda hoja DDD514-Contratapa Julio Villavicencio 20220726CreditRating_PUCP c885da3e1cbe4cb3d9d06e97852db1d9b9d713b31275a14a561f2c9fc0cab844.pdf 89ffadbc051250d5941eebb31d3809cb3e21fc2c7fd2694aea035d9f460b7305.pdf c885da3e1cbe4cb3d9d06e97852db1d9b9d713b31275a14a561f2c9fc0cab844.pdf Introduction Data and methodology Methodology Data Estimation and inference results Conclusion DDD514-ultimas publicaciones