metadata: authors: 0: author: De,Supriyo
1: author: Mohapatra,Sanket
2: author: Ratha,Dilip K.
count: World
docna: 0: docna: Sovereign Credit Ratings, Relative Risk
            Ratings, and Private Capital Flows
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Emerging Markets,Macroeconomic Management,Financial Structures
teratopic: Macroeconomics and Economic Growth,Private Sector Development,Finance and Financial Sector Development
abstracts: cdata!: This paper examines the influence of
            sovereign credit ratings and relative risk ratings on
            private capital flows to 26 emerging and frontier market
            economies, using quarterly data for 1998-2017. A dynamic
            panel regression model is used to estimate the relationship
            between ratings and capital flows after controlling for
            other factors that can influence capital flows, such as
            growth and interest rate differentials and global risk
            conditions. The analysis finds that while absolute ratings
            were an important determinant of net capital inflows prior
            to the global financial crisis in 2008, the influence of
            relative risk ratings increased in the post-crisis period,
            which was characterized by easy monetary policies and global
            liquidity, on the one hand, and greater caution and
            discretion on the part of investors on the other. The
            post-crisis effect of relative ratings appears to be driven
            mostly by portfolio flows. These findings imply that
            emerging and frontier markets need to pay greater attention
            to their relative economic performance and not just their
            sovereign ratings. Tracking changes in relative ratings
            could help predict macroeconomic disturbances resulting from
            volatile portfolio capital movements.
repnme: repnme: Sovereign Credit Ratings, Relative Risk
            Ratings, and Private Capital Flows
display_title: Sovereign Credit Ratings, Relative
            Risk Ratings, and Private Capital Flows
listing_relative_url: /research/2020/09/32413455/sovereign-credit-ratings-relative-risk-ratings-private-capital-flows
topicv3: Balance of Payments m1326965 441,Central Banks m1327453 1235,Monetary Policy m1327609 444,Growth from Emerging Markets m1504462 3135,Investment Entry m1504647 2544
publishtoextweb_dt: 2020-09-17T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Private Sector Development,Finance and Financial Sector Development
origu: JOBS- CCSA - IBRD (HSPJB)
no_of_pages: 37
author_keys: 0: author_key: 1292393
1: author_key: 607120
2: author_key: 99413
model_extractions: Policy Research Working Paper 9401 Sovereign Credit Ratings, Relative Risk Ratings, and Private Capital Flows _Supriyo De Sanket Mohapatra Dilip Ratha_ Social Protection and Jobs Global Practice Migration and Remittances Team September 2020
Policy Research Working Paper 9401

# **Abstract**

This paper examines the influence of sovereign credit ratings and relative risk ratings on private capital flows to 26 emerging and frontier market economies, using quarterly data for 1998–2017. A dynamic panel regression model is used to estimate the relationship between ratings and capital flows after controlling for other factors that can influence capital flows, such as growth and interest rate differentials and global risk conditions. The analysis finds that while absolute ratings were an important determinant of net capital inflows prior to the global financial crisis in 2008, the influence of relative risk ratings increased in the post-crisis period, which was characterized by easy monetary policies and global liquidity, on the one hand, and greater caution and discretion on the part of investors on the other. The post-crisis effect of relative ratings appears to be driven mostly by portfolio flows. These findings imply that emerging and frontier markets need to pay greater attention to their relative economic performance and not just their sovereign ratings. Tracking changes in relative ratings could help predict macroeconomic disturbances resulting from volatile portfolio capital movements.

This paper is a product of the Migration and Remittances Team, Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at sde@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Sovereign Credit Ratings, Relative Risk Ratings, and Private Capital Flows<sup>*</sup>

Supriyo De<sup>§</sup> , Sanket Mohapatra<sup>†</sup> , and Dilip Ratha<sup>§</sup>

> § _The World Bank Group,_ † _Indian Institute of Management, Ahmedabad_

_Keywords:_ emerging markets, frontier markets, private capital flows, post-crisis developments, sovereign credit rating JEL Codes : F21, F30, F65, G15 _________________________

> * This research was supported by grants from the World Bank’s Research Support Budget and funding from IIM Ahmedabad and UTI Asset Management Company. Thanks are due to Kaushik Basu for useful discussions on the relative rating concept. Thanks are also due to Norbert Fiess, Indermit Gill, Poonam Gupta, Ayhan Kose, Jamus Lim, Evis Rucaj and Sergio Schmukler for constructive comments and suggestions. The views expressed in this paper are solely those of the authors and do not represent the views of the institutions they are associated with. Contact emails: sde@worldbank.org, sanketm@iima.ac.in, dratha@worldbank.org.
# **1. Introduction**

Following the global financial crisis of 2008, there were realignments of sovereign ratings within the group of emerging markets (EMs) and frontier markets (FMs), reflecting great heterogeneity in the performance of these economies.<sup>1</sup> In particular, while some economies had minor erosions, others witnessed larger declines in their ratings or outlooks. Capital inflows to several economies with minor rating erosions continued to grow while those with large rating downgrades suffered from declining inflows. This suggests that capital inflows may be driven not so much by absolute credit ratings but by _relative_ ratings, that is, the degree by which a country’s rating is better or worse than others (Basu et al. 2012 and 2013). Relative risk ratings endeavor to quantify this concept (see section 3 for computation of relative ratings).

The relative rating measure is relevant since it does not always move in consonance with absolute ratings. The importance of the relative rating metric lies in analyzing instances when the direction of absolute rating change is different from relative rating movements. This can arise when large economies undergo changes in absolute ratings (usually downgrades) while other economies have smaller or no changes. In all likelihood, investors care more about relative ratings when allocating investment. Therefore, changes in emerging and frontier market ratings vis-à-vis their peers can drive foreign investment reallocations. In the pre-crisis period, capital inflows to emerging economies increased to a great extent. Despite a sharp slowdown during the financial crisis in 2008-09, in the post-crisis period, flows to EMs and FMs recovered. Low policy interest rates and quantitative easing by advanced economy central banks contributed to increased capital flows into emerging economies during the post-crisis period (Fratzscher, Lo Duca and Straub 2016, Lim and Mohapatra 2016). This period also witnessed strong bond issuance by frontier market economies with several sovereigns tapping international capital markets for the first time (Guscina, Guilherme and Presciuttini 2014).<sup>2</sup> But net flows to EMs and FMs displayed considerable heterogeneity during the post-crisis period (Figure 1).

> 1 Our classification of EMs and FMs broadly draws on MSCI, FTSE, S&P and Dow-Jones.

> 2 Several frontier markets obtained ratings and issued sovereign bonds for the first time in international markets. For instance, Zambia obtained a rating from S&P in March 2011 and had its debut bond issue in September 2012. The corresponding dates for Rwanda are December 2011 and April 2013.

2
<!-- Start of picture text --> Average capitalflows (% ofGDP)<br>14<br>12<br>10<br>8<br>6<br>4<br>2-1<br>0 rv<br>-2<br>-4<br>ZESSSESETELTSSEPTESSESSETES<br>RRARSSSSSSSSSSsSSSSSSSSsgssss<br>SK KF SK ANNAN AAN AANA ANN NNN NNO<br>—— Net capital inflows Gross capital inflows<br><!-- End of picture text -->
- b) This is also one of the first papers to juxtapose absolute and relative ratings and reveal their comparative impacts on capital flows in the pre- and post-crisis periods.

This study relies on a comprehensive data set of sovereign ratings, relative ratings, and private capital flows, and other potential correlates of capital flows to analyze the influence of sovereign and relative ratings on both net and gross capital inflows with a particular focus on possible shifts following the global financial crisis in 2008-09. The post-crisis years are a unique period characterized by highly accommodative monetary policies in advanced economies and great heterogeneity in the performance of EMs and FMs.

Using dynamic fixed effects estimation, we control for other factors that can influence capital flows particularly growth and interest rate differentials and global risk conditions. We find evidence that while absolute ratings were an important determinant of capital inflows, especially in the pre-2008 period, the influence of relative risk ratings increased in the postcrisis period. The observed relationship between absolute ratings, relative risk ratings, and capital flows holds even after accounting for the effects of quantitative easing (QE) by the U.S. Federal Reserve in the post-crisis period. This finding suggests that in a post-crisis environment characterized not only by abundant global liquidity but also by greater investor caution, the relative creditworthiness of EMs and FMs compared to their peers has become a more relevant factor in attracting private capital flows. The baseline results are robust to the use of fixed GDP weights in calculating relative risk ratings, to an alternative measure of capital flows (gross capital flows instead of net capital flows), and to the potential endogeneity of absolute and relative ratings as described below. In a combined specification with both absolute ratings and the residual of relative ratings, the relative rating residual retains its positive sign and significance in the post-crisis period indicating an independent effect of relative ratings, which is not driven by sovereign ratings. Disaggregating the flows into foreign direct investment, portfolio flows and other investments (largely commercial bank lending) reveals that the postcrisis effect of relative ratings is driven mostly by portfolio flows.

This paper is organized into eight sections. Following this introductory part, section 2 reviews the relevant literature, and section 3 describes the data and methodology. Section 4 describes the empirical strategy, section 5 discusses the main results and their implications, and section 6 tests the robustness of the results. Section 7 examines the effects of ratings for the different components of capital flows, while section 8 concludes with some implications for policy.

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# **2. Literature review**

This research relates to two strands of literature: first, studies that consider the impact of sovereign rating changes including regional spillovers and the recent literature on relative risk ratings; and second, the push and pull factors in international capital flows including recent research that analyzes the role of highly accommodative monetary policies in advanced economies and global risk conditions during the post-2008 period. Rather than taking a comprehensive view, the literature on capital flows, sovereign ratings, and spillovers has focused on particular forms of capital flows or specific high-frequency financial indicators. Moreover, prior studies have generally considered push and pull factors including absolute sovereign ratings but have not accounted for changes in relative ratings. To address these gaps, this research seeks to take an encompassing view of net and gross capital inflows when considering the impact of sovereign ratings and relative risk ratings on the allocation of international capital flows during the post-financial crisis period.

_2.1 Impact of sovereign rating changes, including regional spillovers, and relative risk ratings_ Several studies have explicitly considered the impact of sovereign ratings on capital flows, bond spreads, and equity prices. Kim and Wu (2008) find strong evidence that sovereign credit ratings affect financial sector development and capital flows. Kim and Wu (2011) find that sovereign credit rating revisions have significant and positive influences on international bank flows of G7 countries to 55 emerging markets. Furthermore, there are regional spillovers: rating improvements in one EM region tend to reduce bank flows to other regions. Cai, Gan, and Kim (2018) using bilateral data on foreign direct investment (FDI) flows from 31 OECD countries to 72 recipient countries find that sovereign credit ratings in both source and recipient countries impact FDI flows. The authors find a positive impact of credit ratings of recipient countries on FDI inflows, suggesting the importance of the overall investment climate as indicated by the sovereign rating. The authors also find that lower-rated FDI donor countries are likely to engage in FDI to high-rated recipient countries. Several studies have examined links between sovereign credit ratings and bond yield spreads. Reisen and Maltzan (1999) use an event study to explore market responses 30 trading days before and after rating announcements and find a significant impact of imminent upgrades and implemented downgrades despite anticipation of rating events. Gande and Parsley (2005) study the effect of a sovereign rating change of one country on sovereign credit spreads of other countries. They find evidence of asymmetric effects whereby negative rating events are particularly correlated with an increase in spreads.

5
Afonso, Furceri and Gomes (2012) use an event study analysis to demonstrate significant responses of EU government bond yield spreads to changes in ratings and outlooks, particularly in the case of negative announcements.

Kaminsky and Schmukler (1999) find that during the Asian financial crisis, news about agreements with international organizations and credit rating agencies had the most impact on stock prices. Kaminsky and Schmukler (2002) find that sovereign rating and outlook changes in emerging markets impact not only bond spreads but also stock prices. Impacts of changes are stronger during crises and also generate cross-country contagion. Ferreira and Gama (2007) find that sovereign rating and credit outlook changes of one country have an asymmetric and economically significant effect on stock market returns of other countries. A credit rating downgrade has a negative reaction around the world, but upgrades have no significant impact abroad. Alsakka and ApGwilym (2012) find that rating agencies’ signals affect a country’s exchange rate and also have strong spillover effects on exchange rates of other countries in the same region.

A study by Guscina, Pedras, and Presciuttini (2014) finds that improvements in sovereign ratings have often preceded bond issuances by first-time issuers. The quality of institutions, GDP growth, reduction in debt, and improvement in global risk conditions are all associated with lower cost of borrowing by new issuers. These issuers face a premium over established emerging markets; this additional cost of borrowing tends to rise together with worsening global risk.

A new strand of literature has explicitly formulated the concept of relative risk ratings. Basu et al. (2012) define a new relative performance index and then track how nations have done over time. According to them, major credit rating agencies assign sovereign credit ratings as an absolute grade. In contrast, relative risk ratings indicate how a particular sovereign fares in relation to the rating performance of other sovereigns. Basu et al. (2013) compare relative ratings in 2012 with those of early 2008 and find that while they improved in several developing economies, they deteriorated in crisis-affected high-income countries. Interestingly, India, Jordan, Poland, and the United Kingdom had outlook downgrades, but their relative ratings improved as other countries suffered even worse downgrades. The implications of this have not yet been examined in the literature and this is a gap we seek to address.

6
# _2.2 Push and pull factors in international capital flows_

A large body of literature since the 1990s suggests that capital inflows to developing countries are influenced by both global conditions (push factors) and country-specific (pull) factors (Calvo, Leiderman and Reinhart 1996, Chuhan, Claessens, and Mamingi 1998, Reinhart and Reinhart 2008). Some of this research has focused on domestic vulnerabilities and the quality of institutions as important determinants of access to international credit markets (Gelos, Sahay and Sandleris 2011). Alfaro, Kalemli-Ozcan and Volosovych (2008) and Benassy-Quere, Coupet and Mayer (2007) have also found that international capital flows are influenced by institutional factors such as government stability, absence of corruption, and law and order. Capital inflows may differ in their risk characteristics depending on whether they are in the form of equity or debt: the former implies more sharing of risks with foreigners while the latter can imply a larger burden on the borrowing economy (Gill and Raiser 2012). Domestic factors are expected to be more important drivers of flows during periods of lower risk-aversion (Baldacci, Gupta and Mati 2008) while global factors are likely to be more relevant during high-risk periods. Koepke (2019) provides an extensive review of the literature on the push and pull factors in global capital flows to emerging market economies. The author identifies global risk aversion and interest rates in advanced economies as important push factors for portfolio debt and equity flows to emerging markets. Among pull factors, GDP growth, returns on domestic financial assets, and country risk are related to both portfolio flows and banking flows, but are relatively more important for the latter.

Among research covering the global financial crisis and post-2008 period, Fratzscher (2012) employs a factor model to explain the highly heterogeneous effects of push and pull factors on capital flows across countries. The authors find that push factors were dominant during the global financial crisis but country-specific pull factors were more important in the immediate recovery phase after the crisis. Ahmed and Zlate (2014) find growth and interest rate differentials to be significantly associated with capital flows to emerging economies. They also find that capital controls may have reduced inflows while the sensitivity of portfolio investment flows to interest rate differentials and risk aversion increased in the post-crisis period. Kellard et al. (2020) find that sovereign risk, proxied by 10-year government bond yields, negatively impacts FDI flows both for origin and recipient Eurozone countries, while banking sector stability (proxied by the share of non-performing loans in total loans) matters for FDI flows from origin countries. Several studies have found that loose monetary policy in advanced 7
economies may have contributed to greater risk-taking and increased leverage (Bekaert, Hoerova and Lo Duca 2013, Dell’Ariccia, Laeven and Suarez 2017, IMF 2013). This, in turn, contributed to greater cross-border credit intermediation and investment flows to developing countries (Fratzscher, Lo Duca and Straub 2016, Lim and Mohapatra 2016, Park, Ramayandi and Shin 2014).

Changes in US monetary policy and risk aversion are linked to powerful global financial cycles characterized by large common movements in asset prices, capital flows, and leverage (Miranda-Agrippino and Rey 2015, Rey 2015). Correa et al. (2018) find that monetary policy in source countries influences cross-border banking flows. The authors find evidence of a cross-border bank portfolio channel, whereby tighter monetary conditions erode the net worth of domestic borrowers, leading banks to reallocate credit to safer foreign borrowers. Jaramillo and Weber (2013) find that domestic bond yields in emerging economies are heavily influenced by global risk appetite and liquidity conditions. Cerutti, Claessens, and Puy (2019) find evidence of co-movements in bank, portfolio bond, and portfolio equity flows to emerging markets, but not for FDI flows. The authors report that emerging markets' reliance on global mutual funds, recipient market liquidity, and inclusion in global market indices increase the sensitivity of equity and bond inflows to common global factors. Earlier studies also suggest that time-varying global financial variables are likely to be driven by unobserved common factors conceived as a ‘world business cycle’ (for instance, see Albuquerque, Loayza and Serven 2005).

Several studies also find global risk aversion, captured by the VIX index, to be a significant determinant of capital flows (Bruno and Shin, 2014; Forbes and Warnock, 2012, Ghosh et al. 2014). Nier, Sedik and Mondino (2014) find that for low levels of VIX, country fundamentals are significant drivers of capital flows, but when the VIX index is high, country-specific factors become less important. Abrupt adjustment in global monetary and risk conditions can induce volatility in capital flows, exchange rates, and equity prices, as experienced during the summer of 2013 from expectations of tapering of quantitative easing (Eichengreen and Gupta 2015, Basu, Eichengreen and Gupta 2014). Such events can trigger a reappraisal of emerging economies by investors and result in increased differentiation among emerging economies based on their relative creditworthiness.

8
# **3. Data and methodology**

## _3.1 Sovereign ratings and relative risk ratings_

Systems of converting alphabetical ratings into numerical scores have been proposed in papers such as Cantor and Packer (1996), Dadush and Dasgupta (2001), Gaillard (2009) and Basu, et al (2012).<sup>3</sup> The empirical analysis relies on long-term foreign currency ratings assigned by Standard and Poor’s as this is often used as the indicator for sovereign creditworthiness by international investors, and S&P demonstrates the least dependence on other agencies when rating sovereigns (Alsakka and ap Gwilym 2010). Sovereign ratings obtained from Standard and Poor’s were converted to numeric ratings using the rating scale in Table 1.

**Table 1: S&P ratings, outlooks and numerical scores**

|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|S&P<br>Rating|Outlook|Outlook<br>adjusted<br>score|
|---|---|---|---|---|---|---|---|---|
|||Does not|||||||
|AAA|P|exist|BBB+|P|40|B|P|19|
||S|60||S|39||S|18|
||N|59||N|38||N|17|
|AA+|P|58|BBB|P|37|B-|P|16|
||S|57||S|36||S|15|
||N|56||N|35||N|14|
|AA|P|55|BBB-|P|34|CCC+|P|13|
||S|54||S|33||S|12|
||N|53||N|32||N|11|
|AA-|P|52|BB+|P|31|CCC|P|10|
||S|51||S|30||S|9|
||N|50||N|29||N|8|
|A+|P|49|BB|P|28|CCC-|P|7|
||S|48||S|27||S|6|
||N|47||N|26||N|5|
|A|P|46|BB-|P|25|CC|P|4|
||S|45||S|24||S|3|
||N|44||N|23||N|2|
|A-|P|43|B+|P|22|C||1|
||S|42||S|21|D||0|
||N|41||N|20||||

Notes: Based on Basu et al. (2012). ‘P’ signifies Positive, ‘S’ Stable and ‘N’ Negative.

The availability of a numerical conversion system for sovereign ratings is useful for the analysis of sovereign ratings and their possible links to global economic and financial factors,

> 3 These classifications have a scale of around 20 ratings. Basu, et al. (2012) build upon this system but introduce an innocuous cardinal change of multiplying each number by 3. This modification allows for a richer analysis by including the outlooks into the numerical rating spectrum (see Table 1). The revised measure is a positive monotone transformation of the earlier measure and therefore does not change its basic characteristics. Each rating is divided into three outlooks, namely, ‘positive’, ‘stable’, and ‘negative’. These outlooks indicate, respectively, the possibilities of an upgrade, remaining at the same rating, or having a lower rating.

9
such as interest rates, growth, and volume of international private flows. This numerical scoring system is also the basis of computation of the relative risk ratings below.

Given the above method of converting the combination of each rating grade and outlook to a numerical value, a system for using these to determine relative ratings is developed (Basu et al. 2012). An improvement in the relative rating of a country will usually be accompanied by a worsening of the relative rating of another country and vice versa. The complication arises when the average rating of the overall group of comparator countries changes over time: in this case, it becomes important to compare the rating of a country to the average rating of peers. The insight that larger economies are likely to have greater influence in changing relative ratings influences the choice of weight for a country for computing the average.

Weights based on GDP are used, thus assuming that the larger an economy, the greater its impact on the average rating of the peer group. Each country _i,_ gets a numerical rating at any point in time _t_ denoted as _xit_ . _wit_ represents each country _i_ 's GDP as a ratio of the total GDP of all _other_ countries in a particular peer group or market category (namely, emerging market economies and frontier market economies). The notion of a nation’s ‘relative rating’, that is, how good is the nation’s rating vis-à-vis the average rating of all other countries in the same market, can be formalized in many different ways.<sup>4</sup> A general formulation is: This formulation is computed as the difference between a country’s S&P sovereign ratings and the GDP-weighted average ratings of all _other_ countries in the same country grouping, which, in this paper, are emerging market economies and frontier market economies.<sup>5</sup> Relative ratings are a comparative measure. They are driven not just by the rating performance of the economy in question but also by other comparator economies. This is illustrated by the movements of absolute and relative ratings in the case of some selected countries (Figure 2). In the aftermath of the Asian financial crisis in 1997-98, East Asian countries such as the Republic of Korea which had experienced massive absolute rating downgrades recovered

> 4 For an intuitive representation of the basic idea, see Basu, et al. (2013).

> 5 Relative ratings were calculated for all countries in the relevant groups for which Standard and Poor’s rating information was available. The sample of 17 emerging market economies and 9 frontier market economies used in this study is a subset of these based on data availability for private capital flows and the country-level explanatory variables.

10
quickly in the subsequent years. The absolute rating of the Russian Federation collapsed following its sovereign debt default in 1998. During the subsequent period, other countries (such as Croatia, Jordan and South Africa) had fairly stable absolute ratings but their relative ratings improved (Figure 2).

The summary statistics for the absolute and relative ratings for our sample of emerging markets and developing economies are provided in Table 2. We include countries with at least 8 quarters of data on all variables in both the pre- and post-financial crisis periods. Our final sample contains 26 countries comprising 17 emerging market economies and 9 frontier market economies. There are on average 64.5 quarterly observations (about 16.1 years) per country ranging from 45 to 77 observations with information on all the variables in our baseline model. The average sovereign rating among all countries is 33 (around BBB- stable) on our rating scale of 0-60, ranging from an average rating of 14.49 (B- negative) for Ecuador to 47.96 (AAnegative) for Chile. Relative ratings range from -13.11 on average for Indonesia to 18.64 for Botswana.<sup>6</sup>

# _3.2 Capital flows and control variables_

A comprehensive database on net and gross capital flows is compiled at a quarterly frequency for emerging and frontier market economies with available quarterly data. The main source of capital flows data is the International Monetary Fund’s Balance of Payments Statistics, supplemented with country-sourced data from Haver Analytics.<sup>7</sup> Net private capital inflows are the difference between gross private inflows and gross private outflows. Gross private inflows are the sum of foreign direct investment, portfolio investment, and other investments (comprising largely bank lending) by non-residents net of their own disinvestment in the reporting economy.<sup>8</sup> Similarly, gross private outflows refer to flows by residents, net of their own disinvestment. We use quarterly net capital inflows as our main measure. Quarterly gross capital inflows are used to check the robustness of our results to the alternative definition of

> 6 The absolute ratings and relative ratings have the same cardinal measure but there is no strict correspondence in terms of the values.

> 7 The data collection exercise for capital flows addresses two challenges: 1) Consistency of definitions for specific categories of flows (for instance, there are differences in definitions and, therefore, values between the various sub-components of portfolio flows and debt reported by World Bank World Development Indicators, Global Economic Prospects and the IMF Balance of Payment statistics); 2) Issues due to change in Balance of Payments statistics guidelines. Historical capital flows data currently available from the IMF Balance of Payments statistics (on which the World Bank World Development Indicators data are largely based) are currently inconsistent between the pre-2005 and post-2005 periods, given the transition from BPM5 to BPM6 definitions.

> 8 These are standard components used to aggregate capital flows in the literature (see Ahmed and Zlate, 2014).

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<!-- Start of picture text --> China<br>60 17<br>55 . frenee | 16<br>ee ‘<br>45 -/ih\ fia 1413<br>40 \ ‘ NM Vad 11<br>3530 mewyattNUNSOY 910<br>‘ 8<br>25 7<br>SESSTESEPSEEESSEEEES<br>FBRESSSSSSSSSSSSSSSSSESAANNNNNNNNNNNNNNNNS<br>——S&P Rating ------- Relative Rating [Right scale]<br><!-- End of picture text -->

<!-- Start of picture text --> Jordan<br>45 10<br>40 it 8<br>35 ifs 6<br>30 pots :<br>25 : : ;*wok2 : ots ‘2<br>10 -." tenet -2<br>5 :: : -4<br>QHoaoececeeoeeeeeeedtHFSSSSSSSSSSSSSSggggdt ditt ti tat a<br>AANNNNNNNNNNNNNNNNN SES<br>—— S&P Rating +++++++ Relative Rating [Right scale]<br><!-- End of picture text --> <!-- Start of picture text --> South Africa<br>45 8<br>40 Ms 6<br>35 Pee<br>Fig mots 4<br>25 "Sees 0<br>20 . “Meee,<br>*, 2<br>15 : 4<br>10 Beane 6<br>5 5 tees 8<br>ie) goBESAKCHFRSRBESAKHAIATSHoaoeoeeoeceeeeeeeGddtaAnoooocoodcscaecdce000c0ec0odAANNNNNNNNNNNNNNNNNdtd didi dt di didi ditt did ddddt dt tttttcct ela -10<br>—— S&P Rating «+++ +++ Relative Rating [Right scale]<br><!-- End of picture text --> <!-- Start of picture text --> Croatia<br>45 18<br>.<br>40 Pre 16<br>305 :f OSta eet A 1210<br>20 Sree 6<br>1s10 4sHae*° 4<br>5 tai Lo<br>0 -2<br>SESTLSPERERSEESEEREE<br>BASSSSSSSSSSSSSSESSSSADI NNNNNN NNN NNN NNN NN<br>——- S&P Rating -+**+++* Relative Rating [Right scale]<br><!-- End of picture text --> <!-- Start of picture text --> Russian Federation<br>45 5<br>40 nipfs 0<br>35 The 5<br>F<br>| { -10<br>25 +! ( -15<br>10 1 -25<br>5 ae- -30<br>FRSESSSSSSSSAHARAA<br>daadadaanaadanadaAAnaAAAAaAAAES<br>ee S&P Rating ----- Relative Rating [Right scale]<br><!-- End of picture text -->

<!-- Start of picture text --> Korea, Rep.<br>60 18<br>29 pat 16<br>50 te ooh 14<br>45 o fe : 2<br>. soeet te 8<br>30 ° “eet %e, . 6<br>243 “eee<br>2042 4<br>15 48 2<br>105 -+: 0-2<br>0 godtBSSTARFRTLSESTIIHAIFRTEQooedceoceoeeceeOddtiAnococoeoncaeaaeoncocecceceocaeAAI NNNNNNNNNNNNNNdd dtd dt dt ddd dt dtdti tdtttttcdlddodNNN 4<br>= S&P Rating «eee Relative Rating [Right scale]<br><!-- End of picture text -->
**Table 2: Quarterly sample of 26 emerging and frontier market economies: 1998-2017**

|||**S**|**&P ratin**<br>Std.<br>|**gs**<br>|||**Relativ**<br>Std.<br>|**e ratings**<br>||
|---|---|---|---|---|---|---|---|---|---|
||Obs.|Mean|dev.|Min.|Max.|Mean|dev.|Min.|Max.|
|Botswana*|45|43.35|1.45|42|45|18.64|2.95|15.04|25.84|
|Brazil|77|27.64|5.33|20|36|-8.73|3.04|-15.53|-4.56|
|Bulgaria*|70|30.82|5.96|19|39|7.36|5.61|-6.55|13.50|
|Chile|56|47.96|2.63|43|51|10.16|1.45|8.00|13.59|
|China|77|44.30|6.06|36|51|12.86|2.70|8.23|16.61|
|Colombia|62|30.68|3.76|26|36|-6.10|2.04|-9.22|-2.64|
|Croatia*|65|32.00|3.50|26|36|8.51|5.14|-0.92|16.42|
|Czech Republic|76|45.69|3.88|42|51|10.57|3.24|5.08|17.57|
|Ecuador*|63|14.49|3.60|4|21|-9.72|3.85|-19.84|-4.61|
|North Macedonia*|51|26.64|2.32|24|30|1.63|2.77|-3.12|7.95|
|Hungary|70|35.48|5.67|26|42|0.53|10.55|-15.08|13.54|
|India|68|30.73|2.70|26|33|-6.22|2.65|-9.87|1.51|
|Indonesia|72|23.31|7.70|4|33|-13.11|3.32|-26.08|-6.79|
|Jordan*|77|25.13|1.66|21|27|1.24|3.17|-4.17|7.91|
|Kazakhstan*|46|35.11|2.63|32|39|10.70|2.83|6.43|16.59|
|Mexico|47|37.36|1.34|36|39|-2.03|1.91|-5.09|1.41|
|Morocco*|56|31.13|2.08|27|33|7.11|1.62|3.47|9.70|
|Peru|55|33.66|4.98|24|39|-4.62|2.45|-8.24|-0.51|
|Philippines|76|28.96|4.21|23|36|-6.37|6.19|-15.27|4.04|
|Poland|69|40.40|1.76|37|43|4.26|4.02|-2.38|12.80|
|Russian Federation|56|33.70|3.63|27|40|-4.74|4.41|-12.74|2.73|
|South Africa|73|35.17|3.02|30|39|0.13|4.67|-8.47|6.56|
|Korea, Rep.|72|44.55|4.86|34|54|9.28|2.77|4.53|15.27|
|Thailand|76|37.00|2.73|32|39|1.78|3.18|-2.03|7.14|
|Turkey|61|25.09|4.05|14|30|-12.78|2.07|-16.22|-4.84|
|Ukraine*|61|17.47|5.41|2|24|-6.99|6.65|-26.07|1.21|

*Frontier market economy based on MSCI, S&P and Dow-Jones classifications.

Note: S&P ratings were converted to numeric ratings as described in the text and Table 1. A higher number indicates a better rating. See text for computation of relative ratings for the group of EMs and FMs. The sample includes the quarterly observations used in the baseline estimation in Table 3 for the 1998-2017 period.

All our country-specific controls are also at a quarterly frequency. These include GDP growth differentials and interest rate differentials for our sample of EMs and FMs relative to the average GDP growth of the advanced market economies. These country-level control variables are similar to those used by Ahmed and Zlate (2014) in their study of EM quarterly capital flows.<sup>9</sup> Global factors include an indicator for risk (VIX index) in the baseline specification, and subsequently, an additional variable for the US quantitative easing (QE) episodes. Details of the variables used in the analysis at quarterly frequency are provided in Appendix Table A.1.

> 9 Capital controls are often used as an additional explanatory variable for capital flows in the literature (e.g., Fratzscher, 2012; Ahmed and Zlate, 2014). However, it is possible that controls themselves may be imposed in response to increased flows by policy-makers interested in maintaining financial stability (Ostry et al., 2011; Ghosh and Qureshi, 2016), which can give rise to a positive or insignificant relationship between capital controls and flows instead of a negative relationship. For instance, Forbes and Warnock (2012) find no significant relationship between capital controls and a country’s likelihood of experiencing a surge or stop in capital flows. Given the potential endogeneity of capital controls to capital flows, we do not include it in the specifications presented in this paper. Our main results for sovereign ratings and relative risk ratings, however, are robust to the inclusion of the Chinn-Ito index of capital controls (Chinn and Ito, 2008). These additional robustness results are available from the authors upon request.

13
Summary statistics of the variables are provided in Appendix Table A.2.

# **4. Empirical Strategy**

The methodology for studying the influence of sovereign and relative risk ratings on capital flows builds on a large literature on determinants of capital flows (see, for example, Ahmed and Zlate 2014, Broner et al. 2013, Forbes and Warnock 2012, Kim and Wu 2008) and extends it to include relative risk ratings. The baseline model below captures the influence of sovereign ratings and relative risk ratings controlling for other global and country-specific determinants of capital flows.

The equation for determinants of capital flows is as follows: i = i −1 1 i 2 i 3 i + i (2) FF γγFF + δδ rr + δδ GG + δδ XX ii εε _Fit_ is net capital flows as a share of GDP for country + μμ _i_ in the time period _t_ .<sup>10</sup> _rit_ is either the sovereign rating or relative risk rating as defined above. _Gt_ represents global variables, _Xit_ represents country-specific variables, are country-specific intercepts, and _εit_ is the error term.<sup>11</sup> As discussed earlier, country-specific factors include GDP growth differentials and ii μμ interest rate differentials similar to those used by Ahmed and Zlate (2014). Global factors include an indicator for risk (VIX) for the baseline specification, and subsequently, an additional variable related to US quantitative easing in an alternative specification.

The dynamic fixed effects specification in equation (2) is estimated with quarterly data. The use of quarterly data and the small number of cross-sectional units in our sample largely alleviates the so-called “Nickell bias” that is inherent in dynamic panels with a lagged dependent variable typically when the number of cross-sectional units is relatively large (Nickell 1981).<sup>12</sup> Nonetheless, we use the bias-corrected dynamic fixed effects estimator of

> 10 Net flows are likely to be influenced by both global and domestic factors (Ahmed and Zlate 2014). Given the importance of net flows in the balance of payments position, the response of net flows to sovereign ratings and relative risk ratings is examined here.

> 11 If there is no credit rating information, the country would be dropped from the sample. This, however, should not affect the analysis greatly since the vast majority of countries accessing international capital are rated. 12 Traditional Generalized Method of Moments (GMM) methods, which are appropriate for dynamic panels with a large number of cross-sectional units (large N) can become biased and suffer from loss of efficiency in panels where the number of cross-sectional units is relatively small and when there is strong autocorrelation in the data (Blundell and Bond 1998).

14
Bruno (2005), which is appropriate for unbalanced data such as ours with a small number of countries and time series observations at higher than annual frequency.<sup>13</sup> Splitting the sample between the pre- and post-crisis periods is likely to reveal whether relative ratings gained increased salience for capital allocation in the post-crisis period. Therefore, the benchmark capital flows specification is estimated separately for the pre- and post-crisis periods. Overall, three specifications are estimated: for all years from 1998 to 2017, for the pre-financial crisis period (first quarter of 1998 until the second quarter of 2008), and the postcrisis period (from the second quarter of 2009 until the fourth quarter of 2017). In order to exclude the effect of the immediate aftermath of the global financial crisis, we omit two quarters following the onset of the global financial crisis in the third quarter of 2008 from the post-crisis sample.

During the post-crisis period, advanced economy central banks were broadly in a monetary easing mode, which included quantitative easing (QE) by the US Federal Reserve and other central banks. This resulted in “spillovers” to developing countries in the form of increased capital flows (IMF 2013, Lim and Mohapatra 2016). In the analysis, we control for this liquidity effect using an indicator of QE based on Lim and Mohapatra (2016). In an alternative specification, we also control for the increase in the US Federal Reserve’s asset holdings as a continuous measure of QE. This can also capture changes in capital flows that are driven not by fundamental or institutional factors but possibly by investor sentiment during the QE implementation.

# **5. Results**

This section discusses our main results on the impact of absolute sovereign ratings and relative risk ratings on net capital flows to emerging and developing countries. The regression specifications used are similar to Ahmed and Zlate (2014) with growth differentials, interest rate differentials, and a global risk (VIX) index as explanatory variables. Our variables of interest, the S&P sovereign rating and relative risk rating, were included as additional explanatory variables in separate specifications.

> 13 Bootstrapped standard errors for the dynamic panel data model were calculated with 100 replications.

15
# _5.1 Effect of ratings on net capital flows_

The baseline regression results presented in Table 3 are estimated using a bias-corrected dynamic panel estimator. The results suggest that after accounting for the traditional indicators both absolute sovereign ratings and relative risk ratings are significant predictors of capital inflows (See Appendix Table A.3 for results without absolute or relative ratings). However, the coefficient of the absolute sovereign rating is less significant and smaller in the post-crisis period compared to the pre-crisis period (columns (1) and (2) of Table 3). Moreover, the magnitude of the relative rating variable is larger and more significant in the post-crisis period (columns (3) and (4) of Table 3). This finding is consistent with our expectation that relative risk ratings became more important especially when compared to a decline in the importance of absolute ratings in the post-crisis period.

**Table 3: Sovereign credit ratings, relative risk ratings, and net capital inflows to emerging and developing countries**

||**Abs**|**olute rating**|**Rela**|**tive to peers**|
|---|---|---|---|---|
|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.041<br>(0.038)|0.106***<br>(0.038)|0.096**<br>(0.039)|0.103***<br>(0.038)|
|S&P rating|0.519***<br>(0.081)|0.163*<br>(0.083)|||
|Relative rating|||0.141*<br>(0.083)|0.212***<br>(0.080)|
|Growth diff.|0.118<br>(0.098)|0.263***<br>(0.087)|0.201**<br>(0.099)|0.260***<br>(0.088)|
|Interest rate diff.|0.120|0.436***|0.028|0.465***|
||(0.117)|(0.117)|(0.117)|(0.118)|
|VIX|-0.030|0.055|-0.116***|0.047|
||(0.040)|(0.040)|(0.041)|(0.039)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|780|859|780|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.328|0.237|0.293|0.240|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

The findings suggest that in a situation of abundant global liquidity and strong flows to developing countries but comparative caution among investors given the adverse experiences of the crisis, the relative perception of sovereign risk (i.e. how investors perceive a country’s creditworthiness relative to its peers) played a more important role than the sovereign rating in influencing capital flows in the post-crisis period. These findings for capital flows in the post16
crisis world characterized by a greater role of relative risk ratings and of global risk and a smaller role of absolute sovereign ratings also explains why many relatively lower-rated countries were able to access international capital markets on comparatively favorable terms during the post-financial crisis period (Guscina, Pedras and Presciuttini 2014).

The results for the control variables – growth and interest rate differentials and the VIX index, are broadly along expected lines. Growth differentials are positive and statistically significant in most cases (Table 3), while the interest rate differential is significant in the post-crisis period, consistent with a search for yield in the post-crisis period. The coefficient of the VIX index is negative in the pre-crisis period but statistically significant only for relative ratings. This suggests that while country-specific factors were relevant, global risk conditions appear to materially influence capital inflows in the period prior to the financial crisis. Overall, the results suggest that the model in Table 3 captures the key factors relevant for capital flows.

In addition to statistical significance, it is also important to understand the economic importance of the estimated coefficients for the average country in our sample. The results suggest that a one-notch increase in the sovereign rating is associated with a 0.519 percentage point increase in net capital flows as a share of the recipient country GDP for the pre-crisis sample (column (1) of Table 3). An actual rating upgrade or increase in the sovereign rating, for instance, from B+ to BB-, would represent a 3 notch increase on our 60-point rating scale. Such an upgrade, on average, would be associated with an increase in capital flows equivalent to about 1.6 percentage point of recipient country GDP. Since the average country in our sample receives net capital flows of about 2.8 percent of GDP, the effect of such an upgrade (or downgrade) represents more than half of inflows on average. The effect of sovereign ratings on capital flows is less than third in the post-crisis period (column (2)) compared to the precrisis period, and only marginally significant.

Conversely, for relative ratings, although the coefficient is 0.141 in the pre-crisis period, the effect for the post-crisis period is larger at 0.212. A country’s relative rating can change in three scenarios: when its sovereign rating changes; when the average rating of other countries in the same group changes; or when both happen together. An implication is that even when a hypothetical country X’s sovereign rating remains stable, if the average rating of other countries in the same grouping falls by 3 notches (equivalent to an actual rating downgrade) on our 0-60 rating scale, then capital flows to country X are estimated to rise by 0.423 percent of GDP in the pre-crisis period and a larger 0.636 percent of GDP during the post-crisis period, 17
keeping all other factors unchanged.

_5.2 Quantitative easing and the effect of sovereign ratings and relative ratings during the postcrisis period_ The results on quantitative easing as an additional control in the baseline specification on country ratings and capital flows are presented in Table 4. Focusing on net capital flows in the post-crisis period, we find that adding an indicator for QE episodes to the baseline specification maintains the results found earlier on the larger effect of relative risk ratings compared to absolute sovereign ratings (columns (1) and (2)) for the post-crisis period. The magnitude of the coefficients of absolute and relative ratings is similar to the baseline model. The QE indicator is statistically significant confirming findings on the positive effect of QE from earlier studies (see Lim and Mohapatra 2016).

**Table 4: Quantitative easing, sovereign ratings, and net capital inflows after the financial crisis**

||**QE in**<br>|**dicator**<br>|**QE co**<br>|**ntinuous**<br>|
|---|---|---|---|---|
|Net Inflows|Absolute rating<br>(1)|Relative rating<br>(2)|Absolute rating<br>(3)|Relative rating<br>(4)|
|L.Net Inflows|0.089**<br>(0.037)|0.088**<br>(0.037)|0.099***<br>(0.038)|0.098***<br>(0.038)|
|S&P Rating|0.181**<br>(0.082)||0.176**<br>(0.083)||
|Relative Rating||0.200**<br>(0.079)||0.202**<br>(0.080)|
|U.S QE indicator|1.814***<br>(0.381)|1.728***<br>(0.383)|||
|U.S Fed Purchases|||0.005**<br>(0.002)|0.004**<br>(0.002)|
|Growth diff.|0.215**|0.214**|0.232***|0.231***|
||(0.087)|(0.087)|(0.087)|(0.088)|
|Interest rate diff.|0.471***|0.487***|0.442***|0.462***|
||(0.116)|(0.117)|(0.116)|(0.117)|
|VIX|0.035<br>(0.039)|0.028<br>(0.039)|0.028<br>(0.039)|0.023<br>(0.039)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|859|859|859|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.255|0.257|0.243|0.245|

Notes: The post-crisis sample includes quarterly panel data for 26 emerging and frontier market economies from Q2 2009 until Q4 2017. All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. QE refers to the episodes of Quantitative Easing by the US Federal Reserve. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

18
When a continuous measure of QE (US Fed purchases) is included in the regression, the coefficient of relative rating remains larger than that of absolute rating (columns (3) and (4) of Table 4). This suggests that relative ratings played a larger positive role in influencing capital flows to emerging and frontier market economies than absolute ratings, even after controlling for the US quantitative easing during the post-crisis period.

Our findings suggest that while emerging and frontier economies at all rating levels received higher capital flows during QE implementation, those that improved their _relative_ creditworthiness received even more. The rewards for “good performance” were higher during the times of QE in the form of larger capital inflows. This suggests that even while searching for higher yields in emerging and frontier economies, international investors discriminated between better and worse performers on a relative scale and not only in absolute terms.

# **6. Robustness**

In this section, we test the robustness of the results reported in Section 5 using various alternative specifications. These include the use of lagged explanatory variables; use of fixed instead of variable GDP weights to calculate relative risk ratings; use of gross capital flows instead of net capital flows; inclusion of both sovereign ratings and relative risk ratings in the baseline model; and controlling for potential endogeneity of ratings.

## _6.1 Use of lagged explanatory variables_

In order to address concerns about contemporaneous correlations between capital flows, sovereign ratings, and macroeconomic and global variables used in the baseline model, we reestimate the regression with lagged explanatory variables. The results are reported in Table 5. The key baseline result of a decline in the importance of sovereign ratings and an increase in the role of relative ratings in the post-crisis period holds for this lagged specification.

## _6.2 Use of fixed GDP weights in calculating relative risk ratings_

The formula for relative ratings in equation (1) allows for the country-level GDP weights to vary over time. A possible concern with this approach is that the relative ratings measure may be influenced by substantial changes in GDP weights over the course of time even if absolute ratings of the countries in the peer group remain stable. To address this concern, we hold the GDP weight for each country fixed at the average value for the sample period, recalculate the 19
relative ratings measure with the fixed GDP weights, and re-estimate the baseline regression. The results reported in Table 6 suggest that the baseline result indicating greater relevance of relative ratings for private capital flows in the post-crisis period compared to the pre-crisis period is robust to the use of fixed GDP weights.

# _6.3 Use of gross capital inflows_

As a robustness test for our main results obtained using net capital inflows, we use gross capital flows as the dependent variable instead of gross flows for our baseline specification. The results are reported in Table 7. The baseline results reported in Table 3 are robust to this alternative definition of the dependent variable. The coefficient of the absolute ratings is smaller in the post-crisis period while that of relative ratings is larger. The larger coefficient of relative risk ratings for gross capital flows, compared to that for net capital flows in the baseline results may be explained by foreign investors being more sensitive to relative ratings compared to resident investors. These additional results for gross capital flows, which have been widely used in the literature, substantiate our baseline results for net capital flows.

**Table 5: Use of lagged explanatory variables**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>|**tive to peers**<br>|
|---|---|---|---|---|
|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.044|0.113***|0.092**|0.110***|
||(0.037)|(0.038)|(0.037)|(0.038)|
|L.S&P rating|0.512***<br>(0.094)|0.162*<br>(0.086)|||
|L.Relative rating|||0.170*<br>(0.088)|0.202**<br>(0.080)|
|L.Growth diff.|0.159|0.190**|0.218**|0.189**|
||(0.113)|(0.079)|(0.109)|(0.079)|
|L.Interest rate diff.|0.087|0.406***|-0.009|0.429***|
||(0.112)|(0.132)|(0.113)|(0.132)|
|L.VIX|-0.008|0.073**|-0.093**|0.064*|
||(0.041)|(0.036)|(0.040)|(0.035)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|769|859|769|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.311|0.238|0.293|0.240|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. All the independent variables are subjected to a one-period lag. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 19982017 period. The pre-crisis period includes Q1 1998-Q2 2008 and the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

20
**Table 6: Use of fixed GDP weights in calculating relative ratings**

||Pre-Crisis<br>(1)|Post-Crisis<br>(2)|
|---|---|---|
|L.Net Inflows|0.094**|0.105***|
||(0.039)|(0.038)|
|Relative rating|0.169*<br>(0.087)|0.178**<br>(0.080)|
|Growth diff.|0.198**<br>(0.099)|0.263***<br>(0.087)|
|Int. rate diff.|0.03|0.451***|
||(0.117)|(0.118)|
|VIX|-0.118***|0.055|
||(0.041)|(0.040)|
|Country F.E.|Y|Y|
|Obs.|780|859|
|Countries|26|26|
|Adj. R-squared|0.295|0.238|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

**Table 7: Robustness of baseline results to the use of gross capital inflows**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>|**tive to peers**<br>|
|---|---|---|---|---|
|Gross Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.143***|-0.008|0.191***|-0.011|
||(0.039)|(0.038)|(0.040)|(0.038)|
|S&P rating|0.510***<br>(0.086)|0.370***<br>(0.097)|||
|Relative rating|||0.044<br>(0.089)|0.454***<br>(0.093)|
|Growth diff.|-0.027|0.188*|0.062|0.178*|
||(0.106)|(0.101)|(0.105)|(0.101)|
|Interest rate diff.|0.100|0.167|0.020|0.217|
||(0.125)|(0.135)|(0.124)|(0.135)|
|VIX|-0.203***|0.027|-0.274***|0.010|
||(0.045)|(0.046)|(0.045)|(0.045)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|780|859|780|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.443|0.161|0.414|0.171|

Notes: All regressions have gross capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

21
# _6.4 Inclusion of both sovereign and relative ratings in the baseline model_

Since the calculation of relative ratings includes a country’s own sovereign rating (see equation (1)), there may be concerns that the finding of increased importance of relative ratings in the post-crisis period may be driven by sovereign ratings. As discussed earlier, a change in a country’s relative rating can take place either due to a change in its own sovereign rating or due to a change in the average rating of other countries. Consequently, the effect of relative ratings on capital flows can operate both through a country’s own sovereign rating changes and independently of one’s own sovereign ratings. In order to account for these two effects, we first run a simple ordinary least squares (OLS) regression of relative ratings on sovereign ratings for our baseline sample. The coefficient of the sovereign rating is 0.71, which is statistically significant at 1 percent level, confirming an expected strong relationship between absolute ratings and relative ratings. The adjusted R-squared for the regression is 0.495, implying that almost half of the variation in relative ratings is driven by sovereign ratings. We then estimate the relative rating residuals (the difference between actual and predicted relative ratings), which is the part of relative ratings that is _not_ explained by sovereign ratings. We include this residual along with sovereign ratings and other explanatory variables in the baseline model. The results are reported separately for the pre-crisis and post-crisis periods in Table 8.

**Table 8: Joint effect of relative ratings and absolute S&P ratings on net capital inflows**

|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|
|---|---|---|
|L.Net Inflows|0.039<br>(0.038)|0.103***<br>(0.038)|
|S&P rating|0.511***<br>(0.085)|0.008<br>(0.111)|
|Relative rating residual|-0.086<br>(0.099)|0.589**<br>(0.265)|
|Growth diff.|0.110<br>(0.103)|0.251***<br>(0.087)|
|Interest rate diff.|0.133<br>(0.120)|0.473***<br>(0.116)|
|VIX|-0.018|0.030|
||(0.045)|(0.040)|
|Country fixed effects|Y|Y|
|No. of obs.|780|859|
|No. of countries|26|26|
|Adj. R-squared|0.329|0.242|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The relative rating residuals were obtained from a regression of relative ratings on the S&P rating for the sample period. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

22
The results in Table 8 show that the coefficient of S&P sovereign rating is positive and statistically significant for the pre-crisis period, but much smaller in magnitude and insignificant for the post-crisis period, consistent with the baseline model. The relative rating residual retains its positive sign and significance in the post-crisis period even after the inclusion of S&P sovereign ratings. These findings suggest an independent effect of relative ratings in the post-crisis period, which is not driven by sovereign ratings.

# _6.5 Robustness to potential endogeneity of ratings_

We test whether the baseline results reported in Table 3 are robust to potential endogeneity of sovereign and relative ratings. Although the major international rating agencies attempt to maintain the independence of the rating process, it is possible that ratings may be influenced by the volume of capital inflows, in particular large debt inflows that can raise the ratio of external debt to GDP and thereby influence a country’s rating. In order to account for such potential endogeneity, an instrumental variables approach is used with the current values of sovereign ratings and relative ratings instrumented by a four-quarter (one year) lag of the Institutional Investor country credit rating (IIR). The Institutional Investor rating is based on repeated surveys of economists and risk analysts and is arguably independent of the sovereign rating process of the major international credit rating agencies. Moreover, a four-quarter lag of the instrumental variable is sufficient, in our view, to mitigate any possible feedback effects from capital flows to investors’ contemporaneous risk perceptions.<sup>14</sup> The Durbin Chi-squared and Wu-Hausman test statistics for potential endogeneity are reported for both the sub-periods. The results for the instrumental variable regression for the pre-crisis and post-crisis periods are reported in Table 9.

The coefficient of absolute rating in Table 9 is larger and statistically significant in the precrisis period but is smaller in magnitude and loses significance in the post-crisis period. The coefficient of relative rating, on the other hand, is larger in the post-crisis period compared to the pre-crisis period and statistically significant at the 5 percent level. These results for sovereign and relative ratings are similar to the baseline specification in sign and significance. Moreover, the Durbin Chi-squared and Wu-Hausman test statistics indicate potential endogeneity of only sovereign ratings for the pre-crisis period, which is mitigated by the use

> 14 The instrumental variables are statistically significant in a regression of sovereign and relative ratings on these lagged instruments. The results are available from the authors upon request.

23
of instrumental variables in the specification reported in Table 9. The corresponding test statistics in the second column in the table do not suggest the presence of endogeneity of ratings in the post-crisis period. The robustness of our baseline specification to the use of instrumental variable regressions provides additional support for the main results presented in Section 5.

**Table 9: Robustness of baseline results to potential endogeneity of ratings: Instrumental variable estimates**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>|**tive to peers**<br>|
|---|---|---|---|---|
|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.015<br>(0.036)|0.096***<br>(0.035)|0.085**<br>(0.035)|0.090**<br>(0.035)|
|S&P rating|0.639***<br>(0.102)|0.148<br>(0.114)|||
|Relative rating|||0.156*<br>(0.091)|0.264**<br>(0.118)|
|Growth diff.|0.075|0.270***|0.225**|0.265***|
||(0.100)|(0.081)|(0.100)|(0.080)|
|Interest rate diff.|0.141**<br>(0.072)|0.390***<br>(0.121)|0.000<br>(0.070)|0.445***<br>(0.123)|
|VIX|-0.007|0.028|-0.106***|0.019|
||(0.040)|(0.036)|(0.039)|(0.036)|
|Durbin Chi-Squared|4.054**|0.019|0.013|0.344|
|Wu-Hausman F stat.|3.901**|0.018|0.012|0.331|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|743|822|743|822|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.325|0.235|0.295|0.239|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

# **7. Decomposing net capital inflows**

In this section, we examine whether the observed baseline effects for absolute and relative ratings in the pre- and post-crisis periods reported in the baseline in Table 3 are driven by specific components of net capital flows. Table 10a reports the results of estimating equation (2) for absolute ratings for the three main components of net capital flows – net foreign direct investment (FDI) inflows, net portfolio inflows, and net other investment inflows, which mainly comprises cross-border bank lending. Equivalent results for the three components of capital inflows with relative rating as an explanatory variable are reported in Table 10b.

The results reported in columns (1) and (2) of Tables 10a and 10b show that for net FDI inflows, 24
the effect of both absolute and relative ratings is positive and statistically significant but the magnitude of this effect declines for both types of ratings in the post-crisis period compared to the pre-crisis period. The fall in the effect of relative ratings on net FDI inflows may be accounted for by FDI including equity investment, as well as reinvested earnings and intracompany loans.<sup>15</sup> The latter components may not necessarily relate to portfolio considerations.

However, for net portfolio investment inflows (columns (3) and (4) in Tables 10a and 10b), absolute ratings do not seem to play any role in either period. By contrast, relative ratings are positively related to portfolio inflows during the post-crisis period compared to a much smaller and insignificant effect in the pre-crisis period. For cross-border banking inflows (represented by other investment inflows), reported in columns (5) and (6) in Tables 10a and 10b, absolute ratings appear to be a significantly related to such flows in the pre-crisis period but not in the post-crisis period while relative ratings do not seem to be relevant in either period.

The results suggest that portfolio and banking inflows are closely related to changes in relative ratings in the post-crisis period, which is consistent with the view of such flows being influenced by both abundant global liquidity and the emphasis on relative performance among international investors after the global financial crisis. Foreign direct investment is related to country risk considerations in the pre-crisis period but the decline in the influence of absolute ratings together with the stronger relationship to interest rate and growth differentials in the post-crisis period may be indicative of direct investment being driven more by macroeconomic and corporate considerations.<sup>16</sup>

> 15 According to the IMF’s Balance of Payments Manual (Sixth Edition), FDI includes equity that gives rise to control or influence (defined as 10 or more percent of voting power), and also investment associated with that relationship including investment in indirectly influenced or controlled enterprises, investment in fellow enterprises, intra-company loans, and reverse investment (IMF, 2010). 16 The strong positive relationship of net FDI inflows with global risk is consistent with investors seeking safer assets during times of heightened uncertainty.

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**Table 10a: Decomposition of net capital inflows: Absolute ratings**

||Net FDI<br>|Inflows<br>|Net Portfo<br>|lio Inflows<br>|Net Banki<br>|ng Inflows<sup>††</sup><br>|
|---|---|---|---|---|---|---|
||Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|Pre-Crisis<br>(5)|Post-Crisis<br>(6)|
|L.Net Inflow<sup>†</sup>|0.193***|0.117***|0.053|-0.044|0.033|0.047|
||(0.040)|(0.036)|(0.039)|(0.039)|(0.039)|(0.038)|
|S&P rating|0.183***|0.066**|0.037|0.113|0.261***|0.001|
||(0.054)|(0.032)|(0.045)|(0.076)|(0.071)|(0.065)|
|Growth diff.|-0.016|0.073**|0.014|-0.084|0.118|0.129*|
||(0.067)|(0.033)|(0.056)|(0.081)|(0.088)|(0.069)|
|Interest rate diff.|0.053|0.111**|0.037|-0.005|0.017|0.238***|
||(0.079)|(0.045)|(0.066)|(0.107)|(0.104)|(0.092)|
|VIX|-0.033|0.031**|-0.060***|-0.046|0.061*|-0.056*|
||(0.027)|(0.015)|(0.023)|(0.036)|(0.035)|(0.031)|
|Country fixed effects|Y|Y|Y|Y|Y|Y|
|No. of obs.|781|859|781|859|781|859|
|No. of countries|26|26|26|26|26|26|
|Adj. R-squared|0.347|0.333|0.186|0.051|0.158|0.115|

Notes: All regressions are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticityconsistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.<sup>†</sup> Lagged value corresponds to the inflow category in the row header.<sup>††</sup> _Net other investment inflows_ (Balance of Payments category) largely capture cross-border banking inflows.

**Table 10b: Decomposition of net capital inflows: Relative ratings**

||Net FD<br>|I inflows<br>|Net Portfo<br>|lio Inflows<br>|Net Banki<br>|ng Inflows<sup>††</sup><br>|
|---|---|---|---|---|---|---|
||Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|Pre-Crisis<br>(5)|Post-Crisis<br>(6)|
|L.Net Inflows<sup>†</sup>|0.204***|0.117***|0.053|-0.047|0.051|0.045|
||(0.040)|(0.036)|(0.039)|(0.039)|(0.039)|(0.038)|
|Relative rating|0.130**|0.061**|0.012|0.163**|-0.015|0.053|
||(0.056)|(0.030)|(0.047)|(0.072)|(0.077)|(0.062)|
|Growth diff.|0.018|0.071**|0.021|-0.088|0.167*|0.127*|
||(0.067)|(0.033)|(0.056)|(0.081)|(0.092)|(0.069)|
|Interest rate diff.|0.017|0.112**|0.030|0.020|-0.026|0.259***|
||(0.078)|(0.045)|(0.066)|(0.107)|(0.109)|(0.093)|
|VIX|-0.069**|0.029*|-0.066***|-0.051|0.022|-0.057*|
||(0.027)|(0.015)|(0.023)|(0.036)|(0.037)|(0.031)|
|Country fixed effects|Y|Y|Y|Y|Y|Y|
|No. of obs.|781|859|781|859|781|859|
|No. of countries|26|26|26|26|26|26|
|Adj. R-squared|0.344|0.333|0.185|0.055|0.143|0.116|

Notes: All regressions are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies. The pre-crisis period includes Q1 1998-Q2 2008 while the Post-Crisis period includes Q2 2009-Q4 2017. Heteroskedasticityconsistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.<sup>†</sup> Lagged value corresponds to the inflow category in the row header.<sup>††</sup> _Net other investment inflows_ (Balance of Payments category) largely capture cross-border banking inflows.

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# **8. Conclusions**

This paper examines the impact of both absolute and relative ratings on private capital flows to 26 emerging and frontier market economies during the 1998-2017 period. Particular attention is given to the post-crisis period and to the impact of US quantitative easing. The findings suggest that relative risk ratings (ratings relative to peers) played a larger role in influencing capital flows to emerging and frontier market economies in the post-crisis period compared to the pre-crisis period. In contrast, the impact of absolute ratings declined in the post-crisis period. This effect is evident for net capital flows. The impact of established drivers of capital flows based on country-specific growth differentials and interest rate differentials and global risk factors are consistent with earlier studies in the literature. The effect of relative ratings is more pronounced than absolute ratings even after controlling for quantitative easing in the post-crisis period. Evidently, while there was an increase in global liquidity during quantitative easing, investors also became more discerning in the post-crisis period due to their negative experiences during the financial crisis. The baseline results are robust to the use of fixed GDP weights in calculating relative ratings; to an alternative definition of capital flows (gross instead of net); and to an alternative estimation method (that accounts for potential endogeneity of ratings). Decomposing the flows into net foreign direct investment, net portfolio flows, and net other investments (largely bank lending) reveals that the post-crisis effect of relative ratings is largely driven by portfolio flows.

Official aid to developing countries is likely to remain stagnant as fiscal pressures persist in high-income countries. Private capital will be needed to fill these financing gaps, thus, making ratings more important for developing countries. In these circumstances, policy makers in developing countries must pay attention to not only their own creditworthiness but also how their performance compares to their peer countries in order to attract capital in the post-crisis world. Since relative performance appears to greatly impact volatile portfolio flows, tracking changes in relative ratings could help predict and manage the macroeconomic disturbances resulting from sudden movements of these forms of capital.

The findings of this paper shed light on global and country-specific risks with potential policy implications for private financial flows. This is a pioneering study on the impact of relative ratings on investment behavior and capital flows. Much more needs to be done which lies beyond the scope of this paper. Areas for future research could include the impact of relative 27
ratings on bond spreads, its impact on stock market performance, and possible spillover effects within regions or across peer groups.

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