The Effects of Changes in Bond Ratings on Income Inequality in the Developing World

Ronald J. McGauvran and Glen Biglaiser

Department of Political Science University of North Texas

Abstract

The effects of globalization, as measured by foreign direct investment, portfolio investment, and trade, on income inequality has been the focus of recent scholarly attention. However, missing from the discussion is the role that credit rating agencies (CRAs) play on income distributions. CRAs hold an important responsibility as they rate sovereign bonds, determining the costs of capital for bond issuers, and thus influence economic outcomes. Using a two-stage least squares analysis with instrumental variable approach, to account for possible endogeneity, in developing countries from 1995-2015, we find that bond ratings have significant effects on income inequality. Using a panel vector autoregressive modeling structure we show that sovereign bond rating influence both social spending as well as economic inequality, while not responding to larger economic trends. The findings suggest that developing countries should carry out policies preferred by the CRAs that support The findings suggest that developing countries should carry out policies preferred by the CRAs that support higher bond ratings because the lower cost of capital has the potential to help reduce income inequality.

Keywords: Income inequality; credit rating agencies; globalization; sovereign bond ratings.

Policy convergence toward globalization in less-developed countries (LDCs) has drawn much

scholarly interest (Drezner 2001; Garrett 1998a, 1998b; Mosley 2003; Rodrik 1997, 1998). The

debate surrounding the effects of market-oriented policies on income inequality has further

driven interest in this area. Although Stolper-Samuelson trade theory suggests that LDCs should

benefit from economic integration, because of their lower wage levels and attractiveness to

foreign firms, many studies have found that globalization actually worsens income distribution.

Globalization, as measured by foreign direct investment (FDI) (Alderson and Nielsen 1999; Ha

2012; Huber, et al. 2006; Reuveny and Li 2003) and trade (Ha 2012; Reuveny and Li 2003;

Rudra 2008), exacerbates income inequality,1 while portfolio investment has inconsistent

impacts on inequality (Reuveny and Li 2003; Rudra 2008).

Missing from the globalization and inequality discussion is the role of credit rating

agencies (CRAs). CRAs hold an important responsibility as they rate sovereign bonds,

determining the costs of capital for bond issuers. Although much research in the CRA literature

has investigated the determinants of bond ratings (Archer, Biglaiser, and DeRouen 2007; Breen

and McMenamin 2013; Beaulieu, Cox, and Saiegh 2012; Biglaiser and Staats 2012), the effects

of bond ratings on policies in LDCs has garnered less attention.2 The lack of research is

surprising as developing countries rely ever more frequently on foreign capital from sovereign

bond sales (Sinclair 2005). Indeed, the Bank for International Settlements (2016) projects that

the amount outstanding for all debts issued by public institutions exceeded $685 billion in the

first quarter of 2016. The fact that over the past two decades CRAs have played an increasingly

1 See also Rudra (2002), who shows that globalization leads to the decline of the welfare state in LDCs but that it also can help strengthen democracy (Rudra 2005). 2 For some exceptions that consider the effects of bond ratings on policy, see DiGiuseppe and Shea (2013, 2015). DiGiuseppe and Shea (2015) use credit downgrades from CRAs as an independent variable to explain differences in the fate of democratic and authoritarian leaders.

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critical role for capital-scarce developing countries, suggests that the ratings offered by CRAs

might influence policies connected with income inequality in the LDCs.

Some scholars of the global financial community assume that credit rating agencies are

nothing more than market followers, responding to macroeconomic trends, however there is

some reason to believe that CRAs have the ability to independently affect economic policies

through market yields and capital availability (Cantor & Packer 1996). In emerging democracies,

CRAs can prompt fiscal discipline, which reduces opportunistic short-term borrowing by

governments that risk long-term economic stability for electoral gains (Hanusch & Vaaler 2013).

As White (2010) and Sinclair (2005) have noted, the assessment of sovereign creditworthiness

by CRAs, such as Moody’s, S&P, and Fitch, plays a leading role in lending and investment

decisions taken by finance ministries and central banks.

In this paper, we investigate the effects of changes in bond ratings on income inequality

in the developing world. Using a two-stage least squares (2SLS) analysis with instrumental

variable approach to account for possible endogeneity for up to 78 countries from 1995-2015, we

find evidence that bond ratings from the three main CRAs, Moody’s Investor Services

(Moody’s), Standard and Poor’s (S&P), and Fitch Ratings (Fitch), have significant effects on

income inequality. Specifically, we contend that bond rating downgrades often leads to

increasing income inequality in developing countries. We follow the 2SLS analysis with panel

vector autoregression (pVAR), which assesses granger causality, and find evidence that changes

in CRA ratings cause changes in both social spending and income inequality. This indicates that

sovereign bond rating downgrades lead to both decreases in social spending, and increases in

income inequality.

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Sovereign bond rating downgrades suggest that, because of the growing risk of non-

repayment, the sovereign borrower will now need to offer higher interest rates to attract foreign

creditors. The higher interest rate has a two-pronged effect on income inequality. First, the added

financing charge produced by higher interest rates takes monies away from the government,

resources that could be used for social welfare policies or other government programs that might

help to address income inequality. Second, a negative rating encourages countries to adopt

policies prescribed by the CRAs to boost their ratings, often including economic austerity

measures, contributing to increased job losses and further limiting the availability of state funds

to those most in need.

Alternatively, sovereign bond rating upgrades allow countries to reduce interest rates on

their bonds, lowering the cost of capital. The extra savings provide governments with

opportunities to increase social spending or invest in areas such as job creation, which affects

income inequality.3 Additionally, a higher bond rating enhances policy freedom, enabling

countries to go away from economic austerity reforms and pursue a variety of policies that may

include addressing income inequality. Indeed, we would expect politicians in countries

experiencing bond upgrades and improvements in their economic conditions to steer clear of

austerity measures because such policies, except under highly unusual circumstances, are

extremely unpopular with the general public and would jeopardize the politicians’ political

survival (Geddes 1994).

3 See also Nafziger (1997), who contends that foreign capital encourages economic development and lowers income inequality because it allows LDCs to invest more than they save and consume more than they produce. See also Mosley and Singer (2008, 406), who argue that foreign capital increases growth and development for the economy overall. Again, such overall benefits should help lessen income inequality.

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The findings presented here hold important implications concerning income inequality in

the developing world. First, the results complement previous studies,4 suggesting that CRAs can

impact policy making in the developing world because such capital-scarce countries are most

vulnerable to capital market pressures (Mosley 2003; Sinclair 2005). Second, the fact that bond

ratings can negatively (or positively) affect inequality in developing countries builds on works in

the international political economy literature. From the negative perspective, the finding that

CRA bond downgrades increase income inequality supports studies by Reuveny and Li (2003),

Rudra (2008), and Wibbels (2006), who contend that efficiency pressures of globalization lead to

cost cutting at the absolute worst time in LDCs. From the positive perspective, and bolstering the

work by Rodrik (1997) and Katzenstein (1985) on the compensation hypothesis, we find that

governments earning bond upgrades have an opportunity to compensate the losers from

globalization via social spending or other government programs, which enhances income

equality. The take away from our paper is that LDCs should carry out policies preferred by the

CRAs that support higher bond ratings because the lower cost of capital has the potential to help

reduce income inequality.

In the first section, we discuss the literature on globalization and income inequality. We

also develop hypotheses showing how changes in bond ratings influence income inequality. In

section two, we present a method for testing our hypotheses and discuss our data. We provide the

results and offer anecdotal cases studies in sections three and four, respectively. Section five

concludes the paper.

4 See Block and Vaaler (2004) and Schwarcz (2001).

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1. Income Inequality and Changes in Bond Ratings in the Developing World

Income inequality has garnered much scholarly attention over the past half century. Beginning

with the classic work of Kuznets (1955), who found a U-shaped curvilinear relationship between

income inequality and economic growth, scholars have spent ample time studying the connection

between economic development and inequality.5

The economic policy strategy of globalization, which has gained popularity since the late

1980s, has fueled interest in understanding the determinants of income inequality. Contrary to

the Stolper-Samuelson trade theory, which suggests that abundance of unskilled workers and the

low wages they earn should attract much interest from foreign firms and help bring about an

economic boom in LDCs, researchers have found the opposite to be true.6 A common concern

raised with economic integration is that opening markets to foreign competition and reduced

government spending, elements commonly associated with globalization, exacerbates income

inequality in the LDCs (Garrett 1998b; Ha 2008; Rodrik 1998). Because financial budgets for

developing countries are typically inadequate, such countries tend to have limited social safety

nets to address jobs losses caused, in part, by economic integration and increased foreign

competition. Thus, on the surface, globalization appears to spur greater income inequality

(Kaufman and Segura-Ubiego 2001; Wibbels 2006).

Research suggesting a positive relationship between globalization and income inequality

have prompted studies that disaggregate globalization into its component parts. Several works

show that FDI promotes increased income inequality (Alderson and Nielsen 1999; Ha 2012;

5 See Chang and Ram (2000), who show that high growth promotes lower inequality at all income levels, undermining the inverted-U pattern contended by Kuznets. 6 See Garrett and Mitchell (2001), Greider (1997), and Rodrik (1997), who argue that globalization should reduce social safety nets in developed countries and Cameron (1978) and Katzenstein (1985), who claim the opposite, maintaining that market openness leads to increases in social spending.

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Huber, et al. 2006; Reuveny and Li 2003), indicating that foreign investment contributes to a

race to the bottom,7 where multinational corporations “pressure host governments to cut welfare

expenditures and curb labor unions to reduce wages, both of which hurt the lower and middle

classes” (Reuveny and Li 2003, 580).8 Research on trade, another prominent component of

globalization, similarly finds that commerce increases the wage differential between local skilled

workers and their unskilled counterparts, further reinforcing income inequality (Ha 2012, 543;

Reuveny and Li 2003; Rudra 2008).

Unlike the negative effects of FDI and trade on economic equality, studies that

investigate the impact of portfolio investment on inequality report inconsistent results (Reuveny

and Li 2003; Rudra 2008). Although some research shows that government policies engineered

to draw in foreign financial capital (e.g., lowering taxes, reducing government spending, and

removing restrictions on financial flows) may harm workers and benefit the wealthy in LDCs,

thus stimulating income inequality (Germain 1997; Strange 1996),9 others find that countries

opening capital markets to foreign investors are incentivized to act more prudently in

governmental policymaking and can always use funds earned from economic integration to

address income inequality (see Reuveny and Li 2003, 581-582).

Despite the extensive literature concerning the impact of globalization on income

inequality, absent from the conversation is the importance of sovereign bonds as a capital source

7 Some studies indicate that FDI can improve the wealth of a country in a “climb to the top” fashion, based on capital and technology improvements foreign firms bring to the host country (Drezner 2001; Moran 2002; Mosley and Uno 2007). However, the benefits of capital and technology may only accrue to skilled workers at the expense of workers with fewer skills, further exacerbating income inequality (Ha 2012; Nafziger 1997). 8 See also Nafziger (1997), who contends that foreign firms can always also use the threat of sending their operations elsewhere during bargaining over wage issues, culminating in lower salaries and increasing the likelihood for income inequality. 9 See also Stiglitz (2002), who argues that International Monetary Fund (IMF) officials require borrower nations maintain open capital markets that not only harm the debtor nations but come at the behest of treasury and financial officials in developed countries, who benefit from financial openness.

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and the part played by CRAs on policy formation in the developing world. After FDI, sovereign

bonds are among the fastest growing sources of global capital. Indeed, corporate and sovereign

bond sales for LDCs have greatly increased over the past three decades, growing from about $1

trillion in 1980, to more than $28 trillion in 2000, to greater than $130 trillion in 2010 (World

Bank 2015). CRAs play a pivotal role with regard to sovereign bonds because they determine the

interest rates countries charge on their bond issues and the net amount of capital they are able to

realize in the process (Schwarcz 2001; Sinclair 2005). From the perspective of developing

countries, the hope is that they will receive a sufficiently high rating from the CRAs and thus

avoid having to offer extremely high interest rates to attract investors. Higher ratings help to

reduce the risk premium on bond issuances, which enable LDCs to offer lower interest rates and

lessen the costs of capital.

In their capacity as capital gatekeepers, CRAs constantly monitor sovereign bonds

issuers. The agencies regularly review the economic and political circumstances in countries,

providing updates and offering outlooks/watches, before moving a bond rating up or down.

When CRAs upgrade or downgrade a sovereign bond rating, the change could have spillover

effects in realms beyond the financial community that impact the overall economic welfare of the

populace in the LDC.10 Much like foreign firms in host countries, who hire local employees for

their operations and stimulate jobs for local businesses that supply products to the foreign

entities, the same may be true for bond ratings issued by CRAs. Specifically, ratings directly

affect the costs of capital for the government, which impacts the government resources available

for addressing issues including income inequality. Tied in with the role of CRAs, the question

10 See Tomz (2007), Simmons (2000), and Mosley (2003) on the importance of reputation in one area having a snowballing influence on the reputation of other economic areas.

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we ask in this paper, is whether bond rating downgrades (upgrades) affect income inequality

(equality) in bond issuing countries?

Before examining the impact of CRAs on income inequality, we note that previous

studies have investigated the effects of CRAs on social spending. As Borensztein and Panizza

(2009) and Matsaganis (2011) show, CRA downgrades increase the probability of bond default

and raise the likelihood of declines in social welfare spending. By the same token, an extensive

literature documents the negative relationship between social spending and income inequality

(Chu, Davoodi, and Gupta 2000; Cornia 2004; Huber, Mustillo, and Stephens 2008; Giddens

2013; Lustig, Pessino, and Scott 2014). Low spending on social programs, and education in

particular (Abdullah, Doucouliagos, and Manning 2015), tends to exacerbate inequality, as the

poor have fewer opportunities to succeed without government intervention. However, missing

from the literature are the effect of changes in bond ratings on income inequality. We contend

that bond ratings influence the availability of government funds, which produce changes in social

policies, that impact income inequality. We consider the connection between CRAs and income

inequality directly here, a relationship that previous scholarship has failed to test directly.

We argue that LDCs receiving bond rating downgrades are more likely to see a surge in

income inequality for two reasons. First, as already noted, sovereign bond issuers incur an

increase in the cost of capital following rating downgrades. A risk premium associated with

rating downgrades means that sovereign bond issuers will need to raise interest rates on their

bonds to attract prospective buyers. Given the increased cost of capital to government and its

budget, the likelihood is that expanding financing for social spending and other programs to

support the middle class and poor, who may be suffering as a result of globalization, is probably

not feasible. Unlike developed countries that usually have higher income levels and stronger

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bond ratings, and who presumably can increase domestic social spending to care for those who

lose because of economic interdependence (Garrett 1998b; Ha 2008; Rodrik 1998),11 LDCs

generally are in a more precarious economic position. Increased costs of capital make it even

more challenging to address issues of income inequality.

Second, after a ratings downgrade, bond issuers often recognize that they must change

their economic practices if they hope to receive higher bond ratings in the future. The need to

modify policy is especially vital for developing countries who have the greatest need to lower

capital costs. CRAs indicate that among the practices necessary to receive higher bond ratings is

decreasing government spending, lowering debt burdens, and getting one’s house in economic

order (Moody’s 2015; S&P 2004; Fitch 2002).12 CRAs are most interested in seeing countries

pursue economic policies that boost their financial situation and support their repaying of

sovereign bond debt. Much like the recommendations offered by the IMF for countries facing

economic difficulties, CRAs similarly favor economic austerity measures that not only reduce

government spending, leaving additional monies to repay debts, but that also are formulated to

support longer term financial stability. Policies designed to lower income inequality, such as

increased government spending on social programs (e.g., healthcare, education, or social

security), seem unlikely to solve debt repayments and probably will exacerbate them. Because

CRAs tend to favor more fiscally responsible states, LDCs have fewer economic incentives13 to

invest in social spending and instead may be driven by what is known as the “efficiency

11 See Garrett and Mitchell (2001), Greider (1997), and Rodrik (1997), who argue that globalization should reduce social safety nets in developed countries. 12 See also Biglaiser and DeRouen (2007), who argue that specific economic policies tied to globalization affect sovereign bond ratings. 13 Of course, countries have political incentives to maintain and expand social and governmental programs that benefit their constituents and some will continue to maintain, or even increase, government spending even when the financial situation goes against what appears to be fiscally irresponsible policies. What we are arguing is that generally changes in bond ratings influence economic policy decisions in the developing world.

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hypothesis,” cutting government spending and limiting the financing of the welfare state in order

to improve their efficiency and economic competitiveness in global markets.14 Although

developed countries can respond to economic shocks by “borrowing on capital markets and

spending countercyclically on social programs,” LDCs have less access to capital markets during

difficult times and are most likely to “cut social spending at exactly the times it is most needed”

(Wibbels 2006, 435).15 Similarly, Rudra (2008, 37) notes, social security contributions capture

an uncompetitive type of spending that “should be in greatest jeopardy given the ‘efficiency’

pressures of globalization.” Given the discussion above, we expect countries receiving rating

downgrades lower their overall government spending, including in areas tied to social programs

and government transfers, leading to increased income inequality.

Alternatively, we anticipate bond rating upgrades have a positive impact on income

equality. First, an upgrade reduces the cost of capital for the sovereign issuer. Based on the

improved rating, the LDC can lower the interest rates offered on its bonds, gaining a capital

savings windfall that could be used for policies including social safety nets that promote

advances in income equality. Moreover, an upgrade provides countries with greater policy

freedom. As the recent Brexit vote suggests, globalization has winners and losers. Countries that

receive rating upgrades are in a better position to compensate the losers via larger social

spending, or other government programs, in what is referred to as the “compensation hypothesis”

(Katzenstein 1985; Kaufman and Segura-Ubiergo 2001; Rodrik 1997). Of course, there is no

guarantee that rating upgrades will result in countries compensating the losers from

globalization, but they are more inclined to increase government spending that lowers income

14 See Garrett (1998b) for a discussion on the efficiency versus compensation hypotheses. 15 See also Mosley (2003), who contends that developing nations have less flexibility with policy and are more prone to conform to the convergence view of market-oriented policies sponsored by international financial market actors.

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inequality relative to countries receiving downgrades. Given the pressures emanating from those

frustrated by globalization, enhancing income equality seems a rational response to maintaining

constituent support, an interest of parties from all political stripes and of concern to democratic

and authoritarian governments alike.

Based on the prior discussion, we propose two hypotheses on the effects of CRAs on

income inequality.

Hypothesis 1: In the year after a sovereign bond rating downgrade (upgrade), income inequality will tend to increase (decrease) in developing countries. Hypothesis 2: Sovereign bond rating downgrades (upgrades) will have a lasting influence on income inequality (equality) in developing countries.

2. Research Design and Methods

Our analysis uses a sample of up to 78 developing countries between 1995 and 2015 to assess the

effect of changes in CRA bond ratings on income inequality. Our analysis includes all cases for

which data are available, which represent a good cross section of developing countries from

Africa, Asia, Central and Eastern Europe, Latin America, and the Middle East. We analyze the

years 1995-2015 because the number of developing countries receiving bond ratings takes off

around 1995, giving us greater country coverage. Starting the analysis in 1995 also provides us

with a good opportunity to evaluate changes in income inequality since the countries in our

sample have an average duration of at least 14 years of bond ratings data.

2.1 Endogeneity Concerns

There is more than a little anecdotal evidence linking changes in sovereign bond ratings to

income inequality, and some of these will be discussed later. However, there is some concern

that the relationship is endogenous, with income inequality responding to the same

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macroeconomic trends that the CRAs evaluate to determine sovereign bond ratings. How the

agencies determine the ratings is not at all obvious because the CRAs closely guard the precise

matrices they employ in their analysis.

CRAs assess the likelihood of future bond default over a 3-5 year period after the initial

rating (Biglaiser and Staats 2012; Gaillard 2011). The CRAs inform us that they incorporate

various economic and political factors into their ratings methodology, providing some guidance

to bond issuers. Among the economic variables, Moody’s, S&P, and Fitch all stress that

countries maintain policies that support economic growth and development. Moody’s (2015)

points to several specific economic factors including GDP growth, GDP per capita, inflation, and

debt levels to evaluate the likelihood of debt repayment. Fitch (2002) similarly assesses the

balance of payments situation, real (and nominal) GDP, and a host of other economic factors.

S&P (2004) considers much the same economic factors that draw on economic growth prospects,

monetary flexibility, external liquidity, and debt burden. The three CRAs also note the critical

role of political factors, with political institutions that promote stability, durability, transparency,

and predictability receiving much interest. Such institutions also serve as a constraint against

rash and adverse action, a serious issue for CRAs (Moody’s 2008; S&P 2008; Fitch 2002). What

is clear is that CRAs prefer countries with stable and promising economic and political

conditions over the medium to long term that reduce the likelihood of debt default (Biglaiser and

DeRouen 2007).

Since income inequality is often affected by changes to larger economic trends (Kuznetz

1955; Dabla-Norris et al. 2015) and political factors (Giddens 2013; Acemoglu et al. 2015),

which are suspected to comprise the ratings calculations made by CRAs, we expect that income

inequality and sovereign bond ratings have a cyclical relationship. Because of concerns about the

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causal direction in our analysis, where bond ratings affect income inequality and income

inequality possibly impacts ratings, the first stage of our analysis will employ a 2SLS approach –

a method we discuss later in this section.

2.2 Data

The dependent variable, income inequality, is a country-year Gini coefficient of net income

inequality from the Standardized World Income Inequality Database (SWIID) (Solt 2015). The

SWIID contains a standardized measure of income inequality, taken from combined sources and

employing the Luxemburg Income Study as the baseline, which is comparable across counties

and years.16 Due to the comparability and coverage of the SWIID, it is widely used in similar

panel data research (Huber and Stephens 2012; Ostry, Berg and Tsangarides 2014).17 The

calculated Gini index is “exactly one half of the relative mean difference, which is defined as the

arithmetic average of the absolute values of differences between all pairs of incomes” (Sen 1973,

30-31). The Gini coefficient is created by placing the distribution of all incomes within a

population along a Lorenz curve to generate a value that ranges between 0 and 100. A value of 0

indicates that all individuals within a population share the state’s income equally, and 100 shows

that only one person controls 100 percent of the nation’s wealth.

Our independent variable of interest is the change in annual sovereign bond ratings. We

first difference of the variable, thus measuring the change in bond ratings from one year to the

next, to obtain the annual variation of the bond ratings. For our measure of sovereign bonds

ratings, we use data from each of the three largest CRAs – Moody’s, S&P, and Fitch. Because

16 For further defense of the SWIID, see Solt (2015). 17 We ran robustness tests with the All the Ginis (ATG) inequality data from Milanovic (2014) and found similar results. The results are available from the authors.

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not all the agencies provide coverage for the same countries and years, we have an unbalanced

data set. We also convert CRA rating letters into ordinal numbers on a seventeen point (0-16)

scale, a common practice in the CRA literature (Block and Vaaler 2004; Vaaler, Schrage, and

Block 2006), with higher bond ratings indicating better rating scores. We further lag the change

in CRA sovereign bond ratings by one year.

To ensure that the findings from our analysis are not a product of a spurious relationship,

and consistent with previous literature, we include a litany of economic and political variables as

controls which have been identified as primary determinants for income inequality. Among the

economic covariates, to control for the level of economic development we include the log of

GDP/capita as well as lnGDP/capita squared to account for the possibility of a non-linear

relationship (Kuznets 1955). We also include variables consistent with the literature to control

for levels of globalization and economic openness including inflation (IMF 2016), current

account balance (World Bank 2015), natural resources (World Bank 2015), and trade (World

Bank 2015). We also control for levels of social spending18 with a measure of social spending

(education expenditures as % GNI).19 The models utilize demographic factors, as a measure of

country magnitude, such as size of population, logged pop, and total life expectancy that have

potentially important effects on income inequality. Larger populations, for instance, place

additional pressures on government social services without necessarily providing economic

incentives. Indeed, LDCs often face challenges collecting tax revenues to fund social services,

18 Following the previous literature (Castro-Leal et al. 1999, Chu, Davoodi and Gupta 2000), we employ measures of education and healthcare spending as our proxies for social spending. 19 As a robustness check we included different measures of social spending including: expenditures on education (% of total government expenditures), health expenditures per capita (% of GDP), and total health expenditures (% of total government expenditures). We obtained similar results regardless of the social spending measure employed in the models. We present results for education in Appendix 5, 6, and 7.

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fostering greater likelihood for inequality. Both size of population and total life expectancy come

from the World Bank (2015).

For the political determinants of income inequality, we include democracy, regime

durability, and government ideology. Democracy has the potential to produce drastic effects on

inequality,20 therefore we employ the Freedom House imputed Polity2 measure,21 which has

been shown to perform better in terms of validity and reliability than its counterparts (Hadenius

and Teorell 2005). The Freedom House measure places democracy on a 0 to 10 scale, with

higher values referring to more democratic countries. Political regimes in LDCs can be unstable,

and this instability can lead to prolong shifts in inequality. We account for instability by

controlling for regime durability,22 using the number of years since the most recent regime

change (Marshall and Jaggers 2006). Executive ideology is also expected to affect government

concern for addressing income inequality.23 We measure executive ideology by recording 1 for

leftist leaders and 0 for executives from all other parties. Ideology data come from Beck et al.

(2001). Since current levels of income inequality are heavily dependent on previous levels, we

follow common practice and include a lag of the dependent variable to account for serial

dependence (Acemoglu et al. 2015). Additionally, to account for unobserved country-specific

and year-specific trends, we include country and year fixed effects.24 All political and economic

controls are lagged a year because there is often a delay for CRAs to recognize changes in

20 On the role of democracy as an explanatory factor for income inequality, see Lee (2005). 21 As a robustness check we included a dichotomous measure of democracy from Cheibub, et al. (2010) in place of Polity 2 and the main findings remained significant (see Appendix 5, 6, and 7). 22 We also tested political stability with The World Bank’s Worldwide Governance Indicators for political stability and we found little change in the main results (see Appendix 5, 6, and 7). 23 See Ha (2012) for the importance of political ideology, namely leftist governments, for reducing inequality. 24 A Hausman specification test, which tests model fit under different constraints, indicates that the country- and year-level effects are adequately modeled by a fixed-effects framework

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political and economic circumstances. We provide summary statistics and descriptions for all

variables in Appendix 1.

2.3 Method

One of the methodological difficulties in determining the direction and strength of the

relationship between changes in CRA sovereign bond ratings and income inequality rates is

appropriately accounting for both exogenous and endogenous factors that can produce mis-

specified models and incorrectly interpreted quantitative relationships. Since both CRA ratings

and inequality respond to exogenous economic factors and can lead to a spuriously identified

relationship, we follow the lead of previous research and employ a 2SLS analysis with

instrumental variable approach to account for possible endogeneity (Sylwester 2000; Barro and

Lee 2005; Bang et al. 2016; Balestra and Varadharajan-Krishnakumar 1987).

The Instrumental Variable estimator can avoid the bias that Ordinary Least Squares

(OLS) estimates suffer from (absence of inconsistency) when independent variables in the

regression are correlated with the error term in the equation of interest. In accordance with

previous research, the first step to justify the 2SLS estimator is testing for the presence of

endogeneity with a Hausman endogeneity test. Then we must show that instrumental variables

are exogenous to the endogenous components of explaining variables, conditional on the control

variables. Finally, we need to verify that the instruments are valid and not correlated with the

error-term in the equation of interest through an over-identifying restrictions test.

The selection of instrumental variables requires identifying factors that are correlated to

changes in the endogenous independent variable while not being correlated (exogenous) to

changes in the primary dependent variable. Fortunately, previous research provides a wealth of

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possible instruments which have been shown to produce expected change in bond ratings while

also lacking a theoretical connection to income inequality. We expect that rule of law, bond

default, and perceptions of political corruption to serve as appropriate instruments. Rule of law,

or latent judicial independence, has previously been identified to have an effect on CRA ratings

(Biglaiser and Staats 2012). Sovereign bond default (Biglaiser and DeRouen 2007; Gaillard

2011) and the perceptions of corruption25 in government (Depken and Lafountain 2006;

Connolly 2007) also have effects on the bond rating calculations made by the CRAs, while

lacking a direct relationship with inequality aside from the indirect route through bond ratings

change. Thus, our methodology includes the following models:

𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐶𝐶ℎ𝑅𝑅𝑅𝑅𝑅𝑅𝑎𝑎𝑖𝑖𝑖𝑖 = 𝛾𝛾0 + 𝛾𝛾1(𝐶𝐶𝑢𝑢𝑢𝑢𝑎𝑎 𝑜𝑜𝑜𝑜 𝐿𝐿𝑅𝑅𝐿𝐿)𝑖𝑖𝑖𝑖 + 𝛾𝛾2(𝐷𝐷𝑎𝑎𝑜𝑜𝑅𝑅𝑢𝑢𝑢𝑢𝑅𝑅)𝑖𝑖𝑖𝑖 (1) +𝛾𝛾3(𝐶𝐶𝑜𝑜𝐶𝐶𝐶𝐶𝑢𝑢𝐶𝐶𝑅𝑅𝑅𝑅𝑜𝑜𝑅𝑅)𝑖𝑖𝑖𝑖 + 𝛼𝛼1𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜉𝜉𝑖𝑖 + 𝜚𝜚𝑖𝑖 + 𝜐𝜐𝑖𝑖𝑖𝑖

𝐼𝐼𝑅𝑅𝑎𝑎𝐼𝐼𝑢𝑢𝑅𝑅𝑢𝑢𝑅𝑅𝑅𝑅𝐼𝐼𝑖𝑖𝑖𝑖 = 𝛾𝛾0 + 𝛾𝛾1(𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐶𝐶ℎ𝑅𝑅𝑅𝑅𝑅𝑅𝑎𝑎)𝑖𝑖𝑖𝑖−1 + (𝐼𝐼𝑅𝑅𝑎𝑎𝐼𝐼𝑢𝑢𝑅𝑅𝑢𝑢𝑅𝑅𝑅𝑅𝐼𝐼)𝑖𝑖𝑖𝑖−1 (2)

+𝛽𝛽2𝑋𝑋𝑖𝑖−1 + 𝜉𝜉𝑖𝑖 + 𝜚𝜚𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

In the two equations, X is a set of exogenous control variables that are included in first-stage

regressions. For the first and second equations, ʋ and ε, respectively denote the error terms, while

𝜉𝜉 and 𝜚𝜚 represent the country and year fixed effects. The instrumental variables are the three

right side variables in model 1.

3. Results The Chi2 value of the Durbin–Wu–Hausman test demonstrates that OLS is inconsistent, which

indicates the presence of endogeneity within the model and suggests the use of an instrumental

variable approach. To establish the relevance of the instruments, we present the results of the

25 Our measure of political corruption relies on Transparency International’s Corruption Perceptions Index 10-point scale from most to least corrupt.

17

first stage models in Table 1. Table 1 illustrates the first-stage estimates of the 2SLS analysis

with and without the full battery of controls. The control variables have been omitted to make

interpretation of the instruments easier. We present the full models in Appendix 2. The

Kleinbergen-Paap LM statistic provides support that the instruments are not under-identified, and

thus are relevant. Additionally, the Hansens J test of overidentification, which tests whether an

instrument is valid (i.e., uncorrelated with the error term), indicates that the instruments satisfy

the exogeneity requirement. These results are robust across different specifications. The first

model for each of the CRAs only controls for the instruments and the lagged dependent variable.

Under the assumption that the independent variables are excludable conditional on these

variables, this model specification already yields an unbiased coefficient of interest in the second

stage without additional controls. However, once the significant controls are included in the

second models for each of the CRAs, none of the controls significantly affects the relevant

coefficients on the instruments, their significance, or the under or over-identification statistics.

This indicates that the instruments are still valid and relevant once the other controls are

included.

[Table 1 About Here]

3.1 Instrumental Variable Analysis

We now proceed to the second stage of the 2SLS regression, and our primary findings, to

discuss the effects of CRA change on income inequality in developing countries. In Table 2, for

columns 1, 3, and 5, we present results that correspond to models with just economic and

demographic controls, while the results shown in columns 2, 4, and 6 correspond to the full

specification including political controls. The results provide evidence in support of hypothesis

one. The negative and significant relationship for change in CRA rating indicates that as CRAs

18

downgrade sovereign bonds, the expected levels of income inequality within a country will

increase. Likewise, if a CRA increases a country’s sovereign bond rating, that country likely will

experience a decrease to income inequality. This finding is consistent for all three CRAs and for

both model specifications. On average, a nation experiencing a one point increase (decrease) in

its sovereign bond rating, leads to a 0.7 points decrease (increase) in a country’s expected level

of income inequality, which is a substantial change. A one point change in a sovereign bond

rating produces an expected change in a nation’s level of income inequality by at least 28%, and

as much as 42%, of a standard deviation within one year. Though this change may sound small, a

change of this size is far from common, as only about 15% of the sample experienced a change

of this magnitude in a single year. These findings indicate that a change in CRA sovereign bond

ratings can have a substantial and almost immediate impact on a nation’s level of income

inequality. This finding is robust to different model specifications and to different samples,

which vary because not all CRAs rate all possible countries and because of missing data in some

of the additional controls.26

[Table 2 About Here]

Consistent with the literature, a number of the controls reach statistical significance

including the lagged dependent variable, GDP per capita and its polynomial, population, life

expectancy, and democracy. Supportive of work by Kuznets (1955), we find a curvilinear

relationship between economic growth and inequality, indicating that an increase in economic

growth raises inequality, but only to a point.

Next we show how changes in CRA sovereign bond ratings affect inequality in the long

run by testing the effects of different lags on income inequality. The results from these models,

26 See Appendix 3, which provides graphical details for CRA country year change for all countries in the sample.

19

which are identical to those in Table 2, are presented in truncated form in Table 3 and provide

strong support for Hypothesis 2. The effects of a change in a sovereign bond rating affects levels

of income inequality between 2 and 5 years after the change, depending on the CRA. The results

indicate that the effect of a change in sovereign bond ratings is strongest and most significant

after 2 years, and becomes non-significant after 3 years for Moody’s and S&P, and 5 years for

Fitch. The results from Table 3 also are presented graphically in Appendix 4.

[Table 3 About Here]

3.2 Panel Vector Autoregression Analysis

As an alternative to the 2SLS models (Sims 1980), pVAR is an approach to causal

modeling in which each dependent variable is regressed on lagged values of itself and the other

dependent variables. pVAR treats all variables within the VAR system as endogenous, while

allowing for exogenous shocks onto the system. We employ a pVAR in a generalized method of

moments (GMM) framework (Holtz-Eakin, Newey and Rosen 1988, Abrigo and Love 2015) 27,

and implement Granger (1969) causality tests to determine the interaction of the dependent

variables. The dependent variables are sovereign bond ratings, income inequality, social

spending, and macroeconomic trends. We employ the same measure for sovereign bond ratings,

income inequality, social spending as the 2SLS equation and model macroeconomic trends with

logged GDP per capita.

[Table 4 About Here]

27 Various estimators based on GMM have been proposed to calculate consistent estimates with fixed time period and many observations. With our assumption that errors are serially uncorrelated, the first-difference transformation may be consistently estimated equation-by-equation by instrumenting lagged differences with differences and levels of the dependent variables from earlier periods as proposed by Anderson and Hsiao (1982). Though traditional VAR analysis requires many more than are present in our analysis, for a second-order panel VAR, instruments in levels require that 𝑇𝑇𝑅𝑅 ≥ 5 observations are present for each country in the sample.

20

The VAR analysis, which covers economic data from 1995 through 2015, includes a lag

of two years (determined by AIC) and two exogenous variables: logged population and life

expectancy. Table 4 reveals interesting set of causal relationships between the factors analyzed.

It appears that income inequality is affected by all three factors, indicating that income inequality

is being influenced by CRA ratings irrespective to both social spending and macroeconomic

changes. This is the case when all three ratings agencies are examined. Table 4 reveals very little

evidence that Bond ratings are being influenced by the economic trends being analyzed, while

bond ratings are influencing social spending.

These preliminary findings indicate that the relationship between sovereign bond ratings

and income inequality may be operating as we theorized, that bond downgrades produce a

restriction in available economic resources, resources that could be used for social welfare

policies or other government programs that might help to address income inequality.

Additionally, these findings serve to strengthen our assertion that bond ratings are more than just

market followers, that they exert a real and distinguishable effect on the economic fortunes in

countries that issue sovereign bonds.

4. Discussion

Reviewing our hypotheses, we find strong support for hypothesis 1 (sovereign bond rating

downgrades (upgrades) contribute to increased (decreased) income inequality in LDCs). The

results also provide firm footing for hypothesis 2 (the influence of sovereign bond change has a

lasting influence on income equality). The findings suggest that CRA bond upgrades and

downgrades appear to have important effects on income inequality.

In this section, we present anecdotal evidence from, Malaysia, and Uruguay to

demonstrate the working mechanism of our theoretical argument. In both countries, we find that

21

rating downgrades produced declines in government social spending. The downgrades also

increased the probability that government initiated cost-cutting reforms associated with

economic austerity measures, amplifying income inequality. We also note that bond rating

upgrades complement increases in social spending and higher rates of income equality.

Malaysia is an example of bond rating downgrades supporting increases in income

inequality and upgrades contributing to an improvement in income equality. During the

immediate post-war period, when most of the developing world implemented import substitution

industrialization (ISI) policies – producing finished goods for their domestic market that later

contributed to severe balance of trade and payment crises – Malaysia, along with many other

East Asian countries, followed an export-oriented strategy that generated significant economic

benefits. Indeed, Malaysia experienced high growth rates averaging nearly 7% in the 1960s,

almost 8% in the 1970s, and approximately 6% in the 1980s (World Bank 2015). Between 1988

and 1996, Malaysia’s witnessed an average growth rate of 8.9% with a low inflation rate of

between 3-4% (Ariff and Abubakar 1999, 417). Given the impressive economic numbers,

Malaysia along with other East Asian Tigers served as role models for the developing world.

Malaysia’s great economic fortunes faced calamity in the latter part of the 1990s.

Following the collapse of the Thai Baht in 1997 based on currency speculation pressures,

Malaysia, much like the rest of East Asia, found itself in a financial crisis. In 1998, Malaysia's

currency, the Ringgit, dropped by nearly 50%, its stock market fell by more than 60 percent

(Ariff and Abubakar 1999, 417), and its growth rate contracted by 7.4% (The Economist 2007).

Not surprisingly, ratings on Malaysia’s sovereign bonds also crashed, with Moody’s and S&P

downgrading Malaysia’s bonds from a score of twelve in 1997, to nine in 1998, and seven in

1999. Because of the dramatic decline in their ratings and the financial crisis, the Malaysian

22

government led by Prime Minister Mahathir Mohamad adopted a tight fiscal and monetary

program, slashing the public sector budget by 18%, and postponing or cancelling several large-

scale infrastructure projects (Ariff and Abubakar 1999, 419-420). As part of the fiscal program,

the government immediately reduced spending on health and education (World Bank 2015). The

financial crisis and policies designed to rectify it also led to income inequality climbing nearly

7% from 1997 to 2001 (Solt 2015).

Following the economic crisis, the policies installed by Prime Minister Mahathir began to

support economic recovery. By 2002, the economy grew by more than 5% and continued to

grow to 5.9% in 2006 (The Economist 2007). The higher growth rates were in sync with

Moody’s bond upgrades from 7 in 2000, to 8 in 2001, to 9 in 2003, and finally 10 in 2005. Public

health and education expenditures also increased as the rating upgrades improved. The end result

was that income inequality dropped more than 16% from 2001 to 2006 (Solt 2015).

Uruguay also demonstrates the connection between changes in bond ratings and income

inequality. Dating back to the early 1900s and the leadership of José Batlle y Ordóñez, Uruguay

always has attempted to support dialogue between the political parties and consensus building on

policy making, in hopes of promoting social reforms and greater income equality (Alisky 1969).

In the late 1980s and early 1990s, much like the rest of Latin America, economic difficulties

caused largely by upholding ISI policies for more than thirty years led Uruguay to initiate

market-oriented, neoliberal reforms. Opening its markets to international competition exposed

Uruguay, as well as most other countries in the region, to global shocks. Worse for Uruguay, its

main trading partner and next door neighbor, Argentina, experienced economic chaos during the

early 2000s, placing Uruguay in even greater harm’s way.

23

As Argentina’s economy imploded, Uruguay followed suit, leading to a collapse in its

bond rating. Moody’s, S&P, and Fitch all downgraded Uruguay’s sovereign bonds from a score

of seven in 2001, to three in 2002, to one in 2003. Uruguay also defaulted on its bonds in 2003.

In a span of less than three years, Uruguay’s income inequality rose nearly 3% (Solt 2015), a

significant amount given Uruguay’s long-standing concern for social welfare. Indeed, in 2002

and 2003, education expenditures fell 37% and 21%, and public health expenditures similarly

dropped 12% and 14%, respectively.

The economic downturn led Uruguayan voters to elect for the first time a presidential

candidate from the Frente Amplio, a party which had historically held an ideologically-

entrenched, Marxist perspective, in 2004. President Tabaré Vázquez quelled fears about his

economic views, appointing the respected economist, Danilo Astori, as his Minister of the

Economy, who would maintain market-oriented policies preferred by the CRAs (Biglaiser 2016).

Bond ratings slowly recovered, with Fitch increasing its score to three in 2005, four in 2008, five

in 2010, and seven a few years later. Vázquez and his presidential successor from the Frente

Amplio, José Mujica, continued the market course but also implemented social reforms

(Biglaiser 2016). Both Vázquez and Mujica dramatically increased spending on education and

public health during their years in office, which had important effects on income inequality.

Indeed, income inequality declined by more than 10% from 2006 to 2013 (Solt 2015). Once

again, bond rating downgrades promoted increased income inequality while bond rating

upgrades contributed to significant improvements in income equality.

24

5. Conclusion

Previous empirical research on the relationship between globalization and income inequality has

studied economic measures including FDI, trade, and portfolio investment. Absent from the

literature is the role of CRAs and their rating of sovereign bonds, a financial instrument of

growing importance given the dramatic increases in bond issues over the past two decades. Much

like international financial institutions or foreign governments put pressures on countries to

change policy course, changes in the ratings by CRAs can serve as a catalyst for policy reforms,

which affect income inequality in the developing world.

In the first study that we know of to consider the effects of CRAs on income inequality,

we find that rating agencies have a positive or negative effect on income inequality depending on

the change in the bond rating. In cases where the CRAs institute rating downgrades, the

likelihood of higher income inequality increases in developing countries. Alternatively, in

instances where the CRAs carry out rating upgrades, the probability rises of reduced income

inequality. Additionally, we find that changes in CRA sovereign bond ratings affect income

inequality, having a lasting influence over a number of years. We surmise that the cost of capital

and leverage held by the CRAs influence the policies of bond issuers. In situations of rating

downgrades, the increased cost of capital combined with the need by bond issuers to placate the

agencies, lead to the adoption of fiscally-constraining governmental policies, resulting in

growing income inequality. On the other hand, in circumstances of rating upgrades, the reduced

cost of capital along with the higher bond ratings provide the LDCs with added policy freedom,

enabling them to deal with the possible losers from globalization.

The results reported in this paper complement previous studies, indicating that the rating

decisions of CRAs can and do affect policy making in the developing world (Block and Vaaler

25

2004; Mosley 2003; Schwarcz 2001; Sinclair 2005). Similar to the pressures that the IMF

imposes on borrower nations (Stiglitz 2002), the financial power of CRAs hold substantial sway

over policy making, especially in the LDCs. In addition, the findings build on earlier work tied to

the efficiency and compensation hypotheses, showing how bond rating downgrades or upgrades

can promote cost cutting (Reuveny and Li 2003; Rudra 2008; Wibbels 2006) or enhanced

government spending (Rodrik 1997; Katzenstein 1985), respectively.

The analysis and results presented in this article serve as a first step in understanding the

influence that CRAs have on developing countries. While the results reported here say a lot

about this influence, we recognize the possible limitations of using aggregate data for our

quantitative analysis. Ideally, for future research, we invite further inquiries as to the specific

motivations of leaders and government officials in individual countries. Qualitative analysis

incorporating interviews with past and present political leaders and government policy makers

would offer an important opportunity to gain a deeper understanding of the quantitative results.

Analysis of government archives is another way to obtain greater details about the policy making

process. Leaving aside the potential limitations, we believe that the robustness of our results

along with the anecdotal cases provide solid footing for our main premise about the role of

changes in CRA ratings on income inequality.

Bond ratings present many new avenues for future research. One area for future inquiry is

the role played by CRAs on income inequality in the developed world. Many authors have

suggested that developed countries have greater resources to address challenges caused by

globalization than do LDCs and thus compensation for losers should be more common, even

during ratings downgrades, in developed countries. But is this true, particularly in the context of

26

the Brexit vote? Additionally, the effects of the IMF, another important international institution,

on income inequality merits investigation.

Although many interesting projects lie ahead for understanding the factors that affect

income inequality, our paper represents a first step toward understanding how bond ratings

influence inequality. The findings should inform governments about the benefits of

implementing policies favored by the CRAs for receiving higher bond ratings and subsequently

lowering income inequality in the developing world.

27

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34

Summary of Tables

Table 1 – First Stage Regression Moody’s S&P Fitch (1) (2) (1) (2) (1) (2) Rule of Law -.561***

(.203) -.236** (.115)

-.196 (.190)

-.330 (.224)

-.445** (.224)

-.326** (.154)

Bond Default -.430** (.199)

-.293 (.212)

-.623*** (.195)

-.514*** (.198)

-.380** (.194)

-.427** (.214)

Corruption (CPI) .356** (.180)

.618*** (.201)

.449*** (.166)

.711*** (.187)

.772** (.211)

1.074*** (.235)

Controls No Yes No Yes No Yes Obs. 844 761 873 790 714 639 Countries 6 62 78 70 69 61 Avg. Obs. per group 12.4 12.3 11.2 11.3 10.3 10.5 Hansen J statistic p<.05 .216 .214 .319 .311 .201 .197 Kleibergen-Paap rk LM statistic 18.45 18.56 16.05 16.17 18.97 19.11 F-Test : Prob>F .000 .000 .000 .000 .000 .000 Rho .214 .902 .289 .922 .385 .905 Dependent Variable: Change in CRA bond rating. All regressions include country and year fixed effects plus a lagged dependent variable. Significance level: * p<.1; ** p<.05; *** p<.01.

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Table 2 – Second Stage Regression Moody’s S&P Fitch (1) (2) (1) (2) (1) (2) Variables of Interest

Change CRA Ratingt-1 -.716** (.356)

-.797** (.395)

-.511** (.256)

-.544** (.273)

-.600** (.244)

-.679** (.266)

Gini t-1 .907*** (.024)

.900*** (.025)

.905*** (.021)

.899*** (.022)

.873*** (.023)

.868*** (.024)

Economic Controls GDP/capita (ln) 8.082***

(2.812) 8.447*** (2.910)

7.293*** (2.060)

7.716*** (2.127)

10.345*** (2.799)

11.850*** (2.959)***

GDP/capita2 (ln) -.413*** (.156)

-.432*** (.164)

-.382*** (.121)

-.404*** (.127)

-.524*** (.170)

-.613 (.181)

Inflation -.001 (.001)

.000 (.001)

-.001 (.001)

-.001 (.001)

.000 (.001)

.000 (.001)

Current Account Balance .000 (.000)

.000 (.000)

.000 (.000)

.000 (.000)

.000 (.000)

.000 (.000)

Natural Resources .002 (.008)

.002 (.009)

.001 (.006)

.000 (.006)

.012 (.008)

.011 (.008)

Trade -.003 (.004)

-.004 (.004)

.000 (.003)

.000 (.003)

-.004 (.004)

-.004 (.004)

Social Spending (education) .006 (.043)

.004 (.045)

.008 (.039)

.003 (.041)

-.017 (.044)

-.017 (.046)

Demographic Controls Population (ln) -1.092

(1.040) -1.154 (1.075)

-1.149 (.861)

-1.208 (.883)

-3.083 (1.078)

-3.034*** (1.116)

Life Expectancy -.109* (.060)

-.133** (.066)

-.078 (.051)

-.094* (.054)

-.085*** (.056)

-.116* (.060)

Political Controls Polity2 .120**

(.059) .122**

(.053) .223**

(.088) Regime Durability .003

(.008) -.001

(.006) .001

(.007) Government Ideology .056

(.155) -.024

(.124) -.150

(.160) Constant -8.503*

(20.633) -8.073

(21.098) -5.457

(14.792) -5.810

(15.293) 12.944

(19.997) 6.713

(20.964) Obs. 774 760 811 789 648 638 Countries 63 62 73 70 62 60 Adjusted R2 .766 .882 .791 .875 .762 .751 Prob>F .000 .000 .000 .000 .000 .000 Dependent Variable: Gini Coefficient (SWIID). Note: Second stage regression corresponding to Table 1. All regressions include country and year fixed effects and all standard errors are robust. Significance level: * p<.1; ** p<.05; *** p<.01;

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Table 3 – Long Term Effects Moody’s S&P Fitch

T -.642* (.366) [809]

-.540* (.294) [841]

-.535** (.305) [676]

t-1 -.797** (.395) [760]

-.544** (.273) [789]

-.679** (.266) [638]

t-2 -.756* (.435) [735]

-.717* (.423) [766]

-1.291** (.507) [605]

t-3 -.209 (.330) [695]

-.166 (.360) [702]

-.971** (.415) [576]

t-4 .149 (.278) [647]

.271 (.336) [645]

-.653* (.390) [.094]

t-5 .228 (.249) [591]

.477 (.311) [593]

-.218 (.346) [461]

Note: The table reports β-Coefficients for different lags of the dependent variable (change in CRA ratings) for the different ratings organizations at different lags. These equations are otherwise equal to the regressions in table 1 and 2 * p<.10; ** p<.05; *** p<.01;

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Table 4 – Granger Tests for CRA Ratings and Economic Trends Moody’s S&P Fitch

Independent Variable

Dependent Variable

Independent Variable

Dependent Variable

Independent Variable

Dependent Variable

CRA Rating (.080) → Gini CRA Rating (.081) → Gini CRA Rating (.096) → Gini

Social Spending (.028) → Social Spending (.080) → Social Spending (.084) →

GDP per capita (.053) → GDP per capita (.094) → GDP per capita (.069) →

Gini (.232) CRA Rating Gini (.337) CRA Rating Gini (.040) → CRA Rating

Social Spending (.136) Social Spending (.282) Social Spending (.825)

GDP per capita (.129) GDP per capita (.276) GDP per capita (.920)

Gini (.527) Social Spending Gini (.173) Social Spending Gini (.174) Social Spending

CRA Rating (.005) → CRA Rating (.048) → CRA Rating (.091) →

GDP per capita (.794) GDP per capita (.276) GDP per capita (.336)

Gini (.060) → GDP per capita Gini (.140) GDP per capita Gini (.422) GDP per capita

CRA Rating (.362) CRA Rating (.486) CRA Rating (.419)

Social Spending (.624) Social Spending (.746) Social Spending (.718)

Note: Numbers in parentheses are p-values. The arrows indicate that the independent variable Granger causes the dependent variable at a significance level of 0.1, using Block Exogeneity Wald Tests. Logged population and life expectancy modeled as exogenous variables.

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Appendix Appendix 1 – Descriptive Statistics Variable Mean Std. Dev Min Max Description & Source

Gini Coefficient 39.89 1.91 18.72 68.10 Gini coefficient of net income inequality, from SWIID version 5.0 (Solt 2015).

∆ Moody's Bond Rating 0.02 0.71 -11 3 The first difference of CRA rating letters in ordinal scale (0-16), (from Moody’s website).

∆ S&P Bond Rating 0.03 0.68 -9 2 The first difference of CRA rating letters in ordinal scale (0-16), (from S&P website).

∆ Fitch's Bond Rating 0.06 0.65 -6 2 The first difference of CRA rating letters in ordinal scale (0-16), (from Fitch website).

Logged GDP per capita 8.09 0.17 5.33 10.56 Natural log of gross domestic product per capita in constant 2005 US$ (World Bank 2015).

IMF Inflation 110.21 36.31 0.00 931.13 Annual inflation rate (IMF 2015).

Current Account Balance (Millions US$) 1727.39 7483.59 -81108 420568

The sum of the balance of trade (goods and services exports less imports), net income from abroad, and net current transfers (World Bank 2015).

Natural Resources 21.99 5.44 0 99.67 Total ores and metals exports plus fuel exports (% total merchandise exports) (World Bank 2015).

Trade 87.33 13.45 12.37 439.66 Exports plus imports (% GDP) (World Bank 2015). logged Population 16.34 0.08 11.17 21.03 Natural log of national population (World Bank 2015).

Life Expectancy 69.85 1.77 43.53 82.35

Number of years a newborn infant would be expected to live if patterns of mortality at the time of birth remained constant (World Bank 2015).

Polity 2 7.15 0.73 0.25 10 Scale ranges from 0-10 where 0 is least democratic and 10 most democratic (Freedom House 2015).

Democracy (dummy) 0.69 0.00 0 1 Dummy variable for nations with a democratic government. For more information, see Cheibub, et al. (2010).

Regime Durability 17.63 6.05 0 95

The number of years since the most recent regime change defined by a three point change in the polity 2 score over a period of three years or less. (Marshall et al. 2014).

Political Stability -0.12 0.23 -2.81 1.54 Measures of perceptions of the likelihood that the government in power will be destabilized or overthrown (Kaufmann, et al. 2010).

Government Ideology 0.29 0.20 0 1 Dummy variable, 1 for leftist party control and 0 for all other parties (Beck, et al. 2001).

Corruption -0.13 0.17 -1.50 2.42

Transparency International’s Corruption Perceptions Index 10-point scale from most to least corrupt (Transparency International 2015).

Education Spending 4.05 0.58 0.60 18.19

Education expenditures, including wages and salaries excluding capital investments in constant 2005 US$ (United Nations Statistical Yearbook).

Healthcare Spending 3.32 181.44 0.55 7.60

Sum of public and private health expenditures as a ratio of total population in constant 2005 US$ (World Health Organization Global Health Expenditure database).

Bond Default 0.03 0.07 0 1

We obtain sovereign bond default data from S&P (2006, 2008b, 2011), with a one indicating a bond default in a given year and a 0 no default at all.

Rule of Law -0.12 0.14 -1.69 1.77 Latent Judicial Independence scores derived from eight rule of law indicators (Linzer and Staton 2015).

All the Gini’s 39.48 2.03 31.42 46.22 Annual average Gini coefficient from ATG dataset (Milanovic 2014).

Note: The full sample was used for calculating the values in this table. All standard deviations are average within country SDs.

39

Appendix 2 – First Stage Regression (Full Model) Moody’s S&P Fitch (1) (2) (1) (2) (1) (2) Rule of Law -.561*

(.203) -.236* (.115)

-.196 (.190)

-.330 (.224)

-.445* (.224)

-.326* (.154)

Bond Default -.430* (.199)

-.293 (.212)

-.623* (.195)

-.514* (.198)

-.380* (.194)

-.427* (.214)

Corruption (CPI) .356* (.180)

.618* (.201)

.449* (.166)

.711* (.187)

.772* (.211)

1.074* (.235)

Gini -.023 (.020)

-.018 (.017)

-.031 (.027)

-.020 (.016)

-.011 (.018)

-.005 (.018)

GDP/capita (ln) 1.563 (2.023)

.247 (1.69)

-1.073 (2.284)

GDP/capita2 (ln) -.006 (.122)

.071 (.103)

0.138 (.139)

Inflation -.001 (.001)

-.001 (.001)

0.000 (.001)

Current Account Balance .000 (.000)

.000 (.000)

0.000 (.000)

Natural Resources .000 (.007)

-.001 (.005)

0.000 (.006)

Trade -.009* (.003)

-.002 (.003)

0.000 .(003)

Social Spending (education) -.003 (.033)

-.017 (.032)

-0.032 (.035)

Population (ln) 1.749* (.738)

1.755* (.697)

1.656* (.893)

Life Expectancy -.103* (.039)

-.113* (.036)

-0.138* (.040)

Polity2 .022 (.045)

.003 (.044)

0.056 (.067)

Regime Durability .001 (.006)

.000 (.005)

-0.002 (.005)

Government Ideology .169 (.101)

.049 (.096)

0.144 (.116)

Constant -32.626* (13.463)

-26.842* (11.903)

-18.529 (16.428)

Obs. 844 761 873 790 714 639 Countries 64 62 78 70 69 61 Avg. Obs. per group 12.4 12.3 11.2 11.3 10.3 10.5 Hansen J statistic p<.05 .216 .214 .319 .311 .201 .197 Kleibergen-Paap rk LM statistic 18.45 18.56 16.05 16.17 18.97 19.11 F-Test : Prob>F .000 .000 .000 .000 .000 .000 Rho .214 .902 .289 .922 .385 .905 Dependent Variable: Change in CRA bond rating. All regressions include country and year fixed effects plus a lagged dependent variable. Significance level: * p<.05;

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Appendix 3: CRA Country Year Change for Countries Included in Sample

Figure 1

Figure 2

41

Figure 3

42

Appendix 4: Effect of Change in CRA Sovereign Bond Ratings over Different Lags

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Appendix 5 Robustness Check Models with Different Controls and Moody’s Rating as Primary IV (1) (2) (3) (4)

Change Moody’s Ratingt-1 -.777** (.392) -.886* (.519) -.607* (.351) -2.146*** (.735) Gini t-1 .889*** (.025) .878*** (.032) .886*** (.025) .088 (.067) GDP/capita (ln) 13.137*** (3.728) 9.388** (3.681) 4.013 (2.981) 4.889 (7.208) GDP/capita2 (ln) -.740*** (.233) -.449** (.205) -.193 (.169) -.286 (.425) Inflation .000 (.001) -.001 (.001) .000 (.001) -.001 (.003) Current Account Balance .000 (.000) .000 (.000) .000 (.000) .000 (.000) Natural Resources .001 (.009) -.003 (.011) .006 (.009) -.016 (.023) Trade -.003 (.004) -.007 (.006) -.002 (.004) -.058*** (.011) Social Spending (education) -- .018 (.052) -.001 (.044) .161 (.127) - Social Spending (healthcare) .001* (.000) -- -- -- Population (ln) -1.392 (1.087) -1.433 (1.447) -1.067 (1.227) 3.685 (2.770) Life Expectancy -.167*** (.064) -.148** (.075) -.165** (.072) .231 (.148) Polity2 .126** (.058) -- .174*** (.062) -.031 (.157) -Democracy Dummy -- -.009 (.265) -- -- Regime Durability .003 (.008) .004 (.010) -- .028 (.021) -Political Stability -- -- -.155 (.164) -- Government Ideology .027 (.155) .081 (.189) .101 (.152) 1.182*** (.395) Constant -19.125 (2.362) -6.915 (28.550) 12.731 (23.117) -57.673 (52.566)

Obs. 764 760 655 663 Countries 63 62 63 58 Adjusted R2 .856 .897 .909 .749 Prob>F .000 .000 .000 .000 Dependent Variable Models 1-3: Gini Coefficient (SWIID); Model 4: Gini Coefficient (Annual average Gini coefficient from ATG dataset). Note: Second stage regression corresponding to Table 1. All regressions include country and year fixed effects and all standard errors are robust. Significance level: * p<.1; ** p<.05; *** p<.01;

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Appendix 6 Robustness Check Models with Different Controls and S&P’s Rating as Primary IV (1) (2) (3) (4)

Change S&P Ratingt-1 -.518* (.271) -.561* (.319) -.374* (.208) -1.022 (.644) Gini t-1 .890*** (.022) .881*** (.026) .879*** (.023) .037 (.061) GDP/capita (ln) 12.771*** (2.923) 8.811*** (2.535) 4.862** (2.292) -3.554 (5.257) GDP/capita2 (ln) -.742*** (.188) -.439*** (.152) -.259* (.134) .140 (.315) Inflation .000 (.001) -.002 (.001) .000 (.001) -.002 (.002) Current Account Balance .000 (.000) .000 (.000) .000 (.000) .000 (.000) Natural Resources -.001 (.006) -.002 (.007) .001 (.006) -.034* (.018) Trade .002 (.003) -.001 (.004) .002 (.003) -.046*** (.008) Social Spending (education) -- .016 (.045) -.006 (.043) .167 (.107) - Social Spending (healthcare) .001** (.000) -- -- -- Population (ln) -1.534* (.891) -1.360 (1.150) -1.235 (1.021) 2.923 (2.213) Life Expectancy -.130** (.054) -.117* (.062) -.100* (.057) .275** (.123) Polity2 .129** (.053) -- .202*** (.058) -.159 (.133) -Democracy Dummy -- .141 (.209) -- -- Regime Durability -.002 (.006) .004 (.008) -- .022 (.016) -Political Stability -- -- -.008 (.176) -- Government Ideology -.051 (.123) -.040 (.139) .006 (.121) 1.030*** (.317) Constant -16.268 (14.949) -6.579 (19.394) 8.302 (17.566) -5.528 (38.476)

Obs. 791 691 697 695 Countries 70 70 73 66 Adjusted R2 .842 .833 .874 .698 Prob>F .000 .000 .000 .000 Dependent Variable Models 1-3: Gini Coefficient (SWIID); Model 4: Gini Coefficient (Annual average Gini coefficient from ATG dataset). Note: Second stage regression corresponding to Table 1. All regressions include country and year fixed effects and all standard errors are robust. Significance level: * p<.1; ** p<.05; *** p<.01;

45

Appendix 7 Robustness Check Models with Different Controls and Fitch’s Rating as Primary IV (1) (2) (3) (4)

Change Fitch’s Ratingt-1 -.640** (.269) -.662** (.314) -.608** (.248) -1.431** (.721) Gini t-1 .857*** (.024) .879*** (.029) .841*** (.025) .037 (.067) GDP/capita (ln) 18.379*** (4.280) 13.756*** (3.638) 11.999*** (2.946) 6.717 (7.345) GDP/capita2 (ln) -1.044 *** (.274) -.699*** (.224) -.626*** (.180) -.463 (.447) Inflation .000 (.001) -.001 (.001) .000 (.001) .000 (.003) Current Account Balance .000 (.000) .000 (.000) .000 (.000) .000 (.000) Natural Resources .010 (.008) .011 (.010) .012 (.008) -.046* (.025) Trade -.002 (.004) -.007 (.005) -.003 (.004) -.054*** (.011) Social Spending (education) -- -.003 (.051) -.041 (.050) .222* (.125) - Social Spending (healthcare) .001* (.000) -- -- -- Population (ln) -3.373*** (1.124) -2.581* (1.431) -3.068** (1.231) 7.463** (2.941) Life Expectancy -.154** (.060) -.126* (.072) -.115* (.065) .245* (.142) Polity2 .227*** (.086) -- .201** (.088) -.083 (.231) -Democracy Dummy -- .378 (.345) -- -- Regime Durability .001 (.007) .005 (.003) -- .036* (.019) -Political Stability -- -- .387* (.208) -- Government Ideology -.179 (.157) -.145 (.181) -.052 (.164) .883** (.411) Constant -9.087 (20.906) -8.671 (25.668) 7.974 (22.151) -122.52** (54.40)

Obs. 639 545 577 560 Countries 61 61 62 57 Adjusted R2 .759 .696 .764 .502 Prob>F .000 .000 .000 .000 Dependent Variable Models 1-3: Gini Coefficient (SWIID); Model 4: Gini Coefficient (Annual average Gini coefficient from ATG dataset). Note: Second stage regression corresponding to Table 1. All regressions include country and year fixed effects and all standard errors are robust. Significance level: * p<.1; ** p<.05; *** p<.01;

46