# **3.3. Estimation results**

Sovereign risk reflects the perception by market participants or rating agencies of the ability of a government to fully meet its debt obligations. As these perceptions are influenced by a variety of socio-economic factors, we estimate how public investment quality affects the relationship between public investment and sovereign risk while controlling for a large number of variables that have been found to be associated with sovereign risk.

As mentioned, we expect that higher levels of public investment will increase sovereign risk in countries with low investment quality and reduce sovereign risk in countries with high investment quality. To test this hypothesis, we estimate the following model: where SRc,t is sovereign risk in country c, year t, PINVc,t is public investment over GDP in country c year t, PIQc,t is the public investment quality index for country c year t (either the static index PIQ-F or the dynamic index PIQ-D), Xc,t−1 is a matrix of country-year controls (all lagged to attenuate simultaneity), τt are year fixed effects, and ρc are either region or country fixed effects.<sup>24</sup> As mentioned, we use two measures of sovereign risk: sovereign ratings and the log of 5-year CDS spreads.

In the set-up of Equation 3.1, our main parameter of interest is γ. This interactive term measures how the relationship between public investment varies with public investment quality. Given that our dependent variable is increasing in sovereign risk, we expect to find a negative value of γ, indicating that public investment is positively associated with sovereign risk when the quality of public investment is low and negatively associated with sovereign risk when the quality of public investment is high. Note that PIQ’s average value is close to zero. Hence, in the set up of Equation 3.1, the parameter β shows the correlation between public investment and sovereign risk for the country with an average level of investment quality. We also center the public investment variable by subtracting its sample mean (0.07). Thus, the parameter δ indicates the correlation between

> 24 The list of control variables includes: general government gross debt (percent of GDP), total reserves (percent of GDP), log of exchange rate (LCU per USD, period average), CPI inflation, real GDP growth, real GDP per capita (constant 2015 USD, log), current account balance (percent of GDP), Laeven-Valencia financial crisis dummy variables (systemic banking crisis, currency crisis, debt crisis, and debt restructuring), private credit as a share of GDP, natural resource rents (percent of GDP), log of real net ODA received, Chinn-Ito financial openness index, foreign trade (percent of GDP), net FDI inflows (percent of GDP). As the sample for which we have CDS data is smaller than the sample for which we have credit ratings data, we try to preserve observations by reducing the number of controls in baseline parsimonious specifications and show that the results are robust to using the full set of controls. We also check that collinearity is not an issue (the correlation matrix is reported in Table A5). The reverse causality from sovereign risk to public investment is also not a concern: the correlation between sovereign risk measures and public investment ratio is low, and regressions of public investment ratio on sovereign risk, controlling for other variables in the specification, also does not yield a statistically significant effect of sovereign risk on public investment.

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