data. However, to check for consistency and robustness, we repeat these estimates using dynamic ordinary least squares (DOLS) with yearly observations.<sup>12</sup> In addition, we control for short-term disequilibria through macro and financial crisis dummies (see below). To control for correlation of error terms, we use country clustering; to capture worldwide shocks, we use time fixed effects. To allow for possible asymmetries according to countries’ balance of payments position (and, hence, to capture traces of nonlinearities), we introduce current account deficit dummies in the estimation of  _e_ and  _r_ . To test for heterogeneity between countries at different levels of development, we divide the sample of countries by income (low, middle, high) and run separate sets of regressions for each income class. Finally, to test for differential saving propensities across sectors (households, corporates, public and external) we also present the results of saving regressions broken down by sectors, based on a smaller sample of about 50 countries.

To make countries comparable, we first control for the level of economic development (as proxied by per capita GDP), which, importantly, also controls for the more systematic component of the Balassa-Samuelson effect in the real exchange rate regression.<sup>13</sup> We then include as additional controls key structural and institutional features (trade and capital openness, demographics, dependence on natural resources, dependence of foreign-earned income as determined by net unrequited transfers, quality of institutions, country size); fiscal policy (the fiscal balance and public sector consumption); exposure to external shocks (terms of trade changes, safe haven effects);<sup>14</sup> and the country’s record of macroeconomic and financial stability, as proxied by the incidence of inflationary or external debt crises. We distribute these controls across the five structural equations based on reasonable priors.

To eliminate the valuation biases that terms of trade changes introduce in nominal macro variables, we use the UN database that provides national accounts in real terms (i.e., where each component of aggregate demand is deflated by its own price deflator).<sup>15</sup> To compare real exchange rate _levels_ over time and across countries (rather than real exchange rate changes over time for the same country), we use as a measure of the real exchange rate the World Bank’s purchasing power parity (PPP) conversion factor divided by the country’s nominal exchange rate with respect to the U.S. dollar.<sup>16</sup> The latter measures, relative to the U.S., the cost of the bundle of goods and services that makes up the gross domestic product of a country.<sup>17</sup> We regress both the real exchange rate and the country rating against net domestic saving, where the latter equals the current account and is expressed as the difference between

> 12 Dynamic ordinary least squares (DOLS) has been shown to provide efficient estimators of long-run relationships in panel data with co-integrated variables (Stock and Watson, 1993).

> 13 Thus, the residual (uncontrolled for) component of real exchange rates is more likely to reflect demand side than supply side effects.

14 Safe haven effects are measured by interacting market volatility (the VIX) with a safe haven dummy that equals one for the United States, Switzerland and Japan (and zero for all other countries).

15 For example, the investment-to-GDP ratio is likely to be underestimated if nominal variables are used in times of significant terms of trade gains because the GDP deflator rises faster than the investment deflator.

16 Rodrik (2008) uses a similar index albeit from a different database (Penn World Tables instead of the World Bank’s WDI). Note that because the exchange rate is expressed in terms of dollars, an appreciation is reflected in an increase in _e_ .

17 Although the PPP conversion factor series was recently updated (after this project was initiated), we continued to use the former version (with data up to 2012) because we detected in the updated version some counterintuitive adjustments for some key Latin American countries.

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