for U.S. mutual funds by Chevalier and Ellison (1997), among others. It also permits us to estimate how investors react to changes in the conditions experienced by the countries in which funds invest, measured by crisis at the country of destiny. Furthermore, investors are also affected by shocks such as global crises and changes in the conditions at their country of origin, which can lead to change their investments. For example, investors may feel richer and desire to invest more internationally during good times. But it could also be the case that investors prefer to invest more internationally when conditions in their home countries worsen, because international markets might provide better prospects in relative terms. Ex-ante, all these effects are not obvious. Investors may react to different types of shocks pro-cyclically, countercyclically, or not react at all.

We sequentially regress the injections to a fund on a weighted country crisis dummy, a dummy variable taking the value one during periods of global turmoil, lagged fund returns, and the returns of the fund’s country of origin.<sup>23,24</sup> This is akin to an augmented version of the specification estimated by Sirri and Tufano (1998) for U.S. mutual funds.<sup>25</sup> In addition, the regressions include, alternatively, fixed effects at the fund, month, and country of origin-month levels. Standard errors are clustered at the country of origin-month level to control for correlation in injections to funds located in the same country.<sup>26</sup> The results reported in Table 4 show that injections to both equity and bond funds fall when the countries of destiny are affected by crises (Column (1)) and in periods of global crises (Column (2)). On the contrary, injections increase in response to the lagged returns of the fund (Column (3)), which are observable by the underlying investors, and in response to increases in the contemporaneous returns in the country of origin of the fund (Column (4)), which capture local conditions. Interestingly, among both equity and bond funds, the coefficient on lagged

> 23 In the regressions, we normalize the injections to a fund (given by Equation (2)) by the average assets instead of the initial assets to isolate fluctuations in injections from fluctuations in initial assets. Results using injections normalized by initial assets (available upon request) are qualitatively and quantitatively similar to those reported here, but estimators are less precise because of the additional volatility of the denominator in the expression.

> 24 The weighted country crisis dummy is constructed using yearly country crisis data, weighted by the fund’s country portfolio weights at the beginning of the year. The crisis variable comes from Broner et al. (2010) and dates a crisis the years when a country suffers at least a banking, debt, or currency crisis, according to indicators widely used by the literature. The periods of global turmoil are: July 1997-December 1997 (the Asian crisis), August 1998-December 1998 (the Russian crisis), March 2001-December 2001 (the dotcom bust, September 11, and the Enron scandal), and September 2008-June 2009 (the global financial crisis). Fund returns are computed from fund-price data. Returns of the fund’s country of origin are measured using a broad equity price index from the country where the fund is located. Funds that are domiciled in Luxembourg are matched with country returns from Belgium because there are no available indexes for bonds and equity from Luxembourg.

> 25 Sirri and Tufano (1998) include a longer set of lags of injections and fund returns in their specification. We also estimated a version including up to three lags of both variables obtaining similar results.

> 26 Clustering estimations by month yields very similar results to using clusters by country of origin-month.

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