and includes active and dead cross-regional and regional equity and bond funds registered in various domiciles globally.<sup>10</sup> These funds invest in over 124 developed and developing countries around the world. For each fund and month, the dataset contains the total net asset (TNA) value of the fund denominated in U.S. dollars, the percentage of fund assets allocated to each country (which we call _country weights_ or _weights_ and are non-negative), and the percentage held in cash. The dataset has both actively and passively managed funds with different investment scopes: global, emerging markets, and different regional funds (Table 1). The data also contain information on the fund domicile, the family (investment or asset management company), and main currency denomination.<sup>11</sup> We generally use the term “fund type” to refer to any of these dimensions of fund characteristics, clarifying the precise dimension when necessary.

To perform the empirical analyses, we cleaned the original data in standard ways, reducing the sample in about 15% and the total of funds to 1,076.<sup>12</sup> The final dataset on country allocations contains 7,429,000 observations on the investments of the included mutual funds across countries and time. There is substantially more data (cross sectional and time series) and variety of funds for equity funds than for bond funds.<sup>13</sup> We complement the analysis by collecting additional data from other sources to compute inflows and outflows to funds and countries. To calculate monthly injections into each fund, we collect data on fund prices (Net Asset Values, NAVs) from Bloomberg and Datastream (DS) that we match to the corresponding funds from EPFR Global by name and family. We are able to match about 90% of the funds in our cleaned sample, ending up with 896 and 106 equity

> 10 Over the years, several papers have used the different versions of the EPFR data to analyze mutual fund investments in emerging and developed countries. See, for example, Kaminsky et al. (2001), Borensztein and Gelos (2003), Gelos and Wei (2005), Broner et al. (2006), Fratzscher (2011), and Jotikasthira et al. (2012). 11 Our sample covers mainly open-end mutual funds. While EPFR Global data contain some closed-end funds, their importance is relatively small. Moreover, many of the closed-end funds they cover allow for monthly or quarterly subscriptions and redemptions, and are therefore not truly closed.

> 12 We conducted two basic cleanings. First, we removed fund-time periods where the data was reported at a frequency other than monthly. This excludes some funds that report quarterly data during part of the sample period. Second, we excluded funds that report data for less than 12 months in the entire sample (unless they are present until the end of the sample period).

> 13 Equity mutual funds contain nine types of funds (of global and regional nature). There are a total of 965 mutual funds with 6,867,500 usable observations. Instead, bond mutual funds encompass two types of funds (global and global emerging markets), and include a total of 111 mutual funds. The total number of observations (country weights and cash) for bond funds is 561,500.

7