inflows, before this is separated into portfolio bond and equity flows, in columns ( _D5_ ) and ( _D6_ ), respectively.<sup>31</sup> We draw several conclusions from this exercise.

First, and most remarkable, is the distinction between FDI and both portfolio and loan flows. For FDI inflows, exempting the lagged dependent variable, only the institutional investor variable and GDP enter with a (marginally) significant coefficient.<sup>32</sup> This result underscores the importance of political and institutional risk as determinants of FDI, which has ample support in the literature (Alfaro _et al._ 2008; B ́enassy-Qu ́er ́e _et al._ 2007; Busse & Hefeker 2007; Dailami _et al._ 2012). This result also corroborates with evidence from gravity-type models of FDI (which finds larger FDI flows between bilateral pairs with larger pairwise GDP), and the more general stylized fact that gross FDI inflows tend to be countercyclical and the least volatile among different financial flows (Contessi _et al._ 2013). It is nevertheless useful for us to recognize that the insensitivity of FDI inflows to global variables applies even when considering gross (rather than net) flows, and that QE has had little impact on this large and stable source of developing country cross-border finance. In contrast, both portfolio capital and bank lending respond more to both global drivers. A related observation is that portfolio and loan flows react mainly to the global “push” factors (due to economic conditions in high-income countries), as opposed to country-specific “pull” variables.

Second, comparing portfolio with loan flows, the latter clearly responds more to the unobservable effect of QE (the coefficient on the indicator variable is 0.021 versus 0.018), suggesting that more so than for the other flows, QE operated through channels other than the modeled channels to boost bank lending. In contrast, measurable transmission channels for QE are routinely larger for portfolio flows. For example, the coefficient on the yield curve (corresponding to the portfolio rebalancing channel) is an order of magnitude as large as that for loans; an analogous argument can be made for the short-term interest rate (for the liquidity channel). The number of statistically significant coefficients is also larger for portfolio flows.<sup>33</sup> Taken together, these first two findings strongly suggest that it is portfolio flows, and especially bond capital, that are most sensitive to QE. In contrast, FDI—which is the most stable compo-

> 31Since the decomposed series are generally less persistent than the aggregate flows data (with comparable average time periods), the magnitude of bias between the different approximations is virtually identical (Bruno 2005). Accordingly, we relax the bias correction to just _O_ � _T_ <u>1</u> � for the estimates in Table 5. Since the panels in the decomposed series also include smaller samples and can be more unbalanced, we also increase the number of bootstrap replications to 200.

> 32Both variables consistently enter with a positive and significant across our baseline and robustness specifications.

> 33The relative insensitivity of bank lending to observable fundmentals in specification ( _D2_ ) may strike some as contrary to findings, especially from the crisis literature, that loans appear to respond to key global factors during crisis events (Adams-Kane, Lim & Jia 2012; Broner _et al._ 2013). We do not see an inconsistency here, since loan flows may well respond strongly to fundamentals in a crisis environment (which we control for), but not under non-crisis conditions. Moreover, it is important to recognize that our results pertain to _post-2000 data only_ , which is our period of interest. Consequently, studies that rely on a longer span of bank lending data may well uncover somewhat ifferent relationships.

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