Statistics (LBS).<sup>9</sup> We define our main dependent variable of interest, aggregate _gross financial inflows_ , as the sum of changes in foreign holdings of these three categories of assets (portfolio, FDI, and loans) in the developing economy, net of their own disinvestment in each of these three flows. In our robustness checks, we also draw on EPFR Global’s Global Fund Flows and Allocations Data—which compiles secondary market transactions of bond and equity purchases in emerging market mutual funds—to obtain a complementary gross inflow measure; we define this alternative measure as _gross fund inflows_ .<sup>10</sup> Other additional control variables were obtained from the IFS and the World Bank’s World Development Indicators, supplemented by data from Haver Analytics and Datastream where gaps exist.

Our main independent variables of interest is a suite of variables designed to capture the effects that occurred during QE episodes accruing to unobservables. We consider three alternative primary measures, all of which are global in nature: an indicator variable that corresponds to any of the three distinct periods for which a QE program was implemented, separate indicator variables for each of the three episodes, and a continuous measure of QE interventions based on expansions in the size of the central bank’s balance sheet.<sup>11</sup> For the indicator variables, our coding scheme for the start/end quarters defines a quarter as belonging to the implementation window if the total number of implementation days exceeded half the days in any given quarter (e.g. QE1 operations, which began on December 16, 2008, is coded as starting 2009Q1, while QE2, which came into effect on November 3, 2010, is coded as beginning 2010Q4) (precise details of this coding scheme are provided in the appendix). In our baseline, we consider only QE operations by the U.S. Federal Reserve (which we subsequently expand in robustness checks to allow for QE operations in other major advanced-economy central banks).

We use a number of distinct measures to capture each of the potential transmission channels for QE. For each channel, we include a primary indicator (or set of indicators), which we use in our more parsimonious specification, and additional secondary indicators, which are distinct but related alternative measures that we introduce in an extended specification.

> 9The IFS data do include a residual category, “other investments,” that includes loans as a subcomponent. However, this category also includes other forms of cross-border finance (such as trade credit and cash) that are of a fundamentally different nature from bank loans, which make it harder to draw inferences when we disaggregate by flow type. The main advantage of using the “other investments” data is that they aggregate consistently with outflows to yield the balance of payments. Since ensuring this consistency is not important for our application, we use the more clearly-delineated LBS data instead.

> 10Since only relatively few countries report this breakdown to the IMF, the IFS data provide relatively scant country coverage. While our alternative measure of gross inflows represents only a relatively small segment of the total market for financial assets, it tends to closely track actual balance of payments flows remarkably well (Miao & Pant 2012), and serves as a useful robustness check for our main dependent variable.

> 11It is conceivable that these indicators are capturing unobservable effects that are nevertheless directly attributable to the observable channels, and merely reflect a change in the structural relationship along these channels during the QE period. We explore this possibility by examining interaction effects of the QE episode indicator with our other channel measures in Subsection 5.2.

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