internationally, quickly and powerfully, and in particular that the volatility of foreign lending is higher than that of domestic lending. Lending by bank subsidiaries appears to be more procyclical and “crunched” during the recent crisis.<sup>8</sup> More relevant for this study is the evidence from Popov and Udell (2012). Focusing on the Eastern European countries in our data set, these authors show that firms were more credit constrained if they were dealing with banks that experienced a decline in equity and Tier 1 capital, as well as losses on financial assets. Riskier firms as well as firms with fewer tangible assets were more affected by positive or negative shocks suffered by banks.

We account for both foreign and domestic shocks to demand, _DomesticDemandShockjt_ and _ForeignDemandShock−jt_ , in our estimation framework. Following Coulibaly, Sapriza, and Zlate (2012), foreign demand shocks are captured as the percentage change in GDP in the destination country weighted by the shares of the exports from country of firm _i_ at time _t −_ 1 to each destination country.<sup>9</sup> Domestic demand shocks are computed as the percentage change in domestic GDP, consistent with the measure of foreign demand shocks. We also use the firm-specific level of utilization of facilities and manpower (“capacity”) as alternative measures of demand shocks for robustness checks. The measures are not included in the baseline regressions as they are not available for all time periods: less than 40% of the firms reported their capacity level in the last two rounds of the surveys.

The vector _Xijt_ contains the additional firm-level characteristics accounting for the heterogeneity among firms. Because of data limitations, we cannot calculate total factor productivity _a_ ` la Olley and Pakes (1996), but define the labor productivity as the log of the ratio of total sales to employment, a widely used alternative in the trade literature. Even though basic, this proxy is preferable to more elaborate measures of productivity in the absence of input or output prices, as in our case. We account for the age of the firm, computed as the difference between the time of the survey and the year the firm began operating in a given country. We also control for the size of the firm, including a binary variable that takes value one if the firm has more than one but less than 19 employees. Industry fixed effects are also added: the firms in the sample belong to the real estate (8%), manufacturing (27%), construction (13%), retail (40%), hotel and restaurant (5%), and transportation (8%) industries.

The estimation of Equation 1 will be done through a linear probability model (LPM). While the LPM tends to generate biased and inconsistent estimates, as discussed in Horrace and Oaxaca (2006), it is not a large concern given the structure of our data. Irrespective of the set of covariates

> borrowers with the home bias of lenders’ loan origination increasing by approximately 20% if the bank’s home country experiences a banking crisis. The larger contraction in lending during the financial crisis by foreign-owned banks relative to locally-funded domestic banks is documented also by Ongena, Peydr ́o, and Van Horen (2015) that focus specifically on countries from the ECA region and Turkey.

> 8For example, using U.S. data from the recent financial crisis, Cetorelli and Goldberg (2012) show that foreign banks pulled significant funding from their U.S. branches during the Great Recession with sizable effects on their U.S. lending. They estimate that the average-sized branch experienced a 12% net internal fund withdrawal and that, on average, for each dollar of funds transferred internally to the parent, branches decreased lending supply by about 40 to 50 cents. Similar findings are also documented in Coulibaly, Sapriza, and Zlate (2012).

> 9The BEEPS data do not track the firm-specific export destinations. We use aggregate trade data from the World Integrated Trade Solutions (WITS) database.

8