Abstract
Global banks played a significant role in the transmission of the 2007 to 2009 crisis to emerging market economies. This paper examines the relationships between adverse liquidity shocks on main developed-country banking systems to emerging markets across Europe, Asia, and Latin America, isolating loan supply from loan demand effects. Loan supply in emerging markets was significantly affected through three separate channels: a contraction in direct, cross-border lending by foreign banks; a contraction in local lending by foreign banks’ affiliates in emerging markets; and a contraction in loan supply by domestic banks resulting from the funding shock to their balance sheet induced by the decline in interbank, cross-border lending. Policy interventions, such as the Vienna Initiative introduced in Europe, influenced the lending channel effects on emerging markets of head office balance sheet shocks. Moreover, openness to international funding was not the main vehicle of propagation. Rather, it was exposure to international funding from source country banking systems that were ex ante more likely to suffer from the liquidity shock.
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Notes
See also the discussion by Crystal, Dages, and Goldberg (2001) and by Calomiris and Powell (2001). Additionally, globalization of banking weakens the lending channel for monetary policy within the United States, while extending the transmission of U.S. policy and liquidity shocks to foreign markets (Cetorelli and Goldberg, 2008). The home market shocks are transmitted into the lending of foreign affiliates. At the same time, such internal capital markets mean that foreign bank subsidiaries do not need to rein in their credit supply during a (local) financial crisis at the same time that domestically owned banks need to (De Haas and Lelyveld, 2010).
The dramatic collapse of global trade in goods and services during the crisis has spawned a debate about the reasons for this collapse. Comparative facts on the downturn are provided by Imbs (2010). Some studies posit that banking and trade credit disruptions played a key role (Amiti and Weinstein, 2009; Chor and Manova, 2009). Other studies dispute the central role of trade credit, instead arguing that global demand and the expanded role of vertically integrated production account for most of the observed collapse of trade (Eaton and others, 2010; Levchenko, Lewis, and Tesar, 2010; Yi, Bems, and Johnson, 2010), or that the collapse was a manifestation of an inventory adjustment (Alessandria, Kaboski, and Midrigan, 2010).
Internal capital markets have received earlier attention in domestic banking contexts. For example, Houston, Marcus, and James (1997) emphasize active internal capital markets in banking organizations, with banks relying on related entities in a bank holding company to get insulation from localized shocks within the United States. Likewise, Ashcraft (2008) shows that bank holding companies are a source of strength to their affiliates, while Campello (2002) shows that parent bank insulation from access to external capital markets extends to small affiliated banks, leaving them less vulnerable to shocks than other small banks that are unaffiliated. See also Ashcraft and Campello (2007). The application to global banks by Cetorelli and Goldberg (2008) argues that there is often internal borrowing and lending between parent organizations and their overseas affiliates. Correa and Murry (2009) consider the cross-border lending dynamics.
Our approach focuses on transmission channels as a result of existing bilateral ties between source and destination countries. A complementary take is to look at potential “contagion” effects, so that transmission occurs also through “third” country channels. van Rijckeghem and Weder (2003) find evidence of significant transmission of shocks through such channels.
Schnabl (2009) provides another recent example of loan supply shock identification. He uses the 1998 Russian default as a negative credit supply shock to international banks and analyzes its impact on bank lending in Peru. With data on individual firms, he controls for credit demand by examining firms that borrow from several banks.
In retaliation to unanticipated nuclear tests in India in May 1998, Pakistan followed through in a matter of days with its own nuclear tests. As a result of such tests, both countries were promptly sanctioned by the international community, with the suspension of exchange rate support to the Pakistani rupee as part of the sanction package. This chain of events, unrelated to the functioning of the Pakistani banking industry, ultimately resulted in a severe bank liquidity crunch, since many Pakistani banks had a substantial deposit base in dollar-denominated accounts. The dollars collected through these bank deposits, however, had to be transferred to the government, which upon withdrawal requests from bank clients would eventually release such dollars at the exchange rate at the time of the original deposit. In essence, the government bore all the currency risk on bank deposits. In response to the financial sanctions cited above, the Pakistani government announced the suspension of this convertibility agreement, releasing instead dollars at the current, much devalued exchange rate, effectively imposing a partial default on this liability. Despite the much less favorable conditions, a substantial amount of dollars were withdrawn by depositors, thus determining a severe funding crisis for the Pakistani banking system.
We defer to the original Khwaja and Mian (2008) article for the details of the model.
Khwaja and Mian (2008) actually argue for a possible negative correlation and in their case found evidence consistent with their prior.
The constraint imposed by the fixed effect specification is that, by relying on a within firm comparison of lending by two separate banks, it can only be implemented on the subset of firms borrowing ex ante from more than one bank. This limitations lead to a drastic reduction in sample size in the Khwaja and Mian (2008) exercise (but still leaving them with more than 5,000 unique bank/firm observations). In our case, this is less of an issue, since at an aggregate level only a handful of destination countries in the data set borrow from just one source country. In our regression analysis those destination countries are excluded.
The treatment of local claims in foreign currency in the database makes these proxy variables instead of true representations.
Some gaps appear in the data available in DBSonline, and are owing to confidentiality concerns of the reporting central banks. For example, both Denmark and Finland no longer have a numerous national banking system, as most of their domestic banks have over time been bought up by larger banks from other Scandinavian countries. When reported data are the aggregate from a small number of commercial banks, the reporting central banks may report the observations to the BIS marked with Observation Level Confidentiality C Confidential, and these data are suppressed from export to DBSonline. The bank type B Domestic Banks amounts vis-à-vis developing countries are not in DBSonline, but the bank type A All Banks amounts are available there.
IFS 22d for bank lending means something slightly different for different countries (most often claims on private sector from banking institutions, but sometimes claims on other sectors from deposit money banks or another combination). 22a through c are claims on central government, state and local governments, and nonfinancial public enterprises.
Owing to the confidential nature of the data, we are only authorized to display actual vulnerability figures for a limited subset of the source countries.
In order to address the robustness of the empirics under alternative timing assumptions, we have experimented with a number of alternative definitions for both the pre- and post-periods. The choice of alternative dates does not really have any material impact on the results. Results based on alternative time windows are available upon request.
Higher numbers post crisis are likely driven by a steep increase in the precrisis quarters, so that time averaging yields relatively lower numbers pre than post. We could have chosen the observation right at the quarter before the crisis and the last quarter in the data set to do the comparison, but the time averages have the advantage of smoothing out quarter-specific idiosyncratic factors. In any case, as argued in the main text, the identification does not rely on the simple pre-post comparison on levels but on the comparison in the pre-post growth between the two subgroup of countries.
The results from these regression checks are reassuring. The V measures exhibit high correlations with the fixed effect estimated coefficients from both cross-border and local lending regressions. The correlations vis-à-vis the coefficients from the cross-border lending regression are higher, around 0.6, and highly significant. The correlations vis-à-vis the coefficients from the local lending regression are smaller, ranging between 0.35 and 0.55 across the three V measures, and significant for one of the three measures. This pattern of relative strength in the results will be found in the formal regression analysis as well, in large part probably because of the fact that, as already mentioned above, the sample size for cross-border lending is much greater than that for local lending. We thank Romain Ranciere for suggesting running this test.
Note that the FE specification is based on the comparison of lending growth by at least two different source countries with different degrees of dollar vulnerability to the same destination country. Hence, in what follows we need to restrict the regression analysis by excluding those destination countries that do not maintain flows from at least two source countries. Of course this set is different in the analysis for cross-border lending from that for local lending, but the differences in sample size with the corresponding OLS regressions is explained by the imposition of this constraint.
The data are from the World Bank update to Beck, Demirgüç-Kunt, and Levine (2000).
Goldberg, Kennedy, and Miu (2010) provide details on the availability of dollars to financial institutions outside the United States via central bank dollar swap facilities that were established.
The authors thank Patrick McGuire for pointing this out.
At least for Latin American countries we know of significant tightening in domestic funding sources as a result of the crisis (Jara, Moreno, and Tovar, 2009).
Cetorelli and Goldberg (2008) show that such internal capital markets are activated in U.S. banks in response to monetary policy conditions.
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Additional information
*Nicola Cetorelli is a Senior Economist in the Research Department at The Federal Reserve Bank of New York. Linda Goldberg is a Vice President in the Research Department at The Federal Reserve Bank of New York and a Research Associate of the NBER. The authors acknowledge the valuable comments from anonymous reviewers, Romain Ranciere, and participants in the IMF-Banque de France-Paris School of Economics Conference on Economic Linkages, Spillovers and the Financial Crisis (January 2010). The authors thank Patrick McGuire and Goetz von Peter for their assistance and thoughtful insights on data on cross-country vulnerabilities of banking sectors to dollar market conditions; and Craig Kennedy for his research support.