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International Fiscal-Financial Spillovers:the Effect of Fiscal Shocks on Cross-Border Bank Lending

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Abstract

This paper sheds new light on the degree of international fiscal-financial spillovers by investigating the effect of domestic fiscal policies on cross-border bank lending. By estimating the dynamic response of U.S. cross-border bank lending towards 45 recipient countries to exogenous domestic fiscal shocks (both measured by spending and revenue) between 1990Q1 and 2012Q4, we find that expansionary domestic fiscal shocks lead to a statistically significant increase in cross-border bank lending and the size of the effect is comparable to an exogenous decline in the federal funds rate by about 25 bp (50 bp) for spending (revenue) shocks. The fiscal-financial spillovers we find are independent of changes in monetary policy or financial conditions measured by the VIX. The effects also depend on the sign of the fiscal shocks and the underlying economic conditions of a source country. While capital controls seem to play some moderating role, we do not find systematic and statistically significant differences in the spillover effects across recipient countries, depending on their exchange rate regime. The extension of the analysis to fiscal shocks for a panel of 16 small open economies largely confirms the U.S. economy’s findings.

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Notes

  1. For example, Beetsma et al. (2008) find that a 1 % GDP increase in public spending is followed by a decline in the trade balance by 0.5% of GDP. Similarly, Feyrer and Shambaugh (2012) find that a 1 % increase in taxes as a share of GDP improves the U.S. current account by 0.5% of GDP. By contrast, Kim and Roubini (2008), find that expansionary U.S. fiscal shocks tend to improve the current account and claim that “twin divergence” is a more suitable description of the historical data. More recently, Ilzetzki et al. (2013) and Kim (2015) find that the sign of the effect of a fiscal deficit on the current account depends on structural characteristics, such as capital openness and exchange rate regimes.

  2. However, since a bulk of cross-border bank lending in our dataset consists of bank loans and deposits rather than a bank’s holding of debt securities, our analysis remains silent about other types of financial linkages such as international bond flows with the increasing importance in shaping global liquidity recently.

  3. In contrast, we do not directly measure cross-border fiscal multipliers through financial linkages—an analogy to measuring fiscal multipliers through trade linkages as in Auerbach and Gorodnichenko (2013) and Goujard (2017)—for the following reasons. First, we are mainly interested in the consequence of domestic fiscal policy on cross-border financial stability in the financially integrated world, thereby shedding new light on the link that was never analyzed before. Second, unlike trade linkages that enter directly to the demand component of GDP, financial linkages measured by the volume of cross-border banking flows are expected to affect output indirectly.

  4. While the data is made public by the BIS at the aggregate level, the data on bilateral claims and liabilities between reporting (source) and counterparty (recipient) countries is available to reporting central banks.

  5. Adjusted changes in amounts outstanding are calculated, as an approximation for flows. In addition to exchange rate fluctuations, the quarterly flows in the locational datasets are corrected for breaks in the reporting population. In Table A.1 in the online appendix, we summarize the data availability in the BIS International Banking Statistics.

  6. We confirm that our main findings are hardly affected by such data-cleaning procedures. For example, censoring the dependent variables at the one percentile of the distribution or including the all observations hardly changes the main findings qualitatively.

  7. Under the maintained assumption that the structural VAR is correctly specified, the patterns should be the same.

  8. We also include a linear time trend, but it hardly changes the estimation results.

  9. The inclusion of \( {\alpha}_{i,j}^h \) is more flexible than controlling for any set of common time-invariant regressors, as those commonly used in the gravity model of international finance and controls simultaneously for any time-invariant characteristics specific to a country i and a country j, respectively.

  10. When policy rates are not available, we use interbank rates. When interbank rates are not available, we use money market rates.

  11. Estonia, Latvia, Slovenia, and Ukraine among the countries presented in Table 1 are dropped in this analysis.

  12. The larger effect on cross-border bank lending of a tax cut than government spending—when they are measured as a share of GDP—is in line with the finding of Mountford and Uhlig (2009) that deficit-financed tax cuts have a larger effect on GDP than deficit-financed spending.

  13. See Choi and Furceri (2018) for detailed discussions on choosing control variables.

  14. We also test the robustness of our results using two lags and find that the impulse response functions are very similar. To save space, this result is available upon request.

  15. The category “other investment” is the residual in the balance of payment statistics and includes loans, currency and deposits, and trade credits.

  16. Our conclusions hardly change when we use export growth instead of import growth.

  17. As an attempt to identify the source of the real exchange rate response, we replace the real effective exchange rate with the nominal effective exchange rate. We find that the nominal effective exchange rate also depreciates significantly, implying that the real exchange rate depreciation following the expansionary fiscal shock is largely driven by the nominal exchange rate depreciation.

  18. We obtain a similar result by using the Wu-Xia shadow federal funds rate (Wu and Xia 2016) instead since 2009.

  19. Fiscal shocks identified by Ramey and Zubairy (2018) and monetary policy shocks identified by Coibion (2012) are indeed near orthogonal (correlation of 0.01).

  20. The response of cross-border bank lending to the fiscal shocks hardly changes when we replace the VIX with U.S. economic policy uncertainty constructed by Baker et al. (2016).

  21. The estimation period is from 1990Q1 to 2008Q4, as the monetary policy shock series is only available until 2008Q4.

  22. The exogenous monetary policy shocks have a significantly negative effect on cross-border bank lending (Figure B.7 in the appendix), which is in sharp contrast to the finding of Cerutti et al. (2017) and Correa et al. (2017), who find a positive effect of domestic monetary tightening on cross-border bank lending using the policy rate itself as a measure of monetary policy shocks. On the other hand, our finding is consistent with Avdjiev and Hale (2018) and Albrizio et al. (2020), who find a negative relationship between the federal funds rate and U.S. cross-border bank lending when an increase in the federal funds rate is driven by exogenous changes in the monetary policy stance.

  23. Unlike the previous case of cross-border asset positions, we cannot take the log of net position. Instead, we normalize the net position by the size of a recipient country’s GDP: \( {y}_{j,t}=\frac{claims_{j,t}-{liabilites}_{j,t}}{GDP_{j,t-1}} \) and \( {y}_{j,t+h}=\frac{claims_{j,t+h}-{liabilites}_{j,t+h}}{GDP_{j,t-1}} \).

  24. Our results are robust to the weighted regression where observations are weighted by the recipient country’s nominal GDP.

  25. We thank an anonymous referee for this helpful suggestion. We use a standardized version of Swamey’s test for slope homogeneity implemented by Bersvendsen and Ditzen (forthcoming). When h > 0, the null hypothesis is always rejected at the 5% significance level.

  26. The results, available upon request, are similar when considering a measure of output gap instead.

  27. Our results hardly change when using alternative parameter values of θ between 1 and 6.

  28. We download the updated annual dataset from Shambaugh Exchange Rate Regime Classification and use the most basic measure of the exchange rate regime employed in Shambaugh (2004). https://www2.gwu.edu/~iiep/about/faculty/jshambaugh/Shambaughexchangerate.pdf provides further details on the construction of the dataset.

  29. The base countries for pegs are obvious; the base for nonpegs, while conceivably difficult to isolate, are in fact almost equally obvious. Most countries generally only peg to one country during the sample and nearly all peg at some point, thus revealing the base. Further, those that do switch bases, tend to switch directly from one peg to another (e.g., Ireland in 1979), so no ambiguous middle float exists. For the few countries that do not peg, currency history is used and the dollar in very rare cases (Japan) where no obvious other choice exists. After the introduction of the euro, all euro-zone countries in our sample are treated as pegged to each other.

  30. Only a quarter of recipient country-time observations are classified as a pegged regime.

  31. The classification of counties in advanced and merging market economies follows the IMF World Economic Outlook.

  32. The average dollar funding share is 0.57 and its standard deviation is 0.21, confirming substantial heterogeneity. The dollar share is higher in emerging market economies (0.63) than advanced economies (0.48). The dollar funding share is also higher in the sample of floaters (0.58) than peggers (0.47).

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Correspondence to Davide Furceri.

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Choi, S., Furceri, D. & Yoon, C. International Fiscal-Financial Spillovers:the Effect of Fiscal Shocks on Cross-Border Bank Lending. Open Econ Rev 32, 259–290 (2021). https://doi.org/10.1007/s11079-020-09606-2

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