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Corporate globalization and bank lending

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Abstract

This paper investigates the effect of corporate globalization on bank loan contracts, as reflected in both price and non-price loan terms. We show that globally diversified firms receive more favorable valuation from creditors than domestic firms do. Specifically, we find strong evidence that global firms are charged lower loan rates, and are spared the more restrictive non-price contractual terms such as short maturity and collateral requirements. Our results are robust to various extensions, including controlling for firms’ endogenous choice of globalization, recognizing the joint determination of loan terms, and using alternative measures of global diversification. Our study contributes to the international business literature as the first comprehensive investigation of how global diversification affects bank lending.

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Notes

  1. For example, Focarelli and Pozzolo (2001) show that cross-border mergers and acquisitions in banking are considerably rarer than in other industries. Levine (1996) shows that the market shares of foreign-owned banks are generally below 10%.

  2. Berger, Dai, Ongena and Smith (2003) examine bank financing in 20 European nations, and find that corporations in those countries tend to borrow from local or regional banks rather than from global banks.

  3. We are grateful to Sreedhar Bharath for providing access to the SAS codes that we used to compute the EDF.

  4. Following Bharath et al. (2011), Security is equal to 0 if a loan's security information is not recorded in the Dealscan. Similar results are obtained if we exclude Security from the regression.

  5. For line of credit, the loan size is measured by the total amount of the line of credit.

  6. Among all global firms, the average level of globalization measured by the foreign sales ratio is 0.327 (32.7%). Given that the dependent variable is expressed as a logarithm and the estimated coefficient of Globalization is −0.121, one would expect a 3.957% (−0.121 × 0.327) drop in bank loan spreads when a domestic firm embarks on the average level of foreign sales.

  7. For a similar application of PSM, refer to Bharath et al. (2011).

  8. Since the mid-1990s, over 15% of M&A deals with US acquirers have involved a target company based in a foreign country.

  9. The state export data are obtained from the US Census Bureau.

  10. Closely related to our study, Bharath et al. (2011) study the impact of prior lending relationships on bank loan contracts, and Berger et al. (2005) examine the impact of bank size on bank lending activities. Interestingly, after controlling for the potential endogeneity (lending relationship and bank size, respectively) using the IV analysis, the magnitude of the coefficient of interest increases considerably in both studies (approximately 5.5 times).

  11. Bound, Jaeger, and Baker (1995) and Staiger and Stock (1997) suggest a rule of thumb that the instruments are weak if the F-statistic on the excluded instruments in the first stage is less than 10.

  12. Less significant, albeit at a 10% level, the negative coefficient of foreign assets ratio might be attributed to the fact that an international spread of assets leads to asset opaqueness and thus a less transparent borrowing firm, whereas foreign sales and the number of foreign segments are more recognizable to a lender.

  13. Asset maturity is a weighted average of current assets divided by the cost of goods sold, and net PPE divided by depreciation and amortization – as defined in Barclay and Smith (1995) and Bharath et al. (2011).

  14. As a robustness check, we use both asset maturity and the regulated industry dummy as the IVs of debt maturity. We obtain results very similar to those reported in Table 6. The regulated industries are industries with SIC codes between 4900 and 4999.

  15. The average level of globalization as measured by the foreign sales ratio is 0.327 (32.7%). Given that the dependent variable is expressed as a logarithm, and the estimated coefficient of Globalization is −0.295, we would expect a 9.647% (−0.295 × 0.327) drop in bank loan spreads when a domestic firm embarks on an average level of foreign sales.

  16. We follow Sufi (2007) and Bharath et al. (2011) in identifying lead banks. A lender is defined as the lead lender if it has one of the following roles reported in the Dealscan: lead arranger, agent, administrative agent, arranger or lead bank.

  17. We construct the lending distance measure following Petersen and Rajan (2002) and Bharath et al. (2011). We use the zip codes of a borrowing firm's headquarters and of its lender to calculate the distance between them. The information on a borrower's location (identified by its zip code) can be obtained from Compustat. To identify lenders’ geographic locations, we extract the zip codes of lenders from Dealscan company information database or Federal Deposit Insurance Corporation's Institution Directory database. Once we have identified the zip codes of the borrowing firm and the lending bank, we match each zip code with its geographic coordinates (latitude and longitude) provided by US Census Gazetteer Zips file (available at http://www.census.gov/tiger/tms/gazetteer/zips.txt). The last step is to calculate the distance between two coordinates using the following formula:

    where 3949.99 is the radius of Earth in miles; lat 1 and lat 2 are latitudes for location 1 and location 2, respectively; and long 1 and long 2 are longitudes for location 1 and location 2, respectively. This formula yields the distance between two locations in miles.

  18. Since the dependent variable is expressed as a logarithm, the estimated coefficient of a dummy variable represents the percentage effect of the independent variable on the dependent variable.

  19. We also include the weighted average of loan type and loan purpose dummies to control for the impacts of loan types and loan purposes at the aggregated level. A weighted average loan type (purpose) dummy can be viewed as the proportion of the corresponding loan type (purpose) in the total amount of a deal.

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Acknowledgements

We thank the editor, David M. Reeb, and two anonymous referees for very helpful comments. Qiu is grateful for the financial support from the Social Sciences and Humanities Research Council of Canada.

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Authors and Affiliations

  1. China Securities Regulatory Commission, Beijing, China

    Shujing Li

  2. DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada

    Jiaping Qiu

  3. Department of Economics, Carleton University, Ottawa, Ontario, Canada

    Chi Wan

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  1. Shujing Li
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Correspondence to Jiaping Qiu.

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Accepted by David Reeb, Area Editor, 23 May 2011. This paper has been with the authors for three revisions.

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Li, S., Qiu, J. & Wan, C. Corporate globalization and bank lending. J Int Bus Stud 42, 1016–1042 (2011). https://doi.org/10.1057/jibs.2011.29

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