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Goetz: Federal Reserve Bank of Boston, email@example.com. Levine: Brown University and the NBER, firstname.lastname@example.org. We received very helpful comments from John Boyd, Vicente Cunat, Todd Gormley, Juan Carlos Gozzi, Bill Keeton, David Levine, Yona Rubinstein, David Webb, and seminar participants at Brown University, Harvard Business School, the Haas School of Business, the London School of Economics, University of California, Berkeley, University of Michigan, Federal Reserve Bank of Richmond, and the 7th Annual Conference on Corporate Governance Conference at Washington University of St. Louis. The paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the Federal Reserve Bank of Boston or the International Monetary Fund, its Executive Directors, or the countries they represent.
The corresponding reporting form is called FR Y-9X. More information is available at: http://www.federalreserve.gov/reportforms/ReportDetail.cfm?WhichFormId=FRY-9C.
We exclude subsidiaries that exclusively engage in foreign activities since they do not contribute to domestic diversification, which is the focus of our study.
Income Diversity is computed as follows:
Net interest income is Total interest income minus Total interest expenses. Other operating income includes net fee income, net commission income, and net trading income. In turn, Asset Diversity is computed as:
Net loans is Total loans net of loan loss provisions, and Other earning assets include all earning assets other than loans (such as Treasuries and other fixed income securities, including mortgage-backed securities).
A data set matching Call Report and CRSP identifiers is available on the website of the Federal Reserve Bank of New York, see http://www.newyorkfed.org/research/banking_research/datasets.html.
All four measures of diversification enter negative and significantly if we limit the sample to bank holding companies without international activities.
Deng and Elyasiani (2008) distinguish between diversification and distance. As a robustness test, we control for distance and obtain the same results on diversification.
The closest state receives a weight of one and the farthest a weight of zero:
In this first step of the gravity-deregulation model, we tried several variations. Since many BHCs do not diversify, the dependent variable has many zeros. Thus, we estimate a Tobit rather than a linear OLS model. This yields stronger results than those reported below.
Furthermore, Rubinstein (2011) has critiqued the Frankel and Romer (1999) approach because the fixed effects from the zero stage enter in a nonlinear manner when aggregating to produce the instrument for the first-stage. Thus, we also conducted the analyses in two ways to address this concern. First, we excluded all fixed effects from the zero-stage. Second, we did the estimation in the zero-stage with the fixed effects, but did the projections to form the instrumental variables while setting the coefficients on the fixed effects in the zero-stage to zero. All of the results hold. Indeed the magnitude of the coefficient in the valuation regression increases markedly.