## _wp1250

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---

### I. Research question and framing
- Primary question: Does geographic diversification boost corporate valuation, focusing on U.S. bank holding companies (BHCs).
- Theoretical channels:
  - Positive: economies of scale, risk diversification, easier outside monitoring of diversification for banks (Chandler, 1977; Gertner et al., 1994; Houston et al., 1997; Berger et al., 1999; Diamond, 1984).
  - Negative: intensified agency problems when firms become geographically dispersed (Jensen, 1986; Jensen and Meckling, 1986; Jensen and Murphy, 1990; Scharfstein and Stein, 2000).
- Identification challenge: causal inference is difficult because diversification decisions may be endogenous to firm valuations.

### II. Empirical setting, data, and sample
- Why U.S. BHCs:
  - Regulatory variation in interstate branching deregulation across states and time provides exogenous variation.
  - Detailed quarterly data on BHC subsidiaries and consolidation available.
  - Banks’ role across the economy makes valuation effects important.
- Data sources and scope:
  - Federal Reserve Financial Statements for Bank Holding Companies (FR Y-9X) and Call Reports for subsidiaries.
  - Market capitalization from CRSP (end-of-quarter market capitalization).
  - State demographics from the Bureau of Economic Analysis.
  - Interstate deregulation dating from Amel (1993) extended to 2007 via state regulators.
- Final sample:
  - 28,337 BHC-quarter observations of 756 BHCs.
  - Time period: second quarter of 1986 to last quarter of 2007.
  - Exclude observations below the 1st and above the 99th percentile of q.
- Tobin’s q definition and presentation:
  - Tobin’s q = (stock market capitalization + book value of total liabilities + minority interest + perpetual preferred stock) / book value of total assets.
  - For expositional purposes q multiplied by 100 in tables.

### III. Measures of diversification and controls
- Four geographic diversification measures:
  - Dummy = 1 if BHC has subsidiaries in more than one state.
  - Fraction of assets held in out-of-state affiliates.
  - 1 − Herfindahl-Hirschman Index of a BHC’s assets across states (increasing in diversification).
  - Average distance (in miles) between BHC headquarters and affiliated subsidiaries.
- Activity diversity controls:
  - Income Diversity and Asset Diversity indexes (values between zero and one).
- Other controls:
  - ln(Total assets), ln(Operating income), growth rates, capital-to-asset ratio, return on equity.
  - State-level: median level of q, concentration of banking assets (HHI), real growth rate of state personal income.
- Sample construction details:
  - Subsidiaries linked to ultimate parent using Call Reports (RSSD9364).

### IV. OLS findings and dynamics
- Cross-sectional correlations:
  - Without BHC fixed effects: positive association between geographic diversity and q (consistent with Deng and Elyasiani, 2008).
  - With BHC fixed effects: strong negative relationship between diversity and q (except fraction of out-of-state assets sometimes insignificant).
- Event dynamics:
  - Event-window regression covering 10 quarters before to 10 quarters after first diversification shows q falls after first geographic diversification and the drop grows for a few quarters afterwards; no pre-trends detected.
- Illustrative economic magnitudes (OLS):
  - Median nondiversified BHC switching to median level of diversity associated with a drop in q of about 0.4 (i.e., about 0.4% since median q is about 100).
  - This translates into a drop in market capitalization of the average bank of about $15 million.
  - Aggregated across sample banks, OLS estimates suggest a drop in bank capitalization of about $20 billion.
- Robustness:
  - Negative association robust to controls for acquisitions/sales (share of assets acquired or sold in quarter), location of subsidiaries (subsidiary-state dummies), activity diversity, accounting oddities around M&A.

### V. Instrumental variables strategies and IV results
- Motivation: address endogeneity (valuations could cause diversification).
- Two IV strategies implemented:
  1. State-time instruments exploiting interstate branching deregulation
     - Instrument sets (nine state-time instrument sets):
       - Years since interstate branching deregulation (linear, squared, and nonparametric year dummies up to ten years).
       - Ln(Number of accessible states) and weighted variant (distance-weighted).
       - Ln(Market Population) and weighted variant (population of accessible states excluding home state).
       - Ln(Market Potential) = Market Population / home-state population, and weighted variant.
     - First-stage evidence:
       - Deregulation increased cross-state diversity; explanatory power improves when instruments weight accessible states by distance or population.
       - F-test values for joint significance of instruments (Panel B, Table 5): 32.08, 67.30, 15.94, 6.864, 23.38, 6.335, 19.88, 16.58, 36.74 (by instrument column).
       - For seven of nine instrument sets, F-test > ten.
       - Hansen J-tests (not reported) fail to reject instrument validity for these seven instruments.
     - Second-stage (2SLS) evidence:
       - Exogenous increases in cross-state diversity reduce Tobin’s q; exception: Ln(Number of accessible states) (weak first stage).
       - Economic examples:
         - A one standard deviation increase in the asset diversity index implies decreases in q of about 30 percent of its standard deviation (regressions (4) or (5)), over 40 percent (regressions (6) or (7)), and about 12 percent with other regressions.
         - New Jersey illustration: changing from completely closed to completely open implied average q fall by almost 5 percent; aggregating across U.S. banking system would be a drop in market capitalization of about $225 billion.
       - 2SLS estimates are between 10 and 20 times larger in absolute value than OLS estimates (consistent with positive selection of higher-q banks into diversification biasing OLS toward zero).

  2. Gravity-deregulation BHC-county instrumental variables
     - Two-step procedure:
       - Zero-stage gravity regression for Share_b,i,j,t (percentage of BHC b’s assets held in state j at time t) on:
         - Distance_b,i,j (distance in 100s of miles between BHC headquarters county and state j’s capital).
         - Ln(pop_i,t / pop_j,t) population ratio.
         - Interactions with whether BHC’s home county is in the top-third by population.
         - Fixed effects: combinations of δ_b, δ_i, δ_j, δ_i,j, δ_t, δ_i,t; estimation only where legal entry into state j is feasible given regulations (projected shares set to zero where prohibited).
       - Aggregation: use predicted bilateral shares (zero where prohibited) to compute predicted Herfindahl index of assets across states for each BHC-quarter; use predicted index as instrument for actual diversity.
     - Zero-stage results (Table 6):
       - Distance (in 100 miles) has negative effects on foreign-state shares with coefficients: -1.165***, -1.100***, -1.912***, -0.243***, -1.948*** across specifications.
       - Negative effect of Ln(population ratio) on shares; county population moderates distance and population effects.
       - Observations: 1,123,007 and 1,122,940 (reported across specifications).
     - First-stage and second-stage (Table 7):
       - First-stage F-tests: 12.84, 11.98, 12.08, 14.35, 12.47, 8.182 across columns (instrument useful; most >10).
       - Second-stage 2SLS results indicate geographic diversity reduces Tobin’s q:
         - Example coefficients in Table 7 Panel A for Tobin’s Q column: -33.740*** (standard error 12.237), and related estimates: -31.718** (12.478), -28.338*** (9.298), -40.632*** (12.704), -55.587*** (20.366).
       - Decomposition (columns (2) and (3), Table 7): impact of diversification operates primarily through changes in market capitalization rather than changes in other components of Tobin’s q ((Total Liabilities + Perpetual Preferred Stock)/Total Assets).
       - Robustness:
         - Results robust to excluding BHCs with international subsidiaries.
         - Results remain (and strengthen) when excluding quarters during and up to two quarters after acquisitions/sales of subsidiaries.
     - Advantages:
       - Differentiates among BHCs within the same state and quarter, allowing BHC fixed effects and state-quarter fixed effects.
       - Helps show that effects are due to diversification per se and not state-level increases in competition following interstate agreements.
     - Economic magnitudes:
       - Regulatory-induced changes in diversity that differentially affect BHCs by location reduce Tobin’s q by between five and ten percent when a state goes from completely closed to completely open (county-time instrument estimates larger than state-time instrument estimates).

### VI. Interpretation and conclusion
- Main conclusion: Exogenous increases in BHC geographic diversity—identified using state-time deregulation instruments and a gravity-deregulation BHC-county instrument—reduced Tobin’s q for BHCs.
- Interpretation:
  - Evidence consistent with increased geographic diversity raising complexity and agency problems, making it more difficult for outside investors to monitor insiders and enabling larger private-rent extraction by insiders, thereby lowering market valuations.
  - Findings emphasize the importance of agency and corporate governance frictions in assessing the net effect of geographic diversification for financial firms.

*Source: _wp1250 (PDF chapter/section) — canonical URL provided*

### 1. Summary Statistics ..................................................................................................

### 1. Summary Statistics

### Major themes and sectional breakdown
- 1. Summary Statistics .............................................................................................................23
- 2. Differences between Diversified and Undiversified Bank Holding Companies ..............24
- 3. Geographic Diversification and Bank Holding Company Value ......................................25
- 4. Geographic Diversification and Bank Holding Company Value:  Controls ......................26
- 5. Geographic Diversification and Bank Holding Company Value:  
  Instrumental Variables based on Interstate Branching Deregulation .............................27
- 6. The Relationship between Population, Distance and BHC Asset Holdings: Zero-Stage ..30
- 7. Geographic Diversification and Bank Holding Company Value:  
  BHC-Specific Instrumental Variables Based on a Gravity-Deregulation Model ...........31

### Figures listed
- Figure 1. The Dynamic Impact of Geographic Expansion on q ........................................................21
- Figure 2. Differences in q before (bilateral) Interstate Banking Agreement .....................................22

*Source: _wp1250 - 1. Summary Statistics (PDF chapter/section) — canonical URL provided*

### References .............................................................................................................

### _wp1250 - References

### I. Research question and framing
- Primary question: Does geographic diversification boost corporate valuation, focusing on U.S. bank holding companies (BHCs).
- Theoretical channels:
  - Positive: economies of scale, risk diversification, easier outside monitoring of diversification for banks (Chandler, 1977; Gertner et al., 1994; Houston et al., 1997; Berger et al., 1999; Diamond, 1984).
  - Negative: intensified agency problems when firms become geographically dispersed (Jensen, 1986; Jensen and Meckling, 1986; Jensen and Murphy, 1990; Scharfstein and Stein, 2000).
- Identification challenge: causal inference is difficult because diversification decisions may be endogenous to firm valuations.

### II. Empirical setting, data, and sample
- Why U.S. BHCs:
  - Regulatory variation in interstate branching deregulation across states and time provides exogenous variation.
  - Detailed quarterly data on BHC subsidiaries and consolidation available.
  - Banks’ role across the economy makes valuation effects important.
- Data sources and scope:
  - Federal Reserve Financial Statements for Bank Holding Companies (FR Y-9X) and Call Reports for subsidiaries.
  - Market capitalization from CRSP (end-of-quarter market capitalization).
  - State demographics from the Bureau of Economic Analysis.
  - Interstate deregulation dating from Amel (1993) extended to 2007 via state regulators.
- Final sample:
  - 28,337 BHC-quarter observations of 756 BHCs.
  - Time period: second quarter of 1986 to last quarter of 2007.
  - Exclude observations below the 1st and above the 99th percentile of q.

### III. Measures of diversification and controls
- Four geographic diversification measures:
  - Dummy = 1 if BHC has subsidiaries in more than one state.
  - Fraction of assets held in out-of-state affiliates.
  - 1 − Herfindahl-Hirschman Index of a BHC’s assets across states (increasing in diversification).
  - Average distance (in miles) between BHC headquarters and affiliated subsidiaries.
- Activity diversity controls:
  - Income Diversity and Asset Diversity indexes (values between zero and one).
- Other controls:
  - ln(Total assets), ln(Operating income), growth rates, capital-to-asset ratio, return on equity.
  - State-level: median level of q, concentration of banking assets (HHI), real growth rate of state personal income.
- Sample construction details:
  - Subsidiaries linked to ultimate parent using Call Reports (RSSD9364).
  - Tobin’s q computed as (stock market capitalization + book value of total liabilities + minority interest + perpetual preferred stock) / book value of total assets; for expositional purposes q multiplied by 100 in tables.

### IV. OLS findings and dynamics
- Cross-sectional correlations:
  - Without BHC fixed effects: positive association between geographic diversity and q (consistent with Deng and Elyasiani, 2008).
  - With BHC fixed effects: strong negative relationship between diversity and q (except fraction of out-of-state assets sometimes insignificant).
- Dynamics around diversification:
  - Event-window regression covering 10 quarters before to 10 quarters after first diversification shows a marked break: q falls after first geographic diversification and the drop grows for a few quarters afterwards; no pre-trends detected.
- Economic magnitudes from OLS (illustrative):
  - Example: an estimated coefficient implies median nondiversified BHC switching to median level of diversity associated with a drop in q of about 0.4 (i.e., about 0.4% since median q is about 100).
  - This translates into a drop in market capitalization of the average bank of about $15 million.
  - Aggregated across sample banks, OLS estimates suggest a drop in bank capitalization of about $20 billion.
- Robustness:
  - Negative association robust to controls for acquisitions/sales (share of assets acquired or sold in quarter), location of subsidiaries (subsidiary-state dummies), activity diversity, accounting oddities around M&A.

### V. Instrumental variables strategies and IV results
- Motivation: address endogeneity (valuations could cause diversification).
- Two IV strategies:
  1. State-time instruments exploiting the dynamic, state-specific process of interstate branch deregulation (1978–1995 and later Riegle-Neal Act of 1994).
     - Nine state-time instrument sets:
       - Years since interstate branching deregulation (linear, squared, and nonparametric year dummies up to ten years).
       - Ln(Number of accessible states) and weighted variant (distance-weighted).
       - Ln(Market Population) and weighted variant (population of accessible states excluding home state).
       - Ln(Market Potential) = Market Population / home-state population, and weighted variant.
     - First-stage evidence (Panel B, Table 5): deregulation increased cross-state diversity; explanatory power improves when instruments weight accessible states by distance or population.
     - F-test values for joint significance of instruments (Panel B, Table 5): 32.08, 67.30, 15.94, 6.864, 23.38, 6.335, 19.88, 16.58, 36.74 (by instrument column).
     - Instrument validity evidence:
       - For seven of nine instrument sets, F-test > ten.
       - Hansen J-tests (not reported) fail to reject instrument validity for these seven instruments.
       - No evidence of pre-deregulation trends in q predicting deregulation timing; historical evidence suggests chaotic interstate agreement formation not driven by BHC valuations.
     - Second-stage (2SLS) evidence (Panel A, Table 5):
       - Exogenous increases in cross-state diversity reduce Tobin’s q; exception: Ln(Number of accessible states) (weak first stage).
       - Economic examples:
         - A one standard deviation increase in the asset diversity index implies decreases in q of about 30 percent of its standard deviation (regressions (4) or (5)), over 40 percent (regressions (6) or (7)), and about 12 percent with other regressions.
         - New Jersey illustration: changing from completely closed to completely open implied average q fall by almost 5 percent; aggregating across U.S. banking system would be a drop in market capitalization of about $225 billion.
       - 2SLS estimates are between 10 and 20 times larger in absolute value than OLS estimates (consistent with positive selection of higher-q banks into diversification biasing OLS toward zero).

  2. Gravity-deregulation model to construct BHC-county-specific instruments (BHC-level IV):
     - Two-step procedure:
       - Zero-stage gravity regression for Share_b,i,j,t (percentage of BHC b’s assets held in state j at time t) on:
         - Distance_b,i,j (distance in 100s of miles between BHC headquarters county and state j’s capital).
         - Ln(pop_i,t / pop_j,t) population ratio.
         - Interactions with whether BHC’s home county is in the top-third by population (county-level urbanization).
         - Fixed effects: combinations of δ_b, δ_i, δ_j, δ_i,j, δ_t, δ_i,t; estimation only where legal entry into state j is feasible given regulations (projected shares set to zero where prohibited).
       - Aggregation: use predicted bilateral shares (zero where prohibited) to compute predicted Herfindahl index of assets across states for each BHC-quarter; use predicted index as instrument for actual diversity.
     - Zero-stage results (Table 6):
       - Negative effect of Distance (in 100 miles) on foreign-state shares (coefficients: -1.165***, -1.100***, -1.912***, -0.243***, -1.948*** across specifications).
       - Negative effect of Ln(population ratio) on shares, with interactions showing county population moderates distance and population effects.
       - Observations: 1,123,007 (or 1,122,940).
     - First-stage and second-stage (Table 7):
       - First-stage F-tests: e.g., 12.84, 11.98, 12.08, 14.35, 12.47, 8.182 across columns—instrument useful (most >10).
       - Second-stage 2SLS results indicate geographic diversity reduces Tobin’s q:
         - Example coefficients in Table 7 Panel A: 1 − Herfindahl Index (Predicted) coefficient values reported, for Tobin’s Q column: -33.740*** (standard error 12.237) and related estimates in robustness columns: -31.718** (12.478), -28.338*** (9.298), -40.632*** (12.704), -55.587*** (20.366).
       - Decomposition (columns (2) and (3), Table 7): impact of diversification operates primarily through changes in market capitalization rather than changes in other components of Tobin’s q ((Total Liabilities + Perpetual Preferred Stock)/Total Assets).
       - Robustness:
         - Results robust to excluding BHCs with international subsidiaries.
         - Results remain (and strengthen) when excluding quarters during and up to two quarters after acquisitions/sales of subsidiaries.
     - Advantages of BHC-county-level IV:
       - Differentiates among BHCs within the same state and quarter, allowing BHC fixed effects and state-quarter fixed effects throughout.
       - Helps show that effects are due to diversification per se and not state-level increases in competition following interstate agreements.
     - Economic magnitudes:
       - Regulatory-induced changes in diversity that differentially affect BHCs by location reduce Tobin’s q by between five and ten percent when a state goes from completely closed to completely open (county-time instrument estimates larger than state-time instrument estimates).

### VI. Interpretation and conclusion
- Summary conclusion: Exogenous increases in BHC geographic diversity—identified using state-time deregulation and a gravity-deregulation BHC-county instrument—reduced Tobin’s q for BHCs.
- Interpretation:
  - Evidence consistent with view that increased geographic diversity raises complexity and agency problems, making it more difficult for outside investors to monitor insiders and enabling larger private-rent extraction by insiders, thereby lowering market valuations.
  - Findings emphasize the importance of agency and corporate governance frictions in assessing the net effect of geographic diversification for financial firms.

*Source: _wp1250 - References (PDF) — IMF working paper content provided.*

### References

### _wp1250 - References

### Banking diversification, conglomerates, and firm value
- Acharya, V., Hasan, I., and Saunders, A., 2006, “Should Banks be Diversified? Evidence from Individual Bank Loan Portfolios,” Journal of Business, Vol. 79, pp. 1355-411.
- Berger, P., Ofek, E., 1995, “Diversification’s Effect on Firm Value,” Journal of Financial Economics, Vol. 37, pp. 39 – 65.
- Campa, J., Kedia, S., 2002, “Explaining the Diversification Discount,” Journal of Finance, Vol. 57, pp. 1731-762.
- Custodio, C., 2010, “Mergers and Acquisitions can Explain the Diversification Discount,” Arizona State University, mimeo, W.P. Carey School of Business.
- Demsetz, R.S., Strahan, P.E., 1997, “Diversification, Size, and Risk at Bank Holding Companies,” Journal of Money, Credit, and Banking, Vol. 29, pp. 300-13.
- Denis, D. J., Denis D. K., Sarin, A., 1997, “Agency Problems, Equity Ownership, and Corporate Diversification,” Journal of Finance, vol. 52, pp. 135-160.
- Denis, D.J., Denis D.K., Yost, K., 2002, “Global Diversification, Industrial Diversification, and Firm Value,” Journal of Finance, Vol. 57, pp. 1951-979.
- Graham, J., Lemmon, M., Wolf, J., 2002, “Does Corporate Diversification Destroy Value?” Journal of Finance, Vol. 57, pp. 695–720.
- Jensen, M., 1986, “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers,” American Economic Review, Vol. 76, pp. 332-29.
- Jensen, M.C., Meckling, W., 1986, “Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure,” Journal of Financial Economics, Vol. 3, pp. 305-60.
- Laeven, L., Levine, R., 2007, “Is There a Diversification Discount in Financial Conglomerates?” Journal of Financial Economics, Vol. 85, pp. 331-67.
- Maksimovic, V., Phillips, G., 2002, “Do Conglomerate Firms Allocate Resources Inefficiently Across Industries? Theory and Evidence,” Journal of Finance, Vol. 57, pp. 721-67.
- Servaes, H., 1996, “The Value of Diversification During the Conglomerate Merger Wave,” Journal of Finance, Vol. 51, pp. 1201-225.
- Villalonga, B., 2004, “Diversification Discount or Premium? New Evidence from the Business Information Tracking Series,” Journal of Finance, Vol. 59, pp. 479-506.
- Rajan, R., Servaes, H., Zingales, L., 2000, “The Cost of Diversity: The Diversification Discount and Inefficient Investment,” Journal of Finance, Vol. 55, pp. 35- 80.

### Banking structure, scale, scope, and efficiency
- Berger, A., Demsetz, R., Strahan, P., 1999, “The Consolidation of the Financial Services Industry: Causes, Consequences, and Implications for the Future,” Journal of Banking and Finance, Vol. 23, pp. 135–94.
- Berger, A., Hanweck, G., Humphrey, D., 1987, “Competitive Viability in Banking: Scale, Scope, and Product Mix Economies,” Journal of Monetary Economics, Vol. 20, pp. 501–20.
- Berger, A., Humphrey, D., 1991, “The Dominance of Inefficiencies over Scale and Product Mix Economies in Banking,” Journal of Monetary Economics, Vol., 28, pp. 117-48.
- Ferrier, G., Grosskopf, S., Hayes, K., Yaisawarng, S., 1993, “Economies of Diversification in the Banking Industry: A Frontier Approach,” Journal of Monetary Economics, Vol. 31, pp. 229-49.
- Brickley, J., Linck, J., Smith, C., Jr., 2003, “Boundaries of the Firm: Evidence from the Banking Industry,” Journal of Financial Economics, Vol. 70, pp. 351-83.
- Saunders, A., Walter, I., 1994, “Universal Banking in the United States: What Could We Gain? What Could We Lose?” Oxford University Press.

### Internal capital markets, information, hierarchies, and agency
- Boyd, J. H., Prescott, E.C., 1986, “Financial Intermediary-coalitions,” Journal of Economics Theory, Vol. 38, pp. 211-32.
- Gertner, R., Scharfstein, D., Stein, J., 1994, “Internal vs. External Capital Markets,” Quarterly Journal of Economics, Vol. 109, pp. 1211-230.
- Houston, J., James, C., Marcus, D., 1997, “Capital Market Frictions and the Role of Internal Capital Markets in Banking,” Journal of Financial Economics, Vol. 46, pp. 135-64.
- Scharfstein, D., Stein, J., 2000, “The Dark Side of Internal Capital Markets: Divisional Rent-seeking and Inefficient Investment,” Journal of Finance’ Vol. 55, pp. 2537-564.
- Stein, J., 2002, “Information Production and Capital Allocation: Decentralized Versus Hierarchical Firms,” Journal of Finance. Vol. 57, pp. 1891-921.
- Liberti, J., Mian, A., 2009, “Estimating the Effect of Hierarchies on Information Use,” Review of Financial Studies, Vol. 22, pp. 4057-090.
- Jensen, M.C., Murphy, K.J., 1990, “Performance Pay and Top Management Incentives,” Journal of Political Economy, Vol. 98, pp. 225-64.
- Denis, D.J., Denis D.K., Yost, K., 2002, “Global Diversification, Industrial Diversification, and Firm Value,” Journal of Finance, Vol. 57, pp. 1951-979.

### Distance, geographic diversification, branching, and trade effects
- Amel, D., 1993, “State Laws Affecting the Geographic Expansion of Commercial Banks,” Manuscript, Board of Governors of the Federal Reserve System.
- Degryse, H., Ongena, S., 2005, “Distance, Lending Relationships, and Competition,” Journal of Finance, Vol. 60, pp. 231-66.
- Deng, S., Elyasiani, E., 2008, “Geographic Diversification, Bank Holding Company Value, and Risk,” Journal of Money, Credit and Banking, Vol. 40, pp. 1217-238.
- Jayaratne, J., Strahan, P. E., 1996, “The Finance-growth Nexus: Evidence from Bank Branch Deregulation,” Quarterly Journal of Economics, Vol. 111, pp. 639-70.
- Kroszner, R., Strahan, P., 1999, “What Drives Deregulation? Economics and Politics of the Relaxation of Bank Branching Restrictions,” Quarterly Journal of Economics, Vol. 114, pp. 1437-467.
- Morgan, D., Rime, B., and Strahan, P., 2004, “Bank Integration and State Business Cycles,” Quarterly Journal of Economics, Vol. 119, pp. 1555-584.
- Petersen, M., Rajan, R., 2002, “Does Distance Still Matter? The Information Revolution in Small Business Lending,” Journal of Finance, Vol. 57, pp. 2533-570.
- Mian, A., 2006, “Distance Constraints: The Limits of Foreign Lending in Poor Economies,” Journal of Finance, Vol. 61, pp. 1465-505.
- Frankel, J., Romer, D., 1999, “Does Trade Cause Growth?” American Economic Review, Vol. 89, pp. 379-99.
- Rubinstein, Y., 2011, ‘Estimating the Impact of Trade on Income: Trade Volumes and Trade Partners.” London School of Economics, mimeo.
- Goldberg, Linda S., 2009, “Understanding Banking Sector Globalization,” IMF Staff Papers, Vol. 56, pp. 171-97.

### Corporate governance, incentives, and managerial behavior
- Jensen, M., 1986, “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers,” American Economic Review, Vol. 76, pp. 332-29.
- Jensen, M.C., Meckling, W., 1986, “Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure,” Journal of Financial Economics, Vol. 3, pp. 305-60.
- Petersen, M., Rajan, R., 2002, “Does Distance Still Matter? The Information Revolution in Small Business Lending,” Journal of Finance, Vol. 57, pp. 2533-570.
- Rajan, R., 1992, “Insiders and Outsiders: The Choice Between Informed and Arm’s Length Debt,” Journal of Finance, Vol. 41, pp. 1367-400.
- Rubinstein, Y., 2011, ‘Estimating the Impact of Trade on Income: Trade Volumes and Trade Partners.” London School of Economics, mimeo.

*Source: _wp1250 - References*

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