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Evaluating the Real Effect of Bank Branching Deregulation Comparing contiguous counties across U.S. state borders

Evaluating the Real Effect of Bank Branching Deregulation Comparing contiguous counties across U.S. state borders. Rocco Huang The University of Amsterdam. Presentation plan. Background of bank branching deregulations in the United States

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Evaluating the Real Effect of Bank Branching Deregulation Comparing contiguous counties across U.S. state borders

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  1. Evaluating the Real Effect of Bank Branching Deregulation Comparing contiguous counties across U.S. state borders Rocco Huang The University of Amsterdam

  2. Presentation plan • Background of bank branching deregulations in the United States • Common empirical difficulties in evaluating the real effect of policy changes • Comparing contiguous counties across state borders. Why and how? • Other applications

  3. Branching regulation in the U.S. • US banking market was balkanized. Geographic expansion by branching was restricted in most places. • Banks were not allowed to branch across county boundaries; Some states practiced unit-banking • In a piecemeal fashion, individual states removed restrictions one by one • Exploit the staggered nature of state-level branching deregulations to examine the real effect of policy changes • A benchmark result: Jayaratne and Strahan (QJE, 1997) show that the removal of restriction on bank expansion is accompanied by faster economic growth

  4. Common empirical problems • States in the same region tend to deregulate in waves; State-to-state comparisons involve large heterogeneity . E.g., Texas and Michigan always go different directions • Deregulation can be induced by unobserved growth opportunities – spurious correlation • Regulations have no teeth; How to do evaluations when a policy is powerless? • Ed Kane: “in the 1970s, loophole mining and fabrication became the main business of modern depository institutions.”

  5. A new methodology • Comparing contiguous counties on opposite sides of “regulation change borders” --- a “regression discontinuity” Deregulated County (since 1983) Georgia Florida Regulated County (until 1988)

  6. Why comparing contiguous counties? • Separated only by state borders for exogenous political reasons • Observable factors: contiguous counties are similar in per capita income and economic structure • Similar climate, access to transport, and agglomeration economy • Reduced standard errors of the estimation; Increased power of the test • County-level growth opportunities are unlikely to affect state-level legislative decisions

  7. “Regulation Change Borders” • 37 segments of “regulation change borders” and 279 pairs of contiguous counties. At least three years of gap between the two states’ deregulation timings. Can evaluate 23 events

  8. Deregulation Timeline

  9. The empirical setting • Pre-deregulation period: • 10-year period when both states restricted state-wide branching • Post-deregulation period: • when one of the two states removed restrictions on state-wide branching, while on opposite side of state border it has not yet • In this period, there was regulatory difference across the border. We require it to be at least three years, and it is on average six years. • “Difference-in-differences” Treatment Effect (TE)

  10. Did deregulated states grow faster? • Our new tests show that the effects are very diverse (In Strahan et al., all but six deregulators perform better) • But, how large an effect is statistical significant?

  11. Statistical precision of the results • What is the random odd of a county growing faster than its neighbors in a 15-year period? • Let’s take a counterfactual path. One example: • Nothing happened across NC-SC border in 1980 • We pretend that deregulation took place in North Carolina • And calculate the economic effect of this “deregulation” as if it had actually happened • We repeat the “fictitious deregulations” for all possible scenarios on “non-event borders” • 266 county-pairs X 11 years X either county deregulates first (always five years of gap)= 5,852 different combinations • We obtain an empirical distribution of the “false treatment effects”

  12. Distribution of the “false treatment effects” • The distribution gives us an idea on how easily we can randomly run into certain large treatment effects • Non-parametric approach: do NOT need to describe and model the distribution of economic growth rates • We obtain critical values for different sample size 90% critical value

  13. What do we learn? • A new methodology for policy evaluation • Can be used to evaluate many other financial regulations in the U.S. (bankruptcy law, foreclose law, predatory lending law, etc) • Can be applied to Europe too • DEU-NLD-BEL borders: SME segment fares better in the German side? • Across German Lander borders:Landesbank Rheinland-Pfalz was taken over by LBBW. How are borrowers in Rheinland-Pfalz affected (using Hesse as the control)? • DEU-AUT border: Are there negative effects of letting private banks carry the “Sparkasse” logo?

  14. Thank you!

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