LE Mathias

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Journals favor rejection of the null hypothesis. This selection upon tests may distort the behavior of researchers. Using 50,000 tests published between 2005 and 2011 in the AER, JPE, and QJE, we identify a residual in the distribution of tests that cannot be explained by selection. The distribution of p-values exhibits a two humped camel shape with abundant p-values above 0.25, a valley between 0.25 and 0.10, and a bump slightly below 0.05. The missing tests (with p-values between 0.25 and 0.10) can be retrieved just after the 0.05 threshold and represent 10% to 20% of marginally rejected tests. Our interpretation is that researchers might be tempted to inflate the value of those just-rejected tests by choosing a “significant” specification. We propose a method to measure this residual and describe how it varies by article and author characteristics.

Using 50,000 tests published in the AER, JPE, and QJE, we identify a residual in the distribution of tests that cannot be explained solely by journals favoring rejection of the null hypothesis. We observe a two-humped camel shape with missing p-values between 0.25 and 0.10 that can be retrieved just after the 0.05 threshold and represent 10-20 percent of marginally rejected tests. Our interpretation is that researchers inflate the value of just-rejected tests by choosing ".significant". specifications. We propose a method to measure this residual and describe how it varies by article and author characteristics. (JEL A11, C13).

Journals favor rejection of the null hypothesis. This selection upon tests may distort the behavior of researchers. Using 50,000 tests published between 2005 and 2011 in the AER, JPE, and QJE, we identify a residual in the distribution of tests that cannot be explained by selection. The distribution of p-values exhibits a two humped camel shape with abundant p-values above 0.25, a valley between 0.25 and 0.10, and a bump slightly below 0.05. The missing tests (with p-values between 0.25 and 0.10) can be retrieved just after the 0.05 threshold and represent 10% to 20% of marginally rejected tests. Our interpretation is that researchers might be tempted to inflate the value of those just-rejected tests by choosing a “significant” specification. We propose a method to measure this residual and describe how it varies by article and author characteristics.

The capital of a bank is a key element of their soundness and in this thesis we examine its dynamics. Chapter 2 shows that the introduction of a deposit guarantee leads banks to increase their leverage. However, bank responses are heterogeneous: the effect decreases with size, systemicity and initial capitalization so that the largest banks and the initially least capitalized banks do not respond to the adoption of a deposit guarantee. Chapter 3 proposes a new measure to quantify the aggregate capitalization of banking sectors by considering market discipline and the regulatory framework. This index allows us to study how capital shortfalls relative to a bank-specific implied capital target induce aggregate credit fluctuations. The capitalization index is consistent with the Bank Lending Survey and is significantly correlated with future aggregate credit fluctuations, particularly during episodes of undercapitalization. Chapter 4 is not about bank capital but studies the impact of scientific standards and conventions (statistical significance levels) on the behavior of researchers. We identify irregularities in the distribution of t-stats from empirical papers and interpret these as the result of distorted incentives. Our identification allows us to separate publication bias from what we call inflation bias: the fact that researchers are tempted to inflate marginally rejected tests by choosing a "significant" specification.

This paper empirically investigates the bank leverage adjustments after deposit insurance adoption. Banks are found to increase significantly their leverage after the introduction of deposit insurance. However, the banks’ responses appear to be heterogenous. The magnitude of the change in bank leverage decreases with (i) the size, (ii) the systemicity and (iii) the initial capitalisation of banks so that the most systemic and the most highly leveraged banks are unresponsive to deposit insurance. As a result, implementing a deposit insurance scheme could have important competitive effects.

Explicit deposit insurance is a crucial ingredient of modern financial safety nets. This paper investigates the effect of deposit insurance adoption on individual bank leverage. Using a panel of banks across 117 countries during the period 1986-2011, I show that deposit insurance adoption pushes banks to increase significantly their leverage by reducing their capital buffer. This increase in bank leverage then translates into higher probability of insolvency. Most importantly, I bring evidence that deposit insurance adoption has important competitive effects: I show that large, systemic and highly leveraged banks are unresponsive to deposit insurance adoption.