John Moody published his first railroad security analysis and ratings manual in April 1909. This study analyzes several current issues by looking back at Moody's original intentions for constructing a ratings system. The study analyzes whether Moody intended his ratings to reflect his private information, or rather, to serve some alternative role, as with monitoring conflicts of interests or realizing informational economies of scale. The study uses an ordinal regression approach to evaluate a set of explanatory variables, constructed from both the manual itself and the panic months of 1907, to test the potential information content of Moody's ratings. At the time of Moody's first rating system, the illiquidity of the US Treasury market forced investors to seek alternative ‘high-quality’ securities. Indeed, Moody rated 38.94 percent of railroad bonds as Aaa, and rated 85.25 percent of railroad bonds as A, Aa or Aaa in his universe of railroad bonds rated. To further test the informational content of Moody's ratings, the study pursues a structural default analysis during the panic year of 1907, which yields results that indicate that the default risk of railroad securities was quite low at the time. These results provide justification for the high overall ratings that Moody assigned to railroad securities, and thus their role as near risk-free securities. Therefore, railroad securities, and Moody's ratings, played a particularly important role in the financial system at the time.

Objectives: In the Netherlands, allocation decisions have not yet been explicitly based on the Value of Statistical Life. However, when policy makers decide whether or not to implement life saving interventions this trade-off is made implicitly. This study aimed to gain insights into this trade-off, hereafter referred to as Implicit Value of Statistical Life (IVSL), by means of a retrospective investment analysis of life saving interventions implemented in the Netherlands.

Methods: A literature search was conducted to find life saving intervention cases meeting the requirements for IVSL calculation. A final sample of ten cases was included in the study concerning interventions implemented in different societal sectors. For each case, an IVSL estimate was calculated according to a uniform method.

Results: IVSL estimates derived from the intervention cases differed considerably and ranged from €1 to almost €11 million. Differences were most extreme when comparing IVSL estimates concerning interventions implemented in different societal sectors. However, IVSL estimates also varied greatly between interventions in the same sector and even within the same interventions when critical assumptions were altered.

Conclusion: Our findings suggest that there are great imbalances between societal investments for preventing a statistical death. This highlights the need for further deliberation about how to improve transparency of policy decisions. An approach ex ante determining the Value of Statistical Life by means of empirical methods and based on societal preferences might circumvent the problems associated with the IVSL and needs further exploration.