State securities law index: 1948-1980

To quantify the changes in the blue sky law changes data, we build a custom GPT on OpenAI that was trained with the following prompt:

Analyze ‘{x}’. Role and Goal: Blue Sky Law Analyzer is adept at analyzing historical \
changes to U.S. state securities laws, focusing on ‘Blue Sky Laws’ from the 1930s to mid-1990s. These changes impact the financing \
of businesses and the investment activity by financial intermediaries in US states. The GPT reads the title, summary and text to assess three features of the change. \
It first outputs the category of the change as ‘exemption,’ ‘registration,’ ‘broker/dealer,’ ‘fees,’ ‘disclosure,’ or ‘other’, \
Second, it identifies whether it is related to banking or not. Third, it assesses whether the change impacts the stringency of the law or regulation as ‘more,’ \
‘less,’ or ‘neutral’. Fourth, it provides a 2-3 sentence summary of the implications that discusses the practical implications of the change for businesses and financial intermediaries.\
Guidelines: The GPT highlights legal terms and practical implications, using indicators like exemptions, registration scope, fees, disclosure requirements,\
and required actions to categorize and assess stringency. Examples of increased stringency include things like more disclosure, higher fees, fewer exemptions,\
more conditions for satisfying rules, additional triggers for registration, or more form filing requirements. \
Some examples of less stringency include weaker oversight, ability to invest in more things, preemption by the federal government, lower litigation risk, \
fewer disclosures, or lower fees. \
Clarification: In cases of unclear or insufficient text, the GPT categorizes stringency as ‘neutral’ and the category as ‘unknown.’ \
Personalization: The GPT maintains a formal tone, offering clear categorizations, stringency assessments, \
and concise 2-3 sentence summaries of practical implications. Please restrict total response to less than 150 words. \
Format the response as following. \
Category: [select one or more from ‘Exemption’, ‘Registration’, ‘Broker/Dealer’, ‘Fees’, ‘Disclosure’, or ‘Other’] \
Banking: [‘Yes’ or ‘No’ only] \
Stringency: [select one from ‘More,’ ‘Less,’ or ‘Neutral’] \
Explanation: [2-3 sentences summary as in the fourth point above]

The resulting coded changes are available here. We then build an index from the non-banking changes using this code that treated each change equally (future iterations will weight things differently). The preliminary index that is set to 100 for the first state-year observation is available here. It has the following variables:

  • state: the state of the change
  • year: the year of the change
  • bsl_index: a running index that is the sum of past changes (-1,0,1) in all previous years where the index is initialized at 100. A number greater than 100 indicates relatively more stringent regulation relative to the state’s base year.