The regulatory framework for financial advisors is fragmented, with multiple state and federal regulators. Prior empirical literature on financial advisors has largely focused on a single subset of financial advisors, but we create a database containing brokers regulated primarily by FINRA, investment advisers regulated by the SEC or state securities regulators, and insurance producers regulated by state insurance regulators. There is significant overlap across the regimes; more than 40% of the advisors in our data are registered with more than one regulator. This overlap has implications for labor allocation and market discipline. For example, of the individuals who exit FINRA’s broker regime, 79% were jointly registered in insurance upon exiting FINRA’s regime. This could be efficient if it reflects bad actors who transition to lower risk work, but our evidence shows that these advisors continue to engage in financial planning after they move to the insurance side, as over 90% maintain licenses to sell annuities. Moreover, those who committed misconduct when regulated by FINRA continue to have heightened levels of misconduct in insurance. Our findings have additional implications for regulatory discipline. In 2018 and 2019, FINRA proposed rules designed to nudge “bad” brokers out of the industry. We show that these proposals caused thousands of high-risk brokers to exit the FINRA broker regime, but that the majority of these individuals did not leave financial services—98% are currently registered with state regulators as insurance producers.

We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.

We use a matching method that constructs synthetic counterfactual states to identify the channels that link bank deregulation to financial integration, and thereby to economic growth. We document a positive, but conditional, effect of financial integration on economic growth. We explore the heterogeneous effects of financial integration across states depending on the capital mobility in each state. Our results reveal a correlation between financial integration and subsequent banking sector changes related to an expansion in loan recipients. We show that financial integration democratizes lending and spurs economic growth.