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Compliance-driven investments in technology—or “RegTech”—are growing rapidly. To understand the effects on the financial sector, we study firms’ responses to new internal control requirements. Affected firms make significant investments in ERP and hardware. These expenditures then enable complementary investments that are leveraged for noncompliance purposes, leading to modest savings from avoided customer complaints and misconduct. IT budgets rise and profits fall, especially at small firms, and acquisition activity and market concentration increase. Our results illustrate how regulation can directly and indirectly affect technology adoption, which in turn affects noncompliance functions and market structure.

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group’s interests are threatened. However, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool.

In canonical models of investment dynamics under uncertainty, “time-to-build” in investment decisions implies that uncertainty negatively impacts firm values and aggregate capital productivity. However, capital budgeting, which involves ex-ante information acquisition and state-contingent investment decisions, can potentially ameliorate time-to-build frictions. Reduced-form evidence using firm-level data on sales and investment expectations and errors supports both mechanisms. Incorporating capital budgeting into a standard investment model, our calibrated model reveals that state-contingent investment planning and information acquisition reduce aggregate productivity losses by 41% and 17%, respectively. Moreover, gains from planning accrue primarily to less productive firms, while information acquisition benefits higher productivity ones.

The Dodd-Frank Act shifted regulatory jurisdiction over “midsize” investment advisers from the SEC to state-securities regulators. Client complaints against midsize advisers increased relative to those continuing under SEC oversight by 30 to 40 percent of the unconditional probability. Complaints increasingly cited fiduciary violations and rose more where state regulators had fewer resources. Advisers responding more to weaker oversight had past complaints, were located farther from regulators, faced less competition, had more conflicts of interest, and served primarily less-sophisticated clients. Our results inform optimal regulatory design in markets with informational asymmetries and search frictions.