Lorien Stice-Lawrence

Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.

We show that managers have a propensity to disproportionately report total revenues just above base-ten thresholds (e.g., ten million, thirty million, one billion) and examine motives for and consequences of this behavior. We document that base-ten thresholds are unusually prevalent in revenue targets set in executive compensation contracts, analyst forecasts, and management forecasts. We also show that pressure to beat these targets provides one explanation for the base-ten bias in reported revenues. However, these incentive effects do not offer a complete explanation because base-ten threshold-beating is observed even in the absence of these explicit targets. We further find that when firms beat a base-ten threshold for the first time, they experience increases in news coverage, institutional ownership, liquidity, and analyst following, even after controlling for whether they have beaten other common benchmarks. These results suggest that managers also beat base-ten thresholds in order to increase their overall visibility.

We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store-level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short-term trading profits.

This paper provides evidence on the determinants and economic outcomes of updates of accounting systems (AS) over a 24-year timespan in a large sample of U.S. hospitals. We provide evidence that hospitals update their AS in response to three types of pressures: economic pressures, such as increases in the quality of accounting information driven by vendor rollouts of improved AS; coercive pressures imposed by regulators mandating certain practices, such as internal control practices imposed by Sarbanes–Oxley Section 404; and mimetic pressures for hospitals to conform their AS to those of their peers, such as local county and prominent “celebrity” peers. We find that only economically driven updates lead to economic benefits in the form of lower operating expenses and higher revenues. In contrast, we find some evidence that AS updates prompted by coercive regulatory pressures actually impose economic costs in the form of higher operating expenses.

We document marked trends in 10-K disclosure over the period 1996-2013, with increases in length, boilerplate, stickiness, and redundancy and decreases in specificity, readability, and the relative amount of hard information. We use Latent Dirichlet Allocation (LDA) to examine specific topics and find that new FASB and SEC requirements explain most of the increase in length and that 3 of the 150 topics—fair value, internal controls, and risk factor disclosures—account for virtually all of the increase. These three disclosures also play a major role in explaining the trends in the remaining textual characteristics.