Income disclosure and consumer judgment in a multilevel marketing experiment

Consumer protection concerns have been raised related to income misrepresentation in multilevel marketing (MLM) recruitment. Though not required by law, some MLM firms choose to voluntarily disclose income information about previous participants. Through replication and extension of the experiment created in Bosley, Greenman, and Snyder (2020), we investigate the impact of these disclosures on consumer interest and earnings expectations. We test the external validity of their findings with subjects from Mechanical Turk and explore issues regarding treatment heterogeneity and better-than-average bias. Supporting prior findings, we present evidence that income disclosures, on average, do not significantly affect subjects' interest in the MLM opportunity, but they do decrease earnings estimates for most while increasing earnings estimates for a few. These effects depend on a subject's numeracy skills and whether they see their earnings potential as better-than-average. We also find that asking about earnings estimates first tends to decrease interest.     

Overcoming data barriers in spatial agri-food systems analysis: A flexible imputation framework

Suppressions in public data severely limit the usefulness of spatial data and hinder research applications. In this context, data imputation is necessary to deal with suppressed values. We present and validate a flexible data imputation method that can aid in the completion of under-determined data systems. The validations use Monte Carlo and optimisation modelling techniques to recover suppressed data tables from the 2017 US Census of Agriculture. We then use econometric models to evaluate the accuracy of imputations from alternative models. Various metrics of forecast accuracy (i.e., MAPE, BIC, etc.) show the flexibility and capacity of this approach to accurately recover suppressed data. To illustrate the value of our method, we compare the livestock water withdrawal estimations with imputed data and suppressed data to show the bias in research applications when suppressions are simply dropped from analysis.     

Goode Food Trucks Inc.*

This fictional case centers around a young Dutch couple who emigrated to Canmore, Alberta. Upon arriving, Saul and Rens opened two successful food trucks and are now looking to promote each truck's main chef to truck manager. They plan to offer a performance bonus based on the net income of each truck and have requested a local accounting student to advise them on the new bonus structure. Goode Food Trucks Inc. presents an opportunity for students to demonstrate technical competence in managerial accounting (rate methods, period cost allocation, and data visualization); strategy and governance (employee performance incentives); and financial reporting (reporting needs) while incorporating enabling competencies (communicating and adding value). The case and teaching notes were adapted from principles used to train and evaluate Chartered Professional Accountant (CPA) candidates and tailored to an appropriate level for undergraduate learners. MBA instructors may also use this case to apply multiple concepts in a defined context, thus enhancing a course's “real-life” applicability.