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.     

Marketing insights from text analysis

Language is an integral part of marketing. Consumers share word of mouth, salespeople pitch services, and advertisements try to persuade. Further, small differences in wording can have a big impact. But while it is clear that language is both frequent and important, how can we extract insight from this new form of data? This paper provides an introduction to the main approaches to automated textual analysis and how researchers can use them to extract marketing insight. We provide a brief summary of dictionaries, topic modeling, and embeddings, some examples of how each approach can be used, and some advantages and limitations inherent to each method. Further, we outline how these approaches can be used both in empirical analysis of field data as well as experiments. Finally, an appendix provides links to relevant tools and readings to help interested readers learn more. By introducing more researchers to these valuable and accessible tools, we hope to encourage their adoption in a wide variety of areas of research.

     

Do house prices affect campaign contributions?

Individual campaign contributions are the largest source of financing for U.S. presidential and congressional candidates, though the body of research examining why people give remains small. To help understand these decisions, we estimate the causal impact of house prices on donations across campaigns and parties using an instrumental variables strategy. Our results indicate that an increase in house prices increases ZIP code-level donations to Democratic presidential and congressional candidates, with minuscule or no effect for Republican candidates. The effects in areas with a greater proportion of renters are larger than areas with more homeowners. Since this population is likely to experience higher rents as a result of house price increases, this suggests that pleas for policy may inspire giving. Further, areas with the highest fraction of college educated residents also see the largest effects, when compared to less-educated areas, suggesting a wealth effect exists as well.     

Exploring the potential of SMEs to build individual,organizational, and community resilience through sustainability-oriented business practices

Small- and medium-sized enterprises (SMEs) can have significant resources, capacities, and influence in their communities, suggesting they have the potential to be agents for transformative sustainability. However, SMEs will need to move beyond firm-centered sustainable business practices towards strategic approaches that encompass and contribute to resilience-building processes. Amid the unfolding COVID-19 pandemic, we explored what types of sustainable business practices of SMEs can contribute to individual, organizational, and community resilience. We identified six clusters of practice that are important in this regard. The clusters are not solely technical or “environmental” but rather illustrative of deeper sustainable values shaped by organizational structure, culture, and behavior. This paper suggests that SMEs can pursue transformative approaches to sustainability that are more environmentally, socially, and economically sustainable and better able to withstand shocks like the COVID-19 pandemic and can be significant contributors to community resilience. We conclude with a series of future research priorities critical to examine a largely unexplored nexus in the private sector, the linkages and dynamics between sustainability practice, resilience building, and broader community pathways.     

Historical grounding,political contexts,material hurdles: Towards more in-depth understandings of ‘finance going farming’

Two recently published books—Fairbairn's Fields of Gold and Ouma's Farming as a Financial Asset—now provide the first extensive investigations into finance's engagement with farmland. Both books set out to understand finance's growing interest in farmland from the perspective of the financial actors involved, and inquire how, why, and with what kind of challenges ‘finance has been going farming.’ This review essay discusses the two books in the context of the ‘land rush’ literature. It outlines how they contribute to an advanced understanding of the financialization of farmland in three ways, by (i) embedding finance-farmland intersections historically; (ii) scrutinizing the role of the state within financial farmland investments; and (iii) exploring the hurdles involved in ‘marrying’ finance with farmland. I then critically reflect on the areas that have not been covered by the authors. Critical agrarian studies need to investigate how financialization intersects with the digitization of agriculture, examine life expectancies and afterlives of financialized farms, further ground financial investment in concrete rural spaces, and explore individual motivations and belief systems of its proponents more seriously.     

Redevelopment along arterial streets: The effects of light rail on land use change

We use difference-in-differences approaches and parcel-level data from Minneapolis to estimate the effects of light rail on land use change using alternate definitions of treatment area. Results using circular buffers corroborate previous findings that light rail has virtually no effect on land use change in our study area. In contrast, light rail increases the likelihood of land use change along arterial streets that cross the line at station areas. To accurately model the effects of public transit projects on urban land use, one must consider how potential riders access station areas, rather than assuming accessibility improves radially around a station.     

Place-Based Policies and Nowcasting

There is a growing need to gauge local economic activity in real time. Localised economic challenges have been emphasised in the wake of the COVID-19 pandemic. Real-time trackers (such as OECD trackers) and other nowcasting applications typically correspond to national or highly aggregated regions. In this discussion paper, we briefly explore how unconventional data might be used to produce nowcasts of local economies. We argue that in the absence of traditional nowcasting metrics, efforts to nowcast local economies need a local perspective, with data capture tailored to address heterogeneity across three domains: (1) resources, (2) people and (3) life.