Working Papers

Julie Agnew, Hazel Bateman, Christine Eckert, Fedor Iskhakov, Jordan Louviere, and Susan Thorp

We quantify the widespread and significant economic impact of first impressions and confirmation bias in the financial advice market. We use a theoretical learning model and new experimental data to measure how these biases can evolve over time and change clients’ willingness to pay advisers. Our model demonstrates that clients’ confirmation bias will reinforce the effect of first impressions. Our results also lend support, in a new financial context, to theoretical models of learning under limited memory where people use unclear signals to confirm and reinforce their current beliefs. We find that almost two thirds of the participants in our experiment make choices that are consistent with a limited memory updating process: they interpret unclear advice to be good advice when it comes from the adviser they prefer. Our results show that models that account for behavioral factors such as confirmation bias may be needed to explain some financial decisions.

Michael Keane, Jonathan Ketcham, Nicolai Kuminoff and Timothy Neal

Abstract: We propose new methods to model choice behavior and conduct welfare analysis in complex environments where it is untenable to assume that choices fully reveal preferences. In particular, we investigate how Medicare beneficiaries choose prescription drug plans (PDPs) under the Medicare Part D program. Our approach is novel in that we estimate a multinomial logit model for PDP choice that allows for heterogeneity in both preferences and the behavioral choice process. We find the data can be well characterized by a mixture of three behavioral types: The “rational” type constructs expected out-of-pocket costs E(OOP) rationally, and, ceteris paribus, seeks to minimize premiums plus E(OOP) as theory suggests. The second type constructs expected out-of-pocket (OOP) costs rationally, but puts too much weight on premiums relative to E(OOP) in choosing plans. A third type, who we label “confused,” places weight on irrelevant financial aspects of drug plans, implying they fail to construct E(OOP) rationally. A consumer is more likely to be the “confused” type if they suffer from Alzheimer’s disease and/or depression. We use the model to quantify the monetary and welfare losses that arise from suboptimal decision making for the population, for the behavioral types, and for people with cognitive limitations. We also evaluate policies to simplify the choice set to reduce these losses.

Elena Capatina, Michael Keane and Shiko Maruyama

In the US healthcare system, patients of different socio-economic status (SES) often receive disparate treatment for similar conditions. Prior work documents this phenomenon for particular treatments/conditions, but we take a system-wide view and examine socioeconomic disparities in spending for all medical conditions at the 3-digit ICD-9 level. We also compare SES spending gradients for those covered by private vs. public insurance (Medicare). Using data on adult respondents from the Medical Expenditure Panel Survey 2000-14, we estimate multivariate regressions for individual medical spending (total and out of pocket) controlling for medical conditions, demographics, health, and insurance, separately by sex, education, and age. Within age-sex categories, we assess how spending on each condition varies with education (a proxy for SES). In the predominantly privately insured population aged 24-64, system spending for several of the most socially costly conditions is strongly increasing in education (e.g., breast cancer for women and chest symptoms for men). These disparities are not explained by differences in health, insurance status, or ability-to-pay, suggesting they arise due to discrimination. However, we find no positive SES gradients for individuals over 64 covered by the public Medicare program, suggesting that Medicare plays an important role in improving equity.