In the presence of adverse selection, mergers can increase welfare through a reduction in inefficient sorting. I characterize the sorting externality internalized between merging firms in a tractable discrete choice model. Mergers benefit consumers when the firms are small, willingness to pay is moderately increasing in cost, and consumer costs are skewed. Applying the model to the non-group health insurance market, 13% of potential mergers would improve consumer surplus. In markets where the sorting distortion exceeds $5 per person, nearly one-third of mergers improve consumer surplus, highlighting the importance of considering adverse selection in merger evaluation.

This paper theoretically and empirically examines the role of information in the practice of pharmaceutical detailing (promotional interactions between drug representatives and physicians).We start with a theoretical framework in which pharmaceutical firms target detailing visits to physicians who potentially learn about drug quality and prescribe it to their patients.We derive several predictions about the role of information in these visits, which we then test empirically using Medicare Part D prescriptions and pharmaceutical detailing visit data.We find there is little empirical evidence to support learning as a primary mechanism of detailing visits and, in fact, document strong evidence to the contrary.

We estimate a tractable income process that is consistent with key facts on individual income risk and its variation over the business cycle. In particular, the estimated process generates income fluctuations that display (i) flat and acyclical variance, (ii) volatile and procyclical skewness, (iii) very high kurtosis, and (iv) a moderate rise in within-cohort inequality over the life cycle, all consistent with the US data. Furthermore, the income process captures the predictable nature of business cycle income risk: income changes during a business cycle episode are partly predicted by income levels before that episode. The estimated process features a time-varying distribution of innovations as well as a factor structure for business cycle exposure. Incorporating the estimated process into a business cycle model adds only one state variable—as in the workhorse persistent-plus-transitory income process—making it a tractable option for modelers.

Consumers in private health insurance markets are highly inertial. The literature has repeatedly found consumers are willing to pay thousands of dollars to keep their health plan. However, the causes of inertia are not well understood, despite their importance in determining whether welfare can be improved by reducing inertia and which types of policies would be effective in doing so. Using administrative data from California's Health Insurance Marketplace, we separately identify three sources of inertia-tastes for provider continuity, inattention, and hassle costs-using two-stage models of inattention and health plan choice. We find that eliminating inattention and hassle costs would reduce repeated health plan choice by 53 percentage points and that interventions to reduce inattention and hassle costs are complements. Inattention and hassle costs cost consumers over a billion dollars in foregone consumer surplus in 2018, roughly $1,790 per household per year or half the annual premium paid by the median household, with inattention accounting for the largest source of forgone surplus. We conclude that interventions to reduce inertial plan choice should jointly focus on hassle costs and particularly inattention, but not tastes for provider continuity. (C) 2022 Elsevier B.V. All rights reserved.

We use a novel data set from a private online marketplace to estimate the demand for individual health insurance among a set comprising many high-income households across 18 states. Households earning more than four times the federal poverty level (FPL) are willing to pay $30 to $135 per month to increase the actuarial value of their insurance by 10 percentage points, much less than households earning less than 2.5 times FPL. Higher-income households are also less likely to forgo insurance because of a premium increase. These results are important for understanding the effect of health reform proposals targeting higher-income populations.

Competition in insurance markets affects not only the premium but also the cost-sharing terms—e.g. copays and coinsurance rates— which may affect a patient’s medical decisions and health outcomes. Using medical claims data linked to insurance product choices, I estimate a model in which consumers select an insurance plan and make medical consumption decisions given the cost-sharing terms of their insurance. Firms compete on both the premium and the copay for primary care. A $10 increase in the copay leads to an 8% decrease in medical consumption and a 0.3 percentage point increase in inpatient mortality. Mergers have heterogeneous effects on the primary care copay, leading to between a $6 reduction and $24 increase in mean annual medical consumption. At typical estimates of the value of a statistical life, mergers that increase medical consumption improve welfare as the additional resource use is outweighed by a reduction in mortality risk.

Publicly subsidized private health insurance markets in the United States were created under the assumption that competition would maximize consumer welfare. However, consumers in these markets are willing to pay thousands of dollars to stay in the same health plan, even after accounting for premiums and coverage generosity. While this inertial behavior can undermine welfare gains from competition, it is unclear whether policymakers should attempt to reduce it. One reason not to reduce inertia is that it may result from consumers’ desire to maintain continuity of care with their health care providers, which has strong positive effects on health. On the other hand, inertia may result from inattention and hassle costs. Using administrative data from California’s Health Insurance Marketplace, we present descriptive evidence of inertial plan choice and inattention. We then separately identify three sources of inertia—tastes for provider continuity, inattention, and hassle costs—using two-stage models of inattention and plan choice. We find that nearly all inertial plan choice results from inattention and hassle costs, the former more so than the latter. As a result of these two sources of inertia, consumers lost over a billion dollars in forgone surplus in 2018, or $1,440 to $1,584 per household per year—roughly half the annual premium paid by the median household in California’s Marketplace. We conclude that inertia is amenable to policy remediation, and that interventions to reduce hassle costs and especially inattention can improve consumer welfare and efficiency in private health insurance markets.

In the presence of adverse selection, firms have an incentive to offer products that appeal to low-cost consumers and lead high-cost consumers to purchase insurance elsewhere. This incentive is worst in highly competitive markets and absent in a monopoly. Through this interaction, regulations to address adverse selection and competition policy should be considered complements. This paper estimates a model of imperfect competition in the non-group health insurance market that directly characterizes the relationship between adverse selection and market power using novel choice data from a private online marketplace and a risk prediction model. The largest welfare cost comes from high markups. Current policies targeting adverse selection are successful in reducing the welfare cost of extensive margin selection and inefficient sorting. However, these policies also increase the potential harm of mergers, and in the most concentrated markets, the firms recapture much of the added surplus through higher markups.

These notes derive formulas for the market shares, first derivatives, and second derivatives of the nested logit model. Section 1 examines on a one-level nesting structure, and Section 2 examines a two-level nesting structure. The notes were originally prepared as part of the Miller and Weinberg (2017) research project, 1 though they have proven useful in a number of other projects as well. We have prepared corresponding functions in R that return the market shares and derivatives given the structural parameters as inputs.

We use Monte Carlo experiments to evaluate whether "upward pricing pressure" (UPP) accurately predicts the price effects of mergers, motivated by the observation that UPP is a restricted form of the first order approximation derived in Jaffe and Weyl (2013). Results indicate that UPP is quite accurate with standard log-concave demand systems, but understates price effects if demand exhibits greater convexity. Prediction error does not systematically exceed that of misspecified simulation models, nor is it much greater than that of correctly specified models simulated with imprecise demand elasticities. The results also support that UPP provides accurate screens for anticompetitive mergers. (C) 2017 Elsevier B.V. All rights reserved.

We use Monte Carlo experiments to study how pass‐through can improve merger price predictions, focusing on the first order approximation (FOA) proposed in Jaffe and Weyl []. FOA addresses the functional form misspecification that can exist in standard merger simulations. We find that the predictions of FOA are tightly distributed around the true price effects if pass‐through is precise, but that measurement error in pass‐through diminishes accuracy. As a comparison to FOA, we also study a methodology that uses pass‐through to select among functional forms for use in simulation. This alternative also increases accuracy relative to standard merger simulation and proves more robust to measurement error.

The relationship between rising premiums and lower pay was already well known in academic literature. Our research simply measured how the ACA has affected the relationship between health insurance premiums, small business wages, and employment. While there was no significant relationship between healthcare premiums and employment before the ACA, since 2010 small businesses have slowly started shedding jobs and reducing wages. We found that, on average, employees who work a full year for a business with 50-99 employees lose $935 annually due to ACA regulations, while employees of businesses with 20-49 employees, on average lose $827.50 annually.

We analyze the accuracy of first order approximation, a method developed theoretically in Jaffe and Weyl (2012) for predicting the price effects of mergers, and provide an empirical application. Approximation is an alternative to the model-based simulations commonly employed in industrial economics. It provides predictions that are free from functional form assumptions, using data on either cost pass-through or demand curvature in the neighborhood of the initial equilibrium. Our numerical experiments indicate that approximation is more accurate than simulations that use incorrect structural assumptions on demand. For instance, when the true underlying demand system is logit, approximation is more accurate than almost ideal demand system (AIDS) simulation in 79.1 percent of the randomly-drawn industries and more accurate than linear simulation in 90.3 percent of these industries. We also develop, among other results, (i) how accuracy changes across a variety of economic environments, (ii) how accuracy is affected by incomplete data on cost pass-through, and (iii) that a simplified version of approximation provides conservative predictions of price increases.