Nonparametric Estimates of Demand in the California Health Insurance Exchange
We develop a new nonparametric discrete choice methodology and use it to analyze the demand for health insurance in the California Affordable Care Act marketplace. The methodology allows for endogenous prices and instrumental variables, while avoiding parametric functional form assumptions about the unobserved components of utility. We use the methodology to estimate bounds on the effects of changing premiums or subsidies on coverage choices, consumer surplus, and government spending on subsidies. We find that a $10 decrease in monthly premium subsidies would cause a decline between 1.8% and 6.7% in the proportion of subsidized adults with coverage. The reduction in total annual consumer surplus would be between $63 and $74 million, while the savings in yearly subsidy outlays would be between $209 and $601 million. We estimate the demand impacts of linking subsidies to age, finding that shifting subsidies from older to younger buyers would increase average consumer surplus, with potentially large impacts on enrollment. We also estimate the consumer surplus impact of removing the highly-subsidized plans in the Silver metal tier, where we find that a nonparametric model is consistent with a wide range of possibilities. We find that comparable mixed logit models tend to yield price sensitivity estimates towards the lower end of the nonparametric bounds, while producing consumer surplus impacts that can be both higher and lower than the nonparametric bounds depending on the specification of random coefficients.
We thank Nikhil Agarwal, Stéphane Bonhomme, Steve Berry, Øystein Daljord, Michael Dinerstein, Liran Einav, Phil Haile, Kate Ho, Ali Hortaçsu, Simon Lee, Chuck Manski, Sanjog Misra, Magne Mogstad, Adam Rosen, Ariel Pakes, Áureo de Paula, Jack Porter, Bernard Salanié, Thomas Wollman, and participants in many conferences and seminars for helpful comments and feedback. Five anonymous referees and the co-editor provided comments that led to substantial improvements in the paper. Research supported in part by the Becker Friedman Institute Health Economics Initiative and by National Science Foundation grant SES-1426882. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Research supported in part by National Science
Foundation grant SES-1530538.