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«By JONATHAN D. KETCHAM, NICOLAI V. KUMINOFF AND CHRISTOPHER A. POWERS* We develop a structural model for estimating the welfare effects of poli- cies ...»

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41 In the LE scenario, the fact that the reassignment is based solely on cost allows for the possibility that consumers making suspect choices will be harmed by being reassigned to plans with lower risk protection or quality. Also in this scenario, being assigned to a default plan does not eliminate the hassle cost of learning to navigate a plan offered by a different insurer (e.g. prior authorization paperwork, new pharmacy networks, new customer service protocols). To account for this we recalibrate the model so that the policy reduces the cost of switching to the low-cost default from ̂ Δ Δ to ̂ Δ. Under this interpretation, the welfare-relevant hassle costs are the difference in the estimated cost of switching between brands relative to switching within brands. The continued presence of navigation costs reduces the share of enrollees choosing their assigned default to 23% which, in turn, reduces the average changes in premiums, oop expenditures, and plan quality experienced by winners and losers albeit with no qualitative changes in the pattern of results.37 Even with less than one quarter of consumers switching, the reduction in premiums translates to savings to taxpayers of $54 per enrollee or about $420 million per year. Table 8 shows that the share of consumers who benefit, their average welfare gain, and the implications for government spending and insurer revenue are virtually unchanged if we repeat the estimation and simulation under the assumption that consumers have myopic expectations of their own drug needs for the upcoming year.

Finally, the last column of Table 7 illustrates the importance of what may seem like a small detail—the design of the opt out feature. We repeat the simulation except that now we make it costly for enrollees to switch back to their previously-chosen plans. Intuitively, people may incur a cost from paying attention to the new policy, learning how the opt out feature works, determining whether they expect to prefer their newly assigned default to their old plan and, if not, exercising their opt out option. Under the assumption that everyone faces the same opt out cost we solve for the cost needed to set the average change in expected welfare to zero. It ranges from a low of $73 in the LE scenario with This approach may still overstate benefits to the extent that ̂ and represent latent preferences. As we increase the post-policy cost 37 of switching to the new default option to ̂ Δ Δ the benefits to consumers approach zero. The extreme case in which ̂ and are entirely latent preferences is equivalent to reverting to the pre-policy equilibrium in which case the policy has no effect on consumer welfare.

–  –  –

Table 9 reports the sensitivity of our main estimates for consumer welfare, taxpayer spending, and insurer revenue to several alternative specifications of our model. The columns match the main policy scenarios summarized in the tables and figures of the previous section, and the first three rows of Panel A repeat the results from those scenarios for convenience.

Panel B summarizes results from the alternative approach in which we replace the assumption that consumers have perfect foreknowledge about their future drug costs and distribution with an assumption that their expectations are fully myopic, e.g. based on their prior year’s drug consumption (the “ex ante” approach reported in Ketcham et al.

2012 and Ketcham, Lucarelli and Powers 2015). This involves using the ex ante measures of expected cost to reclassify choices as suspect or non-suspect, estimating the logit models based on that information, and repeating the policy analysis. The results are qualitatively identical to those from the ex post assumption reported in the article. The main quantitative difference is that the welfare gains from personalized decision support and default assignment are larger because the ex ante approach assumes there is no information asymmetry between consumers and the government.

Panels C and D of Table 9 report the sensitivity of our main results to two alternative approaches to defining suspect choices under the baseline approach using ex post drug claims to determine plan costs and choice of dominated plan. Panel C ignores the MCBS knowledge question and defines choices as suspect based solely on dominated plans.

Panel D uses a more inclusive definition based on the union of dominated plan choices, the knowledge question, and being able to reduce expenditures by more than 50%. Moving from C to D increases the set of choices labeled as suspect from 18% to 47%, with the base results in Panel A fitting logically between these figures. Altering how suspect choices are defined has little effect on our main results. This remains true if we further 43 expand the set of suspect choices to include people who could have reduced expenditures by more than 33% (Table A8). The reason is that of the three suspect choice indicators considered here, the choice of a dominated plan has the largest effects on our estimates for. This means that when we classify a greater share of choices as suspect, the difference between and declines. More people benefit from certain simplifications to choice architecture, but the average gain among those who benefit is smaller. These effects offset each other in a way that leads to small increases in expected welfare in some scenarios and small decreases in expected welfare in others.

–  –  –

As a next set of robustness checks, we refine the sample in multiple ways. In Panel E we exclude 640 enrollees who entered the market mid-year. A potential concern in that they may have been forward looking with respect to the following year’s drug needs at the time they made their enrollment decisions, especially as they neared or entered the open enrollment period for the following year. Given that they represent a small share of our overall sample (7%) is it not surprising that dropping them has little effect on our results. In Panel F we drop the 3,740 enrollees (38% of our sample) who had help choosing a plan or relied on a proxy to choose a plan for them. While the smaller sample reduces the statistical significance in the relationships between observed demographics and the probability of making an uninformed decision, the logit estimates and subsequent policy implications are virtually identical to the full sample. This suggests that while the research value of having access to better information on how family, friends, and advisors influence decision making is self-evident, in our context of Medicare Part D it does not alter the predicted effects of policy reforms.

E. Summary

We have developed a structural model capable of evaluating who would win and who would lose from a wide range of paternalistic reforms in a differentiated product market.

We used the model to evaluate three prospective policies that have been proposed to simplify the markets for prescription drug insurance created under Medicare Part D. Our analysis was enabled by a novel combination of administrative records and survey data on consumers’ knowledge of the market, their enrollment decisions, and the financial consequences of those decisions for a nationwide sample of the Medicare population. We used the data to first identify which consumers appear to make informed decisions that reveal their preferences to us then to estimate separate models of decision making for the informed and misinformed groups.

46 The results from our policy experiments suggest that CMS’s recent proposal to simplify the choice process by reducing the number of drug plans would reduce welfare for the median consumer and increase transfers to insurers. In contrast, our results suggest that providing personalized information about the potential savings from switching plans and assigning people to low-cost default plans would benefit the median enrollee. However, even under the most optimistic assumptions, these gains are always less than 19% of consumer expenditures. They are typically under 10%, and are often overshadowed by transfers from insurers to taxpayers. We note three limitations with our study. First, our analysis largely excludes supply-side responses to the prospective policies apart from the adjustments in premiums and the insurers’ decisions about which plans to provide under menu restrictions. Second, our analysis does not embed any responses by consumers in their decisions about whether to participate in the PDP market or not. Given the large taxpayer subsidies to all PDP enrollees, such enrollment decisions likely have large effects on expected consumer surplus and taxpayer costs to the extent that such decisions change under the prospective policies. Third, our study holds constant the drugs consumed across plans and under alternative policies, again excluding some potentially welfare-relevant changes from the policies. We consider each of these limitations as important avenues for further research.


Abaluck, Jason, and Jonathan Gruber. 2011. “Choice Inconsistencies among the Elderly:

Evidence from Plan Choice in the Medicare Part D Program.” American Economic Review. 101(4): 1180-1210.

Agarwal, Sumit, John Driscoll, Xavier Gabaix, and David Laibson. 2009a. “The Age of Reason: Financial Decisions over the Life-Cycle and Implications for Regulation.” Brookings Papers on Economic Activity, Fall: 51:117.

Agarwal, Sumit and Bhashkar Mazumder. 2013. “Cognitive Abilities and Household Financial Decision Making”. American Economic Journal: Applied Economics.

5(1): 193-207.


Allcott, Hunt and Dmitry Taubinsky. 2015. “Evaluating Behaviorally-Motivated Policy:

Experimental Evidence from the Lightbulb Market.” Forthcoming, American Economic Review.

Allcott, Hunt and Judd B. Kessler. 2015. “The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons.” NBER Working Paper #21671.

Ambuehl, Sandro, B. Douglas Bernheim and Annamaria Lusardi. 2014. “The Effect of Financial Education on the Quality of Decision Making”. NBER Working Paper #20618.

Bayer, Patrick and Christopher Timmins. 2005. “On the Equilibrium Properties of Locational Sorting Models.” Journal of Urban Economics, 57(3): 462-77.

Bernheim, B. Douglas and Antonio Rangel. 2009. “Beyond Revealed Preference: ChoiceTheoretic Foundations for Behavioral Welfare Economics-super.” The Quarterly Journal of Economics, 124(1): 51-104.

Bernheim, B. Douglas, Andrey Fradkin, and Igor Popov. 2015. “The Welfare Economics of Default Options in 401(k) Plans.” Forthcoming in American Economic Review.

Busse, Meghan, Devin Pope, Jaren Pope, and Jorge Silva-Risso. 2015 “The Psychological Effect of Weather on Car Purchases”. Forthcoming in Quarterly Journal of Economics.

Camerer, Colin, Samuel Issacharoff, George Lowenstein, Ted O’Donoghue, and Matthew Rabin. 2003. “Regulation for Conservatives: Behavioral Economics and the Case

for “Asymmetric Paternalism”“. University of Pennsylvania Law Review. 151(3):


Carlin, Bruce, Simon Gervais, and Gustavo Manso. 2013. “Libertarian Paternalism, Information Production, and Financial Decision Making.” Review of Financial Studies, 26(9): 2204-28.

Chetty, Raj, John N. Friedman, Soren Leth-Petersen, Torben Heien Nielsen, and Tore Olsen. 2015. “Active vs. Passive Decisions and Crowd-Out in Retirement Savings Accounts: Evidence from Denmark”. Forthcoming in the Quarterly Journal of Economics.

48 Choi, Syngjoo, Shachar Kariv, Wieland Müller and Dan Silverman. 2014. “Who is (More) Rational?” American Economic Review, 104(6): 1518-50.

Cohen and Liran Einav. 2007. “Estimating Risk Preferences from Deductible Choice.”.American Economic Review, 97(3): 745-788.

Fang, Hanming, Michael P. Keane, and Dan Silverman. 2008. “Sources of Advantageous Selection: Evidence from the Medigap Insurance Market.” Journal of Political Economy, 116(2): 303-350.

Federal Register. 2014. “Policy and Technical Changes to the Medicare Advantage and the Medicare Prescription Drug Benefit Programs for Contract Year 2015 (CMSP).” January 10: 1917-2073. Accessed online September 1, 2015, http://www.gpo.gov/fdsys/pkg/FR-2014-01-10/html/2013-31497.htm.

Galiani Sebastian, Alvin Murphy, and Juan Pantano. 2015. “Estimating Neighborhood Choice Models: Lessons from a Housing Assistance Experiment”. Forthcoming in the American Economic Review.

Handel, Benjamin R. 2013. “Adverse selection and inertia in health insurance markets:

When nudging hurts.” American Economic Review, 103(7): 2643-2682.

Handel, Benjamin R. and Jonathan T. Kolstad. 2015. “Health Insurance for “Humans”:

Information Frictions, Plan Choice, and Consumer Welfare.” Forthcoming, American Economic Review.

Handel, Benjamin R., Jonathan T. Kolstad, and Johannes Spinnewijn. 2015. “Information Frictions and Adverse Selection: Policy Interventions in Health Insurance Markets.” NBER Working Paper #21759.

Health and Human Services. 2014. “Proposed HHS Notice of Benefit and Payment Parameters for 2016 Fact Sheet.” November 20, 2014. Accessed online November 27, 2015, https://www.cms.gov/cciio/resources/fact-sheets-and-faqs/downloads/fact-sheetpdf.

Heiss, Florian, Daniel McFadden, and Joachim Winter. 2010. “Mind the Gap! Consumer Perceptions and Choices of Medicare Part D Prescription Drug Plans.” In Research Findings in the Economics of Aging. ed. David A. Wise. Chicago, IL: University of Chicago Press.

Howell, Benjamin L., Jennifer Wolff, and Bradley Herring. 2012. “Medicare Beneficiary Knowledge of the Part D Program and Its Relationship with Voluntary Enrollment.” Medicare Research & Review. 2(4): E1-E21.

Hsu, John, Vicki Fung, Mary Price, Jie Huang, Richard Brand, Rita Hui, Bruce Fireman, and Joseph P. Newhouse. 2008. “Medicare beneficiaries’ knowledge of Part D prescription drug program benefits and responses to drug costs.” Journal of the American Medical Association, 299(16): 1929{1936.

Kahneman, Daniel, Peter P. Wakker, and Rakesh Sarin. 1997. “Back to Bentham? Explorations of Experienced Utility.” Quarterly Journal of Economics 112(2): 375-405.

Kariv, Shachar and Dan Silverman. 2013. “An Old Measure of Decision-making Quality Shed New Light on Paternalism.” Journal of Institutional and Theoretical Economics 169(1): 29-44.

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