Essays on Social Insurance
Access StatusFull text of the requested work is not available in DASH at this time ("dark deposit"). For more information on dark deposits, see our FAQ.
MetadataShow full item record
CitationPrinz, Daniel. 2021. Essays on Social Insurance. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
AbstractThis dissertation consists of four chapters on the economics of social insurance programs.
In the first chapter, Bastian Ravesteijn and I study the consequences of employer cost sharing in disability insurance. We develop a framework to study optimal disability insurance when employers exhibit moral hazard and show that the optimal system takes into account employer-side moral hazard and selective hiring. We illustrate these insights using a reform in the Netherlands that extended experience rating to temporary workers. Using this reform, we document a 24 percent decrease in disability inflow. We also find an increase in worker selection, accounting for 14 percent of the overall decrease. Using our model, we evaluate the normative implications of the experience rating policy. We conclude that, given reasonable assumptions, the policy improved welfare and additional employer responsibility would further add to social welfare.
In the second chapter, Ithai Lurie, Nicole Maestas, Corbin Miller, and I study the distribution of disability insurance receipt across employers in the United States. We merge the universe of 2000-2018 W-2 earnings records to the universe of 2000-2018 SSA-1099 forms to estimate the Social Security Disability Insurance (SSDI) claiming rate of each employer's employees. We document large variation across industries in claiming rates. We also show that SSDI claiming rates correlate with characteristics of firms that signal firm quality. There is a positive association between firm size and employee SSDI claiming, except for the largest firms, which have lower employee claiming rates. In addition, we document a negative association between employee wages and SSDI claiming.
In the third chapter, Tal Gross, Timothy Layton, and I study the role of liquidity sensitivity in healthcare consumption and its implications for the design of health insurance for low-income beneficiaries. Some consumers lack the cash needed to pay for medical care. As a result, they either delay care until they can pay for it or they forgo the care altogether. To test for such a possibility, we study the distribution of monthly Social Security checks among Medicare Part D enrollees. When Social Security checks are distributed, prescription fills increase by 6-12 percent. In that sense, drug consumption of low-income Medicare recipients is ``liquidity sensitive." We then study recipients who transition onto a program that eliminates copayments. When those recipients do not face copayments, their drug consumption becomes less liquidity sensitive. That finding implies that, beyond risk protection, generous insurance also provides recipients with the ability to consume healthcare when they need it rather than when they have cash. Further, we find that recipients whose drug consumption is most liquidity sensitive exhibit price elasticities of demand that are twice the size of the average elasticity, suggesting that more-generous insurance causes recipients both to re-time prescription filling and also to start filling prescriptions that they otherwise would not fill. We present a stylized model that uses this finding to call into question the conventional interpretation of demand-response to price as solely inefficient moral hazard.
In the fourth chapter, Edward Kong and I study the impact of various non-pharmaceutical interventions on unemployment insurance claiming during the Covid-19 pandemic. We use high-frequency Google search data, combined with data on the announcement dates of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic in U.S. states, to disentangle the short-run direct impacts of multiple different state-level NPIs in an event study framework. Exploiting differential timing in the announcements of restaurant and bar limitations, non-essential business closures, stay-at-home orders, large-gatherings bans, school closures, and emergency declarations, we leverage the high-frequency search data to separately identify the effects of multiple NPIs that were introduced around the same time. We then describe a set of assumptions under which proxy outcomes can be used to estimate a causal parameter of interest when data on the outcome of interest are limited. Using this method, we quantify the share of overall growth in unemployment during the COVID-19 pandemic that was directly due to each of these state-level NPIs. We find that between March 14 and 28, restaurant and bar limitations and non-essential business closures can explain 6.0 percent and 6.4 percent of UI claims respectively, while the other NPIs did not directly increase own-state UI claims. This suggests that most of the short-run increase in UI claims during the pandemic was likely due to other factors, including declines in consumer demand, local policies, and policies implemented by private firms and institutions.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37368209
- FAS Theses and Dissertations