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Item National Instant Criminal Background Check and Youth Gun Carrying(American Academy of Pediatrics, 2020-01) Timsina, Lava R.; Qiao, Nan; Mongalo, Alejandro C.; Vetor, Ashley N.; Carroll, Aaron E.; Bell, Teresa M.; Economics, School of Liberal ArtsBackground: Despite being unable to purchase firearms directly, many adolescents have access to guns, leading to increased risk of injury and death. We sought to determine if the National Instant Criminal Background Check System (NICS) changed adolescents' gun-carrying behavior. Methods: We performed a repeated cross-sectional study using National Youth Risk Behavior Survey data from years 1993 to 2017. We used a survey-weighted multivariable logistic regression model to determine if the NICS had an effect on adolescent gun carrying, controlling for state respondent characteristics, state laws, state characteristics, the interaction between the NICS and state gun laws, and time. Results: On average, 5.8% of the cohort reported carrying a gun. Approximately 17% of respondents who carried guns were from states with a universal background check (U/BC) provision at the point of sale, whereas 83% were from states that did not have such laws (P < .001). The model indicated that the NICS together with U/BCs significantly reduced gun carrying by 25% (adjusted relative risk = 0.75 [95% confidence interval: 0.566-0.995]; P = .046), whereas the NICS independently did not (P = .516). Conclusions: Adolescents in states that require U/BCs on all prospective gun buyers are less likely to carry guns compared with those in states that only require background checks on sales through federally licensed firearms dealers. The NICS was only effective in reducing adolescent gun carrying in the presence of state laws requiring U/BCs on all prospective gun buyers. However, state U/BC laws had no effect on adolescent gun carrying until after the NICS was implemented.Item Regression-Based Causal Analysis from the Potential Outcomes Perspective(Taylor & Francis, 2020-01) Terza, Joseph V.; Economics, School of Liberal ArtsMost empirical economic research is conducted with the goal of providing scientific evidence that will be informative in assessing causal relationships of interest based on relevant counterfactuals. The implementation of regression methods in this context is ubiquitous. With this as motivation, we detail a comprehensive regression-based potential outcomes framework for causal modeling, estimation and inference. This framework facilitates rigorous specification of the effect parameter of interest and makes clear the sense in which it is causally interpretable, when appropriately defined in a potential outcomes setting. It also serves to crystallize the conditions under which the effect parameter and the underlying regression parameters are identified. The consistent sample analog estimator of the effect parameter is discussed. Juxtaposing this framework with a stylized version of a commonly implemented and routinely applied modeling and estimation protocol reveals how the latter is deficient in recognizing, and fully accounting for, conditions required for identification of the relevant effect parameter and the causal interpretability of estimation results. In the context of an example, we demonstrate the conceptual advantages of this general potential outcomes framework for regression modeling by showing how it resolves fundamental shortcomings in the conventional approach to characterizing and remedying omitted variable bias.Item Validating Imputation Procedures to Calculate Corrected Opioid-Involved Overdose Deaths, Marion County, Indiana, 2011-2016(Sage, 2020-01) Gupta, Sumedha; Cohen, Alex; Lowder, Evan M.; Ray, Bradley R.; Economics, School of Liberal ArtsObjectives: Understanding the scope of the current opioid epidemic requires accurate counts of the number of opioid-involved drug overdose deaths. Given known errors and limitations in the reporting of these deaths, several studies have used statistical methods to develop estimates of the true number of opioid-involved overdose deaths. This study validates these procedures using a detailed county-level database of linked toxicology and vital records data. Methods: We extracted and linked toxicology and vital records data from Marion County, Indiana (Indianapolis), during a 6-year period (2011-2016). Using toxicology data as a criterion measure, we tested the validity of multiple imputation procedures, including the Ruhm regression-based imputation approach for correcting the number of opioid-involved overdose deaths. Results: Estimates deviated from true opioid-involved overdose deaths by 3% and increased in accuracy during the study period (2011-2016). For example, in 2016, 231 opioid-involved overdose deaths were noted in the toxicology data, whereas the corresponding imputed estimate was 233 opioid-involved overdose deaths. A simple imputation approach, based on the share of opioid-involved overdose deaths among all drug overdose deaths for which the death certificate specified ≥1 drug, deviated from true opioid-involved overdose deaths by ±5%. Conclusions: Commonly used imputation procedures produced estimates of the number of opioid-involved overdose deaths that are similar to the true number of opioid-involved overdose deaths obtained from toxicology data. Although future studies should examine whether these results extend beyond the geographic area covered in our data set, our findings support the continued use of these imputation procedures to quantify the extent of the opioid epidemic.Item US Trends in COVID-19–Associated Hospitalization and Mortality Rates Before and After Reopening Economies(JAMA Network, 2021-06) Gupta, Sumedha; Georgiou, Archelle; Sen, Soumya; Simon, Kosali; Karaca-Mandic, Pinar; Economics, School of Liberal ArtsImportance After abrupt closures of businesses and public gatherings in the US in late spring 2020 due to the COVID-19 pandemic, by mid-May 2020, most states reopened their economies. Owing in part to a lack of earlier data, there was little evidence on whether state reopening policies influenced important pandemic outcomes—COVID-19–related hospitalizations and mortality—to guide future decision-making in the remainder of this and future pandemics. Objective To investigate changes in COVID-19–related hospitalizations and mortality trends after reopening of US state economies. Design, Setting, and Participants Using an interrupted time series approach, this cross-sectional study examined trends in per-capita COVID-19–related hospitalizations and deaths before and after state reopenings between April 16 and July 31, 2020. Daily state-level data from the University of Minnesota COVID-19 Hospitalization Tracking Project on COVID-19–related hospitalizations and deaths across 47 states were used in the analysis. Exposures Dates that states reopened their economies. Main Outcomes and Measures State-day observations of COVID-19–related hospitalizations and COVID-19–related new deaths per 100 000 people. Results The study included 3686 state-day observations of hospitalizations and 3945 state-day observations of deaths. On the day of reopening, the mean number of hospitalizations per 100 000 people was 17.69 (95% CI, 12.54-22.84) and the mean number of daily new deaths per 100 000 people was 0.395 (95% CI, 0.255-0.536). Both outcomes displayed flat trends before reopening, but they started trending upward thereafter. Relative to the hospitalizations trend in the period before state reopenings, the postperiod trend was higher by 1.607 per 100 000 people (95% CI, 0.203-3.011; P = .03). This estimate implied that nationwide reopenings were associated with 5319 additional people hospitalized for COVID-19 each day. The trend in new deaths after reopening was also positive (0.0376 per 100 000 people; 95% CI, 0.0038-0.0715; P = .03), but the change in mortality trend was not significant (0.0443; 95% CI, −0.0048 to 0.0933; P = .08). Conclusions and Relevance In this cross-sectional study conducted over a 3.5-month period across 47 US states, data on the association of hospitalizations and mortality with state reopening policies may provide input to state projections of the pandemic as policy makers continue to balance public health protections with sustaining economic activity.Item Plan Choice And Affordability In The Individual And Small-Group Markets: Policy And Performance—Past And Present(Project Hope, 2019-04) Abraham, Jean M.; Royalty, Anne B.; Drake, Coleman; Economics, School of Liberal ArtsThe individual and small-group health insurance markets have experienced considerable changes since the passage of the Affordable Care Act in 2010, affecting access, choice, and affordability for enrollees in these markets. We examined how health plan access, choice, and affordability varied between the individual on-Marketplace, individual off-Marketplace, and small-group markets in 2018. We found relatively similar outcomes across the three markets with respect to deductibles and out-of-pocket spending maximums. However, the small-group market maintained greater plan choice and lower premiums—outcomes that appear to be associated with higher insurer participation. States may consider a variety of policy proposals such as reinsurance or the introduction of a public option to increase insurer participation and improve the plan choices offered in the individual market.Item Dirty hands on troubled waters: Sanitation, access to water and child health in Ethiopia(Wiley, 2019-11) Manalew, Wondimu S.; Tennekoon, Vidhura S.; Economics, School of Liberal ArtsIn this paper, we investigate the impact of access to drinking water sources and sanitation facilities on the incidence of diarrheal diseases among children below 5 years of age in Ethiopia using the propensity score matching technique with a polychotomous treatment variable. We find that among the water sources traditionally considered as improved, only water piped into dwelling, yard or plot leads to a large percentage point reduction in diarrhea incidence. The other water sources, generally believed as clean, are not effective in reducing diarrhea even compared with some of the unimproved water sources. We also find that some unimproved water sources and sanitation facilities are less inferior than they are believed to be. These results suggest that the traditional way of categorizing different types of improved and unimproved water sources and sanitation facilities into a dichotomous variable, “improved” or “unimproved”, could be misleading as it masks the heterogeneous effects of the water sources and the sanitation facilities.Item Incentives for motivated experts in a partnership(Elsevier, 2018-08) Liu, Ting; Ma, Ching-to Albert; Mak, Henry Y.; Economics, School of Liberal ArtsA Principal has a set of projects, each having different benefit potentials, and each requiring a basic technology from one of two experts and time inputs from both experts. Experts enjoy motivation utilities from production, but have private information of their own motivation preferences and project potentials. Technology and time-input choices are experts’ private decisions. Experts form a Partnership, which designs a sharing rule and a gatekeeping protocol to determine experts’ priority on technology choice. Using a linear cost-share contract that lets experts make minimum profits, the Principal implements the first best by delegating all decisions to the Partnership.Item Two-Stage Residual Inclusion Estimation in Health Services Research and Health Economics(Wiley, 2018-06) Terza, Joseph V.; Economics, School of Liberal ArtsOBJECTIVES: Empirical analyses in health services research and health economics often require implementation of nonlinear models whose regressors include one or more endogenous variables-regressors that are correlated with the unobserved random component of the model. In such cases, implementation of conventional regression methods that ignore endogeneity will likely produce results that are biased and not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts. They call this method two-stage residual inclusion (2SRI). In the present paper, I offer a 2SRI how-to guide for practitioners and a step-by-step protocol that can be implemented with any of the popular statistical or econometric software packages. STUDY DESIGN: We introduce the protocol and its Stata implementation in the context of a real data example. Implementation of 2SRI for a very broad class of nonlinear models is then discussed. Additional examples are given. EMPIRICAL APPLICATION: We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997). CONCLUSION: It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers.Item Two-stage residual inclusion estimation: A practitioners guide to Stata implementation(2017) Terza, Joseph V.; Economics, School of Liberal ArtsAbstract. Empirical econometric research often requires implementation of nonlinear models whose regressors include one or more endogenous variables—regressors that are correlated with the unobserved random component of the model. In such cases, conventional regression methods that ignore endogeneity will likely produce biased results that are not causally interpretable. Terza, Basu, and Rathouz (2008, Journal of Health Economics 27: 531–543) discuss a relatively simple estimation method (two-stage residual inclusion) that avoids endogeneity bias, is applicable in many nonlinear regression contexts, and can easily be implemented in Stata. In this article, I offer a step-by-step protocol to implement the two-stage residual inclusion method in Stata. I illustrate this protocol in the context of a real-data example. I also discuss other examples and pertinent Stata code.Item Managing imperfect competition by pay for performance and reference pricing(Elsevier, 2018-01) Mak, Henry Y.; Economics, School of Liberal ArtsI study a managed health service market where differentiated providers compete for consumers by choosing multiple service qualities, and where copayments that consumers pay and payments that providers receive for services are set by a payer. The optimal regulation scheme is two-sided. On the demand side, it justifies and clarifies value-based reference pricing. On the supply side, it prescribes pay for performance when consumers misperceive service benefits or providers have intrinsic quality incentives. The optimal bonuses are expressed in terms of demand elasticities, service technology, and provider characteristics. However, pay for performance may not outperform prospective payment when consumers are rational and providers are profit maximizing, or when one of the service qualities is not contractible.