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This article uses a unique panel data set to examine the relationship between high school marijuana use and annual earnings at age 29. The authors estimate a series of OLS models that incrementally add potential confounding variables, including marijuana use at age 29. The analysis finds that part of the negative relationship reflects an indirect pathway whereby early marijuana use affects human capital accumulation, which in turn affects earnings. Moreover, the authors find evidence that the remaining association between early marijuana use and earnings, after controlling for differences in human capital, reflects the cumulative effect of marijuana use on cognitive ability and motivation. (JEL J30, I12)

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For California, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in California will fall to 4 percent; without the law, it would remain at 20 percent. The model projects that total state government spending on health care will be 7 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Connecticut, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Connecticut will fall to 5 percent; without the law, it would remain at 11 percent. The model projects that total state government spending on health care will be 10 percent lower for the combined 2011–2020 period than it would be without the ACA, mostly because of federal subsidies for residents who would have been covered by Connecticut's state-run health insurance program (State-Administered General Assistance).

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Montana, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Montana will fall to 3 percent; without the law, it would remain at 18 percent. The model projects that total state government spending on health care will be 3 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Texas, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Texas will fall to 6 percent; without the law, it would remain at 28 percent, the highest in the nation. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Illinois, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Illinois will fall to 3 percent; without the law, it would remain near 15 percent. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.

The Veterans Access, Choice, and Accountability Act of 2014 addressed the need for access to timely, high-quality health care for veterans. Section 201 of the legislation called for an independent assessment of various aspects of veterans' health care. The RAND Corporation was tasked with an assessment of the Department of Veterans Affairs (VA) current and projected health care capabilities and resources. An examination of data from a variety of sources, along with a survey of VA medical facility leaders, revealed the breadth and depth of VA resources and capabilities: fiscal resources, workforce and human resources, physical infrastructure, interorganizational relationships, and information resources. The assessment identified barriers to the effective use of these resources and capabilities. Analysis of data on access to VA care and the quality of that care showed that almost all veterans live within 40 miles of a VA health facility, but fewer have access to VA specialty care. Veterans usually receive care within 14 days of their desired appointment date, but wait times vary considerably across VA facilities. VA has long played a national leadership role in measuring the quality of health care. The assessment showed that VA health care quality was as good or better on most measures compared with other health systems, but quality performance lagged at some VA facilities. VA will require more resources and capabilities to meet a projected increase in veterans' demand for VA care over the next five years. Options for increasing capacity include accelerated hiring, full nurse practice authority, and expanded use of telehealth.