Health Insurance Coverage and Outcomes

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CASE STUDY:
Health insurance coverage and outcomes

Consider the following two tables from Chapter 1 in Angrist and Pischke’s 2015 econometrics textbook, Mastering Metrics.

Table 1.1 compares 7 characteristics of men and women observed living as married couples in the 2009 wave of the U.S. National Health Interview Survey. Average characteristics are listed along the rows, and the columns show different groups identified by gender and also by whether the person (and typically the household) has any health insurance. Columns (3) and (6) show the difference in the average characteristic that is associated with having any health insurance, and underneath it is the standard error of the difference in parentheses. For example, column (6) in the top row shows that women with health insurance report having 0.39 unit better health with a standard error of 0.04. A difference that is greater than about 2 of its standard errors is considered statistically significant.

Table 1.4 compares 5 measures of health-care use and 4 measures of health outcomes across four different composite treatment arms of the RAND Health Insurance Experiment. These arms are labeled “catastrophic,” which is a like a control group with only health insurance against catastrophically large expenses; “deductible,” which is a treatment arm with somewhat more generous insurance; “coinsurance,” which is a treatment arm with more generous insurance; and “free,” which is a treatment arm where health care is effectively free and fully subsidized by insurance. For each of the 5 + 4 variables shown across the rows, the first column shows the average level in the control group, followed by the average difference between the control group and the next most generous insurance plan. Below the average differences are their standard errors in parentheses. The rightmost column (5) shows the average difference between the control group and the other 3 treatment arms combined. For example, column (5) in the top row shows that RAND HIE participants with any insurance above catastrophic coverage had an additional 0.90 face-to-face visit on average, with a standard error of 0.20, indicating that difference was statistically significant.

Answer and discuss the following questions in complete sentences. A good strategy would be to write a short paragraph for each question.

1. How can there be uninsured people in Table 1.1 in 2009? Would there be a similar group in 2023? Reference specific insurance programs.
2. Were insured people observed as healthier, less healthy, or the same as uninsured people in 2009 in Table 1.1? Were insured people better off socioeconomically, less well off, or the same as uninsured people? Are these two observations consistent or inconsistent with each other?
3. What is a randomized controlled trial (RCT), like the RAND Health Insurance Experiment? Why are average differences across treatment arms in an RCT more plausibly showing us causal effects of the treatment? Generally speaking, what is the treatment in the RAND HIE?
4. If we believe the RAND HIE and Table 1.4 more than we believe the NHIS and Table 1.1, what are the causal effects of insurance on the demand for health care? What are the causal effects of insurance on health itself? Do not descend into small details; state the general results. But cite one statistic from the table when answering each of these two sub-questions, two statistics in total.
5. Physicians and other staff often cite their personal dedication to keeping all people healthy as a motivating reason to have entered medicine in the first place. Is this consistent or inconsistent with the results of the RAND HIE regarding the generosity of health insurance and health outcomes? Discuss.