component typically find little impact on take-up (Capuno et al. 2016; Wagstaff et al. 2016; Thornton et al. 2010; Banerjee et al. 2021; Malani et al. 2021; Das et al. 2016). A second possibility is that there are non-price barriers to take-up such as administrative burdens arising from eligibility requirements. Interventions that offer administrative assistance with enrolling in health insurance indeed find that takeup increases, with an effect size equivalent to that of premium subsidies for six months (Capuno et al. 2016; Thornton et al. 2010; Banerjee et al. 2021; Malani et al. 2021). This is sufficiently large that a simple explanation rooted in the opportunity cost of time is unlikely. But so far, no consensus has emerged on the extent to which low demand reflects low expected value rather than administrative burdens; this remains very much at the frontier of the research on health insurance.

# Why Has Health Insurance Not Improved Health Outcomes? Supply Constraints

The most obvious explanation for why health insurance has not improved health outcomes is that there is little capacity for quality improvement to begin with—doctors are overworked, do not have the right equipment, and may not have the right skills. In this section we will show that, in contrast to this view and despite severe deficits in quality, there is in fact considerable room for improvement. In the next section, we then consider what we view as a more likely possibility—that government health insurance in LMICs has had an adverse effect on provider incentives, ultimately undermining the objectives of these schemes in a way that can _worsen_ the quality of health care.

## Quality of Care in LMICs

That the quality of care in LMICs can be very poor is not in dispute. Das et al. (2012) present one example of how standardized patients—healthy people recruited from the local community and then extensively trained to present the same case to multiple providers—are treated in the Indian state of Madhya Pradesh when they present with crushing chest pain the night before and extreme anxiety.<sup>10</sup> After appropriate questioning, the doctor should refer this patient to a hospital, suggest an electrocardiogram (ECG) and

> 10 Like “audit studies” in the economics of discrimination, the standardized patient approach is frequently regarded as the gold standard for measuring quality, at least for primary outpatient care. It allows researchers to abstract from omitted variable bias due to unobserved patient and case-mix across providers and mitigates the possibility that health care providers act differently when they know they are being observed (Leonard and Melkiory 2006). Most importantly, researchers can compare the physician’s treatment with evidence-based clinical guidelines and evaluate the accuracy of treatment decisions even in cases where the condition is misdiagnosed, a task that is difficult to accomplish even with well-maintained patient charts as researchers do not know the true underlying illness of the patient. Studies across multiple countries and tracer conditions have shown that standardized patients can be deployed in large samples, leading to valid and reliable results with low detection rates and provider behavior consistent with the belief that they were dealing with a real patient. See Das et al. (2012) and Kwan et al. (2019).

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