Health Econometrics: Volume 294

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

Little is known about perceptions of medical expenditure risks despite their presumed relevance to the demand for health insurance. This is the first study to examine households’ beliefs about their future spending on health care. The study made a unique elicitation of subjective probabilities of medical expenditures from rural Ethiopians participating in a panel survey and offered the opportunity to enrol in a health insurance programme. The vast majority of respondents give logically consistent responses to the subjective probability questions. The data indicate that the cross-sectional variance of realized expenditures, which is often used to proxy risk exposure, greatly overestimate the risk faced by any single household. Consistent with the serial correlation observed in realized expenditures, expectations are positively correlated with past expenses. They are revised upward in response to an increase in realized expenditure and, to some extent, they predict expenditure incurred in the year ahead. Despite containing information on future medical expenditures, there is no evidence that expectations influence the decision to take out health insurance, although plans to insure are positively related to the perceived volatility of expenses.

These results suggest that adverse selection may not threaten the viability of voluntary health insurance. A caveat is that measurement error in the reported probabilities may weaken the test for adverse selection. Notwithstanding this limitation, measurement of household-specific distributions of future medical expenses is feasible and avoids relying on the cross-sectional variance, which provides an upwardly biased estimate of medical expenditure risk.

This chapter reviews the existing empirical evidence on how social insurance affects health. Social insurance encompasses programs primarily designed to insure against health risks, such as health insurance, sick leave insurance, accident insurance, long-term care insurance, and disability insurance as well as other programs, such as unemployment insurance, pension insurance, and country-specific social insurance programs. These insurance systems exist in almost all developed countries around the world. This chapter discusses the state-of-the art evidence on each of these social insurance systems, briefly reviews the empirical methods for identifying causal effects, and examines possible limitations to these methods. The findings reveal robust and rich evidence on first-stage behavioral responses (“moral hazard”) to changes in insurance coverage. Surprisingly, evidence on how changes in coverage impact beneficiaries’ health is scant and inconclusive. This lack of identified causal health effects is directly related to limitations on how human health is typically measured, limitations on the empirical approaches, and a paucity of administrative panel data spanning long-time horizons. Future research must be conducted to fill these gaps. Of particular importance is evidence on how these social insurance systems interact and affect human health over the life cycle.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

In the public debate, immigration is often viewed as a threat to the access and the quality of health care services. The health needs of immigrants and refugees pose new challenges to health care systems. This chapter reviews the recent economic literature on immigration and health. We discuss the main methods used to study the health immigrant trajectories and the effects of immigration on demand and supply of health care in both destination and sending countries.

This chapter considers the analysis of a cost-effectiveness dataset from an econometrics perspective. We link cost-effectiveness analysis to the net benefit regression framework and explore insights and opportunities from econometrics and their practical implications. As an empirical illustration, we compare various econometric techniques using a cost-effectiveness dataset from a published study. The chapter concludes with a discussion about implications for applied practitioners and future research directions.

Anchoring vignettes have become a popular method to adjust self-assessed data for systematic differences in reporting behaviour to aid comparability, for example, of cross-country analyses. The method relies on the two fundamental assumptions of response consistency and vignette equivalence. Evidence on the validity of these assumptions is equivocal. This chapter considers the utility of the vignette approach by considering how successful the method is in moving self-assessed reports of health mobility towards objective counterparts. We draw on data from the Survey of Health, Ageing and Retirement in Europe (SHARE) and undertake pairwise country comparisons of cumulative distributions of self-reports, their objective counterparts and vignette adjusted reports. Comparison of distributions is based on tests for stochastic dominance. Multiple cross-country comparisons are undertaken to assess the consistency of results across contexts and settings. Both non-parametric and parametric approaches to vignette adjustment are considered. In general, we find the anchoring vignette methodology poorly reconciles self-reported data with objective counterparts.

We estimate the parental investment response to the child endowment at birth, by analysing the effect of child birth weight on the hours worked by the mother two years after birth. Mother’s working hours soon after child birth are a measure of investments in their children as a decrease (increase) in hours raises (lowers) her time investment in the child. The child birth endowment is endogenously determined in part by unobserved traits of parents, such as investments during pregnancy. We adopt an instrumental variables estimation. Our instrumental variables are measures of the father’s health endowment at birth, which drive child birth weight through genetic transmission but does not affect directly the mother’s postnatal investments, conditional on maternal and paternal human capital and prenatal investments. We find an inverted U-shape relationship between mothers worked hours and birth weight, suggesting that both low and extremely high child birth weight are associated with child health issues for which mothers compensate by reducing their labour supply. The mother’s compensating response to child birth weight seems slightly attenuated for second and later born children. Our study contributes to the literature on the response of parental investments to child’s health at birth by proposing new and more credible instrumental variables for the child health endowment at birth and allowing for a heterogeneous response of the mother’s investment for first born and later born children.

This chapter aims at providing an understanding of the research and devlopment (R&D) process in the pharmaceutical industry, by exploring the methodological challenges and approaches in the assessment of the determinants of innovation in the pharmaceutical industry. It (i) discusses possible methodological approaches to model occurrence of events; (ii) describes in detail competing risks duration models as the best methodological option in light of the nature of pharmaceutical R&D processes and data; (iii) concludes with an estimation strategy and overview of potential covariates that have been found to correlate with the likelihood of failure of R&D pharmaceutical projects.

In healthcare, overuse and underuse of medical treatments represent equally dangerous deviations from an optimal use equilibrium and arouse concerns about possible implications for patients’ health, and for the healthcare system in terms of both costs and access to medical care. Medical liability plays a dominant role among the elements that can affect these deviations. Therefore, a remarkable economic literature studies how medical decisions are influenced by different levels of liability. In particular, identifying the relation between liability and treatments selection, as well as disentangling the effect of liability from other incentives that might be in place, is a task for sound empirical research. Several studies have already tried to tackle this issue, but much more needs to be done. In this chapter, we offer an overview of the state of the art in the study of the relation between liability and treatments selection. First, we reason on the theoretical mechanisms underpinning the relationship under investigation by presenting the main empirical predictions of the related literature. Second, we provide a comprehensive summary of the existing empirical evidence and its main weaknesses. Finally, we conclude by offering guidelines for further research.

Hospitals are complex organisations accounting for most of total health expenditure. They play a critical role in providing care to patients with high levels of need. A key policy concern is that patients receive high quality care. Policymakers have attempted to influence hospital quality in different ways. This chapter focuses on three key policy levers: the extent to which hospital competition and higher hospital tariffs (of the DRG type) can stimulate quality, and whether non-profit hospitals provide higher or lower quality than for-profit ones. The chapter outlines key methodological challenges and selectively reviews the main findings from the literature. While several studies suggest that hospital competition reduces mortality rates for heart attack cases when hospital tariffs are fixed (under a DRG system), at this stage is unclear whether the effect holds across a range of quality indicators. Moreover, the limited literature on hospital mergers tends to suggest that hospital quality does not change following a merger. Finally, whether non-profit hospitals provide higher or lower quality varies across regions and institutional arrangements. The economic theory suggests several mechanisms with opposite effects on quality. To guide policy, future work needs to further unpack the various mechanisms through which these three key policy issues affect hospitals incentives.

The goal of this contribution is to shed light on the benefits for research in health care coming from the use of administrative data, especially in terms of measuring hospitals’ outcomes. The main approaches to health outcome evaluation are reviewed and the possible improvements deriving from the use of administrative data are highlighted. Administrative data may be an essential element in the process of gathering to the public true rankings of health care organizations, reducing the degree of asymmetric information that typically arises in health care. Patients will be more aware of the best institutions, which will induce most of them to demand to be admitted in them, taking into account the costs associated with distance and with the severity of the illness. This in turn may ask for a reorganization of the sector, closing some organizations and expanding others, having as final goal to improve the health status of the population, without income barriers. This is one of the first attempts to provide an overview of the advantages that administrative data may gather in health care.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

The search for more effective policies, choice of optimal implementation strategies for achieving defined policy targets (e.g., cost-containment, improved access, and quality healthcare outcomes), and selection among the metrics relevant for assessing health system policy change performance simultaneously pose continuing healthcare sector challenges for many countries of the world. Meanwhile, research on the core drivers of healthcare costs across the health systems of the many countries continues to gain increased momentum as these countries learn among themselves. Consequently, cross-country comparison studies largely focus their interests on the relationship among health expenditures (HCE), GDP, aging demographics, and technology. Using more recent 1980–2014 annual data panel on 34 OECD countries and the panel ARDL (Autoregressive Distributed Lag) framework, this study investigates the long- and short-run relationships among aggregate healthcare expenditure, income (GDP per capita or per capita GDP^_HCE), age dependency ratio, and “international co-operation patents” (for capturing the technology effects). Results from the panel ARDL approach and Granger causality tests suggest a long-run relationship among healthcare expenditure and the three major determinants. Findings from the Westerlund test with bootstrapping further corroborate the existence of a long-run relationship among healthcare expenditure and the three core determinants. Interestingly, GDP less health expenditure (GDP^_HCE) is the only short-run driver of HCE. The income elasticity estimates, falling in the 1.16–1.46 range, suggest that the behavior of aggregate healthcare in the 34 OECD countries tends toward those for luxury goods. Finally, through cross-country technology spillover effects, these OECD countries benefit significantly from international investments through technology cooperations resulting in jointly owned patents.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.