Economic considerations for social distancing and behavioral based policies during an epidemic

https://doi.org/10.1016/j.jhealeco.2013.01.002Get rights and content

Abstract

Public policies intended to induce behavioral change, specifically incentives to reduce interpersonal contacts or to “social distance,” increasingly play a prominent role in public disease response strategies as governments plan for and respond to major epidemics. I compare social distancing incentives and outcomes under decentralized, full control social planner, and constrained social planner, without health class specific control, decision making scenarios. Constrained social planner decision making, based on non-health class specific controls, can in some instances make society worse off than decentralized decision making (i.e. no intervention). The oft neglected behavior of recovered and immune individuals is important for welfare and health outcomes.

Highlights

► A joint model of SIR dynamics and strategic behavior in response to infectious disease. ► Immune individuals may undersupply contacts relative to the social optimum. ► Non-targeted social distancing policies may be welfare decreasing relative to no intervention. ► The behavior or immune individuals has important implications for health and social welfare.

Introduction

Infectious disease epidemics are scary, and behavioral adaptation has been a part of human response to infectious disease for centuries. Recently, the World Health Organization (2006), governments (Stern and Markel, 2009), and public health experts (Ferguson et al., 2005, Glass et al., 2006, Halloran et al., 2008) have emphasized the potential importance of public policies designed to elicit behavioral changes in preparing for and responding to infectious disease epidemics. Specifically, these strategies provide incentives, some quite strong, to reduce interpersonal contacts or to “social distance.” Social distancing policies can be in the form of public facility shut downs (e.g., mass transit and school closures), propaganda campaigns, and other attempts to reduce the ordinary contact rate among people. Economics has a clear role to play in mapping incentives through micro-level behaviors to macro-level outcomes for health and measures of social welfare.

If policies are aimed at changing individual behavior, then one must consider the ways in which heterogeneous agents will respond to infection risk (Funk et al., 2010, Gersovitz, 2011). Prior economic studies of infectious disease transmission have developed macro-dynamic models of socially optimal disease control and eradication in humans via treatment and vaccination (Barrett and Hoel, 2007, Boulier et al., 2007, Francis, 2004, Gersovitz and Hammer, 2003, Gersovitz and Hammer, 2004), in animals via culling strategies (reviewed in Horan et al., 2011b, Horan et al., 2010), and in vector-borne diseases (Gersovitz and Hammer, 2005). These models show that individuals underinvest in prevention and control of disease, and that policy intervention can be welfare enhancing. Moreover, Almond and Mazumder (2005) and Almond (2006) show that the impacts of an epidemic can persist long after the epidemic fades. Yet, public interventions must be undertaken with care. Smith et al. (2009) and Keogh-Brown et al. (2010) simulate social distancing interventions using a computable general equilibrium model and find that the costs of proposed public health interventions may outweigh the cost of the disease. Chen et al. (2011) show that social distancing interventions may have ambiguous results on endemic disease equilibria. Social distancing and quarantine policies may be “overdone” and decrease welfare (Fenichel et al., 2011, Keogh-Brown et al., 2010, Mesnard and Seabright, 2009).

Epidemiologists and economists increasingly recognize that consumer heterogeneity and micro-level decision making are essential in determining how epidemics evolve and how policy interventions affect this evolution. Economists have developed models to describe how individuals engage in adaptive or strategic behavior, including treatment and vaccination (Francis, 1997, Geoffard and Philipson, 1996, Philipson, 2000), reductions in risky sexual behavior (Auld, 2003, Kremer, 1996), migration behavior (Mesnard and Seabright, 2009), and generic risk mitigation through reducing social contacts (Chen et al., 2011, Fenichel et al., 2011). A key component of models with strategic behavior is that the current health state of an individual influences his incentives to engage in different behaviors (Auld, 2003, Fenichel et al., 2011). This type of adaptive response is largely missing from epidemiological models that express an individual's rate of social contacts as a function of observable attributes that are exogenous to the epidemic such as sex and age (reviewed by Funk et al., 2010).1

Susceptible individuals may have incentives to avoid infection for their own wellbeing even if they are not concerned with the overall state of public health. Hence, one can think of individuals as having partial ownership over the state of public health or alternatively the public health state can be modeled as an impure public good (Bell and Gersbach, 2009). However, public health interventions are difficult to design. For example, vaccinations are wasted if they are given to individuals who would not have become infected or have already contracted an infection. Treatment, if available, may be well targeted at infected individuals, but may not prevent latent individuals from spreading infection. These targeting challenges are exacerbated when considering social distancing policies, where the first best policy must account for the mapping between endogenous behavioral responses to the policy and the resulting spread of infection. Policies that abstract from this feedback mechanism could have perverse effects. There is a need for economic analysis, but to have policy impact such analysis must also be grounded in the nearly hundred-year-old epidemiological modeling tradition (Kermack and McKendrick, 1929) to gain traction in non-economic spheres – the spheres that dominate actual policy decision making.

In this paper, I develop an integrated epidemiological-economic model of social distancing and strategic economic behavior during an epidemic. I contrast three types of decision making: decentralized decision making, socially optimal decision making in the sense of maximizing the discounted net present utility of the ex ante representative agent (this defination of social welfare follows Chakraborty et al., 2010 and is explain in more detail below; Francis, 1997, Francis, 2004, Geoffard and Philipson, 1996), and a constrained social planner who tunes a policy instrument to be as efficient as possible given the inability to target the epidemic itself, specifically, I consider a social planner who constrains all individuals to the same behavior regardless of health class. This third mode of decision making approximates the thinking that drives many real-world social distancing policies (though these are seldom optimized), such as school closures, (Cauchemez et al., 2008, Glass et al., 2006), public transit shut downs, and other policies put forth by “frontline” infectious disease epidemiologists (Ferguson et al., 2005, Fraser et al., 2009). I show that targeting social distance policies by health class is important for maximizing social welfare and that it in some cases it is possible for indiscriminant, but optimally tuned, decision making to make society worse off than allowing for decentralized decision making. In other words, seemingly “second-best” interventions that tune a non-targeted control to maximize social welfare, conditional on the constrained choice set, can potentially lead to lower wellbeing than not intervening, suggesting that indiscriminant policies to reduce contacts that are not optimally tuned may not satisfy the creed “do no harm.” A relative loss of welfare can potentially occur from non-targeted policies, compared to decentralized decision making, because designing policies for the “average” individual may impose a strong constraint that erodes welfare more than incomplete markets for disease prevention. This result, which is easily overlooked in infectious disease epidemiology, highlights the need for analysis that integrates economic modeling with epidemiological theory, and contributes to the broader literature on commons problems with partial ownership (Stavins, 2011).

Section snippets

An epidemiological model with behavioral response

Most modern epidemiology builds on the compartmental epidemiological modeling framework introduced by Kermack and McKendrick (1929) and popularized by Anderson and May (1979). Our model follows in this tradition, and closely follows Fenichel et al. (2011). Specifically, I consider a communicable disease that causes significant utility loss to infected individuals, but does not cause mortality.2

Numerical illustration

In order to show how different decision frameworks lead to different epidemiological and welfare outcomes, I develop a numerical illustration based on decision making under decentralized decision making, a social planner, and a constrained social planner. Furthermore, I examine adaptive and rational expectations models for the case of decentralized decision making, and explore the case when a social planner is constrained to provide a minimum number of contacts but is otherwise free to target

Discussion and conclusion

Reduced form models suggest that social distancing, defensive behavioral changes, appear to be important in epidemics (Caley et al., 2007). Increasingly, social distancing is not just discussed as decentralized behavioral change, but is also discussed in the context of policy to avert losses associated with epidemics (World Health Organization, 2006). To evaluate these policies and the behavioral incentives they generate requires an economic decision model (Heckman, 2010). The current paper

Acknowledgements

Thoughtful comments by members of the National Institute for Mathematical and Biological Synthesis SPIDER working group, Charles Perrings, and Nick Kuminoff contributed to previous versions of this manuscript. I thank two anonymous reviewers for their thoughtful suggestions. This publication was made possible by Grant number 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health. Its contents are solely the responsibility of the

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