The Allocation of Time and Risk of Lyme: A Case of Ecosystem Service Income and Substitution Effects


Forests are often touted for their ecosystem services, including outdoor recreation. Historically forests were a source of danger and were avoided. Forests continue to be reservoirs for infectious diseases and their vectors—a disservice. We examine how this disservice undermines the potential recreational services by measuring the human response to environmental risk using exogenous variation in the risk of contracting Lyme Disease. We find evidence that individuals substitute away from spending time outdoors when there is greater risk of Lyme Disease infection. On average individuals spent 1.54 fewer minutes per day outdoors at the average, 72 U.S. Centers for Disease Control and Prevention, confirmed cases of Lyme Disease. We estimate lost outdoor recreation of 9.41 h per year per person in an average county in the Northeastern United States and an aggregate welfare loss on the order $2.8 billion to $5.0 billion per year.

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  1. 1.

    Given large imperfections in medical service and health insurance markets, the degree to which treatment costs reflect true economic cost or transfer payments is an important research question beyond the scope of this paper. Some of these expenditures are certainly real costs. By excluding them from our analysis we provide a lower bound on the total welfare loss associate with Lyme Disease.

  2. 2., or the ridiculously costly approach of vaccinating short-lived intermediate hosts like mice (Tsao et al. 2004).

  3. 3.

  4. 4.

    We assume that Lyme Disease related decisions do not influence income from labor, which implies time is not reallocated to labor. We find no evidence of time reallocation to labor in the empirical section of the paper.

  5. 5.

    The canonical model (Freeman et al. 2014; Phaneuf and Requate 2017) express U slightly more generally as U(yzmL). In the canonical model individuals experience an ambient level of the quality attribute, in this case L. In our setting, individuals only experience a level of L if they consume y, which is a non-essential good. This restriction allows us to derive an outdoor time demand function that nests inside the general demand function for y presented in the canonical model. Importantly, everyone who alters behavior to avoid infection suffers a welfare loss from Lyme Disease, not just the people who contract infection.

  6. 6.

    Increased use of an area by people does not increase the prevalence of infected ticks. Humans do not shed enough pathogen to infect new ticks, so there is no feedback from people to quantity of infected ticks.

  7. 7.

    The direct effect of a change in Lyme Disease risk is multiplicatively separable in the Slutsky equation due to our restrict that the quality effect of Lyme Disease risk to enter through the full price of consuming outdoor recreation.

  8. 8.

    A list of all activities included in the analysis is provided in Table 3 in the appendix. All activities are also limited using the ATUS variable TEWHERE to include only those taking place outdoors and away from home.

  9. 9.

    The forward orthogonal transformation of x is defined as

    $$\begin{aligned} x_{i,t}^{*}\equiv \sqrt{\frac{T_{it}}{T_{it}+1}}\left( x_{it}-\frac{1}{T_{it}} \sum _{h>t}x_{ih}\right) , \end{aligned}$$

    where the sum is taken over all future available observations, \(T_{it}\) (Roodman 2009). This transformation preserves observations when there are gaps within panels that would otherwise be removed under a first-difference transformation.

  10. 10.

    When transformed lagged observations are used as instruments in the levels Eq. 4a, the conventional first-difference transformation, \(x_{i,t} -x_{i,t-1}\), is applied. The forward orthogonal deviations transform would be inappropriate for lags because it would include the contemporaneous observations as part of the average future observations, which is hypothesized to be endogenous motivating the instrumental variables approach to begin with.

  11. 11.

  12. 12.

    The complete list is in the Appendix in Table 3.

  13. 13.

    All Arellano–Bond models use orthogonal deviations for cases as instruments, as well as a measure of the predicted tick habitat in that geography as an additional instrument for Lyme cases.

  14. 14.

    The vast majority of infectious disease models are either first-order differential or difference equations models or first-order Markov models. Therefore, theory suggests that we would not expect correlation in the errors to persist for greater than one time period.

  15. 15.

    Siderelis and Smith (2013) use an average stay length of 3 h in state parks. Using their estimate, we find an aggregate of 412 million days were lost.

  16. 16.

    To our knowledge this is the first time this information in the ATUS has been used for travel cost analysis.

  17. 17.

    The income variable in the ATUS is categorical, so we assume individuals work 2,080 h per year (40  h a week for 52  weeks) and use a weighted average of the income variable. While the ATUS is a stratified random sample of US households, the strata are not on income, and the survey maybe oversampling lower income households.

  18. 18.

    There are issues with estimating the exact welfare loss due to the availability of substitutes that are also leisure activities. We suspect this is common in the literature where seemingly dissimilar alternative leisure activities are not considered as substitutes (e.g., nature-based outdoor activities and indoor activities).


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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 and NSF. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIGMS. This work was also funded by NSF Grant No. 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program. S.R.M was supported by the NatureNet Science Program of The Nature Conservancy.

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Correspondence to Kevin Berry.



See Tables 3 and 4.

Table 3 Activities in analysis, limited to those taking place outdoors and away from home
Table 4 Robustness checks for the Arellano–Bond GMM specification, parameter estimates and p values

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Berry, K., Bayham, J., Meyer, S.R. et al. The Allocation of Time and Risk of Lyme: A Case of Ecosystem Service Income and Substitution Effects. Environ Resource Econ 70, 631–650 (2018).

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  • Adaptation
  • Resource allocation
  • Risk
  • Economic-Epidemiology
  • American Time Use Survey (ATUS)
  • Travel cost