Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
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.
http://www.cdc.gov/lyme/, or the ridiculously costly approach of vaccinating short-lived intermediate hosts like mice (Tsao et al. 2004).
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.
The canonical model (Freeman et al. 2014; Phaneuf and Requate 2017) express U slightly more generally as U(y, z, m, L). 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.
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.
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.
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.
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.
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.
The complete list is in the Appendix in Table 3.
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.
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.
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.
To our knowledge this is the first time this information in the ATUS has been used for travel cost analysis.
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.
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).
References
Allan BF, Keesing F, Ostfeld RS (2003) Effect of forest fragmentation on Lyme disease risk. Conserv Biol 17:267–272
Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297
Arellano M, Bover O (1995) Another look at the instrumental variable estimation of error-components models. J Econom 68:29–51
Bayham J, Kuminoff NV, Gunn Q, Fenichel EP (2015) Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic. In: Proc. R. Soc. B. The Royal Society, p 20150814
Bieri DS, Kuminoff NV, Pope JC (2013) National expenditures on local amenities. Working Paper (2014)
Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econom 87:115–143
CDC (2015) No Title. In: County-level Lyme Dis. data from 2000-2014 Microsoft Excel file. http://www.cdc.gov/lyme/stats/index.html
Census (2015) No Title. In: Popul. Estim. http://www.census.gov/popest/data/index.html
Cesario FJ (1976) Value of time in recreation benefit studies. Land Econ 52:32–41
De Groot R, Wilson M, Boumans R (2002) SPECIAL ISSUE: the dynamics and value of ecosystem services: integrating economic and ecological perspectives, a typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol Econ 41:393–408
De Groot R, Brander L, Van Der Ploeg S et al (2012) Global estimates of the value of ecosystems and their services in monetary units. Ecosyst Serv 1:50–61
Fenichel EP, Castillo-Chavez C, Ceddia MG et al (2011) Adaptive human behavior in epidemiological models. Proc Natl Acad Sci 108:6306–6311
Fenichel EP, Kuminoff NV, Chowell G (2013) Skip the trip: Air Travelers’ behavioral responses to pandemic influenza. PLoS One 8:e58249
Fisher I (1906) The nature of capital and income. The Macmillan Company, New York
Foster W, Just RE (1989) Measuring welfare effects of product contamination with consumer uncertainty. J Environ Econ Manage 17:266–283
Freeman AM III, Herriges JA, Kling CL (2014) The measurement of environmental and resource values. Resources For the Future Press, Washington, DC
Fry J, Xian G, Jin S et al (2011) National Land Cover Database (NLCD). Photogramm Eng Remote Sens 77:858–864
Gallagher DR, Smith VK (1985) Measuring values for environmental resources under uncertainty. J Environ Econ Manage 12:132–143
Herrington JE (2004) Risk perceptions regarding ticks and Lyme disease: a national survey. Am J Prev Med 26:135–140
Herrington JE, Campbell GL, Bailey RE et al (1997) Predisposing factors for individuals’ Lyme disease prevention practices: Connecticut, Maine, and Montana. Am J Public Health 87:2035–2038. doi:10.2105/AJPH.87.12.2035
Larson DM, Shaikh SL (2004) Recreation demand choices and revealed values of leisure time. Econ Inq 42:264–278
Larson DM, Shaikh SL, Layton D (2004) Revealing preferences for leisure time from stated preference data. Am J Agric Econ 86:307–320
McConnell KE (1985) The economics of outdoor recreation. Handb Nat Resour Energy Econ 2:677–722
Narasimhan R (2015) WeatherData: get weather data from the web. R package version 0.4.1. https://cran.r-project.org/web/packages/weatherData/index.html
Patz JA, Confalonieri UEC, Amerasinghe FP, Chua KB, Daszak P, Hyatt AD, Molyneux D, Thomson M, Yameogo L, Lazaro MM, Vasconcelos P, Rubio-Palis Y, Campbell-Lendrum D, Jaenisch T, Mahamat H, Mutero C, Waltner-Toews D, Whiteman C (2005) Human health: ecosystem regulation of infectious diseases. In: Ecosystems and Human Well-Being: Current State and Trends, vol 1. The Millennium Ecosystem Assessment, Island Press, Washington DC, pp 391–415
Perrings C, Castillo-Chavez C, Chowell G et al (2014) Merging economics and epidemiology to improve the prediction and management of infectious disease. Ecohealth 11:464–475
Phaneuf DJ, Smith VK (2005) Recreation demand models. Handb Environ Econ 2:671–761
Phaneuf DJ, Requate T (2017) A course in environmental economics theory, policy, and practice. Cambridge University Press, New York
Phillips CB, Liang MH, Sangha O et al (2001) Lyme disease and preventive behaviors in residents of Nantucket Island, Massachusetts. Am J Prev Med 20:219–224
Roodman D (2009) How to do xtabond2: An introduction to difference and system GMM in Stata. Stata J 9(1):86–136
Shogren JF, Crocker TD (1999) Risk and its consequences. J Environ Econ Manage 37:44–51
Siderelis C, Smith JW (2013) Ecological settings and state economies as factor inputs in the provision of outdoor recreation. Environ Manage 52:699–711
Smith VK (1987) Uncertainty, benefit-cost analysis, and the treatment of option value. J Environ Econ Manage 14:283–292
Springborn M, Chowell G, MacLachlan M, Fenichel EP (2015) Accounting for behavioral responses during a flu epidemic using home television viewing. BMC Infect Dis 15:1
StataCorp (2011) Stata statistical software: release 12. StataCorp LP, College St
Tsao JI, Wootton JT, Bunikis J et al (2004) An ecological approach to preventing human infection: vaccinating wild mouse reservoirs intervenes in the Lyme disease cycle. Proc Natl Acad Sci 101:18159–18164. doi:10.1073/pnas.0405763102
US Department of Labor. Bureau of Labor Statistics (2015) American Time Use Survey User’s Guide: Understanding ATUS 2003 to 2014. US Bureau of Labor Statistics, Washington, DC
Varian HR (1992) Microeconomic analysis, 3rd edn. Norton, New York
Webster BH, Bishaw A (2007) Income, earnings, and poverty data from the 2006 American Community Survey. U.S. Government Printing Office, Washington
Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econom 126:25–51
Zinsstag J, Schelling E, Roth F et al (2007) Human benefits of animal interventions for zoonosis control. Emerg Infect Dis 13:527
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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). https://doi.org/10.1007/s10640-017-0142-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10640-017-0142-7