Valuing National Forest Recreation Access: Using a Stratified On-Site Sample to Generate Values Across Activities for a Nationally Pooled Sample

The Forest Service controls vast quantities of natural resources including timber, wildlife, watersheds, air sheds, and ecosystems. For many of these resources, recreation is one of the primary uses of the natural asset. Recreation visits taken to National Forests are not "purchased" in the same type of market as other goods (e.g., timber, grazing, or housing). The price of, and ultimately benefit received from, recreation to National Forests cannot be estimated via traditional market prices and quantities. Alternate methods must be employed to estimate the value of recreation access. We use on-site survey data from the Forest Service's National Visitor Use Monitoring database (2000-2003) and stated preference demand estimation methods to model annual recreation trip-taking behavior to National Forests. We then use these models to derive estimates of per-visit net economic benefits across regions and activities. In 2000, the FS began conducting systematic research into recreation visitation levels on National Forest lands under the National Visitor Use Monitoring Project (NVUM). From 2000 to 2003 NVUM has collected data from 120 National Forests providing information on the number of annual visits, primary activity, local area expenditures, satisfaction with facilities, and limited demographic information. These data were collected using an on-site stratified random sampling scheme resulting in over 90,000 completed surveys. Using the NVUM data we estimate the net economic value (NEV) of recreation on National Forest lands. The dataset used to estimate these values contains 73,655 observations. Using a truncated negative binomial estimator, weighted by a composite factor that adjusts for the stratified, on-site nature of the data, we have estimated a series of pooled, multi-site recreation demand models and calculated net economic values for recreational visits to the National Forests for each of fourteen activities and four RPA regions (Pacific, Rocky Mountain, Northern, and Southern) on a per visit per individual value and for a per activity day per individual basis. Our results indicate that for most models and specifications, adjusting for the choice based sampling frame by using a truncated, weighted, stratified, negative binomial estimator, as well as accounting for regional and activity differences, reduces the estimate of the average per day and per activity day values. Forest managers and others involved in managing, planning, and administering resources used for recreation often need an estimate of the economic value of the resource. For many of these resources non-market analysis must be used to generate this information. For forest recreation, many of the values currently available come from secondary sources or from small samples. The values estimated using NVUM represent an improvement over many of the currently available forest recreation values because of the unique nature of the large-scale, stratified random sample.Resource /Energy Economics and Policy,

Accounting for Geographic Heterogeneity in Recreation Demand Models

Spatial differences in site characteristics and user populations may result in heterogeneity of recreation preferences and values across geographic regions. Non-linear mixed effects models provide a potential means of accounting for this heterogeneity. This approach was tested by estimating a national-level recreation demand model with encouraging results.Resource /Energy Economics and Policy,

Estimating the Net Economic Value of National Forest Recreation: An Application of the National Visitor Use Monitoring Database.

Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

Estimating Travel Cost Model: Spatial Approach

travel cost model, spatial analysis, Environmental Economics and Policy,

Oil and Gas Production and Economic Growth in New Mexico

This paper examines the relationship between energy production and economic growth in New Mexico using cross section data for the state's 33 counties in Census years 1960, 1970, 1980, 1990 and 2000. The central question is whether or not New Mexico's counties are subject to the resource curse, a phenomenon documented frequently in the literature. Most empirical studies of the resource curse hypothesis have used national or state level data and a broad definition of natural resources. In contrast, this analysis uses county level data with a focus on oil and gas extraction. The estimated models suggest that oil and gas extraction in New Mexico counties has had a small but positive effect on income, employment and population. Similar results were obtained when the model was estimated for 925 counties in 13 energy producing states for the year 2000.economic growth, energy, resource curse,

Valuing National Forest Recreation Access: Using a Stratified On-Site Sample to Generate Values Across Activities for a Nationally Pooled Sample

The Forest Service controls vast quantities of natural resources including timber, wildlife, watersheds, air sheds, and ecosystems. For many of these resources, recreation is one of the primary uses of the natural asset. Recreation visits taken to National Forests are not "purchased" in the same type of market as other goods (e.g., timber, grazing, or housing). The price of, and ultimately benefit received from, recreation to National Forests cannot be estimated via traditional market prices and quantities. Alternate methods must be employed to estimate the value of recreation access. We use on-site survey data from the Forest Service's National Visitor Use Monitoring database (2000-2003) and stated preference demand estimation methods to model annual recreation trip-taking behavior to National Forests. We then use these models to derive estimates of per-visit net economic benefits across regions and activities. In 2000, the FS began conducting systematic research into recreation visitation levels on National Forest lands under the National Visitor Use Monitoring Project (NVUM). From 2000 to 2003 NVUM has collected data from 120 National Forests providing information on the number of annual visits, primary activity, local area expenditures, satisfaction with facilities, and limited demographic information. These data were collected using an on-site stratified random sampling scheme resulting in over 90,000 completed surveys. Using the NVUM data we estimate the net economic value (NEV) of recreation on National Forest lands. The dataset used to estimate these values contains 73,655 observations. Using a truncated negative binomial estimator, weighted by a composite factor that adjusts for the stratified, on-site nature of the data, we have estimated a series of pooled, multi-site recreation demand models and calculated net economic values for recreational visits to the National Forests for each of fourteen activities and four RPA regions (Pacific, Rocky Mountain, Northern, and Southern) on a per visit per individual value and for a per activity day per individual basis. Our results indicate that for most models and specifications, adjusting for the choice based sampling frame by using a truncated, weighted, stratified, negative binomial estimator, as well as accounting for regional and activity differences, reduces the estimate of the average per day and per activity day values. Forest managers and others involved in managing, planning, and administering resources used for recreation often need an estimate of the economic value of the resource. For many of these resources non-market analysis must be used to generate this information. For forest recreation, many of the values currently available come from secondary sources or from small samples. The values estimated using NVUM represent an improvement over many of the currently available forest recreation values because of the unique nature of the large-scale, stratified random sample

Accounting for Geographic Heterogeneity in Recreation Demand Models

Spatial differences in site characteristics and user populations may result in heterogeneity of recreation preferences and values across geographic regions. Non-linear mixed effects models provide a potential means of accounting for this heterogeneity. This approach was tested by estimating a national-level recreation demand model with encouraging results