This chapter examines the links between the trade in tropical timber products and deforestation in Indonesia. It reviews some of the evidence suggesting that timber production is a factor in tropical deforestation, and the role of timber trade policy in Indonesia in influencing this process by affecting forest-based industrialization. These issues are of particular concern to Indonesia, as the country has recently banned sawnwood exports to encourage further rapid development of plywood processing. The chapter develops a partial equilibrium timber trade model of Indonesia to analyze the effects of various policy interventions on the trade and tropical deforestation. It considers the model to simulate several policy options, including the impacts of sawnwood export taxes/effective bans, import bans imposed by consumer countries, revenue-raising import taxes, and increased harvesting costs associated with "sustainable management". The chapter summarizes the results of the policy analysis, and discusses the policy options open to the Government of Indonesia and importing countries.

This paper discusses contributions that women at the intersection of agricultural economics and environmental and resource economics have made over the past several decades to their profession, through both research and leadership. We highlight research contributions in the following areas: land use and conservation, non-market valuation, environmental policy design, and climate and energy economics. Key examples of leadership within the Agricultural and Applied Economics Association and the Association of Environmental and Resource Economists are also discussed. We conclude with some brief personal reflections regarding our experience working at this interface.

We propose a new method for analyzing multiple-destination recreation trips and apply it to visitation at national parks in the southwestern United States. We use conventional random utility theory and treat groups of parks (portfolios) as choice alternatives. We consider one choice occasion per respondent and condition that choice on the person visiting at least one park in the choice set, so the participation decision (go/no-go) is not modeled. Trip cost includes time, travel, lodging, and food cost for visiting all sites in the portfolio. Variation in trip cost is generated by variation in the location where individuals enter and exit the southwestern region and by variation in the specific set of parks in each portfolio. We use specialized sampling weights to correct for on-site sampling. Finally, we provide estimates of per trip losses for closing one or more of the parks.

This paper examines the effect water quality has on property values in Maryland's Anne Arundel County, located along the western shore of Chesapeake Bay. The study uses hedonic pricing techniques to accomplish this, which was an area lacking examination at the time this paper was written. The problem lies in how scientific water quality variables used (such as dissolved oxygen, nitrogen, and phosphorus) cannot be perceived by homeowners like air particulates can in air pollution. It is only when algae blooms and fish kills occur that homeowners notice, which is related to the perceived water clarity. The water quality variable used is fecal coliform bacteria and willingness to pay (WTP) is estimated for marginal improvements in this variable and a onetime improvement in a particular area. The model estimates fecal coliform count significantly decreases property values. The study was also able to address many of the statistical faults previously encountered in hedonic pricing techniques when reaching all of these values. A change of 100 fecal coliform counts per 100 mL is estimated to produce a 1.5% negative change in property prices. This is a significant amount of change in the fecal coliform count, but a wide variety of values was measured and must be accounted for. For the eight specifications of the variables lot size and water quality, the range of mean effect on predicted price for a 100 count change is from a low of $5,114 to a high of $9,824. To test the specific site improvement now that fecal coliform levels are shown to depress property values, a particular area of 41 residential parcels suffering from fecal coliform counts ranging from 50 counts per 100 mL to 240 counts per 100 mL is used to demonstrate the model's effectiveness. The projected increase in property values based on a 100 counts per 100 mL decrease was approximately $230,000, or about 2% of the aggregate assessed value over $10 million dollars. In order for all properties in the area to achieve 200 counts per 100 mL, or the state standard, 494 properties would need improvement that would benefit by $12.145 million dollars in property value increase (although the estimate is likely an upper bound).

The emergence of urban-rural space, as evidenced by the expansion of low-density exurban areas and growth of amenity-based rural areas, is characterized by the merging of a rural landscape form with urban economic function. Changing economic conditions, including waning transportation and communication costs, technological change and economic restructuring, rising real incomes, and changing tastes for natural amenities, have led to this new form of urban-rural interdependence. We review the recent research on the causes and consequences of this growth at regional and metropolitan scales, discuss advances in empirical and theoretical economic models of urban land-use patterns at spatially disaggregate scales, and highlight research on environmental impacts and the efficacy of growth controls and land conservation programs that seek to manage this growth. The paper concludes with future research questions and needs. These include spatially disaggregate and accurate data, improved causal inference and structural modeling, and dynamic models that incorporate multiple sources of spatial and agent heterogeneity and interactions.

Using a panel of parcel-level data we estimate a hazard model and find strong evidence that the mere existence of an option to preserve farmland delays decisions to convert farmland to developed uses by about six years, a reduction in median conversion time of 12 to 43% depending on parcel size. Where such delays allow local governments to improve infrastructure or implement stricter growth control measures, benefits of a preservation option may be even more long term. Also, increases in the variance of returns to development tended to slow conversion for parcels with all but the highest lot capacities.

We investigate the dynamics and spatial distribution of land use fragmentation in a rapidly urbanizing region of the United States to test key propositions regarding the evolution of sprawl. Using selected pattern metrics and data from 1973 and 2000 for the state of Maryland, we find significant increases in developed and undeveloped land fragmentation but substantial spatial heterogeneity as well. Estimated fragmentation gradients that describe mean fragmentation as a function of distance from urban centers confirm the hypotheses that fragmentation rises and falls with distance and that the point of maximum fragmentation shifted outward over time. However, rather than outward increases in sprawl balanced by development infill, we find substantial and significant increases in mean fragmentation values along the entire urban-rural gradient. These findings are in contrast to the results of Burchfield et al. [Burchfield M, Overman HG, Puga D, Turner MA (2006) Q J Econ 121:587-633], who conclude that the extent of sprawl remained roughly unchanged in the Unites States between 1976 and 1992. As demonstrated here, both the data and pattern measure used in their study are systematically biased against recording low-density residential development, the very land use that we find is most strongly associated with fragmentation. Other results demonstrate the association between exurban growth and increasing fragmentation and the systematic variation of fragmentation with nonurban factors. In particular, proximity to the Chesapeake Bay is negatively associated with fragmentation, suggesting that an attraction effect associated with this natural amenity has concentrated development.

PREFACE. 1. SETTING THE STAGE. 1.1. Oil Spills and Valuation. 1.2. Where Do We Begin? 1.3. The Purpose and Approach of the Book. 1.4. The Maintained Assumptions. 1.5. What the Book Omits. 1.6. A Look Ahead. 2. WELFARE ECONOMICS FOR PRICE CHANGES. 2.1. Introduction. 2.2. Compensation Measures. 2.2.1. Willingness to Pay and Willingness to Accept. 2.3. From Behavior to Welfare Measures. 2.3.1. So What is Wrong with Consumer Surplus? 2.4. From Ordinary Demands to Welfare. 2.4.1. Multiple Price Changes. 2.5. Income and Welfare Effects. 2.5.1. Endogenous Income. 2.6. Non-Linear Budget Constraints. 2.7. Conclusions. 3. THE CONCEPT OF COMPLEMENTARITY. 3.1. Introduction. 3.2. The Basic Problem. 3.3. The Public Good as an Attribute. 3.3.1. Weak Complementarity. 3.3.2. Can Weak Complementarity Be Tested? 3.4. Weak Complementarity and Marshallian Demands. 3.4.1. The Willig Condition. 3.5. Welfare without Weak Complementarity. 3.6. Conclusions. 4. IMPLEMENTING WEAK COMPLEMENTARITY. 4.1. Introduction. 4.2. Specifying Demand as a Function of Quality. 4.2.1. Translations of Utility Functions. 4.2.2. Utility Parameters as a Function of Quality. 4.3. Weak Complementarity and Household Production. 4.3.1. Household Production and Constant Marginal Costs. 4.3.2. Incorporating Time Costs. 4.3.3. Time in Incomplete and Partial Demand Systems. 4.3.4. On-Site Time and Non-linear Budget Constraints. 4.4. Information and Behavioral Change. 4.5. Quality Changes and Induced Price Effects. 4.5.1. Induced Price Changes. 4.6. Conclusions. 5. MEASURING WELFARE IN DISCRETE CHOICE MODELS. 5.1. Introduction. 5.2. The Basic Discrete Choice Model. 5.3. Welfare in the Random Utility Model. 5.3.1. More Welfare Calculations with the Linear Model. 5.3.2. Welfare Measurement with Imperfect Information. 5.4. Generalizing Discrete Choice Models. 5.4.1. Nested Models: Relaxing the IIA Property. 5.4.2. Mixed Logit Models: A Further Generalization. 5.5. The Larger Consumer Choice Problem. 5.5.1. The Role of Income. 5.5.2. The Frequency of Choice. 5.5.3. The Generalized Corner Solution Model. 5.6. The Hedonic Travel Cost Model. 5.6.1. The Structure of the Model. 5.6.2. The Hedonic Cost Function. 5.6.3. Making Sense of the Story. 5.6.4. Welfare Measures in the Hedonic Travel Cost Model. 5.7. Conclusion. 6. HEDONIC MODELS OF HETEROGENOUS GOODS. 6.1. Introduction. 6.2. The Theory of Hedonic Models. 6.2.1. Rosen's Bid Function. 6.2.2. The Hedonic Price Function. 6.3. Welfare Measures in Hedonic Markets. 6.3.1. Defining 'Pure Willingness to Pay'. 6.3.2. Revealing 'Pure Willingness to Pay'. 6.3.3. Welfare Effects of Exogenous Events. 6.4. Some Econometric Issues. 6.4.1. Estimating the Hedonic Price Function Only. 6.4.2. Recovering Information on Preferences. 6.5. The Housing Choice as a Discrete Choice. 6.5.1. Drawbacks of Discrete Choice Housing Models. 6.6. Conclusions. 7. HEDONIC WAGE ANALYSIS. 7.1. Introduction. 7.2. Hedonic Wages in Theory. 7.2.1. The Simple Model. 7.2.2. Revising the Model: The Wage vs Risk Trade-off. 7.2.3. Important Underlying Assumptions. 7.2.4. The Determinants of the Hedonic Function. 7.2.5. The Anomaly of Safer Jobs and Higher Pay. 7.2.6. Endogenous Sorting. 7.2.7. Welfare with the Hedonic Wage Model. 7.3. Estimating the 'Value of a Statistical Life'. 7.3.1. Data Sources for Wage and Risk Variables. 7.3.2. Variability in Specifications. 7.3.3. Fragility of Estimates of the Wage-Risk Trade-off. 7.3.4. The Challenge of Transferring $VSL$ Estimates. 7.4. Wage Hedonics and Locational Amenities. 7.4.1. The Roback Model. 7.4.2. Migration and Disequilibrium. 7.4.3. Welfare Interpretations. 7.4.4. Locational Amenities in a Discrete Choice Framework. 7.5. Conclusions. 8. PUBLIC GOODS IN HOUSEHOLD PRODUCTION. 8.1. Introduction. 8.2. The Structure of the Problem. 8.2.1. A Simple Result for Constant Marginal Costs. 8.3. Restrictions on the Demand for an Input. 8.3.1. The Case of a Separable Production Relationship. 8.3.2. Demand for Essential Inputs. 8.3.3. Wea