THREE CHAPTERS ON THE ECONOMICS OF LAND CONSERVATION
Bibliographic record
Abstract
In this dissertation, I investigate the distributional impacts of land conservation on households of varying race and ethnicity, tenure, and income as well as household decisions to locate near conserved lands based on characteristics such as partisan affiliation. While land conservation is considered a public good by most, answering who benefits from conservation, and who chooses to live near conservation are valuable insights for policy makers looking to both provide adequate conservation and ensure that conservation policy is equitable and achievable.\nIn Manuscript 1, I quantify the benefits from newly conserved lands to homeowners in Massachusetts using a hedonic pricing model with fine-scale spatial fixed effects. I find that on average, a 10 acre increase in conserved open space within a quarter mile increases property values by 0.18% or $659. After establishing that properties receive a premium from new conservation, I calculate the dollar gains that each household in Massachusetts received from conservation between 1998 and 2016. Then, I use a descriptive analysis to estimate how the capitalized benefits from conservation are distributed among households of different income, race, or ethnicity. In general, I show that White and wealthier homeowners receive disproportionately more dollar benefits than lower income or minority homeowners, and this pattern holds at both a local and national scale.\nIn Manuscript 2, I estimate the potential financial impacts to renters from housing price responses from gains in conservation at the block group level. I use census and conservation data for the entire coterminous U.S. to show how housing prices responded to newly conserved lands between 2000 and 2014. I implement a propensity score matching approach to match block groups that experienced conservation during this period to characteristically similar areas that did not. I model both home and rental price responses using a first difference model with a series of spatial fixed effects, state-level interactions, and base controls. Results suggest that while homeowner property values increased in response to gains in conservation during this period, rental prices did not respond. These results hold under an expansive set of robustness checks and model extensions.\nIn the final manuscript I study the location decisions of households and the value they place on proximity to conserved land in their home choices based on partisan affiliation. Specifically, I test whether Democrats, Republicans, or Unaffiliated households have a different willingness to pay (WTP) for open space proximity using a residential sorting model in three distinct cities. While partisan groups seem to diverge in behavior at the ballot box, our results indicate that there is no statistical difference in WTP between partisan groups when it comes to sorting across space.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".