The Economic Impact of Expenditures by Local Governments and Nonprofits on Property Values: Evidence from 41 Large Texas Cities
Bibliographic record
Abstract
This dissertation uses property values to investigate the economic effect of public expenditures for operations and capital improvements on place value. Given the increasing role of nonprofit services in augmenting those of cities and school districts, the dissertation research investigates whether nonprofit expenditures join those of cities and school districts as Tiebout commodities, thereby contributing to place value. Furthermore, the research examines whether those expenditures contribute to reducing the inequities in the distribution of property wealth. The conceptual framework for the dissertation is the Tiebout model and its various extensions. The model proposes that individuals have different preferences for public goods and services, and there are many jurisdictions that vary in the services provided. Consequently, individuals shop around for the community that best matches their preferences and locate in the one that maximizes their utility. If the model correctly predicts households' behavior, then the quality of public goods and services provided by a community will affect its desirability. The more attractive a community, the higher the demand for its properties, which results in higher property values. The dissertation research finds that city public capital spending positively impacts property values in two ways. Property values respond positively to (1) the announcement of capital investments (i.e., ongoing capital expenditures), and (2) the amenity created when capital projects become operational (i.e., when operating expenditures are combined with capital stock). The results also show that nonprofit capital stock and spending on operations affect property values differently depending on the nonprofit category. The findings further reveal that local public and nonprofit spending benefit owners of lower and medium valued properties more than owners of higher valued properties. This finding suggests that local government and nonprofit spending contribute to reducing inequities in the distribution of property wealth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".