Bibliographic record
Abstract
This dissertation consists of three chapters, each exploring research questions within the field of Urban Economics. Over the course of three papers, I examined how real estate markets and local economic activity responded to certain urban phenomenons, using large and novel administrative datasets from the United Kingdom and Portugal. In the first chapter, I delved into the impact of Business Improvement Districts (BIDs) on housing markets, in Greater London. BIDs are entities funded by business owners, which provide additional public services, within their perimeter. By employing cutting-edge difference-in-difference techniques, I calculate that BID openings led to an increase in house prices by at least 3%, with no visible effect on housing supply. Moreover, I show that blocks exposed to BIDs display a lower growth in the share of minorities and unemployed residents. In the second chapter, I estimate the effects of short-term rentals on house prices, in the two Portuguese metropolitan areas, using a policy change in 2014 that led to an exponential variation in the number of such type of accommodation. I employ a two-way fixed effects model, retrieving that a one-unit increment in the number of short-term rentals per quarter results in a 0.142%-0.272% increase in the value of transactions, over the period 2010-2017. I also document positive spillovers to commercial properties and a decrease in the number of transactions of new buildings. In the last chapter, I continue to explore the economic consequences of short-term rentals, by analysing its impact on the performance of local businesses. Hence, I estimate the effect of exposure to short-term rentals, at the civil parish level, on the evolution of certain outcome variables between 2016-2019. Higher exposure to short-term rentals is positively linked with firm closure, especially for low performance firms. It also leads to increases in sales for both resident and tourist-oriented, with the latter also experiencing increments in the number of employees, wages and liquidity. Moreover, higher treatment intensity increases the probability of an entry firm being tourist-oriented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".