Three Essays in Behavioral Economics and Macroeconomics: Unraveling Celebrity Influence on Philanthropy, Racial Disparities in Donation Decisions During the COVID-19 Pandemic, and the Impact of Foreign Direct Investment on Economic Growth in Saudi Arabia
Bibliographic record
Abstract
This dissertation encompasses three chapters. Two delve into behavioral aspects of charitable donations during the COVID-19 pandemic, investigating celebrity influence and racial disparities, as well as risk preferences. The third chapter shifts to macroeconomics, examining the relationship between Foreign Direct Investment (FDI) and economic growth in Saudi Arabia over a long-term horizon. The first chapter investigates the effectiveness of celebrity endorsements on charitable giving during the COVID-19 pandemic. Participants' donation decisions were compared after exposure to celebrity and non-profit expert endorsements. Logistic regression and Ordinary Least Squares regression were used to analyze the impact of independent variables on the likelihood and total amount of donations. Findings suggest that celebrities did not significantly affect overall donation behavior, consistent with previous research. The study found no significant difference between celebrity and expert endorsements in terms of donation decisions, underscoring that the primary challenge for non-profit organizations is outreach, as the choice of messenger appears to have minimal impact on donation decisions. The second chapter investigates donation decisions during the COVID-19 pandemic, specifically examining racial disparities in charitable giving and the relationship between risk preferences and donations. The analysis is based on the financial contributions made by the average US citizen to food banks in the fourth quarter of 2020. The study finds that a substantial portion of the population (57%) was willing to support charitable causes during this challenging period. Additionally, it reveals that Black participants were more likely to donate and, on average, donate more than individuals from other racial groups. This finding aligns with previous evidence highlighting the generosity of Black individuals in charitable giving. Contrary to some prior results, the study uncovers that risk-averse individuals, as indicated by their frequent use of masks during the pandemic, were more likely to donate. These insights shed light on the role of empathy and donation motivations, offering valuable implications for fundraising campaigns targeting diverse racial groups and individuals with different risk preferences. The third chapter explores the implications of Foreign Direct Investment (FDI) on Saudi Arabia's economic growth, a topic of critical importance amidst the country's ongoing economic diversification under Vision 2030. We employ Autoregressive Distributed Lag (ARDL) models to scrutinize the effects of FDI intensity on the Kingdom's economic performance, utilizing annual data. Results reveal no significant short-term impact of FDI on economic growth. However, there is a notable long-run equilibrium relationship among FDI, inflation, interest rates, and GDP per capita growth. Historical crises and real interest rates also significantly influence economic growth. These findings echo the existing literature on the non-significant short-term effect of FDI on Saudi Arabia's economic growth, while pointing to potential long-term relationships.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".