A Quantitative Study Of The Impact Of Foreign Aid On Economic Growth And Human Development Index In Afghanistan From 1960-2020
Bibliographic record
Abstract
The aim of this thesis is to study the impact of aggregate foreign aid (FAID), foreign direct investment (FDI) and exports on economic growth measured by real gross domestic product (GDP) and Human development index (HDI) in Afghanistan from 1960 to 2020. The effectiveness of foreign aid on economic growth is highly contested among scholars and it is open to further research. The existing literature also lacks a comprehensive analysis on Afghanistan; thus, this study will add significant findings to this debate by using Afghanistan as a case study. After the 9/11 attacks on the World Trade Center, the U.S. and its European allies invested heavily in Afghanistan to promote democracy and establish a functioning state. In addition, many other nations such as Japan, Canada, India, and Australia also provided humanitarian assistance to the war-ravaged economy of Afghanistan. This study uses time series regression (1960-2020) and an auto-regressive distributed lag model (ARDL) to explore relationships between foreign aid and economic growth and foreign aid and human development index. ARDL is utilized because it includes the lag effect which allows the use of lagged values of the dependent variable. Other variables such as exports and foreign direct investment are also included to make the analysis more comprehensive. Findings suggest foreign aid has a positive and significant impact on GDP in the long-run and short-run. However, foreign aid didn’t impact HDI in Afghanistan. Foreign aid did increase the income and livelihood of many Afghans. However, it was not sustainable as data shows that since the U.S. withdrawal from Afghanistan in August 2021, the GDP has contracted almost 30%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".