The Impact of FDI Inflows on Poverty Reduction: Empirical Evidence from Egypt
Bibliographic record
Abstract
Foreign direct investment (FDI) is a major driver of international economic integration. With the right policy framework, FDI can provide financial stability, promote economic development and enhance the well-being of societies. It is generally considered by many international institutions, politicians and economists, as a factor promoting the economic growth of the recipient/ host country, as well as solving the economic problems of developing countries. This can be achieved through allowing the host country to; improve its competitive position; transfer technology and knowledge between economies; promote its products on a larger scale in international markets. In addition to all these benefits, FDI is considered as an important source of capital for the host country. In the light of this, this paper aims to determine the impact of FDI on poverty in Egypt during the period of 1961 to 2018 using Autoregressive distributive lag model (ARDL) Since there is no single variable that can capture poverty in Egypt, three variables have been used as proxy to poverty which are Household Consumption (POV1), Infant Mortality rate (POV2), and Life Expectancy at birth (POV3). After combining the results, some policy recommendations are proposed to enhance the impact of FDI on poverty reduction in Egypt which in turn affects economic growth.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".