Tourism-Led Growth and Risk of the Dutch Disease: Dutch Disease in Turkey
Bibliographic record
Abstract
The Dutch disease phenomenon refers to the adverse effects of the supply of natural resources and production in the tradable sectors specifically the manufacturing sector. Corden and Neary (1982) and Corden (1984) developed the core model of the Dutch disease that it explains a large amount of foreign money to inside the country will appreciate real exchange rate and cause both the spending and reallocation of resources between non-tradable and tradable sectors that it will lead the country to de-industrialisation The Dutch disease is generally related to the export of natural resources; however, it can be caused by any factors that increase the flow of foreign currency into a country. According to Copelend (1991), the tourism sector is one of the most important sectors that can be the cause of the Dutch disease. Holzner (2010) called the effect of the Dutch disease on tourism-dependent countries the “Beach Disease”. The aim of this study is to investigate whether the growing tourism sector in Turkey has caused resource movement and a spending effect that have led the Turkish economy to experience the Dutch disease. The Turkish economy is one of the emerging markets that in the past few decades has experienced noticeable growth in the tourism sector, to the extent that, according to the World Travel and Tourism Council (WTTC, 2017), travel and tourism’s contribution to GDP in Turkey was 12.5% in 2016. By using several methods, such as non-linear and linear ARDL bounds tests and structural VAR, this study aims to investigate whether the Turkish economy experienced Beach Disease over the period from 1976 to 2017. Empirical evidence demonstrates that due to the growth of the tourism sector, the Turkish economy is suffering from symptoms of Beach Disease, such as de-industrialisation and resource allocation to non-tradable sectors. The results show that the Turkish economy has suffered from the Dutch disease due to a growing tourism sector, which has led to de-industrialisation and unstable long-term growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".