Bibliographic record
Abstract
For three decades, Korea was held up as an economic icon. The country's typical blend of high savings and investment rates, autocratic political systems, export-oriented businesses, restricted domestic markets, government capital allocation, and controlled financial systems were hailed as ideal ingredients for the strong economic growth of developing countries (Shapiro, 1999). However, in July 1997, a currency turmoil erupted in Thailand, spreading to Korea and other countries. This article investigates a number of practical issues about the Korean crisis of 1997--interlinked economies, the causes of the crisis, and policy responses. The Korean Economy Before The Crisis Prior to the crisis, the Korean economy was characterized by a dichotomy between a strong real economy and a growing financial problem. In 1996, Korea had experienced a severe trade shock and a cutback in business investment following a boom in the early 1990s. These forces had reduced GDP growth from an annual rate of 9 percent in 1994-95 to 7 percent in 1996. The first quarter of 1997, when GDP growth fell to 5.7 percent, appeared to mark a successful soft landing (OECD, 1998). Although domestic demand was weak, GDP still grew 6 percent during the first three quarters of 1997. Other macroeconomic statistics, such as inflation (4.3 percent) and unemployment (2.3 percent) were low by Korean historical standard as well as OECD standards. At the same time, the current account deficit, which had soared to $23 billion in 1996, had been cut in half by the third quarter of 1997. Thus, many analysts concluded that the economy was firmly underpinned by cautious monetary and fiscal policies. An Economic Crisis in Thailand Spread Throughout the World International capital flows caused booms and busts for Thailand's economy. How could an economic crisis in an emerging economy, such as Thailand's, could spread throughout the world. Thailand's economy surged until early 1997 partly because the Thais found they could borrow dollars at low interest rates overseas more cheaply than they could the baht at home. By late 1996, foreign investors began to move their money out of Thailand because they worried about Thais' ability to repay. In February 1997, foreign investors and Thai companies rushed to convert their baht to dollars. The Thai central bank responded by buying baht with its dollar reserves and raising interest rates. The rise in interest rates drove prices for stocks and land downward. This dynamic situation drew attention to serious problems in the Thai economy: a huge foreign debt, trade deficits, and a banking system weakened by the heavy burden of unpaid loans. The Thai central bank ran out of dollars to support the baht. On July 2, 1997, the central bank stopped to defend the baht's fixed value against the dollar. And then the currency lost 16 percent of its value in one day. Investors and companies in the Philippine, Malaysia, Indonesia, and Korea realized that these economies shared all of Thailand's problems. So, investors and companies rushed to convert local currencies into dollars. And then, the peso, ringgit, rupiah, and won toppled in value like dominos in a row. In the fourth quarter of 1997, the International Monetary Fund (IMF) arranged emergency rescue packages of $18 billion for Thailand, $43 billion for Indonesia, and $58 billion for Korea. By the end of 1998, the Asian crisis of 1997 spread to Russia, Brazil, and many other countries. Again, the IMF arranged bail-out packages of $23 billion for Russia in July 1998 and $42 for Brazil in November 1998. This means that since mid-summer 1997, IMF-led rescue packages for Asia, Russia, and Brazil racked up some $184 billion to keep world markets safe. In theory, capital is a boon, enabling developing countries to reduce poverty and raise living standards. But the theory does not always work smoothly. Countries mismanage the inflows. …
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".