The rationale for adopting inflation targeting : the case of Indonesia
Bibliographic record
Abstract
In the wake of the New Order under Soeharto, Indonesia conducted a rehabilitation and stabilization programme to boost its economy that nearly collapsed. It started by implementing fixed exchange rate under the Bretton Woods.The collapse of Bretton Woods made many countries (including Indonesia) adopt crawling-peg exchange rate, where the exchange rate was the main tool to achieve intermediate targets (low unemployment rate, economic growth, etc.) and low inflation rates in the long run (exchange rate targeting).After years relying on intermediate targets by conducting short-term manipulation of monetary policy (monetary targeting) to achieve other goals, such as higher employment rate and output, in the early 1990s pioneered by Canada, Sweden, New Zealand, and Great Britain, they began to focus on the inflation rate itself. This was followed by some developing countries such as Brazil, Chile, Mexico, Poland, South Africa, and Czech Republic.Under this regime, the central bank has to announce a target for inflation in the medium term, and is responsible for achieving that target. To conduct this, the central bank must be operationally independent of government influence. Based on the experiences of some countries adopting Inflation Targeting, Indonesia has been trying to implement this policy. Due to some lacks in the initial conditions for conducting Inflation Targeting, Indonesia has not purely adopted this policy. Starting from 2000, the Board of Governors of Bank Indonesia started to implement this policy by projecting that the core inflation was between 3%-5%, with 2% inflationary impact of the government's price and incomes policy, over and above that target. This thesis analyses preparation of Bank Indonesia in adopting Inflation Targeting.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".