The effect of inflation on income inequality: Evidence from a non-linear dynamic panel data analysis in indonesia
Bibliographic record
Abstract
This research investigates the impact of inflation on income inequality in Indonesia. This study is part of a comprehensive examination investigating which monetary policy can be utilized to lessen inequality. As a central bank objective, inflation can influence the distribution of income, wealth, and endogenous consumption, hence defining inequality. This study employed dynamic panel data analysis for linear autoregressive data using the generalized method of moments (GMM) for both first differences GMM (FD-GMM or AB-GMM) and system GMM (Sys-GMM or BB-GMM) with regional data from 58 cities in 2010-2020. The Arellano-Bond estimator reveals a positive and statistically significant association between inflation and inequality. When inflation rises, the purchasing power of the poor will decline, while the wealthiest will benefit as their non-cash assets proliferate. This study finds, indirectly, that Indonesia’s monetary policy can play a crucial role in lowering income distribution gaps. As one of the nations with an inflation-targeting framework, the Indonesian Central Bank can target the inflation rate by considering inequality. The ITF becomes the most effective monetary policy for stabilizing prices and promoting economic stability. The ITF reduces income inequality by reducing inflation rates. The study also finds that, similar to other emerging nations, economic growth in Indonesia exacerbates inequality. Poverty can be reduced by increased economic growth, but the positive impact of development on the wealthy is significantly more significant than on the poor. Therefore, economic expansion increases inequality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".