The Effect of Income, Health, Education, and Social Capital on Happiness in Indonesia
Bibliographic record
Abstract
<p class="a"><span lang="EN-US">The purpose of the study is to analyze the effect of income, health, education, and social capital on happiness in Indonesia. The data was taken from National Survey of Social Economic conducted by National Bureau of Statistics of Indonesia in 2012. Ordered probit model was used as estimation technique due to ordinal dependent variable and normal distribution of error assumption. The findings show happiness is affected by absolute income, physical perceived health status, mental health, medium and high level of education, trust in leader, participation on society’s activities, tolerance, and help for and from others. However relative income, gender, household head, junior high school, and trust to neighbors do not have significant effect on happiness. Easterlin paradox does not exist in Indonesia because income has positive impact on happiness. Satisfaction on works, financial, family harmony, and leisure time also have significant effect on happiness. Satisfaction on family harmony is the most important factor than others. From demographic variables, it was known that happiness is not different across gender, household head, and low education people. People who are married, live in urban areas, live outside Java and Bali islands, and have more children are found happier. Happiness-age relationship indicates U-shaped curve. Happiness tends to decrease over time until people reach 51 years old. For all three level of happiness, some predictors do not have significant marginal effect on happiness namely relative income, gender, low education level, and trust to neighbors. Meanwhile the variables of household head and number of infant are not robust. </span></p>
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".