Impact of Oil Palm Expansion on Farmers’ Crop Income and Poverty Reduction in Indonesia: An Application of Propensity Score Matching
Bibliographic record
Abstract
In order to solve serious problem on the lack of job opportunity and poverty in Indonesia, oil palm expansion driven by smallholders have been taken into the economic development agenda. The evidence shows that oil palm expansion by smallholders have a great performance for improving livelihood of rural community. Thus, this study aims to estimate the causal effect of oil palm expansion on farmers’ livelihoods in Indonesia. Using cross-sectional data from 271 households in Riau Province, the determinants of farmers’ decisions to expand oil palm farm size and the impacts of expansion are analyzed. Propensity Score Matching was employed in order to deal with self-selection biased in the evaluation of oil palm expansion impact. In the first step, logit model was applied to analyze the determinant of oil palm expansion. In the second step, each observation is matched with a similar propensity score value in order estimate the average treatment effect for the treated (ATT). Empirical results show that number of family members actively involved in oil palm cultivation, farmers’ financial assets, contract farming, and distance to the market are significantly associated with likelihood for expanding farm size. Positive and significant impacts of crop income from oil palm and per capita expenditures, confirms that oil palm expansion help reducing the problem of job opportunity and poverty in Indonesia. This study implicates that, to improve oil palm expansion practice in Indonesia, several schemes must be considered: enhancing human resources development, integrating oil palm marketing schemes, and improving infrastructure facilities.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".