2000年-世界发展银行全球_Measurements_of_Poverty_in_Indonesia___1996_1999_and_Beyond_50页_1mb

Summary of "Measurements of Poverty in Indonesia: 1996, 1999, and Beyond"

Core Content

This working paper explores the challenges and methods of measuring poverty in Indonesia using consumption data from 1996 and 1999. The authors, Menno Pradhan, Asep Suryahadi, Sudarno Sumarto, and Lant Pritchett, focus on two key issues: the definition of regionally consistent poverty lines and the expansion of the narrow measure of poverty to include other welfare dimensions.

The paper is part of the World Bank's effort to develop a national poverty reduction strategy for Indonesia and is based on the full SUSENAS sample of 65,000 households. It uses an iterative method to define poverty lines that reflect equivalent real incomes across urban and rural regions, and it compares these results with those from the BPS method.

Main Issues and Findings

1. Regionally Consistent Poverty Lines

• Challenge: Defining a poverty line that reflects the same material standard of living across regions with different price levels.
• Method: The authors use an iterative approach to determine poverty lines, starting with an initial reference group and refining it based on the resulting poverty estimates.
• Key Insight: The poverty line is defined based on the consumption pattern of a reference population, which in turn affects the basket of goods and the resulting poverty line.
• Formula: The food poverty line (FPL) is calculated using the formula:
  $$
  FPL_j = \sum_{k=1}^{52} \bar{q}k(\widetilde{e}) * \hat{p}{kj}(\widetilde{e}) * \left(\frac{2100}{\sum_{k=1}^{K} \bar{q}_k(\widetilde{e}) * c_k}\right)
  $$
  where $\bar{q}k$ is the average quantity of commodity $k$ consumed by the reference population, $\hat{p}{kj}$ is the predicted price of commodity $k$ in region $j$, and $c_k$ is the unit calorie value of commodity $k$.

2. Expansion of the Poverty Measure

• Conceptual Challenge: Expanding the narrow measure of poverty based on consumption to include other dimensions of welfare.
• Approach: The authors use an Engel curve to estimate the non-food component of the poverty line, based on the expected non-food consumption for those whose total consumption equals the FPL.
• Formula: The full poverty line (PL) is calculated as:
  $$
  PL_j = FPL_j + NFA_j = FPL_j * (2 - \omega_j)
  $$
  where $NFA_j$ is the non-food allowance and $\omega_j$ is the share of food in total consumption.

Key Information

• Reference Group Importance: The choice of reference group significantly affects the estimated poverty line. Different prior beliefs about the reference group can lead to vastly different poverty rates.
• Iterative Method: This method ensures consistency by refining the reference group based on the poverty line derived from it.
• Poverty Profiles: The paper presents poverty profiles by location (urban and rural), occupation, and educational attainment.
• Regional Poverty Rates: Using the iterative method, the poverty rate in Indonesia was estimated at 27.13% in February 1999, implying around 55.8 million poor people.
• Comparison with BPS Method:
  • National Level: The iterative method gives a slightly higher poverty rate (27.13%) compared to the BPS method (23.55%).
  • Urban-Rural Differences: The iterative method shows a much greater disparity in poverty rates between urban and rural areas (16.34% vs 34.10%) compared to the BPS method (19.98% vs 25.85%).
  • Rank Correlation: The iterative method and BPS method show a high rank correlation (0.92), indicating similar rankings of provinces by poverty levels.
• Impact of the Crisis: The economic crisis led to a significant deterioration in household welfare, which is reflected in the poverty incidence. The iterative method estimates a larger change in poverty rates compared to the BPS method.

Conclusion

• The paper highlights the importance of using an iterative method to define regionally consistent poverty lines.
• It emphasizes that the poverty rate is highly sensitive to the choice of reference group and the assumptions made about the basket of goods.
• The findings suggest that the differences in poverty rates between urban and rural areas may be more influenced by methodological choices than by actual welfare differences.
• The iterative method provides a more accurate and consistent measure of poverty, especially in times of economic crisis.