2011年-世界发展银行全球_Using_Repeated_Cross-Sections_to_Explore_Movements_in_and_out_of_Poverty_44页_1mb

Summary of "Using Repeated Cross-Sections to Explore Movements in and out of Poverty"

Core Content

This working paper explores the use of repeated cross-sectional data to estimate poverty mobility, which refers to the movement of individuals or households into or out of poverty over time. Traditional panel data, which track the same households over multiple periods, are often unavailable, especially in developing countries. The authors propose a method that allows for the estimation of bounds on poverty mobility using only two or more rounds of cross-sectional surveys.

Main Views

• Poverty Mobility is Crucial for Policy: Understanding whether poverty is transitory or chronic is essential for designing appropriate policy interventions, such as safety nets for transitory poverty or more activist policies for chronic poverty.
• Limitations of Panel Data: Panel data are rare and often not available, making it difficult to assess poverty dynamics. The authors aim to address this gap using repeated cross-sections.
• Theoretical Framework: The paper introduces a statistical method based on the assumption that the same population is sampled in both rounds and that the error terms (unexplained variation) are non-negatively correlated.
• Bounds on Poverty Mobility: The method estimates both upper and lower bounds for poverty mobility, which can be used to infer the likely range of mobility without exact panel data.
• Non-Parametric and Parametric Approaches: Two methods are introduced: a non-parametric approach that makes no assumptions about the error distribution, and a parametric approach that assumes a bivariate normal distribution for the errors, allowing for tighter bounds.

Key Information

Methodology Overview

• Two Rounds of Cross-Sectional Data: The paper uses data from two survey rounds to estimate poverty mobility.
• Model Specification: A linear projection model is estimated in the first round using time-invariant characteristics. These predicted values are then used to estimate the unobserved first-period consumption or income for individuals in the second round.
• Bounds Estimation:
  • Upper Bound: Assumes independence between the error terms in the two rounds.
  • Lower Bound: Assumes perfect correlation between the error terms in the two rounds.
• Validation: The method is tested using genuine panel data from Vietnam and Indonesia, where it is compared against true panel estimates.

Empirical Application

• Vietnam VHLSS: Two rounds of data from 2006 and 2008, with a rotating panel module for validation.
• Indonesia IFLS: Two rounds (1997 and 2000), with high re-survey rates (94.4% and 95.3% respectively).
• Results: The method provides reasonable bounds on poverty mobility, with the true panel estimates typically falling within these bounds. The width of the bounds decreases as the model becomes more richly specified or when parametric assumptions are applied.

Robustness and Considerations

• Robust to Measurement Errors: The method is robust to classical measurement errors, and the lower bound is also robust to general non-classical errors, provided assumption 2 is not violated.
• Assumptions:
  • Assumption 1: The population sampled in both rounds is the same.
  • Assumption 2: The correlation between the two error terms is non-negative.
• Limitations: The method may not be as effective for younger or older households due to the instability of their economic status over time.

Conclusion

The paper demonstrates that repeated cross-sectional data can be used to estimate poverty mobility, even in the absence of panel data. By deriving upper and lower bounds based on different assumptions about the correlation between error terms, the authors provide a flexible and robust approach for analyzing poverty dynamics. The method is particularly useful for countries where panel data are scarce, and it allows for insights into the nature and duration of poverty, as well as the factors associated with mobility.

Structure of the Paper

1. Introduction: Highlights the importance of poverty mobility for policy and the lack of panel data in many developing countries.
2. Theoretical Bounds: Introduces the concept of bounds on poverty mobility using two rounds of cross-sectional data and presents Theorems 1 and 2 for upper and lower bounds.
3. Non-Parametric Bounds: Describes the non-parametric approach to estimating mobility bounds, including the steps for generating predicted consumption and calculating probabilities.
4. Sharpening the Bounds: Discusses how including more detailed characteristics can improve the precision of the bounds.
5. Datasets: Explains the use of Vietnam and Indonesia panel data to validate the method.
6. Conclusion: Summarizes the findings and the potential of the method for poverty mobility analysis in settings with limited panel data.