2004年-世界发展银行全球_The_RMSM-XP__A_Minimal_Poverty____________Module_for_the_RMSM-X_47页_558kb

Summary of the RMSM-X+P: A Minimal Poverty Module for the RMSM-X

Core Content

The RMSM-X+P is a new tool developed by the World Bank to enhance poverty and social indicators analysis within the existing RMSM-X framework. It adds a Poverty Module that includes three worksheets: poverty, education, and health. This module allows users to assess the impact of macroeconomic shocks on poverty and related social indicators such as literacy and infant mortality.

Main Viewpoints

• Purpose: The RMSM-X+P aims to provide a more comprehensive analysis of poverty reduction by integrating social indicators into the macroeconomic modeling framework.
• Focus: It moves beyond the narrow focus on the partial correlation between growth and poverty, enabling a broader evaluation of the effects of various adjustment policies.
• Implementation: The model is based on a standard World Bank tool, making it accessible to users already familiar with RMSM-X with minimal additional training.
• Structure: It includes a poverty equation, an education equation, and a health equation, each linking specific social indicators to macroeconomic and structural factors.

Key Information

1. Poverty Equation

The poverty equation is used to estimate the incidence of poverty (measured by the headcount index) based on the following factors:

• Inflation: High inflation tends to reduce the real purchasing power of the poor, increasing poverty incidence.
• Education: Higher literacy rates are associated with lower poverty levels.
• Per Capita Real GDP: Economic growth is expected to reduce poverty.
• Openness: Trade openness can reduce poverty through direct and indirect channels, though the relationship may be non-linear.
• Income Distribution: More unequal income distribution is linked to higher poverty incidence.

The equation is estimated using pooled data from low-income countries and is used to project poverty levels based on macroeconomic projections from RMSM-X.

2. Education Equation

The education equation links the adult literacy rate to the following factors:

• Per Capita Real GDP: Higher income levels are associated with increased educational attainment.
• Urbanization Rate: Urban areas offer better access to education, leading to higher literacy rates.
• Per Capita Public Education Expenditure: Increased public spending on education improves the capacity of the education system and enhances literacy.

The equation is estimated using cross-country time series data and is used to project literacy rates for the poverty equation.

3. Health Equation

The health equation uses the infant mortality rate as a proxy for the general level of health and links it to:

• Per Capita Real GDP: Economic growth improves health outcomes.
• Per Capita Public Health Expenditure: Higher public health spending reduces infant mortality.
• Education: Better education levels are associated with improved health outcomes.

The equation is also estimated using cross-country time series data and is used to project health indicators for the poverty equation.

Estimation and Projections

• Data and Methodology: The equations are estimated using pooled regression analysis with historical data from low-income countries.
• Variables: Key variables include inflation, literacy rate, real GDP per capita, trade openness, Gini coefficient, urbanization rate, and public education and health expenditure.
• Projection Sources: Poverty and social indicators are projected using outputs from the RMSM-X model and exogenous assumptions for certain variables.

Linking with RMSM-X

• The Poverty Module is integrated into the RMSM-X model.
• Poverty headcount index is the primary poverty measure.
• Social indicators (literacy and infant mortality) are projected using the education and health equations, respectively.
• The model allows for a more holistic assessment of poverty and social outcomes in response to macroeconomic shocks.

Example Application

• The model is demonstrated through an analysis of the effects of changing public education spending.
• This example illustrates how the RMSM-X+P can be used to evaluate the impact of policy changes on poverty and social indicators.

Conclusion

• The RMSM-X+P is a relatively simple yet effective tool for poverty analysis.
• It enables a more nuanced understanding of how macroeconomic policies influence poverty and social indicators.
• It is particularly useful for countries with limited data and capacity to implement more complex models.

References

• Agénor, P. (2002, 2004a)
• Dollar, D. & Kraay, A. (2001, 2002)
• De Gregorio, J. & Lee, J. W. (1999)
• Foster, J. & Székely, M. (2001)
• Gundlach, M. et al. (2001)
• Ravallion, M. & Chen, S. (1997)
• Romer, C. & Romer, D. (1998)
• World Bank (2000a, 2000b, 2000c)

Tables and Figures

• Tables:

  • Table 1: Poverty Regression
  • Table 1a: Poverty Regression with log of per capita real GDP fitted with 1 period lag
  • Table 1b: Poverty Regression with log of per capita real GDP fitted with 1 and 2 period lag
  • Table 2: Education Regression
  • Table 2a: Education Regression with public education expenditure averaged over previous 4 periods
  • Table 3: Health Regression
  • Table 3a: Health Regression with average public health expenditure
  • Table 3b: Health Regression with lagged public health expenditure

• Figures:

  • Figure 1: Poverty Sheet - Overview
  • Figure 2: Health and Education Sheets - Overview
  • Figure 3: Poverty Module - Details
  • Figure 4: Education Sheet - Base Run Values
  • Figure 5: Literacy Rate - Base Run Plot
  • Figure 6: Poverty Headcount - Base Run Plot
  • Figure 7: Infant Mortality Rate - Base Run Plot
  • Figure 8: Increased Literacy Rates
  • Figure 9: Decreased Poverty Incidence
  • Figure 10: Decreased Infant Mortality Rates

Appendix

• Appendix 1: Calibration Note
• Appendix 2: Poverty Module Formulae and External links to the Poverty Module