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A social engineering model for poverty alleviation.

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This study introduces a new, self-consistent poverty line derived from India

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Area of Science:

  • Economics
  • Computational Social Science
  • Econometrics

Background:

  • Traditional poverty lines are arbitrary and based solely on basic food consumption.
  • Existing poverty metrics fail to account for total consumption distribution and income-expenditure dynamics.
  • A single expense descriptor is an exogenous variable, not reflecting actual income-expenditure statistics.

Purpose of the Study:

  • To develop a self-consistent, endogenous poverty line using comprehensive income-expenditure data.
  • To integrate all expenditure modes (food and non-food) into a unified poverty measure.
  • To provide an advanced tool for economic policy formulation.

Main Methods:

  • Utilized extensive income-expenditure statistics from India.
  • Developed an agent-based stochastic model of market exchange.
  • Employed advanced Machine Learning tools for probabilistic parameter estimation.

Main Results:

  • Derived a novel endogenous poverty line that reflects total consumption distribution.
  • Successfully combined labor, commodity, and asset market outcomes into a consumption-based poverty measure.
  • Demonstrated the model's capability to capture complex market dynamics.

Conclusions:

  • The proposed consumption-based poverty measure offers a more accurate and holistic assessment of poverty.
  • This method provides a superior tool for economic policy formulation compared to traditional poverty lines.
  • The integration of Machine Learning enhances the precision of poverty measurement.