HICM: An approach towards Harmonizing Indian Census Migration data and its applications

2026-04-14

AI Summary

This study addresses long-standing limitations in India’s inter-state migration census data, which have been plagued by uneven state-level coverage and inconsistent measurement practices, leading to systematic biases that undermine analytical reliability. For the first time, the paper systematically disentangles measurement bias from representativeness bias and introduces a data-driven Harmonized Inter-State Migration (HICM) framework. Integrating statistical diagnostics, imputation, smoothing, and bias correction techniques, HICM standardizes and reconciles migration data across states and time. The proposed approach delivers a reproducible, bias-aware preprocessing and validation pipeline that substantially enhances structural consistency and temporal stability. Empirical results demonstrate that the corrected data significantly improve the credibility of migration network analyses, offering policymakers more accurate evidence for informed decision-making.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Knowledge Representation and Reasoning: Preferences
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data integration and cleaning
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Reliable analysis of migration is critically dependent on the quality and consistency of the underlying data. Indian migration data, primarily derived from decennial census records, are affected by systematic gaps arising from uneven coverage and measurement inconsistencies across states and time. This paper presents a data-centric framework, HICM, for harmonizing Indian census migration data recorded under the Indian census and correcting prominent sources of bias prior to downstream analyses. We explicitly identify two types of bias across three decades of migration data: measurement bias and representativeness bias. We propose to address these gaps through principled pre-processing, mitigation, and validation strategies grounded in statistical diagnostics. An empirical evaluation of harmonized Indian interstate migration data reveals that bias-aware data correction substantially improves the consistency in the structure of the data and enhances the reliability of subsequent temporal analysis results. By improving data quality through reproducible data imputation and smoothing, this work advances migration analytics and provides a robust foundation for policy-relevant longitudinal network analysis of Indian internal migration.

Problem

• migration data
• data harmonization
• measurement bias
• representativeness bias
• census data

Innovation

• data harmonization
• migration bias correction
• statistical diagnostics
• reproducible imputation
• longitudinal network analysis