HICM: An approach towards Harmonizing Indian Census Migration data and its applications
For researchers and policymakers studying Indian internal migration, this work addresses data quality issues in census records, enabling more reliable longitudinal analysis.
The paper introduces HICM, a framework to harmonize Indian census migration data by correcting measurement and representativeness biases across three decades, improving data consistency and reliability for temporal analysis.
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.