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The paper introduces HICM, a data-centric framework to harmonize Indian census migration data by addressing measurement and representativeness biases present across states and time. They mitigate these biases through statistical pre-processing, imputation, and smoothing techniques. Empirical evaluation demonstrates that HICM significantly improves data consistency and the reliability of temporal analysis of Indian interstate migration.
Cleaning up messy census data with statistical methods can reveal hidden patterns in Indian migration, offering a more reliable foundation for policy decisions.
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.