Data Note: Alternative Name Encodings - Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage

Accurate data linkage across large administrative databases is crucial for addressing complex research and policy questions, yet linkage errors-stemming from inconsistent name representations-can introduce biases, predominantly for names not given in English. This data note examines the impact of romanisation on linkage accuracy, focusing on Chinese names and comparing standardised systems (Jyutping and Pinyin) with the non-standardised Hong Kong Government Cantonese Romanisation (HKG-romanisation). We identify three primary issues: language-specific variations in romanisation, the loss of tonal information inherent to tonal languages, and discrepancies in name order conventions. Using a dataset of 771 Hong Kong student names, our analysis reveals that standardised romanisation systems enhance the uniqueness and consistency of name representations, thereby improving linkage precision and recall compared to HKG-romanisation. Specifically, Jyutping and Pinyin achieved over 95% recall in blocking strategies, whereas HKG-romanisation only reached 68.8%. Incorporating tonal information further improved recall. These findings underscore the necessity of adopting standardised, tone-sensitive romanisation systems and flexible database designs to reduce linkage errors and promote data equity for under-represented groups. We advocate for the implementation of phonetic encodings in databases, alongside language-specific pre-processing protocols, to ensure more inclusive and accurate data linkage processes.

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Publication Details

Journal
International Journal for Population Data Science
Published
2025-03-11
DOI
https://doi.org/10.23889/ijpds.v8i5.2935
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
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article

Data Note: Alternative Name Encodings - Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage

Katie Harron, Robert W Aldridge, Joseph Lam, Ruth Blackburn et al.
International Journal for Population Data Science
Data Quality and Management
article

Data Note: Alternative Name Encodings - Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage

Katie Harron, Robert W Aldridge, Joseph Lam, Ruth Blackburn, Mario Cortina‐Borja
article en

Abstract

Accurate data linkage across large administrative databases is crucial for addressing complex research and policy questions, yet linkage errors-stemming from inconsistent name representations-can introduce biases, predominantly for names not given in English. This data note examines the impact of romanisation on linkage accuracy, focusing on Chinese names and comparing standardised systems (Jyutping and Pinyin) with the non-standardised Hong Kong Government Cantonese Romanisation (HKG-romanisation). We identify three primary issues: language-specific variations in romanisation, the loss of tonal information inherent to tonal languages, and discrepancies in name order conventions. Using a dataset of 771 Hong Kong student names, our analysis reveals that standardised romanisation systems enhance the uniqueness and consistency of name representations, thereby improving linkage precision and recall compared to HKG-romanisation. Specifically, Jyutping and Pinyin achieved over 95% recall in blocking strategies, whereas HKG-romanisation only reached 68.8%. Incorporating tonal information further improved recall. These findings underscore the necessity of adopting standardised, tone-sensitive romanisation systems and flexible database designs to reduce linkage errors and promote data equity for under-represented groups. We advocate for the implementation of phonetic encodings in databases, alongside language-specific pre-processing protocols, to ensure more inclusive and accurate data linkage processes.

International Journal for Population Data ScienceVol. 8(5)
University of Washington (US), Great Ormond Street Hospital (GB), Institute for Health Metrics and Evaluation (US), University College London (GB), Seattle University (US)
Quality Education
Openalex Percentile: Top 95%
Data Quality and Management
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