Shengjie Song

Shengjie Song

Beijing Normal University

CHARLS

Date:

He received his training in the spring of 2022 and travelled to Shandong Province and Guangdong Province for fieldwork in the summer. This is his contribution to Mature Economics.

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Introduction

The China Health and Retirement Longitudinal Study (CHARLS) aims to collect a high quality nationally representative sample of Chinese residents ages 45 and older to serve the needs of scientific research on the elderly.

Contribution

  • Visited more than 15 sample villages and interviewed over 60 middle-aged and elderly people, aiming to collect a set of high-quality micro-data representing households and individuals of Chinese people aged 45 and above.

  • Completed the whole process from procurement to questionnaire survey, medical examination and collection of biological information to analyze the problem of population ageing in China and interdisciplinary research.

Personal Story

Aging is a pressing global issue, and the China Health and Retirement Longitudinal Study (CHARLS) is a crucial database for researching this phenomenon in China. Papers based on CHARLS data have even been published in prestigious journals like Nature(see

example).

As a field interviewer for CHARLS, I have had the privilege of experiencing firsthand the diverse lives of the elderly in China.In the scorching heat of rural Shandong, where the ground was hot enough to cook an egg, my team and I sought refuge in a local home to avoid heatstroke. In the urban villages of Guangzhou, I met migrant workers living in a single-room rental for just 600 RMB and had to go downstairs a long way to shower and use the toilet. While following up with a worker in Dongguan, I witnessed the profound love she had for her husband when she saw his recent photo on my tablet and eagerly asked to take a picture of it with her phone.

In academic papers, these individuals might be reduced to mere data points, excluded for missing responses. However, those of us who have conducted fieldwork understand that data cleaning must be done with empathy and respect for the people behind the numbers.