数字痕迹数据和人口预测:谷歌对美国 COVID-19 婴儿潮的预测有多准?

IF 4.6 2区 社会学 Q1 DEMOGRAPHY
Joshua Wilde, Wei Chen, Sophie Lohmann, Jasmin Abdel Ghany
{"title":"数字痕迹数据和人口预测:谷歌对美国 COVID-19 婴儿潮的预测有多准?","authors":"Joshua Wilde, Wei Chen, Sophie Lohmann, Jasmin Abdel Ghany","doi":"10.1111/padr.12647","DOIUrl":null,"url":null,"abstract":"At the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the “promise and pitfalls” of digital trace data in demographic research.","PeriodicalId":51372,"journal":{"name":"Population and Development Review","volume":"3 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digital Trace Data and Demographic Forecasting: How Well Did Google Predict the US COVID‐19 Baby Bust?\",\"authors\":\"Joshua Wilde, Wei Chen, Sophie Lohmann, Jasmin Abdel Ghany\",\"doi\":\"10.1111/padr.12647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the “promise and pitfalls” of digital trace data in demographic research.\",\"PeriodicalId\":51372,\"journal\":{\"name\":\"Population and Development Review\",\"volume\":\"3 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Population and Development Review\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1111/padr.12647\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DEMOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Population and Development Review","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1111/padr.12647","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DEMOGRAPHY","Score":null,"Total":0}
引用次数: 0

摘要

在美国 COVID-19 第一波流行开始时,人们还不知道该流行病对未来出生率的影响。在本文中,我们评估了数字追踪数据--通常被吹捧为解决传统数据匮乏的灵丹妙药--是否具有准确预测 COVID-19 大流行在美国引起的生育率变化的潜力。具体来说,我们利用与妊娠相关的谷歌搜索数据,对大流行病对美国出生率的影响做出了州一级的动态未来预测。重要的是,这些预测是在 2020 年 10 月做出的(并在 2021 年 2 月进行了修订),远在大流行病对出生率的影响可能被知晓之前。我们的分析预测,在 2020 年 11 月至 2021 年 2 月期间,美国的月出生率将急剧下降约 12%,然后开始反弹,并在 2021 年 8 月之前保持低迷。虽然这些预测在低谷的幅度和时间上基本准确,但在这些下降的实现和反弹的速度上却存在重大失误。这种对事前预测的事后评估有力地证明了数字跟踪数据在人口研究中的 "前景和缺陷"。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital Trace Data and Demographic Forecasting: How Well Did Google Predict the US COVID‐19 Baby Bust?
At the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the “promise and pitfalls” of digital trace data in demographic research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.80
自引率
4.00%
发文量
60
期刊介绍: Population and Development Review is essential reading to keep abreast of population studies, research on the interrelationships between population and socioeconomic change, and related thinking on public policy. Its interests span both developed and developing countries, theoretical advances as well as empirical analyses and case studies, a broad range of disciplinary approaches, and concern with historical as well as present-day problems.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信