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.

中文翻译：

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数字追踪数据和人口预测：Google 对美国 COVID-19 婴儿半身像的预测效果如何？


在美国第一波 COVID-19 爆发时，该流行病对未来出生率的影响尚不清楚。在本文中，我们评估了数字追踪数据（通常被誉为解决传统数据稀缺问题的灵丹妙药）是否具有准确预测美国 COVID-19 大流行引起的生育率变化的潜力。具体来说，我们使用与怀孕相关的谷歌搜索数据，对大流行对美国出生率的影响进行了州级动态未来预测。重要的是，这些预测是在 2020 年 10 月做出的​​（并于 2021 年 2 月修订），远早于人们可能知道大流行的出生影响。我们的分析预测，2020 年 11 月至 2021 年 2 月期间，美国月出生人数将大幅下降约 12%，然后开始反弹，同时保持低迷状态直至 2021 年 8 月。虽然这些预测在低谷的幅度和时间方面总体上是准确的，在这些减少的实现和反弹速度方面存在重大失误。这种对事前预测的事后评估有力地证明了人口研究中数字追踪数据的“前景和陷阱”。