Beyond Boom and Bust: Heterogeneous Fertility Effects of the COVID-19 Pandemic

项目摘要 COVID-19大流行到来之际，美国的出生率已连续多年下降。相反 与最初预测的“COVID婴儿潮”相比，早期证据显示， 出生率下降。 但美国各地的生育模式差异很大，COVID-19的死亡率和健康影响 还因地点、种族/民族和社会经济地位而异。因此，我们预计， 大流行对生育率的影响。然而，现有的数据使这一问题难以研究-生育率， 在美国，家庭收入是不可用的，即使在国家一级，也不是按组合的费率表 种族/民族和收入或平等。该项目旨在利用一个新的受限数据源， 小组在国家儿童与发展委员会资助的“增加获得避孕药具的机会：机会红利？" 项目（R 01-HD 101480 -01，2020-2024），以评估COVID-19大流行对生育力的影响。 在与美国人口普查局的合作中，我们将使用我们创建的数据集“人们生活中的生殖” （RIPL），它提供了研究生育率如何变化所需的完整计数个人水平纵向数据。 在2019冠状病毒病大流行期间，人们的年龄、州、种族和民族、家庭收入和出生顺序各不相同。 使用此数据集，我们将链接多个美国行政数据源，包括个人税务申报微观数据， 2010年十年一次的人口普查和社会保障数据，以（目标1）计算美国所有年龄段的生育率。 2015年至2021年按州和人口分组分列的妇女情况。这些比率-计算如下： 种族/民族、家庭收入、均等以及这些特征的大多数组合--代表了一个重要的 与目前公开的费率相比，详细程度有所提高。 使用大流行前时期（2015-2019年）计算的发病率，我们将（目标2）使用人口预测 生成反映各州和人口统计数据的2020-2021年估计生育率水平的反事实率 在没有COVID-19大流行的情况下，该亚组。我们使用一种预测方法， 在像当代美国这样的生育环境中， 李-卡特方法是人口预测的基石 我们将（目标3）通过比较以下因素，评估COVID-19大流行对生育率影响的异质性： 与2020-2021年目标1下构建的观察到的利率相反。预测 反事实使我们能够估计大流行期间生育率变化的部分，这是由于 大流行，并比较不同年龄组，社会人口亚组，胎次， 和国家。我们预计，我们也将能够公开发布一个子集的费率表和预测 让其他研究人员能够访问这个重要的新数据源。

PROJECT SUMMARY The COVID-19 pandemic arrived at a time when U.S. birth rates had been declining for several years. Contrary to initial predictions of a “COVID baby boom,” early evidence shows that the pandemic coincided with a steeper decline in birth rates. But fertility patterns vary dramatically across the U.S., and the mortality and health effects of COVID-19 have also varied by location, race/ethnicity, and socioeconomic status. Therefore, we expect significant variation in the pandemic’s effect on fertility. However, currently available data make this difficult to study - fertility rates by household income are not available in the U.S., even at the national level, nor are rate schedules by combinations of race/ethnicity and income or parity. This project seeks to leverage a new restricted data source, which our team has produced under the NICHD-funded "Increased access to contraception: an opportunity dividend?" project (R01-HD101480-01, 2020-2024), to assess the effect of the COVID-19 pandemic on fertility. In partnership with the U.S. Census Bureau, we will use a dataset we created, Reproduction in People’s Lives (RIPL), which provides the full count individual-level longitudinal data needed to study how changes in fertility during the COVID-19 pandemic have varied by age, state, race and ethnicity, household income, and birth order. Using this dataset, we will link multiple U.S. administrative data sources, including individual tax filings microdata, the 2010 decennial Census, and social security data, to (Aim 1) calculate age-specific fertility rates for all U.S. women by state and demographic subgroup for the years 2015 through 2021. These rates - calculated by race/ethnicity, household income, parity, and most combinations of these characteristics - represent a significant improvement in level of detail over current publicly available rates. Using the rates calculated for the pre-pandemic period (2015-2019) we will (Aim 2) use demographic forecasting to generate counterfactual rates that represent estimated 2020-2021 fertility levels by state and demographic subgroup in the absence of the COVID-19 pandemic. We use a forecasting method that is demonstrated to perform well over the short- to medium-term in fertility contexts like the contemporary U.S. and which is based on the Lee-Carter method, a cornerstone of demographic forecasting. We will (Aim 3) assess heterogeneity in the COVID-19 pandemic’s effect on fertility by comparing these counterfactuals to the observed rates constructed under Aim 1 during the years 2020-2021. The forecast counterfactuals allow us to estimate the portion of fertility change over the pandemic period that is the result of the pandemic, and to compare this portion across different age groups, sociodemographic subgroups, parities, and states. We anticipate that we will also be able to publicly release a subset of the rate schedules and forecasts we generate, allowing other researchers access to this important new data source.