Sciences-Po, Paris, France.
OECD Statistics and Data Directorate, Paris, France.
PLoS One. 2019 Jan 11;14(1):e0209562. doi: 10.1371/journal.pone.0209562. eCollection 2019.
We build models to estimate well-being in the United States based on changes in the volume of internet searches for different words, obtained from the Google Trends website. The estimated well-being series are weighted combinations of word groups that are endogenously identified to fit the weekly subjective well-being measures collected by Gallup Analytics for the United States or the biannual measures for the 50 states. Our approach combines theoretical underpinnings and statistical analysis, and the model we construct successfully estimates the out-of-sample evolution of most subjective well-being measures at a one-year horizon. Our analysis suggests that internet search data can be a complement to traditional survey data to measure and analyze the well-being of a population at high frequency and local geographic levels. We highlight some factors that are important for well-being, as we find that internet searches associated with job search, civic participation, and healthy habits consistently predict well-being across several models, datasets and use cases during the period studied.
我们构建模型来估计美国的幸福感，依据是从谷歌趋势网站获取的不同词汇搜索量变化。幸福感的估算系列是根据词群的加权组合得出的，这些词群是通过内生识别来拟合盖洛普分析公司为美国每周收集的主观幸福感衡量指标，或为 50 个州每两年收集的衡量指标。我们的方法结合了理论基础和统计分析，我们构建的模型成功地估计了大多数主观幸福感衡量指标在一年预测期内的样本外演变。我们的分析表明，互联网搜索数据可以作为传统调查数据的补充，以便在高频和本地地理水平上衡量和分析人口的幸福感。我们强调了一些对幸福感很重要的因素，因为我们发现，与求职、公民参与和健康习惯相关的互联网搜索在研究期间的几个模型、数据集和用例中都能持续预测幸福感。
