Abstract
Climate change is a global problem that has a significant impact on the world’s economy and society.
To effectively address climate change and other societal challenges, policymakers often require reliable estimates of relevant variables at a sub-national level. Nationally representative surveys are not
often designed for this purpose. In this study we propose to use small area estimation techniques
to obtain reliable estimates of the proportion of people very and extremely worried about climate
change at regional level. A novel aspect of our approach is that we include non-traditional auxiliary
information, specifically web data, into our model. For the data used in this paper, our results show
that incorporating web data yields more reliable estimates than the model without them. Finally, we
also acknowledge and address certain limitations associated with the use of web data in small area
estimation
To effectively address climate change and other societal challenges, policymakers often require reliable estimates of relevant variables at a sub-national level. Nationally representative surveys are not
often designed for this purpose. In this study we propose to use small area estimation techniques
to obtain reliable estimates of the proportion of people very and extremely worried about climate
change at regional level. A novel aspect of our approach is that we include non-traditional auxiliary
information, specifically web data, into our model. For the data used in this paper, our results show
that incorporating web data yields more reliable estimates than the model without them. Finally, we
also acknowledge and address certain limitations associated with the use of web data in small area
estimation
Original language | English |
---|---|
Pages (from-to) | 30-37 |
Journal | The Survey Statistician |
Volume | 90 |
Early online date | 2024 |
Publication status | Published - Jul 2024 |
Keywords
- Digital Trace Data
- Data Integration
- Attitudes
- Fay-Herriot model