| 来源类型 | Project
|
| 规范类型 | 研究项目
|
| Mobile Exclusion Modelling |
| Sarah Hughes; Jonathan Gellar
|
| 开始日期 | 2018
|
| 结束日期 | 2019
|
| 资助机构 | FinMark Trust
|
| 语种 | 英语
|
| 概述 | Mathematica developed a predictive model to estimate financial inclusion (access to banking systems) in eight countries in Africa and Asia.", |
| 摘要 | These countries are: Tanzania, Uganda, Nigeria, Kenya, Pakistan, Bangladesh, India, and Indonesia. The focus of the study was to test if we could accurately estimate financial inclusion using data from mobile SMS surveys, which are much less expensive than more traditional face-to-face surveys but also systematically under-represent certain target groups, including the rural poor, women, and the elderly. In order to adjust our estimates to account for this non-representative sampling, we used a technique called multilevel regression with post-stratification (MRP, or “Mr. P”). MRP is widely used across the social sciences to adjust non-representative data, and has shown to provide more efficient estimates than traditional (frequentist) post-stratification in cases with many small post-stratification cells. We found MRP to perform well with the SMS data. We also explored novel approaches of combining data from multiple survey modalities, including computer-assisted telephone interview (CATI) data and face-to-face data, with the larger SMS survey data. |
| URL | https://www.mathematica.org/our-publications-and-findings/projects/mobile-exclusion-modelling
|
| 来源智库 | Mathematica Policy Research (United States)
|
| 资源类型 | 智库项目
|
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/491182
|
推荐引用方式
GB/T 7714 |
Sarah Hughes,Jonathan Gellar. Mobile Exclusion Modelling. 2018.
|
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