Using large-scale data for new measurements in Afghanistan
Joshua Blumenstock, Professor
Information, School of
Closed. This professor is continuing with Fall 2023 apprentices on this project; no new apprentices needed for Spring 2024.
Over the past decade, the rapid proliferation of mobile phones, satellites, and other digital sensors has created tremendous opportunities to measure human behavior. These data also provide the foundation for the growing interdisciplinary field of computational social science. Non-traditional “big” data hold unique potential in developing and conflict-affected environments, where traditional sources of quantitative data are often out of date, unreliable, and subject to political capture and censorship. Our team has pioneered the use of mobile phone data, high-resolution satellite and multispectral remote sensing data to measure poverty, internal migration and forced displacement, as well as social cohesion.
Furthering this research agenda, we are exploring if such data can be used to measure economic production, as well as trust in institutions. Specifically, we have been developing methods to estimate opium production from high-resolution satellite imagery. More speculatively, we hope to develop a measure of trust in government security services using mobile phone data.
We have access to terabytes of data from Afghanistan's largest mobile phone operator, in the form of anonymized call records. This unique data set enables us to infer locations of individuals, as well as a wealth of other information. Other than mobile phone metadata, we will explore data from other sources, such as satellites, open street maps, financial institutions, censuses, household surveys.
With these data, we are uniquely positioned to be able to answer important and pressing questions, such as:
1. How can these new measurements be rigorously validated using data from more traditional and trusted sources?
2. Can these measures be validated when traditional sources do not exist?
3. What important social science questions can be answered with the availability of such measures?
Role: The undergraduate apprentice would have the opportunity to shape the research from the start of this project. This includes brainstorming ideas on what questions can and cannot be answered, exploring the data to extract insights, and identifying promising directions.
Depending on the student’s interest, there may be the opportunity to explore methods related to machine learning, causal inference, etc., as well as to develop new estimation frameworks. The undergraduate would be expected to be able to complete well-defined research tasks, but will also have the freedom to propose and pursue promising directions of their own interest.
Qualifications: All URAP apprentices, irrespective of the specific role or assignment, are expected to be extremely self-motivated, attentive to detail, and meticulous in their approach to data analysis. Apprentices must be able to work independently and be excited to take responsibility and initiative for ensuring their work meets exacting standards of quality.
Specific qualifications for this position include:
- Proficiency in R or Python (required)
- Experience manipulating large data sets (required)
- Ability to derive insights from data and communicate these clearly (required)
- Knowledge of statistical/econometric methods (preferred)
- Proficiency with visualization tools (preferred)
- Knowledge of Afghanistan society (preferred)
Day-to-day supervisor for this project: Xiao Hui Tai, Post-Doc
Hours: to be negotiated
Off-Campus Research Site: Zoom (during covid)
Related website: http://didl.berkeley.edu
Related website: http://didl.berkeley.edu