CEGA’s Digital Credit Observatory (DCO) invites proposals for small grants of up to $50,000 each (including indirect costs) to support new survey modules, new treatment arms, sample size expansion allowing for gender disaggregation, descriptive studies, and/or pilots of new gender-focused interventions that help us answer important questions related to digital credit (understood to mean mobile phone-based loans that are instant, automated, and remote) and women’s economic empowerment:

  1. How do digital credit products and related lending practices economically empower–and disempower–women?
  2. What are the key barriers that women face to accessing and using digital credit products? To what extent are these barriers gender-specific? What can this teach us about the design of digital credit products and related lending practices aiming to promote financial inclusion and economic empowerment for women?

Please read carefully through the application guidelines (RFP) and direct any questions to our staff team at digitalcredit@berkeley.edu 

ATAI Data Analyst (PhD Student)

The Center for Effective Global Action (CEGA) is a hub for research on global development, with a network of over 100 academic researchers extending across the University of California, Stanford University, and the University of Washington. Our faculty affiliates design and test solutions for the problems of poverty, generating actionable evidence for policy-makers in less developed countries. Using rigorous field trials, behavioral experiments, and tools from data science, we measure and maximize the impacts of economic development programs throughout the world. 

We are seeking outstanding PhD student applicants to support the Agricultural Technology Adoption Initiative (ATAI), based at either UC Berkeley, or UC San Diego.

Agricultural Technology Adoption Initiative

Co-implemented by CEGA and the Abdul Latif Jameel Poverty Action Lab (J-PAL) at MIT, ATAI generates data and insights needed to inform productive investments in agricultural development. The program is led by a consortium of leading social scientists who conduct large-scale randomized evaluations in direct partnership with implementers like the CGIAR, World Bank, international NGOs, and national ministries of agriculture. More than one hundred thousand individuals have been surveyed to date as part of ATAI evaluations. Per ATAI policies, each of these data are to be made publicly-available as a resource for the global policy and research community, while ensuring that personally identifiable information is protected. Valuable information is included in these resources, including household and plot-level information on prices, production, and a variety of farmer welfare indices. In addition, since June 2015, ATAI has required all full-scale RCTs to collect information on program costs for the interventions being evaluated. This year ATAI launched renewed activities to generate learning across its portfolio of field experiments, including a mandate to better utilize the wealth of primary data collection we have commissioned.

Scope of Work

The PhD student hired will collate and examine publicly available ATAI datasets, identify and advance current and future opportunities for meta-analysis, and help encourage data publication. 

This position will be part-time (estimated 50% time, to be finalized with the selected candidate), with a start date to be set (preferred during the summer of 2019, although we will consider early start dates for fall semester/quarter of 2019). This position has the opportunity to extend, meaning that a hire who produces sufficient quantity and quality of work in the first semester could be asked to stay on in the following semester/quarter(s). 

The position will work for ATAI’s faculty leads Craig McIntosh (UC San Diego), Jeremy Magruder (UC Berkeley), as well as Tavneet Suri (MIT), with support from CEGA and J-PAL staff.

Specific tasks include the following:

Aggregation, analysis, and display of existing data resources:  Over the past eight years, ATAI has supported more than 50 studies, more than half of which are randomized evaluations in 17 different countries across Sub-Saharan African and South Asia. You will aggregate the data resources publicly available thus far. As data is brought together, you will collaborate with ATAI’s faculty leads to identify ways to link this information in a manner that allows for cross-study comparability. Opportunities to link these efforts to external resources (for example, UN FAO, the University of Washington Evans School Policy Analysis and Research Group and the World Bank’s Living Standards Measurement Survey) will be explored and implemented where fruitful. 

Advise and support new data collection efforts: You will support the program’s faculty directors and staff in making recommendations of specific indicators that could be collected across contexts as a way to facilitate meta-analysis ex ante. ATAI will be commissioning a set of common indicators, as well as “diagnostic” top-up data collection exercises to encourage researchers to collect information on areas where good descriptive data is lacking and they are already headed to the field. In the immediate term, diagnostic data collection exercises could investigate gender dynamics, food security, "inclusion" across the income distribution, soil tests or environmental assessments, and/or improved agricultural input or output measurement approaches.

Core Responsibilities:

  • Become familiar with ATAI’s datasets (including fields, data types, and constraints), and the software used to view and analyze them (GIS skills strongly preferred);
  • Collate ATAI's datasets, collaborating with J-PAL’s knowledge management and data quality team to support regular flow of data publication, tracking project completion and compliance with data publication policies;
  • Identify datasets that can be linked with ATAI survey data, perform linkages, and display results;
  • Perform data analysis and report summary statistics, and findings to ATAI faculty in a clear, logical, and reproducible manner. As the opportunity arises, develop visualizations, summary statistics, and presentations, which use existing ATAI data to inform policy and research communities on the advancement of smallholder agriculture;
  • Work with faculty leaders to suggest key hypotheses to test using ATAI data and other data sources;
  • Work closely with faculty leaders and staff from ATAI to develop the foundations of a robust data analytics research agenda for the program;
  • Collaborate with J-PAL’s knowledge management and data quality team to assess and implement options for cost effectiveness analysis across the ATAI portfolio;
  • Serve as resource for ATAI on survey questions, modules, and data collection strategies for ATAI’s new “transformation metrics”; assess opportunities for additional “diagnostic” data collection exercises, advise faculty directors, and propose data collection methods.
  • Understand and adhere to ATAI’s policies and protocols regarding data security and confidentiality.

Required Qualifications

  • Current PhD student at UC Berkeley or UC San Diego. Training in microeconomics, econometrics, and statistics is required.
  • 2+ years of hands-on experience wrangling large, heterogeneous data sets, particularly demonstrating aptitude with spatial data; candidates should also demonstrate competence working with survey, administrative, and/or time series data;
  • 2+ years of hands-on experience using GIS software as well as R, Stata, SAS, and/or Microstrategy, preferably with strong experience using Python and/or other scientific computing languages

Desired Qualifications

  • PhD students in Economics, Agricultural & Resource Economics or similar programs are preferred. 
  • Familiarity with randomized controlled trials
  • Experience cleaning, labelled, and documenting datasets for public repositories;
  • Experience working in developing countries (preferably in a research-related capacity);
  • Experience designing, managing, and implementing different data gathering strategies, including: (i) semi-structured interviews, (ii) focus groups, and (iii) surveys;
  • Interest in agricultural development and ways in which quantitative research can be used to create valuable global public goods;
  • Experience developing data visualizations (using Tableau or similar platform) that are accessible to non‐academic audiences;
  • Ability to interact with individuals at all levels in a fast‐paced environment, sometimes under pressure, while remaining flexible, proactive, tactful, resourceful and efficient, and with a high level of professionalism and confidentiality.

Application Requirements

Please upload brief cover letter, CV and contact information for two academic references to the "ATAI Data Analyst (PhD Student)" Submittable page.

Applications will be reviewed on a rolling basis until the position is filled; deadline to apply is June 30, 2019.

About RT2

The Berkeley Initiative for Transparency in the Social Sciences (BITSS) will hold its next Research Transparency and Reproducibility Training (RT2) in Washington DC, September 11-13, 2019. This RT2 is organized in partnership with the World Bank’s Development Impact Evaluation (DIME) group.

RT2 provides participants with an overview of tools and best practices for transparent and reproducible social science research. The curriculum is developed and delivered by academic leaders in the open science movement and there is space for collaborative work and hands-on skill building. Participants are encouraged to bring their own research questions and ideas to seek support and feedback from instructors and other attendees. Learn more about previous RT2 events here.

Curriculum

Find a tentative agenda here. RT2 curriculum focuses on the following topics:

  • Ethics and Mertonian norms for research transparency and reproducibility
  • Scientific misconduct and researcher degrees of freedom
  • Improved specification through study pre-registration and pre-analysis plans
  • Computational reproducibility and approaches to replication
  • Techniques for reproducible meta-analysis
  • Hands-on practice with version control using Git (GitHub or the Command Line)
  • Dynamic documents with R and Stata
  • Data management and de-identification for data sharing
  • Appropriate use of statistics and interpretation of statistical evidence

Faculty

Faculty members include: Luiza Andrade (World Bank, Economics), Lorena Barba (George Washington University, Engineering), Graeme Blair (UCLA, Political Science), Fiona Burlig (University of Chicago, Economics), Benjamin Daniels (World Bank, Economics), Charlie Ebersole (University of Virginia, Psychology), Sean Grant (University of Indiana, Public Health), Fernando Hoces de la Guardia (BITSS, Public Policy), Maggie Jones (US Census, Economics), Roshni Knincha (World Bank, Economics), Edward Miguel (BITSS/UC Berkeley, Economics), Joseph Simmons (University of Pennsylvania, Psychology), Soazic Elise Wang Sonne (World Bank, Economics), and Alexa Tullett (University of Alabama, Psychology).

Eligibility

RT2 is designed for researchers in the social and health sciences, with particular emphasis on economics, political science, psychology, and public health. Participants are typically (i) current Masters and PhD students, (ii) postdocs, (iii) junior faculty, (iv) research staff, (v) librarians and data stewards, and (vi) journal editors, funders, and research managers curious about the implications of transparency and reproducibility for their work.

RT2 curriculum is most applicable to research that uses quantitative or mixed methods. Applicants should have proficiency in R or Stata.


Fees and Financial Support

Tuition for RT2 is $1,500 for three days, including breakfast, lunch, and a networking reception on September 13. This tuition fee does not cover costs for travel and accommodation.

Financial Support: For up to 30 participants, BITSS offers partial or full financial support by waiving the tuition fee and covering travel, lodging and child care. Financial support will be awarded based on a combination of merit and need, taking into account our commitment to facilitate access to RT2 for diverse and underserved populations. Note that the deadline for applications seeking support from BITSS has now expired.


Application Process

Submit an application using the form below. The application includes questions regarding (i) your motivation for participating in RT2 and (ii) how you expect your participation to contribute to scaling-up education and training on research transparency and reproducibility.

Applications should also include a Curriculum Vitae or Resume. Letters of Reference are also accepted, though they are not required. Please submit these as attachments in the online application portal.


Selection Process

BITSS aims to select no more than 40 participants for RT2. With this in mind, the number of applications for RT2 tends to exceed the number of available spaces.

BITSS staff will lead the selection process with oversight from the BITSS Faculty Director. Competitive selection will be based on the (i) quality of application materials and expected impact and (ii) balance across disciplines, gender, and institutions.

Applications from self-funded participants will be considered on a rolling basis. Self-funded applicants will be assessed based on the same eligibility criteria as those requesting financial support.


Application Timeline

The deadline for applicants seeking financial support from BITSS has now expired. Notifications of acceptance for applicants with financial support will be sent no later than June 14, 2019.

BITSS will consider applications for self-funded participants on a rolling basis through 11:59 pm PT August 28, 2019.


Contact

Reach out to BITSS Senior Program Associate Aleksandar Bogdanoski (abogdanoski@berkeley.edu) with any questions about RT2 DC.

Center for Effective Global Action