Paul Gertler, Professor

Closed (1) Project 1: Can We Build Entrepreneurs and Better Negotiators? A Field Experiment in Uganda (Machine Learning and Big Data)

Applications for fall 2021 are now closed for this project.

We study the medium-term impacts (3.5y) of the Skills for Effective Entrepreneurship Development (SEED) program, an innovative in-residence 3-week mini-MBA program for high school students modeled after western business school curricula and adapted to the Ugandan context. The program featured two separate treatments: the hard-skills MBA features a mix of approximately 75% hard skills and 25% soft skills; the soft skills curriculum has the reverse mix. Using data on 4,400 youth from a nationally representative sample in a 3-arm field experiment in Uganda, the 3.5 year follow-up demonstrated that training was effective in improving both hard and soft skills, but only soft skills were directly linked to improvements in self-efficacy, persuasion, and negotiation. The skill upgrade was rewarded in substantially higher earnings and youth in both groups were more likely to start enterprises and more successful in ensuring their businesses' survival.

Furthermore, with exciting and novel data on hand, we are poised to answer some of the following questions:
- Can we improve youth's ability to negotiate?
- What makes a good negotiator?
- Which tactics are more successful (e.g., 'split the difference', win-win arguments, cheap talk, aggressive bidding)? What is the role of fairness in shaping outcomes?
- Which personality skills matter (e.g., inter- and intra-personal skills)?

Data to answer these questions are generated in the context of the SEED RCT and a lab-in-the-field experiment with real stakes (a Negotiation Task). We have access to a rich data set, including recordings and transcripts for several rounds of negotiation for a large sample of youth.

This is an exciting project that touches upon different strands of the economic and methodological literatures including behavioral economics, negotiations, machine learning methods for text analysis and other novel statistical and econometric techniques.

The team is also gearing up to start 8-year data collection for the SEED study. The follow-up instruments will be designed to shed light on the underlying mechanisms and components through which SEED operates and yields lasting impacts, and to inform the debate on the optimal combination of soft- and hard-skills in the design of entrepreneurship training programs.

Students who work on this project will receive training on best practices in analyzing data, conducting literature reviews, and questionnaire development.

Tasks:
- Clean and analyze data
- Review literature on soft-skills measurements (task based, biometrics, self reported) and entrepreneurship
- Support during questionnaire development and testing phases

Day-to-day supervisor for this project: Laura Chioda, Staff Researcher

Qualifications: - Data analysis using Stata, R, or Python - Strong written and oral communication skills - Experience with Big Data and Machine Learning Methods, especially in the context of text analysis -Strong ability to anticipate, plan, prioritize, and meet deadlines - Capacity to handle multiple projects at once, effectively manage time, and interface confidently with co-workers - Self-motivated, detail-oriented, hard worker who enjoys working in teams - - Desire to pursue graduate school in economics (and related fields), public policy, or data science a plus - Excellent grades in econometrics, statistics, and/or biostatistics required.

Weekly Hours: 9-11 hrs

Related website: http://www.paulgertler.com/
Related website: https://www.nber.org/papers/w28845

Closed (2) Project2: Lab for Inclusive FinTech (LIFT): Building an Inclusive and Sustainable Digital Economy

Applications for fall 2021 are now closed for this project.

The Lab for Inclusive FinTech (LIFT) is a new and exciting initiative housed at the Institute for Business and Social Impact at Haas.

LIFT seeks a creative, ambitious, and mission-driven student with strong communication skills and a passion for economic development and FinTech to help take LIFT's media and policy engagement strategy to the next level. Students will have the opportunity to interact with industry, NGO, and research leaders and contribute to fostering a healthy, inclusive, and sustainable digital economy.

• LIFT links academic researchers with industry partners and policymakers to reimagine the design and reach of digital financial services for unbanked, underserved, and vulnerable populations around the world by tackling the complex barriers to financial inclusion leveraging technology.
• LIFT functions as a real-world lab. It forges partnerships with industry, NGOs, and policy makers, leading to the formulation of bottom-up questions/problems and solutions. It combines rigorous evaluation and frontier research with Fintech, big data, and AI.

LIFT’s three pillars:
(1) Research: high-quality impact evaluations and advanced data analytics
(2) Strengthening Community of Practice: field-building though careful designed field experiment to design, test, solutions for and in collaboration with industry/governments partners
(3) Facilitate Innovation and Experiential Learning: train the next generation of leaders in fintech to ensure diversity and inclusivity is the cornerstone of the industry

LIFT places a strong emphasis on new technologies (Fintech, Insurtech, and, Blockchain Applications) designed
(1) to support, grow, and ensure financial inclusion by removing constraints and creating opportunities for individual and SMEs/firms at the bottom of the pyramid
(2) to enable and promote sustainable development, green growth, and social impact: sustainable digital finance, impact investing, climate fintech etc.

Tasks:
- Write timely and exciting blog posts/policy notes with quick turnarounds
- Develop multimedia content, disseminate through social media, monitor press coverage, and support website operations
- Support event planning (conference, round tables etc) and promote event on social media. These event will feature leaders and innovators in the FinTech, Policy Makers, and Researchers
- Develop web content for LIFT and related initiatives's websites
- Track web and social media analytics and report metrics

Day-to-day supervisor for this project: Laura Chioda

Qualifications: - Advanced writing skills with precise, clear, and vibrant language - Relevant communications experience and a demonstrated interest in evidence-based policy-making and related economics topics (e.g., fintech, economics, behavioral economics, and economic development, public policy etc.) - Strong ability to anticipate, plan, prioritize, and meet deadlines - Capacity to handle multiple projects at once, effectively manage time, and interface confidently with co-workers - Self-motivated, detail-oriented, hard worker who enjoys working in teams - Demonstrate professionalism and discretion, exhibit good judgment when sharing news and information publicly - Flexible and able to adapt to change - Eye for design and style, both in web and in print

Weekly Hours: 9-11 hrs

Related website: http://www.paulgertler.com/

Closed (3) Project3: Evaluating the effectiveness of California's Child Welfare System in protecting victims of child abuse and neglect

Applications for fall 2021 are now closed for this project.

Victims of child maltreatment are one of society’s most vulnerable populations. Abuse and neglect put children at high risk for poor later life outcomes, including low earnings, unemployment, criminal activity, and poor health. Child welfare systems are designed to investigate allegations of abuse and neglect and respond to substantiated ones by providing services and/or removing a child. However, the evidence to inform how the system should intervene and when is extremely sparse.

This project uses over two decades of administrative data from California and economic theory to study how the child welfare system affects the later life outcomes for child maltreatment victims, as well as the policy levers to improve service quality. Using quasi-experimental research designs, we will leverage historical policy variation across California’s 58 counties. This project works closely with the California Department of Social Services.

This project uses “big data” (over 100 million observations) that is highly sensitive in nature. Access is made possible through a data user agreement between the California Policy Lab and the California Department of Social Services.

The URAP working on this project will receive training in applied econometric analysis, use of longitudinal data, and develop an understanding of social worker decision making and child welfare processes.

Tasks may include but are not limited to:
- Cleaning, merging, and analyzing complex data
- Interpreting results from quasi experimental research designs
- Producing compelling graphs to visualize results

Day-to-day supervisor for this project: Nicole Perales, Ph.D. candidate

Qualifications: Experience with R required. Experience managing “big data” preferred. Familiarity with quasi-experimental research designs preferred. Desire to pursue graduate school in public health, economics, public policy, or data science preferred. Additionally, the URAP must be willing to undergo a basic background check to be granted access to the California Policy Lab’s secure server. Because data access is only possible through remote access to the secure server, good internet is also required for this project.

Weekly Hours: 6-8 hrs

Closed (4) Project4: Healthcare and behavioral economics in Chile (2 projects)

Applications for fall 2021 are now closed for this project.

Project 1: Can nudges improve the behavior and health of patients living with chronic diseases? An impact evaluation with Chile’s ministry of health.

This project uses ‘big data’ (over 100 million observations) from four sources: 1) electronic health records from primary care visits with biomarkers and measures of health behaviors, 2) medication refill data from pharmacies, 3) hospitalization data and 4) mortality records.

Students working on this project will receive training in econometric data analysis in particular cutting-edge quasi-experimental methods, use of complex longitudinal and big data, and develop an understanding of chronic diseases management and its evaluation.

We are in the late stages of analysis and looking for a student to assist in cleaning and analyzing two new large administrative datasets, and creating publication-ready tables. Experience with R required. Please email an R code sample to cboone@berkeley.edu in addition to your portal application.


Project 2: Heuristic thinking in primary care.
Clinical practice guidelines aim to standardize medicine and important clinical decisions, but health care providers still have substantial discretion in providing care: not only do they decide which patients to test for a given disease, they also decide whether to act on the results of the test and proceed with diagnosis and treatment. How these decisions are made is not well understood. I investigate whether clinicians use heuristics - mental shortcuts – to make decisions.
Seeking a candidate who is fluent in Spanish (required!) and interested in public health and/ medical research to help with reading clinical practice guidelines, scraping information from the web, and potentially conducting phone interviews with clinicians in Chile.


Day-to-day supervisor for this project: Claire Boone, Graduate Student

Qualifications: Day-to-day supervisor for this project: Claire Boone, Graduate Student Qualifications: For both: desire to pursue graduate school in public health, economics, public policy or data science a plus. Excellent grades in econometrics, statistics, and/or biostatistics required. Prefer candidates who are open to joining the project for fall and spring semesters.

Weekly Hours: 6-8 hrs

Off-Campus Research Site: Weekly meetings can either be in person or on zoom.