Jeremy Magruder, Professor

Closed (1) Meet Your Future: Job Search Effort & Aspirations of Young Jobseekers

Applications for fall 2021 are now closed for this project.

Context : Job-seekers in developing frameworks face a number of search barriers to quality employment. The most recurring ones in the literature range from liquidity constraints for travel costs to the high opportunity cost of search-time due to job availability in the informal sector. In addition to these, young job-seekers often do not know how or where to search for jobs. They even cannot assess their own skills in relation to what employers want and are likely to have distorted expectations of wage levels and working conditions. Importantly, these barriers are magnified right in the most vulnerable phase of a career called the transition into the labour market.

Objectives: The objective of this study is to examine how career-coaching and job search assistance from “the future you” can help influence trainees’ expectations and labour market trajectories.

Methodology: In order to measure the effects of career-coaching, we collected survey data and set up a randomized control trial (RCT), where the administered intervention consists of a set of trade-tailored meetings between “the future you,” an alumnus of the VTI and the trainees. In order to measure the impacts of career-coaching, we randomised alumni assignments at the trainee level.

As part of the intervention we recorded, transcribed and translated all the one-to-one interactions between the alumni and the trainees to be able to track the content (what information is shared) but also the form of the conversation (through sentiment analysis).


The undergraduate(s) who join this project are expected to support the conceptualization and implementation of the text analysis and text analytics part of the project. Specifically, they will help:
- Categorize interactions by sentiment and topic;
- Identify important information within the “scripts”;
- Perform text classification, topic analysis, sentiment analysis, topic modeling, and intent detection;
- Detect patterns across hundreds of texts, resulting in graphs (e.g. word clouds), reports, tables etc.

Day-to-day supervisors for this project: Livia Alfonsi, graduate student

(Mainly remote)

Day-to-day supervisor for this project: Livia Alfonsi

Qualifications: • Proficiency in Text Analysis and Text Analytics [Required] • Interested in (development) economic research [Preferred] • Knowledge of LaTeX [Preferred]

Weekly Hours: 6-8 hrs

Closed (2) The Ugandan Shecession

Applications for fall 2021 are now closed for this project.

The student selected will primarily work on a study that looks at COVID-19 disproportionate impacts on female employment in Uganda (“The Shecession of 2020”). Existing research postulates that women in Sub-Saharan Africa suffered disproportionately from the COVID-19 economic recession. However, no data is yet available to support these predictions. This study addresses that knowledge gap. We leverage a panel dataset we collected for a sample of young and successful Ugandan workers prior, during, and following the pandemic. We confirm a disproportionate effect on women's employment. Specifically, we document three main findings. First, we show that the lockdown, Africa's strictest one, reduced female employment by 50% and male employment by 30%, generating a gender gap in employment that did not previously exist in this population. Second, we show that six months after the end of the lockdown, the gender gap has halved in magnitude but persists. Third, we find that conditional on employment, women are substantially less likely to be employed in their VTI-trained sectors. These results persist both one and six months following the lockdown, suggesting that in addition to the differential impact on labor force participation, there is a reduction in human capital accumulation and individual productivity.

An additional phone survey of our sample of young workers and entrepreneurs in July-August 2021 (one year after the total lockdown was lifted) will complement the first one by documenting the dynamics of the recovery from the crisis. In particular, we will measure our key outcomes in the month before the interview, and will also gather data on expectations, time use, and job search to track their evolution.

1) Manage and organize the data coming in from the field to the central server (using the World Bank Survey Solutions Software);
2) Support data cleaning and data management activities (e.g. merge latest survey round to previous and creation of the variables for the analysis);
3) Assist the research team in data analysis as needed.
4) Contribute to report writing.

Day-to-day supervisor for this project: Livia Alfonsi, Graduate Student

Qualifications: • Proficiency in STATA [Required] • Interest in development economic research [Preferred]

Weekly Hours: 9-11 hrs