Chemonics / FEWS NET 7
An early-warning frame for interpreting labor and urban vulnerability signals.
Project overview
The Lebanon labor project examined casual, daily, and informal work in urban areas, especially among locally poor households. Public evidence from the previous decade was turned into a taxonomy, DELLM extraction workflow, qualitative panel dataset, and decision-support dashboards with statistics, trends, lessons learned, and recommendations.
Project story
The FEWS NET 7 Lebanon work focused on casual, daily, and informal labor in urban areas, especially among locally poor households. The source base included academic articles, grey literature, intergovernmental and government reports, and NGO materials from the previous decade.
The project built a taxonomy for labor dynamics, then trained DELLM to extract relevant qualitative excerpts and label them against that structure. Those excerpts were standardized into a panel dataset and translated into dashboards that show labor categories, target populations, seasonal variation, lessons learned, trends, and recommendations for future analysis and decision support.
Example deliverables
Concrete outputs or working assets the project produced or supported.
Decision problem
Chemonics and FEWS NET 7 needed a clearer evidence base on casual, daily, and informal labor in urban Lebanon, especially among locally poor households. The work had to draw from public evidence over the prior decade and turn scattered labor-market information into analysis that could inform early warning and future decision support.
What the work involved
Built a taxonomy for labor dynamics in Lebanon, including categories such as formal employment, informal labor markets, seasonal work, self-employment, casual labor, and daily labor.
Trained DELLM to extract relevant excerpts from academic articles, grey literature, intergovernmental and government reports, and NGO materials from the previous decade.
Standardized qualitative excerpts into a panel dataset and created dashboards showing labor categories, target populations, seasonal variation, lessons learned, trends, and actionable recommendations.
Outcome
Sharper interpretation of urban livelihood risks for early warning and response planning.
Evidence inputs
Review focus
Bring one decision, report, review process, or evidence problem. We will help identify the smallest useful place to begin.
We use cookies to enhance your browsing experience on our website. By continuing to use our site, you consent to our use of cookies. For more information, please review our Privacy Policy.
