Apply

Research Assistant - Innovation and Business Cycles

Support research examining how economic recessions affect innovation patterns using US Census and patent data. Responsibilities include: preparing and merging large datasets (patent quality measures, inventor data), conducting empirical analyses on innovation during the 2001 and 2008 recessions, tracking inventor mobility across firms, and potentially learning SAS for big data processing. Ideal candidate has experience with economic data analysis, patent databases, and statistical software. Weekly collaboration with faculty.

  • Location:

    Boston

  • Semester:

    Summer 2026

Application

Doctoral Program
Current year in program
Doctoral Program Status
Accepted file types: pdf, Max. file size: 250 MB.
Attach a PDF of your resume here
This field is hidden when viewing the form
This field is hidden when viewing the form

  • Project Title

    Innovation and Business Cycles

  • Faculty / Project Lead

    Santiago Caicedo

  • Project Description

    This research project examines how business cycles affect innovation quality, investigating whether recessions produce better or worse patents. Using Census Bureau restricted microdata (LEHD and LBD) linked to USPTO patent records, the researchers will analyze how the composition of inventors and firms changes during economic expansions and recessions, and whether these compositional shifts explain observed patterns in patent quality. The project tracks career trajectories of "recession inventors" versus "expansion inventors" and examines the persistence of initial economic conditions on innovative performance. Preliminary findings suggest innovation quality is counter-cyclical, with patents created during recessions receiving more citations.

  • Qualifications Necessary

    Strong proficiency in statistical software (Stata, R, or Python); Experience with data management and merging large datasets; Knowledge of econometric methods and regression analysis; Attention to detail and ability to work with complex data structures; Basic understanding of patent data or innovation economics (preferred); Willingness to learn new software (SAS) and work with confidential Census data; Strong organizational skills and ability to work independently; Experience with academic research projects (preferred)

  • Hours per Week

    20 Hour Position