Diversity at MPC

Here's what we want.

At the Minnesota Population Center, we believe that the creation of a diverse and inclusive community is central to maintaining the excellence of all of our projects. Our definition of diversity extends beyond race and gender and includes socioeconomic status, religion, gender identity and expression, and more. As a center, we aspire to represent the diversity of our city, our region, and our world and to create a space that encourages and embraces inclusiveness, equal opportunity, and respect.

Here's how we live it.

In 2015, the MPC developed a diversity enrichment initiative with the goal of increasing the diversity of our community. Since then, we've been welcoming Diversity Fellows every summer. 

See for yourself.

Read about our inaugural Fellowship program in the 2015 newsletter.

Meet our most recent Fellows on the Office for Equity and Diversity blog.

Read about the results of a project from the 2017 Fellowship.

Learn about some of the work former Fellows have done on the IPUMS blog:

MPC Diversity Fellows

Diversity Fellowship Program

The Diversity Fellowship at the Minnesota Population Center is designed to help recruit underrepresented undergraduate and graduate students to work on IPUMS data infrastructure projects or with MPC member faculty on demographic research projects. 

The Summer 2018 program will run from June 11 through August 17.

Summer 2018 Projects

Exploring Spatiotemporal Patterns & Accessibility of Nice Ride in Twin Cities with Professor Somayeh Dodge & Senior Data Analyst Derek Burk

Fellows will work with Dr. Dodge and Mr. Burk to explore patterns of Nice Ride trips from 2010 – 2017. Bike sharing is a means of green transportation and contributes to a healthy lifestyle. An effective bike sharing system should be safe, affordable, and accessible to the community it serves. Fellows working with Dr. Dodge will analyze spatiotemporal patterns of bike sharing trips in the Twin Cities to determine if access to the bike sharing service is equal for neighborhoods of different demographic characteristics. This knowledge is essential for modeling and designing equitable and accessible bike sharing systems. You will explore the following research questions: Are there any regularities in Nice Ride usage in space (spatial patterns) across the Twin Cities and time (season, monthly, weekly, daily patterns)? Are there any associations in Nice Ride usage and the demographic characteristics of the neighborhoods in which Nice Ride operates? Is there any difference in access to Nice Ride in different neighborhoods? Where are underserved or overserved populations and areas? This research will provide important insights into biking patterns of Nice Ride users and spatiotemporal variability and accessibility of Nice Ride service.

Socioeconomic Characteristics of Public Housing Residents in the 1940 Census with Professor Ryan Allen & Spatial Analysis Director Dave Van Riper

Fellows will work with Dr. Allen and Mr. Van Riper to reveal the historical effect of the 1934 public housing policy. Additionally, students will determine the specific socioeconomic characteristics of individuals who benefitted from public housing in the 1940s by comparing addresses of public housing residents to enumerated district data from the 1940 Census. This research will allow us to identify residents of public housing in 18 cities spread across the US at the time of the Census. You will compare the resident characteristics of The Housing Division of the Public Works Administration to those residents in Wagner-Steagall Act. This comparison will allow us to gain insight into how the construction of the specific public housing affected the demographic profile and the socioeconomic status of neighborhoods with newly constructed public housing. You will seek to answer questions regarding the socioeconomic status of households in various public housing units. You will generate descriptive statistics for the public housing residents, the pool of public housing residents, and the neighborhoods around public housing developments. You will also create data visualizations based on the descriptive statistics or other exploratory data analysis.

Security Services & Violence in African Countries with Professor Elizabeth Boyle & Research Scientist Lara Cleveland

Fellows with work with Dr. Boyle and Dr. Cleveland to better understand whether greater professionalization among African police forces is associated with a lower incidence of violence against civilians. You will use data from IPUMS International and the Armed Conflict Location & Event Data (ACLED). You will combine information on police officers including education and employment status to determine professionalization. You will conduct exploratory analysis to determine the best ways to measure these variables. You will link ACLED on violence against civilians to IPUMS International data, based on year and geographic units. You will explore if these data can answer the question of if where racial, ethnic, or religious characteristics of police vary significantly from the people they are policing, the police are more likely to resort to violence.

Measuring Impacts of Heterorgeneity on Poverty and Employment Pathways Among Boomers with Professor Phyllis Moen, Research Scientist Dr. Sarah Flood, & Research Scientist Dr. José Pacas

Fellows will work with Dr. Moen, Dr. Flood, and Dr. Pacas to examine relationships between poverty and short-term changes in employment among older adults (ages 50-79). We will consider demographic differences in the poverty-employment relationship over the last decade and relationships between employment patterns and changes in poverty. The focus of this project will be on age- and gender-specific patterns since women and the old tend to experience higher rates of poverty than men and working-age adults. You will use panel data from the Current Population Survey (IPUMS CPS) to construct employment paths. You will code data, link observations over time, and empirically identify employment paths. You will create data visualizations to demonstrate these relationships. You will conduct multivariate analyses to understand relationships between age, gender, poverty, and employment patterns as well as relationships between age, gender, employment patterns, and changes in poverty.


Fellows will work extensively with data of various types, including historial census and survey data form the United States as well as spatial data. They may interpret, edit, and format technical documentation. They also may analyze data in statistical packages and record the findings systematically. Some fellows help to prepare data for distribution through MPC's data dissemination websites. Fellows are expected to carry out a variety of other tasks as needed for data preparation, data dissemination, and research. Fellows will be expected to learn new software and techniques as necessary, perform work in a timely manner while being attentive to details, and show initiative in solving problems.

Fellows will report to and be mentored by research scientists, senior data analysts, faculty, software developers, or other professional staff working on the assigned project. They may collaborate with principal investigators, other research assistants, post-doctoral associates, and other project and Center staff. Graduate Fellows will be asked to be a peer mentor to their Undergraduate Fellows team member.


  • Professional mentorship: each Fellow is paired with two mentors working on the assigned project. Graduate Fellows will gain mentorship experience. Undergraduate Fellows will gain peer mentorship from Graduate Fellow team members.
  • Professional development: Fellows will participate in professional development workshops over the course of the summer, in addition to weekly cohort meetings.
  • Paid summer stipend: graduate students — 10 weeks, 20 hours per week, $22.95/hr; undergraduate students — 8 weeks, 20 hours per week, $10.71/hr.


Required: Students must be currently enrolled in an undergraduate or graduate program. The MPC works with students from many disciplines across campus. Students must have: excellent written and oral communication skills, excellent computer skills and ability to work in a technical environment, good interpersonal skills, reliability and attention to detail, and the ability to act independently and as part of a diverse team environment. Students must be willing to embrace new technologies and skills.

Additional Selection Criteria: Students should self-identify with a historically underrepresented group which includes, but is not limited to: African Americans, American Indians, Hispanic/Latino/a Americans, Asian Americans. We also consider first generation students, women students in tech fields, LGBTQ students, students with disabilities. Students may have: knowledge of a major statistical package (Stata, SAS or SPSS), experience analyzing census or survey microdata, experience with HTML, Unix, and XML metadata, and/or use and knowledge of ArcGIS or other GIS software packages. We are especially eager to recruit students who are interested in learning new skills and who could use MPC/IPUMS data in their own research.

Application Instructions

Please apply by emailing a cover letter and resume/CV to Mia Riza, Diversity Fellowship Coordinator, at mpc-jobs@umn.edu.

Your cover letter should which project/s you have a special interest in and for which you are most qualified. Projects described above. Please also include in your cover letter why a summer fellowship with a concentration on diversity is of interest to you.

The search committee will begin its review of applications immediately upon receipt; interviews will take place in March. Applicants will be notified of selection or non-selection on or before April 20. Questions concerning the application process may be addressed to mpc-jobs@umn.edu.

Application deadline: March 15, 2018

Past Projects

Summer 2017 projects

Summer 2016 projects

Summer 2015 projects

The University of Minnesota shall provide equal access to and opportunity in its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression.