GEOG 456 A: Internal Migration in the United States: Methods, Models, and Theories

Geography 456: Internal Migration in the United States: Methods, Models, Theories

Overview

This course is about internal migration in the United States and the use of quantitative methods, statistical analysis, and data science techniques to study this migration.

Each year in the U.S. about 11 million people move across county lines and five million move to a different state. By comparison, about one million immigrants have entered the country from abroad annually in recent years (pre-pandemic). Thus, interstate migration flows are five times larger than the annual inflow of immigrants from abroad (see CPS Historical Migration/Geographic Mobility Tables., particularly, Table A-1. Annual Geographic Mobility Rates, By Type of Movement: 1948-2021). These comparisons reveal that internal migration, even at the long-distance scale of interstate migration, has a larger effect on local and regional population change in the U.S. than immigration. The course will overview histories of U.S. internal migration and discuss theories of migration that attempt to explain why and where people migrate.

A substantial component of the class will include instruction on how to access and process U.S. internal migration survey and administrative data and use statistical and visualization methods for analyzing these data to summarize geographical and temporal trends in U.S. internal migration.

Students will do assignments that use these migration data and methods.  They will use R and Quarto for these assignments.

Migration theory will guide the empirical work in assignments.

Recommended Background

Familiarity or comfort with using R and RStudio is highly recommended for this class.

We will use this programming environment to process and shape downloaded migration data and to analyze and visualize that data. Assignments and the final project will use/write/publish Quarto/R scripts from RStudio.

Learning Objectives

At the end of the class, you will have learned:

• about key migration events in American history (e.g. Great Migration of the mid-twentieth century, the decline of migration rates in the late twentieth century, ) and theories for explaining who moves and their destination geographies.
• how rates and patterns of migration changed during the pandemic period. It is too soon to tell whether any of those changes, such as they are, have persisted. We need data through 2024 at least to assess this question
• about the long-term decline in US internal migration
• how to access contemporary and historical survey and administrative data on U.S. internal migration.
• techniques and R code for shaping, analyzing, and visualizing that data.
• quantitative measures and statistical analysis techniques for studying migration
• how to use these data science and statistical skills to investigate historical and contemporary internal migration by geography and population sub-groups (age, education, nativity, race and ethnicity, etc.).
• how to test hypotheses about US migration rates and flows derived from migration theory
• how to write descriptions of these theories, of the hypotheses and the methods used to test them, and discussions of the results

Expectations and Responsibilities of Students:

Students are responsible for reading the material and watching the online lectures and assimilating the material from each by taking notes. Failure to do so will make it difficult to perform well on the assignments. The lectures and readings are essential for understanding how to do the assignments and to make sense of the results you generate in your write-ups.

Grade Breakdown

Online recorded lectures

Each week there will be up to three recorded lectures. These will be 20-30 minutes in length. They will review key theoretical concepts, explain methods, and discuss data processing and computational issues associated with the topics for that week (see below).  They will also introduce and outline each assignment.

Online office hours

There will be scheduled online discussion and consultation sessions each week, which will last for 60-75 minutes.  In these sessions you can discuss any questions you have about the readings, and we can troubleshoot your coding and computational problems. 

Possible Assignments (subject to change)

There will be four data-based assignments (assignments given in weeks 1, 2, 3 and 4) that will require combinations of downloading, shaping, analysis, visualization and modeling of migration data. You will have 5-7 days to complete each assignment, depending on the assignment.

You will do this work using R and Quarto, knitting your annotated results as to an html or pdf file that you upload to Canvas.

I will provide template scripts to get you started in some of these assignments. You should start your assignment by making sure you can get this template to run.  Then you will adapt and extend the template to complete the assignment.  As your skills progress I expect you to extend these templates further and generate your own code and computations.

Each assignment generally has two parts, and successful completion of both is necessary to get a good score:

1. Data processing, analysis and visualization
2. Text describing and evaluation what the analysis and visualizations reveal in relation to theory and expectations.

Assignment 1:

• The objectives of this assignment are to install RStudio, write a Quarto script that reads in a data file and generates a table and a chart, and knits the script to an html file.

Assignment 2: Migration Rates

• The objectives of this assignment are to download American Community Survey (ACS) Data from IPUMS USALinks to an external site., read this data in a R Quarto script and calculate and chart some migration rates by age, education and race/ethnicity.

Assignment 3: Mapping Migration Rates

• The objectives of this assignment are to use your American Community Survey (ACS) Data extract from assignment one to
  • Map migration rates by state
  • Map migration rates by education by state
  • Map migration rates by race and ethnicity (using the categories used in assignment one) by state
  • Map migration rates by age cohorts by state

Assignment 4: Exploring and Summarizing the Geographies of Migration Flows

• The objectives of this assignment are to use your ACS extract to
  • Calculate summary measures of migration flows including migration efficiencies
  • Estimate a spatial interaction model predicting migration flows

Approximate Weekly Topics and Possible Readings (subject to change)

Week 1. Overview of migration, migration rates, accessing migration data, and R/RStudio/Quarto

• What is migration? Scales of migration.
• Migration rates.
• Migration data. Downloading and shaping ACS data from IPUMS.
• Installing R/RStudio and required libraries.
• Readings:
  • Molloy, R., C. L. Smith, and A. Wozniak. 2011. Internal Migration in the United States. Journal of Economic Perspectives 25 (3):173–196.
  • Bell, M., E. Charles-Edwards, D. Kupiszewska, M. Kupiszewski, J. Stillwell, and Y. Zhu. 2015. Internal Migration Data Around the World: Assessing Contemporary Practice. Population, Space and Place 21 (1):1–17.
  • Read about and access ACS data at IPUMS: https://usa.ipums.org/usa

Week 2. Migration theory and exploring variation in migration rates

• Migration theory: Why do people move? Who moves?
• Variations by age, education, race
• Variations across space and time.
• Readings:
  • Wright, R., and M. Ellis. 2016. Perspectives on Migration Theory: Geography. In International Handbook of Migration and Population Distribution, International Handbooks of Population., ed. M. J. White, 11–30. Dordrecht: Springer Netherlands http://link.springer.com/10.1007/978-94-017-7282-2 (last accessed 18 January 2016).
  • Bernard, A., M. Bell, and E. Charles‐ 2014. Life-Course Transitions and the Age Profile of Internal Migration. Population and Development Review 40 (2):213–239.
  • Wright, R., and M. Ellis. 2019. Where science, technology, engineering, and mathematics (STEM) graduates move: Human capital, employment patterns, and interstate migration in the United States. Population, Space and Place 25 (4):e2224.

Week 3. Population structure and migration rates, migration histories, lifetime migration, migration flows

• Age structure and variation in migration rates over space and time, standardization. Does aging of US population structure explain migration decline?
• Longitudinal data and migration histories. Lifetime migration.
• Migration flow matrix (we can use ACS/Census sample data or download annual IRS annual flow data).
• Readings:
  • Plane, D. A. 1992. Age-Composition Change and the Geographical Dynamics of Interregional Migration in the U.S. Annals of the Association of American Geographers 82 (1):64–85.
  • Cooke, T. J. 2013. Internal Migration in Decline. The Professional Geographer 65 (4):664–675.
  • Access and download IRS migration data: https://www.irs.gov/statistics/soi-tax-stats-migration-data

Week 4. Describing, summarizing, explaining flows.

• Descriptive summaries of migration flows (net migration, effectiveness, distance, visualization).
• Readings:
  • Ellis, M., R. Wright, and M. Townley. 2014. The Great Recession and the Allure of New Immigrant Destinations in the United States. The International Migration Review 48 (1):3–33.
  • Rees, P., M. Bell, M. Kupiszewski, D. Kupiszewska, P. Ueffing, A. Bernard, E. Charles-Edwards, and J. Stillwell. 2016. The Impact of Internal Migration on Population Redistribution: an International Comparison. Population, Space and Place.
  • Gu, Z. Circular Visualization in R. https://jokergoo.github.io/circlize_book/book (last accessed 18 October 2022).
  • RPubs - Chord Diagram Visualization of UN Migration Data. https://rpubs.com/SmilodonCub/582650 (last accessed 18 October 2022).

Week 5. Modeling flows.

• Gravity/spatial interaction model
• Readings:
  • Dennett, A. 2018. Modelling population flows using spatial interaction models. Australian Population Studies 2 (2):33–58.
  • Dennett, A. 2012. Estimating flows between geographical locations: ‘get me started in’ spatial interaction modelling. UCL Centre for Advanced Spatial Analysis. http://www.indigo-sandbox.ucl.ac.uk/bartlett/casa/pdf/paper181 (last accessed 7 March 2016).