Assignments

• Today
  • Final Project Assignment 1: Research question and data source
  • Check your submission (self-contained)
• Thursday (3/14)
  • Readings

Agenda

1. Final Project Review
2. Workshop on Final Project Essentials

Research Question

Does moving to a new city reduce the political engagement of young people?

• I hypothesize that moving to a new city will reduce young people’s likelihood of engaging in political or civic action
  • Youth have low levels of political engagement, often driven by lack of information and experience
  • Youth that move have less information and experience with engagement in their new city
  • Youth that move probably have fewer social ties, and ties are important for facilitating engagement

Research Design

• Design: Estimate the relationship between whether or not an individual moved to a new city to attend university and their level of political engagement
• Assumption: Individuals that moved to a new city had similar propensities to participate to those that did not, conditional on observable covariates
• Diagnosis: Unrealistic!
• Plan: Build confidence by ruling-out potential differences in baseline propensity to participate among moving and non-moving students

Researh Context

• Students at Addis Ababa University (AAU)
  • Youth frequently move to a new city in order to obtain education
  • AAU is Ethiopia’s top university, and students from around the country move to study there
  • Universities are important sites of political socialization

Building Confidence

Observable implications

• Identify specific types of participation that are more and less likely to be affected by whether at student moves
  • Some forms of participation rely on social ties or information about the environment, while others do not
• Account for the length of time since respondents moved to their new city
  • As students become more embedded, the gap between moving and non-moving students should become smaller

Building Confidence

Conditioning

• Students that move from one urban place to another urban place
  • Students that move from one city to another city will be less different (in their propensity to participate) than those moving from rural to urban
• Students with similar socio-economic status (SES)
  • Students with similar SES will be less different (in their propensity to participate) than those with similar SES

Building Confidence

Placebo tests

• Moving may affect feelings about your individual efficacy, but not the efficacy of youth in general
• Moving may affect your participation recent engagement, but not an opportunity to engage that was provided within the survey

Data and Variables

Data

• Representative survey of 825 AAU students
• 2 waves (May-June, October-November of 2022)

Variables

• Outcome: survey questions measuring political participation
• Treatment: whether student is originally from Addis Ababa
• Building confidence: feelings of political efficacy, number of years since move, urban or rural origin, SES, etc.

Creating Index Measures

When to create an index measure

• When you have many ways of measuring a single concept
  • This is true for outcome measures, treatment measures, and covariates

Benefits of index measures

• Simplifies analysis (fewer graphs, tables, etc.)
• Reduces number of hypotheses being tested

Additive Scale

What is an additive scale?

• Simple sum across columns (index = column_1 + column_2)

When to use an additive scale

• When variables are measured on a common scale
• When you are interested in a cumulative amount of something
  • Number of times someone engaged in a specific behavior
  • Amount of money from several different sources

Additive Scale

Benefits of additive scales

• Interpretability: number on the original scale
• Simplicity: Just plain addition

Averaged Z-Scores

What is a z-score?

• \(Z = (X - \mu) / \sigma\)
• Standardized: Mean of 0 and standard deviation of 1

When to use averaged z-scores

• When variables are measured on different scales
• When variables cannot be summed

Averaged Z-Scores

Benefits of averaged z-scores

• Interpretability: Standard deviations from the mean
• Outlier detection: abs(3)

Fancier index techniques

• Principal Component Analysis
• Factor Analysis
• Inverse Covariance Weighting

Interaction Terms

What is an interaction term?

• Simple linear models assume that the effect of predictors is independent of other factors
• Interaction terms allow us to estimate the difference in the slope of a predictor across unit characteristics

\[ Y_i = \alpha + \beta_1 X_{i1} + \beta_2 X_{i2} + \beta_3 X_{i1}*X_{i2} + \epsilon_i \]

Interaction Terms

What are interaction terms used for?

• Heterogeneous effects
• Difference-in-differences

Example: Continuous outcome with two binary predictors

• \(\alpha\): Intercept when \(X_{i1}\) and \(X_{i2}\) are 0
• \(\beta_1\) Slope when \(X_{i2} = 0\)
• \(\beta_2\) Difference in \(\alpha\) between \(X_{i2}=0\) and \(X_{i2}=1\)
• \(\beta_3\) Difference in \(\beta_1\) between \(X_{i2}=0\) and \(X_{i2}=1\)