• This calendar and reading assignments may change at the discretion of the teaching team.
  • Readings for a given week are to be completed by the time of the class on that date
  • You can read in any order you like but we have ordered readings for each week in what we think may make the most sense.
  • For most weeks, we have included supplemental readings. They serve to provide additional perspectives on the methodological or substantive topics of that week. They are not required, but could be insightful to your future academic or professional work.

Unit 1: A Common Vocabulary

Week 1: January 8

Objectives:

  1. Articulate in words and simple graphical representations challenges in identifying causal relationships in quantitative data
  2. Articulate in words and using simple mathematical terms a framework for identifying causal relationships in quantitative data
  3. Describe (conceptually) unit fixed effects and their strengths (and limitations) in research designs seeking to identify causal relationships
  4. Describe the conceptual approach to identifying causal effects using the difference-in-differences framework

Readings:

  1. Murnane and Willett. (2011). Methods Matter, Chapters 1-5, 7
  2. Hoxby, C. (2016). The immensity of the Coleman data project. Education Next, 16(2), 64-69. https://www.educationnext.org/the-immensity-of-the-coleman-data-project/.

Lecture:

Assignment:

  • Student survey due Jan. 10 (see email or Canvas)

Further readings:

  1. Murnane and Willett, Chapter 6
  2. Angrist and Pischke (2009), pp. 1-110
  3. Angrist and Pischke (2014), Chapters 1-2
  4. Cunningham (2021), Chapters 1-4
  5. Clark, T.S. & Linzer, D.A. (2015). Should I use fixed or random effects? Political Science Research and Methods, 3(2), 399-408.

Unit 2: Difference-in-differences

Week 2: Asynchronous Online

Objectives:

  1. Describe threats to validity in difference-in-differences (DD) identification strategy and multiple approaches to address these threats.
  2. Using a cleaned dataset, estimate multiple DD specifications in R and interpret these results

Readings:

  1. Murnane and Willett, Chapter 8
  2. Dynarski, S.M. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. The American Economic Review, 93(1), 279-288.

Lecture:

Assignment:

  • Submit written responses to Class 2 Questions by 12:00pm, Jan. 16
  • Complete DARE #1 by 11:59pm, January 21

Further readings:

  1. Angrist and Pischke (2009), Chapter 5
  2. Angrist and Pischke (2014), Chapter 5
  3. Cunningham (2021), Chapters 8-9

Week 3: January 22

Objectives:

  1. Describe threats to validity in difference-in-differences (DD) identification strategy and approaches to address these threats
  2. Conduct applied difference-in-difference analysis and interpret these results
  3. Describe conceptual approach to regression discontinuity analysis

Readings:

  1. Liebowitz, D.D., Porter, L. & Bragg, D. (2022). The effects of higher-stakes teacher evaluation on office disciplinary referrals. Journal of Research on Educational Effectiveness, 15(3), 475-509. Online Appendix

Lecture:

Assignment:

  • Submit Research Project Proposal by 11:59pm, February 2

Further readings:

  1. Zeldow, B. & Hatfield, L. (2019). Difference-in-differences.
  2. Baker, A. (2019). Difference-in-differences methodology.
  3. de Chaisemartin, C. & d’Haultfoeuille, X. (2021). Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey.
  4. Roth, J., Sant’Anna, P.H.C., Bilinski, A. & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218-2244.

Unit 3: Regression Discontinuity

Week 4: January 29

Objectives:

  1. Describe conceptual approach to regression discontinuity analysis
  2. Assess validity of RD assumptions in applied context
  3. Conduct RD analysis in simplified data and interpret results substantively

Readings:

  1. Murnane and Willett, Chapter 9
  2. Angrist, J.D. & Lavy, V. (1999). Using Maimonides’ Rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 114(2), 533-575.
  3. Dee, T.S. & Penner, E.K. (2017). The causal effects of cultural relevance: Evidence from an Ethnic Studies curriculum. American Educational Research Journal, 54(1), 127-166.

Lecture:

Assignment:

  • Complete DARE #2 by 11:59pm, February 4

Further readings:

  1. Angrist and Pischke (2009), Chapter 6
  2. Angrist and Pischke (2014), Chapter 4
  3. Cunningham (2021), Chapter 6
  4. Ludwig, J. & Miller, D. (2007). Does Head Start improve children’s life chances? Evidence from a regression discontinuity design. Quarterly Journal of Economics, 122(1), 159-208.

Week 5: February 6

Objectives:

  1. Implement RD design in simplified data and interpret results
  2. Assess basic assumptions of RD design
  3. Describe the conceptual and simple mathematical approach to identifying causal effects using the Instrumental Variables approach

Readings:

  1. Holden, K.L. (2016). Buy the book? Evidence on the effect of textbook funding on school-level achievement. American Economic Journal: Applied Economics, 8(4), 100-127. Online Appendix

Lecture:

Assignment:

  • Work on research project

Further readings:

  1. Lee, D. & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(June), 281-355.
  2. Calonico, S., Cattaneo, M., Farrell, M. & Titiunik, R. (2017). rdrobust: Software for regression-discontinuity designs. The Stata Journal, 17(2), 372-404.
  3. Pei, Z., Lee, D.S., Card, D. & Weber, A. (2021). Local polynomial order in regression discontinuity designs. Journal of Business and Economics Statistics, Online.

Unit 4: Instrumental Variables

Week 6: February 12

Objectives:

  1. Describe conceptual approach to instrumental variables (IV) analysis
  2. Assess validity of IV assumptions in applied context
  3. Conduct IV analysis in simplified data and interpret results

Readings:

  1. Murnane and Willett, Chapters 10 and 11
  2. Dee, T.S. (2004). Are there civic returns to education? Journal of Public Economics, 118(4) 1495-1532.
  3. Angrist, J.D., Cohodes, S.R., Dynarski, S.M., Pathak, P.A. & Walters, C.R. (2016). Stand and deliver: Effects of Boston’s charter high schools on college preparation, entry, and choice. Journal of Labor Economics, 34(2), 275-318.

Lecture:

Assignment:

  • Complete DARE #3 by 11:59pm February 18

Further readings:

  1. Angrist and Pischke (2009), Chapter 4
  2. Angrist and Pischke (2014), Chapter 3
  3. Cunningham (2021), Chapter 7
  4. Angrist, J.A and Krueger, A.B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15(4), 69-85.

Week 7: February 19

Objectives:

  1. Implement instrumental variables estimatation in simplified data and interpret results
  2. Assess basic assumptions of IV design in an experimental setting with imperfect compliance
  3. Describe the conceptual approach of using selection on observables to defend causal inferences about the effects of treatment

Readings:

  1. Kim, J.S., Capotosto, L., Hartry, A. & Fitzgerald, R. (2011). Can a mixed-method literacy intervention improve the reading achievement of low-performing elementary school students in an after-school program? Results from a randomized controlled trial of READ180 Enterprise. Educational Evaluation and Policy Analysis, 33(2), 183-201.

Lecture:

Assignment:

  • Work on research project

Further readings:

  1. Lee, D.S., McCrary, J., Moreira, M.J. & Porter, J.R. (2021). Valid t-ratio inference for IV. NBER Working Paper Series No. 29124.
  2. Baicker, K. et al. (2014). The Oregon experiment — Effects of Medicaid on clinical outcomes. The New England Journal of Medicine, 368(18), 1713-1722.

Unit 5: Matching

Week 8: February 26

Objectives:

  1. Describe conceptual approach to matching analysis
  2. Assess validity of matching approach
  3. Conduct matching analysis in simplified data using both propensity-score matching and CEM; interpret and compare results

Readings:

  1. Murnane and Willett, Chapter 12
  2. Diaz, J.J. & Handa, S. (2006). An assessment of propensity score matching as a nonexperimental impact estimator: Evidence from Mexico’s PROGRESA program. The Journal of Human Resources, 41(2), 319-345.

Lecture:

Assignment:

  • Complete DARE #4 by 11:59pm, March 3.

Further readings:

  1. Cunningham (2021), Chapter 5
  2. Dehejia, R.H. & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics, 84(1), 151–161. https://doi.org/10.1162/003465302317331982.
  3. Iacus, S. M., King, G., & Porro, G. (2011). Causal inference without balance checking: Coarsened Exact Matching. Political Analysis, 20(1), 1–24. https://doi.org/10.1093/pan/mpr013.

Week 9: March 4

Objectives:

  1. Describe conceptual approach to matching analysis
  2. Assess validity of matching approach
  3. Conduct matching analysis in simplified data using both propensity-score matching and CEM; interpret and compare results

Readings:

  1. Murnane and Willett, Chapters 13 and 14
  2. Umansky, I, & Dumont, H. (2021). English Learner labeling: How English Learner status shapes teacher perceptions of student skills and the moderating role of bilingual instructional settings. American Educational Research Journal, 58(5), 993-1031. Online Appendix

Lecture:

Assignment:

  • Prepare to present Research Project Presentation in class on March 11

Further readings:

  1. King, G., Nielsen, R., Coberly, C., Pope, J.E. & Wells, A. (2011). Comparative effectiveness of matching methods for causal inference. Working Paper. http://j.mp/2nydGlv.
  2. King, G. & Nielsen, R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27(4), 435-454. https://doi.org/10.1017/pan.2019.11.

Unit 6: Presentations

Week 10: March 11

Readings:

  • None!

Assignment:

  • Submit Final Research Project by 5:00pm March 20th.