[SOLVED] ECON5094 Detailed Reading List Part 2 SQL

ECON5094 Detailed Reading List

Part 2 – Tanya Wilson

The course companion texts are

• “Mostly Harmless Econometrics” by J. Angrist and J.S. Pischke, Princeton Uni-versity Press (noted in reading list as MHE)

• Blundell, R., & Dias, M. C. (2009). Alternative approaches to evaluation in em-pirical microeconomics. Journal of Human Resources, 44(3), 565-640. (noted in reading list as BCD)

I would advise all students intending to conduct empirical analysis in their PhD thesis to read these texts in their entirety. Relevant chapters/article sections corresponding to each week’s lectures are indicated.

For leisure reading:

• The Economist as Detective by Claudia Goldin – link to paper

• The Economist as Plumber by Esther Duflo – link to paper

• “Freakonomics” by Steve Levitt and Stephen Dubner

For each unit I have included key papers on the econometric methods used, references for the papers discussed in the lectures and additional papers which use the method described in the relevant lecture. * indicates required reading. Everything else is op-tional. Those papers in bold font have supplementary data files available – usually on the respective journal’s website.

1 – Randomised Experiments

Methodological papers:

* MHE – Chapters 1 and 2

* BCD – Sections I, II and III

1. Athey, S., & Imbens, G. W. (2017). The Econometrics of Randomized Experi-ments. In Handbook of Economic Field Experiments (Vol. 1, pp. 73-140). North-Holland.

2. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal ef-fects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455.

3. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688.

4. Banerjee, A., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., Shotland, M. and Walton, M., 2017. From proof of concept to scalable policies: challenges and solutions, with an application. Journal of Economic Perspectives, 31(4), pp.73-102.

Empirical papers:

1. Bertrand, Marianne, and Sendhil Mullainathan. 2004. ”Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.” American Economic Review, 94 (4): 991-1013.

2. Krueger, A. B. (1999). Experimental estimates of education production functions. The Quarterly Journal of Economics, 114(2), 497-532.

3. Miguel, E., & Kremer, M. (2004). Worms: identifying impacts on education and health in the presence of treatment externalities. Econometrica, 72(1), 159-217.

4. Angrist, J., & Lavy, V. (2009). The effects of high stakes high school achievement awards: Evidence from a randomized trial. American Eco-nomic Review, 99(4), 1384-1414.

5. Bandiera, O., Barankay, I., & Rasul, I. (2009). Social connections and incentives in the workplace: Evidence from personnel data. Econometrica, 77(4), 1047-1094.

2 – Natural Experiments, Differences-in-Differences

Methodological papers:

* MHE – Chapter 5

* BCD – Section IV

1. * Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of business & economic statistics, 13(2), 151-161.

2. Rosenzweig, M. R., & Wolpin, K. I. (2000). Natural “natural experiments” in economics. Journal of Economic Literature, 38(4), 827-874.

3. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates?. The Quarterly Journal of Economics, 119(1), 249-275.

Empirical papers:

1. Smith, G. C., & Pell, J. P. (2003). Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised con-trolled trials. BMJ, 327(7429), 1459-1461.

2. Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylva-nia. The American. Economic Review, 84(4).

3. Braakmann, N., Chevalier, A. and Wilson, T., 2024. Expected returns to crime and crime location. American Economic Journal: Applied Economics, 16(4), pp.144-160.

4. Bleakley, H. (2010). Malaria eradication in the Americas: A retro-spective analysis of childhood exposure. American Economic Journal: Applied Economics, 2(2), 1-45.

5. Duflo, E. (2001). Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment. American Economic Review, 91(4), 795-813.

6. Meghir, C., & Palme, M. (2005). Educational reform, ability, and family background. American Economic Review, 95(1), 414-424.

7. Card, D. (1990). The impact of the Mariel boatlift on the Miami labor market. ILR Review, 43(2), 245-257.

3 – Instrumental Variables and Regression Discontinuity Design

Methodological papers:

* MHE – Chapter 4 and 6

* BCD – Section VI and VII

1. * Angrist, J. D., & 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.

2. Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endoge-nous explanatory variable is weak. Journal of the American statistical association, 90(430), 443-450.

3. Staiger, D., & Stock, J. H. (1997). Instrumental Variables Regression with Weak Instruments. Econometrica: Journal of the Econometric Society, 557-586.

4. Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.

5. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal ef-fects using instrumental variables. Journal of the American Statistical Association, 91(434), 444-455.

6. * Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of economic literature, 48(2), 281-355.

7. Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69(1), 201-209.

8. Lee, D. S., & Card, D. (2008). Regression discontinuity inference with specification error. Journal of Econometrics, 142(2), 655-674.

9. Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of econometrics, 142(2), 615-635.

10. McCrary, J. (2008). Manipulation of the running variable in the regression discon-tinuity design: A density test. Journal of econometrics, 142(2), 698-714.

11. Gelman, A., & Imbens, G. (2018). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business & Economic Statistics, 1-10.

Empirical papers:

1. Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school at-tendance affect schooling and earnings?. The Quarterly Journal of Eco-nomics, 106(4), 979-1014.

2. Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lot-tery: evidence from social security administrative records. The Ameri-can Economic Review, 313-336.

3. Angrist, J., & Evans, W. (1998). Children and Their Parents’ Labor Supply: Evidence from Exogenous Variation in Family Size. The Amer-ican Economic Review, 88(3), 450-477

4. Anderson, M. L., & Matsa, D. A. (2011). Are restaurants really su-persizing America?. American Economic Journal: Applied Economics, 3(1), 152-88.

5. Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational psychology, 51(6), 309.

6. Lemieux, T., & Milligan, K. (2008). Incentive effects of social assistance: A re-gression discontinuity approach. Journal of Econometrics, 142(2), 807-828.

7. Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic achievement. The Quarterly Journal of Economics, 114(2), 533-575.

8. Lee, D. S. (2008). Randomized experiments from non-random selection in US House elections. Journal of Econometrics, 142(2), 675-697

9. Carpenter, C., & Dobkin, C. (2009). The effect of alcohol consump-tion on mortality: regression discontinuity evidence from the minimum drinking age. American Economic Journal: Applied Economics, 1(1), 164-82.