Applied Microeconometrics.
Fall 2008
Professor: Ruben Enikolopov
Teaching Assistant:
The goal of the course is to familiarize you with a range of techniques used in applied
microeconometrics. The emphasis in the course will be on issues that arise in working with data and
practical considerations in using various econometric techniques rather than their theoretical
underpinning. It is assumed that you already have a sufficient knowledge of the basic econometric
theory.
Grading
The grade for the course will be based on three computer based problem sets (60%) and final exam
(40%). The problem sets will involve analysis of the data using the methods discussed in class. The
exam will contain questions on a published paper in applied microeconometrics handed out in
advance.
Books
Wooldridge, J. “Econometric Analysis of Cross Section and Panel Data” (JW)
Cameron, C. and Trivedi, P. “Microeconometrics: Methods and Applications” (CT)
Syllabus
The reading list includes papers that will be discussed in class or provide good discussion of the
relevant topics. Reading the papers is not required, but is highly recommended.
1. Research design. Causality. Potential outcomes approach. CT Ch 2, 25; JW Ch 18.
a. Angrist and Krueger (1999) “Empirical Strategies in Labor Economics” in Handbook
of Labor Economics, vol. 3A.
b. Krueger, A. (1993) “How Computers Have Changed the Wage Structure: Evidence
from Micro Data,” Quarterly Journal of Economics, 108 (1), 33-60.
c. DiNardo, J., and Pischke, J.-S. (1997) “The Returns to Computer Use Revisited:
Have Pencils Changed the Wage Structure Too?” Quarterly Journal of Economics
112(1), 291-303.
2. Measurement error problems. CT Ch 16.
a. Griliches, Zvi. (1986) "Economic Data Issues," in Handbook of Econometrics,
Volume III.
b. Hausman, J. (2001) “Mismeasurement Variables in Econometric Analysis: Problems
from the Right and Problems from the Left,” Journal of Economic Perspectives,
15(4), 57-67.
3. Incidental parameter problem. CT Ch 23.2; JW Ch 15.8
a. Lancaster, T. (2000) “The Incidental Parameter Problem Since 1948," Journal of
Econometrics, 95, 391-413.
4. Panel data. Fixed effects vs. random effects vs. clustering. Difference-in-Difference
estimators. CT Ch 21, 22.
a. Chamberlain G. ( 1987) “Panel data” in Handbook of Econometrics.
b. Aschenfelter, O. and Krueger,A. (1994) “Estimates of economic return to schooling
from a new sample of twins,” American Economic Review, 84(5), 1157-1173.
c. Donohue, J., and S. Levitt (2001) ‘‘The Impact of Legalized Abortion on Crime,’’
Quarterly Journal of Economics, 116(2), 379–420.

Joyce, T. (2004) ‘‘Did Legalized Abortion Lower Crime,’’ 39(1) Journal of
Human Resources 1–28.

Donohue, J., and S. Levitt (2004) ‘‘Further Evidence that Legalized Abortion
Lowered Crime: A Reply to Joyce,’’ 39 Journal of Human Resources 29–49.

Foote, Christopher L., and Christopher F. Goetz. (2005) ‘‘Testing Economic
Hypotheses with State-Level Data: A Comment on Donohue and Levitt.’’

Donohue, John J., III and Steven D. Levitt (2006), “Measurement Error,
Legalized Abortion, and the Decline in Crime: A Response to Foote and
Goetz (2005),” NBER WP #11987.

Dills, A., and Miron, J. (2006) “A Comment on Donohue and Levitt’s (2006)
Reply to Foote and Goetz (2005)”
d. Bertrand, M., Duflo, E. and Mullainathan, S. (2004) “How Much Should We Trust
Difference in Differences Estimates?” Quarterly Journal of Economics 119, 249-275.
5. Instrumental Variables. Two main assumptions. Weak Instruments problem. CT Ch 4, JW
Ch 5.
a. Angrist, J. and Krueger, A. (1991) “Does compulsory schooling attendance affect
schooling and earnings?” Quarterly Journal of Economics 106, 979-1014.
b. Bound, J., Jaeger, D., and Baker, R. (1995) “Problems with instrumental variable
estimation when the correlation between the instruments and the endogenous
explanatory variable is weak,” Journal of the American Statistical Association 90
(430), 443-450.
c. Staiger, D., and J. Stock (1997): “Instrumental variables regression with weak
instruments,” Econometrica, 65, 557-86.
d.
Moreira, M. and L. Cruz (2005) “On the Validity of Econometric Techniques With
Weak Instruments: Inference on Returns to Education Using Compulsory School
Attendance Laws,” Journal of Human Resources, 40(2), 393-410.
6. Random coefficients. Implications for IV estimation and matching vs. regression. CT Ch 25,
JW Ch 18.
a. Angrist, J. (1998) “Estimating the labor market impact of voluntary military service
using social security data on military applicants,” Econometrica, 66(2), 249-288.
b. Imbens, G. and J. Angrist (1994) “Identification and Estimation of Local Average
Treatment Effects," Econometrica, 62, pp. 467-75.
c. Angrist, J.D., G.W. Imbens and D.B. Rubin (1996), “Identification of Causal Effects
Using Instrumental Variables,” (with discussion) Journal of the American Statistical
Association, 91, 444-472.
7. Refutability. Placebo regressions.
a.
Card, D. (1990) “The impact of the Mariel boatlift on the Miami labor market,”
Industrial and Labor Relations Review, 43, 245-257.
b.
Enikolopov, R., Petrova, M. and Zhuravskaya, E. (2008) “Media and Political
Persuasion in Young Democracies: Evidence from Russia”, working paper.
8. Discontinuity analysis. CT Ch 25.
a. DiNardo, J., and D.S. Lee, (2004) “Economic Impacts of New Unionization on
Private Sector Employers: 1984-2001,” Quarterly Journal of Economics 119, 13831441.
b. Lee. D, Moretti, E., and Butler M. (2004) “Do Voters Affect or Elect Policies?
Evidence from the U.S. House,” Quarterly Journal of Economics, 119(3), 807-859.
9. Quantile regressions.
a. Koenker, R., and K. Hallock (2001) “Quantile Regression,” Journal of Economic
Perspectives, 15, 143-156.
b. Angrist, J., Bettinger, E., and M. Kremer (2005) "Long-Term Educational
Consequences of Secondary School Vouchers: Evidence from Administrative
Records in Colombia," forthcoming in American Economic Review.
c.
10. Limited depended variables. CT Ch 14-16, JW Ch 15, 16.
a. Chay, K. and J. Powell (2001) “Semiparametric Censored Regression Models,"
Journal of Economic Perspectives, 15, 29-42.
b. Powell, J. (1984) “Least Absolute Deviations Estimation for the Censored Regression
Model," Journal of Econometrics, 25, 303-25.
c. Powell, J. (1986b) ‘Symmetrically Trimmed Least Squares Estimation for Tobit
Models," Econometrica,” 54, 1435-60.
d. Billett, Matthew T., and Hui Xue, 2006, The Takeover Deterrent Effect of Open
Market Share Repurchases, Journal of Finance, forthcoming.