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Methods: General textbooks in econometrics recommended for program impact evaluation

• Angrist, Joshua and Jorn-Steffen Pischke, (2008), “Mostly Harmless Econometrics: An Empiricists’s Companion”, Princeton University Press.   
• Cameron, A. Colin and Pravin K. Trivedi, (2005), “Microeconometrics: Methods and Applications”, Cambridge University Press.
• Lee, Myoung-jae, (2005), “Micro-Econometrics for Policy, Program, and Treatment Effects”, Oxford University Press.
• Shadish, William, Thomas Cook and Donald Campbell, (2002), “Experimental and quasi-experimental designs for generalized causal inference”, Houghton Mifflin.
• Wooldridge, Jeffrey, (2002), “Econometric Analysis of Cross Section and Panel Data”. Cambridge, MA: MIT Press.
• NATIONAL BUREAU OF ECONOMIC RESEARCH - SUMMER INSTITUTE 2007 - What's New in Econometrics?
• Gertler, P.J. Martinez, S. Premand, P. Rawlings, L.B. & Vermeersch, C.M.J., (2011), Impact Evaluation in Practice, The World Bank, Washington DC.

Methods: Background reading

• Angrist, Joshua and Alan Krueger, (1999), “Empirical Strategies in Labor Economics”, in Orley Ashenfelter und David Card, eds. Handbook of Labor Economics, Volume 3A, Amsterdam: North-Holland. 1277-1366.  
• Blundell, Richard and Monica Costa Dias, (2008), “Alternative approaches to evaluation in empirical microeconomics”, The Institute for Fiscal Studies Department of Economics, UCL, CeMMAP working paper CWP26/08.
• Blundell, Richard, Lorraine Dearden and Barbara Sianesi, (2005), “Evaluating the Impact of Education on Earnings in the UK: Models, Methods and Results from the NCDS,” IFS working paper. 
• Duflo, Esther, Rachel Glennerster and Michael Kremer, (2008),“Using Randomization in Development Economics Research: A Toolkit”, in T. Paul Schultz and John Strauss eds. Handbook of Development Economics, Volume 4, Chapter 61, North Holland.
• Hamermesh, Daniel, (1999), “The Art of Labormetrics,” NBER Working Paper No. 6927.
• Heckman, James, Robert LaLonde and Jeffrey Smith, (1999), “The Economics and Econometrics of Active Labor Market Programs,” In Orley Ashenfelter and David Card, eds., Handbook of Labor Economics, Volume 3A, Amsterdam: North-Holland. 1865-2097.  
• Imbens, Guido and Jeffrey Wooldridge, (2009), “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, 47, 5–86.  
• McCloskey, Deirdre and Stephen Ziliak, (1996), “The Standard Error of Regressions,” Journal of Economic Literature, 34,: 97-114.
• Parker, Susan W., Luis Rubalcava and Graciela Teruel, (2008), “Evaluating Conditional Schooling and Health Programs”, Handbook of Development Economics, edited by T. Paul Schultz and John Strauss, Volume 4, Chapter 62, North Holland.
• Ravallion, Martin, (1999), “The Mystery of the Vanishing Benefits: Ms. Speedy Analyst's Introduction to Evaluation,” World Bank Policy Research Working Paper No. 2153.
• Ravallion, Martin, (2008), “Evaluating Anti-Poverty Programs”, Handbook of Development Economics, edited by T. Paul Schultz and John Strauss, Volume 4, Chapter 59, North Holland
• Smith, Jeffrey, (2000), “A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies,” Swiss Journal for Economics and Statistics, 136, 1-22.
• Smith, Jeffrey, (2004), “Evaluating Local Economic Development Policies: Theory and Practice,” In Alistair Nolan and Ging Wong, eds., Paris: OECD, 287-332.  
• Todd, Petra, E. (2008), “Evaluating Social Programs with Endogenous Program Placement and Selection of the Treated”, Handbook of Development Economics, edited by T. Paul Schultz and John Strauss, Volume 4, Chapter 60, North Holland
• Heckman, James J. 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture." Journal of Political Economy 109(4): 673-748.
• Baker, Judy. 2000. Evaluating the Impact of Development Projects on Poverty: A Handbook for Practitioners. Directions in Development, World Bank, Washington, D.C.
• Banerjee, Abhijit V. and Duflo, Esther (2009). "The Experimental Approach to Development Economics". Annu. Rev. Econ. 1:151–78.
• Leeuw, Frans and Vaessen, Jos (2009). "Impact Evaluations and Development: NONIE Guidance on Impact Evaluation". NONIE, Washington D.C.

Methods: Social experiments

• Burtless, Gary, (1995), “The Case for Randomized Field Trials in Economic and Policy Research,” Journal of Economic Perspectives, 9, 63-84.   
• Duflo, Esther, Rachel Glennerster and Michael Kremer, (2008), “Using Randomization in Development Economics Research: A Toolkit”, Handbook of Development Economics, edited by T. Paul Schultz and John Strauss, Volume 4, Chapter 61, North Holland
• Heckman, James, (1992), “Randomization and Social Policy Evaluation,” In Charles Manski and Irwin Garfinkel, eds., Evaluating Welfare and Training Programs, Cambridge, MA: Harvard University Press, 201-230. (Also available as NBER Technical Working Paper 107)
• Heckman, James and Jeffrey Smith, (1995), “Assessing the Case for Social Experiments,” Journal of Economic Perspectives, 9, 85-100.  
• Heckman, James, Jeffrey Smith and Nancy Clements, (1997), “Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts,” Review of Economic Studies, 64, 487-535.  
• Heckman, James, Neil Hohmann, Jeffrey Smith and Michael Khoo, (2000), “Substitution and Drop Out Bias in Social Experiments: A Study of an Influential Social Experiment,” Quarterly Journal of Economics, 115, 651-694.  
• Holland, Paul, (1986), “Statistics and Causal Inference,” Journal of the American Statistical Association, 81, 945-970
• Imbens, Guido and Joshua Angrist, (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 63, 467-475.
• Bloom, Howard S. (2005), “Learning More from Social Experiments: Evolving Analytic Approaches”, Russell Sage Foundation Publications
• Orr, Larry L., (1998), “Social Experiments: Evaluating Public Programs With Experimental Methods”, Sage Publications, Inc

Methods: Non-experimental

General Matching
 

• Rosenbaum, Paul, (1984), “The Consequences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment,” The Journal of the Royal Statistical Society, Series A, 147: 656-666.   
• Rosenbaum, Paul and Donald Rubin, (1985), “The Bias Due to Incomplete Matching,” Biometrics, 41,103-116.    
• Rubin, Donald, (1979), “Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies,” Journal of the American Statistical Association, 74, 318-328.   
• Abadie, Alberto, and Guido Imbens, (2006), “Large Sample Properties of Matching Estimators for Average Treatment Effects,” Econometrica, 74, 235-267.
• Busso, Matias, John DiNardo and Justin McCrary, (2008), “Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects,” Unpublished manuscript, University of Michigan.  
• Caliendo, Marco and Sabine Kopeinig, (2008), “Some Practical Guidance for the Implementation of Propensity Score Matching,” Journal of Economic Surveys, 22, 31-72.  
• Hahn, Jinyong, (1998), “On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects,” Econometrica. 66, 315-331.   
• Heckman, James, Hidehiko Ichimura and Petra Todd, (1997), “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program,” The Review of Economic Studies, 64: 605-654. 
• Heckman, James, Hidehiko Ichimura, and Petra Todd, (1998), “Matching as an Econometric Evaluation Estimator,” Review of Economic Studies, 65, 261-294.    
• Hirano, Keisuke, Guido Imbens, and Geert Ridder, (2000), “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” NBER Technical Working Paper No. 251.  
• Lalonde, Robert, (1986), “Evaluating the Econometric Evaluations of Training Programs Using Experimental Data,” American Economic Review, 76, 604- 620.  
• Rosenbaum, Paul and Donald Rubin, (1983), “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score,” Journal of the American Statical Association, 79, 516-525.  
• Rosenbaum, Paul and Donald Rubin, (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, 70, 41-55.   
• Smith, Jeffrey and Petra Todd, (2001), “Reconciling Conflicting Evidence on the Performance of Propensity Score Matching Methods,” American Economic Review, 91, 112-118.   
• Imbens, Guido. 2004. “Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review.” Review of Economics and Statistics 86(1): 4-29.

Assumptions on Matching
 

• Rosenbaum, Paul, (1984), “From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment,” Journal of the American Statistical Association, 79, 41-48.  
• Rosenbaum, Paul, (2001), “Stability in the Absence of Treatment,” Journal of the American Statistical Association, 96, 210-219.

Multiple Treatments  
 

• Imbens, Guido, (2000), “The Role of the Propensity Score in Estimating Dose-Response Functions,” Biometrika, 87, 706-710.  
• Lechner, Michael, (2001), “Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption,” In Michael Lechner and Friedhelm Pfeiffer, eds., Econometric Evaluation of Active Labour Market Policies,  Heidelberg: Physica, 43-58.   
• Lechner, Michael, (2001), “Program Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labor Market Policies,” Review of Economics and Statistics, 84, 205-220.

Matching in a Dynamic Context
 

• Abbring, Japp and Gerard van den Berg, (2004), “Analyzing the Effect of Dynamically Assigned Treatments Using Duration Models, Binary Treatment Models and Panel Data Models,” Empirical Economics, 29, 5-20.  
• Fredriksson, Peter and Per Johansson, (2003), “Program Evaluations and Random Program Starts,” IFAU Working Paper. No. 2003:1. 
• Lechner, Michael and Ruth Miquel, (2001), “A Potential Outcome Approach to Dynamic Programme Evaluation – Part I: Identification,” Discussion paper 2001-16, University of St. Gallen.
• Robins, James, (1986), “A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect,” Mathematical Modelling, 7:1393-1512.
• Sianesi, Barbara, (2004), “An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s,” Review of Economics and Statistics, 86, 133-155.  

Matching – Common support
 

• Black, Dan and Jeffrey Smith, (2004), “How Robust is the Evidence on the Effects of College Quality? Evidence from Matching,” Journal of Econometrics, 121: 99-124.
• Dehejia, Rajeev, (2005), “Practical Propensity Score Matching: A Reply to Smith and Todd,” Journal of Econometrics, 125, 355-364.  
• Dehejia, Rajeev and Sadek Wahba, (2002), “Propensity Score Matching Methods for Non-experimental Causal Studies,” The Review of Economics and Statistics, 84, 151-161. 
• Dehejia, Rajeev and Sadek Wahba, (1999), “Causal Effects in Non-experimental Studies: Reevaluating the Evaluation of Training Programs,” Journal of the American Statistical Association, 94, 1053-1062.   
• Lechner, Michael, (2001), “A note on the common support problem in applied evaluation studies,” Discussion Paper 2001-01, Department of Economics, University of St. Gallen. 
• Smith, Jeffrey and Petra Todd, (2005), “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?” Journal of Econometrics, 125, 305-353.  
• III-2) Selection on unobservables

Longitudinal Methods
 

• Abadie, Alberto, (2005), “Semiparametric Difference-in-Differences Estimators,” Review of Economic Studies 72, 1-19. 
• Altonji, Joseph G, and Rosa L. Matzkin (2005), “Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors,” Econometrica 73, 1053-1102. 
• Athey, Susan and Guido W. Imbens (2006), “Identification and Inference in Nonlinear Difference-In-Differences Models,” Econometrica 74, 431-497.  
• Bertrand, Marianne, Esther Duflo and Sendhil Mullainathan, (2004), “How Much Should We Trust Differences in differences Estimates?” The Quarterly Journal of Economics, 119, 249-275. 
• Blundell, Richard, Monica Costa Dias, Costas Meghir and John Van Reenan. 2004, “Evaluating the Employment Impact of a Mandatory Job Search Assistance Program,” Journal of the European Economic Association, 2, 569-606.
• Bound, John, David Jaeger, and Regina Baker, (1995), “Problems with Instrumental Variables Estimation When The Correlation Between the Instruments and The Endogenous Explanatory Variables is Weak,” Journal of the American Statistical Association, 90, 443-50.  
• Cameron, Colin, Jonah Gelbach, and Douglas Miller, (2008), “Bootstrap-Based Improvements for Inference with Clustered Errors,” The Review of Economics and Statistics, 90, 414-427.
• Donald, Stephen, and Kevin Lang, (2007), “Inference with Differences in Differences and Other Panel Data,” The Review of Economics and Statistics, 89, 221-233.
• Eissa, Nada, (1996), “Labor Supply and the Economic Recovery Tax Act of 1981,” In Martin Feldstein and James Poterba, eds., Empirical Foundations of Household Taxation, Chicago: University of Chicago Press. 5-32.  
• Hansen, C.B. (2007a), “Asymptotic Properties of a Robust Variance Matrix Estimator for Panel Data when T is Large,” Journal of Econometrics 141, 597-620.  
• Hansen, Christian B. (2007b), “Generalized Least Squares Inference in Panel and Multilevel models with Serial Correlation and Fixed Effects,” Journal of Econometrics 140, 670-694. 
• Hausman, Jerry. A. and Guido Kuersteiner (2005), “Difference in Difference Meets Generalized Least Squares: Higher Order Properties of Hypotheses Tests,” Boston University Department of Economics Working Paper Number 2005-010.
• Heckman, James and Jeffrey Smith, (1999), “The Pre-Programme Dip and the Determinants of Participation in Social Programmes: Implications for Simple Programme Evaluation Strategies,” Economic Journal, 109, 313-348. 
• Heckman, James and Richard Robb, (1985), “Alternative Methods of Evaluating the Impact of Interventions,” In James Heckman and Burton Singer, eds., Longitudinal Analysis of Labour Market Data.  New York: Cambridge University Press. 156-245.
• Kiefer, Nicholas M., (1980), “Estimation of Fixed Effect Models for Time Series of Cross-Sections with Arbitrary Intertemporal Covariance,” Journal of Econometrics 14, 195-202. 
• McKinnish, Terra, (2000), “Model Sensitivity In Panel Data Analysis: Some Caveats About the Interpretation of Fixed Effects and Differences Estimators,” University of Colorado. 
• Meyer, Bruce, (1995), “Natural and Quasi-Experiments in Economics,” Journal of Business and Economic Statistics, 13, 151-161.  
• Moffitt, Robert, (1991), “Program Evaluation with Nonexperimental Data,” Evaluation Review, 15, 291-314. 
• Solon, Gary, (1984), “Estimating Autocorrelations in Fixed-Effects Models,” NBER Technical Working Paper Number 032.  
• Wooldridge, Jeffrey M. (2005), “Fixed Effects and Related Estimators for Correlated Random-Coefficient and Treatment Effect Panel Data Models,” Review of Economics and Statistics 87, 385-390.

 

Combining Longitudinal with Matching
 

• Eichler, Martin and Michael Lechner, (2002), “An Evaluation of Public Employment Programmes in the East German State of Sachsen-Anhalt,” Labour Economics, 9, 143- 186.  
• Heckman, James , Hidehiko Ichimura, Petra Todd and Jeffrey Smith, (1998), “Characterizing Selection Bias Using Experimental Data,” Econometrica, 66, 1017- 1098.   
• Rosenbaum, Paul, (2001), “Stability in the Absence of Treatment,” Journal of the American Statistical Association, 96: 210-219.

 

Instrumental Variables
 

• Abadie, Alberto, (2002), “Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models,” Journal of the American Statistical Association, 97, 284-292.
• Abadie, Abadie, (2003), “Semiparametric Instrumental Variable Estimation of Treatment Reponse Models,” Journal of Econometrics, 113, 231-263.
• Angrist, Joshua D., and Guido W. Imbens, (1995), ”Two–Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity,” Journal of the American Statistical Association, 90, 431-442.
• Angrist, Joshua D., Guido W. Imbens, and Donald Rubin, (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444-455. 
• Angrist, Joshua D, and Alan B. Krueger, (2001), “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments”, Journal of Economic Perspectives, 15, 69-85.
• Björklund, Anders and Robert Moffitt, (1987), “The Estimation of Wage Gains and Welfare Gains in Self-Selection Models,” Review of Economics and Statistics, 69, 42- 49.  
• Heckman, James, (1997), “Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations,” Journal of Human Resources, 32, 441-452.  
• Heckman, James, Justin Tobias and Edward Vytlacil, (2000), “Simple Estimators for Treatment Parameters in a Latent Variable Framework with an Application to Estimating the Returns to Schooling,” NBER Working Paper No. 7950.  
• Heckman, James and Edward Vytlacil, (2001), “Policy-Relevant Treatment Effects,” American Economic Review, 91, 107-111.  
• Imbens, Guido and Joshua Angrist (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 61, 467-476.
• Vytlacil, Edward, (2002), “Independence, Monotonicity, and Latent Index Models: An Equivalence Result,” Econometrica, 70, 331-342.   

Regression Discontinuity Design
 

• Buddelmeyer, Hielke, and Emmanuel Skoufias, (2004), “An Evaluation of the Performance of Regression Discontinuity Design in Progresa,” World Bank Policy Research Working Paper No. 3386.
• Cook, Thomas, (2008), “Waiting for Life to Arrive”: A History of the Regression-Discontinuity Design in Psychology, Statistics, and Economics, Special Issue on Regression Discontinuity Design, 142, pp. 636-654.
• Hahn, Jinyong, Petra Todd and Wilbert van der Klaauw, (2001), “Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design,” Econometrica,69, 201-209.  
• Imbens, Guido., and Karthik Kalyanaraman, (2008), “Optimal Bandwidth Selection in Re- gression Discontinuity Designs,” unpublished manuscript, Department of Economics, Harvard University.
• Imbens, Guido and Lemieux, Thomas, (2008), “The regression discontinuity design--Theory and applications,” Journal of Econometrics, Special Issue on Regression Discontinuity Design, 142, 611-614.
• Lee, David S. and David Card, (2008), “Regression Discontinuity Inference with Specification Error, forthcoming”, Special Issue on Regression Discontinuity Design, 142, 655-674.
• Lemieux, Thomas and Kevin Milligan, (2008), “Incentive Effects of Social Assistance: A Regression Discontinuity Approach”, Journal of Econometrics, 142, 807-828.
• McCrary, Justin, (2008), “Testing for Manipulation of the Running Variable in the Regression Discontinuity Design”, Special Issue on Regression Discontinuity Design, 142, 698-714.

Control Function
 

• Blundell, Richard and Monica Costa Dias, (2008), “Alternative approaches to evaluation in empirical microeconomics”, The Institute for Fiscal Studies Department of Economics, UCL, CeMMAP working paper CWP26/08.
• Heckman, James, (1976), “The common structure of statistical models of truncation, sample selection and limited dependent variables, and a simple estimator for such methods,” Annals of Economic and Social Measurement, 5, 475-492.
• Vytlacil, Edward, (2002), “Independence, Monotonicity, and Latent Index Models: An Equivalence Result,” Econometrica, 70, 331-341.

Methods: Others

IV-1) Ex ante evaluations

• Heckman, James J. (2000), "Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective," Quarterly Journal of Economics, 115, 45-97.
• Hurwicz, Leonid (1962), "On the Structural Form of Interdependent Systems," Logic, Methodology and Philosophy of Science, edited by Ernest Nagel, Pattrick Suppes and Alfred Tarski. Stanford, Calif.: Stanford University Press.
• Ichimura, Hidehiko and Christopher Taber (2002), "Semiparametric Reduced-Form Estimation of Tuition Subsidies," American Economic Review, 92, 286-92.  
• Lise, Jeremy, Seitz, Shannon, and Jeffrey Smith, (2003), "Equilibrium Policy Experiments and the Evaluation of Social Programs," working paper.
• Lumsdaine, Robin, James Stock and David Wise, (1992), "Pension Plan Provisions and Retirement: Men and Women, Medicare, and Models," D. A. Wise (ed.) Studies in the Economics of Aging, Chicago: University of Chicago Press.
• Marschak, Jacob (1953), "Economic Measurements for Policy and Prediction," William Hood and Tjalling Koopmans, eds., Studies in Econometric Method, New York: John Wiley, 1-26.
• McFadden, Daniel, Antti Talvitie and Associates, (1977), "Validation of Disaggregate Travel Demand Models: Some Tests," Urban Demand Forecasting Project, Final Report, Volume V, Institute of Transportation Studies, University of California, Berkeley.
• Todd, Petra and Kenneth Wolpin, (2005), “Ex Ante Evaluation of Social Programs,” Working paper. 
• Wise, David, (1985), "A Behavioral Model Verses Experimentation: The Effects of Housing Subsidies on Rent," Methods of Operations Research, 50, Verlag Anton Hain.  

IV-2) Quantile treatment effects

• Abadie, Aberto, Joshua D. Angrist, and Guido W. Imbens, (2002), “Instrumental Variables Estimation of Quantile Treatment Effects,” Econometrica, 70, 91-117.
• Bitler, Marianne, Jonah Gelbach and Hilary Hoynes, (2006), “What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments,” American Economic Review, 96, 988-1012.
• Frölich, Markus and Blaise Melly (2008) “Quantile treatment effects in the regression discontinuity design”. IZA Discussion paper 3638.

IV-3) Bounds    

• Horowitz, Joel and Charles Manski, (1995), "Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations," Working Paper No. 9525, Wisconsin Madison - Social Systems.
• Lee, David, (2002), “Trimming For Bounds On Treatment Effects With Missing Outcomes,” Technical Working Paper No. 277.
• Manski, Charles, (1989), “The Anatomy of the Selection Problem,” Journal of Human Ressources, 24, 343-360. 
• Manski, Charles, (1990), “Nonparametric Bounds on Treatment Effects,” American Economic Review, 80, 319-323. 
• Manski, Charles, Gary Sandefur, Sara McLanahan, and D. Powers, (1992), “Alternative Estimates of the Effect of Family Structure During Adolescence on High School Graduation,” Journal of the American Statistical Association, 87, 25-37.

IV-3) Partial equilibrium and structural models  

• Todd, Petra and Kenneth Wolpin, (2006), “Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility: Assessing the Impact of a School Subsidy Program in Mexico,” American Economic Review, 96, 1384-1417.

 

IV-4): General equilibrium evaluation

• Attanasio, Orazio, Costas Meghir and Ana Santiago, (2004), “Education choices in Mexico: using a structural model and a randomized experiment to evaluate PROGRESA,” IFS Working Papers, EWP04/04.
• Calmfors, Lars, (1994), “Active Labour Market Policy and Unemployment--A Framework for the Analysis of Crucial Design Features,” OECD Economic Studies, 22, 7-47.  
• Cohen-Goldner S. and Z. Eckstein, (2009), “Estimating the Return to Training and Occupational Experience: The Case of Female Immigrants”, forthcoming Journal of Econometrics, () 
• Heckman, James, Lance Lochner and Christopher Taber, (1999), “General Equilibrium Cost Benefit Analysis of Education and Tax Policies,” NBER Working Paper No. 6881.  
• Lise, Jeremy, Shannon Seitz and Jeffrey Smith, (2004), “Equilibrium Policy Experiments and the Evaluation of Social Programs.” NBER Working Paper No. 10283.
• Meghir, Costas, (2006), “Dynamic models for policy evaluation”, IFS Working Paper WP0608.
• Heckman, James J., Lance Lochner, and Christopher Taber. 1998. "General-Equilibrium Treatment Effects: A Study of Tuition Policy." The American Economic Review 88(2): 381-86.

 

IV-5) Miscelaneous

• Banerjee, Abhijit and Esther Duflo, (2008), “The Experimental Approach to Development Economics,” Working Paper NBER W14467.
• Browning, Edgar, (1971), “Incentive and Disincentive Experimentation for Income Maintenance Policy Purposes,” American Economic Review, 61, 709- 712.
• Deaton, Angus, (2009), “Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development,” Working Paper NBER W14690.
• Heckman, James, (1995), “Randomization as an Instrumental Variable,” NBER Technical Working Paper No. 184.
• Heckman, James and Sergio Urzua, (2009), “Comparing IV with Structural Models: What Simple IV Can and Cannot Identify?,”  NBER Working Paper No. 14706.
• Imbens, Guido, (2009), “Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)”, (2009)Harvard University, unpublished manuscript
• Ravallion, Martin, (2009), “Should the Randomistas Rule?,” The Economists' Voice, 6  
• Rodrik, Dani, (2008), “The New Development Economics:  We Shall Experiment, But How Shall We Learn?,” Unpublished manuscript, John F. Kennedy School of Government Harvard University.

Applications: Social experiments

• Bruhn, Miriam and David McKenzie. 2008. "In pursuit of balance: randomization in practice in development field experiments," Policy Research Working Paper Series 4752, The World Bank
• Alcott, Hunt, Dean Karlan,  Markus Mobius, Tanya Rosenblat, and Adam Szeidl, (2007), “Community Size and Network Structure”, American Economic Review Papers and Proceedings, 97, 80-85
• Angelucci, M and G. De Giorgi, (2009), “Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption?”, American Economic Review, 99, 486–508.
• Angrist, J. Bettinger, E. Bloom, E. King, E. and Kremer, M. ,(2002), Vouchers for Private Schooling in Colombia: Evidence from a Randomized Natural Experiment. American Economic Review, 92, 1535-1558
• Ashraf, Nava, Dean Karlan and Xavier Gine, (2008), “Finding Missing Markets: Evidence from an Export Crop Adoption and Marketing Intervention in Kenya”, American Journal of Agricultural Economics
• Banerjee, Abhijit , Shawn Cole, Esther Duflo and Leigh Linden, (2007), “Remedying Education: Evidence from Two Randomized Experiments in India,” (with), Quarterly Journal of Economics, 122, 1235-1264, 
• Banerjee, Abhijit, Rukmini Banerji, Esther Duflo, Rachel Glennerster, Stuti Khemani, (2008), “Pitfalls of Participatory Programs: Evidence from a randomized evaluation in education in India”), forthcoming, American Economic Journal: Economic Policy, (also see CEPR working paper No. DP6781). 
• Bertrand, M. and S. Mullainathan, (2004), “Are Emily and Greg More Employable than Latoya and Tyrone? Evidence on Racial Discrimination in the Labor Market from a Large Randomized Experiment,” American Economic Review, 94, 991-1013
• Bertrand, Marianne, Dean Karlan, Sendhil Mullainathan, Eldar Shafir and Jonathan Zinman, (2010), “What's Advertising Content Worth? Evidence from a Consumer Credit Marketing Field Experiment”, forthcoming Quarterly Journal of Economics
• Chattopadhyay, Raghabendra and Esther Duflo, (2004) “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India,” Econometrica 72, 1409-1443, 2004 
• Duflo, Esther, Michael Kremer and Jonathan Robinson, (2008) “How High are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya”. American Economics Review, Vol. 98, 482-88
• Duflo, Esther, William Gale, Jeffrey Liebman, Peter Orszag, and Emmanuel Saez, (2006), “Saving Incentives for Low- and Middle-Income Families: Evidence from a Field Experiment with H&R Block”, Quarterly Journal of Economics, 121, 1311-1346
• Gertler, Paul. (2004), Do conditional cash transfers improve child health? Evidence from PROGRESA’s control randomized experiment. American Economic Review, 94, 336-341.
• Jamison, Julian and Dean Karlan, (2008), “When Curiosity Kills the Profits: An Experimental Examination”, Games and Economic Behavior.
• Karlan, Dean, (2005), “Using Experimental Economics to Measure Social Capital and Predict Financial Decisions”, American Economic Review, 95(5), 1688-1699.
• Karlan, Dean and John List (2007), “Does Price Matter in Charitable Giving? Evidence from a Large-Scale Natural Field Experiment”, American Economic Review, 97(5), 1774-1793
• Karlan, Dean and Martín Valdivia, (2009), “Teaching Entrepreneurship: Impact Of Business Training On Microfinance Clients and Institutions”, Review of Economics and Statistics
• Karlan, Dean and Jonathan Zinman, (2010) “Observing Unobservables: Identifying Information Asymmetries with a Consumer Credit Field Experiment”, forthcoming, Econometrica
• Karlan, Dean, Sharon Barnhardt and Stuti Khemani, (2009). “Participation in a School Incentive Program in India”, Journal of Development Studies, 45, 369-390
• Kremer, Michael y Eduardo Miguel, (2004), “Worms: Education and Health Externalities in Kenya”, Econometrica, 72, 159-217
• Parker, Susan W. and Skoufias, Emmanuel, (2000), “The impact of PROGRESA on work, leisure, and time allocation”, mimeo, International Food Policy Research Institute, Economia, The Journal of LACEA.
• Parker, Susan W., Luis Rubalcava and Graciela Teruel, (2008), “Evaluating Conditional Schooling and Health Programs”, Handbook of Development Economics, edited by T. Paul Schultz and John Strauss, Volume 4, Chapter 62, North Holland.
• Rosenzweig, Mark R. and Kenneth I. Wolpin, (2000), “Natural “natural” experiments in Economics” Journal of Economic Literature, 38, 827-874
• S. Khandker, G. Koolwal and H. Samad, (2009), "Handbook on Impact Evaluation: Quantitative Methods and Practices", World Bank, October 2009

Applications: Non-experimental Methods

II-1) Selection on observables

II-1)-a) Matching methods

• Angrist, Joshua, D, (1998), “Estimating the Labor Market Impact on Voluntary Military Service Using Social Security Data on Military Applicants”, Econometrica, (66) , 248-288.
• Bassi, Laurie, (1984), “Estimating the Effects of Training Programs with Nonrandom Selection,” Review of Economic Studies, 66, 36–43
• Card, David E. and Daniel Sullivan, (1988), “Measuring the Effect of Subsidized Training on Movements in and out of Employment”, Econometrica, 56, 497-530
• Cave, George, and Hans Bos, (1995), “The Value of a GED in a Choice-Based Experimental Sample,” (New York: Manpower Demonstration Research Corporation, 1995).
• Czajka, John, Sharon M. Hirabayashi, Roderick J. A. Little, and Donald B.Rubin, (1992), “Projecting from Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics,” Journal of Business and Economic Statistics 10, 117–131.
• Dehejia, Rajeev, and Sadek Wahba, (1999), “Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs,” Journal of the American Statistical Association 94, 1053–1062.
• Diaz, Juan Jose and Sudhanshu Handa, (2006), “An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator: Evidence from Mexico’s PROGRESA”, The Journal of Human Resources, 61, 320-345
• Fraker, Thomas, and Rebecca Maynard, (1987), “Evaluating Comparison Group Designs with Employment-Related Programs,” Journal of Human Resources 22, 194–227.
• Friedlander, Daniel, David Greenberg, and Philip Robins, (1997) “Evaluating Government Training Programs for the Economically Disadvantaged,” Journal of Economic Literature 35, 1809–1855.
• Heckman, James, Hidehiko Ichimura, and Petra Todd, (1997), “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme,” Review of Economic Studies 64, 605–654.
• Jalan, Jyotsna and Martin Ravallion, (2003). "Estimating the Benefit Incidence of an Antipoverty Program by Propensity-Score Matching," Journal of Business & Economic Statistics, American Statistical Association, 21, 19-30
• Levine, David and Gary Painter, (2003), “The Schooling Cost of Teenage Out of Wedlock Childbearing: Analysis with a Within-School Propensity Score Matching Estimator”. Review of Economics and Statistics, 84, 884-900

 
II-2) Selection on unobservables

1. Longitudinal Methods

• Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, (2007), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,” NBER Technical Working Paper Number 335.
• Abadie, Alberto, and Javier Gardeazabal, (2003), “Economic Costs of Conflict: A Case Study of the Basque Country,” American Economic Review 93, 113–132. 
• Ashenfelter, Orley and David Card, (1985), “Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs,” Review of Economics and Statistics 67, 648-660. 
• Dehejia, Rajeev and Sadek. Wahba, (1999), “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs,” Journal of the American Statistical Association 94, 1053-1062. 
• Donohue, John, III and Steven, Levitt, (2001) "The Impact of Legalized Abortion on Crime." Quarterly Journal of Economics, 116, 379-420
• Duflo, Esther, (2001), Schooling and labor market consequences of school construction in Indonesia: evidence from an unusual policy experiment, American Economic Review 91, 795-813.
• Galiani, Sebastian., Gertler, Paul y Schargrodsky, Ernesto, (2005). Water for Life: The Impact of the Privatization of Water Services on Child Mortality. Journal of Political Economy, 113, 83-120
• Gruber, Johnatan H. (1994), “The Incidence of Mandated Maternity Benefits,” American Economic Review 84, 622-641.
• Jensen, Robert and Emily Oster, (2008), “The Power of TV: Cable Television and Women's Status in India”, forthcoming Quarterly Journal of Economics
• Meyer, Bruce, W.Kip Viscusi, and David L. Durbin (1995), “Workers’ Compensation and Injury Duration: Evidence from a Natural Experiment,” American Economic Review 85, 322-340. 
• Wolfers, Justin, (2006): “Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results,” American Economic Review, 96(5): 1802-1820.

      

2. Instrumental Variables

• Angrist, Joshua, (1990), “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records,” American Economic Review, 80, 313-335.
• Angrist, Joshua and Alan Krueger, (1992), “The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples,” Journal of the American Statistical Association 87, June.
• Angrist, Joshua and Adriana Kugler, (2003), “Protective or Counter-Productive? Labor Market Institutions and the Effect of Immigration on EU Natives,” Economic Journal, Royal Economic Society, 113, 302-331
• Bjorklund, Anders and Robert Moffitt, (1987), “The Estimation of Wage Gains and Welfare Gains in Self–Selection Models”, Review of Economics and Statistics, 69, 42–49.
• Butcher, Kristin and Ann Case. (1994). “The Effects of Sibling Sex Composition on Women’s Education and Earnings,” Quarterly Journal of Economics, 109, 531-564. 
• Oreopoulos, Philip (2006). “Estimating average and local average treatment effect of education when compulsory schooling laws really matter”. American Economic Review, 153-175

 

3. Regression Discontinuity Design

• Angrist, Joshua .D. and Victor Lavy (1999), “Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement”, Quarterly Journal of Economics, 114, 533-575
• Black, Sandra, (1999), “Do Better Schools Matter? Parental Valuation of Elementary Education”, Quarterly Journal of Economics 114, 577-599.
• Card, David, Alexandre Mas, and Jesse Rothstein, (2006), “Tipping and the Dynamics of Segre- gation in Neighborhoods and Schools”, Unpublished Manuscript, Department of Economics, Princeton University.
• Chay, Kenneth, and Michael Greenstone, (2005), “Does Air Quality Matter; Evidence from the Housing Market”, Journal of Political Economy 113, 376-424.
• Di Nardo, John., and David S. Lee,(2004), “Economic Impacts of New Unionization on Private Sector Employers: 1984-2001”, Quarterly Journal of Economics 119, 1383-1441.
• Gerber, Alan, Daniel Kessler, and Mark Meredith, (2008), “The Persuasive Effects of Direct Mail: A Regression-Discontinuity Approach”, NBER Working Paper 14206.
• Lee, David S., (2001), “The Electoral Advantage of Incumbency and the Voter’s Valuation of Political Experience: A Regression Discontinuity Analysis of Close Elections,” unpublished manuscript, Department of Economics, University of California.
• Lee, David S., (2008), “Randomized Experiments from Non-random Selection in U.S. House Elections”, Journal of Econometrics, 142, 675-697.
• Lee, David S., Moretti, Enrico, and Mathew J. Butler, (2004), “Do Voters Affect or Elect Policies?Evidence from the U.S. House”, Quarterly Journal of Economics 119, 807-859.
• Lemieux, Thomas and Kevin Milligan, (2007), “Incentive Effects of Social Assistance: A Regression Discontinuity Approach”, Special Issue on Regression Discontinuity Design, 142.
• Ludwig, Jens, and Douglas Miller,(2005), “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design”, NBER working paper 11702.
• McEwan, Patrick, and Joseph Shapiro, (2007), “The Benefits of Delayed Primary School Enrolment: Discontinuity Estimates using exact Birth Dates”, Unpublished manuscript.
• Van Der Klaauw,Wilbert, (2002), “Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression–discontinuity Approach”, International Economic Review 43, 1249- 1287.