The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Mostly Harmless Econometrics: An Empiricist's Companion
Abstract
The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Variance-based approaches build on a counterfactual understanding of causation - often developed as the potential outcomes framework (Woodward, 2003; Rubin, 2005; Angrist and Pischke, 2009).”
From Multi-Method Research in the Social Sciences: A Review of Recent Frameworks and a Way Forward (2020) · cites this paper as Angrist and Pischke, 2009
In the citing paper
“Put another way, with a 'within-school' analysis, the failure to account for stable characteristics mathematically cannot bias the estimate (Angrist & Pischke, 2009).”
From Fostering Belonging, Transforming Schools: The Impact of Restorative Practices (2023) · cites this paper as Angrist & Pischke, 2009
In the citing paper
“The benefit of a within-school approach is that it accounts for all time-invariant endogeneity.”
From Fostering Belonging, Transforming Schools: The Impact of Restorative Practices (2023) · cites this paper as Angrist & Pischke, 2009
In the citing paper
“The coefficients for the pre-treatment periods are statistically insignificant, indicating that the parallel trends assumption holds (Angrist and Pischke 2009).”
From AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burden (2025) · cites this paper as Angrist and Pischke 2009
In the citing paper
“The inclusion of PSA-specific monthly time trends enables an assessment of whether the findings hold after taking into account that each PSA is implementing cameras at different time points and may have diverging trends (Angrist & Pischke, 2009).”
From Police reform in public housing contexts: Body‐worn cameras, surveillance, and harm reduction in New York City Housing Authority Developments (2024) · cites this paper as Angrist & Pischke, 2009
In the citing paper
“We recognize, of course, that quasi-experimental methods such as instrumental variables and regression discontinuity designs allow for causal conclusions to be drawn from observational snapshots when investigators make strong, explicit assumptions and articulate a credible identification strategy 27-30 .”
From Quantifying the prevalence and impact of overreaching causal claims in social science (2026) · cites this paper as [28]
In the citing paper
“Strictly speaking, observational data in the form of statistical covariation of causes and outcomes across many cases does not enable causal inferences to be made unless we assume that the data have the character of a natural experiment that enables us to claim that our population is split (either temporally or spatially) into a treatment and control group in which everything else is constant (Ang”
From Multi-Method Research in the Social Sciences: A Review of Recent Frameworks and a Way Forward (2020) · cites this paper as Angrist and Pischke, 2009
In the citing paper
“we tested a differences-in-differences approach26”
From Firearm and Nonfirearm Violence After Operation Peacemaker Fellowship in Richmond, California, 1996–2016 (2019) · cites this paper as [26]
Citations
Cites
Arellano, M., & Bond, S. (1991) · The Review of Economic Studies · doi:10.2307/2297968 · Cited by 33,272
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
(1963) · Medical Entomology and Zoology · Cited by 15,085
Initial conditions and moment restrictions in dynamic panel data models
Blundell, R., & Bond, S. (1995) · Working Paper Series · doi:10.1920/wp.ifs.1995.9517 · Cited by 14,755
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
(2002) · Cited by 13,211
Biases in dynamic models with fixed effects
Nickell, S. (1981) · Econometrica · doi:10.2307/1911408 · Cited by 8,735
The Colonial Origins of Comparative Development: An Empirical Investigation: Reply
Acemoğlu, D., Johnson, S., & Robinson, J. A. (2012) · American Economic Review · doi:10.1257/aer.102.6.3077 · Cited by 6,050
Cited by
Sorted by citations
Difference-in-differences with variation in treatment timing
Goodman-Bacon, A. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2021.03.014 · Cited by 8,046
A Practitioner’s Guide to Cluster-Robust Inference
Colin Cameron, A., & Miller, D. L. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.317 · Cited by 5,037
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
How Much Should We Trust Staggered Difference-in-Differences Estimates?
Baker, A. C., Larcker, D. F., & Wang, C. C. Y. (2022) · Journal of Financial Economics · doi:10.1016/j.jfineco.2022.01.004 · Cited by 3,149
Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516047113 · Cited by 2,486
Haans, R. F. J., Pieters, C., & He, Z. (2015) · Strategic Management Journal · doi:10.1002/smj.2399 · Cited by 2,534
Financial Literacy, Financial Education, and Downstream Financial Behaviors
Fernandes, D., Lynch, J. G., & Netemeyer, R. G. (2014) · Management Science · doi:10.1287/mnsc.2013.1849 · Cited by 2,152
What's trending in difference-in-differences? A synthesis of the recent econometrics literature
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.03.008 · Cited by 2,027
Who Creates Jobs? Small versus Large versus Young
Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00288 · Cited by 1,946
Endogeneity in Empirical Corporate Finance1
Roberts, M. R., & Whited, T. M. (2013) · Handbook of the Economics of Finance · doi:10.1016/b978-0-44-453594-8.00007-0 · Cited by 2,459
LEUZ, C., & WYSOCKI, P. D. (2016) · Journal of Accounting Research · doi:10.1111/1475-679x.12115 · Cited by 1,760
Propensity Score Matching in Accounting Research
Shipman, J. E., Swanquist, Q. T., & Whited, R. L. (2016) · The Accounting Review · doi:10.2308/accr-51449 · Cited by 1,657
The Oregon Health Insurance Experiment: Evidence from the First Year*
Finkelstein, A. N., Taubman, S. L., Wright, B. J., Mira Bernstein, A., B, J. G. A., Newhouse, J. P., ALLEN, H. L., D, K. B. A., & Oregon Health Study Group (2012) · The Quarterly Journal of Economics · doi:10.1093/qje/qjs020 · Cited by 1,661
Corporate Sustainability: First Evidence on Materiality
Khan, M., Serafeim, G., & Yoon, A. (2016) · The Accounting Review · doi:10.2308/accr-51383 · Cited by 1,959
Bias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624
Big Data: New Tricks for Econometrics
Varian, H. R. (2014) · The Journal of Economic Perspectives · doi:10.1257/jep.28.2.3 · Cited by 1,588
Bustos, P. (2011) · American Economic Review · doi:10.1257/aer.101.1.304 · Cited by 1,552
The role of information and financial reporting in corporate governance and debt contracting
Armstrong, C. S., Guay, W. R., & Weber, J. P. (2010) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2010.10.001 · Cited by 1,494
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
Abadie, A. (2021) · Journal of Economic Literature · doi:10.1257/jel.20191450 · Cited by 1,486
Burgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153
Cai, X.-Q., Lu, Y., Wu, M., & Yu, L. (2016) · Journal of Development Economics · doi:10.1016/j.jdeveco.2016.08.003 · Cited by 1,205
Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018) · Journal of International Marketing · doi:10.1509/jim.17.0151 · Cited by 1,302
Elasticities and the Inverse Hyperbolic Sine Transformation
Bellemare, M. F., & Wichman, C. J. (2019) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.12325 · Cited by 1,178
Common Errors: How to (and Not to) Control for Unobserved Heterogeneity
Gormley, T. A., & Matsa, D. A. (2012) · Review of Financial Studies · doi:10.1093/rfs/hht047 · Cited by 1,157
Corporate governance, incentives, and tax avoidance
Armstrong, C. S., Blouin, J. L., Jagolinzer, A. D., & Larcker, D. F. (2015) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2015.02.003 · Cited by 1,073
Doubly robust difference-in-differences estimators
Sant’Anna, P. H. C., & Zhao, J. (2020) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.06.003 · Cited by 1,047
The Changing of the Boards: The Impact on Firm Valuation of Mandated Female Board RepresentationPreprint
Ahern, K. R., & Dittmar, A. K. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1364470 · Cited by 1,011
From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior
Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. K. (2015) · Journal of Marketing · doi:10.1509/jm.14.0249 · Cited by 1,156
ESG disclosure and financial performance: Moderating role of ESG investors
Chen, Z., & Xie, G. (2022) · International Review of Financial Analysis · doi:10.1016/j.irfa.2022.102291 · Cited by 1,094
Pre-Colonial Ethnic Institutions and Contemporary African Development
(2013) · Econometrica · doi:10.3982/ecta9613 · Cited by 953
Design-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917
The Estimation of Causal Effects by Difference-in-Difference Methods
Lechner, M. (2011) · Foundations and Trends® in Econometrics · doi:10.1561/0800000014 · Cited by 935
Craig, P., Cooper, C. C., Gunnell, D., Haw, S. J., Lawson, K. D., Macintyre, S., Ogilvie, D., Petticrew, M. P., Reeves, B. C., Sutton, M., et al. (2012) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2011-200375 · Cited by 966
Kline, P., & Moretti, E. (2013) · The Quarterly Journal of Economics · doi:10.1093/qje/qjt034 · Cited by 906
Politicized Places: Explaining Where and When Immigrants Provoke Local Opposition
HOPKINS, D. J. (2010) · American Political Science Review · doi:10.1017/s0003055409990360 · Cited by 1,223
Miller (1983) Revisited: A Reflection on EO Research and Some Suggestions for the Future
Miller, D. (2011) · Entrepreneurship Theory and Practice · doi:10.1111/j.1540-6520.2011.00457.x · Cited by 966
Online Display Advertising: Targeting and Obtrusiveness
Avi Goldfarb, B., & Tucker, C. E. (2011) · Marketing Science · doi:10.1287/mksc.1100.0583 · Cited by 801
Lagged Explanatory Variables and the Estimation of Causal Effect
Bellemare, M. F., Masaki, T., & Pepinsky, T. B. (2017) · The Journal of Politics · doi:10.1086/690946 · Cited by 880
Cocreation of Value in a Platform Ecosystem: The Case of Enterprise Software1
Ceccagnoli, M., Forman, C., Huang, P., & Wu, D. J. (2012) · MIS Quarterly · doi:10.2307/41410417 · Cited by 794
Platform competition: Strategic trade‐offs in platform markets
Cennamo, C., & Santalo, J. (2013) · Strategic Management Journal · doi:10.1002/smj.2066 · Cited by 780
Should I Use Fixed or Random Effects?
Clark, T. S., & Linzer, D. A. (2014) · Political Science Research and Methods · doi:10.1017/psrm.2014.32 · Cited by 783
Crowdfunding: Geography, Social Networks, and the Timing of Investment Decisions
Agrawal, A., Catalini, C., & Goldfarb, A. (2015) · Journal of Economics & Management Strategy · doi:10.1111/jems.12093 · Cited by 1,019
Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults
Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Sanbonmatsu, L. (2012) · Science · doi:10.1126/science.1224648 · Cited by 747
Difference-in-Differences Estimators of Intertemporal Treatment Effects
de Chaisemartin, C., & D’Haultfœuille, X. (2026) · Review of Economics and Statistics · doi:10.1162/rest_a_01414 · Cited by 740
Bor, J., Venkataramani, A. S., Williams, D. R., & Tsai, A. C. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31130-9 · Cited by 746
Hodler, R., & Raschky, P. A. (2014) · The Quarterly Journal of Economics · doi:10.1093/qje/qju004 · Cited by 744
Semadeni, M., Withers, M. C., & Certo, S. T. (2013) · Strategic Management Journal · doi:10.1002/smj.2136 · Cited by 743
Local Projections and VARs Estimate the Same Impulse Responses
Plagborg‐Møller, M., & Wolf, C. K. (2021) · Econometrica · doi:10.3982/ecta17813 · Cited by 738
A Crash Course in Good and Bad Controls
Cinelli, C., Forney, A., & Pearl, J. (2020) · Sociological Methods & Research · doi:10.1177/00491241221099552 · Cited by 789
Weiland, C., & Yoshikawa, H. (2013) · Child Development · doi:10.1111/cdev.12099 · Cited by 701
Privacy Regulation and Online Advertising
Avi Goldfarb, B., & Tucker, C. E. (2010) · Management Science · doi:10.1287/mnsc.1100.1246 · Cited by 725
Revisiting event study designs: robust and efficient estimation
(2022) · arXiv (preprint) · doi:10.47004/wp.cem.2022.1122 · Cited by 607
Instrumental variable methods for causal inference
Baiocchi, M., Cheng, J., & Small, D. S. (2014) · Statistics in Medicine · doi:10.1002/sim.6128 · Cited by 711
Eight Myths About Causality and Structural Equation Models
Bollen, K. A., & Pearl, J. (2013) · Handbooks of sociology and social research · doi:10.1007/978-94-007-6094-3_15 · Cited by 680
How Much Should We Trust Staggered Difference-In-Differences Estimates?Preprint
Baker, A., Larcker, D. F., & Wang, C. C. Y. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3794018 · Cited by 670
Retrospective Voting Reconsidered
Healy, A., & Malhotra, N. (2013) · Annual Review of Political Science · doi:10.1146/annurev-polisci-032211-212920 · Cited by 667
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
Lin, W. T. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas583 · Cited by 686
MOSTLY POINTLESS SPATIAL ECONOMETRICS?*
Gibbons, S., & Overman, H. G. (2012) · Journal of Regional Science · doi:10.1111/j.1467-9787.2012.00760.x · Cited by 632
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Imai, K., & Kim, I. S. (2021) · Political Analysis · doi:10.1017/pan.2020.33 · Cited by 623
“Our result is also related to that of Arkhangelsky et al. (2018) who shows that the weighted two-way fixed effects estimator can improve the synthetic control method of Abadie et al.”
MethodResultRising food prices, food price volatility, and social unrest
Bellemare, M. F. (2015) · American Journal of Agricultural Economics · doi:10.1093/ajae/aau038 · Cited by 636
The Value of Financial Statement Verification in Debt Financing: Evidence from Private U.S. Firms
Minnis, M. (2011) · Journal of Accounting Research · doi:10.1111/j.1475-679x.2011.00411.x · Cited by 621
Valid post-selection inference
Berk, R. A., Brown, L., Buja, A., Zhang, K., & Zhao, L. H. (2013) · The Annals of Statistics · doi:10.1214/12-aos1077 · Cited by 624
Exposure to untrustworthy websites in the 2016 US election
Guess, A. M., Nyhan, B., & Reifler, J. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0833-x · Cited by 587
Airports, Air Pollution, and Contemporaneous Health
Schlenker, W., & Walker, W. R. (2015) · The Review of Economic Studies · doi:10.1093/restud/rdv043 · Cited by 610
US epidemiology of cannabis use and associated problems
Hasin, D. S. (2018) · Neuropsychopharmacology · doi:10.1038/npp.2017.198 · Cited by 578
The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data
Hummels, D., Jørgensen, R. L., Munch, J. R., & Xiang, C. (2014) · American Economic Review · doi:10.1257/aer.104.6.1597 · Cited by 581
A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences
Goertz, G., & Mahoney, J. (2012) · Cited by 574
National Institutions and Subnational Development in Africa *
Michalopoulos, S., & Papaioannou, E. (2013) · The Quarterly Journal of Economics · doi:10.1093/qje/qjt029 · Cited by 581
Scale and skill in active management
Pástor, Ľ., Stambaugh, R. F., & Taylor, L. A. (2015) · Journal of Financial Economics · doi:10.1016/j.jfineco.2014.11.008 · Cited by 547
Medicaid Increases Emergency-Department Use: Evidence from Oregon's Health Insurance Experiment
Taubman, S. L., ALLEN, H. L., Wright, B. J., D, K. B. A., & Finkelstein, A. N. (2014) · Science · doi:10.1126/science.1246183 · Cited by 578
The Econometrics of Randomized Experiments
Athey, S. C., & Imbens, G. W. (2017) · Handbook of economic field experiments · doi:10.1016/bs.hefe.2016.10.003 · Cited by 561
Burgess, S., & Labrecque, J. A. (2018) · European Journal of Epidemiology · doi:10.1007/s10654-018-0424-6 · Cited by 569
Field, L. C., Lowry, M. B., & Mkrtchyan, A. (2013) · Journal of Financial Economics · doi:10.1016/j.jfineco.2013.02.004 · Cited by 567
Cyclicality of credit supply: Firm level evidence
Becker, B., & Ivashina, V. (2013) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2013.10.002 · Cited by 535
A large-scale analysis of racial disparities in police stops across the United States
Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Jenson, D., Shoemaker, A., Ramachandran, V., Barghouty, P., Phillips, C., Shroff, R., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0858-1 · Cited by 552
The Estimation of Causal Effects by Difference-in-Difference MethodsPreprint
Lechner, M. (2011) · RePEc: Research Papers in Economics · Cited by 552
Rishika, R., Kumar, A., Janakiraman, R., & Bezawada, R. (2013) · Information systems research · doi:10.1287/isre.1120.0460 · Cited by 550
Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality
Evans, D. M., & Smith, G. D. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090314-050016 · Cited by 529
The separation of ownership and control and corporate tax avoidance
Badertscher, B. A., Katz, S. P., & Rego, S. O. (2013) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2013.08.005 · Cited by 511
Playing it safe? Managerial preferences, risk, and agency conflicts
Gormley, T. A., & Matsa, D. A. (2016) · Journal of Financial Economics · doi:10.1016/j.jfineco.2016.08.002 · Cited by 509
Who Creates Jobs? Small vs. Large vs. Young
Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2010) · National Bureau of Economic Research · doi:10.3386/w16300 · Cited by 496
The Impact of No Child Left Behind on Student Achievement
Dee, T. S., & Jacob, B. (2011) · Journal of Policy Analysis and Management · doi:10.1002/pam.20586 · Cited by 487
Are Manipulation Checks Necessary?
Hauser, D., Ellsworth, P. C., & Gonzalez, R. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00998 · Cited by 562
Pustejovsky, J. E., & Tipton, E. (2018) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2016.1247004 · Cited by 492
Have Instrumental Variables Brought Us Closer to the Truth
Jiang, W. (2017) · The Review of Corporate Finance Studies · doi:10.1093/rcfs/cfx015 · Cited by 491
On the relation between corporate social responsibility and financial performance
Awaysheh, A., Heron, R. A., Perry, T., & Wilson, J. I. (2020) · Strategic Management Journal · doi:10.1002/smj.3122 · Cited by 488
The Consequences of Mandatory Corporate Sustainability Reporting
Ioannou, I., & Serafeim, G. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1799589 · Cited by 484
Creditor rights and innovation: Evidence from patent collateral
Mann, W. (2018) · Journal of Financial Economics · doi:10.1016/j.jfineco.2018.07.001 · Cited by 479
The Effect of Institutional Ownership on Payout Policy: Evidence from Index Thresholds
Crane, A. D., Michenaud, S., & Weston, J. P. (2016) · Review of Financial Studies · doi:10.1093/rfs/hhw012 · Cited by 475
Exporting under trade policy uncertainty: Theory and evidence
Handley, K. (2014) · Journal of International Economics · doi:10.1016/j.jinteco.2014.05.005 · Cited by 473
Institutional Ownership and Corporate Tax Avoidance: New Evidence
Khan, M., Srinivasan, S., & Tan, L. (2016) · The Accounting Review · doi:10.2139/ssrn.2779809 · Cited by 473
Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models
Breen, R., Karlson, K. B., & Holm, A. (2018) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041429 · Cited by 468
Civil War and Social Cohesion: Lab‐in‐the‐Field Evidence from Nepal
Gilligan, M., Pasquale, B., & Samii, C. (2013) · American Journal of Political Science · doi:10.1111/ajps.12067 · Cited by 475
Malhotra, N. A., Margalit, Y. M., & Mo, C. H. (2013) · American Journal of Political Science · doi:10.1111/ajps.12012 · Cited by 463
Demand Effects in Survey Experiments: An Empirical Assessment
MUMMOLO, J., & PETERSON, E. (2019) · American Political Science Review · doi:10.1017/s0003055418000837 · Cited by 548
Use of allele scores as instrumental variables for Mendelian randomization
Burgess, S., & Thompson, S. G. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt093 · Cited by 452
THE MONOPOLY OF VIOLENCE: EVIDENCE FROM COLOMBIA
Acemoğlu, D., Robinson, J. A., & Santos, R. J. (2013) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2012.01099.x · Cited by 454
Executive Compensation and the Maturity Structure of Corporate Debt
Brockman, P., Martin, X., & Unlu, E. (2010) · The Journal of Finance · doi:10.1111/j.1540-6261.2010.01563.x · Cited by 443
Spatial Panel Data Models Using Stata
Belotti, F., Hughes, G., & Mortari, A. P. (2016) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1701700109 · Cited by 504
Clots‐Figueras, I. (2010) · Journal of Public Economics · doi:10.1016/j.jpubeco.2010.11.017 · Cited by 438
Metadata
- Authors
- Angrist, J. D., & Pischke, J.-S.
- Venue
- Princeton University Press eBooks
- Publisher
- Princeton University Press
- Published
- 2009-12-31
- OpenAlex
- W2577227262
- Semantic Scholar
- a432377e8c4e782ff764701f50f84dca3c10663b
- Cited by
- 9,467
- References
- 173
- Citations by year
- 2026: 1243 · 2025: 1060 · 2024: 736 · 2023: 670 · 2022: 615 · 2021: 607 · 2020: 577 · 2019: 542 · 2018: 553 · 2017: 457 · 2016: 455 · 2015: 459 · 2014: 432 · 2013: 363 · 2012: 277
- FWCI
- 38.43
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Princeton University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- applied econometrics, linear regression, instrumental variables, natural experiment, difference-in-differences, regression discontinuity design, quantile regression, standard errors, causal inference, econometric methodology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
