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Giovanni Mellace

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Roberta Di Stefano & Giovanni Mellace, 2024. "The inclusive Synthetic Control Method," Papers 2403.17624, arXiv.org.

    Cited by:

    1. Tello-Pacheco, Mario, 2023. "Los “spillovers” del COVID-19 sobre el empleo y el ingreso en Perú," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 42(75), pages 161-195, January.
    2. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    3. Dan S. Rickman & Hongbo Wang, 2023. "Creating and maintaining film clusters: Synthetic control method analysis of the enactment and repeal of US state film incentives," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 363-392, April.
    4. Aleksandar Kešeljević & Rok Spruk, 2024. "Estimating the effects of Syrian civil war," Empirical Economics, Springer, vol. 66(2), pages 671-703, February.
    5. Dan S. Rickman & Hongbo Wang, 2024. "Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching‐regression approach," Growth and Change, Wiley Blackwell, vol. 55(2), June.
    6. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.

  2. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 25 Apr 2024.

    Cited by:

    1. Goff, Leonard, 2024. "A vector monotonicity assumption for multiple instruments," Journal of Econometrics, Elsevier, vol. 241(1).

  3. Federico Crudu & Michael C. Knaus & Giovanni Mellace & Joeri Smits, 2022. "On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models," Papers 2211.09502, arXiv.org.

    Cited by:

    1. Bonev, Petyo, 2023. "Behavioral Spillovers," Economics Working Paper Series 2303, University of St. Gallen, School of Economics and Political Science.

  4. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.

  5. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.

  6. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.

    Cited by:

    1. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    2. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.

  7. Federico Crudu & Giovanni Mellace & Zsolt Sandor, 2017. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 761, Department of Economics, University of Siena.

    Cited by:

    1. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    2. Anatolyev, Stanislav & Sølvsten, Mikkel, 2023. "Testing many restrictions under heteroskedasticity," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org.
    4. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
    5. Tom Boot & Didier Nibbering, 2024. "Inference on LATEs with covariates," Papers 2402.12607, arXiv.org.
    6. Matsushita, Yukitoshi & Otsu, Taisuke, 2024. "A jackknife Lagrange multiplier test with many weak instruments," LSE Research Online Documents on Economics 116392, London School of Economics and Political Science, LSE Library.
    7. Max-Sebastian Dov`i, 2021. "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables," Papers 2101.09543, arXiv.org, revised Mar 2021.
    8. Max-Sebastian Dov`i & Anders Bredahl Kock & Sophocles Mavroeidis, 2022. "A Ridge-Regularised Jackknifed Anderson-Rubin Test," Papers 2209.03259, arXiv.org, revised Nov 2023.
    9. Chao, John C. & Swanson, Norman R. & Woutersen, Tiemen, 2023. "Jackknife estimation of a cluster-sample IV regression model with many weak instruments," Journal of Econometrics, Elsevier, vol. 235(2), pages 1747-1769.
    10. Anna Mikusheva & Liyang Sun, 2024. "Weak identification with many instruments," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages -28.
    11. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
    12. Luther Yap, 2024. "Inference with Many Weak Instruments and Heterogeneity," Papers 2408.11193, arXiv.org, revised Sep 2024.
    13. Johannes W. Ligtenberg & Tiemen Woutersen, 2024. "Multidimensional clustering in judge designs," Papers 2406.09473, arXiv.org.
    14. Tom Boot & Johannes W. Ligtenberg, 2023. "Identification- and many instrument-robust inference via invariant moment conditions," Papers 2303.07822, arXiv.org, revised Sep 2023.
    15. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

  8. Dahl, Christian M. & Huber, Martin & Mellace, Giovanni, 2017. "It's never too LATE: A new look at local average treatment effects with or without defiers," Discussion Papers on Economics 2/2017, University of Southern Denmark, Department of Economics.

    Cited by:

    1. Fiorini, Mario & Stevens, Katrien, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2021-01, University of Sydney, School of Economics.
    2. Dan Black & Joonhwi Joo & Robert LaLonde & Jeffrey Smith & Evan Taylor, 2020. "Simple Tests for Selection: Learning More from Instrumental Variables," Working Papers 2020-048, Human Capital and Economic Opportunity Working Group.
    3. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 25 Apr 2024.
    4. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Jan 2024.
    5. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
    6. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

  9. Kongstad, L.P. & Mellace, G. & Olsen, K.R., 2016. "Can the use of Electronic Health Records in General Practice reduce hospitalizations for diabetes patients? Evidence from a natural experiment," Health, Econometrics and Data Group (HEDG) Working Papers 16/25, HEDG, c/o Department of Economics, University of York.

    Cited by:

    1. Olsen, Kim Rose & Laudicella, Mauro, 2019. "Health care inequality in free access health systems: The impact of non-pecuniary incentives on diabetic patients in Danish general practices," Social Science & Medicine, Elsevier, vol. 230(C), pages 174-183.

  10. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "The finite sample performance of estimators for mediation analysis under sequential conditional independence," Economics Working Paper Series 1415, University of St. Gallen, School of Economics and Political Science, revised Nov 2014.

    Cited by:

    1. Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
    2. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2021. "Empirical Monte Carlo Evidence on Estimation of Timing-of-Events Models," IZA Discussion Papers 14015, Institute of Labor Economics (IZA).
    3. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    4. Mellace, Giovanni & Pasquini, Alessandra, 2019. "Identify More, Observe Less: Mediation Analysis: Mediation Analysis Synthetic Control," Discussion Papers on Economics 12/2019, University of Southern Denmark, Department of Economics.
    5. Dr. Daniel Kasser Tee, 2022. "Mediating Effect of Customer Satisfaction on the Relationship between Service Quality and Customer Loyalty in the Ghana Banking Industry," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 9(4), pages 17-26, April.
    6. Giovanni Mellace & Alessandra Pasquini, 2022. "Mediation Analysis Synthetic Control," Temi di discussione (Economic working papers) 1389, Bank of Italy, Economic Research and International Relations Area.
    7. Arun Advani & Tymon Sloczynski, 2013. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP64/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute of Labor Economics (IZA).
    9. Hugo Bodory & Martin Huber & Michael Lechner, 2022. "The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates," Papers 2212.07379, arXiv.org.
    10. Giovanni Mellace & Alessandra Pasquini, 2019. "Identify More, Observe Less: Mediation Analysis Synthetic Control," CEIS Research Paper 474, Tor Vergata University, CEIS, revised 20 Nov 2019.
    11. Bijwaard, Govert & Alessie, Rob & Angelini, Viola, 2018. "The Effect of Early Life Health on Later Life Home Care Use: The Mediating Role of Household Composition," IZA Discussion Papers 11729, Institute of Labor Economics (IZA).
    12. Bellani, Luna & Bia, Michela, 2017. "The Long-Run Impact of Childhood Poverty and the Mediating Role of Education," IZA Discussion Papers 10677, Institute of Labor Economics (IZA).
    13. Stephen Whelan, 2017. "Does homeownership affect education outcomes?," IZA World of Labor, Institute of Labor Economics (IZA), pages 342-342, April.

  11. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism," Economics Working Paper Series 1414, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. N. N., 2017. "WIFO-Monatsberichte, Heft 6/2017," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), June.
    3. Amelie Schiprowski, 2020. "The Role of Caseworkers in Unemployment Insurance: Evidence From Unplanned Absences," CRC TR 224 Discussion Paper Series crctr224_2020_165, University of Bonn and University of Mannheim, Germany.
    4. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    5. Vikström, Johan & Söderström, Martin & Cederlöf, Jonas, 2021. "What makes a good caseworker?," Working Paper Series 2021:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    6. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
    7. Lechner, Michael & Huber, Martin & Strittmatter, Anthony, 2015. "Direct and indirect effects of training vouchers for the unemployed," CEPR Discussion Papers 10650, C.E.P.R. Discussion Papers.
    8. Prifti, Ervin & Daidone, Silvio & Davis, Benjamin, 2019. "Causal pathways of the productive impacts of cash transfers: Experimental evidence from Lesotho," World Development, Elsevier, vol. 115(C), pages 258-268.
    9. Ville Vehkasalo, 2020. "Effects of face-to-face counselling on unemployment rate and duration: evidence from a Public Employment Service reform," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-14, December.
    10. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    11. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    12. Wunsch, Conny & Strobl, Renate, 2018. "Identification of Causal Mechanisms Based on Between-Subject Double Randomization Designs," IZA Discussion Papers 11626, Institute of Labor Economics (IZA).
    13. Viviana Celli, 2019. "Causal Mediation Analysis in Economics: objectives, assumptions, models," Working Papers 12/19, Sapienza University of Rome, DISS.
    14. Annabelle Doerr & Anthony Strittmatter, 2020. "Identifying causal channels of policy reforms with multiple treatments and different types of selection," Papers 2010.05221, arXiv.org.
    15. David G. Lugo‐Palacios & Jonathan M. Clarke & Søren Rud Kristensen, 2023. "Back to basics: A mediation analysis approach to addressing the fundamental questions of integrated care evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2080-2097, September.
    16. Huber, Martin & Solovyeva, Anna, 2018. "On the sensitivity of wage gap decompositions," FSES Working Papers 497, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    17. Joachim Wilde, 2022. "What drives trust of the long‐term unemployed in their caseworkers?," LABOUR, CEIS, vol. 36(2), pages 231-250, June.
    18. Huber, Martin & Schelker, Mark & Strittmatter, Anthony, 2019. "Direct and Indirect Effects based on Changes-in- Changes," FSES Working Papers 508, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    19. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    20. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    21. Farbmacher, Helmut & Huber, Martin & Langen, Henrika & Spindler, Martin, 2020. "Causal mediation analysis with double machine learning," FSES Working Papers 515, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    22. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    23. Huber, Martin & Laffers, Lukáš, 2020. "Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition," FSES Working Papers 514, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    24. Ulrike Huemer & Rainer Eppel & Marion Kogler & Helmut Mahringer & Lukas Schmoigl & David Pichler, 2021. "Effektivität von Instrumenten der aktiven Arbeitsmarktpolitik in unterschiedlichen Konjunkturphasen," WIFO Studies, WIFO, number 67250.
    25. Schütt, Christoph A., 2023. "The effect of perceived similarity and social proximity on the formation of prosocial preferences," Journal of Economic Psychology, Elsevier, vol. 99(C).
    26. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy (IfW Kiel).
    27. Rainer Eppel & Helmut Mahringer & Petra Sauer, 2017. "Österreich 2025 – Arbeitslosigkeit und die Rolle der aktiven Arbeitsmarktpolitik," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), pages 493-505, June.

  12. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Will Dobbie & Jae Song, 2014. "Debt Relief and Debtor Outcomes: Measuring the Effects of Consumer Bankruptcy Protection," NBER Working Papers 20520, National Bureau of Economic Research, Inc.
    2. Clément de Chaisemartin, 2017. "Tolerating defiance? Local average treatment effects without monotonicity," Quantitative Economics, Econometric Society, vol. 8(2), pages 367-396, July.
    3. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2012. "Late Again with Defiers," Working Papers halshs-00699646, HAL.
    4. Fiorini, Mario & Katrien Stevens, 2014. "Assessing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2014-13, University of Sydney, School of Economics.

  13. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.

    Cited by:

    1. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.

  14. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    2. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    3. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    4. Blanco, German & Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2018. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes under Censoring, Selection, and Noncompliance," GLO Discussion Paper Series 288, Global Labor Organization (GLO).
    5. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    6. Lutz Depenbusch & Pepijn Schreinemachers & Stuart Brown & Ralph Roothaert, 2022. "Impact and distributional effects of a home garden and nutrition intervention in Cambodia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(4), pages 865-881, August.
    7. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    8. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.
    9. Daniel Brüggmann, 2020. "Women’s employment, income and divorce in West Germany: a causal approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-22, December.

  15. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity for LATE identification based on inequality moment constraints," Economics Working Paper Series 1143, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Mohamed Coulibaly & Yu-Chin Hsu & Ismael Mourifié & Yuanyuan Wan, 2024. "A Sharp Test for the Judge Leniency Design," NBER Working Papers 32456, National Bureau of Economic Research, Inc.
    2. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    3. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    4. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    5. Patrick Kline & Christopher R. Walters, 2017. "On Heckits, LATE, and Numerical Equivalence," Papers 1706.05982, arXiv.org, revised Oct 2018.
    6. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    7. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
    8. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    9. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    10. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    11. Felipe González & Luis R. Martínez & María Angélica Bautista & Pablo Muñoz & María Mounu Prem, 2019. "Geography of Dictatorship and Support for Democracy," Working Papers ClioLab 28, EH Clio Lab. Instituto de Economía. Pontificia Universidad Católica de Chile.
    12. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.
    13. Bolzern, Benjamin & Huber, Martin, 2017. "Testing the validity of the compulsory schooling law instrument," Economics Letters, Elsevier, vol. 159(C), pages 23-27.
    14. Fiorini, Mario & Stevens, Katrien, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2021-01, University of Sydney, School of Economics.
    15. Dan Black & Joonhwi Joo & Robert LaLonde & Jeffrey Smith & Evan Taylor, 2020. "Simple Tests for Selection: Learning More from Instrumental Variables," Working Papers 2020-048, Human Capital and Economic Opportunity Working Group.
    16. Salm, Martin & Siflinger, Bettina & Xie, Mingjia, 2021. "The Effect of Retirement on Mental Health: Indirect Treatment Effects and Causal Mediation," Other publications TiSEM e28efa7f-8219-437c-a26d-2, Tilburg University, School of Economics and Management.
    17. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    18. Christina Felfe & Martin Huber, 2017. "Does preschool boost the development of minority children?: the case of Roma children," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 475-502, February.
    19. Eduardo Fé, 2021. "Pension eligibility rules and the local causal effect of retirement on cognitive functioning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 812-841, July.
    20. Carmen Aina & Daniela Sonedda, 2022. "Sooner or later? The impact of child education on household consumption," Empirical Economics, Springer, vol. 63(4), pages 2071-2099, October.
    21. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    23. Joshua Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path from Cause to Effect," NBER Working Papers 29726, National Bureau of Economic Research, Inc.
    24. Tymon Sloczynski & Derya Uysal & Jeffrey Wooldridge, 2023. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Rationality and Competition Discussion Paper Series 424, CRC TRR 190 Rationality and Competition.
    25. Hans Fricke & Markus Frölich & Martin Huber & Michael Lechner, 2020. "Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 481-504, August.
    26. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    27. Sarnetzki, Florian & Dzemski, Andreas, 2014. "Overidentification test in a nonparametric treatment model with unobserved heterogeneity," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100620, Verein für Socialpolitik / German Economic Association.
    28. Hussain, M. Azhar & Siersbæk, Nikolaj & Østerdal, Lars Peter, 2017. "Multidimensional Welfare Comparisons of EU Member States Before, During, and After the Financial Crisis: A Dominance Approach," Discussion Papers on Economics 10/2017, University of Southern Denmark, Department of Economics.
    29. Brigham Frandsen & Lars Lefgren & Emily Leslie, 2023. "Judging Judge Fixed Effects," American Economic Review, American Economic Association, vol. 113(1), pages 253-277, January.
    30. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    31. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.
    32. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2017. "Using Instrumental Variables for Inference about Policy Relevant Treatment Effects," NBER Working Papers 23568, National Bureau of Economic Research, Inc.
    33. Xintong Wang & Alfonso Flores-Lagunes, 2022. "Conscription and Military Service: Do They Result in Future Violent and Nonviolent Incarcerations and Recidivism?," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1715-1757.
    34. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.
    35. Dionissi Aliprantis & Francisca G.-C. Richter, 2020. "Evidence of Neighborhood Effects from Moving to Opportunity: Lates of Neighborhood Quality," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 633-647, October.
    36. Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    37. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    38. Martin E Andresen & Martin Huber, 2021. "Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 536-558.
    39. Linbo Wang & James M. Robins & Thomas S. Richardson, 2017. "On falsification of the binary instrumental variable model," Biometrika, Biometrika Trust, vol. 104(1), pages 229-236.
    40. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    41. Darío Tortarolo, 2014. "Female Labor Supply and Fertility. Causal Evidence for Latin America," CEDLAS, Working Papers 0166, CEDLAS, Universidad Nacional de La Plata.
    42. Hongyi Jiang & Zhenting Sun, 2023. "Testing Partial Instrument Monotonicity," Papers 2308.08390, arXiv.org, revised Aug 2023.
    43. James Bisbee & Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2015. "Local Instruments, Global Extrapolation: External Validity of the Labor Supply-Fertility Local Average Treatment Effect," NBER Working Papers 21663, National Bureau of Economic Research, Inc.
    44. Tymon S{l}oczy'nski & S. Derya Uysal & Jeffrey M. Wooldridge, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Papers 2204.07672, arXiv.org, revised Feb 2024.
    45. Rui Wang, 2023. "Point Identification of LATE with Two Imperfect Instruments," Papers 2303.13795, arXiv.org.
    46. Julia Schmieder, 2020. "Fertility as a Driver of Maternal Employment," Discussion Papers of DIW Berlin 1882, DIW Berlin, German Institute for Economic Research.
    47. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    48. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    49. Machado, Cecilia & Shaikh, Azeem M. & Vytlacil, Edward J., 2019. "Instrumental variables and the sign of the average treatment effect," Journal of Econometrics, Elsevier, vol. 212(2), pages 522-555.
    50. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    51. Hofmarcher, Thomas, 2021. "The effect of education on poverty: A European perspective," Economics of Education Review, Elsevier, vol. 83(C).
    52. Brunello, Giorgio & Fort, Margherita & Weber, Guglielmo & Weiss, Christoph T., 2013. "Testing the Internal Validity of Compulsory School Reforms as Instrument for Years of Schooling," IZA Discussion Papers 7533, Institute of Labor Economics (IZA).
    53. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity in sample selection models," Economics Working Paper Series 1145, University of St. Gallen, School of Economics and Political Science.
    54. Yinchu Zhu, 2021. "Phase transition of the monotonicity assumption in learning local average treatment effects," Papers 2103.13369, arXiv.org.
    55. Jiang, Hongyi & Sun, Zhenting, 2023. "Testing partial instrument monotonicity," Economics Letters, Elsevier, vol. 233(C).
    56. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    57. Zhenting Sun & Kaspar Wuthrich, 2022. "Pairwise Valid Instruments," Papers 2203.08050, arXiv.org, revised Jan 2024.
    58. Francis DiTraglia & Camilo García-Jimeno, 2016. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," NBER Working Papers 22621, National Bureau of Economic Research, Inc.
    59. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2020. "Discriminate me — If you can! The disappearance of the gender pay gap among public‐contest selected employees in Italy," Gender, Work and Organization, Wiley Blackwell, vol. 27(6), pages 1040-1076, November.
    60. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.
    61. Öberg, Stefan, 2021. "The casual effect of fertility: The multiple problems with instrumental variables for the number of children in families," SocArXiv peuvz, Center for Open Science.

  16. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.

    Cited by:

    1. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments," NBER Working Papers 22363, National Bureau of Economic Research, Inc.
    2. Laffers, Lukas & Mellace, Giovanni, 2015. "A Note on Testing the LATE Assumptions," Discussion Papers on Economics 4/2015, University of Southern Denmark, Department of Economics.
    3. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    4. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2017. "Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training," GLO Discussion Paper Series 93, Global Labor Organization (GLO).
    5. Huber, Martin & Mellace, Giovanni, 2012. "Relaxing monotonicity in the identification of local average treatment effects," Economics Working Paper Series 1212, University of St. Gallen, School of Economics and Political Science.
    6. Huber, Martin, 2012. "Statistical verification of a natural "natural experiment": Tests and sensitivity checks for the sibling sex ratio instrument," Economics Working Paper Series 1219, University of St. Gallen, School of Economics and Political Science.
    7. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    8. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
    9. Murard, Elie, 2019. "The Impact of Migration on Family Left Behind: Estimation in Presence of Intra-Household Selection of Migrants," IZA Discussion Papers 12094, Institute of Labor Economics (IZA).
    10. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy (IfW Kiel).

Articles

  1. Christian M Dahl & Martin Huber & Giovanni Mellace, 2023. "It is never too LATE: a new look at local average treatment effects with or without defiers," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 378-404.
    See citations under working paper version above.
  2. Mellace, Giovanni & Ventura, Marco, 2023. "The short-run effects of public incentives for innovation in Italy," Economic Modelling, Elsevier, vol. 120(C).

    Cited by:

    1. He, Siyi & Liu, Jinsong & Ying, Qianwei, 2023. "Externalities of government-oriented support for innovation: Evidence from the national innovative city pilot policy in China," Economic Modelling, Elsevier, vol. 128(C).

  3. Crudu, Federico & Mellace, Giovanni & Sándor, Zsolt, 2021. "Inference In Instrumental Variable Models With Heteroskedasticity And Many Instruments," Econometric Theory, Cambridge University Press, vol. 37(2), pages 281-310, April.
    See citations under working paper version above.
  4. Martin Huber & Lukas Laffers & Giovanni Mellace, 2017. "Sharp IV Bounds on Average Treatment Effects on the Treated and Other Populations Under Endogeneity and Noncompliance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 56-79, January.

    Cited by:

    1. Federico A. Bugni & Mengsi Gao & Filip Obradovic & Amilcar Velez, 2024. "Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance," Papers 2406.08419, arXiv.org, revised Jun 2024.
    2. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Norwegian School of Economics, Department of Economics.
    3. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    4. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    5. Xintong Wang & Alfonso Flores-Lagunes, 2022. "Conscription and Military Service: Do They Result in Future Violent and Nonviolent Incarcerations and Recidivism?," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1715-1757.
    6. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.
    7. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    8. Michela Bia & German Blanco & Marie Valentova, 2021. "The Causal Impact of Taking Parental Leave on Wages: Evidence from 2005 to 2015," LISER Working Paper Series 2021-08, Luxembourg Institute of Socio-Economic Research (LISER).
    9. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2017. "Going beyond LATE: Bounding Average Treatment Effects of Job Corps Training," GLO Discussion Paper Series 93, Global Labor Organization (GLO).
    10. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    11. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    12. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    13. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    14. Kory Kroft & Ismael Mourifi'e & Atom Vayalinkal, 2024. "Lee Bounds with Multilayered Sample Selection," Papers 2409.04589, arXiv.org.
    15. Marx, Philip, 2024. "Sharp bounds in the latent index selection model," Journal of Econometrics, Elsevier, vol. 238(2).
    16. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    17. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.
    18. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

  5. Lukas Laffers & Giovanni Mellace, 2017. "A note on testing instrument validity for the identification of LATE," Empirical Economics, Springer, vol. 53(3), pages 1281-1286, November.

    Cited by:

    1. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    2. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.
    3. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    4. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.

  6. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    See citations under working paper version above.
  7. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    See citations under working paper version above.
  8. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    See citations under working paper version above.
  9. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
    See citations under working paper version above.
  10. Martin Huber & Giovanni Mellace, 2014. "Testing exclusion restrictions and additive separability in sample selection models," Empirical Economics, Springer, vol. 47(1), pages 75-92, August.

    Cited by:

    1. Antonio Paolo & Aysit Tansel, 2019. "English skills, labour market status and earnings of Turkish women," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(4), pages 669-690, November.
    2. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    3. Shannon Seitz & Geoffrey Sanzenbacher & Andrew Beauchamp & Meghan Skira, 2014. "Deadbeat Dads," 2014 Meeting Papers 435, Society for Economic Dynamics.
    4. Thomas Bolli & Katherine Caves & Maria Esther Oswald-Egg, 2021. "Valuable Experience: How University Internships Affect Graduates’ Income," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(8), pages 1198-1247, December.
    5. Kenza Elass, 2022. "The multiple dimensions of selection into employment," AMSE Working Papers 2219, Aix-Marseille School of Economics, France.
    6. Christian K. Darko & Kennedy K. Abrokwa, 2020. "Do you really need it? Educational mismatch and earnings in Ghana," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1365-1392, November.
    7. Brendon McConnell, 2022. "Racial Sentencing Disparities and Differential Progression Through the Criminal Justice System: Evidence From Linked Federal and State Court Data," Papers 2203.14282, arXiv.org, revised Apr 2022.
    8. Hermes, Henning & Krauß, Marina & Lergetporer, Philipp & Peter, Frauke & Wiederhold, Simon, 2022. "Early Child Care and Labor Supply of Lower-SES Mothers: A Randomized Controlled Trial," IZA Discussion Papers 15814, Institute of Labor Economics (IZA).
    9. Biavaschi, Costanza, 2016. "Recovering the counterfactual wage distribution with selective return migration," Labour Economics, Elsevier, vol. 38(C), pages 59-80.
    10. Castagnetti, Carolina & Rosti, Luisa & Töpfer, Marina, 2018. "Discriminate me - if you can! The disappearance of the gender pay gap among public-contest selected employees," Discussion Papers 103, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    11. Harry Anthony Patrinos & George Psacharopoulos & Aysit Tansel, 2019. "GLOBALISATION AND GOVERNANCE: Returns to Investment in Education: The Case of Turkey," ERC Working Papers 1903, ERC - Economic Research Center, Middle East Technical University, revised Mar 2019.
    12. Dante Contreras & Roberto Gillmore & Esteban Puentes, 2017. "Self‐Employment and Queues for Wage Work: Evidence from Chile," Journal of International Development, John Wiley & Sons, Ltd., vol. 29(4), pages 473-499, May.
    13. Elass, Kenza, 2024. "Male and female selection effects on gender wage gaps in three countries," Labour Economics, Elsevier, vol. 87(C).
    14. Patrinos, Harry Anthony & Psacharopoulos, George & Tansel, Aysit, 2019. "Returns to Investment in Education: The Case of Turkey," GLO Discussion Paper Series 335, Global Labor Organization (GLO).
    15. Masayuki Hirukawa & Di Liu & Irina Murtazashvili & Artem Prokhorov, 2023. "DS-HECK: double-lasso estimation of Heckman selection model," Empirical Economics, Springer, vol. 64(6), pages 3167-3195, June.
    16. Huber, Martin & Solovyeva, Anna, 2018. "On the sensitivity of wage gap decompositions," FSES Working Papers 497, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    17. Josephine Jacobs & Courtney Van Houtven & Audrey Laporte & Peter Coyte, 2014. "Baby Boomer caregivers in the workforce: Do they fare better or worse than their predecessors?," Working Papers 140001, Canadian Centre for Health Economics.
    18. Chunbei Wang & Le Wang, 2017. "Knot yet: minimum marriage age law, marriage delay, and earnings," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(3), pages 771-804, July.
    19. Abby Alpert & David Powell, 2020. "Estimating Intensive And Extensive Tax Responsiveness," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1855-1873, October.
    20. Jaehee Hwang, 2022. "Who Becomes a Fisherman? A Two-Stage Sample Selection Analysis on Small-Scale Fishery Choice and Income in Korea," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    21. Feng‐Yi Lin & Shen‐Ho Chang & Shaio‐Yan Huang & Teng‐Shih Wang, 2021. "Self‐interested board of director and stock price crash risk in loss‐making firms," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2853-2890, June.
    22. Kenza Elass, 2022. "The multiple dimensions of selection into employment," French Stata Users' Group Meetings 2022 06, Stata Users Group.
    23. Kenza Elass, 2022. "The multiple dimensions of selection into employment," Working Papers hal-03788508, HAL.
    24. Biewen, Martin & Fitzenberger, Bernd & Seckler, Matthias, 2020. "Counterfactual quantile decompositions with selection correction taking into account Huber/Melly (2015): An application to the German gender wage gap," Labour Economics, Elsevier, vol. 67(C).
    25. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    26. Ahn, Soojung & Steinbach, Sandro, 2021. "COVID-19 Trade Actions in the Agricultural and Food Sector," Journal of Food Distribution Research, Food Distribution Research Society, vol. 52(2), July.
    27. Garbay, Sergio & Barrera, Raquel, 2021. "¿Mujeres en suelos pegajosos? Un análisis de la evolución de las distribuciones de ingresos laborales en Bolivia en el periodo 2011-2019," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB) "San Pablo", issue 36, pages 123-168, Noviembre.
    28. Dhamija, Gaurav & Roychowdhury, Punarjit, 2018. "The impact of women's age at marriage on own and spousal labor market outcomes in India: causation or selection?," MPRA Paper 86686, University Library of Munich, Germany.
    29. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2020. "Discriminate me — If you can! The disappearance of the gender pay gap among public‐contest selected employees in Italy," Gender, Work and Organization, Wiley Blackwell, vol. 27(6), pages 1040-1076, November.

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