IDEAS home Printed from https://ideas.repec.org/p/hhs/ifauwp/2019_018.html
   My bibliography  Save this paper

Long-Run Effects of Dynamically Assigned Treatments: a New Methodology and an Evaluation of Training Effects on Earnings

Author

Listed:
  • van den Berg, Gerard J.

    (University of Bristol)

  • Vikström, Johan

    (IFAU - Institute for Evaluation of Labour Market and Education Policy)

Abstract

We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g. unemployment) to be eligible for a treatment,treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not trained at a certain time in unemployment will leave unemployment before training while others will be trained later. We are interested in effects of the treatment at a certain elapsed duration compared to “no treatment at any subsequent duration”. We prove identification under unconfoundedness and propose inverse probability weighting estimators. A key feature is that weights given to outcome observations of non-treated depend on the remaining time in the initial state. We study earnings effects of WIA training in the US and long-run effects of a training program for unemployed workers in Sweden. Estimates are positive and sizeable, exceeding those obtained by using common static methods, and suggesting a reappraisal of training.

Suggested Citation

  • van den Berg, Gerard J. & Vikström, Johan, 2019. "Long-Run Effects of Dynamically Assigned Treatments: a New Methodology and an Evaluation of Training Effects on Earnings," Working Paper Series 2019:18, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    • Handle: RePEc:hhs:ifauwp:2019_018
    as

    Download full text from publisher

    File URL: https://www.ifau.se/globalassets/pdf/se/2019/wp-2019-18-long-run-effects-of-dynamically-assigned-treatments.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    2. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    3. Michael Lechner & Ruth Miquel & Conny Wunsch, 2011. "Long‐Run Effects Of Public Sector Sponsored Training In West Germany," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 742-784, August.
    4. Katarina Richardson & Gerard J. Berg, 2013. "Duration Dependence Versus Unobserved Heterogeneity In Treatment Effects: Swedish Labor Market Training And The Transition Rate To Employment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 325-351, March.
    5. Carolyn Heinrich & Peter Mueser & Kenneth Troske & Kyung-Seong Jeon & Daver Kahvecioglu, 2013. "Do Public Employment and Training Programs Work?," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-23, December.
    6. Regner, Hakan, 2002. "A nonexperimental evaluation of training programs for the unemployed in Sweden," Labour Economics, Elsevier, vol. 9(2), pages 187-206, April.
    7. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    8. Muller, Paul & van der Klaauw, Bas & Heyma, Arjan, 2017. "Comparing Econometric Methods to Empirically Evaluate Job-Search Assistance," Working Papers in Economics 691, University of Gothenburg, Department of Economics.
    9. Bruno Crépon & Marc Ferracci & Grégory Jolivet & Gerard J. van den Berg, 2009. "Active Labor Market Policy Effects in a Dynamic Setting," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 595-605, 04-05.
    10. Conny Wunsch & Michael Lechner, 2008. "What Did All the Money Do? On the General Ineffectiveness of Recent West German Labour Market Programmes," Kyklos, Wiley Blackwell, vol. 61(1), pages 134-174, February.
    11. Ham, John C & LaLonde, Robert J, 1996. "The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training," Econometrica, Econometric Society, vol. 64(1), pages 175-205, January.
    12. Carling, Kenneth & Richardson, Katarina, 2001. "The relative efficiency of labor market programs: Swedish experience from the 1990's," Working Paper Series 2001:2, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    13. Fredrik Andersson & Harry J. Holzer & Julia I. Lane & David Rosenblum & Jeffrey Smith, 2024. "Does Federally Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms," Journal of Human Resources, University of Wisconsin Press, vol. 59(4), pages 1244-1283.
    14. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    15. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
    16. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
    17. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    18. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Doerr, Annabelle, 2022. "Vocational training for female job returners - Effects on employment, earnings and job quality," Labour Economics, Elsevier, vol. 75(C).
    2. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    3. Marco Caliendo & Robert Mahlstedt & Gerard J. van den Berg & Johan Vikström, 2023. "Side effects of labor market policies," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(2), pages 339-375, April.
    4. Doerr, Annabelle, 2022. "Vocational Training for Female Job Returners - Effects on Employment, Earnings and Job Quality," Working papers 2022/02, Faculty of Business and Economics - University of Basel.
    5. Anže Burger & Jochen Kluve & Milan Vodopivec & Matija Vodopivec, 2022. "A comprehensive impact evaluation of active labour market programmes in Slovenia," Empirical Economics, Springer, vol. 62(6), pages 3015-3039, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerard J. van den Berg & Johan Vikström, 2022. "Long‐Run Effects of Dynamically Assigned Treatments: A New Methodology and an Evaluation of Training Effects on Earnings," Econometrica, Econometric Society, vol. 90(3), pages 1337-1354, May.
    2. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    3. Doerr, Annabelle, 2022. "Vocational Training for Female Job Returners - Effects on Employment, Earnings and Job Quality," Working papers 2022/02, Faculty of Business and Economics - University of Basel.
    4. Fitzenberger, Bernd & Furdas, Marina & Sajons, Christoph, 2016. "End-of-year spending and the long-run employment effects of training programs for the unemployed," Freiburg Discussion Papers on Constitutional Economics 16/08, Walter Eucken Institut e.V..
    5. Doerr, Annabelle, 2022. "Vocational training for female job returners - Effects on employment, earnings and job quality," Labour Economics, Elsevier, vol. 75(C).
    6. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    7. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    8. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2015. "Workplace health promotion and labour market performance of employees," Journal of Health Economics, Elsevier, vol. 43(C), pages 170-189.
    9. Marloes Lammers & Lucy Kok, 2021. "Are active labor market policies (cost-)effective in the long run? Evidence from the Netherlands," Empirical Economics, Springer, vol. 60(4), pages 1719-1746, April.
    10. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    11. 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.
    12. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    13. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    14. Andrea Albanese & Bart Cockx & Yannick Thuy, 2020. "Working time reductions at the end of the career: Do they prolong the time spent in employment?," Empirical Economics, Springer, vol. 59(1), pages 99-141, July.
    15. Martin Huber & Michael Lechner & Anthony Strittmatter, 2018. "Direct and indirect effects of training vouchers for the unemployed," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 441-463, February.
    16. Katharina Dengler, 2019. "Effectiveness of sequences of classroom training for welfare recipients: what works best in West Germany?," Applied Economics, Taylor & Francis Journals, vol. 51(1), pages 1-46, January.
    17. Michael Lechner & Blaise Melly, 2007. "Earnings Effects of Training Programs," University of St. Gallen Department of Economics working paper series 2007 2007-28, Department of Economics, University of St. Gallen.
    18. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    19. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    20. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.

    More about this item

    Keywords

    Treatment effects; dynamic treatment evaluation; program evaluation; duration analysis; matching; unemployment; employment.;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:ifauwp:2019_018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ali Ghooloo (email available below). General contact details of provider: https://edirc.repec.org/data/ifagvse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.