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The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach

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Federal and state employment programs for low-skilled workers typically emphasize rapid placement of participants into jobs and often place a large fraction of participants into temporary-help agency jobs. Using unique administrative data from Detroit's welfare-to-work program, we apply the Chernozhukov-Hansen instrumental variables quantile regression (IVQR) method to estimate the causal effects of welfare-to-work job placements on the distribution of participants' earnings. We find that neither direct-hire nor temporary-help job placements significantly affect the lower tail of the earnings distribution. Direct-hire placements, however, substantially raise the upper tail, yielding sizable earnings increases for more than fifty percent of participants over the medium-term (one to two years following placement). Conversely, temporary-help placements have zero or negative earnings impacts at all quantiles, and these effects are economically large and significant at higher quantiles. In net, we find that the widespread practice of placing disadvantaged workers into temporary-help jobs is an ineffective tool for improving earnings and, moreover, that programs focused solely on job placement fail to improve earnings among those who are hardest to serve. Methodologically, one surprising result is that a reduced-form quantile IV approach, akin to two-step instrumental variables, produces near-identical point estimates to the structural IVQR approach, which is based on much stronger assumptions.
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  • David Autor & Susan N. Houseman & Sari Pekkala Kerr, "undated". "The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach," Upjohn Working Papers and Journal Articles ahk17, W.E. Upjohn Institute for Employment Research.
  • Handle: RePEc:upj:weupjo:ahk17
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    1. Drenik, Andres & Jäger, Simon & Plotkin, Pascuel & Schoefer, Benjamin, 2020. "Paying Outsourced Labor: Direct Evidence from Linked Temp Agency-Worker-Client Data," CEPR Discussion Papers 14517, C.E.P.R. Discussion Papers.
    2. Pierre Cahuc & Stéphane Carcillo & Andreea Minea, 2021. "The Difficult School-to-Work Transition of High School Dropouts: Evidence from a Field Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 56(1), pages 159-183.
    3. Jahn, Elke J. & Pozzoli, Dario, 2013. "The pay gap of temporary agency workers — Does the temp sector experience pay off?," Labour Economics, Elsevier, vol. 24(C), pages 48-57.
    4. German Blanco, 2017. "Who benefits from job placement services? A two-sided analysis," Journal of Productivity Analysis, Springer, vol. 47(1), pages 33-47, February.
    5. Hernæs, Øystein M., 2020. "Distributional effects of welfare reform for young adults: An unconditional quantile regression approach," Labour Economics, Elsevier, vol. 65(C).
    6. Okudaira, Hiroko & Ohtake, Fumio & Kume, Koichi & Tsuru, Kotaro, 2013. "What does a temporary help service job offer? Empirical suggestions from a Japanese survey," Journal of the Japanese and International Economies, Elsevier, vol. 28(C), pages 37-68.
    7. Bhaskar Jyoti Neog & Bimal Kishore Sahoo, 2020. "Job Reallocation Dynamics in India: Evidence from Large Manufacturing Plants," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 934-959, August.
    8. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    9. V. Chernozhukov & C. Hansen, 2013. "Quantile Models with Endogeneity," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 57-81, May.
    10. Balestra, Simone & Backes-Gellner, Uschi, 2017. "Heterogeneous returns to education over the wage distribution: Who profits the most?," Labour Economics, Elsevier, vol. 44(C), pages 89-105.
    11. Lars Kunze & Nicolai Suppa, 2020. "Who Is Bowling Alone? Quantile Treatment Effects of Unemployment on Social Participation," SOEPpapers on Multidisciplinary Panel Data Research 1077, DIW Berlin, The German Socio-Economic Panel (SOEP).
    12. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    13. Michael Chletsos & Stelios Roupakias, 2020. "Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006–2016," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1333-1364, November.
    14. Jahn, Elke J. & Rosholm, Michael, 2014. "Looking beyond the bridge: The effect of temporary agency employment on labor market outcomes," European Economic Review, Elsevier, vol. 65(C), pages 108-125.
    15. Susan N. Houseman, 2014. "Temporary agency work," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-27, May.
    16. Vanesa Jorda & Jose M. Alonso, 2020. "What works to mitigate and reduce relative (and absolute) inequality?: A systematic review," WIDER Working Paper Series wp-2020-152, World Institute for Development Economic Research (UNU-WIDER).
    17. Andreea Minea, 2018. "Essays on the social inclusion of young people. Family and labor market pathways," Sciences Po publications info:hdl:2441/7dun75j1019, Sciences Po.

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    More about this item

    Keywords

    training programs; wage differentials;

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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