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How to Increase PhD Completion Rates? An Impact Evaluation of Two Reforms in a Selective Graduate School, 1976–2012


Graduate and doctoral schools around the world struggle to shorten the long time to degree and to prevent high dropout rates. While most of previous research studied individual determinants of PhD completion, we analyze the impact of two structural reforms of the doctoral program on thesis completion at a selective European graduate school. Exploiting a unique PhD dataset covering 30 entry cohorts, we identify reform effects on PhD outcomes using an interrupted time-series regression design. We find that the first reform improved timely completion rates by between 10 and 15 percentage points (according to the specific outcome), whereas the second reform increased completion rates by between 9 and 20 percentage points. Additionally, each reform reduced dropout rates by 7 percentage points. The results are robust to various sensitivity checks. At the end, we discuss lessons learned for those in charge of graduate schools and/or PhD programs.

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Fig. 1
Fig. 2


  1. 1.

    A commonly heard light-bulb joke about PhD programs. For a recent report, see “The Long-Haul Degree”. By Patricia Cohen in the New York Times, April 16, 2010. Accessible online at

  2. 2.

    This number normally includes the time to finish a masters’ degree.

  3. 3.

    Some exceptions exist in the United States, notably Ehrenberg et al. (2007) and Groen et al. (2008), which evaluated the impact of the Andrew W. Mellon Foundation’s Graduate Education Initiative (GEI) in the 1990s that provided funding to improve the quality of PhD programs in humanities and related social sciences.

  4. 4.

    EUI Strategic Review, November 1991, “Beyond Maintenance”. Although this review was published later than the year 1990, the recommendations would affect students starting in the 1989/90 student cohort.

  5. 5.

    “EUI Strategic Review. November 2001 Enhancing and Enlarging. The Future EUI”. The main results of the review were put in place for the student cohort that had started in the year 2000.

  6. 6.

    See for instance the position paper issued by the Coimbra group on doctoral education, a leading coalition of research universities.

  7. 7.

    Our results do not change substantively when we apply the time-restrictions more sharply (i.e. 4 years is 48 months, 5 years is 60 months, and 6 years is 72 months).

  8. 8.

    Unfortunately we were not able to retrieve the departmental assessment of candidates in the selection procedure. We are therefore not able to include any measures of academic ability in our list of covariates. Nevertheless, since the process is so selective, the variance in academic ability may not be all that big.

  9. 9.

    Table 4 in the Appendix reports the detailed results of the autocorrelation tests, which were performed using the user-written command –actest– (Baum and Schafer 2013) in Stata 14.1 (StataCorp 2015).

  10. 10.

    The models were estimated using the user-written command –itsa– (Linden 2015, 2017) in Stata 14.1 (StataCorp 2015).

  11. 11.

    More precisely, Y t is the predictive margin of the outcome of interest, which correspond to the covariate-adjusted predicted probability of graduating within 4, 5 and 6 years and to dropout from the program. More details are provided below.

  12. 12.

    One might argue that interpreting test statistics makes little sense as we have total population data of EUI students from 1976 to 2006. However, the students who effectively studied at the EUI might be seen at as a sample from a universe of students who are eligible for the EUI. Elements of chance in the recruitment resulting from arbitrariness in applying and admitting then creates a mechanism of chance sampling from that super-population, that is, if recruitment were repeated hypothetically different student populations may have been created. Thus, one could interpret standard errors as measures for the variability of the hypothetical distribution of coefficients that could have been produced by other potential recruitment outcomes under the assumption that the null hypothesis is true. Nonetheless, we do not over-interpret test statistics in our setting and use them only in a heuristic way.

  13. 13.

    For checking robustness, we applied a more flexible local linear regression to our data, using optimal bandwidth (Imbens and Kalyanaraman 2012) and lower degree polynomials (Gelman and Imbens 2014). Results (available from the authors upon request) were substantially very similar to the models presented in the article.

  14. 14.

    Detailed results from the logistic regression models from which the predictive margins were derived are available from the authors upon request.

  15. 15.

    Figure 3 shows the (non-adjusted) observed values of the four outcomes under study and the corresponding adjusted predictive margins. Table 6 reports the ITSA performed on the observed, non-adjusted, data.

  16. 16.

    Indeed, the coefficients for REF1 × TIME reported in Table 3, which measure the difference in the slopes after and before the first reform, are not statistically significant.

  17. 17.

    This finding comes from the 2012 Alumni Survey conducted by the EUI on a sample of 945 former PhD students in order to investigate their following occupational placement.


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We would like to thank the European University Institute for providing us with the data and Ken Hulley for precious guidance on the information contained in the dataset. We would like to thank the participants to our seminar at the EUI’s Economics Department (2015) and at the COMPIE Conference at the Catholic University of Milan (2016) for useful comments. We would also like to express our gratitude to Hans-Peter Blossfeld, Andrea Ichino, Michael Grätz, Hannes Kröger, Loris Vergolini, and Nadir Zanini, as well as two reviewers who provided stimulating remarks on a previous version of this article. All remaining errors are our own.

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Correspondence to Moris Triventi.



See Tables 4, 5 and 6 and Fig. 3.

Table 4 Cumby–Huizinga test for autocorrelation in the time-series
Table 5 Predictive margins for the four outcomes from the ITSA models
Table 6 ITSA: results on the reforms effects using observed yearly aggregated outcomes (without controlling for compositional changes)
Fig. 3

Comparison of observed data and adjusted predictive margins of the outcomes under study. Note Predictive margins represent the expected probability of an outcome for a certain year (entry cohort) adjusted for covariates

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Geven, K., Skopek, J. & Triventi, M. How to Increase PhD Completion Rates? An Impact Evaluation of Two Reforms in a Selective Graduate School, 1976–2012. Res High Educ 59, 529–552 (2018).

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  • Doctoral program
  • Time-to-degree
  • Attrition
  • Dropout
  • Interrupted time-series analysis