Ending the Transformation before the Goal is Fulfilled: The Case of Head Start

AuthorIlana Shpaizman
Published date01 March 2023
Date01 March 2023
Subject MatterArticles
Administration & Society
2023, Vol. 55(3) 381 –404
© The Author(s) 2023
Article reuse guidelines:
DOI: 10.1177/00953997221147225
Ending the
Transformation before
the Goal is Fulfilled:
The Case of Head Start
Ilana Shpaizman1
To make a comprehensive policy change, actors often turn to the
gradual path where they introduce small-scale changes hoping that their
accumulation will meet their goal over time. Nonetheless, they often stop
the transformative process before meeting their original goal. This paper
argues that this can be explained by policy learning. When actors learn from
reliable information that the accumulation of the small-scale changes does
not meet their expectations, they stop the transformative process. At the
same time, the policy is not illuminated due to feedback effects and beliefs
by the majority of actors that the small-scale changes are beneficial.
child care, policy learning, gradual transformative change
Actors wishing to promote a significant policy change often face many insti-
tutional, political, and cognitive obstacles (Baumgartner & Jones, 2010). One
way to bypass these obstacles is to gradually meet their goal by introducing
small-scale changes that, over time, lead to a shift from the status quo. This
1Bar Ilan University, Ramat Gan, Israel
Corresponding Author:
Ilana Shpaizman, Department of Political Studies, Bar Ilan University, Ramat Gan 5290002, Israel.
Email: Ilana.shpaizman@biu.ac.il
1147225AAS0010.1177/00953997221147225Administration & SocietyShpaizman
382 Administration & Society 55(3)
strategy has been used in many policy fields in the last 60 years (Streeck &
Thelen, 2005; van der Heijden & Kuhlmann, 2017). Some prominent exam-
ples are the Republican party’s efforts to replace Medicare by gradually intro-
ducing private insurance plans and blocking efforts to update the existing
structures (Hacker, 2004) or the gradual changes made in Social Security
(Béland, 2007).
Nonetheless, when closely examining the various examples, one finds that
often, while actors were able to shift the status quo, they still had not met
their initial goal after many years. For instance, both Medicare and Social
Security are still active. Although some of the transformative processes might
still be in progress, others stagnated and became adaptive; no new initiatives
have been introduced, no expansion of the existing instruments or strategies
has taken place, and most of the attention has turned to adjust existing struc-
tures. All that even though actors did not change their original goals and were
not forced to stop the transformation. This raises the question of why actors
stop the transformative process before meeting their original goal.
So far, we do not have a sufficient answer to this question. For the most
part, existing research on gradual transformative policy change focuses on
the shift from the status quo that has taken place and its causes. However, it
does not examine to what extent this shift has fulfilled actors’ initial goals.
This is important because focusing only on the successes of the gradual trans-
formative change can lead to a distorted valuation of this change strategy,
seeing it as more successful than it is.
The few existing works that look beyond the shift from the status quo have
pointed to the role a powerful opposition can have on the stagnation of the
transformative process (Mandelkern & Koreh, 2018) or to instances when
negative feedback causes actors to reverse the transformation (Shpaizman,
2017). This paper aims to shed light on another mechanism that could operate
in dynamics unfolding over a long period—policy learning.
Learning occurs when there are policy anomalies, discrepancies between
the expectations and the outcomes (Hall, 1993; May, 1992; Sabatier &
Jenkins-Smith, 1993). In a gradual transformative change, actors have expec-
tations about the policy’s immediate outcome and the accumulated effect, the
estimated effect of the accumulation of small-scale changes over time. The
nature of the gradual process makes it likely to experience policy anomalies
regarding the accumulated effect. Since the information processing of actors
is not proportional (Baumgartner & Jones, 2015), actors will pay attention to
the anomalies only when the level of conflict is moderate and when reliable
information accumulates (Jenkins-Smith et al., 2014). Then, policy learning
will take place; actors will update their beliefs about the expected accumu-
lated effect of the instrument. In other words, they will no longer believe that

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