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Policy dynamics and the evolution of state charter school laws

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Abstract

Baumgartner and Jones (1993) showed how radically new policies emerge on government agendas as a consequence of exogenous shocks to policy subsystems displacing privileged interests. But how do these policies evolve post-punctuation? In this paper, we present three different models of policy change. Policies may revert to the old status quo if displaced interests re-assert themselves, or they may be “locked-in” by new interests now reaping the benefits. Alternatively, they may incrementally change as lawmakers “learn” how to better meet target population needs, particularly by witnessing how other jurisdictions address similar problems. We test these models with data on change in state charter schools laws over time. We find that whether old status quos are overthrow, and the fate of charter policies when they are enacted, is influenced more by competing political interests, especially interest groups, than elite and public perceptions of broad systemic crises. Yet, we also find that changing demands on the state and learning from the successes and failures of neighboring states also play significant roles.

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Notes

  1. See www.edreform.com/index.cfm?fuseAction=document&documentID=1964, accessed on July 16, 2008.

  2. A more detailed methodological appendix is available from the lead author upon request.

  3. The 1998 data came from the Book of the States for 1998, while the 2006 data came from the National Conference of State Legislatures at: www.ncsl.org/statevote/partycomptable2006.htm.

  4. We used a coding of 1 and 2 rather than the more traditional 0 and 1 because we did not want to eliminate only partial Democratic control by multiplying it by 0. This approach enhances Democratic power if they control both houses.

  5. We used schools rather than students because so many charter schools in the early years were unable to enroll even a fraction of the number of students applying.

  6. The Institute’s data is available at www.followthemoney.org. The values were quite large, and so as not to produce skewed coefficients, we divided the state contribution by 10,000. Though this reduces the per-state number, the variation is unchanged.

  7. We are aware that the NEA and AFT have also been supportive of charter schools in some states (see Buss 1999), even opening a few of their own, but this did not start happening until many years after the first law was enacted, after these organizations had presumably realized they were not going to stop charter schooling and had better change their political positions to remain relevant. We cannot be certain, but we suspect that doing so kept them from being completely marginalized in many state education policy communities, allowing them to still lobby effectively for greater regulation and oversight of charter schools in those states.

  8. Renzulli and Roscigno (2005) also used membership in the American Federation of Teachers, but they argued it is the NEA that has led the fight against charters, and so we only use the NEA data.

  9. Molnar’s EMO data can be found at http://www.asu.edu/educ/epsl/CERU/.

  10. Although Adequate Yearly Progress (AYP) scores are considered by many scholars to be the best measures of student performance, this data does not exist as far back as 1991, so we were only left with SAT scores. ACT scores were also unavailable for the early 1990s, and so we have relied exclusively on SAT scores.

  11. We recognize the important and ongoing debates over the proper measurement of dropouts. We share with its critics some reservations about the precision and reliability of this indicator, particularly when it would be used as part of a formula for policy sanctions. For our purposes, however, this is a reasonably good addition to test scores and has the advantages of being available and well recognized.

  12. Case et al. 1993 started to investigate the idea that diffusion might happen among state “peer-groups” defined on the basis of characteristics other than geographic proximity, such as demographic and fiscal similarities, an idea that has also been explored somewhat by Volden (2006). Although work as recently as Boehmke and Witmer (2004) has continued to rely on the geographic approach, we did try a couple of non-geographic approaches by separating the states into groups based on population (by 1/4th standard deviation around the mean) and percentage of the population living in urban areas, data from the Census Bureau, and found that these new variables performed virtually the same as our geographic diffusion variable. As the latter still represents the norm in the literature, we decided to keep it in the analyses, although we are willing to provide the alternative estimates upon request. A list of Census Bureau geographic regions can be found at http://www.census.gov/geo/www/reg_div.txt

  13. This is a standard survival model with errors assumed to follow an exponential distribution.

  14. The policy adoption model had a much larger N, and so multi-collinearity was not a problem.

  15. No other explanatory variables changed significance or direction in this limited re-estimation and we are happy to provide interested readers with the full results.

  16. There is some correlation between some of the independent variables, and so we checked the model with a variance inflation test. Each explanatory variable is regressed on all others one at a time. The R 2 of each regression is subtracted from 1 and then divided by 1. If it produces a score in excess of 10 for one regression, then collinearity is a serious problem. Fortunately, the highest score we calculated was 3.45, and so we conclude that multi-collinearity was not a problem.

  17. Though we do not create a new table showing the re-estimated model with the interactive term, it is worth noting that the EMO variable was no longer statistically significant, but it was the only variable to significantly change. The estimation results are available from the lead author upon request. The robust standard error for the interaction term was 0.36.

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Acknowledgements

The authors would like to thank The Spencer Foundation and the Russell Sage Foundation for their generous support of this research.

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Correspondence to Thomas T. Holyoke.

Appendix 1

Appendix 1

Measuring state charter school laws

Our measure of how little regulation a state places on charter schools (its “flexibility”) is drawn from measures developed by CER where five researchers coded a state’s laws along ten dimensions (from Charter Schools at www.edreform.com/_upload/charter_school_laws.pdf):

  1. (1)

    Number of schools: States that permit a number of autonomous charter schools encourage more activity than states that limit the number of autonomous schools.

  2. (2)

    Multiple chartering authorities/binding appeals process: States that permit a number of entities in addition to or instead of local school boards to authorize charter schools, or that provide applicants with a binding appeals process, encourage more activity.

  3. (3)

    Variety of applicants: States that permit a variety of individuals and groups both inside and outside the existing public school system to start charter schools encourage more activity than states that limit eligible applicants to public schools or public school personnel.

  4. (4)

    New starts: States that permit new schools to start up encourage more activity than those that permit only public school conversions.

  5. (5)

    Schools may start without third-party consent: States that permit charter schools to form without needing consent from competing districts or the general public encourage more activity than those that do not.

  6. (6)

    Automatic waiver from laws and regulations: States that provide automatic blanket waivers from most or all state and district education laws, regulations, and policies encourage more activity than states that provide no waivers or require charter schools to negotiate waivers on an issue-by issue basis.

  7. (7)

    Legal/operational autonomy: States that allow charter schools to be independent legal entities that can own property, sue and be sued, incur debt, control budget and personnel, and contract for services, encourage more activity than states in which charter schools remain under district jurisdiction. In addition, legal autonomy refers to the ability of charter schools to control their own enrollment numbers.

  8. (8)

    Guaranteed full funding: States where 100% of per-pupil funding automatically follows students enrolled in charter schools encourage more activity than states where the amount is automatically lower or negotiated with the district.

  9. (9)

    Fiscal autonomy: States that give charter schools full control over their own budgets, without the district holding the funds, encourage more activity than states that do not.

  10. (10)

    Exemption from collective bargaining agreements/district work rules: States that give charter schools complete control over personnel decisions encourage more activity than states where charter school teachers must remain subject to the terms of district collective bargaining agreements or work rules.

CER codes each dimension from 0 to 5 and then sums them for one aggregate measure, but this creates two problems. First, the measures may be biased because CER is an advocacy organization. Second, some dimensions may capture very different attributes of a state’s policy that are lost when summed. We tackle the second issue first. Witte et al. (2003) argue that there are two basic dimensions in state charter school policies, “flexibility” (ease of organizing and running new schools) and “accountability” (oversight of school achievements). The aggregate CER mixes together both of these when they should be kept separate, which suggests that not all ten dimensions should be used. As our concept of interest is the degree to which states have policies facilitating the growth of large and diverse communities of charter schools largely free from regulation and oversight, we selected six of the ten that we felt captured elements of this concept, namely dimensions 2, 6, 7, 8, 9, and 10.

Wong and Shen (2006) argue that because CER’s scales are measuring different concepts, many of them actually change in different directions from year to year, rendering the aggregate measure internally inconsistent and masking crucial variation. We therefore found the differences of all ten dimensions from 1998 to 2006 and calculated the standard deviation for every state, the average being 0.86. Changes in different directions should produce larger standard deviations, so a subset of these dimensions changing in the same direction should produce smaller ones. We found that five of our selected six (excluding 10) plus dimension 5 (third party consent for opening a school) reduced the average of all state standard deviations to 0.76. We feel that dimension 5 does capture freedom from regulation as it removes much of the public’s role in new school approval, so we combine it with 2, 6, 7, 8, and 9 (still excluding 10 which actually increased the average standard deviation when included) for a final conceptually and statistically consistent measure of charter policy. Our choice is subsequently confirmed by a factor analysis where these measures loaded on one dimension (eigenvalue of 3.49, 0.29 the next largest). The 1998 and 2006 codes for each state with charter laws are listed in Table 4.

As for measure validity due to political bias, three of the five members of the Center’s coding team are from research institutions (Nathan, Greene, and Walsh) and we see no obvious flaw in their coding methodology. Apart from the fact that other researchers have found the CER measures to be acceptable (see Kirst 2006; Stoddard and Corcoran 2007), we note that after Witte et al. developed arguably the most valid and reliable coding scheme for state charter laws (see Shober et al. 2006), they found their measure correlated with the full CER measure at 0.82. Not only were they content to use CER scores as a baseline for evaluating their own measure, the high correlation suggests that the CER measure is most likely valid and reliable as well.

Table 4 State CER Scores for 1998 and 2006 (from the six measures we combined)

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Holyoke, T.T., Henig, J.R., Brown, H. et al. Policy dynamics and the evolution of state charter school laws. Policy Sci 42, 33–55 (2009). https://doi.org/10.1007/s11077-009-9077-3

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