New York City Charter 2013

Following the City Charter revision of which eliminated the powerful New York City Board of Estimate on which the President held a seat, the post was seen as largely ceremonial; its only notable responsibility was to cast the deciding vote in the City Council in the unlikely event of a tie. At the time, it was thought likely that the post would be abolished altogether. Mark Green was the first Public Advocate and served through his unsuccessful run for Mayor in He was succeeded by Betsy Gotbaum.

The election to succeed Gotbaum was highly competitive and was won by Bill de Blasio , who later became the first Public Advocate to win the Mayor's office. The Public Advocate is a non-voting member of the New York City Council with the right to introduce and co-sponsor legislation.

Prior to a charter revision, the Public Advocate was also the presiding officer of the Council. These duties, worded somewhat ambiguously, are laid out in Section 24 of the City Charter. The report shall be for the period commencing on the date that the project agreement and any other documents applicable to such project have been executed through the final year that such entity receives assistance for such project, except that, as to projects consisting of a lease or sale of city-owned land, each annual report shall include only 1 a list of each existing lease, regardless of when such lease commenced, and a list of each sale of city-owned land that closed on or after January 1, , and 2 for such leases or sales, any terms or restrictions on the use of the property, including the rent received for each leased property in the prior fiscal year, and for sales, the price for which the property was sold and any terms or restrictions on the resale of the property, and need not include any other information with regard to such lease or sale of a type required for reports for other projects hereunder.

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Information on any such lease shall be included until the lease terminates and information on sales of city-owned land shall be included for fifteen years following closing. The report, other than for leases or sales of city-owned land, shall contain, for the prior fiscal year, the following information with respect thereto: Reports with regard to projects for which assistance was received prior to July 1, need only contain such information required by this paragraph as is available to the city, can be reasonably derived from available sources, and can be reasonably obtained from the business entity to which assistance was provided; xiii for the current reporting year, with respect to the entity or entities receiving assistance and their affiliates, the number and percentage of employees at all sites covered by the project agreement who reside in the city of New York.

For projects in existence prior to the effective date of this local law, information that business entities were not required to report to such [local development corporation] contracted entity at the time that the project agreement and any other documents applicable to such project were executed need not be contained in the report. The report shall be submitted by the statutory due date and shall bear the actual date that the report was submitted. Such report shall include a statement explaining any delay in its submission past the statutory due date.

Upon its submission, the report shall simultaneously be made available in electronic form on the website of the [local development corporation] contracted entity or, if no such website is maintained, on the website of the city of New York, provided that reports submitted in or after shall simultaneously be made available in a commonly available non-proprietary database format on the website of the [local development corporation] contracted entity or, if no such website is maintained, on the website of the city of New York, except that any terms and restrictions on the use or resale of city-owned land need not be included in such non-proprietary database format, and provided further that with respect to the report submitted in in the commonly available non-proprietary database format, the [local development corporation] contracted entity shall include, in such format, the data included in the reports for the period from July 1, to June 30, Reports with regard to projects for which assistance was rendered prior to July 1, , need only contain such information required by this subdivision as is available to the [local development corporation] contracted entity , can be reasonably derived from available sources, and can be reasonably obtained from the business entity to which assistance was provided.

Paragraph b-1 of subdivision 1 of section of the New York city charter, as added by local law number 48 for the year , is amended to read as follows: By March 1, , and by March 1 every two years thereafter, the [local development corporation] entity under contract with the department to provide or administer economic development benefits on behalf of the city , in consultation with the speaker of the city council and other persons selected jointly by the mayor and the speaker of the city council, who have extensive experience and knowledge in the fields of finance, economics, and public policy analysis, shall evaluate the methodology employed for making the determinations required for this report and generate recommendations, where appropriate, on the methodology by which projects receiving economic development subsidies are evaluated.

The department shall present to the [major] mayor and the speaker no later than October 1 of every year in which such evaluation is required, a report containing such recommendations as are presented as a result of this review. Title 22 of the administrative code of the city of New York is amended by adding a new chapter 8 to read as follows: Charters also have access to varying levels of financial resources, from public and private sources. Thus, while charter schools might be effective on average, not all New York City charter schools are equally effective.

The potential for variation in the effectiveness of charter schools is visible in Figures 1—2. As discussed, the RFT studies tend to produce larger estimated effects than do matching studies. Some of that difference could result from differences in research methodologies.

But it could also be due to the fact that the RFT studies focus on a set of highly effective schools, which, by definition, implies variation in charter school quality.

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Dobbie and Fryer is the only RFT study to produce an estimated charter school effect in math worse than those produced by matching estimates in a given year. It is also the only RFT study since Hoxby and Murarka to include a broad set of charter schools rather than only those in a single network.

CREDO , which disaggregates results by school, finds that It finds similar results for ELA. Among networks operating at least four charters in New York, the effect of attending a network school ranges from —0. Keep in mind that the estimated charter school effect can be considered to be causal, but the associations between effectiveness and observed characteristics of the school cannot be considered to be causal. These studies can therefore identify characteristics that tend to be found in the more successful schools, but they cannot prove that the same positive results would occur if adopted in other schools.

CREDO finds that charter schools operated by charter management organizations CMOs —organizations that manage networks of several schools under a common leadership and philosophy—tend to have substantially larger positive effects on their students than do charters operated by non-CMOs.

For charter schools operated by non-CMOs, the study still finds a significantly positive effect in math but an insignificant effect in ELA. The RFT studies that utilize several different charter schools also attempt to find correlations between characteristics and measured school quality.

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Hoxby and Murarka , for example, find that longer school years are associated with superior quality. But they do not find other characteristics that are robustly associated with measured school effectiveness. Dobbie and Fryer present the most thorough analysis of the underlying factors associated with charter school quality to date. They found that an index of five characteristics—frequent teacher feedback, data-guided instruction, tutoring, high standards for students, and additional instruction time—explained nearly half the variation in measured charter school quality.

The one school input found to be significantly related to charter school effectiveness: Differences in financial resources could influence the effectiveness of charters, relative to traditional public schools and other charters. Here, too, the empirical research may be dated. In , the tuition formula for state funding to charter schools was frozen, with some allowances for supplemental aid in later years.

Nevertheless, some aspects of the academic literature remain pertinent. Taxpayers fund New York City charter schools in several ways. The largest source of public funding— intended to cover operating costs—comes from a per-pupil allocation that is set by the state but paid by the school district through a pass-through fund. Charters can receive allocations for other expenses, though these costs tend to be minor. About two-thirds of charter schools also operate rent-free in a traditional public school building,20 while charters that do not operate in a traditional public school building often receive other in-kind support, such as food service and transportation.

The difference in total public funding between charter and traditional public schools in New York is substantial, depending on whether the school is located in a traditional public school building. Baker and Ferris observe that proportionally fewer charter school students fall into each of these categories than do students in traditional public schools.

Still, the difference in the proportion of charter and traditional public school students classified as ELL has dropped from 14 percentage points at the time of the Baker and Ferris study, to only about 7 percentage points today New York City Charter School Center Thus, the cost-adjusted funding difference found by Baker and Ferris is likely—I am not aware of a recent update to this calculation—to be less important today, even as longitudinal results from matching studies suggest that average charter school effects have remained relatively consistent over time.

Charter schools also receive resources from nonpublic sources, especially private foundations. As such, philanthropic funding does not appear to be a primary driver of the generally positive effects of charter schools relative to traditional public schools in New York. The widespread perception that charter schools receive substantial nonpublic resources to supplement their activities appears to be driven by outliers. Using data from IRS forms, Baker and Ferris similarly find that the amount of additional resources from private sources varies dramatically by charter school.

Further, the empirical evidence offers little guide to whether variation in the amount of nonpublic resources available to a charter school has a meaningful impact on performance. For example, Baker and Ferris find no significant relationship between charter school spending including public and private sources and test scores—though this analysis uses aggregate test scores adjusted for student characteristics as a measure of charter school quality, which is not a design that could plausibly lead to a causal estimate of charter school effects.

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There is a common claim, supported by numerous anecdotes reported in the media, that a main reason for the higher test scores in charter schools comes from their systematic removal of their most difficult to educate students. It is certainly plausible that there have been cases when charter schools as well as traditional public schools have not appropriately handled situations with individual students. However, the academic research literature strongly suggests that charter schools have not systematically manipulated their enrollments in this manner. Overall, charter school students are as likely, or less likely, to leave their school for another school in the city—or to leave the school district entirely—as are students in traditional public schools.

IBO compares the mobility patterns of students who, in the fall of , enrolled in a charter school or in the nearest traditional public school.

New York City charter schools outperform in | Bluegrass Institute | January 31,

Using student-level data from grades 3—8 from to , Winters, Clayton, and Carpenter find that the overall yearly attrition between the sectors was very similar but that attrition from charters was significantly lower than attrition from traditional public schools after accounting for student characteristics. Winters, Clayton, and Carpenter evaluate whether charter schools systematically remove students who are not performing well on standardized tests. They use student-level data for —12 to compare the attrition patterns of students in New York City charter and traditional public schools overall and by test scores.

They find that low-performing students are much more likely to exit their school than are higher-performing students, regardless of the sector. Because Winters, Clayton, and Carpenter find that attending a charter school reduced the likelihood that a student exited his or her school, they ultimately find that low-performing students in New York City are significantly less likely to exit charter schools than traditional public schools.

Figure 4 illustrates the proportion of students in each sector in a variety of categories, as of — In addition, the proportion of students with disabilities and those who are ELL is higher in traditional public schools than in charter schools. UFT and Baker and Ferris observe that the proportion of students in charter and traditional public schools who are eligible for free lunch a measure of extreme poverty —rather than simply reduced-priced lunch—is substantially larger in traditional public schools than in charter schools.

According to the index, students who attend charter schools are more disadvantaged, on average, than those who attend traditional public schools. Figure 5 illustrates some changes in the demographic characteristics of New York City charter schools since — In recent years, it appears that the population of students enrolled in charters has become less black, more Hispanic, and with more ELL.

Though the demographic enrollment gaps between charters and traditional public schools Figure 4 are significant, current enrollment trends appear likely to reduce them. Five factors are relevant:. Start with the factors that do not appear to meaningfully affect enrollment gaps. Are lotteries rigged factor 2, above? The RFT studies demonstrate that there is no significant relationship between the characteristics of students participating in the lottery and the characteristics of the students who were randomly offered a seat.

This suggests that the lottery process is fair and does not influence enrollment gaps between sectors.

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Are students with particular characteristics who win the lottery less likely to enroll factor 3? Charter schools, for instance, could inappropriately encourage students with particular characteristics to decline their acceptance offers. Of the RFT studies, only Hoxby and Murarka fully compare the characteristics of students offered a charter seat with those of students who actually enroll. They do find that students with disabilities are significantly less likely to accept a charter school enrollment offer, though the size of the difference is not substantial 7.

Unterman does not present a full set of these descriptive statistics in her RFT analysis of Success Academy schools. Unfortunately, she does not compare the difference in the proportion of lottery winners with ELL status with those who actually enroll. Are students with particular classifications more likely to leave charter schools factor 5? In other words, different rates of student attrition are an unlikely cause of enrollment differences between the sectors.

Are students with particular classifications in charters more likely to experience a change in their classification status factor 4? Winters finds that, overall, charters and traditional public schools were equally likely to newly classify a student as having a disability.

He also finds that charters were significantly more likely to declassify a student out of special education in early elementary grades—which partly drove the widening gap between the two sectors in the proportion of students enrolled in special education in those same grades. Winters a finds that the proportion of ELL students in who no longer had the ELL classification in was significantly higher in charter schools than in traditional public schools—a result likely influenced by the fact that the average student classified as ELL in a charter was more advanced in English than the average ELL student in a traditional public school.

Winters also finds that because the initial proportion of charter school students with ELL status was much smaller than the proportion in traditional public schools, the ultimate impact of declassification was to reduce the special-education gap as students progressed through school. The research evaluating student enrollment data suggests that differences in the characteristics of charter and traditional public school students are not substantially influenced by factors that occur once students enroll in school.

If factors 2—5 do not explain the difference in the characteristics of students in charter and traditional public schools, factor 1—that some types of students are less likely to apply to charters—must explain it. All of the RFT studies find that students who applied to New York City charter schools were far more likely to be black—and less likely to be Hispanic or ELL—and have lower standardized test scores at entry than were students entering traditional public schools.

There does appear to be considerable variation in special-education applications. Hoxby and Murarka , for instance, find that charter applicants were significantly less likely to be in special education than were other students in the city. Yet Dobbie and Fryer find no meaningful difference between the special-education status of applicants and other students in the city.

Corcoran and Cordes even find that applicants to Democracy Prep charter schools were more likely to be in special education than was the average student in the city. Enrollment data on the universe of city schools also suggest that the application stage is the source of the difference in the proportion of students classified as ELL or having a disability.

Winters and IBO find large differences in the proportion of students in special education across the sectors at the point when students enter kindergarten. Winters a and IBO find large differences across the sectors in the proportion of new kindergarten entrants with ELL status. If charter lotteries are fair and certain types of students are not more likely to enroll after winning a lottery seat, the relatively fewer number of disabled and ELL students who enter charter schools is a product of relatively fewer applications by such students.

An expansive charter sector has the potential to affect not only its own students but also those who remain in the traditional public school sector. Some suspect that competition from charter schools incentivizes traditional public schools to improve in order to retain students.