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In determining that the then existing school funding law did not provide adequate funding to "poorer, urban districts," criteria were developed to determine which districts would be classified as special needs districts.

In developing the methodology for assigning this status to school districts, it was determined that among other requirements the district had to be classified in one of the two lowest DFG categories.

The current list of Abbott districts is based on the DFG classification derived from community characteristics that existed in These recommendations were presented to the legislature in an April 11, report.

The DFGs were again included as part of the recommended criteria. Overall, the DFGs play little role in the allocation of state education aid to school districts.

State aid, as calculated in the Comprehensive Education Improvement and Financing Act CEIFA , is determined based on wealth measures equalized property valuation and income and student needs e.

There is one area, however, in which the DFG classifications have a more substantive impact on state aid. In a later ruling Abbott IV , the court required that, as a form of interim relief to the Abbott districts, the state provide enough aid to these districts such that they are able to spend as much as the wealthiest districts to provide regular education services.

This provided the benchmark for regular education funding for the Abbott districts. There are two key reasons the DFGs are updated with the release of new Census data.

First, it is important to use the most current data available to ensure that demographic changes that may have occurred across communities are adequately reflected in the measure.

Second, the updates provide an opportunity to modify the methodology used to determine the DFGs in order to ensure that the classification is as accurate as possible.

To more fully understand the process employed in this update, it is useful to explore how the DFG calculation has changed over the three previous versions.

The consistent decision to rely on this data is due to the fact that it is the only data source available that provides statistically reliable data at the municipal level on a broad range of characteristics commonly used to measure SES.

Since New Jersey school districts overlap with municipalities or a cluster of municipalities , aggregating the census data to the school district level is a straightforward process.

Table 1 is an adaptation of a table included in the DFG report and offers a brief summary of which variables have been used to determine the SES measures for each district and how they have changed over time.

While the table provides a concise depiction of the changes, a more detailed discussion of each variable is in order. The report noted that this methodology makes implicit assumptions regarding how much better additional years of education are without empirical support for these assumptions for example, the method implies that having one to four years of education is twice as good as having no formal education.

To resolve this concern, the analysis used two variables to measure educational attainment: the percent of adults without a high school diploma and the percent of adults with some level of college education.

This avoided the assumptions made by the previous analyses and was grounded in research literature on the benefits of obtaining specific levels of education.

To that end, all three DFG models included an occupational status score. The census data includes the number of people who are employed in broad occupational categories.

Survey results published by A. Reiss provided measures of the level of prestige the general public associates with occupations in these categories.

These scores were used to rank the occupation groups on a scale of 1 least prestigious to 12 most prestigious and a community prestige score was calculated based on the percent of residents who held jobs in each category.

This methodology is very similar to the education measure produced in the first two iterations and has similar shortcomings. While this was noted in the DFG report, experimentation with alternative measures failed to produce better results.

To that end, all three DFG reports measured occupational status in the same manner. This stark difference failed to capture degrees of variation that may exist across districts.

The most recent report dropped the urbanization variable and added population density. This was an attempt to measure the same concept in a more refined manner to capture nuanced differences among the districts that would not be captured in the dichotomous variable.

The first iteration used average family income. In the DFGs, this was switched to median family income, as the average may be skewed by a small number of outlying observations.

This same measure was used in The second analysis changed the measure to capture the percent of workers who received unemployment compensation at some point in the previous year.

The most recent DFG analysis noted that some unemployed individuals do not actually receive unemployment compensation. As such, that report reverted back to the traditional unemployment rate.

This measure does not include individuals who do not live with any relatives. The and analyses used the more inclusive person level poverty rate.

When the DFGs were developed, exploratory analysis suggested that this variable was no longer a useful indicator of SES.

Therefore, it was dropped. The report noted that over time, this has become a less reliable indicator for SES as people became increasingly likely to relocate to pursue better career opportunities.

This variable has not been utilized since the DFG report. While a detailed explanation of this procedure is beyond the scope of this report, a general description will provide better insight into how the DFGs are determined.

PCA is a technique designed to express the information contained in a group of highly correlated variables in a smaller number of variables.

For example, assume a situation in which an analyst has collected height and weight data for a population. PCA could be used to calculate a new variable called a principal component that captures the same information, but with the use of only one variable instead of two.

One could view this combination of the height and weight data as a more generic size measure. This description is very simplified.

In fact, the PCA process will not produce just one principal component. Rather, it will create as many principal components as there are variables in the original analysis.

One would not use all of the principal components, however, because that would be inconsistent with the objective of reducing the number of variables included in the analysis.

This is a reasonable approach if the variables included in the analysis impact the first principal component in a manner consistent with expectations for example, if the results show higher income decreases the first principal component, it is likely that the first principal component is not measuring SES.

Once the PCA analysis has been implemented and the first principal component has defined a numeric measure of relative SES, the districts must be grouped into the DFG classes.

The first two DFG reports utilized a simple method. The districts were grouped into deciles ten groups containing an approximately equal number of districts based on their SES score the first principal component discussed above.

The report noted that this grouping method, while straightforward, was flawed. The process of classifying districts into equally sized deciles did not account for the magnitude of the difference in the SES scores across districts.

This represented a particular problem in the middle of the distribution, where a large number of districts had similar SES scores.

One result of this problem was that in some cases, average test scores were higher in lower DFGs. The analysis classified districts based on the range of SES scores.

These groupings became the eight DFG categories currently used. Given the expanded use of the DFG classification, particularly the lowest and highest categories, efforts were made to preserve the underlying meaning of these groups.

In determining the DFGs using the Decennial Census data, the overarching goal was to continue refining the methodology in ways that will make the calculation more accurate while simultaneously preserving the basic meaning of the DFG classifications particularly the two lowest and two highest categories.

To this end, the department began the process by obtaining feedback from districts regarding modifications that may be required.

Through various means of communication, the department received a significant number of comments. The most common concerns can be classified into one of four categories:.

It should be noted that questions were not raised regarding the statistical technique used to determine the SES scores and the method for grouping districts into DFG classes.

Given the previous and future uses of the DFGs, one key objective is to preserve the underlying meaning of the groupings, particularly at the low and high ends.

In the absence of any compelling reason to modify these methods, the decision was made to continue the same quantitative analysis technique and grouping method used in the development of the DFGs.

The four subject areas raised during various discussions were explored at length in developing the DFGs. The process is discussed and the final decisions made are explained here.

In reviewing the previous DFG analyses and discussing the measure with representatives from school districts, a number of questions were raised with regards to variables that may improve the DFG calculation.

The previous inclusion of one variable, population density, was called into question. When determining whether such variables should be added to the model, several factors were considered:.

Empirical Results: After experimenting with various models, variables that do a poor job of defining SES should be dropped from the final analysis.

In updating the DFGs, six changes in the model specification were tested with the above four considerations in mind.

The empirical analysis is straightforward. The first model was a baseline version that included the same seven variables as the DFGs.

Each additional option made one change to allow a clear comparison to the baseline version. Each variable used is discussed below. Table 2 summarizes the results of the PCA models.

A review of literature on SES does not reveal frequent use of this measure. Furthermore, a table in the DFG report suggests that this variable was substantially weaker than the other six in terms of explaining SES.

The share of explained variance increases by nearly 10 percentage points or 14 percent. However, the percent of students classified as LEP is not an appropriate measure for this analysis as it is at least partly determined by district policy and practice.

The census data provides two variables that could be used to measure this phenomenon: 1 the percent of people between the ages of 5 and 17 who do not speak English well and 2 the percent of households that are "linguistically isolated" households in which no one over the age of 14 speaks English well.

It should be noted that some analysis was done with the first variable when the DFGs were developed. However, the report concluded that this was not a reasonable measure of SES.

The empirical analysis here corroborates those results. Including the percent of individuals who do not speak English well decreases the explained variance by 6.

Including linguistic isolation yields a similarly sized decrease 5. While it appears that further analysis is warranted, it should be noted that the DFG analysis explored using this variable as an alternative to the poverty measure.

It was determined that poverty was a more appropriate variable. In this analysis, the percent of families with children is explored as a supplement to the other variables.

However, the results show a slight decrease in the percent of variable explained 1. As noted in the report, this does not provide information on how poor these individuals are.

While the inclusion of this variable seems intuitive, it caused a small decrease in the percent of variance explained 0. This idea raises two concerns.

First, similar to the percent of students classified as LEP, it is a measure that partly depends on district level decisions.

Second, there appears to be nothing in the research literature on this topic that link disability status to SES.

To explore this linkage, census data are used to estimate the percent of people between the ages of 5 and 20 who have some disability this measure has the benefit of not being affected by district level decision-making.

The explained variance decreases 4. The census data do not include variables that may be used as a proxy for student mobility. As an alternative, data from the School Report Card were aggregated to the school district level to estimate the mobility rate.

Given the above discussion, it appears that the best model should include six variables: percent of adults with no high school diploma, percent of adults with some college education, occupational status, median family income, poverty rate, and unemployment rate.

A considerable number of school districts are engaged in sending-receiving relationships whereby a district educates students from another community on a tuition basis.

There may be situations in which a district receives students from a community with substantially different demographics.

As designed in the past, the DFGs were based on the characteristics of the community in which the district is located, not the communities in which the enrolled students live.

This may lead to a district being classified in an inappropriate DFG. When submitting the Application for State School Aid ASSA data, districts involved in sending-receiving relationships provide information on the community from which their students originate.

It should be noted that this method prevents the assignment of a DFG to non-operating school districts, as these districts do not operate school buildings.

The characteristics of students in these communities will be accounted for in the district where the student actually attends school.

The census data used to calculate the DFGs provide information on the characteristics of the community in which the school districts are located. In general, this provides a reasonable approximation of the demographics of students served by the public schools.

However, some district representatives raised concerns that the demographics of the community are not representative of the students served by the schools.

This situation may occur, for example, in communities where the more privileged children in a community attend non-public schools.

In attempting to address this concern, one needs a data source that provides a broad range of data on demographic characteristics specifically for the students enrolled in public schools.

This data set aggregates information from the Decennial Census at the school district rather than municipal level. More importantly, it also provides information specifically for parents who have children enrolled in public schools.

In theory, these data should be useful in addressing the concern that was raised. Upon release of the data, the department developed estimates of the DFGs based on the characteristics of parents with children enrolled in the public schools.

Detailed analysis of these data suggested that it would not be a suitable replacement for the data used in the past. These data raised two concerns.

First, there were a significant number of school districts in which there were fewer than 70 parents included in the sample.

With all survey data, it is necessary to have a sufficient sample size to ensure the sample is representative of the population in question. While there is not a specific requirement, the Census Bureau uses a sample size of 70 for reporting purposes when writing reports based on other data collections.

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