For the past several years now, the Education Law Center of New Jersey and I have been producing a roughly annual report on the state of school finance systems. As that report has evolved, we have taken advantage of publicly available data to construct more and more indicators. Over the next several months, we will be releasing an update of the funding fairness report and a report in collaboration with Educational Testing Services which will explore in greater depth the relationships among the various indicators across states. I also expect in the near future to be releasing, with support of Shanker Institute, an update of my 2012 report exploring what we know about the relationship between school spending, schooling resources and student outcomes – in other words, the “does money matter” question.
In my last post, I explored national average trends in school spending and schooling resources, and discussed some of the recent literature on the topic. Here, I provide some snapshots of cross-state variations in financial effort, financial inputs, real resource inputs and student outcomes across states.
I begin with a relatively simple model of how effort and funding translates to resources, and how those resources ultimately provide the enabling conditions for the classroom conditions and practices that lead to better student outcomes. Despite the assertions of some, the schooling equation remains relatively simple – Schooling remains a human resource intensive endeavor, requiring competitive wages to recruit quality teachers and other school staff, and requiring sufficient capital outlay as well to provide the setting for schooling. The search for the holy grail of alternative technologies (broadly speaking, any substantive changes to educational organization/practices) that would substantially reduce the costs of achieving the same outcomes, has not, as of yet panned out. I have discussed this issue generally (as well as methods for studying it), and with specific reference to teacher compensation, as well as “chartering” [where the most aggressive technological substitutions in particular have been massive failures thus far].
REALLY SIMPLE MODEL
Building on the findings and justifications provided by Baker (2012 – Update coming soon!!!), we offer Figure 1 as a simple model of the relationship of schooling resources to children’s measurable school achievement outcomes. First, the fiscal capacity of states – their wealth and income – does affect their ability to finance public education systems. But, as we have shown in related research, on which we expand herein, the effort put forth in state and local tax policy plays an equal role (Baker, Farrie & Sciarra, 2010).
The amount of state and local revenue raised drives the majority of current spending of local public school districts, because federal aid constitutes such a relatively small share. Further, the amount of money a district is able spend on current operations determines the staffing ratios, class sizes and wages a local public school district is able to pay. Indeed, there are tradeoffs to be made between staffing ratios and wage levels. Finally, a sizable body of research illustrates the connection between staffing qualities and quantities and student outcomes (see Baker, 2012).
The connections laid out in this model seem rather obvious. How much you raise dictates how much you can spend. How much you spend in a labor intensive industry dictates how many individuals you can employ, the wage you can pay them, and in turn the quality of individuals you can recruit and retain. But in this modern era of resource-free school “reforms” the connections between revenue, spending, and real, tangible resources are often ignored, or worse, argued to be irrelevant. A common theme advanced in modern political discourse is that all schools and districts already have more than enough money to get the job done. They simply need to use it more wisely and adjust to the “new normal” (Baker & Welner, 2012).
But, on closer inspection of the levels of funding available across states and local public school districts within states, this argument rings hollow. To illustrate, we spend a significant portion of this report statistically documenting these connections. First, we take a quick look at existing literature on the relevance of state school finance systems, and reform of those systems for improving the level and distribution of student outcomes, and literature on the importance of class sizes and teacher wages for improving school quality as measured by student outcomes.
Following is a run down of the indicators I will explore herein, for their obvious connections – across states – in Figure 1 above:
Fiscal Indicator 1: State Effort Ratio, or Total State and Local Revenue for Elementary and Secondary Education as a Percent of Gross Domestic Product (State)
Fiscal Indicator 2: Total State and Local Revenue per Pupil for a K-12 District with 10% Census Poverty, 2,000 or more students, in an average wage labor market.
Fiscal Indicator 3: Current Spending per Pupil for a K-12 District with 10% Census Poverty, 2,000 or more students, in an average wage labor market.
Fiscal Equity Indicator 1: Current Spending Fairness Ratio: Predicted current spending per pupil for a district with 30% poverty divided by predicted current spending per pupil for a district with 0% poverty, for K-12 districts with 2,000 or more students, in an average wage labor market.
- Current spending fairness ratio of 1.2 indicates that a high poverty district is expected to have 20% higher per pupil spending than a low poverty district, and the system is progressive.
- Current spending fairness ratio of .80 indicates that a high poverty district is expected to have only 80% of the spending of a low poverty district and the system is regressive.
Real Resource Inputs
Resource Input 1: Teachers per 100 Pupils for a K-12 district with 10% Census Poverty, 2,000 or more students, in an average wage labor market.
Resource Input 2: Competitive Wage Ratio: Predicted wage of elementary and secondary teachers divided by predicted wage of non-teachers working in the same state, with master’s degree, at specific ages.
Resource Input 3:Self Contained [average] Class Size, predicted for a school of at least 300 pupils, in a district with state (and labor market) average poverty rate.
Resource Equity Indicator 1: Teachers per 100 Pupils Fairness Ratio: Predicted teachers per 100 pupils for a district with 30% poverty divided by predicted teachers per 100 pupils for a district with 0% poverty, for K-12 districts with 2,000 or more students, in an average wage labor market.
- Teachers per 100 pupils fairness ratio of .80 indicates that a high poverty district is expected to have 80% of the teachers per 100 pupils of a low poverty district and the system is regressive.
- Teachers per 100 pupils fairness ratio of 1.2 indicates that a high poverty district is expected to have 20% higher teachers per 100 pupils than a low poverty district, and the system is progressive.
Outcome Levels and Disparities
Outcome Level Indicator 1– Low Income Students Performance Level: Standardized difference between actual and expected NAEP scale score for low income students (given mean income of low income families)
Outcome Gap Indicator 1 – Low Income Achievement Gap: Standardized difference in NAEP mean scale scores of low income (free lunch) vs. non-low income children, corrected for differences in the mean income levels of the two groups.
Outcome Gap Indicator 2 –Income Achievement Effect: Statistical relationship across schools within states between school level concentration of low income children and school level expected NAEP mean scale score.
PREVIEW OF CROSS STATE PATTERNS
The following figures reveal the somewhat unsurprising findings:
Note: State income/wealth measures tend to be similarly associated with state revenue and spending levels. That is, revenue/spending levels appear to be about evenly split/explained by wealth/income and effort. For example, low income/wealth but most effort explains the position of Mississippi in the figure.
Note: Changes in effort from 2007 to 2013 are associated with changes in revenue. Many states have reduced their effort and revenue toward public schooling since 2007. That is, it’s not just the economy stupid.
Note: This one seems to be a no-brainer, but it’s always worth clarifying each connection. Yes, more revenue does translate to more current spending. There is no great systematic resource hoarding going on here. Similarly strong patterns exist across districts within states, with a select few outliers in any given year being districts having significant revenue raised for long-term obligations in any given year.
Note: It also turns out that in states where spending is greater in higher poverty districts, so too are staffing ratios. That is, more progressive cross district distributions of spending are associated with more progressive distributions of staffing (where more intensive staffing, including smaller class sizes, are needed for reducing achievement gaps).
Note: And not surprisingly, states with more teachers per 100 pupils also tend to have smaller class sizes (holding school size, location and poverty rates constant).
Notes: And while somewhat weaker correlation, it turns out that states with higher spending tend to have more competitive teacher wages, when teacher wages are compared to non-teacher wages for same age, similarly educated individuals. Note that teacher wages slip more by age 45 and the relationship between state spending and wage competitiveness increases (r=.46). A factor that weakens this relationship is the wage of non-teachers. Non-teachers in northeastern states like CT, NJ, NY or MA are quite high, and thus, even at relatively high school spending levels, it’s hard for teachers wages to keep up. Non-teacher wages in states like WY or VT tend to be much lower, and thus with high school spending, teacher wages in those states are equal to or even higher than non-teacher wages.
Notes: Figure 9 sums up the relationships across states (aggregated across years) between our input indicators and our outcome indicators. All but one run in the expected direction, and our “teachers per 100 pupils fairness” measure is modestly correlated in the expected direction with each outcome measure. That is, states where more teachers per 100 pupils are in higher poverty districts (relative to low poverty districts) tend to have higher NAEP outcomes for low income children, smaller gaps between low income and non-low income children and tend to have less disparity in NAEP outcomes between lower and higher poverty schools.
Summing it up:
- States that apply more effort – spending a greater share of their fiscal capacity on schools – spend more generally on schools;
- These higher spending levels translate into higher statewide staffing levels – more teaching staff per pupil;
- These higher spending levels translate to more competitive statewide teacher wages;
- Increased targeted staffing to higher poverty schools within states is associated both with higher measured outcomes of low income children and with smaller achievement gaps between children from low income and non-low income families.
There’s plenty more to be explored here, and the longitudinal data set (with assistance from William T. Grant Foundation) is starting to really come together.