State of Florida Department of Educationgafla.dadeschools.net/pdf/Florida State Legislative... ·...

REVIEW OF CURRENT PRICE

LEVEL INDEX METHODOLOGY State of Florida Department of Education

DECEMBER 21, 2018 THE BALMORAL GROUP, LLC

165 Lincoln Avenue | Winter Park, FL 32789

REVIEW OF CURRENT PRICE LEVEL INDEX METHODOLOGY | State of Florida Department of Education Page 1 of 66

Table of Contents Executive Summary............................................................................................................................................... 2 Background ........................................................................................................................................................... 4 Literature Review .................................................................................................................................................. 6

Relative to the FPLI and assumptions ............................................................................................................... 6 Relative to methods used in other states ......................................................................................................... 8 FPLI and The Wyoming HWI.............................................................................................................................. 9

Review of Econometric Theories and Assumptions Employed in the Current FPLI ............................................ 12 Assumption 1: Average wages predict the relative costs of hiring school personnel .................................... 13 Assumption 2: The Average Centrality Index of teachers accurately reflects their distribution .................... 13 Assumption 3: County characteristics may be used to improve estimated relative wages ........................... 14 Assumption 4: Wages cannot vary widely between adjacent areas .............................................................. 15 Summary and Analysis .................................................................................................................................... 15

Review of Data Choices and Consistency with the Economic Theories and Assumptions Employed in the Current FPLI ........................................................................................................................................................ 18

Statistical Analysis of the wage data used in the FPLI ................................................................................ 19 Findings of the Review of the Current Price Level Index Methodology. ............................................................ 24 Works Cited......................................................................................................................................................... 25 Appendix A: In-Depth Literature Review ............................................................................................................ 30

Relative to the FPLI and assumptions ............................................................................................................. 30 Relative to methods used in other states ....................................................................................................... 32

The case of Maryland:................................................................................................................................. 32 The case of Washington State .................................................................................................................... 32 The case of New Jersey: .............................................................................................................................. 33 Comparisons between funding allocation approaches .............................................................................. 33

Appendix B: Working Assumptions of the Current FPLI ..................................................................................... 36 Appendix C: Statistics Review ............................................................................................................................. 46 Appendix D: Annotated Bibliography ................................................................................................................. 51 Appendix E: Florida Statute language ................................................................................................................. 65

List of Figures

Figure 1. FPLI Process Logic .................................................................................................................................. 5

Figure 2. Comparable Wage Index Flowchart ..................................................................................................... 11

Figure 3. Impact of Centrality in the 2017 FPLI ................................................................................................... 14 Figure 4. FPLI Index vs. adjustments by County .................................................................................................. 17

Figure 5. Average Annual Wages and k-means breaks ....................................................................................... 19

List of Tables

Table 1. Description of State Methodologies ....................................................................................................... 8 Table 2. Strengths and Weaknesses of approaches to wage indices.................................................................. 10

Table 3. Description of FPLI and Wyoming HWI ................................................................................................. 10

Table 4. Hypotheses and Alternative Hypotheses .............................................................................................. 12

Table 5. Summary of FPLI Process Formulas....................................................................................................... 16

Table 6. List of Data Sources ............................................................................................................................... 18

Table 7. Data Sources.......................................................................................................................................... 20


Executive Summary At the request of the Florida legislature, the Florida Department of Education (DOE) obtained

an independent, third party review of the current index methodology, the methodological basis

for which had not been examined since 2003. This report has been prepared by The Balmoral

Group under contract to the Department of Education to meet the specifications of the

request.

The Commissioner of Education (i.e., the Florida Department of Education) is to annually

compute for each school district in Florida the current year’s district cost differential, based

upon the Florida Price Level Index (FPLI). Florida law specifies how the Index is to be employed

to determine the cost differential1, using a three-year running average and specific factors. The

index is managed and produced by Florida Polytechnic University (FPU) in collaboration with

the University of Florida’s Bureau of Economic and Business Research (BEBR).

The FPLI is a wage-base index used to predict differences in the relative levels of costs for hiring

comparable personnel across school districts in Florida2. The Index is intended to account for

differences that are outside a school district’s control, including what economists call

“amenities” or “disamenities” - things people generally want to live near, or avoid, respectively.

The current FPLI hinges on several economic theories and assumptions. The following four

theories or assumptions broadly capture the FPLI process logic:

1. Average wages in a county are an accurate indicator of the relative costs of hiring school

personnel.

2. Average wages for a county can be adjusted to a level accurately representative of

teachers’/school personnel, using a measure of occupation-specific employment density

and county size.

3. County characteristics may be used to improve estimates of relative wages and/or to

adjust the Index where accurate wage data is sparse.

4. Wages cannot vary widely between adjacent areas.

The report progresses in four stages:

1. A summary of literature relevant to the FPLI. A condensed literature review is contained

in the body of the report, with additional detail and more in-depth analysis in Appendix

A.

2. A summary of how wage or cost of living indices are calculated in other states with

respect to teacher remuneration. The review finds that several other states have

1 See complete language of the relevant Florida Statute in Appendix E. 2 The index is intended to represent the cost of hiring comparable school personnel, although commonly referred to as teachers’ salaries. In Florida, teachers’ salaries constitute about 52% of total school personnel spending.


adjusted their approach in the last few years due to emerging methodologies and better

available data.

3. An assessment of how the assumptions made in the construction of the FPLI are

consistent with economic theory and literature. There are a number of working

hypotheses embedded in the FPLI process; some of the econometric approaches used

are not the norm across other states that use similar index approaches.

4. An assessment of how the data choices made in the construction of the FPLI are

consistent with economic theory and fitness-for-purpose. The data sources are generally

industry standard (e.g. Census); the use and manipulation of the data has changed from

year to year. A table summarizing the changes over time is included in the data review

section.

The report concludes with several recommendations that may improve the FPLI from the

perspective of transparency, accuracy, and fitness-for-purpose. The most significant of these

are:

1. Thoroughly document process steps and data sources to promote understanding and

validate process and calculations, and to ensure that the FPLI represents the legislative

intent.

2. Taking advantage of new County level data OES forthcoming from Census, and other

data sources to consider a Comparable Wage Index based on school personnel-

credentialed comparable occupations (generally, but not for all positions, college-

educated workers).

3. The creation of an advisory group that is conversant in relevant terminology and familiar

with Florida’s education financing may help to address FPLI concerns.


Background Florida is a high-growth state with a highly competitive marketplace for labor, and the allocation of funding

to the education sector is one element of the State’s ability to attract and retain teachers. Schools compete

for teachers (and other school personnel) not just with other schools but with other workplaces that offer

opportunities for a teacher-qualified worker. Therefore, teacher salary is an important component of the

workplace choices teachers make, including whether to seek employment in comparable positions that are

not in the education sector. However local amenities such as school district quality, neighborhood crime or

other factors that economists may not be able to readily measure also need to be considered.

Prior to 2004 the FPLI used a “market basket” approach to assess cost of living variation between counties,

but this was perceived to be biased toward counties with high land costs. It also failed to take into account

the effects of positive amenities that compensate to some degree for high rents. This approach was

subsequently replaced with a wage-based index to account for these issues. Over the intervening period, the

data used in the construction of the FPLI and certain aspects of the methodology have changed, with varying

degrees of transparency. The current wage-based FPLI is constructed in a series of steps as shown in Figure 1

on the following page. It is difficult to ascertain whether the formula and procedures currently in place meet

the original legislative intent of the Index.

Over the past decade, approaches to education funding across the U.S. have evolved with the availability of

better data than was accessible in the past. For example, data that distinguishes rent gradients between

counties and estimates of the responses of actual Florida wages to various commute costs are now readily

available. The wage-based index is one of several approaches currently used across the United States.

The Comparable Wage Index (CWI) is an alternative wage-based index that has been adopted in several other

states. It is based on average wages in a state for jobs that require comparable education and qualifications.

The FPLI in comparison uses the average of all non-education wages. The reason for the difference is that the

FPLI looks to wage data for non-education workers to characterize differences in county level costs to hire

school workers, as opposed to directly comparing school worker compensation levels.

Historically, the data to construct a CWI was not available at the County level, and would be difficult to

replicate meaningfully for Florida. Recent work for the U.S. Department of Education has resulted in county-

level data that is intended to be updated annually. In discussion with practitioners in other states that utilize

this approach, the CWI is reasonably straightforward to implement and explain. In contrast, it is difficult for

practitioners to understand and replicate the current FPLI. The Index managers themselves have reported

that less than half of teacher salary valuation is explained by the FPLI, whereas applications of the CWI has

been found to be able to explain around 92%[1].

[1]Denslow (2015) Taylor (2011) Updating the Wyoming Hedonic Wage Index. Teacher Fixed Effects Model.

Source: TBG Work Product, from review of FPLI documentation, verbal communication with Dr. Dewey, FPU

Figure 1. FPLI Process Logic


Literature Review The current FPLI hinges on several economic theories and assumptions to predict differences in the relative

levels of costs for hiring comparable personnel across school districts in Florida. The following four theories

or assumptions generally capture the FPLI process logic:

1. The average of all wages is an accurate indicator of the costs of living, or representative of the

tradeoffs workers generally are willing to make between amenities and compensation, to work in a

given county.

2. Average wages for a county can be adjusted to a level accurately representative of teachers, using a

measure of occupation-specific employment density.

3. The variables chosen in the hedonic model are accurate predictors of the raw wage index across all

counties

4. Wages cannot vary widely between adjacent areas.

The literature review sets forth a brief summary of published work relevant to the FPLI evaluation. The

review follows in two parts: a general literature review that examines various aspects of the logic and

assumptions carried forward in the design and application of the current FPLI, and a summary of methods

used to allocate school education funding in other States. An in-depth literature review is provided in

Appendix A; Appendix D provides an annotated bibliography.

Relative to the FPLI and assumptions The FPLI is built on a body of economic literature around hedonic models and wage index methodology.

Hedonic modelling is used to predict the value of an object based on its characteristics. For example, teacher

salaries may be influenced by characteristics of the teacher such as years of experience, qualification type

and quality; and other characteristics such as school location, and the cost of living. The methods and

assumptions of Hedonic modelling are concisely described in Rosen (1974)3.

There is a large collection of literature which supports using hedonic modelling to develop an index of

teacher wages. Roback (1988) shows this is because labor markets attract a different level of compensation

depending on the characteristics (or amenities) of the region the worker is located. That is, a teacher living

next to a beach has a different selection of amenities available to them compared to a teacher working in a

rural town. The value of this difference can be seen through a hedonic model.

Although these amenities exist, Stoddard (2005) notes that how a teacher’s salary is adjusted for these

amenities is very important. One method developed by Taylor (2006) is to use a Comparable Wage Index

(CWI) which measures the differences in teacher pay that cannot be directly measured by looking at the

wages of similar occupations and how they vary. The current method of calculating the FPLI with a hedonic

model assumes that the teacher market acts in the say way as the general labor market.

A hedonic wage index cannot directly take into account characteristics that cannot be counted or are

unobservable such as teacher characteristics such as quality (Tuck, 2009). Further, as urban densities

increase, wages and amenities may not fully compensate for higher rents (Ahlfeldt and Pietrosstefani, 2017).

Yet teacher quality is important when looking at how salaries vary between locations. An area with many

positive characteristics will attract workers, allowing the schools to choose the highest quality teachers, while

teachers of a lower quality will distribute across areas with less attractive characteristics. In addition, Winters

3 Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,"


(2010) and Fowles (2015) find that if teacher unions are present in a school, these teacher salaries will be as

much as 18-22% higher. They further find that teacher salaries are positively influenced by the salaries in

nearby districts. Quality, the presence of unions, and neighboring districts are characteristics that are unique

to teachers. This means that these characteristics will not be reflected in the general labor market. The FPLI,

and other wage indices that use the general average wage (or parts of it) address this by aiming to determine

what the relative minimum cost would be to hire teachers of a specific quality within each county. Mishel

(2007) finds that general wages have both diverged from productivity and have become more unequal, such

that average wages may be misleading measures. The local districts make decisions regarding compensating

teachers given the above factors (unions, quality etc).

The FPLI cites literature by Small and Winston (1998) which offers estimates of the value of time as a travel

cost based primarily on short commutes within a city and is heavily influenced by the transport mode and the

perceived quality of this trip. They estimate the travel cost to range between 20% and 100% of the pre-tax

wage rage. The FPLI uses this value when calculating the distance a teacher is likely to commute in order to

access a higher wage in a neighboring county. In making assumptions about travel, it is also important to

consider that commute preferences are not the same between genders and occupations (Bergantio and

Madio, 2018). Further, simple distance relationships cannot fully explain the costs of commuting (Higgins,

2017)

More recent research defines the total travel cost by looking at the value of time spent commuting in

addition to the money cost of commuting (fuel, maintenance etc). Research by Anas (2007) and Bruechner

(2009) have nominate a value of 100% of the wage rate to represent the cost of time spent commuting. The

original FPLI had excluded money costs of commuting when applying a 50% travel cost to determine the

degree of geographical smoothing, however FPU is currently researching this matter.

The methodology used to calculate the Average Centrality Index (ACI) index is outlined in the paper by Dewey

and Montes-Rojas (2009). The ACI is defined as the degree to which an occupation is close to the inner city

CBD, or spread out to the outer city. Lawyers, with a high ACI, work close to the CBD and therefore require

higher pay because of the higher rents (or longer time spent commuting). Conversely, laborers have a low

ACI, and can be paid lower because they do not need to spend as much money on rent close to the CBD.

The ACI is calculated to account for these seemingly systematic differences in costs faced by teachers

compared with non-teachers. That is if a job is highly central to the CBD, the worker should be compensated

for paying higher rent in this location (or longer commuting times).

However, Weinberg (2002) found that labor within metropolitan areas does not move perfectly to suit the

centrality of employment. Glaeser and Rappaport (2006) find that access to public transport counteracts the

sorting of high-value labor to the inner city and low-value labor will be located outside the CBD.

In an attempt to discover how wages are influenced by the presence of positive amenities, and higher

property values, a number of proxies are used.

The FPLI uses a number of county characteristics in order to model how amenities and housing costs affect

wages, including:

the share of population over the age of 65

per capita income

overall population

This is because older people and wealthier people demand for more amenities, and larger populations have a

greater demand for land and housing. If these variables are accurate predictors of the variation in wage levels


across counties, they should also capture any variation in preferences within these groups. However, the

preferences of different types of people for different types of amenities do differ. The different preferences

of rural versus urban workers is well documented in the literature, and sorting of workers - including by age

cohort or by school quality, for example – is also supported by literature including Kuminoff (2013) and Sinha

(2018). Further, the amenities older populations find attractive, such as health care facilities, are unlikely to

be of equal importance to a teacher (Ehlenfeldt 2014, Kuminoff and Pope 2013).

Relative to methods used in other states There are three methodologies used by other states to calculate cost of education indices; comparable

indices, market-basket indices, and hedonic models.

Hedonic modeling: Quantifying career attributes that educators find attractive or repelling using

regression to determine the statistical relationships between teacher characteristics, working

conditions, area conditions, and salary.

Comparable indices: These indices measure the extent to which adjusted earnings of non-teachers

differ between labor markets. In the frame of a CWI, an area’s non-teacher salaries are proxies for

teacher costs.

Market-basket approaches: This approach measures the cost of living between regions. Cost of living

figures are analyzed in each region through the comparison of a specific market-basket of goods

found in every area.

Florida’s FPLI can be thought of as a comparable index (Texas, 2017). Table 1 below lists several methodology

examples from other states.

Table 1. Description of State Methodologies

State Class of Methodologies

Description

Maryland Hedonic Modelling The Geographic Cost of Education Index (GCEI) uses indices for school staff wages based on hedonic models which includes characteristics such as: • local factors (violent crime, average commute times, housing values, unemployment rate, per capita income) • district factors (% of students receiving free/reduced price lunch) • employee variables (age, gender, education, etc)

Washington Comparable Wage index

Researchers who had assisted Washington State modified a CWI published by the National Center of Education Statistics (NCES) to captures regional wage differences for non-teacher residents with college degrees. Regression analyses of earnings and various worker characteristics were applied to various state labor markets. Each district was given a local wage prediction which served as a basis for respective state funding.

New Jersey Comparable Wage index

New Jersey uses a Comparable index (CWI) with a Geographic Cost Adjustment. The CWI is based on the methodology described by Taylor and Fowler (2006) which assumes, similar to other wage indices, that as the costs of


living increase all workers, teacher included, will demand higher salaries. The occupation wage index restricts the analysis to college graduates, who have wage patterns most comparable to beginning teachers.

Wyoming Hedonic Modelling A Hedonic Wage Index (HWI) is used to model teacher salaries based on the characteristics of teachers themselves, similar to the Maryland example above. The HWI is being updated to encompass a CWI. By combining a CWI with hedonic modelling, the new index addresses the issues of the original HWI, and can model the costs of living where wage data is sparse. The Comparable Wage Indices noted are based on data derived from the Census Population Survey, which historically has not been available at the County level. As noted elsewhere in the report, the data is expected to be available at the County level going forward. A graphic representation of the CWI process is provided in Figure 2 at the end of this section.

Colorado Market-Basket Approach

Colorado uses the Colorado School District Cost of Living Analysis to compare the cost of living across state school districts for the family of a typical teacher with a bachelors degree and 10 years or more of experience. Separate demographic details from the U.S Bureau of Labor Statistics are further examined to further expand the cost of living profile. Relative costs are calculated from expenditure data and school districts are ranked accordingly.

FPLI and the Wyoming HWI The wage index method assumes that people will demand higher wages where the costs of living are higher

(or amenities are lower). Wage index methodologies come in two broad categories:

Direct - Those that measure relative wage differences across space directly and apply them to a class

of workers directly, or after some adjustment

Hedonic - Those that hedonically model the wages for a class of workers based on a number of local

and demographic characteristics

The strengths and weaknesses of the above are briefly summarized in Table 2.


Table 2. Strengths and Weaknesses of approaches to wage indices

Strengths Weaknesses

Hedonic estimation of teacher

wages

Models can apply to teachers

where raw data is sparse.

Must be continually re-

specified in order to remain

relevant.

Direct wage-based Indices Based off objectively

observed wage data that is

easy to obtain.

Requires data selection or

manipulation assumptions in

order to make it directly

relevant to the target group.

Data availability is an issue in

sparsely populated areas.

The method employed to generate the FPLI can be thought of as a hybrid of the direct measurement and

hedonic modelling methods. The raw FPLI index uses directly observed wage differences between regions

and uses a centrality index to tailor the index specifically to teachers4. The statistical smoothing process

hedonically models an index that can be applied to areas where wage data is sparse. The geographical

smoothing process that further alters the FPLI has not been found to be applied to any other index based on

wages or the alternative CPI approach. The hedonically estimated CWI method being considered by Wyoming

is also a hybrid technique, and the pair are briefly contrasted in Table 3.

Table 3. Description of FPLI and Wyoming HWI

Florida Price Level Index Wyoming HWI with CWI Adjustment

Adjustments to make the wage index applicable to teachers

Centralization Index to control for spatial sorting of occupations.

Restriction of wages data to college graduates.

Specification of the hedonic adjustments

Population

Per capita income

Proportion of the population over 65

Population density

Distance to major cities

Teacher demographics

School characteristics

Other Adjustments Geographic smoothing

While both hedonic models are attempting to achieve slightly different things, the Wyoming HWI with CWI

adjustment model uses a wider range of variables in the hedonic model, but it is generally less complex in the

number of other adjustments it makes and how it makes them. A chart more fully illustrating the Wyoming

process is given by Figure 2, on the following page.

4 Based on the assumption that teachers, in general, work in less centralized locations than other occupations, or exhibit less agglomeration effects than other occupations.


Figure 2. Comparable Wage Index Flowchart

Baseline

Comparable Wage Index

Place of Work Public Use Microdata Areas

(PWPUMA) Source: U.S. Census

Regression analysis of the most recent census to create a baseline comparable wage

index

The dependent variable is the log of annual wages for

non-educators. The independent variables are

age, gender, race, educational attainment, amount of time worked,

occupation, industry of each individual in the national sample, and an indicator variable for each labor

market area

The estimation excludes self-employed workers,

workers without a college degree, those who work less

than half time or for less than $5,000 per year, and

anyone who has a teaching occupation or who is

employed in the elementary and secondary education

industry

The index is calculated by:

𝑙𝑜𝑐𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑤𝑎𝑔𝑒 𝑙𝑒𝑣𝑒𝑙

𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑤𝑎𝑔𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑙𝑒𝑣𝑒𝑙× 100

Extending the

Baseline CWI

Occupational Employment

Statistics (OES) Source: BLS

The index is calculated by:

𝑂𝐸𝑆 𝑙𝑜𝑐𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑤𝑎𝑔𝑒 𝑙𝑒𝑣𝑒𝑙

𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑤𝑎𝑔𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑙𝑒𝑣𝑒𝑙× 100

For instance, if the OES estimated wage level in 2013 is 5% higher than

the baseline OES estimated wage level, then the 2013 CWI will be 5%

higher than the baseline CWI

Annual regression analysis using OES annual average earnings and employment

by occupation for Metropolitan Areas

The estimation excludes education-related occupations and crosswalks

OES occupations with Census occupations using the National

Crosswalk Service Center

The log of annual average wages for non-educators is regressed against

fixed effects for occupation and location weighted by total

employment for every year since baseline census year


Review of Econometric Theories and Assumptions Employed in the Current FPLI The current FPLI employs the following theories and hypotheses in order to predict differences in the cost of

living between counties and can be summarized as follows:

1. Average wages in a county are an accurate indicator of the relative costs of hiring school personnel.

2. Average wages for a county can be adjusted to a level accurately representative of teachers’/school

personnel, using a measure of occupation-specific employment density and county size.

3. County characteristics may be used to improve estimates of relative wages and/or to adjust the

Index where accurate wage data is sparse.

4. Wages cannot vary widely between adjacent areas given the economic law of one price.

Table 4 summarizes the assumptions and supporting literature, and alternative hypotheses that could also be

considered. Each is addressed in turn, followed by a summary table of findings.

Table 4. Hypotheses and Alternative Hypotheses

FPLI Working Hypothesis Alternative Hypothesis Source

The average of all wages are an

accurate indicator of the

relative wages of school

personnel

Wages over time have not grown uniformly in

line with costs of living and productivity.

The wages of some occupations reflect different

fundamentals to other occupations.

Wages and amenities may not fully compensate

for higher rents in highly dense cities.

Mishel et al (2007);

Ahlfeldt and

Pietrosstefani

(2017)

The Average Centrality Index

accurately adjusts average

wages to a level accurately

representative of teachers

While the average teacher/school worker is less

centrally located than the average worker, the

wide spread of teachers makes applying an

average centrality to all teachers potentially

flawed.

Metropolitan growth studies suggest that the

relationship between city size and wage

differentials becomes more complex as cities

grow.

Dewey and

Montes-Rojas

(2009);

Lee (2015);

Glaeser and

Rappaport (2006);

Weinberg (2002)

The variables chosen in the

formula are accurate predictors

of relative wages across all

counties:

Population

Per capita income

Proportion of the population

over the age of 65

There are a number of instances where these

variables may not accurately capture amenity

levels, e.g. The Villages5.

The approach assumes all teachers have the

same preferences for the same levels and types

of amenities.

While each year’s predicted index model may be unbiased from a statistical standpoint, it is unclear that the process does not consistently bias certain counties above or below the average year on year.

Ehlenfeldt (2014);

Kuminoff and Pope

(2013);

Sinha (2018).

5 The Villages is a retirement community with more than 50,000 residents located in Sumter County, which has a population at this writing of about 115,000 residents. The Master planned area spills over into Lake and Marion Counties.


FPLI Working Hypothesis Alternative Hypothesis Source

Wages cannot vary widely

between adjacent areas

If workers choose a residence-workplace-wage

arrangement that matches their preferences,

commute costs should already be fully

accounted for in the general wage price index

and require no further adjustment.

Commute preferences are not the same

between genders and occupations.

Simple distance relationships are not sufficient

to understand commute behaviors.

Bergantio and

Madio (2018)

Higgins (2017)

Assumption 1: Average wages predict the relative costs of hiring school personnel A hypothesis made by all wage price indices is that workers will all require increases in pay if their costs of

living increase or the amenities they experience decrease. The FPLI uses average wages as a proxy for

changes in the tradeoffs between amenities and compensation and are intended to provide a measure of

cost changes from the prior year. This follows similar assumptions by other wage price indices to measure

changes in costs of living or amenities which is supported by literature. However, two factors that potentially

limit the ability of wages to be used to measure changes in costs of living are noted below:

1. General wages have both diverged from productivity and become more unequal over recent decades

(Mishal et al (2007). Indicating that the gap between wages and the costs of living may change, and is

different between different groups of people.

2. As the density of cities increase, wages and amenities may not fully compensate for higher rents

(Ahlfeldt and Pietrosstefani, 2017). This means that a wage index may not be able to account for

differences in the costs of living between areas of different population size and density.

To construct a wage index that is fit-for-purpose, it may be important to consider whose wages are counted

and whether or not it is possible to compare wages between high density areas and low density areas

effectively. The latter point is relevant to the second assumption.

Assumption 2: The Average Centrality Index of teachers accurately reflects their distribution

The Average Centrality Index (ACI) adjustment attempts to adjust the raw wage index for teachers whom

may face different locational tradeoffs than the average worker6. The ACI measures employment density by

occupation using U.S. Census datasets (PWPUMA) for major MSA’s outside of Florida. For example, the

centrality measure for lawyers who tend to work in CBDs is different than that for teachers, who tend to be

more dispersed throughout a county. The centrality measure by occupation across hundreds of occupations

is applied to Florida counties at the occupation level through a statistical regression.

The diversity of Florida’s populations across communities may be comparable to that of the non-Florida

MSAs chosen for computing the ACI adjustment. Current literature suggests that the tendency of workers to

strongly divide into ‘inner city’ and ‘outer city’ workforces and housing may be changing. As employers

increasingly ‘meet in the middle’ through the establishment of outer-CBD offices and work from home

arrangements, an upper limit on commuting and congestion has been shown to develop (Lee, 2015). This

6 Or conversely, the degree that a given occupation exhibits agglomeration effects, versus that of teachers’/school personnel.


makes the trend of high-paid jobs being centralized in the CBD and lower paying jobs being located further

away from the CBD less relevant over time.

Current labor force conditions in education are also different than those in place fifteen years ago when the

current FPLI methodology was constructed. The nature of education in general has changed dramatically over

the last decade, with the proliferation of virtual schools in Florida as one example. The results of the ACI

regression are applied as

predicted values to the wage

index. The methods appear to

incorporate the predicted values

without the tails of the

distribution.

If alternative working hypotheses

were chosen, or if the tails of the

distribution of the predicted

values were incorporated, it is

not clear how the ACI results

would be affected.

FPU have indicated alternative

approaches or advances in

process to evaluate the ACI of

teachers are currently in research

stage. The impact of the present centrality adjustment on the current FPLI can be seen in Figure 3, which

shows the adjustments as a percentage point change to the index versus county population (log scale).

Assumption 3: County characteristics may be used to improve estimated relative wages

The FPLI makes adjustments using factors to account for differences in wages between counties that appear

in spite of similarities in basic socioeconomic measures, and/or to adjust the Index where accurate wage data

is sparse. The FPLI model assumes that the selected factors – currently, population of 65 or over, per capita

income and population - are correlated with, or potentially proxies for, amenity levels and land prices. This is

because:

1. Older people and wealthier people generally demand more amenities

2. Age distribution may have correlation with relative wages

3. The higher the population, the greater the demand for land and housing

By using County-level data on these statistics, the FPLI formulas seek to discover how wages are correlated

with the presence of positive amenities and/or high property values (or conversely, low amenities and lower

land values). The approach assumes that the factors such as share of people aged over 65 will reflect

indicators of a good (or bad) place to live and work. This may be true of some things but not others, for

example, older people tend to live near places with medical facilities far more than the average person

(Ihlanfeldt (2014); Kuminoff and Pope (2013)). The Villages for instance, could be considered as a high

amenity area for retirees but not for others.

Figure 3. Impact of Centrality in the 2017 FPLI

Source: Dr. James Dewey, FPU


The model used to generate the FPLI in 2017 appears to be “unbiased”7. It is unclear whether or not some

counties are consistently modelled above or below the model’s estimated FPLI in consecutive years. This

could not be tested by the reviewers as data were not available for prior years in time to review for this

report. Feedback from FPU mostly agrees with the above assessment, however, FPU believes that the share

of over 65 population and population in general are still valid proxies for practical reasons: there exist

constraints of available data across the 67 counties that can effectively measure preferences for different

packages of amenities, and secondly, since the model focuses on the margin of overlap between two

counties, preferences are a less significant factor. Further details of the assumptions used in the current FPLI

can be found in Appendix B.

Assumption 4: Wages cannot vary widely between adjacent areas The FPLI assumes that the economic law of one price should prevent the result of the FPLI formulas, for

adjacent counties, from varying too much. The law of one price suggests that people will commute to higher

paying workplaces if the higher pay compensates them for it, therefore wages will adjust to compete with

neighboring areas. This basic principle of the law of one price is generally supported in the literature, and not

contested in this report.

Within the FPLI, amenity and wage tradeoffs embedded in the raw wage index may reflect the value of

proximity to a large or central county or commuting costs. However, the formula introduces a lower limit on

the results of the FPLI based on a number of assumptions:

1. An average speed of 50 mph

2. Distance driven was one half of the difference between population weighted center of the 2 counties

3. Teachers value their time at half of their wage rate

The working hypothesis underlying the adjustment is that the amenity and wage tradeoffs do not already

reflect local values or costs, or that the data quality is sufficiently poor to require further adjustment in order

to do so. Emerging literature suggests alternative hypotheses may also be considered: 1) Commute

preferences are not the same between genders and occupations (Bergantio and Madio, 2018), 2) Simple

distance relationships cannot fully explain the costs of commuting (Higgins, 2017), and 3) The value of time

used by the FPLI omits money costs (Brueckner, 2009; Anas, 2007). It is unclear as to the extent, if any, that

the Index is harmed by the working hypotheses should it fail to be valid for Florida teachers or Florida’s

diverse population. FPU review suggests that research is underway to consider alternative hypotheses for this

reason.

Summary and Analysis A number of working hypotheses are incorporated into the Current FPLI. Owing to evolving thought,

availability of data, and testing of alternative hypotheses, the formulas used to generate the FPLI have varied

from year to year. Table 5 summarizes the incorporation of centrality and other adjustments over the history

of the Index.

The review identified alternative hypotheses that may be as valid as those in place currently. To the extent

that the impacts of FPLI adjustments or formulas based on the current theoretical underpinnings can be

quantified, the effects on the results of potential alternative hypotheses become clearer.

7 An unbiased model is one where the differences between the actual observations and the model’s results are on average, zero.

Table 5. Summary of FPLI Process Formulas

Source: Dr. James Dewey, FPU

Centrality Adjustment Occupation

Concentrated in Large Markets

Occupation Interacted

with Income Variables for statistical smoothing

Year Present Data Centrality Estimated by Interacted with Integrate or Added On

2003 Yes OES Average percent in central county for 11 multi-county MSAs in Ohio and Florida.

Log retail price index

Added On No No Log population, the average log raw index of other counties inversely weighted by distance, and the interaction of the two, and the log retail price index, share of the population 65 and older.

2004 Yes OES

Regressed percent in central counties for 21 Florida MSAs on MSA and occupation indicators, used normalized values of occupation coefficients.

Log retail price index and squared log retail price index

Integrated No No

2005 Yes OES Log housing cost index

Integrated No No

2006 Yes 2000 Census

As described in the 2009 Dewey and Rojas paper. Ratio of expected employment density by occupation location to expected density of average worker location.

Integrated No No Log population, the average log raw index of other counties inversely weighted by distance, and the interaction of the two, and the log retail price index.

2007 Yes 2000 Census Log retail price index

Integrated No No

2008 Yes 2000 Census Integrated No No

2009 Yes 2000 Census Integrated No No

2010 Yes 2000 Census

Log population

Integrated Yes Yes Log population and the log retail price index.

2011 Yes 2000 Census Integrated Yes Yes

2012 Yes 2000 Census Integrated Yes Yes

2013 Yes 2000 Census Integrated Yes Yes Log of expected density, log of population share 65 and over, and their interaction

2014 No

No Yes Log of expected density, log of per capita income

2015 No

No No

Log population, log per capita income, log of the population share 65 and over.

2016 Yes

5 - Year ACS

Regress log employment density at individual workplace for individuals on occupation and MSA indicators, use normalized occupation coefficients.

Log population Integrated

No No

2017 Yes No No

The impact of the adjustments can be seen in the graphics in Figure 4. The graphs show the results of the

FPLI 2017 Index before and after centrality index, statistical smoothing, geographic smoothing, and all

adjustments, and with county center points updated from 2000 to 2010 data8. The Final FPLI, after all

adjustments, is represented by the red line. The data is ordered from the lowest resulting FPLI value (90.67),

with the population shown on the right axis. The effects of the adjustments are most notable and significant

in the smaller counties, gradually decreasing as population size increases, and presumably data availability

increases.

8 Data provided by Dr. Dewey, FPU

Figure 4. FPLI Index vs. adjustments by County


Review of Data Choices and Consistency with the Economic Theories and Assumptions Employed in the Current FPLI The main data sources used in the calculation process of the current index are the:

Bureau of Labor Statistics (BLS)

Bureau of Economic Analysis (BEA)

University of Florida Bureau of Economic and Business Research (BEBR)

Florida Office of Economic & Demographic Research (EDR)

U.S. Census Bureau and American Community Survey (ACS).

Wage data is from the BLS Occupational Employment Statistics (OES) program, which estimates mean annual

wages by occupation at the State and MSA level. OES County-level data is provided directly to FPU by DEO.

Generally speaking, it may be worth mentioning that DEO states OES is not to be used to compare two points

in time due to survey collection methods, changes in classifications, and estimation methodology changes. As

inputs to regression estimates, the use of OES data may result in undetected effects on the calculation

results. Conceptually, this situation is at least partly the rationale used for some of the adjustments made

prior to the final FPLI result.

In the regression dataset, FPU uses a three-year average data for the calculation of the 2017 values, and since

the statue formula already includes the FPLI average of the last three years, the result is that the values are

effectively being averaged twice.

The location of the primary workplace data needed to build the Average Centrality Index (ACI) is from ACS

and obtained through the Integrated Public Use Microdata Series (IPUMS-USA) database and available at the

Place of Work Public Use Microdata Areas (PWPUMA) level.

Depending on the year the index was constructed, various sources for socioeconomic and demographic data

has been utilized. For example, the data used for county-level population-related variables in the most recent

FPLI is from the following sources: total population is from EDR, share of population 65 and up is from

ACS/Census and per capita income is from BEA. All population estimates have some measurement error and

by using common sources, there is potentially less variability in the measurement error. Additionally, there is

literature evaluating data sources for non-teacher occupations to create an index comparable to that of

educators, Taylor (2006). Taylor (2006) discusses three possible sources: Census, the OES program and the

Current Population Survey. Table 6 summarizes the strengths and weaknesses of these data sources as

described by Taylor (2006):

Table 6. List of Data Sources

Data Source Strengths Weaknesses

Census Provides detailed occupation and

demographic data Not done annually

OES Provides annual and more detailed data

on occupation classifications Includes no demographic data

Current

Population

Survey (CPS)

Provides more detailed demographic

data

More limited occupation data

Does not provide sufficient data for a

county level comparison


Table 7 on the following page provides sources of variables used in the different stages of the analysis to

calculate the FPLI index. There are 9 (nine) raw data variables from sources such as Census and BEBR, and

thirteen variables that, in turn, are calculated from raw data sources and used in subsequent calculations or

regression equations.

Statistical Analysis of the wage data used in the FPLI The occupation-specific wage data (2014-2016) was analyzed to understand the distribution and possible

groups in the wage data. To the extent that the wage distribution of school workers is very different than

that of other occupations, the extrapolation of non-school worker data to school workers may require

additional consideration. Cluster analysis was completed as part of initial explorations of the wage

data. Cluster analysis is a way of identifying groups of variables so that the variables in the same group are

more similar (in some sense) to each other than to those in other groups (clusters).

Two types of cluster analyses were used: k-means and Jenks optimization (natural breaks). K-means

algorithms search iteratively for clusters in datasets that minimize the variation within each cluster. Jenks

optimization works similarly with the goal of maximizing the variance between classes; Choice of number of

clusters is required for both methods. Both k-means and Jenks optimization produced similar results in terms

of the breaks between clusters for each occupation type.

The sample size and the cutoffs between clusters are quite different between education and non-education

wages; this can be seen graphically in the scatterplots showing average annual wages and k-means breaks in

Figure 5. Additional statistical analysis of wage data for each of the occupation types: non-education,

education, education administration, and postsecondary education, can be found in Appendix C.

Figure 5. Average Annual Wages and k-means breaks


Table 7. Data Sources

STATA Files Variables Description Source

centrality.dta centrality Centrality index by occupation Calculated from U.S. Census Bureau, American Community Survey (ACS) data

through the Integrated Public Use Microdata Series (IPUMS-USA), at the Place

of Work Public Use Microdata Areas (PWPUMA) level.

https://usa.ipums.org/usa-action/variables/group

county

variables.dta

pop Population by county Florida Office of Economic & Demographic Research (EDR). Population and

Demographic Data - Florida Products.

http://edr.state.fl.us/Content/population-demographics/data/index-

floridaproducts.cfm

pci Per capita income by county Bureau of Economic Analysis (BEA). Personal Income by County, Metro, and

Other Areas.

https://www.bea.gov/data/income-saving/personal-income-county-metro-

and-other-areas

popshare Share of the state's population in a given

county

Calculated in 2017 FPLI-10-2018.xlsx from EDR county-level population data.

http://edr.state.fl.us/Content/population-demographics/data/index-

floridaproducts.cfm dlnpop Log difference between a county's share of

the state population and the share of the

state's population in the average Floridian's

county

sh65up Share of the county's population age 65 up Calculated from U.S. Census Bureau, ACS data via the American Fact Finder.

https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refres

h=t

county wages.dta empl3 Employment by occupation by county The main source of this data is the Bureau of Labor Statistics’ Occupational

Employment Statistics (OES) database. Since this data stream is only available

for national, state, and metro geographical areas, however, county-level data

is provided by the Department of Economic Opportunity (DEO) directly.

meanann Mean annual wage by occupation by county

fpli_stat_smth.dta county

variables

Includes all variables from county

variables.dta

See county variables.dta for variables and sources.

rli Raw Log Index Log of the raw wage index prior to smoothing; calculated with county

variables.dta. See results in 2017 FPLI-10-2018.xlsx in the Regression tab.


http://edr.state.fl.us/Content/population-demographics/data/index-floridaproducts.cfm


https://www.bea.gov/data/income-saving/personal-income-county-metro-and-other-areas

https://www.bea.gov/data/income-saving/personal-income-county-metro-and-other-areas



https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t


STATA Files Variables Description Source

varrli Variance of Raw Log Index Variance of the raw wage index prior to smoothing; calculated with county

variables.dta. See results in 2017 FPLI-10-2018.xlsx in the Regression tab.

metro13 puma

pumaland and

pwpuma.dta

statefip State of Residence Variables used in centrality index calculation; U.S. Census Bureau, American

Community Survey (ACS) data through the Integrated Public Use Microdata

Series (IPUMS-USA), at the Place of Work Public Use Microdata Areas

(PWPUMA) level.


puma Public Use Microdata Area Code

pwstate2 Place of Work State

pwpuma Place of Work Public Use Microdata Area code

pumaland Land area of Public Use Microdata Area

boundary

metw Metro Area Code

metw titles.dta msatitle Metropolitan Statistical Area Name Variables used in centrality index calculation; U.S. Census Bureau, American



(PWPUMA) level.


msastatename Metropolitan Statistical Area State Name

puma land and

metro13.dta /

puma land and

metro13

reduced.dta

met2013 Identifies metro areas of residence using the

2013 definitions for MSAs from the U.S. Office

of Management and Budget (OMB)

Variables used in centrality index calculation; U.S. Census Bureau, American



(PWPUMA) level.


met2013err Identifies the level of mismatch error

between each MET2013 code and the

corresponding 2013 metropolitan statistical

area

puma10

pwpuma00

cross.dta

statefip State of Residence Variables used in centrality index calculation; U.S. Census Bureau, American



(PWPUMA) level.


puma Public Use Microdata Area Code

pwstate2 Place of Work State

pwpuma00 Place of Work Code






Excel Files Tabs Description Source

2010-occ-codes-

with-crosswalk-

from-2002-

2011.xlsx

2002to2010xw

alk

Crosswalks 2002 Census Occupation Codes

and Descriptions to 2010 Census Occupation

Codes and Descriptions

U.S. Census Bureau. https://www.census.gov/topics/employment/industry-

occupation/guidance/code-lists.html

2017 FPLI-10-

2018.xlsx

cnty vars County Variables Used to calculate the Raw Log Index (RLI) and Predicted Raw Log Index (PRLI);

see county variables.dta for variables and sources.

Constraint Constraints on County Dummies Constraints used to calculate the RLI in STATA; county coefficients are

constrained so that the population weighted coefficients sum to 0 (in log

form).

Regression Raw Log Index Calculation The RLI is calculated in STATA with county-level wage data, county and

occupation dummy variables, and the product of the K-12 education centrality

value and the log relative population of the county; see centrality.dta and

county wages.dta for inputs and fpli_stat_smth.dta for results.

Stat Smth Statistical Smoothing Statistical smoothing is applied to the RLI using the PRLI (RLI regressed on

population, per capita income, and share of county population age 65 and

up), resulting in the Log Statistically Smoothed Index (LSSI); see county

variables.dta for variables and sources.

Dist Calc Distance Calculation The distance between the population weighted centroid of pairs of counties is

estimated to account for differences in workforce mobility between counties.

Population weighted centroids of each county are sourced from BEBR.

https://www.bebr.ufl.edu/population/website-article/floridas-population-

center-migrates-through-history

Geo Smth Geographic Smoothing The LSSI values are adjusted downward for commute time (valued at half the

wage rate with an assumed average speed of 50 mph), per Overview of the

FPLI and Work to Improve It, Dewey, 2018. If the max of the commute

adjusted values is larger than the estimated LSSI in a non-major county, the

small county’s index is replaced with the commute adjusted value. If not, the

county retains the LSSI value.

Table Final Table for Report Florida Price Level Index for School Personnel Table published in 2017 Florida

Price Level Index, Dewey, 2018. After geographic smoothing, the imputed

index value is exponentiated to give a relative wage index instead of a log

relative wage index, and scaled to have a population-weighted value of 100.

https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html


https://www.bebr.ufl.edu/population/website-article/floridas-population-center-migrates-through-history

https://www.bebr.ufl.edu/population/website-article/floridas-population-center-migrates-through-history


Source: TBG Work Product, from review of FPLI documentation, verbal communication with Dr. Dewey, FPU

Excel Files Tabs Description Source

centrality for

2017.xlsx

matching Crosswalks Census Occupation Codes and

Descriptions to IPUMS Data

U.S. Census Bureau. https://www.census.gov/topics/employment/industry-

occupation/guidance/code-lists.html & Integrated Public Use Microdata Series

(IPUMS-USA). https://usa.ipums.org/usa-action/variables/group

MSA2013_PUMA

2010_crosswalk

(1).xlsx

MSA2013_PUM

A2010_crosswa

lk

Crosswalk of 2013 definitions for

metropolitan statistical areas (MSAs) to 2010

PUMAs

Variables used in centrality index calculation; U.S. Census Bureau.

https://www.census.gov/topics/employment/industry-

occupation/guidance/code-lists.html & Integrated Public Use Microdata Series

(IPUMS-USA). https://usa.ipums.org/usa-action/variables/group








Findings of the Review of the Current Price Level Index Methodology. The Review of the FPLI considered several elements:

1. The state of current literature and research in the field

2. How wage or cost of living indices are calculated in other states with respect to teacher

remuneration

3. The underlying econometric theory and assumptions used to construct the current FPLI

4. The validity and reliability of data sources used to produce the current FPLI

A number of econometric theories and assumptions underlie the current formulas used to construct the FPLI.

The econometric theory underlying the current FPLI results in a number of adjustments to the Index that are

primarily designed to accommodate sparse data in smaller counties. Over time, literature on the subject of

funding allocations for teacher pay has evolved, and potentially so has the relevance of some of the

assumptions used in the FPLI.

The data sources used to calculate the current FPLI were checked and found to be valid for the current year

index. There is sufficient variation both in data sources and in the formulas used from year to year to make it

difficult for outsiders to understand and validate the process or calculations. In some years, certain

adjustments have not been made at all, owing to poor data availability. As a result, it is difficult to know

whether the current formula represents the original legislative intent.

There are countless items for which local boards have no influence or control, but which affect the decisions

teachers make about where to work and live, and how much pay to accept. In addition to items like teacher

quality or district quality, non-education opportunities for teacher-credentialed workers are not evenly

distributed throughout the State of Florida. Incorporating wages for school worker comparable occupations

may be important to capturing differences in costs and wage levels across counties. The review describes

methods adopted in other states facing similar challenges in recent years, including a Comparable Wage

Index approach that may simplify the Index production and documentation.

Public demands for more transparency in public spending have fueled change in almost every sector of the

U.S. economy. The current FLPI may not be as transparent as stakeholders now require. FPU reports that

requests have been made in the past for stakeholder input. The creation of an advisory group that is

conversant in relevant terminology and familiar with Florida’s education financing may help to address FPLI

concerns.

From the perspective of transparency, accuracy, and fitness-for-purpose, the review has a handful of findings.

The most significant of these are:

1. Thoroughly document process steps and data sources to promote understanding and validate

process and calculations, and to ensure that the FPLI represents the legislative intent.

2. Taking advantage of new County level data OES forthcoming from Census, and other data sources to

consider a Comparable Wage Index based on school personnel-credentialed comparable occupations

(generally, but not for all positions, college-educated workers).

3. The creation of an advisory group that is conversant in relevant terminology and familiar with

Florida’s education financing may help to address FPLI concerns


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policy conclusions. Economic of Education Review, volume 24. Elsevier, pp. 323-339

Taylor, L., and Fowler, W. (2006). A Comparable Wage Approach to Geographic Cost Adjustment (NCES 2006-

321). U.S. Department of Education. Washington, DC: National Center for Education Statistics.

Taylor, L. (2006) Comparable Wages, Inflation, and School Finance Equity. American Education Finance

Association. Pp 349-371

Taylor, L., (2011) Updating the Wyoming Hedonic Wage Index.

Tuck, B. et al. (2009). Local amenities, unobserved quality, and market clearing: Adjusting teacher

compensation to provide equal education opportunities. Economic of Education Review, volume 28. Elsevier,

pp. 58-66

Walker, K. (2018) A Reproducible Framework for Visualizing Demographic Distance Profiles in US Metroplitan

Areas. Spatial Demography 6(3), pp207-233


Washington Association of School Administrators (2017) McCleary Education Funding Plan: Unpacking EHB

2242. Presented at the September meeting of Washington Association of School Administrators (WASA),

Olympia, WA

Weinberg, B.A. (2002) Testing the Spatial Mismatch Hypothesis Using Inter-City Variations in Industrial

Composition. Regional Science and Urban Economics 34 (5) pp 505-532

Winters, J. (2009) Wages and prices: Are workers fully compensated for cost of living differences?

Montgomery, AL: Regional Science and Urban Economics.

Winters J.V. (2010) Teacher Salaries and Teacher Unions: A Spatial Econometric Approach. MPRA Paper No.2

21202. Munich Personal RePEc Archive


Appendices


Appendix A: In-Depth Literature Review The literature review of the FPLI follows in two parts: a general literature review that examines various

aspects of the logic and assumptions carried forward in the design and application of the current FPLI, and

summary of methods used to allocate school education funding in other States.

Relative to the FPLI and assumptions Rosen’s 1974 seminal hedonic papers set forth methods and assumptions used to substantiate an individuals’ willingness to pay for goods and services without clear markets. The paper posits that goods are heterogeneous, and that prices only partially reflect the full spectrum of value a good or service provides. This relationship has been viewed in unique circumstances, including both labor and education policy. Roback (1988) expanded this by looking at regional labor markets as pools of amenities.

Theoretically, if teachers can flexibly move jobs types and residential locations, their occupational wellbeing should be at least as large as that of their surrounding non-teachers; one can infer how much a teacher’s wage difference is due to the area’s amenities using non-teacher wage differences. Getting an appropriate estimate for the effect amenities has on teacher wages depends on regression adjustments. Roback’s model sees wage differences in the same labor markets between different regions as compensating means that account for amenity differences. Similarly, Ardon et al (2000) adjusts teacher salaries from those of non-teachers. While it is possible to statistically link amenities to teacher wages, Stoddard (2005) states that how amenities are adjusted on wage regressions can determine whether amenities are truly significant to teaching wage. Using non-teaching wage premia in all other states as amenity measures can be imperfect, as state non-teacher fixed effects includes other sources of variation. Stoddard addresses this variation by restricting samples to highly similar workers and experimenting with restricted & expanded set of observable characteristics to control for unique area characteristics. Stoddard concludes that wage differences of non-teachers across states can be used to adjust teacher salaries. Taylor (2006) expands this idea further through the creation of a comparable wage index that measures uncontrollable variations in teacher pay through the observation of systematic wage variations in other comparable occupations.

While the development of teacher wage indices from hedonic techniques, as discussed above, is well established in the literature and policy, there are some confounding factors that limit the applicability of a wage-based index to teachers. As Dewey notes, a fundamental assumption of the FPLI is that teacher markets act the same as labor markets more generally. Tuck (2009) identifies a potential issue, that a hedonic wage index that does not take into account variations in teacher quality, is likely to give misleading signals regarding the costs of providing education. That is, schools in high amenity areas are able to choose high quality teachers (which economists cannot observe, but school administrators can), so that the remaining teachers are disproportionately sorted into schools with lower quality amenities and less capacity to pay higher wages. Further confounding the matching-markets between teachers and schools are the asymmetric presence of teacher unions with differing levels of bargaining power with school boards. Winters (2010) and Fowles (2015) perform spatial econometric analyses of teacher’s salaries in the U.S. and finds that salaries are positively influenced by teacher salaries in nearby districts. Winters also further found that the presence of teachers unions increased the salaries of teachers by as much as 18-28%. He concludes that any attempt to determine teacher salaries without taking into account the effects of these factors is likely to be mis-specified. It follows that the labor market for teachers behaves differently to the general labor market. Attempting to predict teacher wages via the average wage is therefore a potentially flawed method. It is important to note that the index is supposed to determine not what teachers are paid, but what the relative minimum cost would be to hire teachers of a specified quality within each county. The local district will make further decisions about how to reward quality, accommodate unions, etc.

Depending on context, various characteristics of a region’s labor market act as amenities or disamenities. Travel time, a popular water-cooler woe in urban sprawls, can serve as a measure of whether a job in a


certain area holds advantage relative to that in another district. The current FPLI cites Small and Winston (1998) who summarize a number of econometric techniques used to estimate the value of time for different travel modes, and nominates a value of time of 50% of the wage rate as a general result. The direct applicability of this to the problem of inter-county commuting is questionable on a number of points: 1) most of the studies of workplace commuting examined by Small and Winston are based on relatively short intra city commutes, 2) they note that estimates of the range of in-vehicle travel costs span from 20% to 100% of the pre-tax wage rate, 3) travel time costs are even higher for walking and waiting, suggesting that these estimates are heavily influenced by the mode of transport and the perceived quality of the trip. Other urban economists who have developed models to examine travel costs (Brueckner 2009, Anas 2007) assume that the time cost of commuting is valued at 100% of the wage rate. Furthermore these studies incorporate time costs with money costs to develop the total travel cost function. The current FPLI does not appear to incorporate money costs such as the costs of fuel or vehicle maintenance/replacement in determining the degree of geographic smoothing between counties; to the extent that money costs are reflected in wage costs, the omission could impact the accuracy of the FPLI. FPU is currently researching this matter.

The concept of a centrality index is used in the current FPLI to account for the fact that the wages of those closer to the centers of cities will grow more in relation to those employed in the city fringes in order to compensate for the higher rents or greater commuting times required to work in the CBD. Dewey and Montes-Rojas (2009) explain the general methodology used to obtain centrality indices for all occupations, including teachers, adopted by the FPLI. The Average Centrality Index (ACI) for those employed in Education, Training, and Library was 1.110, with a standard deviation of 0.395, the second highest variation of the 23 occupational groupings as listed in the paper. This means that those within 1 standard deviation of the mean (approximately 68% of sample) may be as centrally located as people employed in Legal professions (ACI: 1.480, the most centralized occupation) or as dispersed as those employed in Production (ACI: 0.756, the most dispersed occupation second only to Agriculture). This suggests that the notion of an average centrality as a good descriptor of the spatial occupational sorting of teachers within cities is potentially flawed.

The concept of centralization with regards to workforce sorting is explored by Weinberg (2002) in relation to the mismatch hypothesis. The hypothesis suggests that there is a mismatch between the low numbers of jobs available in the centers of cities compared to the high number of people. Evidence is provided that people do not simply locate to where the jobs are, indicating that labor is imperfectly mobile within metropolitan areas.

While Weinberg confirms that wages are higher in central areas relative to the outer suburbs and hinterlands

of cities, the results blur the notion that occupational sorting prevents those with a lower income from living

in downtown areas. This is explored by Glaeser and Rappaport (2006) who find that access to public

transportation, most readily available in central cities, is a force that counteracts the centralization of ‘high

value’ jobs to city centers, and the dispersal of ‘low value’ jobs to the hinterlands. Higgin (2017) also explores

the relationship between travel-time, congestion and individual preferences and finds that researchers need

to look beyond simple relationships between distance and travel time, and incorporate congestion into their

understanding of the costs of commuting.

The heterogeneous commuting behaviors of people facing similar work-residence tradeoffs are explored by

Bergantio and Madio (2018) who find that wage differential affect male and female workers differently.

Female workers in all sample specifications are less likely to commute to a different labor market. They also

find that job stability, and the effect of working in public firms/administrations tends to reduce the chance of

commuting. Lee (2015) finds that, overall, aggregate time measures show no significant deterioration as

populations grow and cities spread. The research implies that the centralization effects may eventually give

way to dual workforce-workplace sorting that places a limit on overall commuting and congestion within

cities, and therefore the relationship between city size and wage differentials becomes more complex.


The relationship between city size or density, rents and wages are also not as clear cut. Although normative

economic theory states that wages or amenities rise to offset the higher rents required by those who work

closer to the city, Ahlfeldt and Pietrosstefani (2017) discover by analyzing over 102 studies that this is not the

case. That is on average increasing density in a city leads to increased rents, and on average wages do not

fully adjust to compensate, even when taking into consideration the increased amenity and resource use

efficiency. The scarcity rent that is created by this effect directly harms renters and first time home buyers.

The results cast some doubt on the ability of a wage index to accurately predict how costs of living differ

between regions of differing population density.

The competing priorities concept leads to an additional issue of preferences. The literature does not support homogeneous assumptions across rural and urban locations. Heterogeneous preferences across rural versus urban workers are documented in the literature, and spatial sorting of workers particularly by age cohort is supported by literature including Kuminoff (2013) and Sinha (2018).

Relative to methods used in other states There are three methodologies used by other states to calculate cost of education indices; competitive wage indices, market-basket indices, and hedonic models.

• Hedonic modeling: Quantifying career attributes that educators find attractive or repelling using regression to determine the statistical relationships between teacher characteristics, working conditions, area conditions, and salary.

• Competitive wage indices: These indices measure the extent to which adjusted earnings of non-teachers differ between labor markets. In the frame of a CWI, an area’s non-teacher salaries are proxies for teacher costs.

• Market-basket approaches: This approach measures the cost of living between regions. Cost of living figures are analyzed in each region through the comparison of a specific market-basket of goods found in every area.

Florida’s FPLI can be thought of as a comparable wage index (Texas, 2017). The below lists several methodology examples from other states.

The case of Maryland: The Geographic Cost of Education Index in Maryland, or the GCEI, was created in

2002 as a funding component to account for the geographic variation of providing comparable education across the state in the foundation program. The GCEI is a weighted index of four components, indices of wage variation for professionals and non-professionals, and index of energy costs, and a fixed amount for other expenditure. The sub-indices for professional and non-professional wages are calculated on a range of statistics including local factors (violent crime, average commute times, house value, unemployment rate), district factors (% of students receiving free/reduced price lunch), employee variables (rave, gender, education, etc) and others (year, per capita income, etc). It is characterized as a hedonic method of determining employee salary. Issues with data collection as is typical with hedonic methods, have been noted by some commentators, providing momentum for a change to a Comparable Wage Index utilizing wage data of professionals similar to teachers.

The GCEI formula does not reduce funding for jurisdictions where educational resources are less expensive. The index provides a dollar-per-student amount of state funds in every district.

Prior to 2012 the State of Maryland paid 100% of retirement costs, but this was amended to require local contribution. This retirement aid is not wealth equalized.

The case of Washington State: As of 2018, the state of Washington decided to allocate school district

teacher funding based on minimum statewide average salaries for each of the three school staffing categories. The state adjusts its salary allocations to reflect regional differences in the cost of hiring staff


through a regionalization factor based on the median single-family residential value of each school district as well as that of all neighboring proximate school districts within 15 miles of the district boundary. Regionalization factors are 6, 12, or 18 percent. District allocations are then are compared to estimated school district total state and local average certified instructional staff salaries for the prior year to determine a regionalization factor increase of 6 percentage points. If the district’s new allocation is less than their estimated total salary, the respective factor increase will apply. The salary allocations incorporate inflationary adjustment, and will be reviewed and re-based every six years for the 2023 to 2024 school year. No district will receive less state funding for their prior annual minimum state allocations.

The Washington State example does not use the general wage rate in order to determine the relative cost of hiring expenses. Rather, it uses the minimum of the average salaries for three school staffing categories (administrative, instructional, and classified), and then adjusts these upwards in brackets depending on surrounding residential land value for each district. It therefore makes the fundamental assumption that the cost of living, and therefore the relative difficulty of recruiting teachers, is tied exclusively to the cost of housing.

The case of New Jersey: New Jersey allocates funding through wealth-equalized aid and categorical aid.

The former aid type considers each district’s property wealth and aggregate income, while the latter is allocated regardless of a district’s self-funding potential. Wealth-equalized aid, also known as the adequacy budget, additionally internalizes the base amount for each level of pre-college public education, weights for at-risk/limited English proficiency students, special education needs, and district speech needs. The base budget is firstly calculated at an elementary per pupil cost of $9,649.00 and is increasingly weighted as pupils move up in grade. The at-risk and LEP student component weights are applied to base numbers on a sliding scale while the special education costs are census-based. Finally, the speech excess cost is multiplied by the state average speech classification rate and district enrollments (New Jersey 2007). In the adequacy budget, the Geographic Cost Adjustment weight is included for intra-regional cost differences. Both salary data from the U.S Census Bureau is used as well as 5-year survey data averages from the American Community Survey. Overall, the index finds occupational salary differences between counties based on place of work, age, gender, race, education levels, and time worked.

The Geographic Cost Adjustment used in New Jersey is based on the Comparable Wage Index methodology described by Taylor and Fowler (2006) which assumes, similar to other wage indices, that as the costs of living increase all workers, teacher included, will demand higher salaries. They produce a demographically adjusted occupation wage index (to control for places that have lower wages simply because a large proportion of the workers are young and inexperienced) and restricts the analysis to college graduates, who have wage patterns more comparable to beginning teachers than, say laborers.

Comparisons between funding allocation approaches

All of the different methods employed that attempt to control for costs of living and therefore provide

schools with a level playing field with regards to teacher recruitment make a number of assumptions. The

three broad categories of controlling for costs of living are hedonic models, market basket indices (e.g.,

consumer price index) approach, and wage index approaches.

The market basket approach bundles different goods and services together to differentiate the cost of

maintaining a certain standard of living across space. Some of these methods attempt to employ a broad

based basket of goods and services, while others, such as Washington State draw a straight line between a

single cost, typically housing, and the cost of living. CPI approaches must either face the prospect of

becoming increasingly irrelevant as consumption, taste, and housing patterns change, or undergo continuous

improvement which is both time consuming and difficult to validate.


The wage index approach typically starts with the assumption that people will demand higher wages where

the costs of living are higher (or amenities are lower). Wage price index methodologies themselves come in

two broad categories, the first measures relative wage differences across space directly, and the other

attempts to estimate the wages for a class of workers based on a number of hedonic and demographic

variables.

The method employed in Maryland is estimates teacher wages based on a number of socio-economic and

hedonic variables. The method employed by New Jersey is a case-study in competitive wage indices whereby

it applies observed wage differences in the general labor market directly to teachers, although it makes a

number of a priori assumptions based on socio-economic constraints in order to make it more directly

applicable specifically to beginning teachers.

It can be considered that the method employed to generate the FPLI is a hybrid of the direct measurement

and hedonic estimation methods. The raw FPLI index is generated using directly observed wage differences

between regions, in this case using a centrality index to control for socio-demographic variables that make it

apply more directly to teachers9. However the statistical smoothing process estimates the index based on a

number of hedonic and socio-economic variables. Therefore the FPLI carries with it both the strengths and

weaknesses of both approaches. While the direct measurement approach of general wages are based off

objectively observed wage data, it often requires some a priori assumptions to be made regarding data

selection or manipulation in order to make it directly relevant to the target group, and it is highly vulnerable

to data availability especially in sparsely populated regions. Meanwhile the estimation method may produce

an index that can statistically demonstrate high predictive power and be confidently applied to teachers

where raw data is sparse, however like CPI methods they must be continually re-specified in order to remain

relevant.

Although not yet currently in effect, the Wyoming legislature is currently in the process of updating their

current Hedonic Wage Index, which estimates teacher salaries based on hedonic characteristics of teachers

themselves. As noted by Taylor (2011) this approach is vulnerable to a number of criticisms including the fact

that district manipulation may be incentivized, and due to the noncompetitive nature of teacher markets

higher spending districts may be misidentified as high-cost districts. The HWI is therefore being updated to

encompass a Comparable Wage Index (CWI), similar to the example of New Jersey illustrated above. By

combining a CWI and with hedonic estimation techniques, it addresses the issues of the original HWI, and

allows the CWI to create a statistically robust estimator of costs of living where wage data is sparse. Balmoral

Group understand that county-level annual CWI data is currently being prepared by the U.S. Department of

Education and the Census Bureau based on this hedonic CWI methodology.

The hedonically estimated CWI method is therefore comparable with the current FPLI as a hybrid technique.

However where the FPLI makes a number of adjustments to correct for spatial sorting (via centrality) and

adherence to the economic law of one price (via geographical smoothing), the hedonic CWI compares

teachers to a professionally comparable workforce, and allows for the fact that large differences in wages of

neighboring counties can occur if their characteristics are sufficiently different. To contrast the characteristics

chosen as variables in the hedonic models of both, Taylor’s CWI uses the demographic characteristics of

teachers, schools and indicators of regional isolation; whereas the FPLI uses population size and the

proportion of the population over the age of 65. While both models are attempting to achieve slightly

different things, Taylor’s models do use a wider range of variables more directly applicable to teachers and

can be contrasted to the FPLI in that light.

9 Based on the assumption that teachers, in general, work in less centralized locations than other occupations.


It should also be noted that, even in non-estimation methods, where assumptions are made based on

demographic or hedonic variables, that these too must be revisited and reevaluated in order to remain

relevant. The extent to which the re-evaluation process happens in practice varies across jurisdictions and

with the difficulty of obtaining data with high spatial and temporal resolution. The switch towards using the

ACS over the Census data is a pattern common between many methods. Re-evaluation/specification of CPI or

estimation method variables used to calculate funding also tend to raise questions around the extent to

which the variables chosen are arbitrary or reinforce bias towards one group or another. The way house

prices (and wages) are treated between jurisdictions is a salient example, because while they might be

correlated with high costs of living, they are also highly correlated with high local capacity to contribute to

land taxes and therefore school funding.

Appendix B: Working Assumptions of the Current FPLI

The table is organized as follows. The “Working Hypothesis” column is a description of the working hypotheses that were identified throughout the

different stages of the calculation of the current FPLI from the provided documentation; “Model Rationale/Justification” contains the authors’ rationale

or justification for the respective hypothesis from the provided documentation; the “Potential issue” column contains potential issues that could arise

from using such hypothesis (these do not necessarily mean that it is wrong or inadequate); the “Literature addressing issue; potential solution” column

has literature with relevant information that can support or challenge a hypothesis. This literature can be relevant for one or more hypothesis.

Working Hypothesis Model Rationale/Justification Potential issue Literature addressing issue; potential solution

Using occupation alone produces an accurate wage index

The authors tested empirically that occupation only is a good proxy for occupation, industry, worker characteristics and education combined

Worker characteristics and skill differences may not reflect important differences in compensation packages, particularly with regard to pension differences in unionized areas.

Literature (Stoddard (2005); Taylor (2006)) suggests that worker characteristics like age, race, full vs part time, whether a worker is self-employed or not, and educational level should be included to make sure that only the wages of similar non-educators workers are in the sample. However, the authors of the current FPLI index validated this based on the tests they performed and due to what data was available at the time of implementation of the index

On average across a large number of occupations, the skill difference may

wash out

The authors considered that when differences between workers are random, these will average out. Since differences are not always random, the interacted occupation against county per capita income to avoid skewness in data

Worker characteristics and skill differences may not be uniform across rural and urban areas.

Literature (Stoddard (2005); Taylor (2006)) suggest that is important to control for skill difference (understood as education level) because the results might be misleading. Similarly to above, this was done in the perspective of data availability at the time of implementation of the index

Teachers’ wages adjust across locations in similar ways to the

average worker in other occupations

The authors tested education wages vs other occupations wages using national data (regression, predicted vs actual values, Variance)

Teachers’ unions in larger school districts may obscure the relationship between

pay and supply

Although, Winters (2010) finds that investigations of the determinants of teacher salaries that ignore the relationship between teacher



Replicated the process with other occupations, and they got similar results with the variance of wages for other occupations that is explained by the wage index

salaries, and the salaries and union activity in neighboring districts are likely to be mis-specified, literature cited throughout the report, agrees that even though the structure of teachers' labor markets are different to other occupations, the decisions the average worker of any occupation has to make, in relation to its salary, residence location or commuting times are similar.

An index based on actual teachers’ pay would inaccurately reflect costs

Creates an incentive for districts to “play” the system

Pay may be higher in districts with stronger unions or greater teacher control of the boards

After controlling for price level and amenities, paying more should help a district recruit better teachers. But this not always happen, the authors provided Okaloosa as an example

McMahon (1994), Hanushek (1999) and Goldhaber et al. (2010) discuss the problems of using a hedonic wage approach with actual teacher salaries to estimate a wage index, with the same arguments as stated by the authors of the current FPLI. However, there are some states such as Maryland and Wyoming that incorporate hedonic wage indices in the estimation of their cost-adjustments

Wages are higher in places with high cost of living and lower in places with

high amenity levels

The authors’ logic is that growth of living costs are accompanied by more demand from employers, then wages are going to be higher. But if “the increased demand for limited land in high cost areas was accompanied by an increased supply of labor (because enough people found it a desirable location), house prices would rise without a corresponding increase in labor costs” (Dewey, 2018)

High correlation between cost of living and amenities

Roback (1988) model sees wage differences in the same labor markets between different regions as compensating means that account for amenity differences, while Stoddard (2005) states that how amenities are adjusted on wage regressions can determine whether amenities are truly significant to teaching wage. He experimented with restricted &



expanded set of observable characteristics to control for unique area characteristics. Winters (2009) finds that workers are willing to accept lower wages to work in more amenable areas. At the same time, when amenities are controlled, wages are higher in high price areas. Ahlfeldt and Pietrosstefani (2017) find that as the density of cities increase, wages and amenities may not fully compensate for higher rents.

Wages cannot be too different between nearby counties

If wages are too different, workers would commute to get the higher wage area. So, competition tend to equalize wages

This seems to defy the point of adjusting for amenities, which can vary greatly between adjacent counties

Fowles (2015) perform spatial econometric analyses of teacher’s salaries in the U.S. and finds that salaries are positively influenced by teacher salaries in nearby districts, while Higgins (2017) finds that simple distance relationships cannot fully explain the costs of commuting

MSAs in Florida have similar occupational characteristics as MSAs

in other states

The centrality index was built based on occupation national data of MSAs across the U.S. In order to be applicable to counties in Florida, these should have similar characteristics from those out of the state counties used to build the index in the first place

It is not clear that Florida exhibits the same patterns as cities in other states, and to the extent that the hypothesis was based on 2000 labor force patterns, current labor force patterns are very different. See the paper by Dewey and Montes-Rojas (2009) where only 7 MSAs are used because these are the only geographies for which sufficient geographic detail exists, ie containing at least 10 PWPUMAs, excluding those where 50% of employment is located in a single

FPU assertion was that these potential issues would be true only if the centrality index was about MSAs. But, since it is only about occupations, the issues would be overcome. Literature including Sinha et al (2018) finds that sorting effects may outweigh traditional hedonic effects; traditional hedonic effects assume homogeneous preferences, and mis-estimate amenity values based on individuals’ location decisions.



PWPMUA. These are Atlanta, Boston, Detroit, Philadelphia, Pittsburgh, Minneapolis, and Washington. The potential issue is that the results from these 7 cities may not be applicable to not only all 272 MSAs but also all counties. The hypothesis that the occupations that tend to locate in denser areas within and outside cities are the same, on all scales, is potentially flawed.

Kennedy (2005)10 addresses the concept of sample selection bias. This occurs when the probability of being in the sample is not random, getting an unrepresentative sample and potentially causing a misleading interpretation of the results. For instance, applying occupational density of metropolitan areas to small towns could lead to biased results as occupations from small towns are not being included in a non-random way from the estimation of the index.

Less Central occupations adjust less than 1 for 1 to changes in the price

level

Dewey and Rojas (2009) paper Inter-city wage differentials and intra-city workplace centralization. The researchers created a ‘centrality index’ which applies an occupation-specific measure of workplace centralization which is consistent across cities. Workers from more central occupations face higher housing or commuting costs, wages vary more with respect to price level, while the opposite happens for workers in less central occupations.

Centrality is discussed in the overview paper as a measure of centrally located employment as a share of total employment by occupation. Indeed most schools are not located in CBDs, but most schools are located within metropolitan areas, as are most teachers. It may be correct to say that teachers are more “dispersed” but using MSAs to confirm it may be the wrong approach. Would this hypothesis hold in large urban areas? 1. The hypothesis of centrality may

work well on occupations such as lawyers and factory workers who will strongly sort themselves into

Timothy and Wheaton (2001) discuss that workers earn more if they are in the larger employment zone of a city. Carlson and Persky (1999) finds that both men and women working in suburban areas earn less than their counterparts with same skills that work close to the downtown. Lee (2015) find that aggregate time measures show no significant deterioration as populations grow and cities spread because land markets have been able to accommodate multiple origin-destination arrangements, the development of the polycentric city embodying this. This makes the relationship between city size and wage differentials become more complex over time.

10 Kennedy, P. (2003) A Guide to Econometrics. The MIT Press. 5th edition.



regions, but not well for occupations such as teachers who may not.

2. See table 3 of Dewey and Montes-Rojas (2009) that reports that the Average Centrality Index (ACI) for those employed in Education, Training and Library is 1.110 with a standard deviation of 0.395. Only Farming, Fishing and Forestry has a higher std. dev. at 0.434.

3. Such a high standard deviation suggests that teachers within 1 standard deviation of the mean may be as centrally located at those employed in the Legal profession (ACI: 1.480) or as dispersed as those employed in Construction and Extraction (ACI: 0.764).

4. Under these circumstances there is some doubt as to the applicability of a single city-wide ACI to all teachers. By the authors own interpretation, taking the simple mean would tend to underestimate wage increases for teachers that are centrally located, and overestimate wage increases for teachers that are more dispersed.

5. Following from 4. it stands to reason that this distortionary effect



is largest where cities are very large.

Teachers in regional areas may be slow to pick up on wage/price increases. But teachers in metro areas might not lag as much.

Most teachers are women and women are more likely to prefer to work in the outskirts of a city, near

where they live

The most recent statistic found in the provided documentation is that 74% of teachers were women (from the 2001 Current Population Survey). Also, the authors referenced Timothy and Wheaton (2001). This study finds that workers earn more in the larger employment zones and that women’s wages have a stronger response to commuting times than men. The study did not study teachers’ wages per se

Age cohort likely trumps gender. Younger workers prefer urban amenities and are more likely to hold a second job. There are other factors to consider where you live (budget) not necessarily close to work. In large urban areas, schools are in denser areas, where living costs might still be expensive.

Timothy and Wheaton (2001), which were referenced by the authors; Carlson and Persky (1999) discusses that women tend to commute less to work and reference other older studies with the same notion. Bergantio and Madio (2018) study inter- and intra-regional mobility between 2004 and 2014. They find that female workers in all sample specifications are less likely to commute to a different labor market and that they are less likely to commute long distances when they have children themselves, noting the competing priorities of motherhood and work

The fraction of population of 65 or older is a proxy value of amenity

levels

“Retirees may be differentially drawn to high amenity areas since the wage reduction due to the presence of high amenities will not affect them” (Dewey, 2004)

Might no longer be true if older population prefer to move to areas like The Villages (which non-retirees would not consider a high amenity area)

Preferences of 65 over cohort are not the same as other age cohorts; Ihlanfeldt (2014) and Kuminoff and Pope (2013) find that preferences are heterogeneous across counties and at different price levels. Dorfman and Mandich (2016) references previous literature that suggests a strong positive correlation between retirees and natural amenities. But, the study also classifies



retirees into groups because retirees are not a homogenous population (using the life cycle theory model), finding that healthcare access is positively related to moving decisions for retirees

Population is a proxy of price level “Population of cities mostly drive land prices” (Dewey, 2004).

Population having lower influence on land prices or price level

Ihlanfeldt (2014) finds that supply elasticity and regulatory regime are greater influences of land prices and price levels

Assumptions of the Commute adjusted Index

Time cost of commuting is half the wage rate

This value is described in the report as widely accepted from this report (Small, Winston 1999 – The demand for transportation: models and application)

FPU’s calculations of the commuting costs appear to only include the time cost, and omit the money costs such as the immediate costs of fuel, or the longer-term costs of purchasing a vehicle and its maintenance costs. If the costs of commuting are higher (as much as twice as high) as what the authors here assume, this would limit geographical smoothing considerably. The value of time estimates reported in Small and Winton (1998) relate directly to studies of urban transportation. They note that other studies of urban commuters in major industrialized cities have found values of in-vehicle time in the range of 20 to 100 percent of the pre-tax wage rate. Despite the large range, they nominate 50% as a “typical” value, with values for walking

See Brueckner (2009) who develops a model city with “realistic” parameter values that include the time cost of commuting to be valued at the full wage. She also includes the money cost of commuting, ie the out of pocked expenses for commuting such as fuel, cars, or public transit. Also see Anas (2007) who assumes that each unit time spent commuting is valued at wT+C where w is the consumer’s wage rate, T is the time taken, and C is the monetary cost. He also notes that in the long-run commuting is at least somewhat discretionary because consumers can change their residence or job location and in making residence-workplace choices the consumer considers, amongst other things, the number of

Average speed of 50 m/h

From the 2004 Denslow/Dewey Research on the Florida Education Program report: “These are chosen because they seem like reasonable approximations. They could be improved if a separate study was funded to investigate more exact values, but we do not expect large changes in the final results.”

Distance driven was one half of the difference between population weighted center of the 2 counties



and waiting two to three times higher. The two main issues with taking 50% as a proxy of inter-city/inter-county travel is that the studies cited here do not look at this pattern of transit. And the wide rage noted by the authors suggest that the quality of transit and user preferences have a large effect that may be directly relevant to individual cases, such as Florida, or even counties within Florida. Although only related to intercity vacation trips, Small and Winton also note that the value of time is likely reflective of differing degrees of urgency. Therefore we might suggest that the value of an intercity commute to work, for which there is little recreational value, and arriving on time is the primary concern, the value of time may very well be higher than if the person in question chose a closer workplace which allowed for a less frantic commute. In theory a wage index should account for the different amounts spent by teachers on discretionary travel, as wages should encapsulate these ‘cost of living’ expenses. However the volatile nature of fuel costs may

discretionary trips that a particular residence-workplace arrangement allows. Higgins (2017) finds that simple distance relationships cannot fully explain the costs of commuting

More recent studies reflect that younger workers are more likely to have a second job, so commuting time would be equal to wage time.



introduce a degree of cost uncertainty on those with long commutes, which in turn may cause teachers to favor a residence-workplace arrangement that minimizes commuting: either by accepting higher rents closer to a city, or accepting lower wages in a smaller county.

Assumption Model Rationale/Justification Potential issue Literature addressing issue; potential solution

Assumptions behind using Ordinary Least Squares (OLS) to estimate the index11

Dependent variable can be calculated as a linear function of a specific set of independent variables, plus a disturbance term

Tests included in the provided documentation: R2, standard errors of

every coefficient, tested different functional forms to test whether variance

of the model vary systematically with county size

The model could have wrong regressors, nonlinearity and changing parameters

Due to time constraints, alternate functional specifications or tests of the current specification were not done as it is a lengthy and slow process. However, from the literature read, researchers use a linear, logarithmic or a combination of both, to estimate wage indices; the current FPLI uses similar form, but in a series of adjustments. Not all of the assumptions or the potential issues have to be relevant to the calculation of the current index. For instance, if the variables were perfectly collinear, then the estimation of the model wouldn’t be possible. But they are all listed as general assumptions

Expected value of the disturbance term is zero

The model could have a biased intercept

Disturbances have uniform variance and are uncorrelated

The model could have heteroscedasticity and autocorrelated errors

Observations on independent variables can be considered fixed in repeated samples

The model could have errors in variables, and autoregression

No exact linear relationship between the independent variables and more observations than independent variables

Multicollinearity

11 Kennedy, P. (2003) A Guide to Econometrics. The MIT Press. 5th edition.



and issues of every model estimated with OLS. Since no model is perfect, it is going to have errors such as heteroscedasticity. However, a concern with the current specification is the concept of “wrong regressors”. FPU asserts that since the model is used for predictive purposes rather than inference, the concept of wrong regressors in this index is not applicable. However it is not known if there has been validation throughout the years.

Appendix C: Statistics Review

The occupation-specific wage data (2014-2016) was analyzed to understand the distribution and possible

groups in the wage data. Cluster analysis was completed as part of initial explorations of the wage data. This

was done to assess the likely groupings of wage data for each of the occupation types: non-education,

education, education administration, and postsecondary education.

Two types of cluster analysis were used: k-means and Jenks optimization (natural breaks). K-means

algorithms search iteratively for clusters in datasets that minimize the variation within each cluster. Jenks

optimization works similarly, with the additional goal of maximizing the variance between classes. Choice of

number of clusters is required for both methods. Both k-means and Jenks optimization produced similar

results in terms of the breaks between clusters for each occupation type. Of note: the middle two clusters

are quite different between education and non-education wages both in terms of sample size and the cutoffs

between clusters. This can be seen graphically in the scatterplots showing average annual wages and k-

means breaks.

Additionally, histograms of wages for non-education, education, education administration, and

postsecondary education were also prepared to check distributions across occupation types. The distribution

of education wage data is biomodal, as a result of differences in occupations and hours worked within the

education occupation group. Postsecondary education wages and education administration wages both

show similar distributions with no strong skewness. The non-education occupation wages are strongly

skewed right (exceeds the median), suggesting that non-education occupation wages are dominated by lower

income occupations.

Source: TBG Work Product

Appendix D: Annotated Bibliography

Literature: Ahlfeldt, G., Pietrosstefani, E. (2017) The Economic Effects of Density: A Synthesis. Spatial

Economics Research Centre, Discussion Paper 210

This paper uses data of 202 estimates of density elasticities from 102 studies to produce a number of

recommended density elasticities for different outcome categories. They apply the elasticities to a model of

an ‘average’ high-income city and find that a 10% increase in density per capita/year is associated with a

$140 increase in wage and a $243 increase in rent. This real decrease in wages is at least partially offset by an

amenity effect of $106 and positive externalities of $29, but it is not eliminated altogether. The implications

are that on average increasing density in a city leads to increased wages, and on average wages do not fully

adjust to compensate, even when taking into consideration the increased amenity and resource use

efficiency. The scarcity rent that is created by this effect directly harms renters and first time home buyers.

The results of this paper contradict to some degree the notion that people who live in highly dense areas will

be compensated fully through higher wages or shorter commute times. This effect is likely to be largest in

very dense metropolitan areas, with implications for the applicability of wage indices to measure cost of

living differences between low and high density areas.

Literature: Albouy, D. and Lue, B. (2015) Driving to opportunity: Local rents, wages, commuting, and sub-

metropolitan quality of life. Journal of Urban Economics 89 (2015) 74–92

The authors examined indices for neighborhood choice and noted that while a single index involves many

simplifications, incorporating commuting costs and place-of-work wages, their model conforms with the

standard model on local rent and wage gradients. The commuting adjustment revealed that willingness-to-

pay to live in the suburbs or in dense areas is higher than simpler measures imply. The place-of-work wage

adjustment revealed that wages offered in central cities are as high as in the suburbs, even though skill levels

are not. Neighborhood quality within metro areas varies substantially, although such differences have less to

do with natural amenities, and more to do with local residents and the artificial amenities they produce.

Literature: Alexander, N., Kim, H. and Holquist, S. (2014). Locating Equity: Implications of a Location Equity

Index for Minnesota School Finance. University of Minnesota - Twin Cities Report Prepared for the

Association of Metropolitan School Districts.

The report examine one aspect of equity: the equalization of different costs of schooling that are associated

with geographic location. The approach used parts of Florida’s (GCEI) formula in part because of Florida’s

widely varying costs, recognizing that Florida’s formula does not focus specifically on education costs. They

set 0.15 as an index, where 85% of the index reflects salary as share of budget (vs 80% in Florida). They

concluded that CWI artificially restricts the range of geographic cost differences faced by districts.

Literature: Anas (2007) A unified theory of consumption, travel and trip chaining. Journal of Urban

Economics. Journal of Urban Economics Volume 62 (2) pp 162-186.

This paper examines the interaction between consumption theories of microeconomics, and travel demand

theory. It proposes that most models of consumption ignore the fact that most consumption requires some

form of travel, and therefore incurs costs, which should be fully accounted for. As he notes, commuting is

important because it competes with leisure and work for the time of consumers. In his model he adopts a

travel cost equation, where unit time spent commuting is valued at wT+C where w is the consumer’s wage


rate, T is the time taken, and C is the monetary cost. In his conclusions he notes that in the long-run

commuting is at least somewhat discretionary because consumers can change their residence or job location

and in making residence-workplace choices the consumer considers, amongst other things, the number of

discretionary trips that a particular residence-workplace arrangement allows. Therefore it follows that wages

do not only compensate for the locus of rents and commute times to and from work, but also discretionary

commute times as well.

Literature: Aten, A. (2006). Interarea price levels: an experimental methodology. Monthly Labor Review.

Washington D.C: Bureau of Labor Statistics, pp. 47 - 61.

The author shows that it is possible to use price observation and weights to estimate interarea price levels for

various expenditure categories in metropolitan urban areas. She does this through a two-step approach to

calculate these price levels for 2003 and 2004. Firstly, price parities are estimated in each area using a

hedonic regression in which the dependent variable consists of the log of observed prices and the

independent variable consist of product characteristics. Secondly, item stratum price parities for each

metropolitan area were aggregated using a variation of the Country-Product-Dummy approach. The author

concludes by posing two questions; will the area price level variances from the first step persist over time and

remain similar across items? How will these estimates fare in state or smaller geographic areas?

Literature: Bergantio, A.S., Madio, L. (2018) Intra- and Inter-Regional Commuting: Assessing the Role of Wage

Differentials. Available at SSRM: https://ssrn.com/abstract=3088449 or

http://dx.doi.org/10.2139/ssrn.3088449

This paper studies inter- and intra-regional mobility between 2004 and 2014 by examining regional wage

differential and individual and regional characteristics. They find that wage differentials influence male and

female workers differently. They confirm that female workers in all sample specifications are less likely to

commute to a different labor market. In addition they confirm that women are less likely to commute long

distances when they have children themselves, noting the competing priorities of motherhood and work.

They also find that job stability, and the effect of working in public firms/administrations tends to reduce the

chance of commuting, explained by the fact that public services are distributed almost equally across cities

and areas, rending commute times shorter.

Literature: Blanciforti, L. and Kranner, E. (1995). Estimating County Cost of Living Indexes: The Issue of Urban

Versus Rural. Morgantown, West Virginia: West Virginia University, pp. 1 – 36.

This paper looks at how cost of living/price indices are ascertained from various geographical levels in order

to review how they are measured. The analysis critically assesses resulting differences from applying the

same analytical methods to urban and rural counties. The authors looked at Florida’s basket-based indices

and found that while housing, population densities, and a dummy variable that indicated the presence of a

seashore greatly accounted for the magnitude of urban price levels, housing was the only dominant variable

in the measuring rural price levels. Heterogeneity amongst explanatory variables does not provide an

accurate measure between urban and rural areas. In addition, the authors found evidence that housing

should be considered separately, as it constitutes about 38% of Florida'

Literature: Brueckner, J.K (2001) Urban Sprawl: Lessons from Urban Economics. Brookings-Wharton Papers

on Urban Affairs, pp 90-94

This paper examines urban sprawl and its underlying causes. In order to do this she develops an econometric

model of a monocentric city whereby residents commute to a CBD, earn wages, and incur commuting and

housing costs. As with most models, high commuting costs are compensated for by low rents, further out


from the CBD. However she notes that rents not only depend on distance from the CBD, but with income. In

her model city the commuting cost parameter includes both a money and time cost as follows: Assuming an

out-of-pocket cost of $0.30 per mile and 250 round trips per year leads to a value of $150 per year for the

money cost of commuting per mile. A yearly income of $40,000 implies an hourly wage of $20, which yields a

time cost per mile of $0.66 assuming a traffic speed of 30 miles per hour (commuting time is valued at the full

wage). Time cost is then $330 per mile per year, yielding a total money plus time cost of $480.

Literature: Claffey, E. (2016) The impact of state and district wealth on educational outcomes (Boston

University Theses & Dissertations)

Thesis determined that state and district income was a predictor of student achievement (National

Assessment of Education Progress scores) and that district income is less impactful in higher income states

than in lower income states. With test passage as the dependent variable, education spending was

statistically significant in Florida.

Literature: Demirgüç-Kunt, A., Klapper, L., and Panos, G.A (2016) Determinants of Saving for Old Age around

the World in Retirement System Risk Management: Implications of the New Regulatory Order, Oxford

University Press, Oxford, UK.

The authors of this chapter investigate the determinants of saving for old age and find that the percentage of

people saving for old age, as well as the amounts saved vary considerably across economies. Their results

demonstrate that the proportion saved varies by demographic characteristics such as gender, education, age,

marital/relationship status, income, wage, and mortgage. They find that urban/regional differences are not

significant in explaining savings in a probit regression. However in a subsequent multinomial probit model for

different types of savings it was found that urban/rural effects were significant. Given the significance of the

demographic variables, it is suggested that not all teachers will necessarily strive to save the same amount,

either spatially, or over the course of their working lives.

Literature: Denslow, D. and Dewey, J. (1999). Report on the Florida Price Level Index: Impact of Dropping

Transportable Commodities. Gainesville, FL: The University of Florida, pp. 1 – 17.

In this paper, Denslow and Dewey recommend a few changes to the FPLI calculation. First, they recommend

dropping 80 out of 117 items forming the basis for the basket-based index; dropping these items were found

to have little systematic impact on the FPLI due to their transferability between counties and thus assumed

state-wide uniform pricing. Second, they recommend using web-based data to calculate the apartment rent

aspect, as there is more web data, more detailed characteristics, and is cheaper to extract. These advantages

were paired with the potential disadvantage of selectivity bias, as not all large complexes are found online.

Literature: Denslow, D. and Dewey, J. (2000). Report on the Florida Price Level Index: Alternative Methods

for Measuring Costs Across Counties. Gainesville, FL: The University of Florida, pp. 1 – 38.

Denslow and Dewey recommend a few changes for the Florid Price Level Index. First, they recommend

cutting the number of items used to index and compare counties from 110 to 31. The authors believe doing

so would reduce sampling noise, yet no proof is provided. Also, the authors conjecture that all 79 cut goods

can be easily transported between counties, implying little to know variation in their prices. Second, they

recommend preparing a spreadsheet that would clarify the calculation of the FPLI to the casual spreadsheet

user. Third, they recommend phasing the use of a wage-based index, as it has technical advantages. The

authors posit that a wage index - unlike a basket index - take account of amenities and is easier and cheaper

to commute by using OESWS data. Lastly, they recommend third party oversight of the evaluation,

maintenance, and improvement of the FPLI.


Literature: Denslow, D., Dewey, J., and Lotfinia, B. (2004). The Florida Price Level Index, The Sparsity

Supplement, and Discretionary Millage. Gainesville, FL: The University of Florida, pp. 1 – 134.

The report by Denslow, Dewey and Lotfinia (2004) considers how appropriate the FPLI, Sparsity Adjustment

and Discretionary Millage are for the Florida Education Finance Program (FEFP). The authors recommend

spatial amenities, such as the compensation of living in a desirable location and its impact on housing and

labor markets, be taking into account through the District Cost Differential (DCD). This would involve

including an amenity adjusted price level index, statistically and geographically smoothed version of the FPLI,

as a proxy spatial cost of living index, denoted as FPLI_AS. It is reported that there is a lack of empirical

evidence to justify the presence of economies of scale and the requirement for a sparsity supplement. The

preferred outcome is to drop it all together, otherwise it should be recalculated based on modern

econometric studies of economies of scale in Florida’s schools. At a minimum the wealth adjustment should

be dropped. The final recommendation is for unequalized discretionary millage to be increased until local

funding equals the national average per UWFTE. If this is unfeasible, it should be unlinked from the sparsity

supplement and the unequalized cap should be increased as much as possible.

Literature: Dewey, J. (2003). 2002 Wage Index. Gainesville, FL: The University of Florida, pp. 1- 30.

The Dewey (2003) report identifies a need to change the measure of the DCD from a price level index to a

labor cost index. The DCD is the basis for the personnel cost adjustment, on the understanding that the same

underlying economic factors which cause the tax base per student to vary will also cause wage and salary

costs to differ. It has been calculated historically on the Florida Price Level Index (FPLI) because of a

lack/absence of good wage and salary data. All else equal, wages adjust for cost of living differences. The

researcher believes that the exclusive use of a cost of living index is incorrect because wage and salary costs

are what the DCD is intending to measure; and wages will also adjust for the amenity differences which will

drive a wedge between market wages and the cost of living. A wage index for 2002 has been calculated using

wage data intended to represent wages at the MSA level. It should approximately equal the price index plus

an amenity adjustment and since the amenity level shouldn’t change much over time, it is expected to follow

a similar trend to the FPLI. However the index requires more work as it recommended that the FPLI is used

for small, non-MSA counties.

Literature: Dewey, J. (2005). Improvements to the 2003 Florida Price Level Index: Background, Theory, Tests

Based on the Current Population Survey, the Relationship between Excess Teacher Turnover in Florida’s

Schools and the Market Basket Approach, and Robustness Checks. Gainesville, FL: The University of Florida,

pp. 1- 71 Gainesville, FL: The University of Florida, pp. 1- 30.

The report is intended to clarify the background information on the DCD, FEFP, FPLI and the theory of index

numbers before presenting these empirical analyses. District Cost Differential is an input price index while

the Florida Price Level Index (a subcomponent of the DCD) is an input price index for labor across Florida’s

counties. Given that wage rates are the price of labor, the FPLI is an approximate wage index according to the

researchers. Cost Of Living (COL) methodologies are used to index variations in wage costs, either through

the ‘market basket approach’ or the ‘comparable wage approach’. While they are both approximates of a

true COL Index, it is determined that, in support of theoretical expectations and other published work, a

comparable wage based approach was significantly superior. The estimate of the FPLI as calculated by the

market basket approach leads to estimates that are sufficiently far from actual relative wages that no

adjustment at all would be more equitable. This was presented by comparing the results of methodology

tests of the FPLI using data from the Current Population Survey with results from tests which apply the

market basket approach against the FPLI_AL. The differences in FPLI_AL and FPLI_P is associated with past


excess teacher separations across Florida’s school districts. The evidence supports the estimated FPLI_AL as

robust for small or substantial changes in the underlying data and estimation methodology.

Literature: Dewey, J. and Montes-Rojas, G. (2009). Inter-city wage differentials and intra-city workplace

centralization. London, UK: City University of London, pp. 1 – 32.

The research by Dewey and Montes-Rojas (2009) creates a measure of job centrality that is occupation

specific so that inter-city wage compensation differentials can be estimated across cities. This is based on the

theories and models associated with intra-city rent and wage gradients and inter-city wage rent differentials,

which are widely used but in isolation of one another. In summary, intra-city rent and wage gradients assess

the impact of proximity to the city centre(s) or city size as factors which increase rent; and inter-city

differentials model the weights firms and workers place on intrinsic characteristics against the differences in

wages and rent when choosing a city to locate to. The researchers have first created a ‘centrality index’ which

applies an occupation-specific measure of workplace centralization which is consistent across cities. Then a

hypothesis is tested that assesses whether wage premia (the elasticity of wages with respect to city size)

increases with the centrality index. The idea is that workers in central occupations face a less desirable

combination of house prices and commuting times when compared with those in non-central occupations.

This is robust if city-specific fixed effects and individual-specific human capital variables are included. The

research is the first attempt at blending the theories of intra-city rent and wage gradients with inter-city

wage and rent differentials.

Literature: Dewey, J. (2017). Consideration of Funding, the District Cost Differential, and the Florida Price

Level Index for the Flagler School District. Lakeland, FL: Florida Polytechnic University, pp. 1 – 9.

This is Dr. Jim Dewey’s response to the Chief Financial Officer of the Flagler School District’s request to

explain why Flagler’s student unit funding is lower relative to its high millage rate. Dewey explains that Flagler

County pays the same effective millage rate as every other district. Next, the document also explains how the

District Cost Differential aspect of base funding equation is calculated and its relevance toward Flagler’s

student funding result. Expected density, an adjusted measure of population density, serves to measure the

DCD, a measurement that reflects the salary differences in labor costs between counties. Dewey explains that

Flagler county, being a relatively sparser and less populated county, had a below average DCD. DCD had the

biggest impact in Flagler County’s funding equation, and was thus reflected in the lower per-student funding.

Literature: Dorfman, J.H, Mandich, A.M, (2016) Senior Migration: Spatial Considerations of Amenity and

Health Access Drivers. Journal of Regional Science, 56 (1) pp. 96-133

This paper assesses the drivers of retiree migration by simultaneously examining the access to healthcare

services and local amenities. The author notes that past literature identifies natural amenities as a driver for

later-life migration, but these works don’t take the need for healthcare access into account. The model

classifies retiree migrants by age (60-74 and 75+) as well as by rural, urban and most urban counties, to

reflect the heterogeneous preferences of this cohort. It was found that access to healthcare through the

channels of health expenditures, hospital beds, and number of doctors, were positively associated with later-

life in-migration. The presence of an existing population aged 65 years and natural amenities were also

significant. The results indicate a variation in natural amenities desired by those locating to rural or urban

counties, thus implying that the preferences of the cohort are heterogeneous compared to the general

working population.

Literature: Duncombe, W., Nguyen-Hoang, P. and Yinger, J. (2014) Measurement of Cost Differentials.

Handbook of Education Finance and Policy, 2nd Edition, edited by H.F. Ladd and M.E. Goertz (Routledge).


The authors find that existing state aid formulas usually contain ad hoc cost adjustments that fall short of the

across-district cost differences estimated by scholars. They attempt to synthesize the research literature on

education cost differences across school districts and to discuss the implications of this literature for state

education aid formulas. They critique the cost function approach and introduce the basis for geographic

adjustment, including COI, CWI, and Hedonic Teacher Cost Indices. They conclude there is no consensus on

the magnitude of cost differentials or the methodologies for their estimation, and the need to make cost

estimation more transparent.

Literature: Florida Department of Education. (2014). Funding for Florida School Districts. Tallahassee: Florida

Department of Education, pp. 1 – 41.

This document breaks down the entire state of Florida’s school funding. FEFP, or the Florida Education

Finance Program is the policy enacted in 1973 which serves as primary mechanism for funding the operating

costs of Florida’s schools. The policy bases primarily bases financial support on student needs rather than the

number of teachers or classrooms. This basis is expressed by accounting for various local property tax bases,

education program costs, costs of living, and equivalent educational programs. Other sources include federal

sources, Public Education Capital Outlay (PECO) funds, Workforce Development Education funds, Adults with

Disabilities funds, and funds for student transportation.

Literature: Fowles, J. (2015) Salaries in Space: The Spatial Dimensions of Teacher Compensation. Public

Finance Review 1 (26)

This paper draws on administrative panel data of all public school teachers in Kentucky from 1997 to 2005.

The author tests for spatial inter-dependence in the teacher remuneration policies of school districts. The

results of the models suggest that a 1% increase in the salary generosity of a district’s distance weighted

neighbors yields a 0.57% increase in salaries within that district, controlling for time and other fixed effects.

The author also finds that, all else held constant, as a district grows, it compensates teachers more

generously. They do not speculate on the reasons but comment that it does reinforce the concerns that small

rural districts struggle to retain and attract high-quality teachers.

Literature: Fowler, W. and Monk, D. (2001) A Primer for Making Cost Adjustments in Education: An Overview,

in Selected Papers in School Finance, 2000–01. Edited by W. Fowler. National Center for Education Statistics,

NCES 2001–378

In A Primer for Making Cost Adjustments in Education, the authors attempt to explain the differences

between educational costs and expenditures, explain the differences in the “unit price” of teachers and

differences over time in the level of inflation, examine existing indices that can be used to make judgments

for these differences in costs, and outline a future plan of action to derive a precise, stable, and accurate

index for school administrators and policymakers to use. The authors address the Barros (1994), McMahon

and Chang (1996), Chambers (1998) models for a geographically-based teacher price index and the need to

adjust indices over time. The difference of examining costs vs expenditures is addressed. They conclude that

in most cases, geographic cost adjustments have not been applied when assessing intra-state fiscal equity

and that there is no consensus on methodology.

Literature: Glaeser, E.L, Matthew, E.K., Rappaport, J. (2008) Why Do the Poor Live In Cities? The Role of Public

Transportation. Journal of Urban Economics 63 (1) pp 1-24

This paper examines the tendency of poor people to live in the centers of large cities, which defies the notion

that low value jobs will tend to be sorted to the outskirts of urban areas. They argue that the primary reason

for this is that public transportation becomes the only feasible method of traveling to and from work for


many people on lower incomes, and therefore living in the suburbs is completely beyond their means due to

the high up-front financial costs of private vehicle ownership, even if the cost of housing in the suburbs in

cheaper in the long term.

Literature: Goldhaber, D., Destler, K., Player, D. (2010) Teacher labor markets and the perils of using hedonics

to estimate compensating differentials in the public sector. Economics of Education Review 29 pp. 1-17

The pitfalls of using hedonic modelling to estimate compensating differentials in public schools are assessed

in this article. Hedonic models were run on public and private schools, and the estimated teacher-salary

returns to individual teacher, school, and community characteristics were compared. While it highlighted the

differences in compensation returned between public and private schools, it is deemed likely that private

school teacher salaries are determined within a more competitive labor market which smooths

compensation in a form that is well estimated by a hedonic model. The public domain market forces may be

distorted which makes using hedonics to estimate the required differentials much harder. This is because

salary differentials may not reflect the necessary compensation for identical teachers in different jobs and

thus it cannot precisely measure the required compensation.

Literature: Griffith, M. (2005) State Education Funding Formulas and Grade Weighting. Education

Commission of the States (ECS).

This paper discusses the different ways each state allocates educational funding. It describes the four main

allocation systems; the foundation/base formula, the modified foundation/base formula, the teacher

allocation system, and the dollar funding per student system, and identifies which states belong to each

system. The paper also provides the weights used by each different allocation system for funding by grade

level and describes the systems used by a few states outside of these four main categories.

Literature: Griffith, M. (2016) “State teacher salary schedules.” Policy Analysis, Education Commission of the

States.

This document is a summary of policy regarding state teacher salary schedules. The reasoning behind the

policy, potential problems with the policy and alternatives to teacher salary schedules are described. The

document also lists the 17 states that have a teacher salary schedule and the minimum starting salary,

maximum salary, average salary, salary based on 10 years of experience and the requirements to receive the

maximum salary in each state.

Literature: Hanushek, E. (1999) Adjusting for Differences in the Costs of Educational Inputs. Rochester, New

York: University of Rochester

This paper analyzes, compares, and contrasts how some academics have adjusted education data for price

differences between areas. The paper builds analyses from previous methods, including the hedonic price

index. The author concludes that a modified hedonic analysis may provide more accuracy, as standard

hedonic reliability may decrease over time. The latter relies on educational salary increases as opposed to

relative-cost changes amongst college-educated workers. Also, these hedonic analysis can only be

constructed for years with sufficient enough data from large teacher surveys.

Literature: Higgins, C.D. (2017) All Minutes are Not Equal: Travel Time and the Effects of Congestion on

Commute Satisfaction in Canadian Cities. Transportation 45(5) pp 1249-1268

This paper examines the relationship between travel-time, congestion, and individual predisposition to

congestion related stress. The authors find that while improvements to travel-time improve commuter

satisfaction overall, reduction in congestion has a disproportional effect on increasing commute satisfaction.


The results suggest that researchers need to look beyond simple relationships between distance and travel

time, and incorporate congestion into their understanding of the costs of commuting.

Literature: Ihlanfeldt, K., Mayock, T. (2014) Housing Bubbles and Busts: The Role of Supply Elasticity. Land

Economics, 90 (1), pp. 79-99

This research aims to build on the understanding of supply elasticity and its effect on housing bubbles and

busts. It is noted that past literature has used proxies for supply elasticity that has been inconclusive in

understanding its impact on the housing market. For this reason the authors uses 21 year panels to directly

estimate the short run price elasticity of housing supply for the 63 counties of Florida. The importance of

supply elasticity as a determinant of price and quantity changes is varied in boom and bust periods as well as

across counties. In areas with higher supply elasticity, there is greater construction and less price

appreciation in a boom period. However, during bust periods the magnitude of price changes were

unaffected by supply elasticity even though there is more new construction in high supply-elasticity areas.

These area differences highlight the heterogeneous supply-elasticity between counties across Florida. The

research goes on to explain this heterogeneity to be a result of differences in land availability and local fiscal

and regulatory environment, potentially obscuring the relationship between population density alone as a

predictor of high housing costs.

Literature: Imazeki, J. (2016, June). A Comparable Wage Index for Maryland. Denver, CO: APA Consulting.

This paper provides an alternative to the previously used Geographic Cost of Education Index (GCEI) for the

state of Maryland using a comparable wage approach. This method uses a model for the wages of

professionals relative to teachers and other professional district workers and a model for the wages of non-

professional workers comparable to the wages of non-professional district workers to evaluate the variation

in wages between districts to form a single index. Due to the availability of the data and simplicity of the

model, this index can easily be updated over time and can be smoothed to mitigate sharp changes in district

funding from year-to-year through the use of an average of previous years’ index values. The authors suggest

that Maryland use the full range of values in the index, not just those above one like in previous years, to

accurately represent the variation between districts.

Literature: Imazeki, J. (2018) Adequacy and State Funding Formulas: What Can California Learn From the

Research and National Context? San Diego State University

This paper reviews the literature on how states fund education and distribute adequate funding to districts

with different characteristics and costs. The paper focuses on how adequate costs are determined, factors

that impact costs of education, how costs are addressed in funding systems, costs that impact funding

systems that aren’t usually accounted for, and structures of different funding systems similar to California. It

helps provide California with a context of where it fits compared to other systems around the country.

Literature: Kuminoff N.V, Pope J.C (2013) The Value of Residential Land and Structures during the Great

Housing Boom and Bust. Land Economics, 89 (1) pp. 1-29

This paper uses a hedonic estimator in identify how the value of residential land and structures changed

across time, 1998-2009. The authors acknowledge recent literature which questions the capacity for hedonic

pricing models to deal with unobserved attributes of houses and neighborhoods. Spatial fixed effects were

employed to determine nonmarket amenities impact on localizing land values and unobserved attributes

were integrated by varying the implicit per unit price of structures between neighborhoods. The paper

identified preferences, wealth, credit and so on, may cause the shape of the house price function to vary


from year to year. This indicates that house prices are not homogenous across time and preferences are

heterogeneous across counties at different price levels.

Literature: Lee, B. (2015) Spatial structure and travel: Trends in commuting and non-commuting travels in US

metropolitan areas. In Handbook on Transport and Development (pp 87-103). Edward Elgar Publishing Ltd

This chapter serves as a comprehensive resource on current literature regarding commute times and

behaviors that have been researched recently. They find that, overall, aggregate time measures show no

significant deterioration as populations grow and cities spread. This is attributed chiefly to the fact that that

land markets have been able to accommodate multiple origin-destination arrangements, so that reasonable

travel times remain available to most people. That is, results generally corroborate the idea that employers

and employees choose locations that favor mutual accessibility, the development of the polycentric city

embodying this. The research implies that the centralization effects may eventually give way to dual

workforce-workplace sorting that places a limit on overall commuting and congestion within cities, and

therefore the relationship between city size and wage differentials becomes more complex.

Literature: Legislative Budget Board Staff of The State of Texas (2017) Overview of Education Indices. Austin,

TX: The State of Texas.

This document provides a description of the Texas cost education index, or CEI, and an overview of different

other CEIs. The Texas CEI is composed of a price component, a scale component, and a composite CEI value.

The price component accounts for regional variation, while the scale component addresses the gap created

by the differences between operating costs and limited revenue generating ability of smaller districts. A

composite CEI value is then formed from both the price and scale components. A comparative description of

different methodologies is provided along with specific state examples; competitive wage indices, market-

basket approaches, and hedonic models are discussed.

Literature: McMahon, W. (1994) Intrastate Cost Adjustments, National Center for Education Statistics,

viewed 11 December 2018, <https://nces.ed.gov/pubs/web/96068ica.asp>

This paper critiques the both the cost of living (COL) and cost of education (COE) index, and offers contextual

adjustments depending on their specific uses. This paper also present a theoretical model and methods of

measuring cost differences between school districts, while presenting results, cost difference amongst areas.

The article deduces that to preserve equity between low-income rural districts and wealthy areas, the

property tax mileage rate will have to be replaced with a more appropriate measure such as per capita

personal income. The method presented in the paper is suggests the exploration of equity in expenditure per

pupil.

Literature: Mishel, Bernstein, Allegretto (2007) the State of Working Class America 2006-2007.

This paper investigates the collective wellbeing of American workers amidst the backdrop of macroeconomic

patterns in 2006 and 2007. A central idea of the paper is that general wages have diverged from productivity

and become more unequal over recent decades. This may indicate that the wage growth for some has

outpaced cost of living, while for others it has lagged behind. Along with faster productivity, strong collective

bargaining institutions, an appropriate minimum wage, and a tight labor market make sure that productivity

gains are realized by everyone and for everyone at the same time.

Literature: National Education Association. (2011). Policy Brief: Federal Education Funding under NCLB:

Fairness Contributor or Inhibitor?

https://nces.ed.gov/pubs/web/96068ica.asp


This policy analysis by the National Education Association (NEA) assesses federal education funding under the

No Child Left Behind policy. The studies discussed found that while high-poverty districts receive three times

more federal funding than low-poverty districts, federal funding is only a small proportion of the total

funding provided to districts and high-poverty districts still receive 7% less funding per student than low-

poverty districts. In general, the level of federal funding is inadequate to make a difference. The impact of

NCLB on this funding is mixed. The average funding per poor child on the district level appeared to increase,

however, when analyzed as a share of total funds over the time period studied, it was only a 2% increase in

funding, due to the two-stage funding process and additional requirements put in place under NCLB. While

there is a requirement that resources between title 1 and non-title 1 school must be the same, staff for title-1

schools were generally less experienced and paid less than non-title 1 schools. The NEA also discusses steps

that Congress should take in order to improve the NCLB and requests better measuring tools and an

increased focus on research and analysis in education policy.

Literature: National Education Association. (2013). Policy Brief: Multiple Indicators of School Effectiveness

This paper justifies the use of multiple indicators as assessments of school effectiveness in comparison to the

more recent No Child Left Behind (NCLB) policy. The pitfalls of the NCLB policy and the reasoning behind the

use of multiple indicators is discussed. The analysis suggests using a framework of three broad areas for

assessing school effectiveness including school inputs, processes and outcomes. The paper also offers a list of

measurable indicators that fall into each category of assessment and also policy recommendations from the

NEA.

Literature: New Jersey Department of Education (2007) A Formula for Success: All Children, All Communities.

Trenton, NJ: State of New Jersey <https://www.state.nj.us/education/sff/>

This paper describes the development of a formula for adequately allocating educational funding to districts

in New Jersey. In keeping with the professional judgement method, a method 13 other states have used to

develop formulas, panels of professionals identified the resources sufficient to support a model school

district and cost data was assigned to those resources. The consultant developed a model used to estimate

the funding necessary for each district in New Jersey using this data. It accounted for districts of different

sizes, demographics and make up and used the Geographic Cost of Education Index to adjust cost data, which

accounts for differences in cost of living and in the ability to hire and retain teachers.

Literature: New Jersey Department of Education (2014) Geographic Cost Adjustment (CGA) Update FY2014.

Trenton, NJ: State of New Jersey <https://www.state.nj.us/education/sff/>

This paper describes the Geographic Cost Adjustment that is used to account for differences in costs across

New Jersey. It compares salaries of similar occupations across different districts in New Jersey and accounts

for differences in demographics and socioeconomic values. This paper discusses what was used to update the

GCA and also shows the change in the adjustment by county due to this update.

Literature: Odden, A., Picus, L., and Goetz, M. (2008) A 50 State Strategy To Achieve School Finance

Adequacy. Annual Meeting of The American Education Finance Association, Denver Colorado

The review does not address intra-state inadequacies but looks instead at an evidence-based approach to

funding and identifies shortfalls based on individual state models applied to a single hypothetical school.

Economies of scale were not addressed.

Literature: Rickman, D., Wang, H., Winters, J. (2015) Adjusted State Teacher Salaries and the Decision to

Teach. IZA DP No. 8984


This paper analyzes relative teacher salaries and decision to teach by comparing the wages of educators

versus non educators by state and job history. They found that salaries are significantly related to whether a

male education major will remain a teacher and that salaries have the greatest effect on this decision for 40-

59 year olds. In terms of subject, relative salaries had the greatest impact on the decision to remain a teacher

for math, science and computer teachers, specifically males. They also found that higher relative salaries

positively impacted female elementary education majors, and both male and female general education

majors to remain a teacher.

Literature: Roback, J. (1988). Wages, Rents, and Amenities: Differences among Workers. Economic Inquiry,

volume 26, pp. 23-41

Studies the relationship between worker wages, rents (for both employers and workers) and amenities. They

built the model based on two types of workers, assuming they are different but the company needs both.

First, they found that the wage of one of the worker is dependent on the other’s wage, preferences, and the

company’s cost function. Secondly, they found the relationship between amenities and wages is not always

negative, while the relationship between rent and amenities is not always positive. This is due to the impact

that amenities have on productivity. If they increase productivity, then wages also increase. The concluded

that wage and rent differentials are because of differences in the amenities a location has.

Literature: Senate Committee Services of the Washington State Legislature (2018). A Citizen’s Guide to

Washington State K-12 Finance. Olympia, WA: Senate Ways and Means Committee and the Senate Early

Learning K-12 Committee.

This document is a compilation of answered frequently asked questions about education funding in

Washington State. Covered topics include legislation, organization and structure of school districts and

funding, current and historical spending, current and historical expenditures per student, per activity and per

program, and determination of salaries and health benefits. This document is meant to provide citizens with

a general overview of finances related to the education system.

Literature: Sinha, P., Caulkins, M.L., Cropper, M.L. (2018) Do Discrete choice approaches to valuing urban

amenities yield different results than hedonic models? NBER Working Paper Series, National Bureau of

Economic Research, Cambridge, MA.

This research compares the approaches of hedonic modeling and discrete choice modelling that are typically

used to value amenities which vary across cities. The work is unique in comparing how estimates of marginal

willingness to pay (MWTP) for small changes in summer and winter temperatures vary by a households

current location, ceteris paribus, rather than comparing the MWTP estimate without a climate location

factor. It was found that the MWTP for warmer winters or cooler summers are estimated quite differently

between the two models because of what information each employ. Discrete modelling incorporates the

costs of moving from ones birthplace, allows for city-specific labor and housing markets, and is more easily

able to measure the impact of urban amenities on all household groups. The hedonic model ignores this basic

psychology, assumes a national labor and housing market, and doesn’t use information regarding market

shares. By assuming homogenous preferences, the hedonic model is deemed less successful at estimating

amenity values because of how it assesses individuals’ location decisions.

Literature: Small, K.A., Winston, C. (1998). The Demand for Transportation: Models and Applications.

Transportation Policy and Economics, eds. Washington D.C.: Brooking Institution.

This chapter acts a meta-analysis of a number of transport cost studies with relations to urban use of private

and public vehicles, freight, and travel type (business, work, or vacation). It summarizes a number of


econometric techniques used to estimate the Value of Time (VOT) for different mode sand types of travel.

Travel between cities or counties for the purposes of regular work are not directly dealt with in the models

discussed as most deal with transport issues within cities. It is from these studies that Small nominates a VOT

of 50% of the wage rate as being a general result, but notes that VOT can range from anywhere between 20%

and 100% of the pre-tax wage rate. The authors also note that the VOT is also likely to be higher for periods

spent walking or waiting for transit, and where the urgency of the trip is greater.

Literature: Springer, M. (2009) “Rethinking Teacher Compensation Policies.” In Performance Incentives: Their

Growing Impact on American K-12 Education, edited by M. Springer. (Brookings Institution Press)

This chapter summarizes the history of teacher compensation reform and evaluates the arguments for and

against reform. It takes a look at the widely used single salary payment type and describes the problems and

arguments against using it. The chapter also analyzes different types of reforms including pay-for-

performance and market based reforms by reviewing experimental studies. The paper summarizes each

study’s sample size, location, study period, study design, unit of measurement, measure of teacher

assessment and results.

Literature: Stoddard, C. (2005). Adjusting teacher salaries for the cost of living: the effect on salary

comparisons and policy conclusions. Economic of Education Review, volume 24. Elsevier, pp. 323-339

This paper addresses how adjusting teachers’ salaries based on cost of living can lead to inaccurate

adjustments. It argues that by doing this, instead of controlling for variation, using cost of living increases the

gaps. Alternatively, Stoddard discusses how amenities affect workers’ salaries (not only teacher salaries) and

how adjusting for them in the regression analysis, provide a better measure of relative wages and student

outcomes She argues that non-teacher salaries can be used to estimate teacher salaries as both value

amenities in similar ways, however, she examines different ways to adjust cost of living as well as the

importance to control for amenities and worker characteristics so that the regression analysis reflect actual

variation in teachers’ salaries.

Literature: Taylor, L., and Fowler, W. (2006). A Comparable Wage Approach to Geographic Cost Adjustment

(NCES 2006-321). U.S. Department of Education. Washington, DC: National Center for Education Statistics.

The Geographical Wage Index (GWI) is used to estimate geographical cost adjustments. This paper develops

the Comparable Wage Index (CWI) as a way to capture intrastate variation in wages of college graduates in

education that the GWI does not capture. Using earnings, occupation, place of work and demographic data

from the 2000 census and regression analysis, the authors created the CWI. The model allows users to

predict the wages that a national representative person would make in any labor area. The wage prediction

of a local market divided by the national average ($47,836 in 1999 dollars) gives the CWI. This model does not

capture variation within a labor market area, only between them.

Literature: Taylor, L. (2006) Comparable Wages, Inflation, and School Finance Equity. American Education

Finance Association. Pp 349-371

This paper examines the pros and cons of using a comparable wage index. This index uses non-teacher wages

to determine variation in teacher wages. Taylor argues that non-teacher occupations are a good proxy for

teacher wages as long as the non-teacher workers have similar characteristics (age, educational levels, and

amenity preferences). Otherwise, the index would have limited explanatory power. According to Taylor,

wages differentials are due to differences in worker, job and location characteristics. Possible data sources

discussed by Taylor include: Census, with the advantage of providing occupation and demographic data, but

not done annually, so the estimated index would serve as a baseline; The Occupational Employment Statistics


(OES) that provides annual and more detailed data on occupations, but no demographics, and Current Survey

Population data, which provides more detailed demographic data, but more limited occupation data.

Literature: Taylor, L., (2011) Updating the Wyoming Hedonic Wage Index.

This paper analyzes the Wyoming Cost of Living Index and analyzes and updates the Wyoming Hedonic Wage

Index (HWI), which are both used to construct the Regional Cost Adjustment (RCA) distributed to school

districts annually. This paper also discusses the advantages and disadvantages of hedonic wage indices, cost

of living indices and comparable wage indices as approaches to developing an RCA. The authors recommend

updating the Wyoming HWI to an auto-regressive random effects HWI, a model which incorporates

information on the distribution of teacher salaries and is still able to measure the impact of costs like

geographical factors and housing. They also suggest that the legislature change the way they use these

indexes to distribute the RCAs.

Literature: Tuck, B. et al. (2009). Local amenities, unobserved quality, and market clearing: Adjusting teacher

compensation to provide equal education opportunities. Economic of Education Review, volume 28. Elsevier,

pp. 58-66

This paper focuses on individual teacher data to address cost differentials of having teaching quality across

localities in Alaska. Since measuring teaching quality is not an easy task as public data is limited, they relied

on data from the Alaska Department of Education and Early Development to determine how much time a

given teacher stays at a given school, and compensation and community characteristics data to determine

the tradeoffs of moving to one school to another.

Literature: Waights, S. (2018) Does the law of one price hold for hedonic prices? London, UK: London School

of Economics

This study estimates a hedonic price model for rail access controlling for different location amenities and

disamenities using historic data in Germany. Then, the author tests for stationarity in the results by using a

panel unit root test, which uses a matrix with the estimated differences of the estimated prices from the first

step. Results suggest that since there are no persistent price differentials across city locations that have rail

access, then the law of one price hold for hedonic prices.

Literature: Walker, K. (2018) A Reproducible Framework for Visualizing Demographic Distance Profiles in US

Metroplitan Areas. Spatial Demography 6(3), pp207-233

This article provides a technical methodology to visualize smoothed relationships between the location

quotient of a specific demographic group for a metropolitan Census tract and the distance between its

centroid and respective urban core. While distance profile visualization is not a new technique, this article

offers a transparent and reproducible way of determining them in free, open source software.

Literature: Washington Association of School Administrators (2017) McCleary Education Funding Plan:

Unpacking EHB 2242. Presented at the September meeting of Washington Association of School

Administrators (WASA), Olympia, WA

This presentation describes the Washington State McCleary Education Funding Plan. It breaks down the

operating budget, salary allocations, enrichment levees and LEA, collective bargaining and supplemental

contracts, accountability and transparency including audits and budgeting, and health benefits for employees

included in the plan.

Literature: Weinberg, B.A. (2002) Testing the Spatial Mismatch Hypothesis Using Inter-City Variations in

Industrial Composition. Regional Science and Urban Economics 34 (5) pp 505-532


This paper investigates the mismatch between the location of jobs in the outer regions of cities, and the

centralization of people, particularly lower income and Black people concentrated in the downtown regions.

They find that labor is imperfectly mobile within metropolitan areas and find that in reality, people on lower

incomes do not necessarily sort into outlying regions in order to live or work. This has direct implications for

econometric studies that assume perfect mobility in labor markets in the long run. They argue that labor

market instruments that increase centralization will therefore increase overall employment.

Literature: Winters, J. (2009) Wages and prices: Are workers fully compensated for cost of living differences?

Montgomery, AL: Regional Science and Urban Economics.

This paper looks at the relationships between wages and prices, specifically, if workers receive higher wages

to offset the costs of living in an expensive locale. If workers receive wages to offset living costs then

theoretically workers should be no better nor less off in any labor market they choose to be in. The author

finds that the relationship holds depending on whether house prices are measured by economic rent instead

or housing values.

Literature: Winters J.V. (2010) Teacher Salaries and Teacher Unions: A Spatial Econometric Approach. MPRA

Paper No.2 21202. Munich Personal RePEc Archive

This paper uses the Schools and Staffing Survey to examine the determinants of teacher salaries in the U.S.

using a spatial econometric framework. The author finds that the salaries for experienced and beginning

teachers are positively influenced by the salaries of nearby districts. The study also finds that union activity

increases the salaries of teachers by as much as 18-28%, but much less for beginning teachers. The author

concludes that any investigation of the determinants of teacher salaries that fails to take into account these

factors is likely to be miss-specified.


Appendix E: Florida Statute language

2018 Florida Statutes

The District Cost Differential is the portion of the Florida Education Finance Program that is addressed in

this report. The salient language is provided herein for reference and context.

Title XLVIII – K-20 Education Code, Chapter 1011, Planning and Budgeting

1011.62 Funds for operation of schools. —If the annual allocation from the Florida Education Finance

Program to each district for operation of schools is not determined in the annual appropriations act or the

substantive bill implementing the annual appropriations act, it shall be determined as follows:

(1) COMPUTATION OF THE BASIC AMOUNT TO BE INCLUDED FOR OPERATION. —The following procedure

shall be followed in determining the annual allocation to each district for operation:

……..

(s) Determination of the basic amount for current operation. —The basic amount for current operation to

be included in the Florida Education Finance Program for kindergarten through grade 12 for each district shall

be the product of the following:

1. The full-time equivalent student membership in each program, multiplied by

2. The cost factor for each program, adjusted for the maximum as provided by paragraph (c), multiplied by

3. The base student allocation.

The base student allocation.

(t) Computation for funding through the Florida Education Finance Program. —The State Board of

Education may adopt rules establishing programs, industry certifications, and courses for which the student

may earn credit toward high school graduation and the criteria under which a student’s industry certification

or grade may be rescinded.

(2) DETERMINATION OF DISTRICT COST DIFFERENTIALS. —The Commissioner of Education shall annually

compute for each district the current year’s district cost differential. The district cost differential shall be

calculated by adding each district’s price level index as published in the Florida Price Level Index for the

most recent 3 years and dividing the resulting sum by 3. The result for each district shall be multiplied by

0.008 and to the resulting product shall be added 0.200; the sum thus obtained shall be the cost

differential for that district for that year.

(3) INSERVICE EDUCATIONAL PERSONNEL TRAINING EXPENDITURE. —Of the amount computed in

subsections (1) and (2), a percentage of the base student allocation per full-time equivalent student or other

funds shall be expended for educational training programs as determined by the district school board as

provided in s. 1012.98.

(4) COMPUTATION OF DISTRICT REQUIRED LOCAL EFFORT. —The Legislature shall prescribe the aggregate

required local effort for all school districts collectively as an item in the General Appropriations Act for each

fiscal year. The amount that each district shall provide annually toward the cost of the Florida Education

Finance Program for kindergarten through grade 12 programs shall be calculated as follows:


2018 Florida Budget Item

Partly in response to individual counties’ prior requests, the 2018 Legislature provided the following language, Chapter 2018-9, Laws of Florida (FY 18-19 Budget and Appropriations):

From the funds in Specific Appropriation 135, $100,000 in nonrecurring funds from the General Revenue Fund is provided to the Department of Education to issue a competitive solicitation to contract with an independent third party consulting firm to conduct a review of the current price level index methodology. A report shall be prepared which provides recommendations to the chair of the Senate Committee on Appropriations, the chair of the House of Representatives Appropriations Committee, and the Executive Office of the Governor’s Office of Policy and Budget by January 1, 2019.

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