An ACS-Based Regional Cost Adjustment for the State of Washington

An ACS-Based Regional Cost Adjustment for the State of Washington Submitted to: Submitted by: Compensation Technical Working Group Quality Education Council Dr. Lori Taylor Texas A&M University May 2012

Executive Summary A regional cost index measures how much more or less it costs each school district to recruit and retain equivalent school personnel. School finance formulas in many states including Alaska, Colorado, Florida, New York, Texas and Wyoming use regional cost indices to direct additional resources to school districts with higher labor costs, so that all districts can afford to hire comparable personnel. There are many strategies that can be used to estimate a regional cost index. This report presents a comparable wage index (CWI) for the state of Washington. The index is based on an analysis of data from the American Community Survey (ACS). The ACS, which is conducted annually by the U.S. Census Bureau, has replaced the decennial census as the primary source of demographic information about the U.S. population. The ACS CWI measures the extent to which the demographically and occupationally-adjusted earnings of non-educators differ from one part of Washington to another. As such, the ACS CWI offers a reliable measure of regional differences in the cost of hiring school district personnel. The ACS CWI indicates that labor costs are above the pupil-weighted state average in the Seattle and Tacoma metropolitan areas, and below the state average in all other parts of the state. The large differences in prevailing wage levels indicated by the ACS CWI mean that it costs more to operate a school district in Seattle and Tacoma than it does to operate a school district in Clarkston or Walla Walla. The ACS CWI indicates that in order to equalize school district resources, the base salary allocations for school districts in Seattle should be 9 percent higher than the state average, and the base salary allocations for school districts in nonmetropolitan eastern Washington should be 14 percent lower than the state average. 1

Introduction School districts in some parts of Washington must pay a premium to attract the same high quality staff available to other districts at lower cost. Districts in urban areas must pay more than other districts to compensate for the higher cost of living. Districts in isolated areas may need to pay a premium to compensate for the lack of local amenities. A regional cost index captures this effect. It indicates how much more or less it costs each school district to recruit and retain equivalent school personnel. School finance formulas in many states including Alaska, Colorado, Florida, New York, Texas and Wyoming use regional cost indices to direct additional resources to school districts with higher labor costs, so that all districts can afford to hire comparable personnel (Lofgren 2007). There are many strategies that can be used to estimate a regional cost index (Taylor 2011). One attractive approach is to recognize that teachers are not the only workers who are sensitive to the cost of living and the general attractiveness of the community. All types of workers demand a higher salary where the price of a home is high, the climate is inhospitable, or the closest movie theater is 100 miles away. A comparable wage index (CWI) measures regional variation in the cost of hiring educators by observing regional variation in the salaries of comparable workers who are not educators (Taylor and Fowler 2006, Rothstein and Smith 1997, Guthrie and Rothstein 1999). Intuitively, if the nurses, accountants and mechanical engineers all earn 10 percent more than the state average for their professions in Seattle, then the cost of hiring teachers in Seattle should also be 10 percent higher than the state average. This analysis presents a CWI for the state of Washington. The index is based on an analysis of data from the American Community Survey (ACS). The ACS, which is conducted annually by the U.S. Census Bureau, has replaced the decennial census as the primary source of demographic information about the U.S. population. It provides information about the earnings, age, occupation, industry, and other demographic characteristics for millions of U.S. workers. The ACS CWI measures the extent to which the earnings of non-educators differ from one part of Washington to another, after adjustments for differences in the demographic and occupational mix of each location. As such, the ACS CWI offers a reliable measure of regional differences in the cost of hiring school district personnel. Advantages and Disadvantages of a CWI The principal advantage of a CWI is that it provides a measure of the cost of education that is completely outside of school district control. There is no risk that school district hiring policies will distort a CWI into confusing high-spending school districts with high cost ones. Because the CWI is based on demographically and occupationally adjusted salaries rather than average salaries, there is also little risk that the CWI will indicate that the wage level is low in an area simply because the labor force is young and inexperienced. Furthermore, unlike a traditional cost-of-living index, a CWI reflects not only differences in the cost of purchased goods and 2

services (like housing) but also differences in amenities (like the climate or access to health care). As such, it is a more complete measure of regional variations in labor cost. The disadvantages of a CWI are threefold. First, comparability is always a concern. If the noneducator population differs substantially from the educator population in terms of age, educational background, or tastes for local amenities, then a CWI may overstate (or understate) the wage differentials that educators will require. A regression-based CWI (like the ACS CWI) largely addresses demographic and occupational differences, but the methodology cannot control for possible differences in tastes between educators and non-educators. If teachers are less attracted to city lights than other workers, a CWI will understate the relative cost of hiring teachers in metropolitan areas. Second, by design, a CWI cannot pick up district-level variations in the price of labor. Every school district in a labor market receives the same index value as every other district in the market. Finally, a CWI reflects labor cost differentials only when labor is mobile. If moving costs prevent workers from moving into (or out of) a particular location, then labor costs in that location may temporarily diverge from what would be expected given local amenities and the local cost of living. For example, employers in fast-growing industries and school districts in fast-growing areas may need to pay a temporary premium to attract workers. A CWI cannot capture this effect. The ACS Comparable Wage Index The National Center for Education Statistics (NCES) publishes a CWI that was specifically designed to capture regional wage differences for college graduates who are not educators. 1 The baseline estimates come from a regression analysis of individual earnings data from the 2000 U.S. Census. Annual updates to that baseline come from regression analyses of occupational earnings data provided by the U.S. Bureau of Labor Statistics (BLS). 2 This analysis updates the baseline analysis of the NCES CWI using public use micro-data from the 2008, 2009 and 2010 ACS surveys. 3 The ACS was chosen for this analysis because the Census Bureau plans to conduct the ACS annually for the foreseeable future, making it possible to update the ACS CWI as the need arises. Over time, updates to the ACS CWI will be more sensitive than the NCES CWI to demographic shifts in the labor force, making the ACS CWI more responsive to regional changes that might arise as Washington moves out of recession into recovery. Like the NCES CWI, the ACS CWI is derived from a regression analysis of individual earnings data. Workers with incomplete data and workers without a high school diploma were excluded from the ACS regression analysis, as was anyone who had a teaching or educational administration occupation or who was employed in the elementary and secondary education industry. Self-employed workers were excluded because their reported earnings may not represent the market value of their time. Individuals who reported working less than half time or for more than 90 hours a week were also excluded, as were workers under the age of 18 and over 3

the age of 80. Finally, individuals employed outside the United States were excluded because their earnings may represent compensation for foreign travel or other working conditions not faced by domestic workers. After these exclusions, the estimation sample retained 2,443,000 employed, high school or college graduates drawn from 447 occupations and 259 industries. The ACS CWI is estimated from nationwide data because the national sample is much larger and yields much more precise estimates of wages by industry and occupation than could be generated using only the ACS data for the state of Washington. For similar reasons, the analysis combines data on college graduates with data on high school graduates who did not finish college, and combines data from the three most recent ACS surveys. Table 1: The ACS Comparable Wage Model Explanatory Variables Estimate Standard Error Educational Attainment High School Diploma -0.0350 0.0013 GED -0.0874 0.0020 Some college, but less than one year 0.0000 One or more years of college, but no degree 0.0166 0.0013 Associate's degree 0.0488 0.0015 Bachelor's degree 0.1828 0.0013 Master's Degree 0.3197 0.0017 Professional Degree 0.3945 0.0033 Doctorate 0.4673 0.0031 White 0.0000 Black -0.1093 0.0012 American Indian -0.0527 0.0039 Chinese -0.1246 0.0030 Japanese -0.0591 0.0058 Other Asian or Pacific Islander -0.1166 0.0018 Other, NEC -0.0498 0.0023 Two or more major races -0.0546 0.0025 Hispanic -0.0879 0.0013 Usual Hours Worked (log) 1.0007 0.0016 Worked 27-39 weeks -0.5632 0.0017 Worked 40 to 47 weeks -0.2424 0.0016 Worked 48 to 49 weeks -0.1035 0.0023 Worked 50-52 weeks 0.0000 Female 0.2522 0.0064 Age 0.0689 0.0002 Age, squared -0.0007 0.0000 Female*age -0.0176 0.0003 Female*age, squared 0.0002 0.0000 Non-English Speaker -0.2522 0.0064 Note: The model also includes 1,341 (3*447) occupational fixed effects, 777 (3*259) industry fixed effects, 2,328 (3*776) labor market fixed effects, and random effects for state. All reported coefficient estimates are statistically significant at the 5-percent level. There are 2,443,000 observations, and the -2 residual log likelihood is 13,381,256. 4

Source: Ruggles et al. (2010) and author s calculations. Table 1 presents the results from the regression analysis. The dependent variable is the log of annual wage and salary earnings. Key independent variables include the age, gender, race, educational attainment, language ability and amount of time worked for each individual in the national sample. The model includes the interaction between gender and age, to allow for the possibility that men and women have different career paths, and therefore different age-earnings profiles. In addition, the estimation includes indicator variables for each labor market area, occupation, and industry for each year (2008, 2009 and 2010). 4 This specification allows wages to rise (or fall) more slowly in some occupations, industries or locations than it does in others. Such flexibility is particularly important because the analysis spans the Great Recession and some industries, occupations or locations fell more sharply and/or are recovering more slowly than others. The labor markets are based on place-of-work areas as defined by the Census Bureau. Census place-of-work areas are geographic regions designed to contain at least 100,000 persons. The place-of-work areas do not cross state boundaries and generally follow the boundaries of county groups, single counties, or census-defined places (Ruggles et al. 2010). Counties in sparselypopulated parts of a state are clustered together into a single Census place-of-work area. All local communities in the United States are part of a place-of-work area. Individuals can live in one labor market, and work in another. Their wage and salary earnings are attributed to their place of work, not their place of residence. 5 Following the NCES CWI, most labor markets in the ACS CWI are either a single place of work, or a cluster of the places-of-work that comprise a metropolitan area. To simplify the ACS CWI, however, the three nonmetropolitan place-of-work areas in eastern Washington have been consolidated into a single labor market. Thus, there are 14 ACS CWI labor markets in the state of Washington. Nine correspond to metropolitan areas Bellingham, Bremerton, Kennewick, Olympia, Portland, Seattle, Spokane, Tacoma, and Yakima while five represent clusters of rural counties. Each Washington school district is associated with one of the 14 labor market areas. As Table 1 illustrates, the estimated model is consistent with reasonable expectations about labor markets. Wage and salary earnings increase with the amount of time worked per week and the number of weeks worked per year. Earnings also rise as workers get older, but the increase is more rapid for men than for women (perhaps because age is not as good an indicator of experience for women as it is for men). Workers with advanced degrees earn systematically more than workers with a bachelor s degree, who in turn earn significantly more than workers with a high school diploma. Whites earn systematically more than apparently comparable individuals from other racial groups. Workers who do not speak English well earn substantially less than other workers, all other things being equal. 5

The predicted wage level in each labor market area captures systematic variations in labor earnings while controlling for demographics, industrial and occupational mix, and amount of time worked. 6 I used the wage model in Table 1 to predict the 2010 wage level in each Washington labor market area, I then matched each Washington school district to the wage prediction for its county of record and calculated the pupil-weighted, statewide average predicted wage. 7 Dividing each local wage prediction by this statewide average yields the ACS CWI. Table 2 presents the ACS CWI for each Washington labor market area. As the table illustrates, the ACS CWI indicates that labor costs are above the pupil-weighted state average in the Seattle and Tacoma metropolitan areas, and below the state average in all other parts of the state. After adjustments for the demographic, industrial and occupational mix, wage levels are 9 percent higher than the state average in the Seattle metropolitan area (the highest-cost labor market in the state) and 14 percent lower than the state average in nonmetropolitan eastern Washington (the lowest-cost labor market in the state). Table 2: The ACS CWI, by Washington Labor Market Area ACS CWI Bellingham Metropolitan Area 94 Bremerton-Silverdale Metropolitan Area 96 Kennewick-Pasco-Richland Metropolitan Area 99 Olympia Metropolitan Area 99 Portland-Vancouver-Beaverton Metropolitan Area 97 Seattle-Bellevue-Everett Metropolitan Area 109 Spokane Metropolitan Area 91 Tacoma Metropolitan Area 103 Yakima Metropolitan Area 94 Island, San Juan and Skagit 93 Adams, Asotin, Chelan, Columbia, Douglas, Ferry, Grant, Garfield, Kittitas, Lincoln, Okanogan Pend Oreille, Stevens, Walla Walla and Whitman 86 Cowlitz, Klickitat and Wahkiakum 91 Grays Harbor, Lewis and Pacific 90 Clallam, Jefferson and Mason 94 State average 100 Note: The pupil-weighted state average is calculated using the FTE student counts from the 2010-11 school year Source: Ruggles et al. (2010) and author s calculations. Figure 1 illustrates the ACS CWI for each Washington school district in 2010. Darker colors indicate higher index values. As the map illustrates, labor costs have a strong geographic pattern in the state of Washington. As a general rule, labor costs are lower in the eastern part of the state, and highest in the central and western regions. The large differences in local wage levels indicated by the ACS CWI mean that it actually costs more to operate a school district in Seattle and Tacoma than it does to operate a school district in 6

Clarkston or Walla Walla. The ACS CWI indicates that in order to equalize school district resources, the base salary allocations for school districts in Seattle should be 9 percent higher than the state average, and the base salary allocations for school districts in nonmetropolitan eastern Washington should be 14 percent lower than the state average. Figure 1: The ACS CWI, 2010 ACS Index 99-109 94-99 93-94 91-93 90-91 86-90 86-86 No data Source: Ruggles et al. (2010) and author s calculations. Comparing the ACS CWI with Regional Differences in the Cost of Housing The wage differentials indicated by the ACS CWI are large, but they are dwarfed by the differences in the cost of housing. According to the U.S. Department of Housing and Urban Development (HUD), a standard, two bedroom apartment rents for $1,098 per month in Seattle, but only $713 per month in Walla Walla, a difference of 54 percent. 8 Because no one spends all of their income on housing, a 54 percent difference in housing costs does not imply a 54 percent difference in the cost of living. What is more interesting is the geographic pattern. Figure 2 maps the HUD fair market rents for the state of Washington. The similarity to Figure 1 is striking. As with the ACS, rents are highest in the Seattle metropolitan area and lowest in the eastern part of the state. The strong geographic pattern to the rents adds further weight to the argument that school districts in Washington face important regional differences in labor costs, and that those differences should be reflected in the funding formula. 7

Figure 2: HUD Fair Market Rents, 2012 Fair Market Rents $1,118 - $1,176 1,060-1,118 1,002-1,060 944-1,002 887-944 829-887 771-829 713-771 655-713 Source: U.S. Department of Housing and Urban Development. Comparing the ACS CWI with Other Labor Cost Indices Figure 3 compares the ACS CWI with two other measures of regional differences in the cost of labor an updated version of the NCES CWI and the HS-CWI. As discussed in Taylor (2012) the Updated CWI reflects regional differences in the prevailing salary for college graduates while the HS-CWI reflects regional variations in the prevailing salary for high school graduates who do not have a college degree. The ACS CWI was estimated from data for both high school and college graduates, so in many ways it represents a blend of the other two indices. As the figure illustrates, the three indicators of labor cost tell very similar stories about regional cost variations in Washington. All three indicate that labor costs are highest in the Seattle metropolitan area, and lowest in nonmetropolitan eastern Washington. They diverge most sharply with respect to the Bremerton metropolitan area, where the Updated CWI and the HS- CWI indicate that labor costs are above average and the ACS CWI indicates that labor costs are below average. Notably, HUD s fair market rents for 2012 are also below average in the Bremerton metropolitan area. 9 8

Figure 3: Comparing the ACS CWI with the HS CWI and the Updated CWI, 2010 Seattle Bellevue Everett Tacoma Olympia Kennewick Richland Pasco Portland Vancouver Beaverton Bremerton Silverdale Clallam, Jefferson and Mason Yakima Bellingham Island, San Juan and Skagit Cowlitz, Klickitat and Wahkiakum ACS CWI Spokane Grays Harbor, Lewis and Pacific Asotin, Columbia, Garfield, Walla Walla and Whitman Adams, Grant, Ferry, Lincoln, Pend Oreille and Stevens Chelan, Douglas, Kittitas and Okanogan HS CWI Updated CWI 20% 15% 10% 5% 0% 5% 10% 15% Percentage Deviation from State Average, 2010 Source: Taylor (2012) and author s calculations using Ruggles et al. (2010) 9

Softening the Sharp Edges Sharp jumps and drops in index values at the border of labor market areas are a source of concern when using the ACS CWI to adjust the base salary allocations. Consider for example, the case of Cascade and Skykomish School Districts. Cascade School District is in Chelan County, where the ACS CWI is 86; bordering Skykomish School District is in King County, where the ACS CWI is 109. Few people would believe that labor costs are nearly 27 percent higher in Skykomish than they are in Cascade (109/86=127). Arguably, wage levels could be lower on the periphery of the Seattle metropolitan area than they are in the center, making the index value for the metropolitan area as a whole an overestimate of labor costs for Skykomish. Fortunately, the Seattle labor market area is made up of multiple Census place-of-work areas, and the ACS data files indicate whether or not a place-of-work area is part of the city center. Therefore it is possible to subdivide the Seattle metropolitan area into two labor markets the central city and the remainder of the metropolitan area and re-estimate the wage model. Table 3: The ACS CWI and Alternative ACS CWI, by Washington Labor Market Area Alternative ACS CWI ACS CWI Bellingham Metropolitan Area 94 94 Bremerton-Silverdale Metropolitan Area 95 96 Kennewick-Pasco-Richland Metropolitan Area 99 99 Olympia Metropolitan Area 99 99 Portland-Vancouver-Beaverton Metropolitan Area 96 97 Seattle-Bellevue-Everett Metropolitan Area, central city 109 109 Seattle-Bellevue-Everett Metropolitan Area, outside central city 108 109 Spokane Metropolitan Area 90 91 Tacoma Metropolitan Area, central city 104 103 Tacoma Metropolitan Area, outside central city 101 103 Yakima Metropolitan Area 94 94 Island, San Juan and Skagit 93 93 Adams, Asotin, Chelan, Columbia, Douglas, Ferry, Grant, Garfield, Kittitas, Lincoln, Okanogan Pend Oreille, Stevens, Walla Walla and Whitman 86 86 Cowlitz, Klickitat and Wahkiakum 90 91 Grays Harbor, Lewis and Pacific 89 90 Clallam, Jefferson and Mason 93 94 State average 100 100 Note: The pupil-weighted state average is calculated using the FTE student counts from the 2010-11 school year Source: Ruggles et al. (2010) and author s calculations Table 3 presents results from replicating the ACS CWI analysis after subdividing the Seattle and Tacoma metropolitan areas into their central and peripheral labor markets. Again, I used the 10

estimated wage model to predict the wage level in each Washington labor market, calculated the pupil-weighted, statewide average, and divided each local wage prediction by this statewide average to yield the alternative ACS CWI. The wage predictions were essentially unchanged for all labor markets except Seattle and Tacoma, but the changes to those markets altered the statewide average, leading to changes in index values for some other labor market areas. As the table illustrates, wages were lower on the metropolitan fringe than they were in the city centers but not by much. The ACS CWI is one percentage point lower on the periphery of King county than it is in the city center. There is no evidence that the index value for Skykomish greatly overstates the cost of labor on the fringes of King County. Meanwhile, the index value for Chelan County does not change, so the gap between Skykomish and Cascade School Districts remains large. The modification has more impact on the index values for the Tacoma metropolitan area. Here, the index value for the city center increased by one percentage point, while the index value for the periphery decreased by two, creating a three percentage point differential between the city center and the surrounding areas. Figure 4 maps the alternative ACS CWI. School districts in King, Pierce and Snohomish counties were matched to the city center using the NCES Common Core of Data s school locale codes and Census Bureau maps. 10 Appendix table A.1 lists the school districts matched to the city center. Figure 4: The Alternative ACS CWI, 2010 ACS Index 108-109 99-108 94-99 93-94 90-93 89-90 86-89 86-86 No data Source: Ruggles et al. (2010) and author s calculations. 11

Smoothing Over Time The ACS CWI and the alternative ACS CWI have been estimated assuming that regional differences in labor cost can change from one year to the next. This assumption is plausible given economic conditions during the three years from 2008 through 2010. It seems reasonable to believe that during the Great Recession wage levels could have been rising or falling in some parts of the state, while holding steady in others. On the other hand, year-to-year changes in wage levels could reflect meaningless noise rather than meaningful trends. Arguably, adjustments to the funding formula should be based on persistent differences in labor cost, not short-term volatility. Therefore, Table 4 presents results from replicating the ACS CWI analysis (after subdividing Seattle and Tacoma) assuming that the geographic pattern of labor costs did not change from 2008 through 2010. (See Appendix Table 2 for the model specifics.) Table 4: The Alternative ACS CWI and 3-Year ACS CWI, by Washington Labor Market Area Alternative ACS CWI Three-Year ACS CWI Bellingham Metropolitan Area 94 92 Bremerton-Silverdale Metropolitan Area 95 97 Kennewick-Pasco-Richland Metropolitan Area 99 99 Olympia Metropolitan Area 99 98 Portland-Vancouver-Beaverton Metropolitan Area 96 97 Seattle-Bellevue-Everett Metropolitan Area, central city 109 110 Seattle-Bellevue-Everett Metropolitan Area, outside central city 108 107 Spokane Metropolitan Area 90 90 Tacoma Metropolitan Area, central city 104 103 Tacoma Metropolitan Area, outside central city 101 101 Yakima Metropolitan Area 94 92 Island, San Juan and Skagit 93 94 Adams, Asotin, Chelan, Columbia, Douglas, Ferry, Grant, Garfield, Kittitas, Lincoln, Okanogan Pend Oreille, Stevens, Walla Walla and Whitman 86 88 Cowlitz, Klickitat and Wahkiakum 90 90 Grays Harbor, Lewis and Pacific 89 89 Clallam, Jefferson and Mason 93 90 State average 100 100 Note: The pupil-weighted state average is calculated using the FTE student counts from the 2010-11 school year Source: Ruggles et al. (2010) and author s calculations As the table illustrates, constraining the analysis so that the geographic pattern of labor cost does not change over time compresses the pattern somewhat. The gap between the highest-cost labor market and the lowest-cost labor market is slightly narrower (25 percent rather than nearly 27 12

percent) as is the gap between the Cascade and Skykomish School Districts. However, the basic story remains unchanged (See Figure 5). Labor costs are above the pupil-weighted state average in the Seattle and Tacoma metropolitan areas, and below the state average in all other parts of the state. Figure 5: The Three-Year ACS CWI, 2008-2010 3-Year ACS Index 107-110 98-107 92-98 90-92 89-90 88-89 88-88 No data Source: Ruggles et al. (2010) and author s calculations. Conclusions All the available evidence indicates that workers in the Seattle and Tacoma metropolitan areas earn substantially higher salaries than similar workers elsewhere in the state. Housing costs are higher. The IRS estimates that even the cost of restaurant meals is higher. 11 It is hard to believe that the cost of hiring teachers isn t also substantially higher in Seattle and Tacoma. Such large differences in labor cost imply equally large differences in the purchasing power of school districts. School districts in high-cost areas must pay higher salaries just to stay even with school districts elsewhere in the state. As a result, the actual costs of providing a basic education are higher in some parts of the state than they are in others. A regional cost index, like the ACS CWI or one of the other wage indices presented here, indicates how much more or less it costs each school district to recruit and retain equivalent school personnel. If the goal of the funding formula is to provide each school district with comparable resources, then the State should consider incorporating a regional cost index into the base salary allocation model. A failure by the state to recognize the regional cost differences in hiring undermines the equity and adequacy goals of the school funding formula. 13

Notes 1 Educators are individuals who are employed in the elementary and secondary education industry (regardless of occupation) or who are employed in an elementary or secondary education occupation (regardless of employer). 2 For more on the estimation of the NCES CWI, see Taylor and Fowler (2006). For information on updates to the NCES CWI, see Taylor (2012). 3 The data for this analysis come from Ruggles et al. 2010. The analysis is based on annual files for each survey administration, and not on the combined three-year file. 4 The model also includes random effects for states. Treating state effects as random rather than fixed ensures that the predicted wage is the same in Kansas City, Kansas as it is in Kansas City, Missouri, while allowing for a correlation in the errors among labor markets within any given state. 5 The analysis does not include any adjustment for commuting time because commuting times are a function of the local housing stock and local rent gradients. An analysis of the relationship between the characteristics of the available housing stocks, wage levels and commuting times is beyond the scope of this report. 6 Formally, the predicted wage level in each market is the least-squares mean for the market fixed effect. The leastsquares mean (or population marginal mean) is defined as the expected value of the mean for each effect (in this context, each market) that you would expect from a balanced design holding all covariates at their mean values and all classification variables (such as occupation or gender) at their population frequencies. 7 The pupil-weighted state average is calculated using the full-time-equivalent student counts from the 2010-11 school year 8 These are the U.S. Department of Housing and Urban Development s Fair Market Rents for 2012. For more on the estimates, visit http://www.huduser.org/portal/datasets/fmr.html. 9 The pupil-weighted state average fair market rent is calculated using the full-time-equivalent student counts from the 2010-11 school year. 10 For a map of the place of work areas, visit http://www2.census.gov/geo/maps/puma/puma2k/wa_puma5.pdf. 11 In 2012, the per diem allowance for food and incidentals is $71 in Seattle, $61 in Tacoma and $46 in rural eastern Washington, For more on the IRS estimates, visit http://www.gsa.gov/portal/category/100120. 14

Bibliography Chambers, J.G. (1998). Geographic Variations in Public Schools Costs (NCES 98-04). U.S. Department of Education. Washington, DC: National Center for Education Statistics Working Paper. Chambers, J.G. (1997). Volume III The Measurement of School Input Price Differences: A Technical Report on Geographic and Inflationary Differences in the Prices of Public School Inputs. U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office. Goldhaber, D.D. (1999). An Alternative Measure of Inflation in Teacher Salaries. In W.J. Fowler, Jr. (Ed.), Selected Papers in School Finance, 1997 99 (NCES 1999-334) (pp. 29 54). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Guthrie, James, and Richard Rothstein. 1999. Enabling adequacy to achieve reality: Translating adequacy into state school finance distribution arrangements. In Equity and adequacy in education finance, edited by H.F. Ladd, R. Chalk, and J.S. Hansen. Washington, D.C.: National Academy Press, pp. 209 59. Fowler Jr., William J., and David H. Monk. 2001. A primer for making cost adjustments in education: Research and Development Report. NCES Report No. 2001 323. Washington, D.C.: National Center for Education Statistics. Rothstein, Richard and J.R. Smith. 1997. Adjusting Oregon education expenditures for regional cost differences: A feasibility study. Sacramento, CA: Management Analysis & Planning Associates, LLC. Ruggles, S. J. Alexander, T., Genadek, K., Goeken, R., Schroeder, M.B. and Sobek, M. 2010. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota. Lofgren, Joanie. 2007. Implementation and use of geographic cost indices in state school funding formulas. Association of Metropolitan School Districts. Manuscript retrieved May 15, 2012 from http://www.amsd.org/wp-content/uploads/2011/11/geographic-cost-indices- _2007_-AMSD.pdf Stoddard, Christiana. 2005. Adjusting teacher salaries for the cost of living: the effect on salary comparisons and policy conclusions. Economics of Education Review 24: 323 39. Taylor, L.L. 2012. But are they competitive in Seattle? An analysis of educator and comparable non-educator salaries in the state of Washington. A report presented to Compensation Technical Working Group and Quality Education Council. Retrieved May 15, 2012 from http://www.k12.wa.us/compensation/pubdocs/competitiveseattle.pdf Taylor, L.L. 2011. Updating the Wyoming Hedonic Wage Index, A report prepared for the Joint Appropriations Committee and the Joint Education Committee. Retrieved May 7, 2012 from http://legisweb.state.wy.us/2011/interim%20studies/taylor_updatingthewyominghedo nicwageindexfinal.pdf 15

Taylor, L.L. 2010. Putting Teachers in Context: A Comparable Wage Analysis of Wyoming Teacher Salaries. A report prepared for the Select Committee on School Finance Recalibration, Retrieved March 7, 2012 from http://legisweb.state.wy.us/lsoweb/schoolfinance/documents/taylorwyomingcompar ablewagesreportfinaldecember2010.pdf Taylor L.L. 2008. Washington Wages: An Analysis of Educator and Comparable Non-educator Wages in the State of Washington, A report to the Joint Task Force on Basic Education Finance. Retrieved December 1, 2010 from http://www.leg.wa.gov/jointcommittees/bef/documents/mtg11-10_11-08/wawagesdraftrpt.pdf Taylor, L.L. 2008. Comparing Teacher Salaries: Insights from the U.S. Census. Economics of Education Review, 27(1): 48-57. Taylor, L.L. 2006. Comparable Wages, Inflation and School Finance Equity. Education Finance and Policy. 1(3): 349-71. Taylor, L.L., and W.J. Fowler, Jr.. 2006. A Comparable Wage Approach to Geographic Cost Adjustment NCES 2006 321. Washington, D.C.: National Center for Education Statistics. 16

Appendix Tables Table A.1: School Districts in the Central City Seattle-Bellevue-Everett Metropolitan Area Bellevue Edmonds Everett Federal Way Highline Issaquah Kent Lake Stevens Lake Washington Mercer Island Tacoma Metropolitan Area Bethel Clover Park Fife Monroe Mukilteo Northshore Renton Seattle Shoreline Snohomish Tukwila Vashon Island Franklin Pierce Puyallup Tacoma 17

Table A.2: The ACS Comparable Wage Model. Three-Year Average Explanatory Variables Estimate Standard Error Educational Attainment High School Diploma -0.0350 0.0013 GED -0.0875 0.0020 Some college, but less than one year 0.0000 One or more years of college, but no degree 0.0166 0.0013 Associate's degree 0.0487 0.0015 Bachelor's degree 0.1827 0.0013 Master's Degree 0.3196 0.0017 Professional Degree 0.3944 0.0033 Doctorate 0.4671 0.0031 White 0.0000 Black -0.1093 0.0012 American Indian -0.0528 0.0039 Chinese -0.1246 0.0030 Japanese -0.0598 0.0058 Other Asian or Pacific Islander -0.1166 0.0018 Other, NEC -0.0497 0.0023 Two or more major races -0.0546 0.0025 Hispanic -0.0879 0.0013 Usual Hours Worked (log) 1.0008 0.0016 Worked 27-39 weeks -0.5632 0.0017 Worked 40 to 47 weeks -0.2423 0.0016 Worked 48 to 49 weeks -0.1035 0.0023 Worked 50-52 weeks 0.0000 Female 0.2525 0.0064 Age 0.0689 0.0002 Age, squared -0.0007 0.0000 Female*age -0.0176 0.0003 Female*age, squared 0.0002 0.0000 Non-English Speaker -0.2522 0.0064 Note: The model also includes 1,341 (3*447) occupational fixed effects, 777 (3*259) industry fixed effects, 778 labor market fixed effects, and random effects for state. All reported coefficient estimates are statistically significant at the 5-percent level. There are 2,443,000 observations, and the -2 residual log likelihood is. 3,374,942. Source: Ruggles et al. (2010) and author s calculations. 18