Identifying and Analyzing Geographic Change to School Districts Colleen D. Joyce U.S. Census Bureau Housing and Household Economic Statistics Division Room 1451-3 Washington, DC 20233-8500 [email protected] The U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) Program produces poverty and income estimates for states, counties, and school districts on an annual basis. These estimates provide updated income and poverty statistics, which are used for the administration of federal programs and the allocation of federal funds to local entities. Although SAIPE’s main reason for producing the estimates is to provide the U. S. Department of Education with the necessary information to allocate Title I funding under the No Child Left Behind Act of 2001, the estimates are used by a variety of data users for a variety of purposes. Some data users use the annual data stand-alone, but others are interested in using the annual estimates to explore how poverty and income has changed over time. SAIPE’s goal is to produce the best estimate possible for a specific point in time. The estimates are not intended to be used in time series analyses. However, should data users choose to analyze the estimates in a time series, it is important they be made aware of the caveats involved with doing so. When a change in the estimate for a specific entity is observed from one estimate year to another, a number of reasons might explain it. These reasons can be roughly categorized into three groups: those involving geographic change, those involving universe change, and those with estimated demographic change. In many cases, the demographic change is what data users are really interested in. However, even when data users can isolate demographic change from geographic and universe changes, there are still numerous issues involved with comparing SAIPE data for the same area across years. These issues have been documented by the SAIPE team, and are outlined on SAIPE’s website.1 Less well documented are geographic and universe change issues. This paper will focus primarily on these two issues, and specifically on how these types of changes are accounted for in the estimates and how the impact of these changes can be determined. Because there is little change in the geography and universe at the state or county level, the paper will focus primarily on the school district estimates. How the Estimates are Created Before looking at the issues associated with analyzing the estimates, it is necessary to have a basic understanding of how the estimates are created. For state and counties, estimates are released for: • the total number of people in poverty; • the number of children under age 5 in poverty (for states only); • the number of related children age 5 to 17 in families in poverty; • the number of children under age 18 in poverty; and • median household income In addition, SAIPE produces the following for school districts eligible for Title I funding under the No Child Left Behind Act of 2001: • the total population; • the number of relevant children age 5 to 17; and • the number of related, relevant children age 5 to 17 in families in poverty 1 Detailed documentation regarding uncertainty in the estimates and cautions associated with comparing modeled estimates of the same county in different years can be found on SAIPE’s website at http://www.census.gov/hhes/www/saipe/ 2 Relevant children or population refers to the children or population served by the school district. For example, the relevant children for an elementary school district that serves kindergarten through grade 8 would be the population age 5-13. For a secondary school district serving grades 9-12, the relevant population would be that population age 14-17. A unified district serving grades K-12 would have a relevant population equal to that population age 5-17. Figure 1 shows the location of elementary, secondary and unified districts. State and County Estimates The poverty and income estimates start with national estimates obtained through the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC). State and county estimates are created using a model-based approach. Inputs to the model include the CPS ASEC data, and other tax and program data such as: • Internal Revenue Service (IRS) tax return data • counts of food stamp participants • Bureau of Economic Analysis (BEA) income data • decennial census estimates • intercensal population estimates School District Estimates Much of the SAIPE models’ input data cannot be uniformly geocoded to geography below that of the county level. It is for this reason that school district poverty estimates are arrived at using a different methodology. Once the estimate for the number of poor children in families in the county has been established, the relevant population is distributed among the school districts in the county. If a school district crosses the county line and is located in more than one county, the county population is distributed only to the piece of the district within the county. 3 Figure 1. Unified, Secondary and Elementary School Districts Unified School Districts Elementary and Secondary School Districts Source: Small Area Income and Poverty Estimates, U.S. Census Bureau The distribution is made using the same proportions that existed in the decennial census. For example, suppose the decennial census estimated 100 poor children in county A, with 50 of those living in district one (50 percent), 25 in district two (25 percent), and 25 in district three (25 percent). The 2002 county estimated number of poor children, as determined by the model, is 200. 100 of those would be assigned to district one (50 percent), 50 to district two (25 percent), and 50 in district three (25 percent). (See Table 1.) That of course, is assuming that the school district geography has not changed since the decennial census. But what if the geography has changed? Table 1. Distributing a County’s Estimated Number of Relevant Children in Poverty Among School Districts Within that County Geographic Census 2000 Census 2000 Entity number of distribution of 2002 estimated number relevant children county’s relevant of relevant children age age 5 to 17 in children in poverty to 5 to 17 in poverty poverty school districts (assuming no geographic changes) County A 100 ------- 200 School 50 50% 100 District One School 25 25% 50 District Two School 25 25% 50 District Three Accounting for geographic change at the state and county level Although rare, should a geographic change occur in any state or county boundary, that change would be accounted for in the models through the input data. IRS data, BEA income data, and food stamp data would be geocoded to the updated geography. Decennial census estimates are retabulated to the new geography through the Geographic Update System to Support Intercensal Estimates (GUSSIE).2 4 Accounting for geographic change at the school district level GUSSIE retabulations are also used to create updated distributions of the number of poor children in whole school districts and school district pieces within counties. Building on the earlier example illustrated in Table 1, now assume that the boundary between school districts two and three has shifted. The original Census 2000 data showed that 50 percent of the poor children in County A were in district one, 25 percent were in district two, and 25 percent were in district three. After GUSSIE processes the boundary change between school districts two and three, the retabulated Census 2000 data shows that of the 100 poor children in the county, 50 of those are living in district one (50 percent), but now only 10 are in district two (10 percent), and 40 are in district three (40 percent). Again assume that SAIPE estimates 200 poor kids in the county in 2002. Based on the Census 2000 retabulation, 100 of those kids will be assigned to school district one (50 percent), 20 to district two (10 percent), and 80 to district three (40 percent). (See Table 2.) Table 2. Example of How the Distribution of a County’s Estimated Number of Relevant Children in Poverty is Distributed Among School Districts Within that County After Geographic Change Geographic Census 2000 Census 2000 2002 Retabulation 2002 Estimate of Entity estimated distribution of of Census 2000 number of number of county’s estimated number relevant relevant relevant of relevant children age 5 to children age children in children in 17 in poverty 5 to 17 in poverty to poverty (after (after boundary poverty school districts boundary change change between between districts districts two and two and three) three) County A 100 ------- 100 200 School 50 50% 50 100 District One School 25 25% 40 80 District Two School 25 25% 10 20 District Three 2 See Appendix A for a description of GUSSIE. 5 If a data user were to look at the estimate of relevant children in poverty in school district two in 2000 and 2002, he or she would see that the number of poor children in the district increases from 25 to 80. What might not occur to the user initially is that part of that increase may not be due to demographic change, but simply to the fact that the district itself is larger, and encompasses population that was previously counted in another district. So how can data users get a sense for how much of a given change in the estimates is due to geographic change and how much of it is demographic change? Examining the income year 2001 and 2002 poverty estimates might help to illustrate. The 2001 and 2002 Poverty Estimates In December 2004, SAIPE released income year 2001 and 2002 poverty estimates for school districts. Two years of data were released as the SAIPE program transitioned from a biennial to an annual release of data. School District Boundary Collection and Differences Between Income Year and Boundary Year Perhaps the first thing that data users should be aware of, is that the estimates for a specific income year do not always correspond with the boundary vintage year. (See Table 3.) Both the 2001 and 2002 estimates were based on school district boundaries as reported for the 2003-04 school year. The Census Bureau collected these school district boundaries in the fall of 2003. The Census Bureau contacts state officials every other year, giving them the opportunity to review the Census Bureau’s school district information and provide updates and corrections to school district names, boundaries, and the grade ranges they serve. Because these changes to school districts are only processed every other year, it is not always possible for the income year to match the school district boundary year. While 6 the income year 2002 estimates released in December 2004 are based on boundaries of a different year (2003-04), the income year 2003 estimates, scheduled for release in late 2005, will also be based on the 03-04 boundaries. The decision was made to use the most recent boundaries (03-04) for the 2001 and 2002 estimates (rather than the 01-02 boundaries), because it allows for more accurate allocation of funds under the No Child Left Behind Act of 2001. Table 3. Relationship of Estimates, Boundaries and Data Releases Estimates Income Year School District Boundary Year Year of Estimate Release 2002 2003-04 2004 2003 2003-04 2005 2004 2005-06 2006 Retabulating the Decennial Census School District updates reported to the Census Bureau are processed through GUSSIE. During GUSSIE processing, Census 2000 data, including total population, population age 5 to 17, relevant population, relevant population in poverty, and housing unit counts, are retabulated to reflect updated boundaries and grade range assignments. The retabulated counts, referred to as the “base” counts, serve as inputs to the production of population and poverty estimates. Because the base counts are a retabulation of the decennial census counts, and because the total counts from the census will not change, any changes in the total school district population base count from one year to the next will almost always be a result of geographic change. The Census 2000 total counts do not change, but the counted are now being assigned to different geography. Likewise, if the total base count for the area does not change but the population of relevant children does, a change in the grade range assignments, or universe, may be the cause. Analysis of the base counts allows us to isolate these geographic changes and analyze the implications of each on the estimates. 7 It should be noted that there are cases where changes in the population base counts result from circumstances other than changes in the boundaries. The Census Bureau is continuously improving our geographic databases. New and more accurate geographic information may show that we are geocoding housing units or group quarters to the wrong geography. Correcting the geocoding can result in units being “moved” to different geography without an actual change in the boundaries having occurred. Of course, we do not need to look at the base counts to determine which districts had boundary or grade range assignment changes, since these changes are reported directly to us by state officials. However, looking at changes in the base counts can be extremely useful in determining the degree to which those changes affected the estimated populations. How Many Districts Experienced These Types of Changes? Comparisons between the school district total population base counts retabulated for the 2001-02 school district boundaries and those retabulated for the 2003-04 boundaries reveal that 3,238 out of 14,2323 school districts, or 22.2 percent, experienced some base population change. (See Figure 2.) Net base count gains for districts ranged from 1 to 40,083 people. Net losses ranged from 1 to 29,927 people. New districts with as many as 16,199 people were created and districts with as many as 23,553 people were dissolved. Table 4 shows the number of districts with changes, broken down by the amount of change, and illustrates that the magnitude of change can vary widely. Of all school districts with changes, 25.1 percent involved net base population changes of 5 people or less. 53.5 percent involved 25 people or less, and over 25 percent involved changes of over 100 people. Figure 3 shows those school districts with numeric change and classifies the magnitude of that change. 8 Figure 2. School Districts with Changes in Base Population: 2001-02 Boundaries to 2003-04 Boundaries Unified with change Unified with no change Elementary and secondary with change Elementary and secondary with no change Elementary with change, secondary with no change Elementary with no change, secondary with change Source: Small Area Income and Poverty Estimates, U.S. Census Bureau