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«THE EFFECT OF SCHOOL FINANCE REFORMS ON THE DISTRIBUTION OF SPENDING, ACADEMIC ACHIEVEMENT, AND ADULT OUTCOMES C. Kirabo Jackson Rucker Johnson ...»

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Models: Results are based on OLS and 2SLS/IV models that include: school district fixed effects, race-specific year of birth fixed effects, race*census divisionspecific linear cohort trends; controls at the county-level for the timing of school desegregation*race, hospital desegregation*race, roll-out of "War on Poverty" & related safety-net programs (community health centers, county expenditures on Head Start (at age 4), food stamps, medicaid, AFDC, UI, Title-I (average during childhood years), timing of state-funded Kindergarten intro); controls for 1960 county characteristics (poverty rate, percent black, education, percent urban, population size, percent voted for Strom Thurmond in 1948 Presidential election*race (proxy for segregationist preferences)) each interacted with linear cohort trends; and controls for childhood family characteristics (parental income/education/occupation, mother's marital status at birth, birth weight, gender).

PSID sample weights are used to account for oversampling of poor families to produce nationally-representative estimates for models with all kids. The firststage model includes as predictors the school-age years of exposure to school finance reform interacted with the respective school district's reform-induced change in school spending. There exists a significant first-stage for both poor and non-poor kids.

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Number of Individuals 14,670 14,670 8,639 8,639 6,031 6,031 Number of School Districts 1,288 1,288 918 918 978 978 Robust standard errors in parentheses (clustered at school district level) *** p0.01, ** p0.05, * p0.10 Data: PSID geocode Data (1968-2011), matched with childhood school and neighborhood characteristics. Analysis sample includes all PSID individuals born 1955-1985, followed into adulthood through 2011.

Models: Results are based on OLS and 2SLS/IV models that include: school district fixed effects, race-specific year of birth fixed effects, race*census divisionspecific linear cohort trends; controls at the county-level for the timing of school desegregation*race, hospital desegregation*race, roll-out of "War on Poverty" & related safety-net programs (community health centers, county expenditures on Head Start (at age 4), food stamps, medicaid, AFDC, UI, Title-I (average during childhood years), timing of state-funded Kindergarten intro); controls for 1960 county characteristics (poverty rate, percent black, education, percent urban, population size, percent voted for Strom Thurmond in 1948 Presidential election*race (proxy for segregationist preferences)) each interacted with linear cohort trends; and controls for childhood family characteristics (parental income/education/occupation, mother's marital status at birth, birth weight, gender).

PSID sample weights are used to account for oversampling of poor families to produce nationally-representative estimates for models with all kids. The firststage model includes as predictors the school-age years of exposure to school finance reform interacted with the respective school district's reform-induced change in school spending. There exists a significant first-stage for both poor and non-poor kids.

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Number of Individuals 8,284 8,284 8,284 8,284 8,284 Number of School Districts 788 788 788 788 788 Robust standard errors in parentheses (clustered at school district level) *** p0.01, ** p0.05, * p0.10 Data: PSID geocode Data (1968-2011), matched with childhood school and neighborhood characteristics. Analysis sample includes all PSID individuals born 1955-1985, followed into adulthood through 2011.

Models: Results are based on 2SLS/IV models that include: school district fixed effects, race-specific year of birth fixed effects, race*census division-specific linear cohort trends; controls at the county-level for the timing of school desegregation*race, hospital desegregation*race, rollout of "War on Poverty" & related safety-net programs (community health centers, county expenditures on Head Start (at age 4), food stamps, medicaid, AFDC, UI, Title-I (average during childhood years), timing of state-funded Kindergarten intro); controls for 1960 county characteristics (poverty rate, percent black, education, percent urban, population size, percent voted for Strom Thurmond in 1948 Presidential election*race (proxy for segregationist preferences)) each interacted with linear cohort trends; and controls for childhood family characteristics (parental income/education/occupation, mother's marital status at birth, birth weight, gender). The first-stage model include as predictors the years of exposure to school finance reform (for relevant ages 5-17; 20-24; 0-4) interacted with the respective school district's reform-induced change in school spending. There exists a significant first-stage for both poor and non-poor kids.

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In order to limit the possibility that school district boundaries were drawn in response to pressure for SFRs, we utilize 1970 school district geographies. The “69-70 School District Geographic Reference File” (Bureau of Census, 1970) relates census tract and school district geographies.

For each census tract in the country, it provides the fraction of the population that is in each school district. Using this information, we aggregate census tracts to 1970 district geographies with Geographic Information Systems (GIS) software. We assign census tracts from 1960, 1980 and 1990 to school districts using this resulting digital map based on their centroid locations.





To construct demographic information on 1970-definition school districts, we compile census data from the tract, place, school district and county levels of aggregation for 1960, 1970, 1980 and 1990. We construct digital (GIS) maps of 1970 geography school districts using the 1969School District Geographic Reference File from the Census. This file indicates the fraction by population of each census tract that fell in each school district in the country. Those tracts split across school districts we allocated to the school district comprising the largest fraction of the tract’s population. Using the resulting 1970 central school district digital maps, we allocate tracts in 1960, 1980 and 1990 to central school districts or suburbs based on the locations of their centroids. The 1970 definition central districts located in regions not tracted in 1970 all coincide with county geography which we use instead.

66Appendix B: Validating the PSID with CCD data

To assuage concerns that our findings are driven by the sampling design of the PSID, we replicate our analysis for high school completion using the CCD data. We focus on dropout rates (grades 9-12) because this is the most reliable data that can be compared across time. Our data span the years 1991-92 to 2008-09. The dropout data from 1991-2001 and 2005-2008 come from the Common Core of Data Local Education Agency Universe Survey for all school districts in the United States. For years 2002-2004, dropout data in the CCD are only available for districts enrolling over 1,000 students. We also compiled a long panel of high school completion for years 1989 through 2010 by counting the number of graduates in a given year per 100 eighth graders four years before. This is a measure of the percentage of 8th graders who graduate.

To validate our PSID analysis, we compute district specific spending increases using the same method as that employed for the PSID data. Using the district specific effects to the graduation data and high school completion data from the CCD by year. We then estimate the effect of increased school spending due to reforms on the district dropout rate and our measure of the high school completion rate.

It is important to note that while one might expect the patterns in the CCD to be similar to those in the PSID, there are numerous reasons to expect some differences between the results presented in the PSID and the CCD samples. First, because these data are at the district level rather than the individual student level and because the CCD data are based on the school district attended (rather than the school district of birth) any effects might reflect changes in school composition that occur as a result of changes in per-pupil spending associated with reforms.

Second, note that the CCD data span a different time period from the sample analyzed in the PSID. While the PSID analysis is based on individuals who were of school age between 1960 and 1992, the CCD data span individuals who would have been school age between 1980 and

2008. Finally, while we analyze the effect of exposure to school spending for an individual over their entre 12 years of public schooling in the PSID, in the CCD we analyze the effect of contemporaneous spending in a given year. In sum, there are numerous reasons to expect differences between the results presented in the PSID and the CCD samples. However, should the results be similar between the CCD data and the PSID sample, this robustness check would indicate that our findings are robust and generalizable.

First, we show the event-study graph for the passage of a court-mandated reform for districts that experienced a 10 percent increase in spending and all other districts separately. We run a regression of the dropout rate on a set of indicator variables denoting the number of years since a court-mandated reform (ranging from -8 to 14). To show the dynamic effect for districts that saw larger increases in spending versus other districts, we interact these dynamic event-time dummies with an indicator equal to one if the districts saw more than a 10 percent increase in per-pupil spending. The event study graphs are presented for the two district types in Figure B.1.

67 The event-study graphs are consistent with spending increases associated with reforms reducing the dropout rate. For both types of districts there is minimal evidence of trending in outcomes prior to reforms (note that the data can only support estimating effects for four years prior to reforms for districts with large spending increases). For both groups of districts there is a steady decrease in the dropout rate after reforms that plateau after about 12 years of exposure. Finally, in regards to dosage, the decrease in the dropout rate is largest in those district that experience larger increases in per-pupil spending. In sum, both the patterns in timing and intensity support a causal effect on dropout in the CCD sample.

To quantify these effects, we implement the same instrumental variables strategy from Section V on the CCD sample. We instrument for per-pupil spending with SPENDj interacted with the number of years of exposure (going from zero to 12), in a regression that includes district fixed effects, time fixed effects, and also a linear in event time. We present a model based on the level of spending in dollars and also models that are based on the natural log of per-pupil school spending. In all models there is a strong first stage (F-statistic50). For both models the OLS regression yields no relationship between spending and dropout, while the 2SLS models show a statistically significant negative relationship between per-pupil spending and dropout. The 2SLS estimates indicate that increasing per-pupil spending by $1,000 will reduce dropout by about one percentage point and a doubling of per-pupil spending would reduce the dropout rate by 10.77 percentage points. Note that this estimate is not directly comparable to that from the PSID sample because this estimate is based on annual spending at the district level, not the cumulative effect of a sustained spending increase (experienced at the student level) for all 12 years of a student’s life. Because we expect the later to be much larger, the results from the CCD data are consistent with those from the PSID. Looking at the number of graduates per 100 8th graders tells a similar story. The OLS results yield a negative relationship between school spending and graduation rates. This would suggest that increasing per-pupil spending actually reduces graduation rates. However, in 2SLS models that rely only on exogenous variation the results have the expected sign. The coefficient on spending ($1,000) is 0.725 and that on the log of spending is 6.96 (both is significant at the five percent level). These point estimates suggest that increasing per-pupil spending by $1,000 would increase the number of graduates per 100 eighth graders by 0.725, and that doubling per-pupil spending would increase the graduates per 100 eighth graders by about 7 students. As with the dropout outcome, this estimate is not directly comparable to that from the PSID sample because this estimate is based on annual spending at the district level, not the cumulative effect of a sustained spending increase (experienced at the student level) for all 12 years of a student’s life. Because we expect the latter to be much larger, the results from the CCD data are consistent with those from the PSID.

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-1

-2

-2

-3

-3

-4

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  Data: District level Common Core Data (1991-2010) matched with per-pupil spending data and estimated district specific school finance reform spending increases from Section V.

Models: Results are based on event-study models that include: school district fixed effects, and year effects The figure plots the estimated years of exposure to school finance reform interacted with the an indicator variable connoting whether the respective school district's reform-induced change in school spending is less than (Left) or more less than (Right) 10 percent.

 

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To provide further evidence of the generalizability of our results we also explore only the state level variation in per-pupil spending associated with the passage of court-mandated school finance reforms. Because the individual level Census data does not include the precise location of birth but does include the state of birth we can only analyze patterns in the data at the state level. If increases in per-pupil spending improved outcomes, given that court-mandated reforms are associated with increased school spending on average, one might expect that cohorts within states that are of school age after reform years should have better outcomes than individuals from the same state who already completed school at the passage of the reforms.



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