Who Bears the Costs of District Funding Cuts? Reducing Inequality in the Distribution of Teacher Layoffs

Abstract

Massive funding cuts to public education took place around the country following the Great Recession. Many school districts were forced to conduct teacher layoffs at a larger scale than any other time in recent history. We show that prior to a district intervention, the layoff process disproportionately impacted historically disadvantaged students in the Los Angeles Unified School District. We then demonstrate the success of a policy designed to reduce inequality in the distribution of teacher layoffs.

Keywords

descriptive analysis economics of education educational policy equity evaluation finance policy policy analysis quasi-experimental analysis secondary data analysis

The Great Recession of 2008 led to unprecedented reductions in public education funding in the United States. As a direct result of recessionary spending cuts, more districts were forced to lay off teachers in the years during and following the Recession than at any other time in recent history (Goldhaber & Theobald, 2013; Shierholz, 2013). Studies show that layoffs harm both teachers and students by increasing class sizes, damaging school culture and morale, contributing to school-level teacher churn, and decreasing individual teachers’ productivity (Goldhaber, Strunk, Brown & Knight, 2016; Guin, 2004; Kraft, 2015; Strunk, Goldhaber, Knight, & Brown, 2015).

Harmful effects of teacher layoffs are particularly concerning because they often disproportionately affect disadvantaged students (e.g., UCLA/IDEA, 2009). This occurs for two reasons. First, many state school finance systems do not provide equitable levels of funding across districts (Baker, Sciarra, & Farrie, 2015; Ushomirsky & Williams, 2015), causing layoffs to be concentrated in districts with lower allocations of state financial support and greater proportions of low-income students and students of color (Estrada, 2012; Knight, 2016; Plecki, Elfers, & Finster, 2010). Second, when layoffs are determined by districtwide seniority, such policies can concentrate layoffs at particular schools within districts (Goldhaber & Theobald, 2013; Levinson & Theisen-Homer, 2015), and novice teachers are often concentrated in the highest poverty schools (Darling-Hammond, 2000, 2004).¹

Some school districts have experimented with policies that mitigate the inequitable distribution of layoffs. For instance, the layoff process in the Charlotte-Mecklenburg school district was conducted on a school-by-school basis during 2008–2009 and 2009–2010 (Sawchuk, 2015). By determining the number of layoffs in each school based on enrollment and revised student-teacher ratio policies, district administrators created a system that allocated budget-based layoffs equitably across all schools in the district (Kraft, 2015).

A different type of policy intervention was implemented in the Los Angeles Unified School District (LAUSD) in 2010–2011 and 2011–2012. After an unprecedented staffing reduction in which almost 5,000 reduction-in-force (RIF) notices were distributed to teachers in March of 2009, the American Civil Liberties Union (ACLU) filed a class action lawsuit alleging that the layoff process in LAUSD disproportionally affected students in South Los Angeles, where schools have the highest concentrations of students of color and in poverty (Reed v. State of California, 2010). A second round of teacher layoffs occurred the following school year, before the State Supreme Court made any decision in the Reed case. Ultimately, the involved parties agreed on a settlement (the “Reed settlement”) that required LAUSD to redirect layoffs in a set of 45 schools with high staff turnover for the third and fourth years of layoffs (2010–2011 and 2011–2012).

Using longitudinal data from LAUSD, we examine how layoffs in the district differentially impacted students across race/ethnicity, heritage language, special education status, and income level both before and after the implementation of the Reed settlement. We show through difference-in-difference analysis that Reed dramatically reduced the extent to which students of color and low-income students were disproportionately impacted. The results of this study have important policy implications as districts around the country continue to struggle to meet budget obligations (Bosman, 2015; Schulte, 2015; Superville, 2016).

In the next section, we provide additional background on the implications of layoffs for schools, teachers, and students. We then review the policy context under which teacher layoffs took place in LAUSD as well as the Reed settlement. The subsequent section provides an overview of our data and analytic approach, and in the final two sections, we discuss findings and policy implications.

Background Literature

Decades of research document the inequitable access to qualified, experienced, and effective teachers across students’ race/ethnicity, socioeconomic status, English language proficiency, and achievement levels (e.g., Clotfelter, Ladd, & Vigdor, 2005; Darling-Hammond, 1998, 2000, 2004; Goldhaber, Lavery, & Theobald, 2015; Ingersoll, 1999; Isenberg et al., 2013; Lankford, Loeb, & Wyckoff, 2002). Seniority-based layoffs, which assign layoffs primarily in reverse order of district seniority (and are required by law in California and many other states around the country; Thomsen, 2014), may disproportionately harm low-income students of color (e.g., Dowell, Whitmore, Hodgman, Littlefield, & Tracey, 2011; Hahnel, Barondess, & Ramanathan, 2011). To this end, several recent policy briefs use aggregate student data to argue that given the distribution of average teacher experience across schools in LAUSD and California, seniority-based layoffs necessarily will be concentrated in high-poverty, high-minority schools (Sepe & Roza, 2010; UCLA/IDEA, 2009).

Two studies simulate how layoffs would be distributed under alternate layoff policies (Boyd, Lankford, Loeb, & Wyckoff, 2011; Goldhaber & Theobald, 2013).² Boyd et al. (2011) find that in New York City, layoff policies based solely on either seniority or value-added measures would result in laid off teachers coming from schools with approximately 80% of low-income students, whereas the district average is approximately 72%. In contrast, simulations based on Washington State data suggest that layoff polices based solely on value-added lead to more equitable distributions than the seniority-based layoff policies that were actually used (Goldhaber & Theobald, 2013). The divergent findings in layoff simulation studies results in part from differences in the way teacher experience is distributed across schools in each context.

Only one prior study examines how layoffs are distributed across students and schools when districts use selection criteria other than seniority and teaching credentials. Kraft (2015) examines layoffs in Charlotte-Mecklenburg Schools, where district administrators determined the number of teacher layoffs at each school based on the difference between the full-time equivalent (FTE) staffing levels generated from the district’s student-teacher ratio policies and the number of FTE teachers employed at each school. Kraft did not find substantial differences in the likelihood of exposure to layoffs by student race/ethnicity or students’ family income level. Although teachers in schools with greater proportions of African American students, lower achievement scores, and higher rates of student absenteeism were slightly more likely to be laid off, the school-by-school determinations of the number of layoffs at each school prevented substantial inequities.

We build off this past work first by drawing on student-level administrative data to demonstrate the inequitable distribution of seniority-based layoffs in a large urban school district. We then examine the impacts of a policy aimed at equalizing the distribution of layoffs across student subgroups.

Policy Context in LAUSD

LAUSD has a similar proportion of students of color and degree of segregation as do other large urban districts, such as New York, Chicago, and Houston (Orfield, Kucsera, & Siegel-Hawley, 2012), making it a useful context to assess the equity implications of budget-based layoffs. Three-quarters of LAUSD students identify as Latina/o, about 9% as Black, 6% Asian, 8% White, and under 1% as Native American, Pacific Islander, or more than one race. Over 85% of students qualified for free or reduced priced meals in at least one year of our data, and approximately 70% are emergent bilinguals.³ Moreover, inexperienced teachers are disproportionately assigned to high-poverty and high-minority schools within LAUSD, although the district’s “teacher experience gaps” are similar to the statewide average (Sepe & Roza, 2010). Thus, when substantial funding cuts stemming from the Great Recession required LAUSD to lay off teachers, the district faced serious equity implications in terms of which schools and students would be affected.

The California Education Code requires that districts lay off teachers in reverse order of seniority within each teaching subject (e.g., math, English, elementary), though some exceptions are permitted.⁴ The Education Code also requires that teachers receive a RIF notice by March 15 that warns the teacher of a possible layoff. By May 15, districts must notify teachers of whether their RIF notice was rescinded or whether they will be laid off at the end of the school year.⁵ From 2008–2009 to 2011–2012, 13.7% of teachers received initial RIF notices, and 4.5% were laid off.⁶ Whether a teacher is RIF-rescinded, laid off, or not impacted by RIFs is important because past research indicates that RIF-rescinded and laid off teachers have lower retention rates and are less productive in the following year (Goldhaber et al., 2016; Strunk et al., 2015).

We split recessionary layoffs in LAUSD into two phases. In Phase I, 2008–2009 and 2009–2010, layoffs were dictated solely by state law, which requires seniority-based layoffs. In Phase II, 2010–2011 and 2011–2012, LAUSD implemented an intervention as a result of the Reed settlement that prevented budget-based layoffs at a subset of schools with the highest levels of teacher turnover (described in greater detail below). The Phase I layoff process exemplifies how layoffs are distributed in large districts under typical seniority-based systems; Phase II demonstrates the distribution of layoffs under a policy intervention intended to stem the inequities resulting from traditional layoff processes.

Phase I: Layoffs During the Preintervention Period

Table 1 provides descriptive statistics of Phase I teacher layoffs for each layoff category outlined previously. The top half demonstrates the strong correlation between seniority and teachers’ likelihood of receiving a RIF or layoff notice. Approximately 28% of novice teachers (with between 0–2 years of experience) were laid off, while 3.4% of mid-career teachers (3–8 years) and 0.6% of veteran teachers (9 or more years) received final layoff notices.

Table 1

Summary Statistics for Teachers by Layoff Threat (Teacher-Year Observations), 2008–2009 to 2009–2010

		RIF
	% Overall	No RIF	RIF-Rescinded	Laid Off
All teachers	52,406	45,571	4,688	2,147
All teachers	52,406	87.0%	8.9%	4.1%
Experience/education
Novice teachers (first–third year)	10.4%	52.7%	19.7%	27.5%
Midcareer teachers (fourth–eighth year)	24.3%	75.4%	21.2%	3.4%
Veteran teachers (ninth year or above)	65.3%	96.7%	2.7%	0.6%
Mean years of experience	9.3	10.1	4.7	2.5
Master’s degree or higher	34.2%	88.5%	8.4%	3.0%
Endorsement area
Special education	12.6%	96.0%	1.7%	2.2%
Math or science	12.9%	86.8%	11.8%	1.4%
Other non-elementary	29.3%	87.2%	7.0%	5.8%
Elementary	45.2%	84.3%	11.4%	4.3%

Note. “No RIF” refers to teachers who did not receive a reduction-in-force (RIF) notice; “RIF-rescinded” implies the teacher received a RIF notice, but it was later rescinded; “Laid off” means the teacher received both a RIF notice and a final laid off notice. The % overall column shows the overall proportion for each teacher characteristic districtwide (within categories, columns sum to 100%). The next three columns show how those characteristics are distributed across the three RIF/layoff categories (rows sum to 100%). For example, 10.4% of teachers are novice, and of those, 52.7% were not RIFed.

The bottom half of Table 1 shows that teachers with special education, math, or science credentials were far less likely to be laid off. In addition to sending RIF and layoff notices to the least experienced teachers within each teaching area, the district also targeted teachers who did not have the appropriate credentials for their teaching area during the first year of layoffs (i.e., were not No Child Left Behind compliant). As a result, other non-elementary credentials had the highest proportion of layoffs during Phase I, and a considerable amount of midcareer teachers received RIF and layoff notices.

Phase II: Layoffs During the Postintervention Period

In February of 2010, the ACLU filed a class action lawsuit (Reed v. State of California, 2010) resulting in a settlement that prevented budget-based layoffs at 45 schools for the second two years of layoffs (2010–2011 and 2011–2012). To identify Reed schools, district staff ranked in order of prior-year school-level teacher turnover all schools in the bottom 30% of Academic Performance Index (API, a composite measure of student test score performance used in California) that were making gains over the previous three years and had at least 15 teachers. The top 35 schools on this list were selected for Reed protection each year. An additional 10 schools were selected through a second set of criteria focused on new schools that would be most heavily impacted by layoffs. In addition, the Reed settlement stipulated that RIF notices redirected from the Reed-protected schools could only be sent to schools in which the proportion of teachers receiving notices was below the district average (see ACLU, 2011; LAUSD, 2015; and the online appendix for more information).

As Table 2 shows, 1.7% of all LAUSD elementary students and 7.1% of middle and high school students were enrolled in schools that were designated as Reed schools in both the 2010–2011 and 2011–2012 school years. An additional 4.2% of elementary students and 10.9% of middle and high school students were impacted by Reed in either of the two treatment years. Reed schools served higher proportions of students of color, emergent bilinguals, and low-income students compared to non-Reed schools. The Reed intervention included 45 of the 696 district schools (6.5%) and an average of 46,624 students (8.7%) during the two years the original Reed policy was in place.⁷

Table 2

Summary Statistics of Student Characteristics by Reed Treatment Group, 2010–2011 to 2011–2012

	Overall	Reed 2011/2012	Reed 2011 Only	Reed 2012 Only	Comparable Schools	All Other Schools
Elementary student observations	543,943	9,516	11,203	11,744	127,152	384,328
Elementary student observations	543,943	1.7%	2.1%	2.2%	23.4%	70.7%
Race/ethnicity
Black	8.2%	13.9%	16.6%	13.0%	10.6%	6.9%
Latina/o	75.3%	85.0%	77.3%	83.9%	81.4%	72.7%
Other race/ethnicity	7.0%	0.7%	4.5%	2.2%	3.7%	8.5%
White	9.5%	0.3%	1.6%	1.0%	4.3%	11.9%
English language status
Emergent bilingual	63.1%	75.7%	70.6%	75.3%	70.6%	59.8%
Native English speaker	36.9%	24.3%	29.4%	24.7%	29.4%	40.2%
Family income
Ever eligible for FRL	88.6%	99.9%	97.7%	99.2%	95.8%	85.4%
Never eligible for FRL	11.4%	0.1%	2.3%	0.8%	4.2%	14.6%
Special education
Any SPED classification	10.8%	9.0%	11.2%	11.5%	10.9%	10.8%
No SPED classification	89.2%	91.0%	88.8%	88.5%	89.1%	89.2%
Middle and high school student observations	524,551	37,185	21,109	36,295	168,370	261,592
Middle and high school student observations		7.1%	4.0%	6.9%	32.1%	49.9%
Race/ethnicity
Black	9.1%	12.7%	12.5%	14.1%	9.7%	7.1%
Latina/o	75.9%	84.0%	80.5%	81.9%	80.5%	70.6%
Other race/ethnicity	7.6%	2.7%	5.4%	2.6%	6.9%	9.7%
White	7.4%	0.7%	1.6%	1.3%	2.9%	12.6%
ELL status
Emergent bilingual	70.8%	78.2%	75.6%	75.4%	75.2%	65.8%
Native English speaker	29.2%	21.8%	24.4%	24.6%	24.8%	34.2%
Family income
Ever eligible for FRL	90.1%	99.1%	97.1%	95.8%	95.4%	84.0%
Never eligible for FRL	9.9%	0.9%	2.9%	4.2%	4.6%	16.0%
Special education
Any SPED classification	11.0%	12.4%	11.0%	11.4%	11.6%	10.4%
No SPED classification	89.0%	87.6%	89.0%	88.6%	88.4%	89.6%

Note. The first column, “Overall,” shows the proportion of elementary and secondary students that fall into each category. Columns 2 through 6 are mutually exclusive groups indicating the treatment group to which each student-observation is assigned. Treatment groups are time-invariant within students. The Reed 2011/2012 column indicates students who were treated by Reed during both the 2010–2011 and 2011–2012 school years. Reed 2011 only and Reed 2012 only refer to students treated only in those years. We define emergent bilingual as any student for whom English is not their heritage language. Other race/ethnicity includes the categories shown in Table 3. FRL = free/reduced price lunch; SPED = special education; ELL = English language learner.

Data and Analytic Approach

We ask two research questions about the distribution of layoffs in LAUSD:

Research Question 1: How are RIFs and layoffs distributed under typical seniority-based layoff policies (in Phase I)?

Research Question 2: How did the Reed settlement impact the distribution of RIFs and layoffs across students (in Phase II)?

We draw on LAUSD administrative data that link students to teachers and schools over a four-year window of observation, from 2008–2009 to 2011–2012. The student-level data include information on student demographics and are linked to teacher-level data sets, which include information on teachers’ experience, educational attainment, endorsement areas, contract status (e.g., temporary, probationary, permanent/tenured, etc.), courses taught each semester, and layoff status. These LAUSD administrative data are merged with public-use school-level data accessed through the California Department of Education.

For elementary students, we examine the likelihood a student’s teacher falls into the following categories: (a) did not receive a RIF notice, (b) received a RIF notice that was rescinded (“RIF-rescinded”), or (c) received a layoff notice. Outcomes for middle and high school students are calculated differently because these students attend non-self-contained classes and therefore have multiple teachers throughout the school day. Middle and high school student outcome measures are the proportion of a student’s teachers who are impacted by RIFs or layoffs. For each middle and high school student, we first calculate the total number of teachers to which the student is assigned throughout the school year (usually between five and seven per semester), and then we calculate the total number of those teachers that fall into each layoff category. We then divide these two numbers to compute the proportion of a student’s teachers that are not RIFed, RIF-rescinded, or laid off each year. Thus, while elementary student outcomes are binary, outcomes for middle and high school students are continuous measures that range from zero to one, with zero indicating that none of a student’s teachers had that outcome (e.g., layoff), and one indicating that all of a student’s teachers had the given outcome.⁸

To examine how layoffs are distributed under typical seniority-based policies, we use descriptive analyses to assess the extent to which RIFs and layoffs disproportionately affected various student groups in the years prior to the Reed intervention. We disaggregate by race/ethnicity, English language status, free/reduced price lunch (FRL) status, and special education enrollment.⁹

We use two strategies to address the question of how Reed impacted the distribution of RIFs and layoffs across students. First, we calculate the same outcome measures for each student group for the Phase II layoffs. We then compare the outcome measures before and after implementation of Reed. This approach shows whether, for example, low-income students were less likely to have their teacher laid off, compared to their higher income peers, when Reed layoff protections were in place.

This first approach is limited because it fails to consider the specific impact of the Reed reform on students in Reed schools. Many changes were occurring in LAUSD during the period of layoffs, including reforms to school governance models and teacher accountability and support systems. Decreases in the inequitable distribution of layoffs shown in the descriptive analyses may not be attributable to the Reed intervention but to other districtwide reforms occurring around the same time. To isolate the impact of the Reed intervention on students in treated schools, we employ a difference-in-difference (DID) framework that uses a group of comparison schools that do not receive the Reed treatment to serve as a control group (Angrist & Pischke, 2009; Imbens & Wooldridge, 2009). Both sets of schools should have had the same responses to any district-wide changes and should only differ in their exposure to the Reed treatment. In short, the DID analysis compares the pre- and postintervention outcomes in treated schools (the first difference) with the pre- and postintervention outcomes in nontreated schools (the second difference). The difference between these two differences (the DID) provides an estimate of the impact of the Reed intervention on students in Reed schools relative to students in similar schools.

In consultation with LAUSD administrators, we identified as our nontreated group schools that would have been selected for Reed if the intervention involved a greater number of schools.¹⁰ We describe this process in detail in the online appendix. As noted earlier, one of the requirements of the Reed settlement was that redirected RIF notices could only be sent to schools in which the proportion of teachers receiving notices was below the district average. As a result, comparison schools received very few if any of the RIFs that were redirected from the Reed schools.¹¹

There are two key assumptions underlying a causal inference from a DID analysis: (a) The treatment and comparison schools show similar trends in the outcome measure prior to treatment, and (b) the policy treatment was exogenous—namely, unanticipated and uncorrelated with other changes that took place at the same time as the policy intervention and were isolated to treatment or comparison schools (Bertrand, Duflo, & Mullainathan, 2004). Figure 1 addresses the first assumption. The graphs show that during the two years prior to Reed implementation, the trend from the first to the second year of layoffs (from 2008–2009 to 2009–2010) of the probability that an elementary student’s teacher received a RIF or layoff notice is similar for students in treatment and comparison schools. Similarly, for students in middle and high schools (graphs on the right side of Figure 1), the trend in the average percentage of a student’s teachers who received a RIF or layoff notice is similar across treatment and comparison schools.

Figure 1.

Predicted probability a student’s teacher received a reduction-in-force (RIF) or layoff notice (elementary, graphs on the left side) and predicted average percentage of a student’s teachers who received a RIF or layoff notice (middle and high school, graphs on the right side), in schools selected for Reed protection in 2010–2011 and 2011–2012 and in comparison schools

To address the second exogeneity assumption, we turn to the way the Reed intervention was implemented. Reed schools were selected by district staff and announced during the spring of the year in which the policy went into effect in each year. School staff were unaware of which schools would be selected in either year of the Reed intervention. Teachers in Reed schools during the first year of Reed (2010–2011) did not know whether their school would be selected a second time, and indeed, about a third of 2010–2011 Reed schools were not selected in 2011–2012. As evidence that no other changes in just the treatment or comparison group schools took place at the same time as Reed was implemented that may affect our estimated treatment effects, we show in online Appendix Table A2 that student demographics did not change significantly between the year prior to implementation and the first and second years of Reed (t tests in the final two columns show that none of the changes in student demographics were statistically significant). Comparison schools thus provide a plausible counterfactual for what would have happened in the treatment schools in the absence of treatment.¹²

For the DID analysis, we focus on outcomes that prior research has linked to higher teacher turnover and lower teacher effectiveness (i.e., Goldhaber et al., 2016; Strunk et al., 2015): (a) whether a teacher receives an initial RIF notice and (b) whether a teacher is laid off.¹³ For elementary students, we run two separate logistic regressions, defining in separate models P_ist(m) as the probability student i’s teacher received an initial RIF notice or received a final layoff, indexing for school s and year t:

\log (\frac{P_{i s t} (m)}{P_{i s t} (0)}) = β_{0} + β_{1} R e e d_{i s t}^{y e a r 1} + β_{2} R e e d_{i s t}^{y e a r 2} + φ R e e d_{i s t}^{y e a r 1} * π_{i s t} + λ R e e d_{i s t}^{y e a r 2} * π_{i s t} + π_{i s t} + X_{i s t} γ + μ_{i s t} .

For middle and high school students, we run identical ordinary least squares regression models predicting the proportion of each student’s teachers who fall into each layoff category. The Reed indicators ( $R e e d_{i s t}^{y e a r 1}$ and $R e e d_{i s t}^{y e a r 2}$ ) are time-invariant variables indicating whether the student will be enrolled in a Reed school in 2010–2011 and in 2011–2012, respectively. π_ist is a set of dummy variables indicating the school year, and X_ist includes school characteristics associated with the likelihood of being targeted for Reed. Our coefficients of interest are ϕ and λ, as they provide the DID estimates. The estimated effect for students treated in just 2010–2011 is the coefficient for the interaction between the year 2010–2011 dummy variable and the $R e e d_{i s t}^{y e a r 1}$ indicator (this coefficient is included in the vector ϕ). The estimated effect for students treated in just 2011–2012 is the coefficient for the interaction between the year 2011–2012 dummy variable and the $R e e d_{i s t}^{y e a r 2}$ indicator (this coefficient is included in the vector λ). For ease of interpretation, we convert all estimated effects for the Reed policy to marginal probabilities and report findings as differences in percentage points for each treatment group. The error term is μ_ist and standard errors are clustered at the school level. The models described previously provide plausibly causal estimates of the extent to which Reed impacted the likelihood that students’ teachers were affected by layoffs.¹⁴

Findings

Teacher Layoffs Prior to the Reed Intervention

The first three columns of Table 3 provide descriptive statistics showing that layoffs were inequitably distributed across student groups during Phase I. For elementary students, we report odds ratios showing the odds that a student in each subgroup had a teacher who was not RIFed, RIF-rescinded, or laid off, compared to the reference group. At the middle and high school levels, for each student subgroup, we report differences from the reference group in the proportion of teachers in each layoff category. Significance tests for elementary students show whether odds ratios for a particular student subgroup are significantly different than 1. For example, for the layoff outcome, an odds ratio of 1 for Black students implies that Black students have the same odds as White students of having their teacher laid off. In contrast, significance tests for middle and high school students show whether differences in the proportion of teachers RIFed/laid off are significantly different than 0. For example, if the difference in the proportion of teachers laid off for Latina/o students is 0, then Latina/o students had the same proportion of teachers laid off as the reference category (White students). Reference categories were selected based on the group that is generally most privileged in educational settings: White, native English speakers and students never eligible for FRL.

Table 3

Odds Ratios for the Likelihood of Having a Teacher in One of Three Layoff Conditions (Elementary) and Differences in the Average Proportion of Teachers in Each Layoff Condition (Middle and High School), Prior to Reed (Phase I Layoffs) and During Reed (Phase II Layoffs)

	A. Phase I Layoffs			B. Phase II Layoffs			C. t Test of Difference
	No RIF	RIF-Resc.	Layoff	No RIF	RIF-Resc.	Layoff	RIF-Resc.	Layoff
	Elementary students (odds ratios)
Total student observations	464,864	62,234	24,501	506,144	42,601	22,616
Total student observations	84.3%	11.3%	4.4%	88.6%	7.5%	4.0%
Black	0.622***	1.489***	1.722***	0.829***	1.183***	1.186***	***	***
Latina/o	0.934***	1.000	1.256***	1.095***	0.949***	0.856***	*	***
Multiple	1.016	0.946	1.099	1.022	0.969	1.005	ns	ns
Native American	1.051	0.834*	1.309*	1.108	0.924	0.882	ns	*
Pacific Islander	0.997	1.082	0.779	1.027	1.129+	0.650+	ns	ns
Asian	0.999	0.935**	1.192***	0.935***	1.121***	0.937*	***	***
White (ref. group)	1.000	1.000	1.000	1.000	1.000	1.000
Emerg. bilingual	0.963	1.029**	1.051***	0.994	1.004	1.010	*	*
Native English (ref. group)	1.000	1.000	1.000	1.000	1.000	1.000
FRL	1.015***	0.925***	0.931***	1.140***	0.890***	0.883***	+	+
Non-FRL (ref. group)	1.000	1.000	1.000	1.000	1.000	1.000
Any SPED	1.063***	0.673***	0.679***	1.493***	0.671***	0.735***	+	**
Non-SPED (ref. group)	1.000	1.000	1.000	1.000	1.000	1.000
	Middle and high school students (average proportion of teachers)
Total student observations (FTE)	506,144	42,601	22,616	459,203	35,347	30,001
Total student observations (FTE)	88.6%	7.5%	4.0%	87.5%	6.7%	5.7%
Black	–0.042***	0.019***	0.022***	0.014***	–0.022***	0.008***	***	***
Latina/o	–0.042***	0.020***	0.022***	0.003***	–0.013***	0.010***	***	***
Multiple	–0.042***	0.022***	0.019***	0.015***	–0.017***	0.002	***	***
Native American	–0.020***	0.006	0.014***	0.000	–0.007*	0.006*	**	*
Pacific Islander	–0.028***	0.017***	0.011***	–0.011**	0.000	0.011***	***	ns
Asian	0.006***	–0.006***	0.000	0.013***	–0.010***	–0.003***	*	**
White (ref. group)	0.922	0.058	0.021	0.871	0.080	0.049
Emerg. bilingual	–0.016***	0.008	0.008***	0.000	–0.004***	0.003***	***	***
Native English (ref. group)	0.897	0.069	0.034	0.875	0.080	0.049
FRL	–0.027***	0.013***	0.014***	0.010***	–0.015***	0.005***	***	***
Non-FRL (ref. group)	0.909	0.064	0.027	0.867	0.081	0.052
Any SPED	0.038***	–0.025***	–0.013***	0.035***	–0.019***	–0.016***	***	***
Non-SPED (ref. group)	0.882	0.077	0.041	0.872	0.069	0.059

Note. This table shows summary statistics with t tests comparing each group to a reference group (e.g., Black students are compared to White students and emergent bilingual students are compared to native English speakers). In the first six columns, significance tests for elementary students (top half) show whether the odds ratio for a particular subgroup is significantly different from 1 (i.e., the odds ratio for the reference group). Significance tests for middle and high school students (Columns 1–6, bottom half) show whether the difference in the proportion of teachers in each RIF/layoff category for a particular subgroup, compared to the reference group, is significantly different from 0. For example, Black students had 2.2 percentage points more teachers laid off, compared to White students, during the Phase I layoffs. The final two columns are t tests assessing whether differences in outcomes were significantly different between Phase I and Phase II layoffs. “RIF-Resc.” refers to students whose teachers received a reduction-in-force (RIF) notice that was later rescinded. “Emerg. bilingual” refers to any student whose heritage language is not English. “FRL” stands for free and reduced price meal eligibility. “Any SPED” refers to any student enrolled in special education.

p < .10. *p < .05. **p < .01. ***p < .001.

The first three columns of the top panel of Table 3 show the results for elementary students. Latina/o students were equally likely to have their teacher RIF-rescinded but had 25.6% greater odds of seeing their teacher laid off compared to White elementary students. Black elementary students had 48.9% and 72.2% greater odds of seeing their teacher RIF-rescinded and laid off, respectively, compared to White students. Both Asian and Native American students were less likely than White students to have a teacher RIF-rescinded but more likely to see their teacher laid off. Emergent bilingual students were assigned to teachers who had 2.9% greater odds of being RIF-rescinded and 5.1% greater odds off being laid off compared to monolingual English speakers.

Surprisingly, we find that elementary students ever eligible for free/reduced price lunch had lower odds of having their teacher RIF-rescinded or laid off compared to those never eligible (the same is true when we consider those eligible in a particular year). This is largely because teacher experience in the elementary grades is roughly evenly distributed across low-income and non–low income students (see Appendix Figure A1 in the online journal). Finally, we find that special education students are less likely to have their teacher RIF-rescinded or laid off compared to those not enrolled in special education. This finding is consistent across all special education categories (not shown).

Phase I results for middle and high school students are shown in the first three columns of the bottom panel of Table 3. Again, the figures for middle and high school students show the difference between the average proportion of a reference group student’s teachers who were not RIFed, RIF-rescinded, or laid off and the proportion of a student in the comparison group’s teachers who were impacted. We find that Phase I RIF and layoffs notices were also inequitably distributed across student race/ethnicity, income level, and language status in the upper grades. Black and Latina/o students both had 2.2 percentage points more of their teachers laid off than did White students. Emergent bilinguals and students ever eligible for FRL had 0.8 and 1.4 percentage points more of their teachers laid off compared to native English speakers and non-FRL students. Disparities are greater when we consider students who had a majority of their teachers RIF-rescinded or laid off (Appendix Table A3 in the online journal). Finally, consistent with the elementary grades, special education students were far less exposed to teachers who were RIFed or laid off.

Teacher Layoffs During the Reed Intervention

The second three columns of Table 3 sho whow layoffs were distributed across students during Phase II. Results for elementary students are shown in the top half of Table 3. For every one of the elementary groups that was disproportionately affected during the Phase I layoffs, we find that the distribution of teachers who were RIF-rescinded and laid off was more equitable with Reed protections in place. For example, Latina/o students in elementary grades became less likely than White students to see their teacher RIF-rescinded or laid off. Although Black students in elementary grades still had 18.3% and 18.6% greater odds of having their teacher RIF-rescinded or laid off in Phase II, the respective figures for the Phase I layoffs were significantly higher. Elementary students who identify in other race/ethnicity categories and emergent bilingual students also experienced dramatic reductions in the extent to which they were disproportionately impacted by the RIF process.

Changes in the distribution of RIF and layoff notices between the first and second phase of layoffs were even more substantial for secondary students, which makes sense given that more middle and high school students were impacted by the Reed intervention (see again Table 2). The second three columns of the bottom half of Table 3 show these results. During Phase II, middle and high school students in every race/ethnicity category had a lower proportion of teachers RIF-rescinded compared to White students (with the exception of Pacific Islanders, who had a roughly equal proportion). Although these student groups maintained a higher proportion of teachers laid off compared to White students during the Phase II layoff period, the degree of inequality reduced substantially. For example, in Phase II, Black and Latina/o students had an average of 0.8 and 1.0 percentage points more of their teachers laid off than White students (who had 4.9% of their teachers laid off), but these figures were down from 2.2 percentage points more layoffs in Phase I, when White students saw only 2.1% of their teachers laid off. The final column of Table 3 provides t tests to demonstrate that RIF and layoff notices were statistically significantly more equitably distributed across various student subgroups during the Phase II layoffs.

The Effect of Reed Layoff Protections

Last, we turn to our analysis of the impact of the Reed settlement on the distribution of RIFs and layoffs. We present our results in Table 4. We find that the Reed intervention significantly reduced the inequitable distribution of RIFs and layoffs. Table 4 shows that for elementary students in Reed schools, the likelihood of having a teacher who received a RIF notice decreased by between 24.2 and 27.4 percentage points, and the likelihood of having a teacher who received a layoff notice declined by between 7.3 and 8.9 percentage points (log odds outcomes are reported in the online Appendix Table A8, but we convert these outcomes to percentage points in Table 4 to ease interpretation).¹⁵ The results are generally similar for students treated in just the first year, the second year, or both (shown in the first, second, and third rows of Table 4).

Table 4

Difference-in-Difference Estimates of the Effect of the Reed Intervention on the Likelihood a Student’s Teacher Receives a RIF or Layoff Notice (Elementary) and on the Average Percentage of a Student’s Teachers that Receive a RIF or Layoff Notice (Middle and High School)

	RIF Notice		Layoff Notice
Elementary students
2011/2012 Reed students	–.274***	(.028)	–.077***	(.016)
2012 only Reed students	–.250***	(.029)	–.073***	(.019)
2011 only Reed students	–.242***	(.025)	–.089***	(.020)
Middle and high school students
2011/2012 Reed students	–.152***	(.013)	–.063***	(.011)
2012 only Reed students	–.120***	(.020)	–.048**	(.015)
2011 only Reed students	–.166***	(.008)	–.074***	(.015)

Note. “RIF” stands for reduction in force and refers to students whose teacher received an initial RIF notice (a warning of potential layoff). “Layoff” refers to students whose teacher received both a RIF notice and a final layoff notice. For middle and high school students, who have more than one teacher throughout the day, we use the average proportion of a student’s teachers that were RIFed or laid off. For both elementary and middle and high school students, there are three different treatment effects corresponding to each of the three different treatment groups: (a) students affected by Reed in both years (2011/2012 Reed students), (b) those affected by Reed in just 2012 (2012 only Reed students), and (c) those affected by Reed in just 2011 (2011 only Reed students). These categories form in part because 13 schools were only treated in 2010–2011 school year, 13 schools were only treated in the 2011–2012 school year, and 32 were treated in both school years (in each year 45 schools were treated). Robust standard errors are in parentheses (clustered at the school level).

p < .01. ***p < .001.

Similarly, we find that Reed substantially lowered middle and high school students’ exposure to the layoff process. In particular, Reed reduced the average proportion of a student’s teachers that were RIFed and laid off by 15.2 and 6.3 percentage points, respectively (for students enrolled in Reed schools in both years). As shown in the bottom two rows of Table 4, the effects of Reed for middle and high school students enrolled in Reed for only one year were similar to the yearly effects for students treated in both years. The effects of the Reed policy can be seen visually in Figure 1. Elementary students in Reed schools experienced decreases in the probability of having a teacher receive a RIF or layoff notice relative to their pretreatment trend. Similarly, for middle and high school students, the average percentage of a student’s teachers who received a RIF or layoff notice declined significantly from the pretreatment trend. In both cases, the trend lines for students in comparison group schools suggest that exposure to layoffs would have been much higher in Reed schools in the absence of the Reed policy.

We conducted a number of robustness checks to support our conclusions. Tables A8 and A9 in the online appendix show that the estimated effects of the Reed policy are similar when based on narrower sets of comparison schools. We also tested whether effects of the Reed intervention differed across student subgroups. Effects were generally similar, which is not surprising given that Reed is a school-level treatment (reported in online Appendix Table A10). This results in more equitable distribution of layoffs because Reed schools serve a far greater proportion of Black and Latina/o students compared to White students.

Discussion and Implications for Policy

This article contributes to understanding of the impacts of education funding cuts that result in teacher layoffs. First, seniority-based layoff policies used in LAUSD caused RIF and layoff notices to be inequitably distributed, disadvantaging students of color, low-income students (in middle and high schools), and emergent bilingual students. This finding is generally consistent with that found in other contexts such as Washington State or New York City, where layoffs conducted under seniority-based policies were found to disproportionately harm disadvantaged students. Given the well-documented within-district disparities in access to well-qualified and effective teachers (e.g., Goldhaber et al., 2015), a layoff system that uses any districtwide teacher characteristic including measures of effectiveness (as opposed to determining layoffs on a school-by-school basis) runs the risk of concentrating layoffs in high-need schools (Boyd et al., 2011).

More importantly, this article demonstrates that districts can implement policies that protect disadvantaged students from the inequitable distribution of layoffs. The Reed policy accomplished its goal of lowering the likelihood that teachers in Reed schools received RIF and layoff notices, thus protecting the students in these schools from having teachers impacted by the layoff process. Without the Reed intervention, the inequitable distribution of layoffs would have continued because after each year of layoffs, the most junior teachers are shuffled into high-poverty, high-minority schools, even as the districtwide average teacher experience increases over time (a process documented in more detail in Goldhaber et al., 2016). It is likely that the Reed-induced increase in equitable RIF and layoff distribution will have real impacts on student achievement. Using the results from Strunk et al. (2015), which highlight the negative effects of the layoff process on teacher effectiveness, we simulate the degree to which the Reed settlement may have affected achievement gaps within LAUSD. We find that Reed likely reduced the layoff-induced expansion of the Black-White and Latina/o-White achievement gaps by 0.0011 and 0.0008 standard deviations of achievement on the California Standardized Tests, respectively. These effects are meaningful given that only 4.5% of teachers were laid off each year and the estimated effects on districtwide achievement gaps are averaged over all 540,000 LAUSD students. Moreover, these estimates ignore any schoolwide effects of layoffs (the online appendix describes more fully the layoff simulations from which we draw these results).

This article also highlights another potential benefit of instituting layoff protections in high-need schools. When districts must conduct budget-based layoffs, protection from layoffs in a subset of schools can serve as a recruitment or retention incentive in hard-to-staff schools by providing teachers in such schools with increased job security. Indeed, Goldhaber et al. (2016) show that teachers in Reed schools, because of the schoolwide protections from budget-based layoffs, were more likely to return to their teaching positions the following year than were otherwise similar teachers in non-Reed schools. Anecdotally, we found that protected schools highlighted their Reed status in teacher job calls. These types of recruitment and retention incentives would not only reduce teacher turnover at little or no direct monetary expense to the district but, as this article shows, would also dramatically improve access to equal educational opportunities for disadvantaged students.

The analysis raises a number of practical issues that district administrators may face when implementing a Reed-style policy. We might expect cost savings to the district since Reed causes more experienced (and more expensive) teachers to be laid off. Although a back-of-the-envelope analysis of the potential cost savings associated with Reed shows that LAUSD did not see substantial savings as a result of Reed, this most likely occurred because of the small scale of the Reed intervention, which impacted only 6.5% of schools each year.¹⁶ Additionally, administrators departing from a seniority-based layoff system may be concerned with the impact on districtwide teacher quality (Winters & Cowen, 2013). We compared value-added measures of teacher effectiveness for teachers who were protected from a RIF notice to those who received a RIF notice as a result of the Reed intervention (the district provided both RIF variables for both years of the Reed intervention). Protected teachers were more junior and, across several value-added specifications, generally less effective than those who received RIF notices as a result of Reed. However, when we compared value-added measures adjusted for experience, Reed-protected teachers were either more effective than Reed-RIFed teachers or differences in effectiveness were not statistically significant (see online Appendix Table A11). Thus, while the Reed policy might lead to a short-term decline in overall teacher effectiveness, this happens solely as a result of experience levels, suggesting that teachers protected through Reed will, on average, grow into equally or more effective teachers as they gain experience.

While the policy caused layoffs to be more equitably distributed across students, our analysis shows that even with Reed protections, most traditionally disadvantaged student groups still faced a disproportionate amount of layoff notices (although not in Reed schools). Again, these results are not surprising given the scale of the Reed intervention, which in each year affected only 8.7% of all students. To that end, we conducted a simulation of the distribution of layoffs in 2010–2011 if Reed included approximately twice as many schools. We found that for 2010–2011, Black elementary students would have had 10.4% greater odds of having their teacher laid off compared to White students instead of 14.7%. At the middle and high school levels, White students would have had 5.6% of teachers laid off, instead of 5.4%. Black students in middle and high schools would have had only a slightly greater percentage of their teachers laid off compared to White students, instead of 1.2 percentage points more, whereas Latina/o students would have had 1.0 percentage points more teachers laid off than White students, instead of 1.4 percentage points. In sum, expansion of the Reed intervention would have further narrowed the gaps in exposure to the layoff process across student racial/ethnic groups; however, even a doubling of the number of schools included would not have closed these gaps.

Districts might also consider scaling up layoff protections in other ways. For instance, while California law permits districts to protect teachers if they have special skills or training that more senior staff do not possess or if they are credentialed in a high-need teaching area such as math, science, or special education (Cal. Educ. Code §44955d, 2016), districts might consider granting protections for teachers with experience working with students of color or in high-poverty schools. If such protections were instituted, low-income students of color would be less likely to experience a disparate impact of layoffs. Such an intervention may also help districts recruit and retain teachers with experience working in high-poverty/high-minority schools, which is a challenge in many large urban districts across the country (Darling-Hammond, 2004; Ingersoll & May, 2011; Quiocho & Rios, 2000; Villegas & Lucas, 2004).

Conclusion

The analyses shown here demonstrate important facets of layoff polices generally and seniority-based policies in large urban districts in particular. Although seniority-based layoffs can create inequitable distributions of layoffs, one approach to addressing this problem is to prevent budget-based layoffs from taking place at the highest-need schools. By selecting schools with the greatest needs and ensuring that a sufficient number of schools are protected, districts can prevent particular student groups from bearing an uneven share of the costs associated with district funding cuts. In the often unavoidable circumstances that force districts to conduct budget-based layoffs, averting inequitable distributions is an important and laudable objective. This study demonstrates the potential for substantial inequities to occur under standard layoff policies but shows how districts can work to mitigate this problem.

Footnotes

Notes

Authors

DAVID S. KNIGHT, PhD, is an assistant research professor at the University of Texas at El Paso Center for Education Research and Policy Studies, 500 W. University Avenue, El Paso, TX 79968; dsknight@utep.edu . His research focuses on educator labor markets, cost-effectiveness analysis, and school finance.

KATHARINE O. STRUNK, PhD, is an associate professor of education and policy at the University of Southern California with a joint appointment in the Rossier School of Education and the Price School of Public Policy, 901E Waite Phillips Hall, 3470 Trousdale Parkway, University of Southern California, Los Angeles, CA 90089; kstrunk@usc.edu . Her research focuses on education governance, teacher labor markets and school district reform.

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