The Impact of School Counselor Resources in Elementary and Middle Grades

Abstract

Prior research indicates that the availability of school counselors—as measured by school counselor-to-student ratios—predicts desired student outcomes in high schools. Less is known about the impact of school counselors in elementary and middle schools, where school counselors take on a broad array of responsibilities related to youth development. Using 6 years of student and school staffing data from North Carolina, we estimated school fixed effect models to assess the associations between school counselor-to-student ratios and student achievement, attendance, and disciplinary outcomes in elementary and middle school grades. We found that when elementary and middle schools have more school counselors, mathematics scores rise and absences decline. We found no conditional association between school counselor ratios and student suspensions. School counselors may be particularly helpful for students experiencing poverty and for students of color.

Keywords

school counseling student achievement student attendance

Although elementary and middle schools typically staff fewer school counselors per student than high schools, 97% of the public elementary schools and 99% of the public middle schools in North Carolina employ at least one part-time school counselor (Bastian et al., 2019). These school counselors provide a broad range of developmental supports to elementary and middle schools, including coordinating socioemotional learning curricula and disciplinary systems and providing individualized supports to students. Elementary and middle school counselors are thus a cornerstone to school-wide efforts to support whole-child learning and development (Carrell & Carrell, 2006).

In this article, we assess the relationship between elementary and middle school counselor staffing ratios and student absence, disciplinary, and achievement outcomes. In doing so, we make an important contribution to the still nascent research literature on the relationship between school counselor-to-student ratios and student outcomes in the elementary and middle school years (Carrell & Carrell, 2006; Carrell & Hoekstra, 2014; Reback, 2010a, 2010b). Our analyses focused on all students enrolled in fourth through eighth grades in North Carolina public elementary and middle schools between the 2009–2010 and 2014–2015 academic years. Using these data, we investigated the relationship between school counselor-to-student ratios and student absences, discipline, and achievement. To better isolate the effects of school counselor ratios, our models employed school fixed effects to account for time-invariant school characteristics and employed a rich set of school-level and student-level control variables, including students’ lagged value on the dependent variable. Our findings suggest that increases in school counseling resources—as measured by ratios—are associated with modest declines in student absences and modest increases in student math achievement. Although one must make a strong set of assumptions about the exogeneity of changes in school counselor staffing in order to interpret these estimates as causal, we argue that our findings are consistent with the hypothesis that increasing school counseling staffing positively effects elementary and middle school students.

School Counseling in Elementary and Middle School Contexts

School counselors have played an important role in providing high school students with course selection and college and career guidance since the early 20th century, but few elementary and middle schools in the United States employed school counselors until relatively recently. In the 1970s, school counselors began to embrace a developmental model that expanded school counselors’ role in schools, giving them responsibility for making links to families and providing students with social and emotional supports. As the profession’s role expanded beyond college and career planning, elementary and middle schools began creating school counseling positions (Baker, 2000).

Elementary and middle school counselors are tasked primarily with helping students develop mindsets and behaviors consistent with their academic, career, and social/emotional goals (American School Counselor Association [ASCA], 2019). Elementary and middle school counselors often teach social/emotional curricula and provide students instruction on study skills. These professionals also often work one-on-one with students, coordinate school-wide disciplinary systems, and help educators design and implement positive behavioral support systems or multitiered systems of support. Further, in many schools, school counselors play a central role in classroom assignment or course scheduling, shaping the composition of teacher teams, distributing special needs students, and placing students in curriculum ability levels (Akos et al., 2005; Barna & Brott, 2013; Rose & Steen, 2015).

Each of the tasks that occupy elementary and middle school counselors has important implications for student outcomes. Prior research has demonstrated that socioemotional learning opportunities and appropriate matches between student needs and their classroom learning environments relate to improvements in student attendance and achievement (Kim & Streeter, 2008). Likewise, the link between school disciplinary culture and—in particular—the implementation of school support systems and student disciplinary outcomes is well established (Okonofua et al., 2016). As such, we expect school counselors to benefit a broad array of elementary and middle school student outcomes, including attendance, exposure to exclusionary discipline, and achievement.

We further expect that elementary and middle school counselors will particularly benefit students who are traditionally underserved in schools, including students of color and students from socioeconomically disadvantaged family backgrounds. Elementary and middle school counselors provide developmental services to the full student body and often deliver intensive services to more vulnerable student populations (Betters-Bubon et al., 2016). The ASCA National Model for school counseling (ASCA, 2019) directs school counselors to examine gaps in school data and prioritize data-informed individual or small group interventions in the form of closing-the-gap plans. These interventions may include cognitive behavioral techniques (Warner et al., 2016), motivational interviewing (Burke et al., 2005), restorative circles (Smith et al., 2018) or a host of indicated supports to close attendance, behavior, or achievement gaps. Further, structural mechanisms, such as multitiered systems of support (Wilkerson et al., 2013; Ziomek-Daigle et al., 2016), often promote school counseling services with clear outcome goals. Overall, school counselors are a social capital investment to enhance learning and development and close pervasive gaps in student outcomes (Bemak et al., 2015).

School Counselor Ratios and Student Outcomes

Kearney et al. (2021) undertook a comprehensive meta-analytic review of published research assessing the relationship between school counselor-to-student ratios and student outcomes, identifying 16 studies that satisfied inclusion criteria (Kearney et al., 2021; Bryan et al., 2009; Bryan et al., 2011; Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Carrell & Carrell, 2006; Carrell & Hoekstra, 2014; Goodman-Scott et al., 2018; Hurwitz & Howell, 2014; Lapan, Gysbers et al., 2012; Lapan, Whitcomb et al., 2012; Pham & Keenan, 2011; Reback, 2010a, 2010b; Utphall, 2006; Woods & Domina, 2014). Although estimated effect sizes varied, this literature consistently suggests that students who attend schools with more school counselors per student tend to achieve more desirable school outcomes.

Several studies documented a positive relationship between school counselor-to-student ratios and high school outcomes. This literature consistently demonstrates that increases in school counselor-to-student ratios are associated with higher student attendance (Carey, Harrington, Martin, & Stevenson, 2012; Lapan, Whitcomb et al., 2012), lower incidence of student discipline (Carey, Harrington, Martin, & Stevenson, 2012), lower rates of high school dropout (Utphall, 2006; Goodman-Scott et al., 2018) and increases in technical proficiency and program completion rates in career and technical education programs (Carey, Harrington, Martin, & Hoffman, 2012). Many studies in this literature also have documented the relationship between school counselor ratios and student transitions to higher education, demonstrating that in high schools with better school counselor-to-student ratios, students were more likely to meet with school counselors to discuss college choices (Bryan et al., 2009; Woods & Domina, 2014), take the SAT (Woods & Domina, 2014), apply to multiple colleges (Bryan et al., 2011), and enroll in 4-year colleges (Woods & Domina, 2014). For example, Hurwitz and Howell (2014) found that adding an additional school counselor increased a high school’s 4-year college going rate by 10 percentage points.

The associations between school counseling ratios and student outcomes are particularly pronounced in schools that serve students experiencing poverty and otherwise educationally disadvantaged students. In Missouri high schools, Lapan, Gysbers et al. (2012) noted that high-poverty schools with a higher school counselor-to-student ratio had higher graduation rates, better school attendance, and fewer disciplinary incidents. Using data from one urban school district, Pham and Keenan (2011) found that a 1% decrease in the ratio of first-generation students to school counselors was associated with a 0.4% decrease in the odds that a highly qualified student would not attend a 4-year college.

By comparison, few studies have investigated the link between elementary and middle school counselor-to-student ratios and student outcomes. Although we know of no studies examining the relationship between school counselor-to-student ratios and student outcomes in middle schools, the available research on elementary school counselors and student outcomes is encouraging. This literature demonstrates a link between school counseling ratios and improved disciplinary outcomes in elementary schools (Carrell & Carrell, 2006; Carrell & Hoekstra, 2014; Reback, 2010a). Likewise, using nationally representative data, Reback (2010b) found a positive association between school counseling ratios and elementary school student behavior and mental health. Results did not replicate in all settings (Reback, 2010a); however, several studies also demonstrated a positive association between school counseling resources and student achievement in elementary school (Carrell & Hoekstra, 2014; Reback, 2010b). Although the bulk of this literature is correlational, Carrell and Carrell (2006) and Carrell and Hoekstra (2014) took advantage of a program that placed University of Florida school counseling interns to generate plausibly causal difference-in-difference estimates of the impact of additional school counselors on elementary student outcomes.

Research Questions

The present study adds to the extant research base on school counselors by estimating the associations between school counselor-to-student ratios and student absences, discipline, and achievement. Our analyses take advantage of year-over-year variation in school counselor ratios to address the following:

1. Are elementary and middle school counselor-to-student ratios associated with student absences, discipline, and achievement outcomes?

2. Does the relationship between school counselor-to-student ratios and student outcomes vary across racial and socioeconomic groups?

Methods

Research Sample

Our research sample included all public (non-charter) elementary and middle schools in North Carolina during the 2009–2010 through 2014–2015 academic years. Our elementary school sample included students in fourth and fifth grades; our middle school sample included students in sixth, seventh, and eighth grades. Overall, our elementary school sample comprised approximately 910,000 unique students in 1392 schools; our middle school sample consisted of more than one million unique students in 674 schools.

Table 1 displays student and school-level descriptive data for the elementary and middle schools in our sample. More than half of the students in our sample were classified as economically disadvantaged (i.e., qualifying for subsidized school meals); nearly half of the students were a racial/ethnic minority. Elementary schools had a slightly higher percentage of limited English proficient students than middle schools. Middle schools enrolled more students than elementary schools. Approximately 40% of the schools in our sample are located in urban or suburban environments; the remaining 60% are in more rural environments.

Table 1.

Student and School-Level Descriptive Data.

	Elementary Grades (4–5)	Middle Grades (6–8)
Student characteristics
Economically disadvantaged	0.541 (0.498)	0.519 (0.499)<
Asian	0.027 (0.161)	0.026 (0.158)
Black	0.255 (0.436)	0.264 (0.441)
Hispanic	0.143 (0.349)	0.127 (0.333)<
American Indian	0.014 (0.118)	0.015 (0.121)<
Multiracial	0.040 (0.196)	0.037 (0.188)
Male	0.512 (0.499)	0.512 (0.499)<
Limited English proficient	0.069 (0.253)	0.054 (0.226)
Formerly limited English proficient	0.053 (0.224)	0.054 (0.225)
Special education	0.162 (0.369)	0.144 (0.351)
Academically gifted	0.144 (0.351)<	0.160 (0.366)
School characteristics
Economically disadvantaged	0.590 (0.247)	0.542 (0.204)
Minority	0.485 (0.267)	0.470 (0.248)
School size	582.80 (201.13)	776.15 (284.58)
City/suburb	0.442 (0.497)	0.433 (0.496)
School counseling and other support personnel resources
School counselor ratio: Number of FTE school counselors per 1000 students	2.54 (1.35)	3.12 (2.41)
Other support personnel: Number of FTE psychologists and social workers per 1000 students	1.45 (3.32)	1.27 (2.58)
Unique students	913,775	1,029,114
Unique schools	1392	674

Note. This table displays student and school descriptive data for the elementary grades (4–5) and middle grades (6–8) students in our sample. Standard deviations are in parentheses.

Measuring School Counselor Ratios

When examining the associations between school counseling ratios and student outcomes, prior research has traditionally focused on the number of school counselors per student at a school, or its inverse, the number of students per school counselor at a school. The ASCA (2019) recommendation of 1 school counselor per 250 students is an example of the latter. Although body count ratios are straightforward for schools to report and policymakers to understand, they do not take into account a school counselor’s level of effort at a given school. For instance, in many school districts, school counselors and other student support personnel (e.g., school psychologists, social workers) work part-time and/or split their time across multiple schools. This suggests that body count ratios may overestimate the school counseling resources available to students at a given school.

Therefore, to measure the school counselor ratios more accurately, we leveraged licensure and certified salary files from the North Carolina Department of Public Instruction. With licensure data, we identified school employees who held a school counselor license in a given academic year. With certified salary files, we identified school counselors who were paid specifically for that position in a given academic year. These data let us calculate traditional body count ratios. The salary data also allowed us to track the number of pay periods school counselors worked at a given school and their full-time equivalency status during each of those pay periods. With these pay period and FTE data, we were able to calculate the number of FTE units that school counselors worked at schools.

For example, school counselors employed at a school for 10 pay periods at 50% FTE worked 500 FTE units at that school (10 pay periods × 50% FTE); school counselors employed at a school for 10 pay periods at 100% FTE earned 1000 FTE units. We summed these FTE units for all school counselors employed at a given school and divided this value by the school’s enrollment. This created our FTE ratio for school counselors—the number of full-time school counselors per 1000 students at the school. We replicated these FTE calculations for school psychologists and social workers to identify the number of other full-time support personnel per 1000 students at the school. We used these other support personnel ratios as control variables in models. This is particularly significant because schools may have to choose how to allocate limited funds within student support personnel. See Bastian et al. (2019) for more details on the process used to calculate these FTE school counselor ratios.

The bottom of Table 1 presents descriptive data on the school counselor and other support personnel ratios in North Carolina elementary and middle schools. FTE school counselor ratios indicate that the state had 2.54 full-time school counselors per 1000 students in elementary schools and 3.12 full-time school counselors per 1000 students in middle schools during the period studied.

Outcome Measures

Our analyses focused on three student outcomes: absences from school, school suspension, and student achievement. For school absences, we focused on two different measures: the total number of days absent over the course of a school year and an indicator flagging students who were chronically absent. Both measures included excused and unexcused absences. In line with many official state definitions of chronic absenteeism, we defined students who miss more than 10% of the school days in a given year as chronically absent (Jordan & Miller, 2017).

We measured student disciplinary outcomes with an indicator variable that distinguishes between students who were ever suspended (in- or out-of-school suspension) during an academic year versus students who were not. Our data include a continuous count of the number of days/times students were suspended; however, because only 7% of elementary grades (4–5) students and 19% of middle grades (6–8) students were suspended in a given year, we focused on the ever suspended indicator.

Finally, we used students’ mathematics and reading test scores from end-of-grade (EOG) exams as a measure of student academic achievement. Nearly all North Carolina public school students take these EOG exams in the spring of their third-through eighth-grade years. We standardized exam scores by year, grade, and subject area across all students in North Carolina public schools.

Table 2 displays descriptive data on these outcome measures for all of the elementary and middle grades students in our analytical sample and for focal student subgroups (by race/ethnicity and socioeconomic status). These data offer a way to benchmark the magnitude of the associations between school counseling ratios and student attendance, suspensions, and achievement. On average, these data showed that elementary grades (4–5) students were absent nearly 6 days during the year while middle grades (6–8) students were absent more than 7 days during the year. Approximately 7% of elementary grades students and 19% of middle grades students were suspended during an academic year. As established in prior studies, large differences existed in our outcomes of interest across student race/ethnicity and socioeconomic indicators. For example, Black students scored lower on standardized assessments and were far more likely to be suspended than their White peers. Students who were economically disadvantaged were more likely to be suspended, were absent from school more often, and scored lower on standardized assessments than their non-economically disadvantaged peers.

Table 2.

Descriptive Data on Student Outcomes (All Students and Focal Subgroups).

	Days Absent	Chronically Absent	Ever Suspended	Std. Math	Std. Reading
Elementary grades (4–5)
All students	5.94	4.96	6.89	−0.00	−0.00
Black	5.56	4.92	14.25	−0.49	−0.44
Hispanic	5.08	3.45	4.54	−0.24	−0.40
White	6.39	5.35	4.01	0.28	0.30
Ever EDS	6.48	6.53	9.68	−0.33	−0.35
Never EDS	5.05	2.37	2.18	0.45	0.45
Middle grades (6–8)
All students	7.17	8.65	19.31	−0.00	−0.01
Black	7.14	9.73	32.58	−0.49	−0.46
Hispanic	6.58	7.55	17.75	−0.25	−0.34
White	7.41	8.41	13.25	0.27	0.28
Ever EDS	8.18	11.76	26.19	−0.35	−0.35
Never EDS	5.67	3.98	8.74	0.39	0.38

Note. EDS = economically disadvantaged status. The top panel of this table displays descriptive data on our outcome measures for all students and key student subgroups in elementary grades (4–5). The bottom panel of this table displays descriptive data on our outcome measures for all students and key student subgroups in middle grades (6–8).

Analyses

We aimed to isolate the effect of FTE school counselor ratios on student absences, discipline, and achievement outcomes. This was challenging because school counseling ratios are a product of several endogenous processes, including the availability of funds to hire school counselors and school leaders’ judgments regarding the need for and role of school counseling and other student support staff at the school. We could not randomly assign different levels of school counseling staffing to schools, and prior analyses clearly indicated that school counselor staffing varies nonrandomly across schools and districts (Gagnon & Mattingly, 2016). In the North Carolina context, schools with relatively high levels of student disadvantage tend to invest more heavily in student support services (Bastian et al., 2019). We attempted to address these concerns and identify the effect of school counseling ratios on student absences, discipline, and achievement by estimating school fixed effect models with a robust set of controls. Although these models do not address all potential sources of bias, they do account for time-invariant school characteristics, observable changes in school composition, student characteristics, ratios for other support personnel, and time trends.

These models take the following general form

Y_{i s t} = β_{0} + β_{1} C o u n s e l o r R a t i o_{s t} + β_{2} O t h e r S u p p o r t R a t i o_{s t} + β_{3} Y_{i s t (n - 1)} + β_{4} X_{i s t} + μ_{s} + σ_{t} + ε_{i s t}

(1)

where

Y_{i s t}

is the outcome of interest for student i in school s and year t;

C o u n s e l o r R a t i o_{s t}

is the FTE school counseling ratio (standardized) for school s in year t;

O t h e r S u p p o r t R a t i o_{s t}

is the FTE ratio (standardized) for other student support staff for school s in year t;

Y_{i s t (n - 1)}

is the lag of the dependent variable;

X_{i s t}

is a vector of student- and school-level controls (including student-level indicators for race/ethnicity, gender, economic disadvantage, currently limited English proficient, previously limited English proficient, special education enrollment, moving schools since the prior spring, and grade-level and school-level measures for student enrollment and the percentage of economically disadvantaged and racial/ethnic minority students);

μ_{s}

is a vector of school fixed effects; and

σ_{t}

is a vector of dummy variables for each academic year in our analyses. We estimated these models separately for students enrolled in elementary grades (4–5) and middle grades (6–8). We also performed supplementary analyses in which we estimated separate models by students’ race/ethnicity and socioeconomic status. Specifically, we estimated separate models for Black, Hispanic, and White students and for students who have ever or never received subsidized school meals during our study period.

These school fixed effect models take advantage of temporal variation in school counselor and other support personnel ratios to estimate the effects of school counseling and other support staff resources on student outcomes. In particular, these models estimated how within-school changes in school counselor FTE ratios predict changes in student absences, discipline, and achievement. Although we controlled for a rich set of student- and time-varying school characteristics, we noted several threats to unbiased identification. One might worry, for example, that district and school leaders invest in school counselors to remedy problems that they see emerging in their schools. Compounding this threat, one might expect enrollments to decline in troubled schools, leading to higher FTE ratios (more school counselors per 1000 students) even in the absence of additional staffing. Both scenarios lead to questions about the extent to which the parallel trends assumption held in our models. We addressed the first of these concerns by controlling for students’ lagged values on the dependent variable. We addressed the second concern by controlling for school enrollment. Because enrollment was already part of our FTE ratio calculations (i.e., FTE units worked/school enrollment), controlling for changes in enrollment with a school fixed effect model allowed us to assess how changes in staffing levels for school counselors predict changes in student outcomes. Because we suspected that schools are more likely to invest in school counselors in response to unmeasured challenges than in response to unmeasured improvements, we contend that any other time-varying school characteristics for which we did not control would likely negatively bias our estimates of interest. As such, we believe our estimates likely represent lower bounds of the effects of investments in school counseling FTE on absences, discipline, and achievement.

A final concern with our school fixed effects approach was the amount of within-school variation in school counselor resources. Without meaningful variation in these resources, detecting a relationship with student outcomes would be difficult. To address this concern, Table 3 presents descriptive data on the year-over-year changes in school counselor resources within elementary and middle schools. In particular, Table 3 displays data on the year-over-year changes in FTE school counselor ratios, which can change due to changes in staffing levels or student enrollment, and the year-over-year changes in FTE units worked by school counselors. Overall, the average year-over-year change in school counseling resources was slightly positive in elementary schools and slightly negative in middle schools. More important, Table 3 suggests that some elementary and middle schools experienced meaningful changes in their school counseling ratios. For example, elementary and middle schools at the 90^th percentile in the distribution of year-over-year changes in school counselor ratios gained one-half of a full-time school counselor per 1000 students (0.522 and 0.543 in elementary and middle schools, respectively). Elementary and middle schools at the 90^th percentile in the distribution of year-over-year changes in FTE units worked gained 200 and 300 FTE units, respectively.

Table 3.

Year-Over-Year Changes in Counseling Resources Within Schools.

	Changes in FTE School Counselor Ratio	Changes in FTE Units Worked by School Counselors
Elementary grades (4–5)
Average year-over-year change	0.040 (0.855)	9.769 (308.75)
10th percentile	−0.416	−160.00
25th percentile	−0.110	0.00
75th percentile	0.169	0.00
90th percentile	0.522	200.00
Middle grades (6–8)
Average year-over-year change	−0.021 (1.059)	−11.610 (397.24)
10th percentile	−0.660	−330.00
25th percentile	−0.161	−1.00
75th percentile	0.164	4.24
90th percentile	0.543	300.00

Note. FTE = full-time equivalent. This table displays descriptive data—the mean and values at the 10th, 25th, 75th, and 90th percentile—on the year-over-year changes in counseling resources within elementary and middle schools. Values in the “Changes in FTE school counselor ratio” column are expressed in full-time school counselors per 1000 students; values in the “Changes in FTE units worked by school counselors” column are expressed in FTE units (where 1000 units would represent one full-time school counselor).

Results

The analyses reported in Tables 4 –6 leverage within-school (over time) variation to estimate the effects of school counselor staffing on student absences, suspensions, and achievement. Overall, results in the top panel of Table 4 indicate that within-school increases in school counselor ratios were associated with decreases in elementary and middle school student absenteeism. Although these associations are modest in size, they are not trivial. In elementary and middle schools, a one standard deviation increase in the FTE school counseling ratio was associated with declines in absenteeism of approximately one 12th of a day. To put this result into perspective, economically disadvantaged students were absent 1.4 days more than non-economically disadvantaged students in elementary grades and 2.5 days more in middle grades (see Table 2). Furthermore, we found that a one standard deviation increase in the FTE school counselor ratio predicted a 0.1 and a 0.2 percentage point decrease in the likelihood of elementary and middle school students being chronically absent. These chronic absence results are also modest in size, at approximately 2% of the state average of chronically absent students in elementary and middle schools.

Table 4.

School Counselor and Other Support Personnel Ratios and Student Absences.

	Elementary Grades (4–5)		Middle Grades (6–8)
Overall	Days Absent	Chronic Absences	Days Absent	Chronic Absences
School counselor ratio	−0.078^** (0.021)	−0.001⁺ (0.000)	−0.081^* (0.040)	−0.002^* (0.001)
Other support personnel ratio	−0.006 (0.023)	−0.001 (0.001)	−0.052 (0.041)	−0.001 (0.001)
Observations	1,147,000	1,147,000	1,638,431	1,638,431
Black students
School counselor ratio	−0.108^** (0.029)	−0.002^** (0.001)	−0.087 (0.059)	−0.003 (0.002)
Other support personnel ratio	0.040 (0.039)	0.001 (0.001)	−0.061 (0.063)	−0.002 (0.002)
Observations	286,160	286,160	420,817	420,817
Hispanic students
School counselor ratio	−0.091^* (0.037)	−0.002 (0.001	−0.158^** (0.058)	−0.005^** (0.002)
Other support personnel ratio	0.036 (0.033)	0.001 (0.001)	0.083 (0.050)	0.004^* (0.002)
Observations	168,771	168,771	212,508	212,508
White students
School counselor ratio	−0.050⁺ (0.027)	0.000 (0.001)	−0.054 (0.043)	−0.002 (0.001)
Other support personnel ratio	−0.035 (0.024)	−0.002⁺ (0.001)	−0.101⁺ (0.054)	−0.002 (0.001)
Observations	599,772	599,772	877,017	877,017
Ever EDS students
School counselor ratio	−0.074^** (0.023)	−0.001 (0.001)	−0.092⁺ (0.048)	−0.003^* (0.001)
Other support personnel ratio	0.021 (0.025)	0.000 (0.001)	−0.014 (0.046)	−0.000 (0.001)
Observations	723,500	723,500	1,009,108	1,009,108
Never EDS students
School counselor ratio	−0.081^* (0.033)	−0.001 (0.001)	−0.047 (0.041)	−0.002 (0.001)
Other support personnel ratio	−0.054^* (0.026)	−0.001^* (0.000)	−0.134^** (0.046)	−0.003^* (0.001)
Observations	423,500	423,500	629,323	629,323

Note. EDS = economically disadvantaged status. This table displays results from models focused on student absences in grades 4–5 (elementary) and in grades 6–8 (middle) in North Carolina. School counselor ratio is a standardized value and all models include student demographics, prior year absences/chronic absent status, school characteristics, and a school fixed effect. +, *, and ** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

Table 5.

School Counselor and Other Support Personnel Ratios and Student Suspension Status.

	Elementary Grades (4–5)	Middle Grades (6–8)
Overall	Ever Suspended	Ever Suspended
School counselor ratio	−0.002 (0.001)	0.000 (0.002)
Other support personnel ratio	0.000 (0.002)	−0.000 (0.003)
Observations	1,277,275	1,923,889
Black students
School counselor ratio	−0.004 (0.003)	−0.001 (0.003)
Other support personnel ratio	0.001 (0.003)	0.002 (0.004)
Observations	327,269	513,525
Hispanic students
School counselor ratio	−0.002 (0.002)	−0.000 (0.004)
Other support personnel ratio	−0.007 (0.005)	0.004 (0.007)
Observations	188,342	251,736
White students
School counselor ratio	−0.001 (0.001)	0.002 (0.002)
Other support personnel ratio	0.001 (0.002)	−0.004 (0.004)
Observations	658,662	1,009,998
Ever EDS students
School counselor ratio	−0.002 (0.002)	0.001 (0.002)
Other support personnel ratio	0.001 (0.002)	0.001 (0.004)
Observations	812,155	1,200,353
Never EDS students
School counselor ratio	−0.003^** (0.001)	0.001 (0.003)
Other support personnel ratio	−0.001 (0.002)	−0.003 (0.004)
Observations	465,120	723,536

Note. EDS = economically disadvantaged status. This table displays results from models focused on student suspension status (ever suspended) in grades 4–5 (elementary) and in grades 6–8 (middle) in North Carolina. School counselor ratio is a standardized value and all models include student demographics, prior year suspension status, school characteristics, and a school fixed effect. +, *, and ** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

Table 6.

School Counselor and Other Support Personnel Ratios and Student Achievement.

	Elementary Schools		Middle Schools
Overall	Mathematics	Reading	Mathematics	Reading
School counselor ratio	0.009^** (0.003)	−0.000 (0.002)	0.005 (0.004)	0.001 (0.003)
Other support personnel ratio	0.005 (0.007)	0.011^* (0.005)	−0.009⁺ (0.005)	0.002 (0.005)
Observations	1,177,167	1,174,596	1,473,031	1,472,564
Black students
School counselor ratio	0.008⁺ (0.004)	0.001 (0.003)	0.004 (0.005)	0.000 (0.004)
Other support personnel ratio	0.007 (0.009)	0.011 (0.007)	−0.004 (0.006)	0.012^* (0.006)
Observations	293,982	293,298	378,218	378,038
Hispanic students
School counselor ratio	0.012^* (0.006)	−0.001 (0.005)	0.014⁺ (0.008)	0.001 (0.006)
Other support personnel ratio	0.019 (0.015)	0.024^* (0.011)	−0.015 (0.009)	−0.017^* (0.008)
Observations	169,912	169,248	196,156	195,857
White students
School counselor ratio	0.002 (0.004)	−0.003 (0.003)	0.008 (0.006)	0.010⁺ (0.006)
Other support personnel ratio	−0.002 (0.011)	0.003 (0.008)	−0.013 (0.010)	0.002 (0.008)
Observations	511,036	510,213	626,182	626,137
Ever EDS students
School counselor ratio	0.007^* (0.003)	−0.001 (0.002)	0.005 (0.004)	0.002 (0.003)
Other support personnel ratio	0.004 (0.007)	0.011⁺ (0.006)	−0.008 (0.005)	0.003 (0.005)
Observations	677,424	675,353	843,488	842,841
Never EDS students
School counselor ratio	0.011^** (0.004)	−0.000 (0.003)	0.006 (0.007)	−0.002 (0.005)
Other support personnel ratio	0.006 (0.011)	0.009 (0.007)	−0.011 (0.010)	0.003 (0.008)
Observations	499,743	499,243	629,543	629,723

Note. EDS = economically disadvantaged status. This table displays results from models focused on student achievement in grades 4–5 (elementary) and in grades 6–8 (middle) in North Carolina. School counselor ratio is a standardized value and all models include student demographics, prior year test scores, school characteristics, and a school fixed effect. +, *, and ** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

Results in the remaining panels of Table 4 detail how investments in school counseling FTE influenced absence outcomes for key student subgroups. In elementary grades, Black and Hispanic students particularly benefitted from increases in school counseling ratios—that is, a standard deviation increase in the FTE school counselor ratio was associated with 0.108 and 0.091 fewer absences, respectively. Likewise, in middle grades, Hispanic students were absent less (0.158 days) when their schools invested more in school counselors. Analyses by economic status showed that in elementary schools, students with and without economic disadvantage benefitted from increases in school counselor ratios. In middle schools, only economically disadvantaged students benefitted from increased investments in school counselor FTE.

In contrast to the absence findings, results in Table 5 provide little evidence to suggest that investments in school counselors reduce the likelihood that elementary and middle school students will be suspended. We found that a one standard deviation increase in the FTE school counselor ratio was associated with a 0.3 percentage point decrease in the likelihood of suspension for non-economically disadvantaged elementary school students. Across all other analyses, overall and by student subgroup, there are precisely estimated null results. Because the outcome in these analyses confounds two potentially distinct processes—problematic student behavior and school disciplinary culture—we are reluctant to conclude that investments in school counseling FTE have no effect on order and behavior in schools. Nonetheless, the results reported here are consistent with a null effect.

The results in Table 6 address the relationships between school counseling FTE ratios and student achievement. Overall, results in the top panel of Table 6 indicate that increases in the school counselor ratio predicted higher adjusted-average achievement in elementary grades mathematics. Specifically, a one standard deviation increase in the FTE school counselor ratio was associated with a nearly 1% of a standard deviation increase in elementary mathematics achievement.

Results from our student subgroup analyses indicated that Black and Hispanic students had higher mathematics achievement when their schools invested in school counselors. This is consistent with the hypothesis that school counseling resources are particularly useful to students of color (Cholewa et al., 2016). For example, a one standard deviation increase in the FTE school counselor ratio predicted adjusted-average achievement approximately 1% of a standard deviation higher for Black and Hispanic elementary school students and 1.4% of a standard deviation higher for Hispanic middle school students. These results are modest in size, especially relative to the achievement differences between White students and students of color shown in Table 2. Investments in school counselors did not predict the mathematics test scores of White students. However, a one standard deviation increase in the FTE school counselor ratio was modestly related to the reading achievement of White middle school students. Analyses by student economic status indicated that elementary grades students—both with and without economic disadvantage—had higher mathematics achievement when their schools had higher FTE school counselor ratios. Results by student economic status were not significant in middle school mathematics or in reading (across grade levels).

The analyses presented in Tables 4 –6 also estimated the relationship between changes in other school support personnel (i.e., school psychologists and social workers) FTE ratios and student outcomes. These analyses are not the focus on this article, and their findings were inconclusive. We found no consistent evidence of a relationship between increases in FTE staffing in school psychologists and social workers and changes in middle or elementary school student absenteeism or disciplinary outcomes. Furthermore, the evidence on the relationship between these other support personnel ratios and student achievement was mixed. We found that increases in other school support personnel FTE ratios were significantly associated with increases in middle school reading scores and decreases in middle school mathematics scores.

Discussion

At a time when schools are being asked to meet the needs of the whole child, it is important to assess whether school counseling resource investments predict desired student outcomes. This focus may be particularly important in elementary and middle schools, where school counselors help students build a foundation for and weather challenges to their academic and socioemotional development. With this motivation, we leveraged statewide data from North Carolina to measure school counselor FTE ratios in elementary and middle schools and to assess whether changes in school counselor FTE ratios predict student absences, suspensions, and achievement.

On balance, we show that school counseling ratios predict desired student outcomes. Comparing within schools, we found that increases in school counseling FTE ratios predicted lower rates of student absenteeism. This is consistent with prior work on school counselors and school attendance (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Lapan, Gysbers et al., 2012). Likewise, we found that more school counselors per student predicted higher adjusted-average achievement in elementary school mathematics. This aligns with work by Carrell and Hoekstra (2014) in Florida elementary schools. The attendance and achievement results from our school fixed effects models are modest in magnitude—approximately one 12th of a day absent and 1% of a standard deviation in mathematics achievement—but take on greater practical significance because they are averaged across all students in the school. Consistent with previous research (Carrell & Carrell, 2006; Mulhern, 2019; Stephan & Rosenbaum, 2013), we found further evidence to suggest that Black and Hispanic students particularly benefitted from attending schools with more school counselors per student. These findings suggest that investments in school counseling personnel may be a useful strategy for schools that are interested in addressing racial and ethnic inequalities in educational outcomes.

In interpreting these findings, we note several aspects of our analyses that were designed to more credibly isolate the effect of investments in school counseling staff on student outcomes. First, we used uniquely detailed staffing and credentialing data to calculate school-level school counselor FTE ratios that accounted for part-time school counselors. Second, we estimated school fixed effect models that controlled for the time-invariant characteristics of schools that may be associated with staffing practices and a rich set of covariates, including the lagged dependent variable. Third, all of our models controlled for the FTE ratios of other support personnel to account for potential trade-offs between investments in different forms of school support personnel. Last, we controlled for school enrollment—in our FTE ratio measures and as a separate covariate in school fixed effect models—to account for schools experiencing enrollment declines and to better assess how changes in school counselor staffing levels predict changes in student outcomes.

Although our work advances inquiry on the influence of school counselors, it is important to detail the limitations of our analyses (and similar research in this field). School counselor ratios, even those that use salary data to take level of effort into account, do not measure students’ direct access to nor quality of interactions with school counselors. We do not know which students are interacting with school counselors nor the frequency with which these interactions occur. The frequency and quality or effectiveness of school counselor-to-student interactions (dosage) may matter as much as, or perhaps even more than, an overall ratio. Role diffusion is a significant issue that may help explain discrepancies between ratio and dosage. The role of the school counselor is not always clear and school counselors are often assigned to noncounseling duties that have been defined as inappropriate by ASCA (ASCA, 2019; Astramovich et al., 2018; Chandler et al., 2018; Paolini & Topdemir, 2013). These duties might include clerical or administrative tasks, testing coordination, or substitute teaching. Even in situations where noncounseling duties took up a relatively small percentage of the school counselors’ time, they still reported that these tasks “impeded upon their ability to engage in direct and indirect student services” (Paolini & Topdemir, 2013, p. 2). Overall, role diffusion suggests that even if school counselor ratios are improved, the nature of school counselors’ specific responsibilities and the efficacy of their work will influence their impact on student outcomes.

Conclusion

Despite limitations, our analyses highlight a range of implications for research, policy, and practice. From a research perspective, the field needs more mixed-methods studies that examine the effects of school counseling ratios alongside explorations of school counselor-to-student interactions and the quality of those interactions. Future studies may also benefit from examining the particular role that school counselors play in elementary or middle school (e.g., the higher use of classroom instruction). For example, elementary and middle school counselors often help ease the transition from elementary school to middle school, a transition that substantially challenges students’ motivation, self-efficacy, and engagement with school.

From a policy and practice perspective, our analyses suggest that states and districts may want to consider the cost/benefit of investments to support the whole child. Policy makers and school leaders should attend to the relationship between school counselors and student outcomes when they make crucial decisions about resource allocations both across and within schools. Our findings suggest that investments in school counseling resources yield important benefits for students in the elementary and middle school grades, and that these benefits are particularly pronounced for students in historically underserved groups. Since our study was not designed to allow a direct comparison of the relationship between various resources and student outcomes, we are not able to make calculations about potential trade-offs facing school leaders who must allocate finite educational resources. However, a comparison of our results with the results of analyses about the effects of classroom size reductions and efforts to improve teacher quality (e.g., Chetty et al., 2014; Jepsen & Rivkin, 2009) is sufficient to suggest that school counselors should remain an important priority in elementary and middle school budgeting decisions.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Thad Domina has spent more than 20 years documenting educational inequalities and looking for educational policies and strategies that help to create a more just, equitable, and inclusive society. https://ed.unc.edu/people/thurston-domina/

Patrick Akos is a former school counselor and his research centers on how best to help others thrive. He utilizes a strengths framework (Galassi & Akos, 2007) for inquiry and practice, which is based in humanistic traditions and is empirically supported by contemporary scholarship (e.g., positive psychology).

Kevin Bastain research interests include educator preparation, labor markets, and on-the-job learning; estimating teacher and principal effectiveness; the distribution of educational resources; and using data to inform the improvement of educator preparation programs.

Jeremy Godwin is a doctoral graduate of the Cultural Studies and Literacies program at UNC-CH.

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