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Why Are So Many Young People Disconnected?

November 25, 2025 Paul Costello1 Uncategorized
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A male teenager lies in his bed looking at his phone. Photo by pressmaster/Adobe Stock
RAND

Stephanie Bonds, Jennie W. Wenger

Expert InsightsPublished Oct 29, 2025

Key Takeaways

  • About 15 percent of young people in middle and high school (ages 11 to 18) become disconnected—that is, not engaging in school, training, or work—by ages 18 to 24.
  • Youth who eventually become disconnected are different from their connected peers across several dimensions at baseline: They are more likely to report symptoms of depression, use substances, engage in delinquent activities, and have weaker social support structures.
  • Suspension in school is a risk factor for later disconnection for males, even after accounting for other observed family and school characteristics.
  • Early pregnancy (before age 18) is a risk factor for disconnection for females, even after accounting for other observed family and school characteristics.

Young people who are neither in school nor working, often called disconnected youth or opportunity youth, face challenges that can lead to lower lifetime earnings, poorer health, and lower socioeconomic outcomes (Belfield, Levin, and Rosen, 2012; MaCurdy et al., 2006; Hair et al., 2009; Lewis, 2021). Disconnection also generates broader social costs through lost productivity and higher social spending. Effective policy solutions require a clear understanding of the factors that lead some youth to become disconnected.

Existing research points to a variety of potential factors that might influence disconnection in young adulthood, including family environment, mental health, educational attainment, and behavioral factors, among others (Cohen and Wills, 1985; Currie and Thomas, 2001; Furstenberg and Hughes, 1995; Heckman, Stixrud, and Urzua, 2006; Hanushek and Woessmann, 2008). However, data on these measures is limited and researchers are rarely able to follow young people prior to and across spells of disconnection.

In this paper, we use data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine who disconnected youth are and what factors precede disconnection (Harris and Udry, 2022). Add Health is a representative sample of adolescents who were in grades 7 through 12 during the 1994–1995 school year and have been followed through 2018. Our sample of disconnected youth consists of individuals aged 18 to 24 who were not in school and not working at the time of the third wave of the Add Health survey (conducted between 2001 and 2002).⁠[1] Nearly all (99.7 percent) of our respondents were connected at baseline, given that the initial sampling was restricted to students in middle and high school.⁠[2]

Overall, our findings highlight both the complexity of the pathways leading to disconnection and the potential for early targeted interventions to alter these trajectories. This paper will be of interest to researchers, policymakers, and practitioners who are developing programs that reconnect youth with education, training, or employment.

Who Is Disconnected?

We first examine the demographic characteristics of those who are disconnected. Overall, 15 percent of youth in our sample are disconnected. Disconnected youth are more likely to be female and twice as likely as connected youth to be a parent (Table 1).⁠[3] This appears to be driven by women with children: 32 percent of women with children are disconnected compared with 12 percent of women without children. Men with children have slightly higher rates of disconnection than do men without children (18 percent versus 13 percent) but much lower than the rate for women with children.

Disconnected youth have lower levels of education than do connected youth (Table 1): They are three times as likely to not complete high school and 50 percent more likely to have only a high school degree. They are far less likely to have attended college. Similar to other observational studies, we find that disconnected youth are more likely than connected youth to be Black and less likely to be White. Being Hispanic, Native American, Asian or Pacific Islander, or another race or ethnicity does not predict disconnection in this sample. Rates of disconnection are similar among those who were born in the United States and those who were not.⁠[4]

Table 1. Means of Demographic Characteristics, by Disconnected Status

Demographic CharacteristicConnectedDisconnectedDifference
Female0.490.550.06*
Parent0.150.310.16***
Less than high school education0.110.330.21***
Completed high school0.290.460.16***
Some college education or more0.600.22–0.38***
Hispanic0.110.120.01
White0.770.63–0.14***
Black0.140.280.14***
Native American0.040.040.01
Asian or Pacific Islander0.040.03–0.01
Other race0.060.060.00
Born in the United States0.930.950.02

SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.

NOTE: We use individual (person) weights to create statistics that are representative of the U.S. population. Asterisks denote statistically significant differences at the 10 percent (*), 5 percent (**), and 1 percent (***) levels, respectively.

What Early-Life Factors Precede Disconnection?

Beyond examining who is disconnected, we also use the panel nature of the data to examine early-life characteristics that precede—and might explain—disconnection.⁠[5] Using the baseline wave that surveyed respondents when they were in school (ages 11 through 18), we construct baseline measures of explanatory variables that are thought to influence disconnection later in life (Table 2).⁠[6]

Table 2. Variable Descriptions

DomainVariables
Home environmentParental education and household income
Academic performance and engagementEnglish language arts (ELA) and math test scores and a binary indicator for suspension at baseline
Mental healthComposite measure of mental health using the Center for Epidemiologic Studies Depression (CES-D) scale
Substance useIndicators of substance initiation, including alcohol, cigarette, and drug use
DelinquencyIndex of delinquency, based on a set of survey measures
Social supportIndex of perceived social support from peers, family, and teachers, based on a set of survey measures

SOURCE: Features information from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022).

Home Environment

The data reveal several patterns (Figure 1). First, household and parental factors appear to be important for disconnection, consistent with the literature on parental factors and child socioeconomic outcomes (for examples of reviews, see Haveman and Wolfe, 1995; Duncan and Murnane, 2011). Students from below the top quartile of income and parental education are nearly twice as likely to be disconnected in young adulthood. Finally, females who had ever been pregnant at baseline (by ages 11 through 18) are significantly more likely to be disconnected later in life.

A bar chart that measures connection (blue) versus disconnection (magenta) across 13 factors
Figure 1. Early-Life Correlates of Disconnection

School: Academic Performance and Engagement

Students scoring within the top quartile in math and ELA test scores are less likely to become disconnected. This is consistent with a large body of research that suggests that academic performance predicts future labor market success (see Currie and Thomas, 2001; Heckman, Stixrud, and Urzua, 2006; Hanushek and Woessmann, 2008; among others). An indicator for whether a student was ever suspended at baseline (“ever suspended”) is significantly correlated with disconnection. This could reflect both behavioral issues and disengagement with school.

Mental Health

Disconnection is correlated with a higher likelihood of being clinically depressed at baseline, as measured by the CES-D scale (Radloff, 1977).⁠[7] This result masks substantial heterogeneity by gender. Young women who report symptoms of depression are significantly more likely to become disconnected. There are no significant differences in depression rates between disconnected versus connected males. As alternative indicators for mental health, we also examine self-reported suicidal ideation and suicide attempts. Both indicators are also higher for disconnected youth overall. Disconnected females have significantly higher suicidal ideation than connected females; disconnected males have higher (although not significantly different) suicide attempts than connected males. These results suggest that disconnected youth are more likely to have faced mental health challenges of some kind during high school.

Behavioral Risk Factors: Substance Use and Delinquency

A body of research links child conduct problems and substance use to poorer adult outcomes across education, employment, and health (Balsa, Giuliano, and French, 2011; Farrington, 2005; Fergusson, Horwood, and Ridder, 2005). We use a series of variables on substance use during high school (including alcohol, cigarettes, and illegal drugs) to examine how substance initiation correlates with disconnection. We also use a set of survey questions on delinquency to create an index for child conduct issues.⁠[8] This includes self-reported answers to questions asking whether the child has painted graffiti, damaged property, lied to parents or guardians about activities, stolen items, taken part in violence, or sold drugs at baseline. Disconnected youth have significantly higher reported cigarette and drug use during high school (the time of the baseline survey) than connected youth. Disconnected youth are 10 percent more likely to have ever smoked cigarettes during high school and nearly 20 percent more likely to have ever used drugs.⁠[9] There is no significant difference in alcohol use between the two groups. This pattern of results is similar across gender. Disconnected youth score significantly higher on the baseline delinquency index, suggesting that behavioral and conduct issues might be an important risk factor for disconnection. This is consistent with the higher likelihood of suspension discussed above.

Social Support

Finally, social support, whether from family, peers, or community, might be one mitigating factor that helps individuals navigate challenges during adolescence and ultimately reduce the risk of disconnection. Strong social ties have been linked to better adult outcomes, including educational attainment, labor market attachment, and psychological well-being (Cohen and Wills, 1985; Furstenberg and Hughes, 1995; Crosnoe and Elder, 2004). We constructed a social support index from a module measuring respondents’ reported feelings of being supported and understood by parents, teachers, and friends. Consistent with the literature, higher social support during high school is correlated with a reduction in disconnection in early adulthood.

Suspension for Males and Early Pregnancy for Females Strongly Correlate with Disconnected Status, All Else Being Equal

Our results so far have examined differences in means across disconnected and connected populations. However, these relationships might be picking up spurious correlation with other factors. As our final analysis, we use multivariate regression analysis to examine all factors together, both overall and separately by gender (Figure 2).⁠[10] This allows us to examine the relationship between each variable and disconnection, holding constant the influence of other factors. “Ever pregnant” and “ever suspended” remain highly positively correlated with disconnection. Examining the data separately by gender, “ever suspended” is only a significant correlate for males.⁠[11] “Ever pregnant” at baseline is, by definition, only a significant correlate for females.

Suspension and early pregnancy might be mediating factors through which the other covariates affect disconnection. We examine correlates of suspension for males and correlates of early pregnancy for females.⁠[12] For males, substance use (drugs and, to some extent, alcohol), high scores on the delinquency index, and being Black are positively correlated with suspension. Higher parental education, household income, ELA scores, and math scores are negatively correlated with suspension. These results are consistent with literature that suggests that school discipline, particularly suspension, is disproportionately applied to males and Black students (Okonofua and Eberhardt, 2015; Skiba et al., 2011). These practices are linked to worse academic and longer-term outcomes (Bacher-Hicks, Billings, and Deming, 2024; Perry and Morris, 2014). Alternative methods, such a restorative justice, have been shown to reduce suspensions, but they may need to be paired with academic supports to sustain achievement (Augustine et al., 2018; Gregory et al., 2016). Such measures may (or may not) serve to reduce eventual disconnection. For females, substance use (drugs), depression, and being Black are correlated with early pregnancy. These are purely correlational, but might shed light on risk factors that precede both early pregnancy and suspension and later disconnection.

Figure 2. Correlates of Disconnection, Regression-Adjusted

Probability of disconnection (with 95% CI)A dot plot comparing the probability of disconnection across many groups like female, household income, parent education, and Hispanic, for example.−0.2−0.100.10.20.30.4Female−0.0080.0470.101Household income (top quartile)−0.063−0.020.023Parent education (top quartile)−0.09−0.0420.006Parent married−0.085−0.0070.071Ever pregnant0.0860.2310.375ELA score (top quartile)−0.082−0.0270.028Math score (top quartile)−0.056−0.0080.041Ever suspended0.0660.1140.163Depression (CES-D)−0.067−0.0110.045Substance use: alcohol−0.089−0.0390.011Substance use: cigarettes−0.072−0.0160.041Substance use: drugs−0.0490.0760.202Delinquency Index−0.044−0.0150.013Social Support Index−0.043−0.024−0.005Hispanic−0.103−0.0210.061Black−0.0230.0810.184Native American−0.142−0.0390.064Asian/Pacific Islander−0.080.0070.093Other race−0.1250.0180.161Born in the United States−0.0870.0240.136

95% confidence intervals in brackets

  • Female = 0.0465 [-0.00783t to 0.101]
  • Household Income (Upper Quartile) = -0.0199 [-0.0625 to 0.0228]
  • Parent Education (Upper Quartile) = -0.0422 [-0.0902 to 0.00581]
  • Parent Married = -0.00722 [-0.0853 to 0.0708]
  • Ever Pregnant = 0.231 (p<0.01) [0.0862 to 0.375]
  • ELA Score (Upper Quartile) = -0.0271 [0.0821 to 0.0279]
  • Math Score (Upper Quartile) = -0.00765 [-0.0560 to 0.0407]
  • Ever Suspended = 0.114 (p<0.001) [0.0655 to 0.163]
  • Depression (CES-D) = -0.0110 [-0.0673 to 0.0453]
  • Substance Use: Alcohol = -0.0388 [-0.0888 to 0.0112]
  • Substance Use: Cigarettes = -0.0155 [-0.0718 to 0.0408]
  • Substance Use: Drugs = 0.0762 [-0.0492 to 0.202]
  • Delinquency Index = -0.0154 [-0.0435 to 0.0128]
  • Social Support Index = -0.0244 (p<0.05) [-0.0434 to -0.00547]
  • Hispanic = -0.0213 [-0.103 to 0.0608]
  • Black = 0.0805 [-0.0230 to 0.184]
  • Native American = -0.0388 [-0.142 to 0.0642]
  • Asian/Pacific Islander = 0.00660 [-0.0800 to 0.0932]
  • Other race = 0.0180 [-0.125 to 0.161]
  • Born in the United States = 0.0244 [-0.0869 to 0.136]
  • Constant = 0.127 (p<0.05) [0.00295 to 0.250]
  • Observations = 1598

SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.

NOTE: This figure plots the coefficients from a linear regression of disconnection on the set of covariates. “White” is the excluded racial category. “Ever pregnant” is coded as 0 if the respondent was male. The upper and lower bounds show the 95-percent CI for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.

The Path Forward

We provide preliminary evidence on the predictive factors that are associated with disconnection, including some of the first analyses using Add Health data to explore these relationships. Even in the years before disconnection emerges, youth who eventually become disconnected are different from their connected peers across several dimensions. They are more likely to report symptoms of depression, experience an early pregnancy, use substances, engage in delinquent activities, have weaker social support structures, and be suspended from school. Early pregnancy and school suspension, in particular, may serve as pathways through which other risk factors translate into later disconnection.

These estimates are descriptive rather than causal, and unobserved factors correlated with our explanatory variables may drive both early-life risks and eventual disconnection. However, this analysis highlights a set of risk factors that are likely to shape longer-run outcomes and provides a foundation for future research.

This work points to several implications for policymakers. First, early identification and prevention of risk factors associated with disconnection is critical. Eventual disconnection is correlated with academic performance, school suspension, early pregnancy, and substance use; this suggests that risks emerge early on. Preventative policies that reduce these risk factors and improve academic performance may reduce the likelihood of later disconnection.

Second, the links between suspension and disconnection suggest that restorative justice programs may not only be effective in reducing suspensions but also in reducing later disconnections. Restorative approaches emphasize repairing harm, relationship-building, and prevention, rather than excluding children from school. Evidence shows that restorative justice practices are effective in reducing suspension rates (Augustine et al., 2018; Gregory et al., 2016). This in turn may reduce the likelihood of youth disconnection by keeping students engaged in school.

Finally, evidence gaps remain. Evaluations of programmatic and policy interventions targeting these explanatory variables might help clarify mechanisms that lead to disconnection. For instance, evaluations of school-based programs that reduce suspensions or evaluations of interventions that delay early pregnancy could shed light on whether addressing these risk factors reduces disconnection. Importantly, future research should explicitly consider disconnection itself as a key outcome, assessing not only whether interventions affect intermediate risk factors but also whether they ultimately reduce the likelihood of youth becoming disconnected.

Appendix. Additional Figures

View Appendix

This appendix presents results from supplementary analyses referenced in the paper. Figure A.1 shows correlates of disconnection disaggregated by gender, Figure A.2 shows correlates of “ever suspended” among males, and Figure A.3 shows correlates of “ever pregnant” among females.

Figure A.1. Correlates of Disconnection: Male Versus Female, Regression-Adjusted

Probability of disconnection (with 95% CI)A dot plot comparing probability of disconnectedness of male versus female across various features like household income, parent education, ELA score, and math score, for example.Household income (upper quartile)−0.500.5Male−0.076−0.0320.013Female−0.1120.0210.154Parent education (upper quartile)−0.500.5Male−0.085−0.0370.011Female−0.164−0.0480.068ELA score (upper quartile)−0.500.5Male−0.067−0.0060.055Female−0.167−0.0770.013Math score (upper quartile)−0.500.5Male−0.074−0.0170.041Female−0.0980.0010.099Ever suspended−0.500.5Male0.0530.110.167Female−0.0290.1070.242Depression (CES-D)−0.500.5Male−0.124−0.0520.019Female−0.0090.0740.158Substance use: alcohol−0.500.5Male−0.104−0.0460.012Female−0.184−0.070.045Substance use: cigarettes−0.500.5Male−0.0510.0040.059Female−0.253−0.1060.041Substance use: drugs−0.500.5Male−0.0280.1180.265Female−0.1920.0420.276Delinquency Index−0.500.5Male−0.037−0.0070.023Female−0.114−0.0560.002Social Support Index−0.500.5Male−0.058−0.035−0.012Female−0.0340.0070.048Hispanic−0.500.5Male−0.109−0.0340.041Female−0.2040.0410.287Black−0.500.5Male−0.0070.1090.226Female−0.157−0.0290.098Native American−0.500.5Male−0.173−0.0810.012Female−0.2180.0990.417Asian/Pacific Islander−0.500.5Male−0.102−0.0230.056Female−0.1210.1640.449Other race−0.500.5Male−0.0740.0610.196Female−0.502−0.1510.2Born in the United States−0.500.5Male−0.090.0170.125Female−0.331−0.0070.317Ever pregnant−0.500.5MaleFemale0.0620.2160.369

95% confidence intervals in brackets

Household Income (Upper Quartile)

  • Male = -0.0317 [-0.0763 to 0.0129]
  • Female = 0.021 [-0.112 to 0.154]

Parent Education (Upper Quartile)

  • Male = -0.0371 [-0.0849 to 0.0108]
  • Female = -0.0476 [-0.164 to 0.0684]

ELA Score (Upper Quartile)

  • Male = -0.00632 [-0.0672 to 0.0545]
  • Female = -0.0772 [-0.167 to 0.0128]

Math Score (Upper Quartile)

  • Male = -0.0169 [-0.0744 to 0.0406]
  • Female = 0.00071 [-0.0980 to 0.0994]

Ever Suspended

  • Male = 0.110 (p<0.001) [0.0531 to 0.167]
  • Female = 0.107 [-0.0288 to 0.242]

Depression (CES-D)

  • Male = -0.0524 [-0.124 to 0.0190]
  • Female = 0.0743 [-0.00944 to 0.158]

Substance Use: Alcohol

  • Male = -0.0461 [-0.104 to 0.0119]
  • Female = -0.0695 [-0.184 to 0.0454]

Substance Use: Cigarettes

  • Male = 0.00379 [-0.0513 to 0.0589]
  • Female = -0.106 [-0.253 to 0.0410]

Substance Use: Drugs

  • Male = 0.118 [-0.0281 to 0.265]
  • Female = 0.0422 [-0.192 to 0.276]

Delinquency Index

  • Male = -0.00701 [-0.0366 to 0.0226]
  • Female = -0.0558 [-0.114 to 0.00225]

Social Support Index

  • Male = -0.0352 (p<0.01) [-0.0581 to -0.0123]
  • Female = 0.00696 [-0.0341 to 0.0481]

Hispanic

  • Male = -0.0339 [-0.109 to 0.0413]
  • Female = 0.0412 [-0.204 to 0.287]

Black

  • Male = 0.109 [-0.00726 to 0.226]
  • Female = -0.0293 [-0.157 to 0.0984]

Native American

  • Male = -0.0809 [-0.173 to 0.0115]
  • Female = 0.0992 [-0.218 to 0.417]

Asian/Pacific Islander

  • Male = -0.0228 [-0.102 to 0.0560]
  • Female = 0.164 [-0.121 to 0.449]

Other race

  • Male = 0.0609 [-0.0742 to 0.196]
  • Female = -0.151 [-0.502 to 0.200]

Born in the United States

  • Male = 0.0173 [-0.0902 to 0.125]
  • Female = -0.00718 [-0.331 to 0.317]

Ever Pregnant

  • Female = 0.216 (p<0.01) [0.0624 to 0.369]

Constant

  • Male = 0.127 (p<0.05) [0.0127 to 0.240]
  • Female = 0.306 [-0.0407 to 0.653]

Observations

  • Male = 1187
  • Female = 414

SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year, and have been followed through 2018.

NOTE: This figure plots the coefficients from a linear regression of disconnection on the set of covariates conducted separately for males and females. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.

Figure A.2. Correlates of Ever Suspended for Males

Probability of suspension: Males (with 95% CI)A dot plot comparing probability of suspension of males across various features like household income, parent education, ELA score, and math score, for example.−0.2−0.100.10.20.30.4Household income (top quartile)−0.083−0.046−0.008Parent education (top quartile)−0.158−0.123−0.089ELA score (top quartile)−0.15−0.112−0.075Math score (top quartile)−0.091−0.057−0.024Depression (CES-D)−0.0390.0070.054Substance use: alcohol0.0020.0420.083Substance use: cigarettes−0.0260.0140.054Substance use: drugs0.0730.190.306Delinquency Index0.0840.1110.138Social Support Index−0.0140.0050.025Hispanic−0.0760.0080.092Black0.170.2350.3Native American−0.0440.0610.166Asian/Pacific Islander−0.0290.0620.152Other race−0.0870.020.128Born in the United States−0.163−0.087−0.011

95% confidence intervals in brackets

  • Household Income (Upper Quartile) = -0.0457 (p<0.05) [-0.0829 to -0.00845]
  • Parent Education (Upper Quartile) = -0.123 (p<0.001) [-0.158 to -0.0885]
  • ELA Score (Upper Quartile) = -0.112 (p<0.001) [-0.150 to -0.0748]
  • Math Score (Upper Quartile) = -0.0574 (p<0.001) [-0.0906 to -0.0242]
  • Depression (CES-D) = 0.00733 [-0.0391 to 0.0538]
  • Substance Use: Alcohol = 0.0424 (p<0.05) [0.00159 to 0.0832]
  • Substance Use: Cigarettes = 0.0143 [-0.0257 to 0.0543]
  • Substance Use: Drugs = 0.190 (p<0.01) [0.0730 to 0.306]
  • Delinquency Index = 0.111 (p<0.001) [0.0841 to 0.138]
  • Social Support Index = 0.00533 [-0.0140 to 0.0247]
  • Hispanic = 0.00800 [-0.0761 to 0.0921]
  • Black = 0.235 (p<0.001) [0.170 to 0.300]
  • Native American = 0.0614 [-0.0435 to 0.166]
  • Asian/Pacific Islander = 0.0617 [-0.0290 to 0.152]
  • Other race = 0.0202 [-0.0872 to 0.128]
  • Born in the United States = -0.0873 (p<0.05) [-0.163 to -0.0114]
  • Constant = 0.356 (p<0.001) [0.262 to 0.450]
  • Observations = 2545

SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year and have been followed through 2018.

NOTE: This figure plots the coefficients from a linear regression of suspension on the set of covariates conducted separately for males. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.

Figure A.3. Correlates of Early Pregnancy for Females

Probability of early pregnancy: Females (with 95% CI)A dot plot comparing probability of early pregnancy across various features like household income, parent education, ELA score, and math score, for example.−0.1−0.0500.050.10.15Household income (top quartile)−0.020.0030.025Parent education (top quartile)−0.028−0.0110.005ELA score (top quartile)−0.0140.0090.033Math score (top quartile)−0.029−0.0090.01Depression (CES-D)0.0270.0540.08Substance use: alcohol−0.027−0.0020.023Substance use: cigarettes−0.0140.0060.025Substance use: drugs0.0290.0910.153Delinquency Index−0.02−0.0090.002Social Support Index−0.0040.0020.009Hispanic−0.0280.0240.076Black0.0030.0370.072Native American−0.052−0.0240.004Asian/Pacific Islander−0.0150.0260.067Other race−0.08−0.0280.025Born in the United States−0.041−0.0010.039

95% confidence intervals in brackets

  • Household Income (Upper Quartile) = 0.00262 [-0.0195 to 0.0247]
  • Parent Education (Upper Quartile) = -0.0114 [-0.0280 to 0.00521]
  • ELA Score (Upper Quartile) = 0.00915 [-0.0144 to 0.0327]
  • Math Score (Upper Quartile) = -0.00921 [-0.0287 to 0.0103]
  • Depression (CES-D) = 0.0538 (p<0.001) [0.0274 to 0.0802]
  • Substance Use: Alcohol = -0.00216 [-0.0270 to 0.0227]
  • Substance Use: Cigarettes = 0.00576 [-0.0135 to 0.0251]
  • Substance Use: Drugs = 0.0908 (p<0.01) [0.0286 to 0.153]
  • Delinquency Index = -0.00895 [-0.0197 to 0.00177]
  • Social Support Index = 0.00241 [-0.00443 to 0.00924]
  • Hispanic = 0.0238 [-0.0282 to 0.0758]
  • Black = 0.0371 (p<0.05) [0.00276 to 0.0715]
  • Native American = -0.0238 [-0.0518 to 0.00415]
  • Asian/Pacific Islander = 0.0262 [-0.0150 to 0.0674]
  • Other race = -0.0277 [-0.0801 to 0.0247]
  • Born in the United States = -0.000757 [-0.0409 to 0.0394]
  • Constant = 0.00562 [-0.0416 to 0.0528]
  • Observations = 1603

SOURCE: Features data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) (Harris and Udry, 2022), a representative sample of adolescents who were in grades 7 to 12 during the 1994–1995 school year and have been followed through 2018.

NOTE: This figure plots the coefficients from a linear regression of early pregnancy on the set of covariates conducted separately for females. “White” is the excluded racial category. The upper and lower bounds show the 95-percent CIs for each covariate. We use individual (person) weights to create statistics that are representative of the U.S. population.

Acknowledgments

We are grateful for the contributions and support of our colleagues Andrew Hoehn, Heather Schwartz, and Jennifer Kondo. We thank Ben Master and Christine Mulhern for their careful reviews. We are grateful to Monette Velasco, Libby Sweeney, Stephanie Lonsinger, and Mirka Vuollo for their assistance with editing and the publication process.

Notes

  1. Employment is measured as working for pay for at least ten hours per week. Therefore, anyone who is working less than ten hours per week or not working for pay (and otherwise not in school) is counted as disconnected by this measure.We examine the period of disconnection during Wave 3; at this point, respondents were 18 to 28 years old, with a median age of 22. Waves 1 and 2 were collected using a sample of adolescents who were in school at the time of the survey, thus disconnection during these waves is, by design, close to 0 percent. Respondents in Wave 4 were aged 25 through 33, and thus outside the age range of disconnected youth, although we can eventually use this sample to examine longer run outcomes of disconnection.For our analysis, we use the Add Health public-use sample, which is one-third of the size of the full restricted-use sample. Future planned analysis will use the full sample. Return to content⤴
  2. This means that our sample differs from the overall population of disconnected youth and those who were already disconnected at the time of high school; examining out-of-school students might reveal different characteristics that correlate with disconnection. Return to content⤴
  3. Seventeen percent of females are disconnected whereas 14 percent of males are disconnected. Return to content⤴
  4. These results are broadly similar to those found in Wenger and Bonds (2025). The differences likely stem from the different periods (Wenger and Bonds use data from 2019–2023) and perhaps from the different sampling frames (Add Health forms a sample from connected youth in high school). Return to content⤴
  5. We note that these results are exploratory and not a comprehensive set of all possible covariates. Future planned work will use the Add Health restricted data, which have the full sample of respondents and a richer set of data, allowing us to calculate disconnection at a more granular level, examine geographic variation, and examine a more comprehensive set of covariates. Return to content⤴
  6. We also examine a binary indicator for physical disability. Physical disability is correlated with disconnection but the sample of those disabled is extremely small (less than 1 percent) and thus our estimate is very imprecise. In future analyses, we will re-estimate this using the restricted-use sample which has three times the number of observations. Return to content⤴
  7. The CES-D scale typically has 20 questions, with a score of 16 or above indicating clinical depression (Radloff, 1977). The version in the Add Health has 19 questions, and so we code 15 or above as clinical depression. Results are robust to an alternative coding of 16 and above. Return to content⤴
  8. We create a means effect index of all 15 questions in the delinquency module to create our delinquency index. Return to content⤴
  9. Illegal drug use includes significantly higher marijuana, cocaine, and “other” drug use; there was no significant difference in inhalant use. Marijuana use was about 30 percent higher in the disconnected sample (from 25 to 33 percent) and cocaine use was about 60 percent higher (from 3 to 5 percent). Both of these differences are statistically significant. “Other” drug use (other drugs not included in marijuana, cocaine, and inhalants) was 12 percent higher in the disconnected sample (from 8 to 9 percent) but not statistically significant. Return to content⤴
  10. We estimated both a linear probability model and a logistic regression. Results are similar across models and so we show results from the linear model here. Return to content⤴
  11. See Figure A.1. in the appendix for these results. Return to content⤴
  12. See Figures A.2. and A.3. in the appendix for these results. Return to content⤴

References

  • Augustine, Catherine H., John Engberg, Geoffrey E. Grimm, Emma Lee, Elaine Lin Wang, Karen Christianson, and Andrea A. Joseph, Can Restorative Practices Improve School Climate and Curb Suspensions: An Evaluation of the Impact of Restorative Practices in a Mid-Sized Urban School District, RAND Corporation, RR-2840-DOJ, 2018. As of September 30, 2025: https://www.rand.org/pubs/research_reports/RR2840.html
  • Bacher-Hicks, Andrew, Stephen B. Billings, and David J. Deming, “The School to Prison Pipeline: Long-Run Impacts of School Suspensions on Adult Crime,” American Economic Journal: Economic Policy, Vol. 16, No. 4, November 2024.
  • Balsa, Ana I., Laura M. Giuliano, and Michael T. French, “The Effects of Alcohol Use on Academic Achievement in High School,” Economics of Education Review, Vol. 30, No. 1, 2011.
  • Belfield, Clive R., Henry M. Levin, and Rachel Rosen, The Economic Value of Opportunity Youth, Corporation for National and Community Service, 2012.
  • Cohen, Sheldon, and Thomas A. Wills, “Stress, Social Support, and the Buffering Hypothesis,” Psychological Bulletin, Vol. 98, No. 2, 1985.
  • Crosnoe, Robert, and Glen H. Elder, Jr., “Family Dynamics, Supportive Relationships, and Educational Resilience During Adolescence,” Journal of Family Issues, Vol. 25, No. 5, 2004.
  • Currie, Janet, and Duncan Thomas, “Early Test Scores, School Quality and SES: Longrun Effects on Wage and Employment Outcomes,” in Solomon Polachek, ed., Worker Wellbeing in a Changing Labor Market, Emerald Group Publishing Limited, 2001.
  • Duncan, Greg J., and Richard J. Murnane, eds., Whither Opportunity? Rising Inequality, Schools, and Children’s Life Chances, Russell Sage Foundation, 2011.
  • Farrington, David P., “Childhood Origins of Antisocial Behavior,” Clinical Psychology & Psychotherapy, Vol. 12, No. 3, 2005.
  • Fergusson, David M., L. John Horwood, and Elizabeth M. Ridder, “Show Me the Child at Seven: The Consequences of Conduct Problems in Childhood for Psychosocial Functioning in Adulthood,” Journal of Child Psychology and Psychiatry, Vol. 46, No. 8, 2005.
  • Furstenberg, Frank F., Jr., and Mary Elizabeth Hughes, “Social Capital and Successful Development Among At-Risk Youth,” Journal of Marriage and the Family, Vol. 57, No. 3, 1995.
  • Gregory, Anne, Kathleen Clawson, Alycia Davis, and Jennifer Gerewitz, “The Promise of Restorative Practices to Transform Teacher-Student Relationships and Achieve Equity in School Discipline,” Journal of Educational and Psychological Consultation, Vol. 26, No. 4, 2016.
  • Hair, Elizabeth C., Kristin A. Moore, Thomson J. Ling, Cameron McPhee-Baker, and Brett V. Brown, “Youth Who Are ‘Disconnected’ and Those Who Then Reconnect: Assessing the Influence of Family, Programs, Peers and Communities,” Child Trends, Vol. 37, July 2009.
  • Hanushek, Eric A., and Ludger Woessmann, “The Role of Cognitive Skills in Economic Development,” Journal of Economic Literature, Vol. 46, No. 3, 2008.
  • Harris, Kathleen Mullan, and J. Richard Udry, “National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994–2018 [Public Use],” dataset, version 25, Carolina Population Center, University of North Carolina–Chapel Hill, Inter-university Consortium for Political and Social Research, August 9, 2022. As of October 7, 2025: https://doi.org/10.3886/ICPSR21600.v25
  • Haveman, Robert, and Barbara Wolfe, “The Determinants of Children’s Attainments: A Review of Methods and Findings,” Journal of Economic Literature, Vol. 33, No. 4, 1995.
  • Heckman, James J., Jora Stixrud, and Sergio Urzua, “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior,” Journal of Labor Economics, Vol. 24, No. 3, 2006.
  • Lewis, Kristen, A Decade Undone: 2021 Update, Measure of America, July 29, 2021.
  • MaCurdy, Thomas, Bryan Keating, Sriniketh Suryasesha Nagavarapu, and David Glick, “Reprint of: Profiling the Plight of Disconnected Youth in America,” Journal of Econometrics, Vol. 243, No.1–2, July 2024.
  • Okonofua, Jason A., and Jennifer L. Eberhardt, “Two Strikes: Race and the Disciplining of Young Students,” Psychological Science, Vol. 26, No. 5, 2015.
  • Perry, Brea L., and Edward W. Morris, “Suspending Progress: Collateral Consequences of Exclusionary Punishment in Public Schools,” American Sociological Review, Vol. 79, No. 6, 2014.
  • Radloff, Lenore Sawyer, “The CES-D Scale: A Self-Report Depression Scale for Research in the General Population,” Applied Psychological Measurement, Vol. 1, No. 3, 1997.
  • Skiba, Russell J., Robert H. Horner, Choong-Geun Chung, M. Karega Rausch, Seth L. May, and Tary Tobin, “Race is Not Neutral: A National Investigation of African American and Latino Disproportionality in School Discipline,” School Psychology Review, Vol. 40, No. 1, 2011.
  • Wenger, Jennie W., and Stephanie Bonds, Understanding Disconnection Among American Youth, RAND Corporation, PE-A4207-1, October 2025. As of October 1, 2025: https://www.rand.org/pubs/perspectives/PEA4207-1.html

https://www.rand.org/pubs/perspectives/PEA4207-2.html??cutoff=true&utm_source=AdaptiveMailer&utm_medium=email&utm_campaign=7014N000001SnimQAC&utm_term=00vQK00000HLuzuYAD&org=1674&lvl=100&ite=301672&lea=4267356&ctr=0&par=1&trk=a0wQK00000GjonRYAR

School daze

November 25, 2025 Paul Costello1 Uncategorized
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A dark school hallway in which students appear in silhouette.

The numbers are staggering.

Nearly one in four 17-year-old boys in the United States has attention deficit hyperactivity disorder. In the early 1980s, a diagnosis of autism was delivered to one child in 2,500. That figure is now one in 31. Almost 32 percent of adolescents have at some point been given a diagnosis of anxiety. More than one in 10 have experienced a major depressive disorder, my colleague Jia Lynn Yang reports.

And the number of mental health conditions is expanding. A child might be tagged with oppositional defiance disorder or pathological avoidance disorder. “The track has become narrower and narrower, so a greater range of people don’t fit that track anymore,” an academic who studies children and education told Jia Lynn. “And the result is, we want to call it a disorder.”

Why did this happen? A lot of reasons. Kids spend hours on screens, cutting into their sleep, exercise and socializing — activities that can ward off anxiety and depression. Mental health screenings have improved.

And then there’s school itself: a cause of stress for many children and the very place that sends them toward a diagnosis.

A slow transformation

In 1950, less than half of American children attended kindergarten. Only about 50 percent graduated from high school. After-school hours were filled with play or work. “But as the country’s economy shifted from factories and farms to offices, being a student became a more serious matter,” Jia Lynn writes. “The outcome of your life could depend on it.” College became a reliable path to the middle class.

Schools leaned into new standards of testing and put in place measures of accountability. The No Child Left Behind Act in 2002 made it federal law.

States rewarded schools for having high scores. They punished them for low ones. “Schools were treated more like publicly traded companies, with test scores as proxies for profits,” Jia Lynn writes. “Before long, schools had public ratings, so ubiquitous they now appear on real estate listings.”

And there were clear incentives to diagnose students with psychiatric disorders: Treatment of one student, especially a disruptive one, could lead to higher test scores across the classroom. And in some states, the test scores of students with a diagnosis weren’t counted toward a school’s overall marks, nudging results higher as well.

The metrics may have gotten many kids the support they needed. Either way, educational policymaking yielded a change: According to one analysis Jia Lynn found, the rate of A.D.H.D. among children ages 8 to 13 in low-income homes rose by half after the passage of No Child Left Behind.

The effect on kids

The pressures on students became extreme. In 2020, Yale researchers found that nearly 80 percent of high schoolers said they were stressed.

And that stress has trickled down to younger and younger kids. Kindergartners learn best through play, not through the rote lessons in math and reading that began to enter classrooms. Preschoolers are not predisposed to sitting still. And yet as they, too, now face greater academic expectations, many are being expelled for misbehavior.

Even the school day became more regimented, with fewer periods of recess — by 2016, only eight states had mandatory recess in elementary schools. Class schedules are packed. “You’ve got seven different homework assignments that you’ve got to remember each night,” one expert told Jia Lynn. “Think of the cognitive load of a sixth-grade boy. I challenge many adults to do this.”

It’s a vicious cycle, where bad outcomes lead to worse outcomes.

And Jia Lynn writes about that beautifully:

By turning childhood into a thing that can be measured, adults have managed to impose their greatest fears of failure onto the youngest among us. Each child who strays from our standards becomes a potential medical mystery to be solved, with more tests to take, more metrics to assess. The only thing that seems to consistently evade the detectives is the world around that child — the one made by the grown-ups.

Read more about schools and the rise of childhood mental health disorders here. Don’t miss the comments that accompany the article, especially from parents and teachers. Many boil down to something a recently retired teacher wrote: “A child’s school day is insane.”

https://mail.google.com/mail/u/2/#inbox/FMfcgzQcqtkKqkGRSdBPhKBjCFgPDdvZ

More Support is Needed to Address Growing Student Need

November 12, 2025 Paul Costello1 Uncategorized
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12 November 2025 | Baltimore, MD –

The Partnership for Student Success (PSS) today announced the publication of a new report, Are K-12 Students Getting the Evidence-Based Supports They Need? Progress and Challenges Four Years After the Pandemic. The report, authored by Dr. Robert Balfanz and Vaughan Byrnes of the Everyone Graduates Center at the Johns Hopkins University School of Education, analyzes findings from a third annual nationally representative survey of K-12 public school principals, fielded by the RAND Corporation in partnership with PSS, to examine the deployment of evidence-based student supports and evolving student need.

The report concludes that four years after the height of the pandemic, there is widespread use of evidence-based and people-powered student supports–such as high-intensity tutoring, mentoring, student success coaching, postsecondary transition coaching, and wraparound supports–in public schools across the United States. But, public school principals indicate that continued growth in these interventions is needed to meet the scale of student needs.

Key findings from the report include:

  • High-intensity tutoring, mentoring, and wraparound supports are each provided in about half of the nation’s K-12 public schools, and in about two-thirds of high-poverty schools, with most schools offering these services providing them to 20% or fewer of their students.
  • Over the past three school years, an estimated 400,000 additional adults have stepped up to support K-12 students in public schools as tutors, mentors, postsecondary advisors, and wraparound support providers.
  • Four years after the height of the pandemic, public school principals report no let-up in student need with 30% to 40% reporting an increase in the number of students needing high-intensity tutoring, mentoring, or wraparound supports.

The report emphasizes that while implementation barriers exist to expanding evidence-based programs, there is a subset of schools that are proving that serving students at scale is possible, and outlines a range of resources and opportunities to support expansion of high-quality programs.

To learn more, read the full report on the Partnership for Student Success’ website or register for a webinar on the report’s key findings on Thursday, December 4th at 3:00pm ET.

###

About the Partnership for Student Success: Based at the Everyone Graduates Center at the Johns Hopkins University School of Education, the Partnership for Student Success is a national coalition dedicated to expanding evidence-based and people-powered student supports for all K-12 students in the United States, with a particular focus on high-impact tutoring, mentoring, student success coaching, postsecondary transition coaching and wraparound/integrated student supports.

Contact: Kate Cochran kcochr17@jhu.edu

Press Release | 2024-25 PSS Principals Survey

More Montgomery County students suspended so far this year

October 29, 2025 Paul Costello1 Uncategorized
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Montgomery County schools update student code of conduct amid equity concerns

The BANNER Talia Richman10/29/2025 5:30 a.m. EDT

More suspensions went to students who are Hispanic, learning English or in special education

Montgomery County schools saw an increase in student suspensions at the start of this school year.

During the first five weeks of classes, the district recorded 296 out-of-school suspensions compared to 230 during the same period last school year.

The 29% increase was driven by more suspensions handed down to Hispanic students, children who are learning English and those who receive special education services.

The numbers — which will be discussed Thursday at the Montgomery County Public Schools board meeting — provide a glimpse into how the district’s new code of conduct is playing out. The data covers the first day of school through Sept. 30.

Read More

Montgomery County is losing students. Here’s why that matters.

October 23, 2025 Paul Costello1 Uncategorized
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Teacher pointing at paper on the floor with students.

Baltimore Banner Talia Richman10/14/2025 1:12 p.m.


Districts across the nation are confronting the consequences of lower birth rates and shifting demographic patterns

Montgomery County Public Schools enrollment dropped to a 10-year low.

Roughly 156,540 students attend county schools — a decrease of more than 2,600 kids since last year, according to preliminary data. While that’s less than a 2% drop, it’s part of a larger pattern of decline since the district’s peak enrollment in 2019.

MCPS predicts a more dramatic dive: a 9% dip from its 2019 peak to the enrollment forecast for 2031.

“This is an uncomfortable conversation for Montgomery County, because this has not been our experience for much of the past few decades,” MCPS Superintendent Thomas Taylor said.

The enrollment dive is part and parcel of big changes planned for Maryland’s largest district. The school system is in the midst of a boundary study for secondary campuses, and Taylor said he wants to expand that effort to elementary schools next.

Montgomery County is not alone in confronting enrollment declines. Districts across the country are confronting the consequences of lower birth rates and shifting demographic patterns.

“We’ve added a lot of housing, and we’ve added a lot of people, and we’ve grown very fast, but something else has changed,” Taylor said. “The percentage of households that have children has dramatically reduced.”

Fewer 5-year-olds

Montgomery County’s declines are driven by drops in kindergarten enrollment and international students.

The kindergarten data is straightforward to explain: Fewer children have been born in the county over the past several years.

The story of international student enrollment is more complex, with Taylor gesturing toward Washington but not going so far as to draw a line to the Trump administration’s aggressive approach to immigration.

“It may be causation, but it’s definitely correlation,” the superintendent said.

Maryland schools are funded based on enrollment, so fewer students in seats means less money flowing toward Montgomery County campuses.

In several cities across the country, school leaders have responded to enrollment drops by closing campuses — a painful choice that often devastates neighborhood families.

Taylor said it’s too soon to tell if those tough choices are in the county’s future.

“Not based on the projections that I have right now,” he said.

By 2031, the district anticipates enrollment dropping just below 150,000 students.

Talia Richman

talia.richman@thebanner.com

Talia Richman

Talia Richman is the Montgomery County education reporter at The Banner. She previously covered schools for The Dallas Morning News. The Education Writers Association has recognized Talia as among the best education beat reporters in the nation. Before her time in Texas, she covered schools and City Hall for The Baltimore Sun.

https://www.thebanner.com/education/k-12-schools/montgomery-county-schools-enrollment-drop-5VZSNAMBUNARNH4Y2VSRKYIWP

3 Insights from the Science of Learning (SoL) Zaretta Hammond

October 14, 2025 Paul Costello1 Uncategorized
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Ready4Rigor logo
This month, my three science of learning insights revolve around metacognition, an often misunderstood aspect of learning.
1. Metacognition requires concentration and quiet because attention is a filter, not a spotlight.
There’s a lot of district-level focus on metacognition: planning, monitoring, and reflecting on one’s thinking as a way to move students through productive struggle. Here’s a quick point to understand about metacognition and students’ attention: Noisy classrooms that boast high engagement might actually be counter-productive to metacognition. Carl Hendricks, professor of Applied Learning Sciences at Academia University in the Netherlands, points out that students rarely suffer from boredom or attention shortages, they suffer from attention surplus – too much stimuli that has to be actively filtered out.  Why does this matter? Because attention is not limitless. That filter has a narrow bandwidth, easily clogged by noise and distraction. If classroom talk is not purposeful, it can be the most powerful distractor of all. The job of teaching is not just to provide something worth noticing, but to protect students from everything that isn’t worth their attention at the moment. 

So, how can you use this information? The solution isn’t to eliminate all talk or stimulation, it’s to be intentional about what demands attention. Teachers whose instruction is grounded in the science of learning create what we might call “attentional architecture”: environments where what is important stands out precisely because what’s unimportant is de-emphasised through well-designed instruction. Read more about what Dr. Hendricks says about it here.
2. Metacognition requires both awareness and an “attack plan.”
When we keep in mind that the main work of metacognition is task analysis, the “attack plan” begins with the move I call Size It Up/Break It Down in Rebuilding Students’ Learning Power. Third grade teachers and researchers, Grace Douglas, Alison Hardy, Katie MacLean, and Sarah Powell (University of Texas at Austin) say that word problems are notoriously challenging for students who have difficulty with mathematics. According to one analysis, word problems make up over 90 percent of the items on high-stakes math tests, so figuring out how to help students improve is an important instructional goal. What students find most difficult in word problems isn’t just the words, but grasping what’s going on in each problem, then translating the scenario into computation, and keeping all this in working memory as they solve the problem slows down computing the answer. “When we reinforce the ineffective keyword strategy,” say Douglas, Hardy, MacLean, and Powell from the study, “we are setting students up for failure, particularly with inconsistent problems.” What’s a better way to solve word problems? Rather than beginning with circling key words, the authors recommend beginning with a metacognitive attack strategy, like this:First, you have to understand the problem. After understanding, make a plan (convert to a computation problem).Carry out the plan.  Look back at your work. How could it be better?How to use this information? There are variations on developing an “attack” strategy, but they have one thing in common: the first step is urging students to take the time to just understand what’s going on, because most students skip this stage. Make this an activity where students talk to each other about what they think is going on in the word problem. This is especially helpful in supporting multilingual students. The second step is choosing a simple attack strategy, ideally with a catchy mnemonic that students can remember. But it all begins with metacognition. Check out the article here.
3. Instructional Illusions: The Illusion of Being Student-Centered
In his new book, Instructional Illusions, Carl Hendricks outlines ten common traps we fall into when it comes to teaching in ways that go against the science of learning. One he points out is the illusion of being student-centered when in reality we structure instruction around teacher-led processing masquerading as student-centered instruction. The most evident sign that we’ve fallen into this trap is over-scaffolding where the teacher is doing most of the cognitive work in the classroom to move students through a lesson. Hendricks suggests that to shift toward an authentically student-centered classroom, we have to help students develop metacognitive awareness of what they already know about a subject, no matter how unrelated it might seem. Why is this a key to a student-centered classroom? Because, as Hendricks points out, there are two principles of learning: Learners come to understand new information in reference to what they already know. My simple way of saying it is: all new learning must be coupled with existing knowledge. Like LEGOs. If a student doesn’t practice metacognitive awareness of what they know, their brain’s synapses aren’t prepped for incorporating new information. How to use this information? Rather than front-loading information we call “background knowledge,” give students time to surface all the ways they might know something about the topic from their everyday lives – movies, songs, memes. Help them make their schema explicit by starting with metacognitive awareness.
 
 2 Quotes: Thinking About Metacognition
Metacognition has many facets, from self-awareness and reflection to understanding our own learning processes and thought patterns. Here are two quotes indirectly related to the topic. “We cannot solve our problems with the same thinking we used when we created them.” — Albert Einstein Einstein reminds us that innovation begins with being aware of our current thinking. “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.” — Marcel Proust, Remembrance of Things Past This quote captures Proust’s insight that true discovery and understanding come from deepening our awareness of what’s already available around us.
 1 Juicy Resource
Here is a great resource: the How Learning and Teaching Happen website, hosted by Academia University of Applied Sciences.
There, you’ll find several free resources that can help you authentically begin incorporating the science of learning. Start with this super helpful, free How Learning and Teaching Happens Implementation Guide. It’s really juicy.

https://www.corwin.com/books/rebuilding-students-learning-267855?inf_contact_key=15c70f0a50151f1159d88f13f4c41feb680f8914173f9191b1c0223e68310bb1

Montgomery County superintendent wants billions of dollars to fix aging schools

October 14, 2025 Paul Costello1 Uncategorized
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The Banner Talia Richman10/13/2025 9:53 p.m. EDTc

Yet the plan covers only about half the district’s needs, Superintendent Thomas Taylor acknowledged

Montgomery County schools Superintendent Thomas Taylor wants to replace Eastern Middle School, renovate Sligo Middle School and close Silver Spring International Middle School as part of a major plan to tackle the district’s aging facilities.

If his proposal is approved, several elementary school buildings could also be replaced by 2031, as could Damascus High School.

Many other campuses should also be on the list for major renovations, the superintendent said, but there just isn’t enough money to cover everything.

“There’s hard truths coming,” Taylor said during a Monday presentation in which he ran through the data that guided his capital improvements program recommendations.

Taylor is asking for a $2.7 billion investment in campus infrastructure over six years. He warned that the enormous figure covers only half of the district’s actual facility needs.

His recommendations are far from a done deal. The school board must sign off on it, and then county government.

As Taylor ran through the numbers, Council Vice President Will Jawando listened near the front of the district’s Rockville boardroom. He said the county must balance competing interests for funds.

Taylor’s presentation was “realistic about the need,” Jawando said. “As far as what we can afford, that’s a different question, right?”

Complaints about campus infrastructure have long dogged Montgomery County Public Schools leaders. The district operates more than 230 buildings, some built more than a half century ago.

“This is a big ask,” Taylor said. “It’s a big ask because our needs are really significant.”

Earlier this year, staff members at a school board meeting decried building conditions they said made them sick. Parents regularly tell board members about problems at their kids’ campuses, from broken HVAC systems to mold.

The school district has more than $740 million worth of overdue HVAC projects alone, Taylor said.

The school district’s old strategy for dealing with facility issues was burying its head in the sand, said Taylor, who has led the district since 2024. That led to a system in which the majority of county public schools need repairs or are functionally unreliable, according to a recent report from the comptroller’s office.

“It took us two decades to get into this mess. It’s going to take two decades to get out of this mess,” Taylor said.

Those who will be stuck waiting on their campus’ turn for renovations are sure to feel frustrated.

“I would love to include them if funds would allow for that,” Taylor said. “They don’t.”

Brigid Howe, president of the Montgomery County Council of PTAs, said she feels for students at schools on the cusp, like Magruder and Wootton high schools, which need repair but don’t make it onto the high-priority list.

Those campuses “have real needs,” she said, “and are having to wait yet again.”

Other changes could be coming too. Taylor said he wants the district to do an elementary school boundary study after it finishes the process at the secondary level.

Shortly before the meeting, Taylor spent 15 minutes on a Zoom call with Silver Spring International Middle School families to give them a heads-up about his proposal.

He rattled through the campus’ myriad problems: poorly designed staircases, subpar bathrooms and other “really scary” issues. He said it’s no longer a tenable learning environment.

“All of that being said, it’s still not the worst facility in Montgomery County Public Schools,” Taylor said.

The superintendent said his plan to close the school will not affect current SSIMS students.

School parents asked how the district will keep their children safe on campus until then. They’ve raised concerns about mold, leaky ceilings, broken railings and other problems that have lingered for years.

“We will continue to make improvements to SSIMS as a building, but just know that it won’t be sweeping, big changes” because it’s not worth pumping that much money into the building and needs are even greater elsewhere, Taylor told them.

The building could eventually be used as a “holding school” for students while their own campuses are undergoing renovations.

Silver Spring International Middle School operates out of what was the original Montgomery Blair High School building, constructed in the 1930s.

That history now “haunts” the facility, Taylor said.

The school board will host community listening sessions over the next several weeks.

The superintendent will formally present his plan to the board on Tuesday.

https://www.thebanner.com/education/k-12-schools/montgomery-county-superintendent-wants-billions-of-dollars-to-fix-up-aging-schools-YDTJVZ2MV5DH7IQMYRXUAB5ZT4/?schk=YES&rchk=YES&edition=montgomery&utm_source=The+Banner&utm_campaign=c7e5e7b4ad-NL_MCD_20251014_0730&utm_medium=email&utm_term=0_-c7e5e7b4ad-592168624&mc_cid=c7e5e7b4ad

What Is the State of the World’s Emotional Health?

October 13, 2025 Paul Costello1 Uncategorized
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Rula's 2025 State of Mental Health Report

From Gallup

Implications for Peace

The global rise in unhappiness over the past decade has been well-documented, yet many leaders have overlooked it because they rely on economic indicators while ignoring daily emotional health.

This oversight matters because negative emotions do not just reflect distress; they narrow people’s focus and erode their coping capacity. When these feelings become chronic, they leave individuals and societies more vulnerable to instability.

As the world’s mood has soured, it has also become less stable, with rising political unrest, more conflicts and higher death tolls.

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

In 2024, adults worldwide reported high levels of daily distress: 

39% felt a lot of worry,
37% felt stress,
32% experienced physical pain,
26% felt sadness
22% felt anger.

All are higher than they were a decade ago. Gallup’s inaugural State of the World’s Emotional Health 2025 report, based on 145,000 interviews across 144 countries and areas, reveals that daily distress may serve as an early-warning signal of fragility, with direct implications for health systems, stability and global development.

The report reveals:

Negative emotions remain elevated. Worry, stress, physical pain, sadness and anger are all higher than they were a decade ago.

Positive emotions are steady. Daily experiences of laughter, enjoyment and feeling well-rested held at long-term averages.

Peace shapes emotions. High levels of anger and sadness go hand in hand with weaker peace on the Global Peace Index, which tracks conflict, and the Positive Peace Index, which gauges the institutions that sustain stability.

Explore the full report to see where distress is deepening, where wellbeing persists and what it could mean for global stability, offering leaders a new way to read risk and to build more stable, healthier societies.

Read the Report

Supporting Children’s Mental Health During Political Changes

October 8, 2025 Paul Costello1 Uncategorized
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Posted on February 19th, 2025

Political changes can create uncertainty and stress, not just for adults but for children as well. Shifts in policies, leadership, and societal discussions can impact their sense of stability, leading to feelings of anxiety, confusion, or fear. During these times, it’s key to provide children with the emotional support and reassurance they need to navigate their feelings in a healthy way.

Understanding Children’s Emotional Responses to Political Changes

Learning about children’s emotional responses to political changes is key for addressing their feelings and supporting their mental health during these times. Children may react with anxiety when they sense that their normal routines and environments are disrupted. Such responses might be more pronounced in children who overhear conversations among adults or see distressing news reports.

They could become unusually clingy, experience stomachaches, or have trouble sleeping. Fear can also manifest, especially when children sense hostility or tension around them, even if they don’t fully grasp the political issues at play. Younger children, in particular, may feel confused about what they see and hear because their cognitive abilities to process complex events are still developing.

Children’s perception of political changes varies significantly across different developmental stages, affecting how they absorb and react to what’s happening. For example, toddlers and preschoolers may not understand the specifics of political events but can pick up on cues from adult conversations and the overall emotional climate at home. Maintaining a calm environment helps reassure them. 

The Impact of Politics on Children

Politics can have a significant impact on children, shaping their views and experiences in various ways. From policies and laws that directly affect their lives to the overall political climate in their communities, children are constantly influenced by the world of politics. Here’s the various ways in which politics can impact children:

  • Political policies and laws can greatly impact the daily lives of children, from education to healthcare and beyond.
  • The political climate in a community can shape a child’s understanding of diversity, inclusion, and social issues.
  • Media coverage of political events and campaigns can expose children to complex ideas and ideologies at a young age.
  • Children from politically involved families may feel pressure to conform to certain beliefs or values.
  • Political discussions and debates in schools can help children develop critical thinking skills and learn about different perspectives.

It is important for adults to be mindful of the impact of politics on children and to have open and age-appropriate conversations with them about current events and political issues. By understanding the influence politics can have on children, we can help encourage and support them in handling the complex world of politics.

Providing Reassurance and Stability

In today’s society, political changes are inevitable. These changes can have a significant impact on children, causing them to feel confused, anxious, and even afraid. As parents, it is our responsibility to help our children understand these changes and provide them with a sense of security and stability. One way to do this is by nurturing strong emotional connections within the family. Here are some tips to help you do just that:

  • Communicate openly and honestly with your children about political changes and their potential impact.
  • Encourage your children to share their thoughts and feelings about these changes without judgment.
  • Validate your children’s emotions and let them know that it is okay to feel scared or uncertain.
  • Take the time to listen to your children and address any concerns or questions they may have.
  • Engage in activities as a family that promote bonding and togetherness, such as game nights, movie nights, or outdoor adventures.
  • Create a safe and welcoming home environment where your children feel comfortable expressing themselves.
  • Lead by example and show your children how to handle political changes in a calm and respectful manner.
  • Remind your children that no matter what happens in the world, they are loved and supported by their family.

By following these tips, you can help cushion the effects of political changes on your children and strengthen the emotional connections within your family. As always, your love and support are the most important things you can provide for your children during these uncertain times.

A Call to Action for Supporting Children

This call to action for support involves everyone — parents, teachers, community members, and political figures. In your community, rally behind initiatives that prioritize children’s mental health support during political transitions. A unified approach can significantly strengthen efforts to establish safe environments where children thrive despite ongoing changes. Foster dialogues with local leaders and school boards to make sure that the well-being of children is at the forefront of policy discussions. Advocate for school programs that address emotional intelligence and stress management, as these can provide children with important tools to express their emotions. 

Your individual efforts, combined with those from community organizations, can also uplift children in need. Non-profits that focus on child mental health, like those occasionally involved in clothes distribution, play a key role in providing necessary resources and support. Lending your time or resources to such organizations not only strengthens social safety nets but empowers children through collective efforts during political shifts. Facilitating conversations about these non-profits within your networks spreads awareness and potentially garners more support. 

Conclusion

Supporting children through political changes is not just critical; it’s a shared responsibility that ties us all together. From the quiet moments of reassurance at home to the concerted efforts of community initiatives, every action counts. Crisis times can lead to heightened anxieties in children, which is why focusing on their mental well-being becomes indispensable. Donations and clothes distribution play a pivotal role here, offering practical support while fostering a sense of belonging and security. When children face uncertainty, knowing they have a robust and caring network can make all the difference.

Can you visualize the profound impact of your involvement? With a little help from everyone, at Josie’s Closet Inc, we can lay a solid foundation for the peaceful future our children deserve. Even small contributions of time or resources can extend a safety net for children whose families need a bit of extra support. By supplying them with necessities like clothing and guidance, we not only address immediate needs but build lasting connections and empower these young minds. 

Now is the time to make a tangible difference. Take action now! Support children’s mental health by donating to Josie’s Closet and help us create a stable, nurturing environment for children in need during political changes. Your support provides key resources that guarantee that children can stand resilient and hopeful as they cope with their complex world. Reach out to info@josiescloset.org for more ways to get involved and lead your children on this meaningful journey. 

https://josiescloset.org/blog/supporting-children-s-mental-health-during-political-changes

Young and “Disconnected” in America

October 8, 2025 Paul Costello1 Uncategorized
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woman standing anxiously while others walk by her in a blur
RAND– The newsletter for policy people Oct. 2, 2025

About one in seven people ages 18 to 24 in the United States are disconnected, meaning they are not engaging in school, training, or work. That’s according to a new RAND paper that explores what the evidence says—and doesn’t say—about this phenomenon.

Disconnection has enormous costs for individuals and for society. In the short term, disconnected youth earn about $12,000 less per year than their peers. And over the long term, they are unlikely to be on a path toward economic prosperity.

Who is disconnected? Here are some of the takeaways:

  • Overall, young men are more likely to be disconnected than young women.
  • But other factors—such as family status, race/ethnicity, and disability status—generally are more important than sex in explaining rates of disconnection.
  • Community factors are also important. For example, rates are higher in areas where fewer adult men are employed.
  • Young veterans, especially female veterans, have high rates of disconnection.
  • Most disconnected youth have at least a high school degree. This suggests that education alone isn’t enough to protect young people from disconnection.

So, what might help disconnected youth secure a brighter future? There is no one-size-fits-all solution, the authors write. Addressing this issue will likely require multiple carefully designed policies and programs.

Read more

https://www.rand.org/pubs/perspectives/PEA4207-1.html?project=&utm_campaign=&utm_content=1759757874&utm_medium=rand_social&utm_source=twitter