Citizen-Readiness Index Methodology and Endnotes

Methodology

Percentage of those not in school and not in the workforce, ages 16 to 24

The percentage of those ages 16 to 24 not in school and not in the workforce, by state (2013), was obtained from Measure of America. The minimum and maximum percentages were 7.6 percent (NE) and 19.8 percent (LA). The range was 12.2 percent (19.9 percent - 7.6 percent = 12.2). In order to assign the states a letter grade and corresponding number of points (A=1 point, B=2 points, C=3 points, D=4 points, and E=5 points), a ranking system was developed by dividing the range by five, the number of letter grades to be assigned (12.2 / 5 = 2.44). This determined the percentage of those not in school and not in the workforce each letter grade would encompass. For example, a state was assigned a letter grade of “A” and one (1) point if its percentage of those not in school and not in the workforce fell between 7.5 and 9.9 percent (7.5 + 2.4 = 9.9). Note: In the letter-grade range calculation, 2.4 was used instead of 2.44 because the percentages provided by Measure of America only extended to tenths of a percentage point, and the scale determination was intended to be kept consistent with that.

The grading scale used for the percentages of those ages 16 to 24, not in school and not in the workforce, was as follows:

• A (1 point) = 7.5 to 9.9 percent
• B (2 points) = 10.0 to 12.4 percent
• C (3 points) = 12.5 to 14.9 percent
• D (4 points) = 15.0 to 17.4 percent
• E (5 points) = 17.5 to 19.9 percent

Each state was assigned a grade and corresponding number of points based on its decimal point score (not reflected in the table, which rounds to a whole number). The following states received a grade based on the below scores, not the scores reflected in the table:

• Idaho: 14.9%
• Maine: 9.8%
• Massachusetts: 9.8%
• North Carolina: 14.7%
• Oregon: 14.8%
• Texas: 14.9%
• Wisconsin: 9.8%

Arrests per 100 people, ages 17 to 24

The total number of arrests for those ages 17 to 24, by state (2014), was obtained from the Federal Bureau of Investigation’s Uniform Crime Report (UCR). In addition, the total population of those ages 17 to 24, by state (2014), was obtained from the U.S. Census Bureau. These total numbers were then used to determine the number of arrests per 100 people ages 17 to 24 in each state. These figures were approximated out to hundredths of a percentage point. Note: This does not refer to unique individuals arrested, but the total number of arrests. It is possible that one person could have accounted for multiple arrests. Due to insufficient reporting, this rate could not be calculated for the following states: Alabama, Florida, Illinois, and New York.

The minimum and maximum arrests per 100 people were 2.77 (HI) and 19.12 (WI), respectively. The range was 16.35 (19.12 arrests – 2.77 arrests = 16.35). In order to assign the states a letter grade and corresponding number of points (A=1 point, B=2 points, C=3 points, D=4 points, and E=5 points), a ranking system was developed by dividing the range by five, the number of letter grades to be assigned (16.35 / 5 = 3.27). This determined the range of arrests per 100 people that each letter grade would encompass. For example, a state was assigned a letter grade of “A” and one (1) point if its number of arrests per 100 people fell between 2.75 and 6.02 (2.75 + 3.27 = 6.02).

The grading scale used for the number of arrests per 100 people, ages 17 to 24, was as follows:

• A (1 point) = 2.75 to 6.02 arrests per 100 people
• B (2 points) = 6.03 to 9.30 arrests per 100 people
• C (3 points) = 9.31 to 12.58 arrests per 100 people
• D (4 points) = 12.59 to 15.86 arrests per 100 people
• E (5 points) = 15.87 to 19.13 arrests per 100 people

Percentage of those ineligible for military service, ages 17 to 24

The percentage of those ineligible for military service, by state (2013), was obtained from the United States Department of Defense. The main disqualifiers from military service are obesity, inadequate education, and a record of criminal activity or drug abuse. The minimum and maximum percentages of those ineligible for military service were 62 percent (HI) and 78 percent (MS), respectively. The range was 16 percent 78 percent - 62 percent = 16.

In order to assign the states a letter grade and corresponding number of points (A=1, B=2, C=3, D=4, and E=5), a ranking system was developed by dividing the range by five, the number of letter grades to be assigned (16 / 5 = 3.2). This determined the percentage of those ages 17 to 24 ineligible for military service each letter grade would encompass. For example, a state was assigned a letter grade of “A” and one (1) point if its percentage of those ineligible for military service fell between 60 and 63 percent (60 + 3 = 63). Note: In order to remain consistent with the whole number percentage for each state, 3.2 was rounded to 3.

The grading scale used for the percentages of those ages 17 to 24, ineligible for military service, was as follows:

• A (1 point) = 60 to 63 percent
• B (2 points) = 64 to 67 percent
• C (3 points) = 68 to 71 percent
• D (4 points) = 72 to 75 percent
• E (5 points) = 76 to 79 percent

Assignment of overall letter grade to each state

After the assignment of a letter grade for each of the three categories, each state was assigned an overall letter grade. To do this, the total number of points accumulated by each state was calculated. The total number of points possible ranged from 3 (if a state earned an “A” in every category) to 15 (if a state earned a “E” in every category). For example, a state earning a “C” (3 points) for “arrests,” a “D” (4 points) for “not in school and not in the workforce,” and a “D” (4 points) for “ineligible for military service” would have a total of 11 points.

The actual, total scores achieved by the states ranged from a minimum of 4 points (HI) to a maximum of 13 points (TN). The range was 9 points (13 points - 4 points = 9). States were assigned overall letter grades based on the following points system:

• A = 4 to 5 total points
• B = 6 to 7 total points
• C = 8 to 9 total points
• D = 10 to 11 total points
• E = 12 to 13 total points

Due to insufficient arrest information for Alabama, Florida, Illinois, and New York, overall letter grades could not be assigned to these states. Washington, D.C. also lacked sufficient arrest data and was excluded from the analysis.

Endnotes

1 Lewis, K., & Burd-Sharps, S. (2015, June). Zeroing In on Place and Race. Measure of America.

2 By age 23, between 25% and 41% of young adults have been arrested, according to: Brame, R., Turner, M. G., Paternoster, R., & Bushway, S. D. (2012). Cumulative prevalence of arrest from ages 8 to 23 in a national sample. Pediatrics, 129(1), 21-27. For the most frequent type of arrests by age, see: Bureau of Justice Statistics (2012, October). “Arrest in the United States, 1990-2010. Table 3: Estimated arrests, by age, 2010.”

3 Pew Research Center. (2014, March). “The Decline in Marriage Among the Young.”

4 Department of Defense. 2013 Qualified Military Available (QMA). Acquired through personal communication with the Accession Policy and Joint Advertising, Market Research and Studies teams at DoD in July 2014. Also see: Wall Street Journal (2014). Recruits’ Ineligibility Tests the Military.

5 Nearly 150,000 parents and children participated in home visiting in 2015, according to: Administration for Children and Families. “Federal Home Visiting Program National Program Brief.” Health Resources and Services Administration (HRSA).

6 Child and Adolescent Health Measurement Initiative (2013). “Overview of Adverse Child and Family Experiences among US Children: 2011-12.” U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB).

7 Bethell, C. D., Newacheck, P., Hawes, E., & Halfon, N. (2014). Adverse childhood experiences: assessing the impact on health and school engagement and the mitigating role of resilience. Health Affairs, 33(12), 2106-2115.

8 Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., et al. (2006). The enduring effects of abuse and related adverse experiences in childhood. European archives of psychiatry and clinical neuroscience, 256(3), 174-186; Baglivio, M. T., Epps, N., Swartz, K., Huq, M. S., Sheer, A., & Hardt, N. S. (2014). The prevalence of adverse childhood experiences (ACE) in the lives of juvenile offenders. Journal of Juvenile Justice, 3(2), 1.

9 Nurse-Family Partnership (2009). “Home Visiting Goals.”

10 Wagner, M. & Clayton, S. (1999). The Parents as Teachers program: Results from two demonstrations. The Future of Children, 9(1), 91-115.

11 Donovan, E.F., et al. (2007). Intensive home visiting associated with decreased risk of infant death. Pediatrics, 119, 1145-1151.

12 Olds, D.L., et al. (1997). Long-term effects of home visitation on maternal life course and child abuse and neglect: Fifteen-year follow-up of a randomized trial. JAMA, 278(8), 637-643.

13 Jones Harden, B., et al. (2012). Early head start home visitation: The role of implementation in bolstering program benefits. Journal of Community Psychology, 40(4), 438-455; Coalition for Evidence-Based Policy (2011, August). HHS’s Maternal, Infant, and Early Childhood Home Visiting Program: Which Program Models Identified by HHS As “Evidence-Based” Are Most Likely To Produce Important Improvements in the Lives of Children and Parents?

14 Caldera, D., et al. (2007). Impact of a statewide home visiting program on parenting and on child health and development. Child Abuse & Neglect, 31(8), 829–852; DuMont, K., et al. (2010). “A randomized trial of Healthy Families New York (HFNY): Does home visiting prevent child maltreatment?” Rensselaer, NY: New York State Office of Children & Family Services and Albany, NY: The University of Albany, State University of New York, 2010.

15 Campbell, F.A., et al. (2012). “Adult Outcomes as a Function of an Early Childhood Educational Program: An Abecedarian Project Follow-Up.” Developmental Psychology. 48(4): 1033-1043 as summarized by Coalition for Evidence-Based Policy. “Social Programs that Work: Abecedarian Project.”

16 21% vs. 37% arrested by age 20 and 12% vs. 28% convicted. Coalition for Evidence-based Policy. “Social Programs that Work: Nurse-Family Partnership.” Original findings from Luckey, D. W., Olds, D. L., et al. (2008). Revised Analysis of 15-Year Outcomes in the Elmira Trial of the Nurse-Family Partnership. Prevention Research Center for Family and Child Health, University of Colorado Department of Pediatrics; and Eckenrode, J, et al. “Long-term Effects of Prenatal and Infancy Nurse Home Visitation on the Life course of Youths: 19-Year Follow-up of a Randomized Trial.” Archives of Pediatric and Adolescent Medicine, January 2010, 164(1), 9-15.

17 In 2010, 43 states provided dedicated funding for home visiting, according to Pew Center on the States. (2011, August). “States and the New Federal Home Visiting Initiative: An Assessment from the Starting Line.” Page 7.

18 Schmit, S. et al. (2015, February 9). “Effective, Evidence-Based Home Visiting Programs in Every State at Risk if Congress Does Not Extend Funding.” Center for Budget and Policy Priorities.

19 Lowell, DI, et al. (2011). A randomized controlled trial of Child FIRST: A comprehensive home-based intervention translating research into early childhood practice. Child Development, 82(1), 193-208; Donovan, E.F., et al. (2007). Intensive home visiting associated with decreased risk of infant death. Pediatrics, 119, 1145-1151.

20 Pew Center on the States. (2011, August). “States and the New Federal Home Visiting Initiative: An Assessment from the Starting Line.”

21 Schmit, S. et al. (2015, February 9). “Effective, Evidence-Based Home Visiting Programs in Every State at Risk if Congress Does Not Extend Funding.” Center for Budget and Policy Priorities.

22 U.S. Department of Health and Human Services (2014, June). “Tribal Home Visiting Programs: Review of the Evidence.” HomVee.

23 Administration for Children and Families. “Federal Home Visiting Program National Program Brief.” Health Resources and Services Administration (HRSA).

24 Cook, S. (2015, February 9). “Will Congress Act Quickly to Save Federally-Funded Home Visiting Programs?” New America.

25 Van Pham, H. et al. (2015, May). “Voluntary Home Visiting Data Book: Assessing Need and Access in California.” Next Generation; Washington State Department of Health (2010, September). “Washington State Home Visiting Needs Assessment: Executive Summary.”; Pennsylvania Partnerships for Children (2014, July). “Evidence-Based Home Visiting In Pennsylvania.”

26 National Center on Education Statistics (2015). The Nation’s Report Card. Institute of Education Sciences, U.S. Department of Education, Washington, D.C.

27 U.S. Department of Education (2015, September). “Public high school four-year adjusted cohort graduation rate (ACGR) by race/ ethnicity and selected demographics for the United States, the 50 states and the District of Columbia: School year 2013-14.” National Center for Education Statistics, Institute of Education Sciences; U.S. Census Bureau. “Sex by Age by Educational Attainment for the Population 18 years and over.” 2014 American Community Survey 1-Year Estimates.

28 Bradbury, B., Corak, M., Waldfogel, J. & Washbrook, E. (2015). Too many children left behind. Russell Sage Foundation

29 National Institute for Early Education Research. “The State of Preschool 2014.” See Table 7 on page 18; Kornrich, S., & Furstenberg, F. (2013). Investing in children: Changes in parental spending on children, 1972–2007. Demography, 50(1), 1-23.

30 Hart, B., & Risley, T. R. (2004). The early catastrophe. Education Review, 17 (1), 110-118.

31 Bradbury, B., Corak, M., Waldfogel, J. & Washbrook, E. (2015). Too many children left behind. Russell Sage Foundation

32 Lochner, L., & Moretti, E. (2004). The effect of education on crime: Evidence from prison inmates, arrests, and self-reports. The American Economic Review, 94(1), 155-189.

33 Cohen, M.A., & Piquero, A.R. (2009). New evidence on the monetary value of saving a high risk youth. Journal of Quantitative Criminology.

34 Reynolds, A. J., et al. (2001). Long-term effects of an early childhood intervention on educational achievement and juvenile arrest A 15-year follow-up of low-income children in public schools. JAMA, 285, 2339-2346; Michigan Great Start Readiness Program evaluation 2012: High school graduation and grade retention findings; Schweinhart, L.J., et al. (2005). Lifetime effects: The High/Scope Perry Preschool study through age 40. Ypsilanti, MI: High/Scope Press

35 Campbell, F.A., Pungello, E. P., Burchinal, M., Kainz, K., Pan, Y., Wasik, B., Barbarin, O. A., Sparling, J. J. & Ramey, C. T. (2012). Adult outcomes as a function of an early childhood educational program: An Abecedarian Project follow-up. Developmental Psychology, 48, 1033-1043.

36 Carnevale, A.P., Smith, N. & Strohl, J. (2013, June). “RECOVERY: Job Growth and Education Requirements Through 2020.” Georgetown University, Center on Education and the Workforce.

37 Barnett, W. S., Jung, K., Youn, M., & Frede, E. C. (2013, March 20). Abbott Preschool Program longitudinal effects study: Fifth grade follow-up. New Brunswick, NJ: National Institute for Early Education Research, Rutgers-The State University of New Jersey.

38 9% vs. 15.3%; Reynolds, A. J., et al. (2001). Long-term effects of an early childhood intervention on educational achievement and juvenile arrest. Journal of the American Medical Association, 285, 2339-2380; 7% vs. 35%; Schweinhart, L. J., et al. (1993). Significant benefits: The High/Scope Perry Preschool study through age 27. Ypsilanti, MI: High/Scope Press.

39 Harlow, C. W. (2003, January). Education and correctional populations. NCJ 195670. Washington, DC: U. S. Department of Justice, Bureau of Justice Statistics.

40 CNA. “2014 Population Representation in the Military Services: Appendix B: Table B-6. Non-Prior Service (NPS) Active Component Enlisted Accessions, FY14: by Education, Service, and Gender with Civilian Comparison Group.”; Pew Research Center. “A Profile of the Modern Military: Education Levels.”

41 Theokas, C. (2010). Shut out of the military: Today’s high school education doesn’t mean you’re ready for today’s Army. Washington, DC: The Education Trust.

42 According to the 2013 Qualified Military Available (QMA), based on personal communication with the Accession Policy and Joint Advertising, Market Research and Studies teams at the Department of Defense in July 2014.

43 Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., et al. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, 108(7), 2693-2698.

44 NYC DHMH, 2012; Centers for Disease Control and Prevention (CDC) (2011). “Obesity in K–8 Students — New York City, 2006–07 to 2010–11 School Years.” Morbidity and Mortality Weekly Report; Robert Wood Johnson Foundation (2012); Robbins, J.M. et al., 2012; Kolbo, J.R., et al., 2012.

45 Department of Defense. 2013 Qualified Military Available (QMA). Acquired through personal communication with the Accession Policy and Joint Advertising, Market Research and Studies teams at DoD in July 2014. Also see: Wall Street Journal (2014). Recruits’ Ineligibility Tests the Military.

46 Schweinhart, L. J., Barnes, H. V., & Weikart, D. P. (1993). Significant benefits: The High/Scope Perry Pre-kindergarten study through age 27. Ypsilanti, MI: High/ Scope Press.

47 Barnett, W.S., & Masse, L.N. (2007). Comparative benefit-cost analysis of the Abecedarian program and its policy implications. Economics of Education Review, 26, 113 – 125.

48 Education Commission of the States (2016, January). State Preschool Funding for 2015-16 Fiscal Year: National trends in state preschool funding.

49 National Institute for Early Education Research. “The State of Preschool 2015.”; New America (2015). “Head Start: An Overview.”

50 National Institute for Early Education Research. “The State of Preschool 2015.”

51 Kids Count Data Center (2014). Analysis of U.S. Census Bureau, American Community Survey data from 2011-2013. “The percentage of children ages 3 and 4 who were not enrolled in nursery school or preschool, by poverty status.”

52 The national average cost per child of a high-quality preschool program is estimated at $8,500. National Institute for Early Education Research. “The State of Preschool 2014.” See Table 7 on page 18.

53 Mashburn, A. J., Pianta, R. C., et al. (2008). Measures of classroom quality in prekindergarten and children’s development of academic, language, and social skills. Child development, 79(3), 732-749.

54 U.S. Department of Health and Human Services. U.S. Department of Education. “What Are Preschool Development Grants?”

55 New America (2015). “Head Start: An Overview.”

56 60% of 20- to 39-year-olds in the U.S. are overweight or obese. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311(8), 806-814.

57 Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311(8), 806-814.

58 State of Obesity (2015). “The Healthcare Costs of Obesity.”

59 Department of Defense. 2013 Qualified Military Available (QMA). Acquired through personal communication with the Accession Policy and Joint Advertising, Market Research and Studies teams at DoD in July 2014. Also see: Wall Street Journal (2014). Recruits’ Ineligibility Tests the Military.

60 Ibid

61 12.4% in 2011 vs. 7.7% in 2002 according to: Department of Defense (2013, February). 2011 Health Related Behaviors Survey of Active Duty Military Personnel. TRICARE Management Activity. Fairfax, VA. Smith, TJ, Marriot, BP, White, A, Hadden, L et. al (2013, June). Military Personnel Exhibit a Lower Presence of Obesity than the General U.S. Adult Population. Military Nutrition Division, U.S. Army Research Institute of Environmental Medicine. Natick, MA.

62 Mission: Readiness (2014, September). “Retreat Is Not an Option: Healthier school meals protect children and our country.”

63 Harvard School of Public Health. “Economic Costs: Paying the Price for Those Extra Pounds.”

64 State of Obesity (2015). “The Healthcare Costs of Obesity.”

65 Lindsay, A. C., Sussner, K. M., Kim, J., & Gortmaker, S. (2006). The role of parents in preventing childhood obesity. The Future of children, 169-186; Institute of Medicine (2016). “Parents Can Play a Role in Preventing Childhood Obesity.”

66 Poti, J. M., Duffey, K. J., & Popkin, B. M. (2014). The association of fast food consumption with poor dietary outcomes and obesity among children: is it the fast food or the remainder of the diet?. The American journal of clinical nutrition, 99(1), 162-171; M. Y. Hood et al (2000). “Parental Eating Attitudes and the Development of Obesity in Children: The Framingham Children’s Study,” International Journal of Obesity.

67 Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869–73; Frieden, T.R., Dietz, W., & Collins, J. (2010). Reducing childhood obesity through policy change: Acting now to prevent obesity. Health Affairs, 29(3), 357-363.

68 Chaloupka FJ and Powell LM. (2009). “Price, Availability, and Youth Obesity: Evidence from Bridging the Gap.” Preventing Chronic Disease: Public Health Research, Practice, and Policy, 6(3): 1-6.

69 Ibid

70 Telama R, Yang X, Viikari J, Välimäki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. Am J Prev Med 2005; 28: 267-73.

71 Mullender-Wijnsma, M. J., Hartman, E., de Greeff, J.W., Doolaard, S., Bosker, R. J., & Visscher, C. (2016). Physically active math and language lessons improve academic achievement: a cluster randomized controlled trial. Pediatrics, 137(3), 1-9.

72 Centers for Disease Control and Prevention (2014). School Health Policies and Practices Study. Trends Over Time: 2000-2014. See the “Physical Education and Physical Activity” table on page 3.

73 National Association for Sport and Physical Education. (2009). Physical education trends in our nation’s schools: A survey of practicing K-12 physical education teachers. Port Washington, NY: Roslow Research Group.

74 48% of high school students attended PE in an average week, according to: Center for Disease Control and Prevention (n.d.) “High School Youth Risk Behavior Survey 2013.”

75 Menschik, D., Ahmed, S., Alexander, M.H., & Blum, E.W. (2008). Adolescent physical activities as predictors of young adult weight. Archives of Pediatrics & Adolescent Medicine, 162(1), 29-33.

76 Fox, M.K., Gordon, A., Nogales, R., & Wilson, A. (2008). Availability and consumption of competitive foods in US public schools. Journal of the American Dietetic Association, 109, S57-S66; Johnston, L.D., et al. (2011). School policies and practices to improve health and prevent obesity: National secondary school survey results, school years 2006–07 and 2007–08. Volume 1. Executive Summary. Ann Arbor, MI: Bridging the Gap Program, Survey Research Center, Institute for Social Research.

77 Johnson, D. B., Podrabsky, M., Rocha, A., & Otten, J. J. (2016). Effect of the Healthy Hunger-Free Kids Act on the Nutritional Quality of Meals Selected by Students and School Lunch Participation Rates. JAMA Pediatrics, 170(1).

78 Ibid

79 Lardner, R. (2009, March 11). “Stress injuries rising due to combat loads.” Military Times; Centers for Disease Control and Prevention (n.d.) “Childhood Obesity Facts.”; Anderson, MK, Grier, T, Canham Chervak, M, Bushman, TT & Jones, BH, Army Institute of Public Health. Association of health behaviors and risk factors for injury: A study of military personnel. Poster session presented at: 141st American Public Health Association Annual Meeting and Expo; 2013 Nov 2-6. Boston, MA.

80 Cohen, J. F., Richardson, S., Parker, E., Catalano, P. J., & Rimm, E. B. (2014). Impact of the new US Department of Agriculture school meal standards on food selection, consumption, and waste. American journal of preventive medicine, 46(4), 388-394.

81 USDA (2016, March 23). “School Meal Certification Data: Percent of School Food Authorities certified for the performance-based reimbursement as of December 2015.”

82 Kernea, T.Y. (2016, July 24). “More exercise now law for schools.” Herald-Citizen.

83 Connors, M. (Mar 5, 2016). “New law sets physical activity requirements in Virginia’s elementary schools.” The Virginian-Pilot.

84 Harvard School of Public Health (2003). Obesity as a public health issue: A look at solutions. Boston.