City College of San Francisco Enrollment Projections and Scenarios. Executive Summary For use in the Strategy Sessions

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1 City College of San Francisco Enrollment Projections and Scenarios Executive Summary For use in the Strategy Sessions Prepared by February 2014

2 OVERVIEW This report summarizes enrollment scenarios and projections for City College of San Francisco through the year The methodology is based on calculating enrollments by age, race/ethnicity, and gender for credit and non-credit students and comparing those shares to official California population projections by county. City College has lost significant enrollment between 2010 and 2013; accordingly, one focus of this report is to identify the student demographic categories underlying these declines, thereby specifying age and race/ethnic categories where gains in enrollment might return the College s enrollment to 2010 levels in the short-term. A second focus is on enrollment scenarios, including four choices that City College can make to increase and manage future enrollment. Even given the recent downturn in enrollment, City College of San Francisco continues to serve a significant share of the population in San Francisco County, as well as the surrounding counties of Alameda, Contra Costa, Marin and San Mateo. These shares are especially substantial for the 15 to 29 year old age groups. Long-term projections to 2030 are for strong growth among young age groups (ages 0 to 19). However, most of the growth 15 to 19 age group will occur after City College s enrollment will be most impacted by steep declines in young to middle-aged adult groups (aged 20 to 39 years-old). Forecast changes in demographics between now and 2030 will require targeted efforts in enrollment management. This report models the potential impact of increasing enrollments by five percent for the years 2015, 2020, 2025, and 2030 across all age groups. The cumulative effect could result in credit enrollments of 34,223 and non-credit enrollments of 45,921 for a total of 80,144 enrollments by SCENARIO DEVELOPMENT Five enrollment scenarios for City College presented in this report based on two types of data. The first set of data was obtained from the California Department of Finance (CDF), and represents population projections for San Francisco County and surrounding counties by race and age categories for the years 2010, 2015, 2020, 2025, and The second set of data, provided by City College of San Francisco, contains enrollment data for students grouped by age, race, gender, and credit/non-credit status. 2 These data sets were matched to generate the scenarios below. 1 California Department of Finance (2013, January). Report P-3: Population Projections by Race/Ethnicity, Detailed Age, and Gender, Retrieved December 7, 2013 at 2 Based on the 27,795 credit enrollments and 32,429 non-credit enrollments in fall 2013 for which we had identifiable age and race/ethnicity information. 2

3 HIGHLIGHT OF THE FINDINGS o The City and County of San Francisco accounted for 73 percent of City College s credit enrollments and 80 percent of non-credit enrollments on average between Fall 2010 and Fall The remaining enrollments were from residents from the surrounding cities and counties. o City College of San Francisco experienced significant declines in credit enrollment (20.3%) and non-credit enrollments (30.0%) between fall 2010 and fall o The largest declines in enrollment between fall 2010 and fall 2013 have been in the 15 to 19 year old age group (a 29 percent decline, or 1,636 enrollments) and the 20 to 24 year old age group (a 17 percent decline, representing 1,824 enrollments) and amongst the Asian population (a loss of 3,228 enrollments, or 24 percent decline). o In fall 2013, City College continued to capture significant shares of the local population. In San Francisco County, 8.3 percent of all 15 to 19 year olds, 11.6 percent of all 20 to 24 year olds, and 4 percent of all 25 to 29 year olds were enrolled in credit courses. In the 35 to 59 year age groups, the range was between 1 and 2 percent for credit. o The largest numerical increases are expected in the Asian, Hispanic, and White populations. Among these categories, Hispanics will grow fastest. o Because City College also draws 27 percent of its credit enrollment from surrounding counties, the population changes and market shares for those counties also contribute to future credit enrollment at City College. In San Francisco County, City College has particularly strong non-credit enrollment shares as well, where between 3 and 6 percent of every age cohort in the county is enrolled in non-credit courses. o Unlike the credit category, age groups in the noncredit area are more evenly spread although the noncredit share for San Francisco was largest for the 20 to 24 age group and those over 65 years of age. o The enrollment scenarios indicate that if City College of San Francisco were to retain its fall 2013 enrollment shares, changes in population would result in only roughly 750 additional credit enrollments by 2030, due largely to significant projected declines in the 20 to 39 age groups in San Francisco county. At the same time, noncredit enrollments would be projected to increase by roughly 5,500 owing to projected population increases in older adults. o The greatest increases in enrollment would be realized through a targeted campaign to raise enrollment numbers incrementally by 5 percent in 2015, 2020, 2025, and 2030, for both non-credit and credit courses across all age ranges. By 2030, these actions would increase enrollments to 80,144 (34,223 credit, and 45,921 non-credit) an increase of approximately 20,000 more enrollments over fall 2013 levels. 3

4 DEMOGRAPHIC FORECAST Table 1 shows projected population changes in San Francisco County from 2010 to 2030, as calculated by the California Department of Finance. The projections show modest growth overall through 2030, with a population increase near 9 percent. Overall modest growth belies significant changes in the youth categories, which are expected to increase dramatically, while the young to middle-age adult groups (aged 20 to 39 yearsold) are expected to decline dramatically. Meanwhile the older adult category is also expected to grow substantially. The largest declines are expected in the working age adult categories: a 14 percent decline In the 25 to 29 age group, a 16 percent decline in the 30 to 34 age group, and an almost 24 percent decline in the 35 to 39 age group. In contrast, the 10 to 14 and 15 to 19 age groups are expected to grow by more than 50% from 2010 to Meanwhile, a large increase in retirement aged adults by 2030 is also predicted for San Francisco county, including a 49 percent increase in year olds, a 63 percent increase in year olds, and a nearly 65 percent increase in year olds. Youth population estimates through 2030 are favorable to the College since there will be an increasing pool of young people from which to draw, especially the 15 to 19 age group. However, in the next several years this increase will not be manifest itself quickly enough to increase the overall size of this critical recruitment pool (Table 2). 4

5 Table 1 Population Projections by Age Categories for San Francisco County, 2010 to 2030 Age Range # Change, 2010 to 2030 % Change, 2010 to ,170 45,349 47,321 45,505 43,304 8, % ,092 32,212 40,724 42,086 39,881 11, % ,166 28,310 31,748 40,272 41,336 14, % 15 to 19 35,709 37,425 38,688 43,353 53,662 17, % 20 to 24 61,690 50,794 54,484 55,930 61, % 25 to 29 89,053 84,670 70,375 75,568 76,454-12, % 30 to 34 79,913 82,130 76,411 61,729 67,088-12, % 35 to 39 70,054 71,912 73,436 67,527 53,267-16, % 40 to 44 62,428 65,920 66,709 67,684 61, % 45 to 49 56,938 57,748 59,918 60,240 60,705 3, % 50 to 54 53,774 53,748 53,464 55,334 55,575 1, % 55 to 59 51,466 51,542 50,860 50,519 52, % 60 to 64 45,284 48,888 48,493 48,001 47,498 2, % ,831 42,015 45,023 44,777 44,298 14, % ,448 26,596 37,381 40,119 39,931 15, % ,594 20,580 22,201 31,462 33,878 13, % ,645 35,270 35,551 37,248 46,044 11, % Total 806, , , , ,847 71, % Taken together, Tables 2 and 3 illustrate a critical challenge for City College s over the next several years. That is, the 20 to 24 and 25 to 29 age groups generate nearly half of the College s credit enrollment and a quarter of non-credit enrollment in fall However, both age categories are forecast to decline significantly through the year There will be moderate growth beyond 2015 for the 20 to 24 age group but this critical age group will not return to 2010 levels until 2030 while the 25 to 29 age group is not forecast to recover to 2010 levels throughout the forecast period (Table 1). The 30 to 34 and 35 to 39 age groups will experience a slight gain from 2010 to 2015, but decrease significantly thereafter. Forecasted trends in youth and young adults are depicted in Figures 1 and 2. A collective decrease in the 20 to 29 age groups through 2015 is offset somewhat by increases at the extremes: the 0 to 9 age groups and 65 + age ranges. In the first instance, however, there will be a considerable time lag before these age ranges mature to college entry age and in the second instance, the preference for programming among the elderly obviously will be markedly narrower than the 20 to 29 age groups. We also observe that adults older than 55 years are twice as likely to be enrolled in non-credit programs, suggesting that future recruitment efforts might be most effective for older age cohorts in this area. 5

6 Table 2 Short-Term Population Projections by Age Categories San Francisco County, 2010 to 2015 Age Range # Change, 2010 to 2015 % Change, 2010 to ,170 45,349 10,179 29% ,092 32,212 4,120 15% ,166 28,310 1,144 4% 15 to 19 35,709 37,425 1,716 5% 20 to 24 61,690 50,794 (10,896) -18% 25 to 29 89,053 84,670 (4,383) -5% 30 to 34 79,913 82,130 2,217 3% 35 to 39 70,054 71,912 1,858 3% 40 to 44 62,428 65,920 3,492 6% 45 to 49 56,938 57, % 50 to 54 53,774 53,748 (26) 0% 55 to 59 51,466 51, % 60 to 64 45,284 48,888 3,604 8% ,831 42,015 12,184 41% ,448 26,596 2,148 9% ,594 20,580 (14) 0% ,645 35, % Total 808, ,124 28,854 4% 6

7 Figure 1 Figure 2 7

8 Table 3 City College Shares of Enrollment by Age, Fall 2010 Credit Enrollment Non-credit Enrollment Age Group # Enrollments % of Total # Enrollments % of Total 15 to 19 5, % 3, % 20 to 24 10, % 6, % 25 to 29 5, % 4, % 30 to 34 3, % 4, % 35 to 39 2, % 3, % 40 to 44 1, % 3, % 45 to 49 1, % 3, % 50 to 54 1, % 2, % 55 to % 2, % 60 to % 2, % % 10, % Forecasts by Race/Ethnicity Predicted shifts in the racial and ethnic composition of San Francisco County from 2010 to 2030 also are remarkable (Table 4). The largest numerical increases are expected in the Asian, Hispanic, and White populations. Among these categories, Hispanics will grow fastest. Table 4 Population Projections by Race/ Ethnicity for San Francisco County, 2010 through # Change, 2010 to 2030 % Change, 2010 to 2030 White 338, ,421 18, % Black 46,758 40,770-5, % American Indian 1,808 2, % Asian 268, ,465 24, % Native Hawaiian 3,145 3, % Hispanic 122, ,542 21, % Other 24,780 36,627 11, % TOTAL 806, ,848 71, % 8

9 ENROLLMENT GAP The City and County of San Francisco accounted for 73 percent of City College s credit enrollments and 80 percent of non-credit enrollments on average between Fall 2010 and Fall Because a significant proportion of enrollments in both categories come from outside San Francisco, enrollment shares also were estimated for the other four counties that are part of the San Francisco-Oakland-Hayward, CA Metropolitan Statistical Area as defined by the U.S. Census Bureau: Alameda, Contra Costa, San Mateo, and Marin Counties. Out-of-state and international enrollments were not considered in this report because they constitute only a tiny proportion of overall enrollment at City College. The recent enrollment declines at City College between fall 2010 and fall 2013 might reversible even given the downward shifts in key demographics illustrated above. A prevalent belief is that recent accreditation issues have limited enrollment, a plausible argument. However, declines in the key 20 to 29 age groups also appear to have played a role and these two factors undoubtedly interact. Figure 3 depicts credit and non-credit enrollment numbers by fall and spring semesters between these time periods. Credit enrollments declined by 7,091 during fall 2010 and 2030, or 20.3 percent; in the same period, non-credit enrollments declined by 14,580, or 31 percent (Table 5). Figure 3 9

10 Table 5 Changes in Credit and Non-Credit Enrollments, Fall 2010 to Fall 2013 Fall 2010 Fall 2013 # Difference % Difference Credit 34,886 27,795-7, Non-Credit 47,009 32,429-14, Shifts in Age and Race/Ethnicity Table 6 shows where enrollments declined the most between fall 2010 and fall This table depicts credit and enrollment patterns by term by race/ethnicity age groups, and credit/noncredit status... Of the 7,091 lost credit enrollments, nearly half (3,228) are attributable to the Asian population; White enrollments declined by nearly 2,000 (Table 6). Of the 14,582 lost non-credit enrollments, nearly a third was Asian (Table 7). Table 6 Changes in Credit Enrollments by Age and Race/Ethnicity, Fall 2010 to Fall 2013 Age Group Total 15 to 19-1,636 (-29%) 20 to 24-1,824 (-17%) 25 to (-16%) 30 to (-20%) 35 to (-24%) 40 to (-25%) 45 to (-26%) 50 to (-20%) 55 to (-20%) 60 to (-23%) (2%) Total -7,127 (-20%) African American -204 (-39%) -217 (-26%) -96 (-20%) -75 (-23%) -33 (-14%) -64 (-31%) 0 (0%) -9 (-6%) -5 (-4%) +17 (31%) +13 (45%) -673 (-21%) Asian -756 (-31%) -1,153 (-24%) -242 (-12%) -252 (-21%) -216 (-28%) -171 (-29%) -156 (-35%) -111 (-29%) -86 (-32%) -50 (-24%) -35, (- 256%) -3,228, (-24%) Hispanic/ Latino -250 (-18%) +135 (7%) -10 (-1%) -128 (-18%) -44 (-0.1%) -27 (-9%) -53 (-20%) -34 (-18%) -13 (-12%) -5 (-8%) -3 (-9%) -432 (-7%) White -238 (-35%) -480 (-24%) -410 (-23%) -152 (-15%) -196 (-28%) -149 (-25%) -142 (-28%) -64 (-14%) -54 (-15%) -104 (-31%) +38 (15%) -1,951 (-22%) Multi-race / Other -166 (-28%) -59 (-7%) -172 (-33%) -109 (-34%) -65 (-37%) -46 (-40%) -42 (-45%) -25 (-34%) -31 (-46%) -21 (50%) -8 (-20%) -744 (-26%) 10

11 Table 7 Changes in Non-credit Enrollments by Age and Selected Race/Ethnicity, Fall 2010 to Fall 2013 Age Group Total Total -2130, (-39.78%) -3036, (-56.43%) -1248, (-73.07%) -1121, (-72.05%) -1131, (-69%) -945, (-72%) -979, (-68.73%) -714, (-74.45%) -461, (-81.51%) -181, (-91.42%) -2636, (-73.64%) -14,582, (-31.22%) African American -306, (-75%) -287, (-56.16%) -102, (-37.92%) -78, (-38.05%) -49, (-30.06%) -52, (-34.9%) -57, (-34.97%) -67, (-38.73%) -15, (-10.87%) -36, (-31.58%) -154, (-30.32%) -1203, (-42.95%) Asian -875, (-66.29%) -1277, (-46.48%) -239, (-17.53%) -254, (-20.03%) -415, (-27.88%) -365, (-22.63%) -556, (-31.7%) -399, (-25.36%) -239, (-16.45%) +30, (2.63%) -318, (-9.2%) -4907, (-25.59%) Hispanic/ Latino -575, (-50.71%) -931, (-38.63%) -650, (-32.27%) -466, (-28.75%) -299, (-25.25%) -247, (-26.05%) -140, (-20.26%) -147, (-24.58%) -46, (-11.98%) -11, (-4.68%) -8, (-1.54%) -3520, (-29.99%) White -156, (-64.73%) -300, (-44.18%) -170, (-29.31%) -160, (-36.53%) -183, (-51.7%) -140, (-45.75%) -122, (-47.29%) -60, (27.03%) -79, (-33.76%) -138, (-40.47%) -476, (-28.78%) -1984, (-37.38%) Multi-race / Other -187, (-47.95%) -206, (-37.12%) -80, (-21.45%) -161, (-35.86%) -187, (-42.02%) -139, (-40.52%) -104, (-41.6%) -38, (-17.51%) -77, (-28.52%) -23, (-8.59%) -1678, (-43.68%) -2880, (-38.91%) The largest percentage loss in credit enrollments (29%, n= 1,636) came in the 15 to 19 age group. The largest drop in absolute numbers in credit enrollments was in the 20 to 24 age group (1,824, 17%). There is a sense of urgency in these drops since the 15 to 19 age group is forecast for only modest growth to 2015 while the 20 to 24 age group is estimated to decline significantly (Table 2). In the non-credit area, the largest decline among age groups was in the 20 to 24 year old cohort (n=3,036, Table 7). There also were also large drops in the age group (n=2,130) and in the 65+ age group (n=2,636). If City College of San Francisco is to regain the credit and non-credit enrollments it has lost in the last three years, the focus should be on prime college-aged students (15 to 24) as well as to Asians and Whites. The older adult category also accounts for a significant number of non-credit enrollments. Predictions for growth in this age category may spell opportunity for City College. A return to 2010 enrollment levels means an increase of 20% in credit enrollments across age cohorts, and an increase of 31% in noncredit enrollments. 11

12 CREDIT AND NON-CREDIT MARKET SHARE Shares are calculated by dividing the enrollments for each age, race/ethnicity, and gender category by the corresponding population estimates for These enrollment shares represent the proportion of the population in each category that enrolled in City College of San Francisco in Fall Current enrollment shares are shown for credit and non-credit enrollment for San Francisco County and for Alameda, Contra Costa, San Mateo, and Marin Counties. 3 City College s enrollment downturn between 2010 and 2013 notwithstanding, these tables indicate that. 8.3 percent of all 15 to 19 year olds in San Francisco were enrolled in credit courses at City College in 2013; 11.6 percent of all 20 to 24 year olds were enrolled; and between 2.5 and 4 percent of year olds were enrolled for credit. In the 35 to 59 year age groups, the range was between 1 and 2 percent for credit. Unlike the credit category, age groups in the noncredit area are more evenly spread although the noncredit share for San Francisco was largest for the 20 to 24 age group and those over 65 years of age. Table 8 Credit Enrollment Shares for San Francisco County, Fall 2013 African Age Range TOTAL American American Indian/ Alaskan Native Asian Hispanic/ Latino Pacific Islander White Multi-race / Other 15 to % 8.65% 5.87% 10.63% 10.20% 7.80% 2.35% 19.31% 20 to % 10.88% 4.39% 17.15% 11.36% 11.78% 5.29% 24.78% 25 to % 8.20% 7.83% 6.39% 4.52% 7.39% 2.14% 7.29% 30 to % 5.65% 5.85% 3.21% 2.83% 9.06% 1.78% 5.36% 35 to % 4.67% 1.86% 2.17% 2.17% 3.50% 1.17% 3.95% 40 to % 3.05% 6.69% 1.67% 1.91% 2.62% 1.18% 3.08% 45 to % 3.79% 4.90% 1.28% 1.74% 2.66% 1.14% 3.22% 50 to % 2.45% 3.73% 1.10% 1.60% 2.42% 1.40% 3.58% 55 to % 2.32% 4.96% 0.73% 1.24% 1.65% 1.20% 3.41% 60 to % 1.69% 0.00% 0.61% 0.81% 1.58% 0.96% 2.58% % 0.44% 2.33% 0.17% 0.23% 0.00% 0.51% 1.95% 3 Previously defined as the San Francisco-Oakland-Hayward, CA Metropolitan Statistical Area: Alameda, Contra Costa, Marin, and San Mateo Counties. We subtract San Francisco County from the MSA as it is considered separately. 12

13 Age Range Table 9 Non-Credit Enrollment Shares for San Francisco County, Fall 2013 TOTAL African American American Indian/ Alaskan Native Asian Hispanic/ Latino Pacific Islander White Multi-race / Other 15 to % 3.11% 1.47% 2.88% 5.82% 2.60% 0.54% 10.62% 20 to % 4.55% 0.63% 7.37% 9.65% 5.44% 1.46% 12.46% 25 to % 4.22% 3.69% 4.44% 7.02% 3.84% 0.65% 6.72% 30 to % 3.64% 4.10% 4.01% 6.71% 5.29% 0.56% 8.41% 35 to % 3.39% 2.49% 4.77% 6.24% 2.72% 0.41% 10.05% 40 to % 2.41% 7.36% 5.85% 5.83% 0.87% 0.42% 9.42% 45 to % 2.41% 3.26% 5.85% 5.83% 2.66% 0.48% 9.82% 50 to % 2.00% 2.13% 5.29% 5.52% 2.42% 0.63% 15.15% 55 to % 2.56% 2.48% 5.30% 4.68% 2.76% 0.65% 19.96% 60 to % 1.96% 2.26% 5.30% 4.12% 3.16% 0.92% 27.19% % 3.91% 5.24% 5.58% 4.08% 0.74% 2.24% 80.93% The 20 to 24 year age group is the modal value for counties outside of San Francisco enrolled in City College s credit program (Table 10). No single age group outside of San Francisco dominates non-credit instruction (Table 11). Table 10 Credit Enrollment Market Shares for Surrounding Counties, Fall 2013 African Age Range TOTAL American American Indian / Alaskan Native Asian Hispanic / Latino Pacific Islander White Multi-race / Other 15 to % 0.55% 0.00% 0.67% 0.48% 1.17% 0.25% 0.87% 20 to % 0.95% 0.65% 1.51% 1.08% 1.42% 0.73% 2.36% 25 to % 0.66% 0.67% 0.87% 0.49% 0.75% 0.51% 1.17% 30 to % 0.39% 0.82% 0.44% 0.21% 0.48% 0.24% 0.81% 35 to % 0.36% 0.53% 0.19% 0.14% 0.15% 0.14% 0.36% 40 to % 0.25% 0.48% 0.14% 0.13% 0.30% 0.09% 0.24% 45 to % 0.28% 0.00% 0.10% 0.11% 0.10% 0.07% 0.19% 50 to % 0.22% 0.24% 0.09% 0.09% 0.10% 0.07% 0.22% 55 to % 0.11% 0.00% 0.07% 0.06% 0.06% 0.05% 0.19% 60 to % 0.07% 0.17% 0.08% 0.06% 0.15% 0.03% 0.11% % 0.02% 0.09% 0.02% 0.01% 0.00% 0.02% 0.13% 13

14 Table 11 Non-credit Enrollment Market Shares for Surrounding Counties, Fall 2013 Age Range TOTAL African American American Indian / Alaskan Native Asian Hispanic / Latino Pacific Islander White Multi-race / Other 15 to % 0.13% 0.18% 0.14% 0.17% 0.18% 0.04% 0.27% 20 to % 0.26% 0.00% 0.46% 0.44% 0.47% 0.15% 0.73% 25 to % 0.21% 0.51% 0.32% 0.33% 0.21% 0.15% 0.79% 30 to % 0.09% 0.16% 0.23% 0.24% 0.26% 0.07% 0.76% 35 to % 0.10% 0.71% 0.15% 0.22% 0.05% 0.04% 0.61% 40 to % 0.12% 0.16% 0.16% 0.19% 0.00% 0.03% 0.58% 45 to % 0.12% 0.00% 0.19% 0.14% 0.15% 0.02% 0.43% 50 to % 0.11% 0.00% 0.21% 0.14% 0.00% 0.02% 0.53% 55 to % 0.09% 0.00% 0.23% 0.14% 0.06% 0.02% 0.72% 60 to % 0.06% 0.00% 0.26% 0.13% 0.00% 0.02% 1.89% % 0.16% 0.36% 0.35% 0.15% 0.13% 0.04% 18.87% 14

15 ENROLLMENT SCENARIOS Based on the shares for Fall 2013 for credit and noncredit enrollment, we simulate what enrollments could look like at City College of San Francisco in in five year increments through the year 2030 for five scenarios: (A) if all current enrollment shares remain constant to 2030; (B) if the enrollment levels of year olds were increased between 2013 and 2030; (C) if enrollment levels of working aged adults aged were increased; and (D) if enrollment levels of older adults (55+) were increased. Finally, scenario E is cumulative and depicts the result of City College increasing market share across all scenarios. Scenario A: Baseline. The baseline or status quo scenario is based on Fall 2013 shares of students by age, race/ethnicity, gender, and credit/non-credit status compared to official CDF projections. This scenario assumes that City College will not increase its current penetration rates of available population segments through In other words, population shifts would dictate future enrollments without any changes in targeted recruitment or improvements in student retention strategies by the City College. Scenario B: Increase Youth (all races). In this scenario, credit and noncredit shares for youth ages 15 to 24 years are increased over the 2013 baseline by 5 percent for these year increments: 2015, 2020, 2025, and This scenario models the effect of increased recruitment of youth and provides City College a benchmark from which to gauge whether its efforts to recruit young are successful. Scenario C: Increase Working Aged Adults (all races). In this scenario, credit and noncredit shares for working aged adults (ages 24 to 54) are increased by 5 percent for these year increments: 2015, 2020, 2025, and This scenario models what enrollment would look like based on increased recruitment efforts of working aged adults and provides City College a benchmark from which to gauge whether its efforts to recruit this age range are successful. Scenario D: Increase Older Adults (all races). This scenario shows what increased recruitment of older adults (55 and over) would do to credit and noncredit enrollments if their share were increased by 5 percent for these year increments: 2015, 2020, 2025, and This scenario models what enrollment would look like based on increased efforts to enroll older adults in credit and non-credit programs and provides City College a benchmark from which to gauge whether its efforts to recruit in this age range are successful. Scenario E: Cumulative. Scenarios B through D are combined with the baseline scenario to illustrate the effect of cumulative recruitment in credit and non-credit programs across youth, working-aged adults, and older adults by 5 percent for these year increments: 2015, 2020, 2025, and This scenario models what enrollment would look like based on overall increased efforts to increase enrollment for all ages across credit and non-credit programs and provides City College a benchmark from which to gauge whether its overall efforts to recruit students are successful. 15

16 Scenario A: Baseline Table 12 represents a baseline scenario for credit and non-credit enrollment estimates through The enrollments projected in this table assume that no special efforts will made to recruit students in any age group or by credit versus noncredit status. It is, therefore, a status quo projection. Because of overall growth overall in San Francisco County predicted by the California Department of Finance, coupled with significant projected population declines in the 24 to 39 age groups by 2030 (see Table 1), credit enrollments are estimated to increase by approximately 750 students through 2030, a small amount. Non-credit enrollments are predicted to increase by 5,657, largely due to increases in the older adult populations and their market share in non-credit enrollments. Credit Enrollments Non-credit Enrollments Table 12 Scenario A: Status Quo Age Range ,048 4,093 4,456 5, ,547 8,385 8,565 9, ,826 4,404 4,352 4, ,946 2,768 2,469 2, ,793 1,853 1,738 1, ,380 1,408 1,451 1, ,137 1,149 1,171 1, ,028 1,017 1,033 1, Total 27,548 26,997 27,207 28,538 Age Range ,422 1,480 1,655 1, ,753 3,584 3,660 3, ,276 2,909 2,780 2, ,947 2,840 2,467 2, ,626 2,827 2,688 2, ,452 2,630 2,803 2, ,188 2,338 2,504 2, ,109 2,094 2,267 2, ,046 2,081 2,084 2, ,976 2,070 2,133 2, ,001 9,577 11,137 12,579 Total 32,796 34,429 36,179 38,086 Total Enrollment, Credit and Non-Credit 60,344 61,426 63,386 66,625 16

17 Scenario B: Increasing Youth Enrollments (15 to 24) Table 13 represents the potential results for increasing credit and non-credit enrollments by five percent by each of the years 2015, 2020, 2025, and 2030 for youth categories (15 to 19 and 20 to 24 age groups). This would produce a total enrollment increase of more than 10,500 enrollments (3,631 additional credit enrollments, and nearly 7,000 additional non-credit enrollments) over 2013 levels by While the 15 to 24 year old group represents a large portion 46 percent, of fall 2013 enrollments the 20 to 24 year old group is projected to decline by 2030 (Table 1). Table 13 Scenario B: 5% Incremental Increases in Youth (15 to 24 years) Enrollment Age Range ,251 4,512 5,159 6, ,974 9,244 9,915 11, ,826 4,404 4,352 4, ,946 2,768 2,469 2, ,793 1,853 1,738 1,535 Credit Enrollments ,380 1,408 1,451 1, ,137 1,149 1,171 1, ,028 1,017 1,033 1, Total 28,178 28,276 29,260 31,426 Non-credit Enrollments Age Range ,493 1,632 1,916 2, ,940 3,951 4,237 4, ,276 2,909 2,780 2, ,947 2,840 2,467 2, ,626 2,827 2,688 2, ,452 2,630 2,803 2, ,188 2,338 2,504 2, ,109 2,094 2,267 2, ,046 2,081 2,084 2, ,976 2,070 2,133 2, ,001 9,577 11,137 12,579 Total 33,055 34,948 37,017 39,351 Total Enrollment, Credit and Non-Credit 61,233 63,224 66,277 70,778 17

18 Scenario C: Increase Working Aged Adults 25 to 54 Table 14 is a scenario that increases credit and non-credit enrollments by five percent by each of the years 2015, 2020, 2025, and 2030 for working aged adults (combining the 25 to 54 year age groups). This would produce a total enrollment increase of 12,065 enrollments over 2013 levels by This is a larger combined enrollment gain over Scenario B (the increase in the youth cohort) because more people in the working adult group tend to enroll in non-credit courses, At the same time, credit enrollments would be slightly lower than for the youth cohort increase (B), largely because the cohort represents a 46 percent share of overall credit enrollments. Table 14 Scenario C: 5% Incremental Increases in Working Aged Adult (25 to 54 years) Enrollments Age Range ,048 4,093 4,456 5, ,547 8,385 8,565 9, ,067 4,855 5,038 5, ,093 3,052 2,858 2, ,883 2,043 2,012 1,839 Credit Enrollments ,448 1,553 1,680 1, ,194 1,267 1,355 1, ,080 1,121 1,195 1, Total 28,204 28,288 29,132 30,924 Age Range ,422 1,480 1,655 1, ,753 3,584 3,660 3, ,868 3,207 3,218 3, ,568 3,131 2,856 2, ,144 3,117 3,112 2,823 Non-credit Enrollments ,938 2,899 3,245 3, ,666 2,578 2,899 3, ,528 2,308 2,625 2, ,046 2,081 2,084 2, ,976 2,070 2,133 2, ,001 9,577 11,137 12,579 Total 33,909 36,032 38,624 41,364 Total Enrollment, Credit and Non- Credit 62,113 64,320 67,756 72,289 18

19 Scenario D: Increase Older Adults (55 and over) Table 15 represents the scenario for increasing credit and non-credit enrollments by five percent by each of the years 2015, 2020, 2025, and 2030 for youth categories (15 to 19 and 20 to 24 age groups). This would produce a total enrollment increase of 10,102 over 2013 levels by A five percent increase in older adults would produce a nearly 14,000 headcount count increase in non-credit enrollments, but only a negligible gain (less than 400) in credit enrollments. This is because the majority of enrollments in this age cohort are of the non-credit variety rather than in credit enrollments. Table 15 Scenario D: 5% Incremental Increases in Older Adult (55+) Enrollments Age Range ,048 4,093 4,456 5, ,547 8,385 8,565 9, ,826 4,404 4,352 4, ,946 2,768 2,469 2, ,793 1,853 1,738 1,535 Credit Enrollments ,380 1,408 1,451 1, ,137 1,149 1,171 1, ,028 1,017 1,033 1, Total 27,640 27,194 27,518 28,948 Age Range ,422 1,480 1,655 1, ,753 3,584 3,660 3, ,276 2,909 2,780 2, ,947 2,840 2,467 2,357 Non-credit Enrollments ,626 2,827 2,688 2, ,452 2,630 2,803 2, ,188 2,338 2,504 2, ,109 2,094 2,267 2, ,292 2,295 2,413 2, ,201 2,282 2,469 2, ,954 10,559 12,893 14,917 Total 29,220 35,837 38,599 41,378 Total Enrollment, Credit and Non-Credit 56,860 63,030 66,118 70,326 Scenario E: Cumulative Scenario 19

20 Table 16 represents the sum of Scenarios B through D. In other words it represents the result of increasing credit and non-credit enrollments by five percent by each of the years 2015, 2020, 2025, and 2030 for all age categories. This would produce a total enrollment increase of nearly 20,000 by An incremental increase across all age cohorts would produce the greatest enrollment gains for City College of San Francisco by 2030, with credit enrollments at 34,223, almost 5,700 more than in the baseline scenario. Non-credit enrollments would increase to 45,921, approximately 7,800 additional enrollments over the baseline scenario. Table 16 Scenario E: 5% Incremental Increases Across All Age Groups Age Range ,251 4,512 5,159 6, ,974 9,244 9,915 11, ,067 4,855 5,038 5, ,093 3,052 2,858 2, ,883 2,043 2,012 1,839 Credit Enrollments ,448 1,553 1,680 1, ,194 1,267 1,355 1, ,080 1,121 1,195 1, Total 28,926 29,764 31,496 34,223 Age Range ,493 1,632 1,916 2, ,940 3,951 4,237 4, ,440 3,207 3,218 3, ,094 3,131 2,856 2,865 Non-credit Enrollments ,758 3,117 3,112 2, ,575 2,899 3,245 3, ,297 2,578 2,899 3, ,215 2,308 2,625 2, ,148 2,295 2,413 2, ,075 2,282 2,469 2, ,401 10,559 12,893 14,917 Total 34,436 37,958 41,882 45,921 Total Enrollments, Credit and Non-Credit 63,361 67,722 73,378 80,144 20

21 STRENGTHS AND LIMITATIONS Strengths. This study uses a market share approach to calculating enrollment projections as explained earlier. These projections, therefore, can be updated as new demographic data become available from the California Department of Finance or other sources. Because these projections are based on stable projective third-party data used throughout California, they are stronger than other methodologies. The market share approach also lends itself to modeling the effect of decisions a college can make to increase the proportion of enrollment in key demographic segments. As the City College of San Francisco gains experience in penetrating demographic shares using the data within this report as a starting point, it will be clear which enrollment management strategies are successful. Finally, because enrollment management is a continuous process, the College is encouraged to use the data presented here as a baseline from which to judge the success of future efforts. Periodic adjustments will be necessary as new population estimates become available and as the College monitors the effectiveness of new enrollment management strategies. Limitations. This study is limited to a degree by its use of official California state projections for future growth, which may differ from other research 4. The accuracy of population estimates from any source is subject to future economic and political events that may create different outcomes. The analyses and simulations presented here do not factor the impact of increased rates of student success. That is, the scenarios offered here might produce higher enrollment than is estimated in this report if current rates of student success were to rise. 4 The Association of Bay Area Governments, for example, has recently estimated that San Francisco County will swell to 1 million by 2030; however CDF data do not support this projection. 21