The Impact of Career Colleges on the Minnesota Economy A Report Provided to the Minnesota Career College Association by New Pharos Consulting January, 2012 New Pharos Consulting Public Policy Research - Data Analytics and Reporting Table of Contents Executive Summary ...................................................................................................................................... 3 Introduction and Study Purpose .................................................................................................................... 6 List of MCCA Institutions ............................................................................................................................ 7 Budgetary, Economic and Demographic Conditions in Minnesota .............................................................. 8 Program Inventory ...................................................................................................................................... 24 Student Demographic, Socio-Economic and Academic Characteristics ................................................... 28 Returns to Post Secondary Education ......................................................................................................... 34 Economic Impact Analysis ......................................................................................................................... 36 Appendices.................................................................................................................................................. 44 New Pharos Consulting Page 2 Executive Summary Career colleges in Minnesota play an important role in the post secondary system in the state of Minnesota. Some of these institutions have a long tradition that is decades old. These operations have an important impact on the Minnesota economy. A group of these institutions, through the Minnesota Career College Association, have sponsored an analysis of this impact. This executive summary provides a synopsis of the findings of this analysis. Higher Education Environmental Background Economic impact analysis without an environmental context is useful, but a bit barren. The first substantive section of the report describes the budgetary, economic and demographic conditions in Minnesota. The major findings of this analysis are: o State spending on higher education for the public institutions and programs like the State Grant that serve both the public and private sector, as a share of the state general fund budget, has been declining for the last two decades. o Pressures from health care spending driven by an aging population coupled with much lower revenues as people retire will create dramatic tensions between various budget areas. o Population change in the next seven to 10 years in prime age groups (ages 17 to 25) is negative and while Minnesota has a net in migration of educated people, the numbers are not large enough to create competitive issues for career college graduates. o Percent increases in tuition and fees over the last decade are lower at career colleges than other sectors. Public sector institutions experience the highest increase. The range between low and high levels of tuition and fees within any particular sector are much smaller at public institutions. Net prices, after all financial aid, are lower at MCCA schools than MPCC institutions, although still quite a bit higher than public institutions. o Health care, technology and educational services are the leading industries in job growth through 2019. o These same fields lead occupational growth during this same seven to 10 year period. o Even with relatively aggressive assumptions from the Georgetown study about educational attainment needs, the state may be producing more associate degrees than will be needed by the economy and is about in line with bachelor degree production. The market may be in certificate production. Program Inventory MCCA institutions offer a variety of programs across a spectrum of study areas. At the same time there is a marked focus on business and health related programs. An inventory of these programs, combined with data on the number of awards made by the institutions suggests the following findings: o o o Of the 38 major program areas identified by the federal government MCCA institutions offers programs in 15. MCCA institutions provided about 5.6 percent of degree awards in Minnesota in 2009-10. ISEEK reports that MCCA school offer courses in 356 different program areas. New Pharos Consulting Page 3 Student Demographic Background Student demographics are an important metric for any single institution or set of institutions. The report presents 21 different variables to describe students attending for-profit institutions. The major findings of this analysis are: o The student population at for-profit schools tends to be more female than male. The for-profit schools have 8.4 % of the population but 10.0 % of the female attendees in the state. o The student population contains a larger share than average of individuals indicating they posses some type of disability. o The student population at for-profit schools is significantly more likely to have dependent children. o Students are much more likely to be independent, again often with children. o Students with dependent children are more likely to be single parents. o The student population at for-profit schools is less likely to be white and much more likely to be black or Hispanic. o These students tend to be from families where parents, relatively speaking, are not as well educated, i.e., they are more likely than students in other sectors to be first generation students. o For-profit schools have a significantly larger veteran population. o The student population is relatively older, more typically 25 years and up. o The student population is more likely to attend part time, part year or a combination rather than full time or full year. o The student population is somewhat more likely to have larger credit card debt. o There is a larger share of "income as an independent student," in large part since there are more independent students at these schools. o The parent income for dependent students is at the low end of the income distribution. o The student population is more likely to be enrolled in a certificate or associate program. Although many programs are offered at the BA level, enrollment tends to be in programs less than four years. o The student population is more likely to be in business or health programs. o The primary academic goal indicated by students is a certificate, associate's degree and to some extent a bachelor's degree. o Students are more likely to consider themselves to be primarily employed full time and attending school part time. o As employees first, these students are more likely to work three quarters or full time. o Because they work longer hours, job earnings tend to be higher than for students in other sectors. o Similarly, overall income tends to be higher for these students. Returns to Post Secondary Education Annual income differences between a high school graduate and an associate's degree and bachelor's degree respectively for Minnesota are determined from the American Community Survey. When lifetime figures are computed and adjusted for projections of real wage growth from the Congressional Budget Office, a person with an associate's degree will earn an additional $570,000 and one with a bachelor's degree will earn an additional $1.4 million compared to the high school graduate. The study reports rates of return to post secondary education. The private rate of return to post secondary education is substantial. Estimates for four year degrees are about 12 percent. Estimates for twoyear degrees, while subject to some disagreement, are about eight percent. New Pharos Consulting Page 4 Economic Impact Analysis The focal point of this report is the evaluation of the economic impact on the state of Minnesota attributable to career colleges. Economic impact takes two forms, the current activity of the institutions and equally important, the impact of the schools on the development of human capital and the increase in earnings for students. This activity has an impact on state and local taxes. The major findings of this analysis are: o Career colleges account for about $1.309 billion annually in total output in the state as measured by income. This includes $595 million directly and $714 million indirectly after all multiplier impacts are taken into account. o Career colleges directly and indirectly produce about 14,900 jobs annually in the state. o Career colleges directly and indirectly produce almost $450 million in earnings annually in the state. o These earning produce about $21 million annually in state income tax revenue. They produce an additional $9 million in sales taxes. o Career colleges directly and indirectly produce nearly $750 million in value added annually in the state. o Career colleges pay about $7.6 million annually in property taxes to state and local government. They provide an addition $4.3 million annually in other state taxes. o Summing the various tax estimates results in total taxes of about $42 million annually. o MCCA institutions produce about 66 percent, or two thirds of the output in the state. These same institutions likewise are responsible for 66 percent of the jobs, earnings and other measures. o Exploratory analysis suggests each year new graduates of career colleges may earn about $300 million more in income than they would had they not gone on to receive a post secondary degree. This produces additional $14 million in income tax revenue for the state. New Pharos Consulting Page 5 Introduction and Study Purpose Career colleges in Minnesota play an important role in the post secondary system in the state of Minnesota. Some of these institutions have a long tradition that is decades old. They provide a set of educational services that meets the needs of hundreds of Minnesota students and families each year and the test of the market every day. While roles can be measured in many ways, one important perspective is the impact of current operations and the development of human capital on the Minnesota economy attributable to these institutions. An advocacy group, the Minnesota Career College Association (MCCA), has engaged New Pharos Consulting to analyze and evaluate this impact. This report is the result of that work. The report also serves a second purpose to member institutions. The document contains substantial background information, including projections of labor force needs and demographics that will help institutions in the planning process. The report is structured in the following way. Section one is an executive summary of the results of the analysis. The second section is a list of the member institutions of MCCA. Sections three, four and five provide an environmental background for the impact analysis. Section three of the study describes the budgetary, economic and demographic conditions in Minnesota. The state budget matters for higher education. Economic and demographic conditions establish the marketplace for higher education. The material covered includes information on the state's budget for higher education, an analysis of general economic and demographic trends, tuition and fee pricing among higher education sectors, industry forecasts and occupational demand projections through 2019 from the Bureau of Labor Statistics and the Department of Employment and Economic Development. These occupation demand projections serve in turn as a basis for the educational requirements needed by the labor force. MCCA institutions offer a variety of programs across a spectrum of study areas. An inventory of these programs, combined with data on the number of awards made by the institutions, is presented in section four. Student demographic, socio-economic and academic characteristics reveal important information about how institutions operate as well as their relative position in the market place. Section five presents data on students in the for-profit sector in Minnesota. Sections six and seven provide the economic impact analysis. Section six of the study describes earnings differences and rates of return to post secondary education. It is a well established fact that people with higher levels of education on average make more income annually than those without this training. The final section of the report describes the impact of career colleges on the Minnesota economy. There are two major sources of this impact, current economic activity and the change in income from the investment in human capital. There are three appendices, the first describing the consultant's background and two with detailed tables noted in the text. A note on institutional labeling; the terms "career college" and "for-profit institution" are used interchangeably in the report. This simply reflects the different labels applied to the same set of institutions by various educational and governmental entities. New Pharos Consulting Page 6 List of MCCA Institutions Academy College Bloomington Art Institutes Intl Minnesota Minneapolis Brown College Mendota Heights DeVry University Edina Duluth Business University Duluth Globe University Woodbury and Minneapolis Herzing University Minneapolis Institute of Production and Recording Minneapolis ITT Technical Institute Eden Prairie, Brooklyn Center and Woodbury Le Cordon Bleu College of Culinary Arts Mendota Heights McNally Smith College of Music Saint Paul Minneapolis Business College Roseville Minnesota School of Business Blaine, Brooklyn Center, Elk River, Lakeville, Moorhead, Plymouth, Richfield, Rochester, Shakopee, St. Cloud National American University Brooklyn Center, Bloomington, Roseville, Minnetonka Northwest Technical Institute Eagan Rasmussen College Blaine, Brooklyn Park, Eagan, Bloomington, Lake Elmo/Woodbury, Moorhead, St. Cloud, Mankato New Pharos Consulting Page 7 Budgetary, Economic and Demographic Conditions in Minnesota Introduction and Summary Economic and demographic conditions establish the marketplace for higher education. The state budget for higher education has a large influence on institutional operations. This section contains a broad range of data describing the budgetary, economic and demographic conditions in Minnesota. The first part presents information on the state's budget for higher education spending and describes how this spending may be influenced in the future. The data presentation draws on work by national experts and the state's economist and demographer. The following parts include an analysis of general economic and demographic trends, tuition and fee pricing among higher education sectors, industry forecasts and occupational demand projections through 2019 from the Bureau of Labor Statistics and the Department of Employment and Economic Development. These demand projections serve as a base for the analysis of degree requirements in the future. Short and Long Term State Budget Environment The 2011 session concluded after the Legislature passed 12 separate budget bills in a special session. This action addressed the immediate problem of funding for the 2012- 2013 biennium and established base funding for the 2014-15 biennium. Longer term state budget pressures are also an important consideration for strategic planning. This section of the study presents information on both the short to medium term funding for higher education and conditions of the longer term environment. Short Term Budget Environment The legislature completed work on the appropriation bills for 2012-13 in July of 2011. The total budget for higher education funding, including the three major program areas-the University of Minnesota, the MnSCU system and the State Grant program-was reduced from the base level by $351 million. All of this reduction was taken from the two public systems which received decreases from base levels of about $193 million at the University and $171 million at MnSCU. The State Grant program actually received an increase of $10.5 million each year with a smaller increase of about $800,000 in the Work Study program. While the additional State Grant funding was higher than the base, it was about $10 million lower than was needed to fully fund the program at expected demand levels. This fact changed with a new forecast of the State Grant in November that shows a surplus for the biennium of about $6 million. Higher education has not been a funding priority of the state for many years. Chart 1 below shows higher education spending as a share of the total general fund budget. The share peaked in the late 1980s, although there was some minor turn in 2006 and 2007. New Pharos Consulting Page 8 Chart 1 State Spending on Higher Education as a Share of all Spending 20% 18% 16% 14% 12% 10% 8% 6% 1959 1965 1971 1977 1983 1989 1995 2001 2007 2013 Funding in the 2014-15 planning numbers is held flat at 2013 appropriated levels. This fact will continue the downward shift in the share of general fund spending for higher education, as other budget areas experience planned increases. While these are planning numbers and not appropriations, it will take affirmative action to improve the levels. These budget levels were decided with revenue numbers projected in the February, 2011 forecast. A new forecast was issued December, 2011 indicating the state would have a substantial surplus the remainder of the biennium. It is highly unlikely that any higher education programs will face funding decreases in the 2012 session. Long Term Budget Issues Tom Gillaspy, the state demographer, and Tom Stinson, the state economist, have developed a long term perspective of the budget environment for the state. There are several selected charts below from their presentation that provide the general picture of this budget environment. It is not a positive picture for higher education. As shown in Chart 2, the historical age distribution of the population has changed over the last three decades. As a percentage share of total population, people between 20 and 40 is lower. All of the age cohorts over 40 years of age are larger. People at older age cohorts have different demands on the public sector- typically human service demands - than younger people. New Pharos Consulting Page 9 Chart 2 The Age Distribution of the Population Has Changed Medium term projections through 2020 and longer term projections through 2060 indicate this pattern will continue. Most of the growth in the population will occur in the older cohorts with low growth in school age children and flat to declining change in 18 to 24 year olds, a group important to higher education. Chart 3 More in the 65 and Older Group than in School Age by 2020 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 18-24 65+ 5-17 This age distribution of population growth has important implication for the labor force. As people age they retire and exit the labor force. This shift exacerbates the public policy problem. As they leave the labor force their income drops and they produce less in the way of state revenue. Stinson and Gillaspy, New Pharos Consulting Page 10 along with the Budget Trends group that met during 2009, find that revenues growth from all sources (income, sales, etc) will drop off. Chart 4 Labor Force Growth Will Slow 1.52% Ave Annual Change 1.6% 1.4% 1.12% 1.2% 1.0% 0.75% 0.8% 0.6% 0.43% 0.4% 0.27% 0.2% 0.13% 0.10% 0.0% 19902000 2005-10 2010-15 2015-20 2020-25 2025-30 2030-35 As people age they demand more in humans service programs. Chart 5 demonstrates health care spending across all age levels. Spending on average increases dramatically for people age 55 and older; in aggregate most health care spending occurs near the end of people's lives. Since this is the prominent growth age cohort, state spending in health care related programs will increase dramatically in the future. Chart 5 Average Health Care Spending Much Higher After Age 55 US Averages in 2004 $12,000 $9,914 $10,000 $9,017 $8,000 $6,694 $6,000 $2,000 $3,571 $3,496 $4,000 $1,855 $2,165 $2,747 $1,074 $1,445 New Pharos Consulting ra ge A ve 75 + 4 65 -7 4 55 -6 4 45 -5 4 35 -4 4 25 -3 4 15 -2 14 5- <5 $0 Page 11 The budget implications of this demographic shift are significant. Given declining revenue growth and increased spending on health services, there is little left for other budget priorities. At this point, there is clearly little political appetite to raise revenues through tax increases. As these pressures evolve over time, the tension between the various spending areas will grow. These facts will make it all the more difficult to increase the share of spending on higher education. Chart 6 State Health Care Spending at Current levels will Consume Budget Resources 8.5% Annual Ave Growth 2008-2033 9% 8% 7% 6% 5% 4% 3.9% 3% 2% 1% 0.2% 0% Revenue Health Care Education & All Other Population Growth and Migration Patterns Population changes at certain age groups establish important conditions for all higher education institutions. Migration patterns in and out of the state are also important considerations for competitive reasons. Table 1 below shows projections by certain ages and years between 2008 and 2017. These have been developed by the state demographer and have been sorted into groups that have relevance to planners at higher education institutions. After 2012, the 17 through 25 year age groups show a marked decline through 2017. Competition between institutions for these students in all sectors will become more intense. The 26 through 35 age cohort, a target population for some career schools, will be increasing. With participation rates out of high school already exceeding 70 percent, other methods will be needed to address enrollment concerns. New Pharos Consulting Page 12 Table 1 Population Projections by Certain Age Groups Though 2017 Age Group 2008 0 to 16 2012 2013 2015 2017 1,180,381 1,205,719 1,216,270 1,242,155 1,269,977 74,263 72,129 75,633 72,366 71,669 72,116 72,172 71,204 71,710 69,468 67,629 73,106 74,705 81,526 88,274 78,691 72,533 74,176 68,495 66,821 71,817 71,168 74,701 81,097 86,963 79,633 73,480 66,715 64,658 70,025 69,153 69,954 70,823 73,194 80,879 89,118 66,957 64,217 68,391 67,063 68,248 68,843 68,655 70,717 75,237 26 to 35 36 to 45 45 and above 687,079 740,553 1,959,118 751,553 688,340 2,143,700 759,727 687,352 2,173,498 779,667 687,120 2,231,760 806,940 691,895 2,293,208 Total 5,220,393 5,469,420 5,511,022 5,595,221 5,680,348 17 18 19 20 21 22 23 24 25 Migration also has an impact on the labor force and competition for jobs. The American Community Survey (ACS) measures the movement of people between states and countries. This data for numbers and educational attainment of movers allows us to analyze the migration patterns of people by various educational levels. Chart 7 shows net migration by education levels between 2007 and 2009 from the ACS. For this period, there was a net outflow of people with levels of education below a bachelor's degree. There is net migration of people into the state with bachelor's degrees and above. These numbers are generally consistent with patterns of migration going back to the 1990's. Minnesota typically has been a net importer of highly educated people. While positive for the economy, the absolute size of these numbers will not have a significant impact on the overall demographics of the population and do not generally pose any competitive threat to graduates of career colleges in the state. However, in specific cases, a career college graduate may very well be competing with a recent graduate from Wisconsin or North Dakota. New Pharos Consulting Page 13 Chart 7 Net Migration by Educational Attainment Level 2007 through 2009 Doctorate degree Professional school degree Master's degree Bachelor's degree Associate's degree One or more years of college, no degree Some college, but less than 1 year High school graduate -6,000 -4,000 -2,000 0 2,000 4,000 6,000 Tuition and Fees Charged at Post Secondary Institutions Prices, or tuition and fee average levels and changes, are important metrics to evaluate higher education. These matter to every stakeholder involved in the system. Students who pay tuition want levels as low as possible. Families concerned about their children's education at affordable prices have an interest. The institutions must run the enterprise and rely heavily on this income. Faculty know that revenue impacts both the quality of the program and their paychecks. Policy makers find themselves in the middle of the debate. An accurate depiction of priced changes requires consistently reported data across time for all the institutions. The tuition and fee information filed with the Office of Higher Education for the State Grant program meets this requirement. This data has a number of distinct advantages. It is timely; data for fall of 2011 is available. It is comprehensive; an institution with students qualifying for the State Grant is required under law to provide tuition and fee data. The rules are consistent across institutions. Finally, the data is audited assuring a higher quality of informational content. The data was separated into seven different groups. These are MCCA member institutions, other forprofit institutions, Minnesota Private College Council member institutions, other non-profit institutions, MnSCU two-year colleges, MnSCU four year universities and the University of Minnesota. This level of sub-sectoral breakdown has not been published before. The data is for the period from 1999-2000 to 2011-12. Simple means across the institutions in each group are used to reflect the average for the group. While this is somewhat less than desirable- weighted averages based on enrollment would be preferredNew Pharos Consulting Page 14 consistent historical weights across all sectors are difficult to obtain. This means for some sectors this data may differ from some published elsewhere.1 Chart 8 and Table 2 show this data. The chart shows growth in the average tuition and fee levels for each group over the time period. To facilitate the analysis, each sector is indexed to 1999-2000 (this simply means each year is divided by the first year so year one equals 1.0). By comparing the different indexes over time we can determine in which group tuition and fees are growing relatively faster or slower. A higher index in one group means tuition and fees have grown at a faster rate in that group. The data indicate that the three public sector groups and "other non-profit institutions" have more than doubled their averages compared to 1999-2000. The MPCC institutions are, on average, right at 2.0 but both for-profit groups are below 2.0. Tuition and fees have grown more quickly in the public sector than the private sector generally. This is not a surprise for public intuitions as they replace declining state funding with tuition revenue. Institutions that are tuition dependent, the private institution in the for-profit and non-profit sectors, have been forced by experience to deal with market based funding. Public institutions are moving into this financial environment as state funding recedes. Chart 8 Change in Average Tuition and Fees by Institutional Group 1999-2000 to 2011-12 U of M 2.5 MnSCU 4 Non-profits MnSCU 2 MPCC MCCA For-profits Index 2.0 1.5 1.0 2000 2002 2004 2006 2008 2010 2012 Academic year 1 OHE publishes weighted means for MnSCU institutions. Tests were done on recent years with enrollment based weights and indicate the results may differ very little. New Pharos Consulting Page 15 Chart 8 above shows the growth in average tuition and fees over time. Equally important are the levels and ranges within each group. Table 2 shows the low, average and high tuition and fee level for three different time periods. The way the table reads is that in each cell the minimum, average and maximum tuition and fee level for that group for the year shown. Year Minimum Tuition and Fee Average Tuition and Fee Maximum Tuition and Fee For example, in the first cell in the upper left hand corner, the average tuition and fee for MCCA schools was $18,336 for the current school year. The minimum, or lowest amount charged by a member of the group was $14,640. The highest level was $23,760. The chart allows one to quickly make comparisons between the sector and across years of not only average levels but the range around these levels. The range is important since key information can be lost in averages. Generally speaking, the range of tuition and fee averages has grown wider over time. This means that regardless of the sector, institutions are increasingly differentiating themselves on price. Table 2 Published Tuition and Fee Averages and Ranges for Selected Years MCCA Other For-profits MPCC Other Non-profits MnSCU 2 Year MnSCU 4 Year U of Minnesota New Pharos Consulting 2011-12 14,640 18,336 23,760 5,904 10,689 22,500 22,280 31,862 42,942 5,100 15,484 25,820 3,365 5,096 5,521 6,341 7,443 8,538 11,096 12,150 13,062 2004-05 11,029 15,010 22,572 5,689 9,258 18,792 15,716 22,782 32,649 3,740 10,054 18,040 3,050 3,978 4,308 4,682 5,598 6,673 8,119 8,903 9,722 1999-00 7,095 9,539 14,429 450 5,955 11,440 9,860 15,883 23,469 1,000 6,863 14,336 2,185 2,349 2,483 2,836 3,177 3,749 4,792 4,898 5,311 Page 16 On average, all private institutions have tuition and fee levels higher than institutions in the public sector. This is unremarkable, given the general subsidy provided to public institutions by the state. MCCA schools are second only to the MPCC institutions, although the difference in sticker price between these two sectors is significant. MPCC schools are noted for their generous aid packages made possible by endowments, but these figures suggest a large amount of aid is needed to equate the prices. The range around the MCCA average is smaller than for the other private schools. MnSCU institutions have relatively tight pricing policies. Again, not unexpected since the tuition levels are set by a central board. Finally, while sticker prices are widely publicized, net prices matter more to students and families. Students typically don't pay the published price of an intuition, but some lower amount after publically and privately funded scholarships and grants are taken into account. Table 3 shows net price by group for 2008-09 from IPEDS. The first column shows the average cost of attendance at the group institutions. Column 2 is average net price and the third column us simply the ratio of net price to cost. While MCCA students have the highest remaining student share, the actual net cost to students is still below MPCC options. Public sector schools remain the lowest cost option for students. What is surprising is how relatively close the three public groups are to one another. Table 3 Costs and Net Prices faced by Students by Institutional Group 2008-09 Average Cost of Attendance MCCA MPCC Other Non-profits MnSCU 2 Year MnSCU 4 Year U of Minnesota New Pharos Consulting Average Net Price $27,381 $20,255 74% $37,903 $22,441 59% $23,884 $16,896 71% $16,863 $10,518 62% $17,166 $11,216 65% $19,693 $13,345 68% Student Share Page 17 Industrial Patterns Projected to 2019 Career colleges train students for jobs in different occupations. But the demand for products and services is typically measured at the industry level. For instance, as people demand more services from the health care industry companies in those industries need more employees. Industry demand, as measured by total employment, is one metric to understand demand for career college graduates. Total employees projections are available from the Department of Employment and Economic Development (DEED). Table 4 shows projected percent changes in employment by industry from 2009 through 2019 for the fastest growing industries. These are reported at the four digit NAICS code level. Health care and technology will be leading industries into the future. Table 4 Projected Employment Growth for top 30 Fastest Growing Industries NAICS Title Code 5416 Management & Technical Consulting Svc 6215 Medical and Diagnostic Laboratories 6216 Home Health Care Services 6214 Outpatient Care Centers 6241 Individual and Family Services 4529 Other General Merchandise Stores 6116 Other Schools and Instruction 5191 Other Information Services 6211 Offices of Physicians 6219 Other Ambulatory Health Care Services 6117 Educational Support Services 5612 Facilities Support Services 8121 Personal Care Services 7113 Performing Arts and Sports Promoters 5331 Lessors, Nonfinancial Intangible Assets 6239 Other Residential Care Facilities 5621 Waste Collection 2389 Other Specialty Trade Contractors 6243 Vocational Rehabilitation Services 5182 Data Processing and Related Services 5613 Employment Services 5616 Investigation and Security Services 5415 Computer Systems Design and Rel Services 5419 Other Professional & Technical Services 2009 Employment 13,012 2,427 17,822 9,234 45,001 14,804 7,017 1,311 62,113 6,140 2,496 758 15,782 2,057 1,201 4,635 3,867 8,917 12,759 6,690 42,179 8,845 27,429 12,411 2019 Employment 21,100 3,900 27,000 13,900 66,700 21,577 9,700 1,780 83,400 8,195 3,300 1,000 20,800 2,687 1,564 6,000 5,000 11,400 16,300 8,500 53,000 11,000 34,100 15,400 Percent Change 62.2 60.7 51.5 50.5 48.2 45.8 38.2 35.8 34.3 33.5 32.2 31.9 31.8 30.6 30.2 29.4 29.3 27.8 27.8 27.1 25.7 24.4 24.3 24.1 Total Change 8,088 1,473 9,178 4,666 21,699 6,773 2,683 469 21,287 2,055 804 242 5,018 630 363 1,365 1,133 2,483 3,541 1,810 10,821 2,155 6,671 2,989 Occupational Patterns Projected to 2019 Career colleges train students for jobs in different occupations. Occupations are tied to skill levels and the work of people as defined by roles and responsibilities of each occupation. Occupational demand projections are key to the planning process of career colleges. These projections can inform strategic planning, programmatic evaluation and operational management. Occupational demand comprises two components, replacement hires and new job openings. Replacement hires are required as people retire and leave the workforce, leave jobs and move to other states or change occupations. New hires occur as emNew Pharos Consulting Page 18 ployers increase the size of their work force in response to economic demand. The occupational demand number of interest is the sum of these two-new hires plus replacement hires. The occupational demand projections used in the report are also from DEED. They are timelier than national projections running from 2009 through 2019 (national projections run from 2008 through 2018). These projections should be viewed as a long term condition that informs discussion and not specific targets. The actual figures are likely to be different as the economy continues to slowly climb out of the deep recession that began late 2007. 2 DEED projects that over the next ten years cumulatively, there will be 885,580 total new hires. However, there are some consistency questions with the treatment of negative change in certain occupations in this total. When summed individually over all 740 individual occupations, total new hires are estimated to be closer to 913,500. While these are ten year aggregates, it may be simpler to divide by ten and use this figure as an annual number. This implies there will be about 91,350 total new hires each year. Table 5 shows the results for the top 25 fastest growing occupations. Except for one or two occupations, the majority of high growth fields are in health care or technology. Table 5 Top 25 Fastest Growing Occupations 2009 through 2019 Occupation Title Biomedical Engineers Personal and Home Care Aides Skin Care Specialists Physician Assistants Home Health Aides Biochemists and Biophysicists Athletic Trainers Network Systems and Data Communications Analysts Financial Examiners Medical Scientists, Except Epidemiologists Veterinary Technologists and Technicians Other Personal Care and Service Workers Veterinarians Radiation Therapists Self-Enrichment Education Teachers 2009 2019 Employment Employment Percent Change Total Change Replacement Hires Total Hires 805 38,122 674 1,352 37,908 301 225 1,422 59,369 1,004 1,940 53,834 424 316 76.6 55.7 49.0 43.5 42.0 40.9 40.4 617 21,247 330 588 15,926 123 91 170 4,760 100 250 3,770 100 80 787 26,007 430 838 19,696 223 171 5,879 905 1,807 1,803 76,656 1,314 197 4,380 8,250 1,263 2,490 2,485 103,020 1,758 261 5,776 40.3 39.6 37.8 37.8 34.4 33.8 32.5 31.9 2,371 358 683 682 26,364 444 64 1,396 1,060 160 370 460 14,670 230 40 670 3,431 518 1,053 1,142 41,034 674 104 2,066 Cardiovascular Technologists and Technicians Dental Hygienists Dental Assistants Personal Financial Advisors Medical Assistants 766 4,088 5,336 2,158 7,327 1,010 5,365 6,976 2,796 9,498 31.9 31.2 30.7 29.6 29.6 244 1,277 1,640 638 2,171 110 830 1,000 230 820 354 2,107 2,640 868 2,991 Nursing, Psychiatric, and Home Health Aides Compliance Officers, Except Agriculture and Construction Pharmacy Technicians Surgical Technologists Healthcare Support Occupations 69,873 90,146 29.0 20,273 6,960 27,233 4,340 6,939 1,825 97,854 5,568 8,893 2,332 124,752 28.3 28.2 27.8 27.5 1,228 1,954 507 26,898 460 1,750 460 10,580 1,688 3,704 967 37,478 2 Details for all occupations can be found at http://www.positivelyminnesota.com/apps/lmi/projections/Results.aspx?dataset=1&geog=2701000000&code= New Pharos Consulting Page 19 Educational Requirements Based on Occupational Demand The occupational demand information is useful to higher education institutions not only for aligning programs with potential demand; it can also be used in an aggregate sense to determine educational needs of the higher education system. The Bureau of Labor Statistics (BLS) uses two approaches to estimate education needs from occupational data. The first is a category system where BLS identifies 11 education or training categories that describe the most significant education or training pathway to employment for each occupation. BLS economists assign occupations to categories on the basis of analyses of qualitative and quantitative information. For example, a retail salesperson may need no more than a high school diploma while a surgeon clearly requires a medical degree. Some jobs need substantial on the job training. Different levels of education or training implied by different skill requirements can be associated with each occupation. 3 The second approach is to measure educational attainment data from the ACS. These data present the percent distribution of workers currently employed in an occupation, broken down by their highest level of education attained. The educational attainment distributions allow data users to better discern whether there are multiple education or training possibilities. For example, because 87 percent of speech-language pathologists have at least a master's degree, it is clear that a getting a master's degree is the most significant source of training in becoming a speech-language pathologist. However, educational attainment data for other occupations may be more varied; for example, 29 percent of computer support specialists have some college, but no degree; 16 percent have an associate degree; and 33 percent have a bachelor's degree. The educational attainment distribution for computer support specialists suggests that there may be more than one way to become fully qualified for this occupation. The data show the highest level of education the survey respondent has attained, which is not necessarily the level of education required for the occupation. BLS provides data that matches individual's education attainment and occupation for 2006, 2007 and 2008. By way of example, Table 6 shows this measure for one occupation, chief executives and for all occupations. The table demonstrates a number of facts. For instance, in 2008, 38.6 percent of chief executives across the country had a least a bachelor's degree while 19 percent had a Masters and 6.2 percent a doctorate or professional degree. Surprisingly, there were some chief executives without even a high school diploma although self-made people who lead their own enterprise are not unheard of in this country. Chief executives clearly tend to posses higher levels of education than the average occupation. The second approach is used in this study. Although there are some disadvantages because the ACS is a survey, the strengths of the approach outweigh these issues. These educational requirements can be applied to each occupation to determine the number of associate degrees, bachelor degrees and so on that must be available to meet the labor force requirements of the economy. By simply summing across all occupations we can determine the totals for each degree level by 2019. One problem with the data is that it does not detail projections for certificates. BLS views these projections as minimum requirements for entry into an occupation. Certificates are viewed as a means to advance within an occupation. New Pharos Consulting 3 This methodology was changed December 6, 2011. This change does not impact this analysis. Page 20 Table 6 Educational Attainment in 2008 for Chief Executives And All Occupations Educational Attainment Percent Distributions Occupation Less than High school Some college, high school diploma or no degree diploma equivalent Associate's degree Bachelor's degree Doctoral or professional degree Master's degree Chief executives 1.8% 16.9% 5.7% 38.6% 19.0% 6.2% 10.0% BLS Educational Distribution 11.9% 27.2% 21.2% 8.8% 20.6% 8.3% 3.8% If we assume that the educational attainment levels in 2019 will remain the same as observed in 2008, we can simply apply these percentages against the projected total hire numbers by occupation for 2019 and determine the several levels of educational training required in the economy. As the economy shifts to higher technology based industries, this approach will reflect the increased need for a higher skilled work force. The results of this exercise for all occupations are shown in Table 7. Again, for these purposes, we have simply divided the numbers by 10 to provide an annual perspective. This table indicates that of the 91,350 new hires each year through 2019, 19,995 will require some college, 8,318 will require at least an associate's degree and 17,564 will require at least a bachelor's degree. Since a BA is a requirement to enter graduate levels of education, a second perspective on demand is to add together the number for bachelor's, master's and doctorate or professional degrees to create an alternative measure of what is needed at the BA level. This total is 27,345 annually. Table 7 Projected Annual Educational Requirements Associated with Total New Job Openings Less than high school diploma High Some Associate's Bachelor's school college, no degree degree diploma or degree equivalent 9,778 25,915 19,995 8,318 17,564 Master's degree 6,709 Doctoral or professional degree 3,071 Total 91,351 The major question for institutions of higher education and policy makers planning for the future is, how do these numbers for annual projections compare against the graduates produced in Minnesota each year? Table 8 is from OHE's web site showing degrees awarded in Minnesota for the last 11 years. The definitions are somewhat different than those used by BLS, but they are close enough to draw some general conclusions. Based on the BLS estimates of educational attainment needs, it appears that the state in New Pharos Consulting Page 21 2010 more than meets the needs for associate and bachelor degrees (even at the 27,345 figure cited earlier). If we roughly equate certificates with some college but no degree then the state is under-producing certificates. Table 8 Degrees Awarded in Minnesota Certificates below Academic Year Bachelor's Assocociates Bachelor's Master's 2000 12,702 11,045 23,181 7,797 2001 12,128 10,910 23,261 8,096 2002 12,396 11,860 24,554 8,518 2003 13,107 13,320 25,789 9,323 2004 14,896 14,209 27,337 11,433 2005 14,094 15,469 28,275 13,052 2006 15,086 15,125 28,911 15,188 2007 14,695 15,825 29,633 16,387 2008 13,713 16,601 30,388 18,012 2009 14,630 17,100 31,278 19,186 2010 15,938 18,468 31,963 21,015 Doctorate (research and professional) 2,402 2,543 2,394 2,537 2,691 2,998 3,357 3,887 3,886 3,874 4,173 The Georgetown Critique Anthony Carnevale and others at the Georgetown Center on Education and Workforce, in a 2010 study, offered a number of critiques of the approach used by BLS. Carnevale asserts that BLS underestimates educational requirements because it essentially holds the education attainment distribution fixed. He notes that the economy is becoming more skill-based so that within each occupation people will need more education to meet the demands of the economy. He cites historical trends to this effect, trends that BLS essentially ignores. Carnevale attempts to address this by using two different projection techniques to change the percentages. He reported that Minnesota was near the top of the country in education needs. Table 9 below brings three different approaches to measure educational attainment needs together. The first line shows the distribution used above from BLS. The second line shows the educational attainment for employed people ages 25 and above in Minnesota from the three-year 2007 through 2009 ACS sample. The third line shows the Georgetown estimates. Both the Minnesota ACS experience and the Georgetown numbers suggest higher levels of education, especially in associate's degrees and bachelor's degrees than does BLS. The ACS data also shows much lower numbers for Minnesota for less than a high school diploma. New Pharos Consulting Page 22 Table 9 Comparison of BLS and Georgetown Estimates for Educational Attainment Distribution All Occupations Less than high school diploma Educational Attainment Percent Distributions High school Some Associate's Bachelor's diploma or college, no degree degree equivalent degree Master's degree Doctoral or professional degree BLS Educational Distribution 10.0% 27.2% 21.2% 8.8% 20.6% 8.3% 3.8% Minnesota ACS 2007-2009 1.1% 25.1% 24.0% 11.9% 25.7% 8.4% 3.9% Georgetown Estimates 6.0% 23.0% 21.0% 13.0% 27.0% 9.0% We can apply the Georgetown percentages in a general way - across all occupations - and determine the impact on the number of associate's and bachelor's degrees that need to be produced. Table 10 shows the results of this exercise. The largest change is in the number of associate's and bachelor's degrees that need to be produced each year. These figures are higher than the BLS numbers but still within the levels being produced in the state today. In 2010, the state produced 18,468 associate's degrees whereas the Georgetown data suggests about 11,600 annually. If we add bachelor's, master's and doctoral or professional degrees from the Georgetown percentages together we have a projected need of 33,205, or a bit more than was produced in 2010. Table 10 Projected Annual Educational Requirements Associated with Total New Job Openings Using Georgetown Percentages Educational Attainment Distributions Source High Less than Some school high school college, no diploma or diploma degree equivalent Associate's degree Bachelor's degree Master's degree Doctoral or professional degree BLS Distribution 9,778 25,915 19,995 8,318 17,564 6,709 3,071 Georgetown Estimates 6,646 21,011 18,832 11,655 24,207 6,172 2,829 The purpose of this report is to provide background information for career colleges to plan and manage respective institutions and to create a context for the analysis of the economic impact of the sector. This is not the venue to decide the issue between the Bureau of Labor Statistics, DEED and the Georgetown Center. However, this is an important question for policy makers to consider. New Pharos Consulting Page 23 Program Inventory Introduction MCCA institutions offer a variety of programs across a spectrum of study areas. At the same time there is a marked focus on business and health related programs. An inventory of these programs combined with data on the number of awards made by the institutions is presented in this section. In summary: o o o MCCA institutions offers program in 15 of the 38 major program areas identified by the federal government MCCA institutions provided about 5.6 percent of degree wards in Minnesota in 2009-10. ISEEK reports that MCCA school offer courses in 356 different program areas. Program Data There are two data sources used in this analysis. The first is ISEEK, a comprehensive career, education, and job resource location that provides information about these three related topics. 4 This organization undertakes workforce development and education projects, promotes a forum for discussion and provides funding for related projects. The ISEEK web site maintains a current list of programs offered at each post secondary institution in the state. The second data source is the program completion data reported in IPEDS. This data includes total awards- a term defined as the sum of certificates, diplomas, associate's degrees, bachelor's degrees and graduate degrees. The data is for 2009-10 and is based on reported first majors. Completion data provides an important metric, especially in an environment of "gainful employment". A simple list of programs would inform the discussion, but it is of limited usefulness. Combining both programs offered with degrees awarded creates a richer context for the actual operations of MCCA institutions. The ideal approach would be a direct link between awards and programs. Unfortunately, a crosswalk between program names used by ISEEK and the IPED's data for each institution is not available. There are two parts contained directly in this section. The first is a summary of completion data reported by institutions aligned by general program areas. The second is a more detailed list of programs that uses the completion data to identify the focus of MCCA institutions across programs. A third part, located in the appendix, is a complete list of programs by award level identified for each of the MCCA schools. Program Summary Table 11 is a list of total completions of awards conferred by all program using the 2010 Classification of Instructional Programs (CIP) for all award levels. 4 See http://www.iseek.org/ New Pharos Consulting Page 24 Table 11 Total Awards for 2009-10 for MCCA Institutions by Major CIP Code IPEDS Completion General Program Area Agriculture, Agriculture Operations and Related Sciences Natural Resources and Conservation Architecture and Related Services Area, Ethnic, Cultural, Gender, and Group Studies Communication, Journalism, and Related Programs Communications Technologies/Technicians and Support Services Computer and Information Sciences and Support Services Personal and Culinary Services Education Engineering Engineering Technologies and Engineering-related Fields Foreign Languages, Literatures, and Linguistics Family and Consumer Sciences/Human Sciences Legal Professions and Studies English Language and Literature/Letters Liberal Arts and Sciences, General Studies and Humanities Library Science Biological and Biomedical Sciences Mathematics and Statistics Military Technologies and Applied Sciences Multi/Interdisciplinary Studies Parks, Recreation, Leisure and Fitness Studies Philosophy and Religious Studies Theology and Religious Vocations Physical Sciences Science Technologies/Technicians Psychology Homeland Security, Law Enforcement, Firefighting, and Related Protective Service Public Administration and Social Service Professions Social Sciences Construction Trades Mechanic and Repair Technologies/Technicians Precision Production Transportation and Materials Moving Visual and Performing Arts Health Professions and Related Programs Business, Management, Marketing, and Related Support Services History Total New Pharos Consulting Total Degrees Percent of and Total Certificates 126 165 386 476 72 46 128 26 255 37 220 10 7 475 1,480 1,327 5,236 2.4% 3.2% 7.4% 9.1% 1.4% 0.9% 2.4% 0.5% 4.9% 0.7% 4.2% 0.2% 0.1% 9.1% 28.3% 25.3% 100.0% Page 25 The list identifies two important facts. First, it provides program areas where MCCA schools focus their offerings and generate graduates. The totals shown are from 2009-10 completions. The 5,236 awards to MCCA students is about 5.6% of the 93,595 awards by all reporting institutions in the state in that year Second, it provides a broad list of program areas where MCCA institutions do not have an academic presence, i.e., the larger market of potential academic presence. The business model used by career colleges direct the types of programs offered, so it is not surprising that there are many areas where MCCA institutions do not offer programs. In those areas where programs are offered, over 29 percent fall in health related fields with another 25 percent in business fields. These are followed by Personal and Culinary Services, Visual and Performing Arts and Computer and Information Sciences. Award Summary Table 12 shows in more detail the number of awards by general area for 2009-10 for each institution. The table only includes programs where there were awards made in that year. Institutions with multiple campuses have been combined. This refines the focus of programs at an institutional level. It identifies the larger schools (Minnesota School of Business, Rasmussen); the breadth (or in some cases the focus such as La Cordon Bleu) of programs; and the various institutions that offer competing programs. New Pharos Consulting Page 26 Total - 80 ni v ers ity titu te o fP rod ITT uct Tec ion hni a nd ca l Re Le In s co r Co titu din rd o te g nB leu Mc Co Na l leg lly eo Sm fC i th u lin Mi Co nn e a ry lleg a po Art eo fM lis s Bu u si Mi sin c nn e e ss so t Co aS lleg cho e ol o Na fB tio n u si al A nes me s rica No r th nU we n iv st T ers ech ity Ra nic sm al I u ss n st en i tu Co te l leg Th e eA rt I nst itut Tot es I al n te rn. Ins ing U He rz Aca General Program Area Communication, Journalism, and Related Programs Communications Technologies/Technicians and Support Services Computer and Information Sciences and Support Services Personal and Culinary Services Education Engineering Engineering Technologies and Engineering-related Fields Family and Consumer Sciences/Human Sciences Legal Professions and Studies Parks, Recreation, Leisure and Fitness Studies Homeland Security, Law Enforcement, Firefighting, and Related Protective Service Public Administration and Social Service Transportation and Materials Moving Visual and Performing Arts Health Professions and Related Programs Business, Management, Marketing, and Related Support Services dem yC o lle Bro ge wn Co ll eg e DeV ry Un iv e rsit Du y luth Bu sin e ss Gl o Un be iv e rsit y Table 12 Number of Awards by General CIP Area by MCCA Institution 2009-10 - - - - - - - - - 106 - - - - 111 - 29 10 - - - 80 110 21 - - - - - - - - - - - - - 7 11 14 24 159 - 18 16 72 7 89 2 50 340 81 95 225 118 61 9 12 4 - 5 7 11 66 - 6 - - - - 28 - - - 18 126 - - - 28 - - - 24 165 371 - 46 - - - 45 105 - 386 476 72 46 - - 17 - 138 27 - 128 26 255 37 89 - - - - 4 115 50 11 461 - - - - 81 127 174 371 135 238 7 51 5 21 89 10 - 28 72 - 71 - - - - 39 - 141 3 595 164 - 220 10 7 475 1,480 553 68 - 411 10 1,327 1,385 131 1,329 366 5,236 14 - 71 26 53 Program Detail Appendix 2 contains a table that drills down the data to a further level of detail. The data shows detail for each separate program by institution and by level of award offered. This table is based on the ISEEK data and, while very useful, contains both advantages and some problems. Since it is centrally collected there is some consistency in the reporting methods. There is a concerted effort on the part of staff to keep the date current. Finally, the data is directed to the same population served by MCCA institutions. However, in some cases the program labels suggest a distinction when one may not exist. For instance, a "Business Administration" program broadly defined may offer the very same courses as a "Business Administration- Accounting" program. Also, some minor errors with the data have been observed in the compilation in this form. New Pharos Consulting Page 27 Student Demographic, Socio-Economic and Academic Characteristics Introduction Student demographic, socio-economic and academic characteristics reveal important information about how post secondary institutions operate. This section presents background data on students in the forprofit sector and comparisons across all sectors of higher education in Minnesota. The data for this analysis is from the 2008 National Post Secondary Student Aid Study (NPSAS). Minnesota was fortunate to be one of the six states over-sampled that year, a fact that provides an incredibly rich data set for analysis. Although MCCA schools cannot be identified separately, the data measuring the for-profit sector should still be relatively reflective of the member institutions. Also, while the survey is somewhat dated, the relationship between the sectors is unlikely to change quickly, so conclusions remain valid. The information is presented in two parts. The first is summary and provides the share of total student population that attend for-profit institutions for 21 different variables. This approach measures information across sectors and is used since it mirrors presentations used at the national level. This summary approach, while useful, doesn't fully reflect the conditions within the for-profit sector. The second section is a more detailed examination of a smaller number of key variables that expand the examination of forprofit institutions. Summary Discussion There are 21 different variables presented describing students at for-profit institutions. A brief synopsis of each variable is provided directly in the text. The full data table is found in Appendix 3. This synopsis together with the table allows the reader to quickly grasp the key demographic, social-economic and academic characteristics of these institutions. In many cases similar student conditions are measured by different variables which in turn reinforce the relationships. For example, if students tend to be full-time employed (work intensity) they also tend to work more (work hours per week) and make more money (income). The first line in Appendix 3 shows the total distribution of students by sector. For example, in 2008, about 8.4 percent of students attended for-profit schools. This figure is the benchmark for each of the other demographic variables. If a variable percentage is larger than the 8.4 percent, the characteristic measured by that variable is more prevalent at for-profit schools. For example, the first variable in the list is gender. The percentage at for-profit schools is 10.5 percent indicating the population in for-profit schools tends to be more female than male, i.e., the percent of female is above the overall proportion of 8.4. To ease identification, the percentage number is boxed for each variable where this is true. Finally, since NPSAS is a sample, the data comes with some level of uncertainty. Wherever a data point has either a single or double exclamation point, confidence intervals are larger and interpretation should be done with caution. Variable Synopsis o The student population at for-profit schools tends to be more female. The for-profit schools have 8.4 % of the population but 10.0 % of the female attendees in the state. New Pharos Consulting Page 28 o The student population contains a larger share of people indicating in the NPSAS survey that they posses some type of disability. o The student population at for-profit schools is significantly more likely to have dependent children. o Students are much more likely to be independent, again often with children. o Students with dependent children are more likely to be single parents. o Students generally are also less likely to be single or divorced o The student population at for-profit schools is less likely to be white and much more likely to be black or Hispanic. o These students tend to be from families where parents, relatively speaking, are not as well educated. The detailed discussion explores first generation students at for-profit schools. o For-profit schools have a significantly larger veteran population. o The student population is relatively older, more typically 25 years and up. o The student population is more likely to attend part time, part year or a combination rather than full time or full year. o The student population is somewhat more likely to have larger credit card debt. o There is a larger share of "income as an independent student," in large part since there are more independent students at these schools. o The parent income for dependent students is at the low end. This is explored below. o The student population is more likely to be enrolled in a certificate or associate program. Although many programs are offered at the BA level, enrollment tends to be in programs less than four years. o The student population is more likely to be in business or health programs. This confirms the information presented in the program inventory. o The primary academic goal indicated by students is a certificate, associate's degree and to some extent a bachelor's degree. o Students are more likely to consider themselves to be primarily employed full time and attending school part time. o As employees first, these students are more likely to work three quarters or full time. o Because they work longer hours, job earnings tend to be higher than for students in other sectors. o Similarly, overall income tends to be higher for these students. New Pharos Consulting Page 29 Detailed Discussion The summary approach presented above does not reflect the conditions within the for-profit sector. This section is a more detailed examination of a smaller number of key variables that expands the description of for-profit institutions. These variables include race, income, parental education and student's debt and are considered key since they address some of the central issues in higher education policy. Since NPSAS is a sample the data comes with some level of uncertainty. Where ever a data point has either a single or double exclamation point confidence intervals are wider and interpretation should be done with caution. Racial Background Access to higher education among various racial groups is an important issue in higher education. Table 13 shows a more detailed perspective on attendance by race across the various sectors. In Minnesota in 2008, nearly 62 percent of students attending post secondary institutions were white. The two largest minority groups were black and Hispanic students, both representing about 14 percent of the population. Asian students made of almost 6 percent. The remaining share is spread among other racially identified groups. In general, the public two year and four year non-doctorate institutions in the state have enrollment patterns that reflect the overall percentages of the population. This is not surprising since most students attending college attend these schools. Four year doctorate public institutions and non-profit institutions have larger white and Asian populations and smaller black and Hispanic populations. For-profit schools serve a higher proportion of minority students than any other sector. Less than one-half of students are white; this is unlike any other sector all of which enroll more than 60 percent white students. Black students represent 26 percent and Hispanic students almost 17 percent of enrollment at for-profit schools. Table 13 Racial Background of Students Percent of Enrollment by Sector White Black or African Hispanic American or Latino Asian American Indian or Alaska Native Native More Hawaiian / than one other Pacific Total Islander Other race Total Enrollment 61.8 14.0 14.1 5.9 0.8 0.7 0.3 2.4 100% Institution sector Public 2-year Public 4-year nondoctorate Public 4-year doctorate 60.2 61.9 69.0 14.4 13.1 10.6 14.8 16.1 9.6 6.1 4.8 6.9 1.0 1.2 0.6 0.9 0.4 0.5 0.3 0.3 0.2 2.3 2.0 2.5 100% 100% 100% Private not-for-profit 4-yr nondoctorate Private not-for-profit 4-year doctorate 67.7 67.8 13.1 10.2 12.1 11.0 3.7 7.5 0.4 0.2 0.7 ! 0.5 ! 0.3 0.3 2.1 2.4 100% 100% Private for-profit 2 years or more 48.9 26.1 16.6 3.4 1.1 ! 0.7 ! 0.4 ! 2.7 100% Attended more than one institution 61.2 12.2 14.4 7.7 0.7 0.8 0.2 2.8 100% New Pharos Consulting Page 30 Family Income Research indicates that family income is a key to the success of a student attending post secondary education. Higher income typically means more access, choice and academic success. Family income may differ significantly between dependent students who rely on parents and independent students who have only their own resources at hand. Table 14 shows family income for both groups. The variable is broken down by sector and into various percentiles. Table 14 Family Income by Dependent Status, Sector and Percentile Dependent Parent Income by Sector and Percentile 10th % 25th % 50th % 75th % 90th % All Sectors 25,942 44,434 73,811 108,480 141,600 Institution sector Public Two-year Public Four-year Private Not-for-profit Four-year Private For-profit Two years or more Attended more than one institution 20,850 27,084 28,744 18,295 23,732 38,802 48,573 51,143 32,379 45,636 62,093 83,767 83,890 56,851 67,329 93,361 111,993 121,918 90,048 101,458 126,449 144,000 177,623 114,348 146,354 Independent Student and Spouse Income by Sector and Percentile 10th % All Sectors Institution sector Public Two-year Public Four-year Private Not-for-profit Four-year Private For-profit Two years or more Attended more than one institution 25th % 50th % 75th % 90th % 5,885 14,942 28,832 52,581 83,451 6,375 2,400 !! 2,999 !! 10,552 7,670 16,150 8,572 ! 15,422.0 ! 16,092 15,429 29,760 21,767 ! 34,811 27,987 29,119 52,793 47,142 68,061 45,791 52,800 87,371 68,280 91,505 73,996 83,451 The top half of the table shows family income for dependent students. Family income for students at forprofit institutions for these students is the lowest at all percentile levels. At the median level of $56,851, income in that sector is only 77 percent of the level for all sectors at $73,811. Based on NPSAS data, forprofit schools serve the lowest income families attending higher education institutions in the state. The story is a bit more complicated for independent students. As indicated above, the student population in for-profit schools tends to be more independent than dependent. The bottom half of the table shows New Pharos Consulting Page 31 family income for independent students and their spouses. At the bottom end of the income spectrum, students in the for-profit sector earn the most at $10,552, not quite double the amount for all students at $5,885 (these numbers are a bit confounded by data at the four year institutions which is not very accurate). This result is not surprising given the fact that students attending for-profit schools simply work more hours in a week while attending school. This dominance disappears in higher income levels. At the median level, independent students at for-profit institutions make less than the median for all students. The spread between for-profit students and all other independent students is greater at incomes above the median. This implies that while students at for-profit schools work more and earn more, they are not making a lot of money at the high end. For independent students, for-profit institutions serve those at the low income levels. Family Education A parent's education can be a strong determinant of the educational path of a child. Highly educated parents tend to beget highly educated children. With demographic patterns shifting towards minority populations, political leaders have focused on the growing need for first generation children to enroll in and graduate from higher education. Table 15 shows the education background of parents by sector. The general pattern is one expected. Students in four year institutions tend to come from families where parents possess a BA, Masters or above. This is highlighted in the boxed area on the right. Table 15 Parent's Highest Education Percent of Total Enrollment by Sector and Level of Education 2 or more Did not High school Vocational Less than Firstyears of complete diploma or or technical two years of Associate's college but no Bachelor's Master's professional Doctoral college degree degree degree degree degree high school equivalent training degree Total 3.6 21.6 8.1 8.4 8.5 4.5 23.8 11.0 4.5 3.3 Institution sector Public 2-year 5.4 27.4 10.0 9.0 9.0 4.4 18.5 8.6 1.9 1.6 Public 4-year nondoctorate Public 4-year doctorate 1.9 1.3 !! 22.5 10.8 10.7 4.5 ! 7.4 7.9 10.3 8.1 ! 4.6 6.2 26.4 32.7 11.5 12.5 ! 1.6 9.7 !! 1.9 5.7 Private Non-profit 4-yr nondoctorate Private Non-profit 4-year doctorate 1.7 4.7 !! 11.3 14.6 5.3 8.7 5.5 6.9 ! 3.7 9.2 3.9 ! 2.7 ! 31.9 25.9 16.2 16.0 11.4 4.9 9.0 ! 3.8 Private For-profit 2 years or more 4.5 ! 37.0 8.1 8.7 8.7 3.7 14.4 7.7 0.6 ! 2.2 Attended more than one institution 3.0 18.0 7.0 10.8 9.0 3.6 25.2 13.3 5.9 ! 2.2 ! New Pharos Consulting Page 32 The most striking relationship in the table is shown in the smaller box on the lower left. Disregarding the uncertainty in the figure of 4.5 percent for parents of students in for-profit schools that did not finish high school, if one adds that number to the 37 percent for parents with only a high school degree, over 41 percent of these students are first generation. These parents have never entered a post secondary institution. The next closest figure would be about 33 percent at public two year institutions. Cumulative Student Debt A major issue in higher education is the level of debt student face upon graduation from college. The last variable in this section examines cumulative debt for students. The NPSAS data allows us to measure this debt load for students attending schools in the various sectors. Table 16 shows cumulative debt by sector for a variety of percentile levels. At the lowest level, total borrowing in the for-profit sector is significantly higher than for students in the other sectors. As one moves up through the range of borrowing, public two year schools remain lower than all other institutions. Students at the highest levels of borrowing at non-profit schools tend to carry the most debt. Table 16 Cumulative Amount Borrowed for Undergraduate 10th % 25th % 50th % 75th % 90th % Total 2,750 5,137 10,964 19,985 32,206 Institution sector (with multiple) Public 2-year 2,000 3,500 7,366 13,410 21,981 Public 4-year nondoctorate Public 4-year doctorate 3,485 2,600 6,908 5,500.0 ! 14,217 10,966 21,579 20,000 34,863 35,000 3,500.0 ! 3,750.0 ! 7,500 9,500 15,689 15,400 26,125 24,284 40,000 39,000 Private for-profit 2 years or more 4,422 7,500 14,112 24,625 35,000 Attended more than one institution 3,500 6,459 12,703 20,000 31,450 Private not-for-profit 4-yr nondoctorate Private not-for-profit 4-year doctorate New Pharos Consulting Page 33 Returns to Post Secondary Education Introduction This part of the study describes income differences and rates of return to post secondary education. It is a well established fact that people with higher levels of education on average make more income annually than those without this training. This annual difference leads to substantial cumulative differences in lifetime earnings. Income data for Minnesota is described in the first part. Part two concerns rate of return estimates. Rate of return analysis is complicated and results from several studies are reported in this section. Education and Income Differences Average wage earnings by educational attainment levels for Minnesota differ as shown in Table 17. The definition of income is important. Data for "wages," a measure of income paid at a job, is used in the analysis. An alternative measure sometimes used in other studies is "earnings," a concept that combines wages and self employment income. Wage information is the more relevant measure for career colleges since graduates are centrally concerned with obtaining a job upon completion. Data is shown for two different population groups- the total population and a more limited age cohort of working people ages 19 to 40. Table 17 Wage Levels by Educational Attainment Total Population Population Total 12th grade, no diploma High school graduate Some college, but less than 1 year One or more years of college, no degree Associate's degree Bachelor's degree Master's degree Professional school degree Doctorate degree Difference between High School and Associate's Lifetime difference Difference between High School and Bachelor's Lifetime difference New Pharos Consulting 5,229,330 $ 61,096 1,110,665 296,051 690,317 364,498 790,823 246,549 69,982 37,481 Average Wage 22,411 13,044 18,230 22,170 24,023 32,512 44,252 55,558 101,821 70,891 14,283 606,263 26,023 1,046,054 Working Population Ages 19 through 40 Average Wage Population 1,213,156 $ 13,832 253,588 83,328 252,372 146,405 309,710 74,068 19,761 7,867 35,405 20,846 24,808 25,410 25,044 34,252 48,418 61,497 94,050 67,897 9,444 375,382 23,610 915,998 Page 34 Total populations figures are typically used in standard analysis. But important distinctions can be lost in these averages. The second population group is presented since it generally reflects a particular demographic group served by MCCA institutions. In addition, the specific identification of working adults is important since 2007 through 2009 was a recessionary period. Annual and lifetime differences between a high school graduate and an associate's degree and bachelor's degree respectively are also shown. Lifetime figures in the table are simply the annual figures multiplied by the relevant number of years assuming no growth in income. Under this simple but straightforward approach, a person with an AA would make about $375,000 more than one with only a high school degree over a lifetime. The same figure for a BA is about 900,000. These figures reflect the standard approach to this type of analysis by assuming flat growth rates. However, it tends to underestimate the true difference between high school and post-secondary education over a lifetime. Real wages grow over time and those with higher incomes will have greater dollar growth since they are starting from a larger base. Using projected real wage growth rates from the Congressional Budget Office's Long Term Budget report, further analysis results in an AA difference of $570,000 and a BA difference of $1.4 million over a lifetime. 5 Both numbers are significantly higher than the simpler approach. These are real dollars that allow students to obtain a higher standard of living. Rate of Return A rate of return estimate indicates how much income is derived from a particular investment expressed in percentage terms. Done correctly, this requires a complicated analysis that is beyond the scope of this project. Rate of return is more than simply subtracting the cost of education from lifetime income. However, there is substantial academic research published on this topic to draw upon. Pascarella and Terenzini indicate a personal rate of return of about 12 percent for a bachelor's degree. This estimate is in line with many other published papers. 6 Returns for two year degrees are subject to more debate, but a typical number used is an eight percent rate of return. 7 5 6 CBO's 2011 Long-Term Budget Outlook, Congressional Budget Office, June 22, 2011 How College Affects Students, Volume 2, Ernest Pascarella and Patrick Terenzini, Jossey Bass, 2005 7 The Education Gospel, The Economic Power of Schooling, W Norton Grubb and Marvin Lazerson, Harvard Press, 2004; Learning and Earning in the Middle, Part I: National Studies of Pre-baccalaureate Education; W. Norton Grubb New Pharos Consulting Page 35 Economic Impact Analysis Introduction This final section of the study describes the impact of career colleges on the Minnesota economy. There are two major sources of this impact, current economic activity and the change in income from the investment in human capital. Career colleges are a part of the larger higher education industry in the state that includes the University of Minnesota, the MnSCU system and non-profit institutions. The delivery of education services, the heart of what these institutions do every day, generates economic activity. Teachers are hired, wages are paid, building are heated in the winter and cooled in the summer, books are purchased, and so on. These operations create a primary impact on the economy. They also create secondary impacts as firms that provide goods and services to institutions purchase their own set of inputs, hire people, pay wages, and so on. This new income creates a third level of economic activity as consumer goods are purchased, homes are built, and so on. This spending and re-spending creates a multiplier effect, a very important concept when determining the total economic impact of career colleges. Career colleges have a second major impact on the economy because they help students improve their skills and knowledge, build their own personal human capital, and, as we saw in the prior section, earn more income annually. There is, on average, a positive rate of return to students that leads to higher lifetime earnings. These higher earnings also have a multiplier impact on the economy, lead to higher consumption and increased taxes for state and local governments. This section of the report is in two parts. The first section describes the economic impact on output, earnings, value added and employment in the state that are attributable to career colleges. The second section describes the impact on the economy of the investment in human capital. Impact Summary The focal point of this report is the evaluation of the economic impact on the state of Minnesota attributable to career colleges. Economic impact takes two forms, the current activity of the institutions and equally important, the impact of the schools on the development of human capital and the increase in earnings for students. This activity has an impact on state and local taxes. The major findings of this analysis are: o o o o o Career colleges account for about $1.309 billion annually in total output in the state as measured by income. This includes $595 million directly and $714 million indirectly after all impacts are taken into account. Career colleges directly and indirectly produce about 14,900 jobs annually in the state. Career colleges directly and indirectly produce almost $450 million in earnings annually in the state. These earning produce about $21 million annually in state income tax revenue. They produce an additional $9.0 million in sales taxes. Career colleges directly and indirectly produce nearly $750 million in value added annually in the state. New Pharos Consulting Page 36 o o o o Career colleges pay about $7.6 million annually in property taxes to state and local government. They provide an addition $4.3 million annually in other state taxes. Summing the various tax estimates results in total state and local taxes of about $42 million annually. MCCA Institutions produce about 66 percent, or two thirds of the output in the state. These institutions likewise are responsible for 66 percent of the jobs, earnings and other measures. Exploratory analysis suggests each year new graduates of career colleges earn about $300 million more in income than they would had they not gone on to receive a post secondary degree, This produces additional $14 million in income tax revenue for the state. Economic Impact Analysis This part of the report describes the direct economic impact of career colleges on the state. The source data is presented, the multiplier concept and source is described, and related tax impacts are discussed. The Data Economic activity can be measured in a number of ways. This analysis identifies four key metrics-output, earning, employment and value added. The first metric is total output, typically measured in impact studies by sales. In the case of career colleges, sales and institutional income are very similar concepts. Institutions sell educational services to students and are paid in the form of tuition dollars or substitute revenue from state and federal government. Post secondary institutions that meet certain criteria are required to report a variety of data to the federal government under the Integrated Postsecondary Education Data System (IPEDS). IPEDS is a rich data set that provides information on enrollment, employment, student costs, graduation levels and revenue and expenditures at individual institutions. This serves as the primary source of output data for the study. IPEDS provides all of the information needed for MCCA member institutions. This reported data has been confirmed by the institutions. For a variety of reasons a number of other career colleges do not provide revenue data to IPEDS. These tend to be either very small schools (very low enrollment) or schools with a very specialized curriculum (for example Aviva College of Midwifery and Maternal Child Health or the Cutting Edge Pet Grooming School). All career colleges in Minnesota must be either licensed or registered with the state and annually file financial information with the Office of Higher Education. The Office has provided aggregate income data for the largest institutions that do not file information with IPEDS. Career institutions in the state directly account for approximately $595 million in output annually in the state. Of this, about $547 million is reported through IPEDS and an additional $48 million is from OHE reports. The combined data provides a fairly complete description of output of career colleges in the state. Multipliers Career institutions directly account for approximately $595 million in output annually in the state. A complete picture of the impact would include not only this direct spending but also secondary, indirect spending in the economy created by this initial activity. The sum effect of this is referred to as a multipNew Pharos Consulting Page 37 lier effect. For accurate estimates of economic impact, economic multipliers specifically estimated for the higher education sector in the state of Minnesota are needed. This specificity is important since the multiplier impact can vary for different industries and geographic locations. Analysis of economic impact can utilize large regional economic models to measure total impact. These models may be well constructed and very elaborate. They are also expensive to construct and operate. A reasonable alternative exists that has been developed by the Bureau of Economic Analysis (BEA). This system has been in place for many years, used across a wide variety of applications and has proven credibility. The summary in the box below describes this system. Multipliers for output, earnings, employment and value added have been obtained from BEA for this project. These multipliers are specific to higher education in the state of Minnesota. They are referred to as Type II multipliers and capture the direct, indirect, and induced effects on the economy. Even though direct measures of employment, earnings and value added are not available, the multipliers shown can be used to estimate the impacts from the output data on these three metrics. They are estimated based on 2008 regional data so they are current with the other data used in the analysis. These are shown in Table 18. Table 18 Economic Multipliers for Higher Education in Minnesota Output Earnings 2.2005 .7552 Employment 25.0675 Value Added 1.2616 These multipliers have fairly straightforward interpretations. The output multiplier means that for every one million dollars in direct output in higher education, $2.2 million in total output occurs in the economy. The earnings multiplier means that for every one million dollars in direct output in higher education, $755,200 in total earnings occurs in the economy. The employment multiplier means that for every one million dollars in direct output in higher education, roughly 25 jobs are produced in the economy. The final multiplier for value added reflects a measure that is similar to gross domestic product. The value added multiplier means that for every million dollars in direct output in higher education, $1.26 million in value added occurs in the economy. A Description of the RIMS II Multipliers Effective planning for public and private-sector projects and programs at the state and local levels requires a systematic analysis of the economic impacts of the projects and programs on affected regions. Systematic analysis of economic impacts must account for the inter-industry relationships within regions because these relationships largely determine how regional economies are likely to respond to project and program changes. Regional input-output multipliers, which account for inter-industry relationships within regions, are useful tools for regional economic impact analysis. In the 1970's, the Bureau of Economic Analysis (BEA) developed a method for estimating regional multipliers known as RIMS (Regional Industrial Multiplier System). Using RIMS II for impact analyses has several advantages. RIMS II multipliers can be estimated for any region composed of one or more counties and for any industry or group of industries in the New Pharos Consulting Page 38 national input-output table. The cost of estimating regional multipliers is relatively low because of the accessibility of the main data sources for RIMS II. According to empirical tests, the estimates based on RIMS II are similar in magnitude to the estimates based on relatively expensive surveys. To effectively use the multipliers for impact analysis, users must provide geographically and industrially detailed information on the initial changes in output, earnings, or employment that are associated with the project or program under study. The multipliers can then be used to estimate the total impact of the project or program on regional output, earnings, or employment. RIMS II is widely used in both the public and private sector. In the public sector, for example, the Department of Defense uses RIMS II to estimate the regional impacts of military base closings, and state departments of transportation use RIMS II to estimate the regional impacts of airport construction and expansion. In the private sector, analysts, consultants, and economic development practitioners use RIMS II to estimate the regional impacts of a variety of projects, such as the development of theme parks and shopping malls. Economic Impact- Output, Earnings, Jobs and Value Added The determination of the economic impact is a simple matter of applying the multipliers to the income data obtained from IPEDS and OHE. Table 19 shows the results of this exercise. Table 19 Total Economic Impact of Career Colleges on the Minnesota Economy (Millions of dollars) Economic Metric Output Employment Earnings Value Added Direct $595 Indirect $714 Total $1,309 14,912 $ 450 $ 750 Tax Impacts The earnings generated through the economic activity of career colleges produce income tax receipts for the state. The total earnings number after the multiplier effect is about $450 million. Since this is an aggregate number, an average effective income tax rate can be applied to estimate annual income taxes attributable to career colleges. Information used in the November 2011 state forecast from the Department of Management and Budget reflects an average effective rate of 4.56 percent. Applying this to the earnings estimate results in approximately $21 million in income tax revenue. 8 8 An average effective income tax rate for Minnesota for 2009 is determined by dividing total income tax liability by adjusted gross income for that year. This approach compensates for exemptions and other income deductions. This information is from the Minnesota Department of Management and Budget New Pharos Consulting Page 39 The activity of career colleges also produces local property taxes and other state taxes. Property taxes are estimated using relationships between output and property taxes in national input-output accounts. This produced an estimate of annual property taxes paid of $7.6 million. Property taxes paid by several MCCA institutions were identified through county web sites to determine the reasonableness of this number. This same national input-output approach was used to estimate other state tax revenue from production activities of $4.3 million annually. This approach only used sales and insurance taxes so it is a bit conservative. Operations of career colleges may also lead to increased motor vehicle licenses, gas and other taxes. In addition to taxes due to operations, earnings themselves lead to increased sales taxes. The 2011 tax incidence study produced by the Minnesota Department of Revenue estimates an effective tax rate at 2.0 percent at incomes of roughly $45,000. The $45,000 is used as a proxy for annual earnings implicit in the total earnings figure of $450 million. Applying the 2.0 percent to this figure results in an increase in sales taxes of roughly $9.0 million annually. Income Produced from the Investment in Human Capital IPEDS reported over 8,000 students in 2009-10 graduated from all career colleges with a variety of degrees and certificates across a number of occupational fields. A key question is what are the employment and earnings experience of students as they leave career colleges? The analysis of student demographics in section five utilized the NPSAS data for Minnesota to broadly describe student characteristics. This data does not include information on either employment or earnings after graduation. However, another survey from the federal government, the Baccalaureate and Beyond Longitudinal Study, does provide information on jobs for each sector of higher education. The data is limited to students with four year degrees so it is an incomplete picture for career schools. In addition, the data is not Minnesota specific although regional data on the Upper Midwest is provided. If one assumes that regional data reasonably approximates the experience in Minnesota, some important information is available. 9 As described in section five of the report, investment in post secondary education, on average, results in higher income over a person's lifetime. Although the data to fully measure this impact is not available, some preliminary information from available data on the impact for career college graduates in Minnesota is presented Employment and Income after Graduation The Baccalaureate and Beyond Longitudinal Study contains data for students graduating with a four-year degree in 2007-08. There is also a follow-up with those same students on earnings and employment in 2009. This information is shown in the two tables below. Table 20 shows median income the year following graduation. This is 2009 earnings for a person graduating in 2008. The median income for all working graduates is $31,190. The median for graduates 9 The Upper Midwest region includes data for the Great Lakes (IL IN MI OH WI) and the Plains (IA KS MN MO NE ND SD) states New Pharos Consulting Page 40 from for-profit institutions is $35,000 or more than 12 percent higher than the level for all students. In fact, for the region, income for students graduating from for-profit institutions is higher than any other sector. Clearly students leaving career colleges with four year degrees are doing well in the job market compared to their peers from other institutions. Table 20 Median Income the Year Following Graduation by Sector Total Median Income in 2009 31,190 Institution sector in 2007-08 Public 2-year Public 4-year non doctorate Public 4-year doctorate Private nonprofit 4-yr non doctorate Private nonprofit 4-year doctorate Private for-profit 2 years or more Attended more than one institution ? 31,000 30,000 31,990 34,486 35,000 30,389 Table 21 concerns employment and enrollment the year following graduation. The data shown is a percent of the total for that sector. Of all graduates, 52.7 percent had a full time job and were not enrolled. The figure for for-profit schools was 62.2 percent, well above the total and again the highest among all sectors. Students in the other sectors did continue some enrollment patterns and working part time. This suggests they may have continued to either attend graduate school or other educational opportunities since jobs in 2009 were difficult to find. The other important data piece in the table is the percentage of students still looking for work. In this case a higher percentage of for-profit graduates were unemployed and not enrolled. Students that graduate from for-profit schools tend to enter the job market more aggressively that students in the other sectors. New Pharos Consulting Page 41 Table 21 Employment and Enrollment Status by Sector Employment and Enrollment Status in 2009 Percent of Total by Sector One full-time job, not enrolled One part-time job, enrolled Unemployed, not enrolled (%) (%) (%) 52.7 5.5 6.1 ? 58.4 51.1 54.2 53.9 62.2 40.7 ? 4.4 6.8 3.7 4 0.7 !! 8.0 ! ? 7 5.9 5.8 6.2 8.5 ! 4.9 Estimates Total Institution sector in 2007-08 Public 2-year Public 4-year non-doctorate Public 4-year doctorate Private nonprofit 4-yr non-doctorate Private nonprofit 4-year doctorate Private for-profit 2 years or more Attended more than one institution Income from Human Capital Investment Career college graduates in 2010 (and all previous years that are living and working in Minnesota) are enjoying a higher level of income and standard of living because of their investment in higher education. In order to measure the impact of this investment on income, data on employment and earnings for all graduates from career colleges living and working in Minnesota would be needed. The increased income for these graduates would be compared against likely incomes these students would earn if they still had only high school diplomas. Unfortunately, this data does not exist. However, it is a useful exercise to provide some very exploratory measures of this impact from the data that is available. Table 22 shows the results of this exploratory exercise. Data on graduates of career schools is available from IPEDS from 2003 through 2010. For these purposes, the total number of degrees was reduced by 15 percent under the assumption that this was a fair assessment of people either not working due to economic conditions, personal choice or migration from the state. Data on the difference in income between a person with only a high school degree and one with a postsecondary degree was provided in section five of this report. The reduced number of graduates is multiplied by the income difference. The results indicate that graduates of career colleges, for the time period shown in Minnesota, annually earn about $300 million more than they would if they had only had a high school degree. This number is suggestive and subject to a number of the assumptions outlined. But clearly it is likely to underestimates the annual true figure since these institutions have been graduating students for decades and these students are still in the work force. As above this additional income increases income tax revenues. Using the same effective tax rate as above, state income taxes would be higher by about $14 million. Since this is exploratory this figure is not included in the tax total above. New Pharos Consulting Page 42 Table 22 Annual Income due to Investment in Degrees at Career Colleges Year 2003 2004 2005 2006 2007 2008 2009 2010 Associate's 1,990 2,214 2,629 2,311 2,487 2,694 2,614 3,589 Number of Reported Degrees Bachelor's Master's 177 36 257 38 370 66 501 95 574 126 628 162 742 193 1,123 238 Professional 38 46 39 52 42 51 50 Total 20,528 4,372 954 318 Total times .85 17,449 3,716 811 270 Income Difference 9,444 23,610 36,689 69,243 Total Incremental Income by Degree 164,793,447 87,739,482 29,750,948 18,716,248 Total Incremental Income across all Degree New Pharos Consulting 301,000,124 Page 43 Appendix 1 Summary of Background and Experience of New Pharos Consulting Mark Misukanis, Ph. D. Dr. Mark Misukanis is a principle in New Pharos Consulting. He possesses over 32 years of experience working in numerous policy areas in state government including tax research, education spanning early childhood through postsecondary education, overall state budgeting covering the programs of every state agency and other policy areas such as economic development and commerce. He spent the first six years of his career in the Department of Revenue operating an extremely large economic model of the state (the REMI model contains literally thousands of equations) producing economic projections as well as simulating alternative policy options. This experience is utilized in the economic impact in this project. He spent 12 years as the fiscal and policy analyst for the education funding committee in the State Senate. In this position he worked on a number of education programs and developed numerous funding formulas. This experience also included cost analysis of school district spending patterns. He has published numerous policy reports on education and other policy areas in the state during this career. He spent eight years as the Senior Budget Analyst and Director of the Office of Fiscal Policy and Analysis in the Senate and has a full understanding of a broad range of funding areas (education, health, human services) and is considered an expert in state budget policy. From 2004 through 2011 he served as Director of Finance and Research for the Minnesota Office of Higher Education. In that role, he managed the daily operations of the Financial, Administrative Operations and Policy Research Divisions of the Office. He also managed the regulatory staff and possesses a strong understanding of private post secondary institutions in the state Dr. Misukanis served as Acting Director of the Agency during 2009. Dr. Misukanis is currently an adjunct faculty at Hamline's Department of Public Administration. He has completed other education projects recently through the Department. During 2009, he prepared a report on cost of living indexes for school districts across the state. This work was done on behalf of Parent's United, a group that represented all of the major education associations in the state. In 2008, he worked with Education|Evolving, a local education group on a project investigating the allocation of school district funds directly to school buildings Dr. Misukanis holds a Ph. D. in Education Policy and Administration from the University of Minnesota and has completed Master's work in Economics with a focus on public finance at the University of Wisconsin and a Bachelor's degree in Economics from the University of St. Thomas. New Pharos Consulting Page 44 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 al Tot nd usse n Ras m 1 1 1 1 Col lege ical t Te chn 2 1 Gra ver Uni Nor thw es nal Am er a Sc Nat io Le 2 1 ITT 2 1 2 Min nes ot nea poli s ican of B hoo l ines Bus hC usin ess sC olle ge usic olle ge o fM f Cu Col lege o leu Mc Nal ly S mi t te titu Cor don B Tec hni cal Ins of P rod ucti Inst i zing Her tute Uni ver sity sity nive r Glo be U th B lina r eco rdin g on a sity ver Uni usin ess y DeV r Dul u rsit yU nive ge olle wn C Bro 1 Min 3 1 nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Accounting AAS Associate BS Certificate Diploma Accounting - Banking AAS Accounting - Financial Accounting AAS Accounting - Financial Investigation AAS Accounting and Financial Management GC Master's Accounting and Tax Specialist AAS Administrative Secretary AAS Diploma Advertising BS Aircraft Dispatch Certificate Applied Information Technology BAS Applied Management AAS BS Architectural or Engineering Drafting/Design and C AAS Audio Engineering Diploma Audio Production Diploma Audio Production and Engineering AAS yA r ts Institutions a Number of Degrees 13 3 1 5 1 3 1 1 1 1 1 1 2 1 1 2 2 2 1 1 1 1 1 1 1 1 3 2 1 1 1 1 1 1 1 1 1 45 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 2 1 1 2 1 1 2 1 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 al Tot nd usse n Ras m 1 1 1 1 1 Col lege ical t Te chn 1 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u DeV r Bro wn C olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Aviation Business AAS B.S. Nursing BS Bachelor of Science in Computer Information System BS Bachelor of Science in Technical Management BS Baking & Pastry AAS Certificate Business AAS Business Administration AAS Bachelor's BS MBA Business Administration - Accounting BS Business Administration - Financial Management BS Business Administration -- Financial Management BS Business Administration - Human Resources AAS Business Administration - Information Systems BS Business Administration - Information Technology BS Business Administration - International Business BS Business Administration - Internet Marketing AAS BS Business Administration - Management BS Business Administration - Marketing BS yA r ts Institutions a Number of Degrees 1 1 1 1 1 1 1 1 2 1 1 1 1 12 5 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 46 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution 1 1 Inst itu te sity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 nd Tot usse n al Col lege ical Ras m 1 1 Gra ver t Te chn ican Uni Nor thw es nal Am er hoo l of B Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern tute Art Inst i Col lege New Pharos Consulting Aca dem y Degree by Progam Business Administration Online BS Business Administrative Assistant Diploma Business Management BS Business Management - Business Administration AAS Business Management - Business Management BS Business Management - Call Center Management AAS Business Management - Child Development AAS Business Management - Entrepreneurship AAS Business Management - Human Resource AAS Business Management - Marketing and Sales AAS CAD Drafting AAS Diploma Computer and Electronics Engineering Technology AAS Computer Animation AAS Certificate Computer Drafting and Design AAS Computer Information Systems BS Computer Programmer AAS Diploma Computer Science BS Corrections AAS yA r ts Institutions a Number of Degrees 2 2 2 2 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 47 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 2 1 1 3 1 2 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 al nd Tot usse n Ras m 1 1 2 1 1 Col lege ical t Te chn 1 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Le 1 1 Mc Nal ly S mi t te titu Ins hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u rsit yU nive wn C Bro DeV r olle ge y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Cosmetology Business AAS Crime Scene Evidence Associate Criminal Justice AAS BS Criminal Justice (MBS Online) AAS Criminal Justice (MSB Online) BS Criminal Justice: Client Services/Corrections Bachelor's Criminal Justice: Criminal Offenders Bachelor's Criminal Justice: Homeland Security Associate Bachelor's Criminal Justice: Investigation/Law Enforcement Bachelor's Criminal Justice: Psychology Bachelor's Culinary Arts AAS Culinary Arts AAS Culinary Management BS Database Administration AAS Dental Assistant AAS Diploma Dental Hygiene AAS Design Management BS Digital Art & Design BS yA r ts Institutions a Number of Degrees 2 2 1 1 9 4 5 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 48 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 al nd Tot usse n Ras m 1 1 1 1 Col lege ical t Te chn 1 1 Gra ver Uni Nor thw es nal Am er ican of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her Inst i Uni ver sity sity Glo be U nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Digital Entertainment and Game Design BS Digital Film & Video Production BS Digital Photography BS Digital Video & Media Production AAS Digital Video and Media Production BFA Early Childhood Education Associate Early Childhood Education: Child & Family Studies Associate Early Childhood education: Child Development AAS Associate Early Childhood Education: Special Needs Associate Electronic Commerce Management GC Electronics and Communications Engineering Technology BS Electronics and Computer Technology Bachelor's Engineering Program AAS Entrepreneurship GC Exercise Science Associate Fashion & Retail Management BS Financial Analysis GC Front Office Support Diploma Game and Application Development AAS yA r ts Institutions a Number of Degrees 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 49 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 al nd Tot usse n Ras m 2 1 1 1 Col lege ical t Te chn 1 1 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam BS Game Design and Development BS Graphic Design AAS BS Certificate Diploma Graphic Design Media AAS Health Care Management BS Health Coding Diploma Health Fitness Management - GU and MSB Online only MS Health Fitness Specialist AAS BS Health Information Technician AAS Health Information Technology AAS Health Services Management GC Healthcare Coding Diploma Healthcare Management Bachelor's Hip Hop Diploma Hospitality Management BS Human Resource Management GC Master's Human Services Associate Human Services Online yA r ts Institutions a Number of Degrees 1 1 1 6 3 1 1 1 1 1 3 3 1 1 2 2 4 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 50 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 al nd Tot usse n Ras m 1 1 1 1 1 1 Col lege ical t Te chn 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern tute Art Inst i Col lege New Pharos Consulting Aca dem y Degree by Progam AAS Information Management: Game and Simulation Production Bachelor's Information Management:: Digital Design and Animation Bachelor's Information Security GC Information Systems Management GC Master's Information Systems Management - Computer Information Technology AAS Information Systems Management - Database Administration AAS Information Systems Management - Network Administration AAS Information Systems Management - Networking Security and Forensics AAS Information Systems Management - Web Programming AAS Information Systems Security BS Information Technology AAS BS Information Technology -- Computer Network Systems AAS Information Technology -- Multimedia AAS Information Technology -- Software Applications and Programming AAS Information Technology /Net Admin/Microsoft BS Information Technology Emphasis/Programming BS Information Technology/Management Info. Systems BS Information Technology-Network Management/Microsoft BS yA r ts Institutions a Number of Degrees 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 3 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 51 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 al Gra nd Tot usse n Ras m 1 1 1 1 1 Col lege ical t Te chn Nor thw es nal Am er ican Uni ver usin ess of B hoo l Nat io a Sc Min nes ot nea poli s Min 1 1 2 1 1 1 1 sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Le Mc Nal ly S mi t te titu Ins hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Interactive Media and Graphic Design AAS Interior Design AAS BS Interior Planning with AutoCAD AAS Law Enforcement Associate Certificate Le Cordon Bleu Patisserie and Baking AAS Legal Admin Assistant/Secretary AAS Diploma Legal Administrative Assistant Diploma Legal Office Assistant Certificate Management BS Master's Management Accounting AAS Massage Therapy AAS Diploma Master of Accounting and Financial Management Master's Master of Business Administration GC Master's MBA Master of Business Administration Master's MBA Master of Business Administration (GU Online Division) MBA Master of Business Administration (MSB Online Division) yA r ts Institutions a Number of Degrees 2 2 3 1 2 1 1 2 1 1 1 1 2 1 1 2 2 1 1 2 1 1 2 2 8 4 4 1 1 3 1 1 1 2 1 1 1 1 1 52 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 2 1 1 2 1 1 1 2 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 al Tot nd usse n Ras m 1 1 1 1 Col lege ical t Te chn 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam MBA Master of Management Master's Master of Science in Management MS Master of Science in Management (GU Online Division) MS Master of Science in Management (MSB Online Division) MS Masters in Human Resources Management Master's Media Arts & Animation BS Media Business BS Medical Administration Certificate Medical Administration AAS Medical Administrative Assistant AAS Diploma Medical Assistant AAS Certificate Diploma Medical Billing & Coding Certificate Medical Billing & Coding Online AAS Diploma Medical Billing and Insurance Coding AAS Diploma Medical Laboratory Technician AAS Medical Staff Services Management AAS Medical Transcriptionist yA r ts Institutions a Number of Degrees 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 6 3 3 14 8 1 5 1 1 2 1 1 2 1 1 1 1 1 1 1 53 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 3 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 Tot nd Ras m usse n al Col lege ical t Te chn 1 1 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern tute Art Inst i Col lege New Pharos Consulting Aca dem y Degree by Progam AAS Microcomputer Support Specialist AAS Multimedia Design and Development Bachelor's Music Business AAS BA Diploma Music Composition BA Music Performance BA Music Performance: AAS AAS Diploma Music Performance: AFA Associate Music Producer AAS BA Music Production: AAS AAS Network & Communications Management BS Network & Communications Mgmt GC Master's Network Administration AAS Certificate Network Development AAS Network Management AAS Nursing BSN Nursing - RN to BSN BS yA r ts Institutions a Number of Degrees 1 1 1 1 1 5 3 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 2 1 1 54 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 al nd Tot usse n Ras m 2 1 2 1 1 1 1 Col lege ical t Te chn 1 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti Inst i zing Her be U Glo tute Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam Office Management Certificate Paralegal AAS BS Certificate Paralegal Online AAS Paralegal Studies AAS BS Pharmacy Technician AAS Phlebotomy Technician Diploma Photography BFA Post-baccalaureate Paralegal Certificate PBC Practical Nursing AAS Diploma Pro Tools Certificate Certificate Pro Tools Professional Certificate Professional Nursing Associate Professional Pilot AAS Certificate Project Management GC Master's Project Management Certificate Public Administration Master's Radio Broadcasting yA r ts Institutions a Number of Degrees 1 1 7 4 2 1 1 1 2 1 1 2 2 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 55 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 2 1 2 1 al nd Tot usse n Ras m 1 1 2 1 1 1 1 Col lege ical t Te chn 1 Gra ver Uni ican nal Am er Nor thw es of B hoo l Nat io a Sc Min nes ot nea poli s Min usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Mc Nal ly S mi t te titu Ins Le hni cal Tec ITT usic lina r eco rdin g of P rod ucti tute zing Her be U Glo Inst i Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam AAS Recording Engineer: Emphasis Live Sound Diploma Recording Technology: AAS AAS Diploma RN to BSN Completion Program - MSB Online only BSN Sales and Marketing AAS Security Administration AAS Software Development AAS Software Engineering Technology AAS BS Software Programming AAS Surgical Technologist AAS Technical Management BS Technical Support Certificate Television Production AAS The Art of Cooking Certificate Therapeutic Massage AAS Diploma Transportation Business AAS Travel & Hospitality AAS Diploma Veterinary Technology AAS yA r ts Institutions a Number of Degrees 1 1 1 2 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 5 3 56 Appendix 2 MCCA Detailed Program Offerings by Degree by Institution Inst itu te sity 1 1 1 1 1 1 2 1 1 31 20 16 13 38 12 5 10 2 15 17 45 43 2 54 al Tot nd usse n Ras m 1 1 1 1 1 1 33 Col lege ical t Te chn 1 1 1 1 1 1 1 Gra ver Uni Nor thw es nal Am er ican of B hoo l Nat io a Sc Min nes ot nea poli s Min 1 usin ess sC olle ge Bus hC ines olle ge o fM f Cu Col lege o leu Cor don B Le Mc Nal ly S mi t te titu Ins hni cal Tec ITT usic lina r eco rdin g of P rod ucti Inst i zing Her be U Glo tute Uni ver sity sity nive r usin ess th B Dul u wn C Bro DeV r olle yU nive ge rsit y Uni ver on a sity nd R Inc . Min nes ot nal atio s In tern Art Inst i tute Col lege New Pharos Consulting Aca dem y Degree by Progam BS Visual Communication AAS Visual Communications/Graphic Design BS Visual Communications/Multimedia BS Visual Effects & Motion Graphics BS Web Design Certificate Web Design AAS Web Design & Interactive Media AAS BS Web Graphic Design Associate Wireless Communications GC Grand Total yA r ts Institutions a Number of Degrees 2 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 356 57 Appendix 3 Demographic, Social-Economic and Academic Variables for Minnesota Higher Education Sectors Percent of Total by Variable Public 2Year Private NotPrivate Not-for- for-profit 4Public 4-year Public 4-Year profit 4-yr Year Non-doctorate Doctorate Nondoctorate Doctorate Private For- Attended More profit 2 Years Than One or More Institution Total Total Enrollment 39.9 9.4 20.1 7.4 6.2 8.4 8.7 ! 100% Gender Male Female 42.7 37.9 11.0 8.2 18.1 21.5 7.5 7.3 6.5 6.1 6.1 10.0 8.1 ! 9.0 ! 100% 100% Disability: Has some type of disability No Yes 38.7 50.9 9.4 8.9 21.3 8.2 !! 7.6 5.5 6.4 4.8 ! 8.1 11.3 8.5 ! 10.4 ! 100% 100% Dependency and marital status (separated is unmarried) Dependent 29.5 Indep, no deps, unmarried/separated 49.3 Indep, no dependents, married 56.3 Indep, with deps, unmarried/separated 55.3 Indep, with dependents, married 58.8 11.3 7.6 6.8 4.8 7.0 28.1 13.6 9.6 ! 8.5 ! 3.0 ! 11.1 1.9 ! 3.5 1.2 2.8 ! 7.5 5.7 5.7 ! 2.4 ! 4.3 3.4 13.5 8.4 20.7 15.9 9.0 ! 8.3 9.6 ! 7.1 ! 8.4 ! 100% 100% 100% 100% 100% Single parent independent students Not a single parent Single parent 38.0 55.3 9.9 4.8 21.5 8.5 ! 8.1 1.2 6.7 2.4 ! 6.9 20.7 8.8 ! 7.1 ! 100% 100% Marital status Single, divorced, or widowed Married Separated 36.0 58.0 46.4 10.0 6.9 1.7 !! 23.5 5.0 0.0 8.4 3.0 0.0 6.6 4.7 4.0 !! 6.8 13.6 41.3 8.7 8.8 ! 6.6 !! 100% 100% 100% Race/ethnicity (with multiple) White Black or African American Hispanic or Latino Asian American Indian or Alaska Native More than one race 39.6 51.5 39.0 28.8 68.3 27.6 10.3 4.7 6.4 ! 6.9 4.1 ! 6.7 ! 19.3 9.4 22.6 !! 38.4 13.8 !! 38.5 ! 8.1 3.2 7.1 !! 6.6 1.2 !! 4.1 ! 6.5 4.8 !! 5.8 ! 7.5 ! 0.0 3.2 !! 7.4 17.3 12.6 ! 4.9 ! 9.9 ! 14.5 ! 9.0 ! 9.1 ! 6.6 ! 6.8 ! 2.6 !! 5.5 !! 100% 100% 100% 100% 100% 100% New Pharos Consulting Fields with single or double exclamation points have wide confidence intervals 58 Appendix 3 Demographic, Social-Economic and Academic Variables for Minnesota Higher Education Sectors Percent of Total by Variable Public 2Year Private NotPrivate Not-for- for-profit 4Public 4-year Public 4-Year profit 4-yr Year Non-doctorate Doctorate Nondoctorate Doctorate Private For- Attended More profit 2 Years Than One or More Institution Total Total Enrollment 39.9 9.4 20.1 7.4 6.2 8.4 8.7 ! 100% Parent's highest education level Do not know parent's education level Did not complete high school High school diploma or equivalent Vocational or technical training Less than two years of college Associate's degree 2 or more years of college but no degree Bachelor's degree Master's degree or equivalent First-professional degree Doctoral degree or equivalent 63.5 59.1 50.5 49.3 43.0 42.0 38.7 31.0 31.1 16.3 20.0 4.5 4.9 9.7 12.3 8.2 11.3 9.5 10.4 9.8 3.4 ! 5.3 4.3 !! 7.0 !! 10.1 11.1 ! 18.9 19.1 ! 27.8 27.6 22.9 42.8 ! 35.3 0.8 !! 3.5 3.9 4.8 4.8 3.2 6.4 ! 9.9 10.8 18.6 ! 20.4 6.4 8.1 !! 4.2 6.7 5.1 ! 6.8 3.8 ! 6.8 9.1 6.7 ! 7.3 14.0 10.4 ! 14.4 8.4 8.7 8.5 6.9 5.1 5.9 1.1 !! 5.8 6.5 ! 7.0 ! 7.2 ! 7.5 ! 11.2 ! 9.1 ! 7.0 ! 9.2 ! 10.5 ! 11.2 !! 6.0 !! 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Veteran status Not a veteran Veteran 39.7 47.3 9.4 7.4 ! 20.4 3.9 !! 7.5 2.0 !! 6.3 2.7 !! 8.0 27.3 8.6 ! 9.4 ! 100% 100% Age as of 12/31/07 18 or younger 19-23 24-29 30-39 40 or older 35.5 31.1 51.4 51.3 62.4 9.6 11.0 7.4 6.5 6.0 26.3 ! 26.9 10.0 ! 9.4 4.1 ! 13.3 9.8 2.1 3.3 ! 0.9 7.0 ! 7.2 5.3 3.4 5.0 3.2 ! 4.4 14.7 17.4 15.4 5.1 ! 9.5 ! 9.1 ! 8.8 ! 6.2 ! 100% 100% 100% 100% 100% Attendance pattern Full-time/full year, 1 institution Full-time/full year, 2+ institution Full-time/part year Part-time/full year, 1 institution Part-time/full year, 2+ institution 25.2 0.0 49.4 62.1 0.0 13.2 0.0 9.8 5.8 0.0 36.7 0.0 8.2 !! 8.3 0.0 12.7 0.0 10.5 2.3 ! 0.0 9.4 0.0 6.4 ! 4.4 0.0 2.9 0.0 11.3 17.0 0.0 0.0 100.0 4.5 ! 0.0 100.0 100% 100% 100% 100% 100% New Pharos Consulting Fields with single or double exclamation points have wide confidence intervals 59 Appendix 3 Demographic, Social-Economic and Academic Variables for Minnesota Higher Education Sectors Percent of Total by Variable Public 2Year Private NotPrivate Not-for- for-profit 4Public 4-year Public 4-Year profit 4-yr Year Non-doctorate Doctorate Nondoctorate Doctorate Private For- Attended More profit 2 Years Than One or More Institution Total Total Enrollment 39.9 9.4 20.1 7.4 6.2 8.4 8.7 ! 100% Part-time/part year 60.5 7.5 7.3 ! 1.4 !! 3.1 13.9 6.3 !! 100% Credit cards: Balance due on all credit cards Less than $500 $500-999 $1,000-1,999 $2,000-2,999 $3,000 or more 39.2 31.9 36.0 34.7 40.5 12.8 12.7 ! 10.7 ! 11.0 14.1 7.1 !! 33.1 ! 26.1 ! 19.9 ! 23.2 ! 18.4 ! 4.6 ! 6.1 ! 8.5 ! 2.5 !! 8.0 ! 6.3 ! 9.1 !! 6.1 ! 5.4 5.8 ! 4.8 !! 2.4 ! 10.7 ! 3.4 ! 8.7 ! 6.6 !! 9.7 ! 9.1 !! 10.9 !! 100% 100% 100% 100% 100% Total income by dependency Dependent: Less than $10,000 Dependent: $10,000-$19,999 Dependent: $20,000-$29,999 Dependent: $30,000-$39,999 Dependent: $40,000-$49,999 Dependent: $50,000-$59,999 Dependent: $60,000-$69,999 Dependent: $70,000-$79,999 Dependent: $80,000-$99,999 Dependent: $100,000 or more 43.6 39.7 34.2 31.8 34.5 39.0 35.7 33.0 24.7 20.5 7.3 ! 9.3 ! 7.8 9.2 13.0 13.8 17.2 12.3 13.1 9.4 20.6 !! 21.6 ! 24.9 ! 36.4 19.0 13.8 ! 13.2 ! 28.3 29.5 38.5 6.0 !! 10.3 !! 9.7 ! 6.5 11.0 9.1 13.4 ! 9.1 14.0 12.6 5.5 ! 6.8 6.6 !! 2.9 !! 10.2 ! 8.8 ! 5.5 !! 6.2 7.0 9.3 5.4 7.0 ! 4.2 ! 4.7 5.9 ! 2.8 4.3 3.9 ! 1.9 ! 2.2 11.6 !! 5.2 ! 12.5 ! 8.6 6.4 ! 12.7 ! 10.7 ! 7.3 ! 9.8 ! 7.5 ! 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Independent: Less than $5,000 Independent: $5,000-$9,999 Independent: $10,000-$19,999 Independent: $20,000-$29,999 Independent: $30,000-$49,999 Independent: $50,000 or more 49.5 54.7 51.7 55.0 55.4 56.9 5.9 7.8 7.0 4.2 6.3 8.2 22.6 12.5 ! 9.0 ! 6.7 !! 6.8 ! 5.0 !! 1.2 !! 1.9 !! 0.7 !! 3.4 ! 1.9 3.0 ! 7.2 !! 4.8 ! 2.9 1.8 ! 4.9 5.8 7.8 10.1 20.1 20.8 16.8 12.2 5.9 8.1 ! 8.8 8.1 ! 7.9 ! 9.0 ! 100% 100% 100% 100% 100% 100% Dependent parent income Less than $36,000 $36,000-66,999 $67,000-104,999 37.2 36.1 27.1 8.3 13.9 12.8 25.6 17.3 30.8 8.1 9.9 11.9 5.8 7.7 6.7 5.3 4.4 2.8 9.7 ! 10.7 ! 7.9 ! 100% 100% 100% New Pharos Consulting Fields with single or double exclamation points have wide confidence intervals 60 Appendix 3 Demographic, Social-Economic and Academic Variables for Minnesota Higher Education Sectors Percent of Total by Variable Public 2Year Private NotPrivate Not-for- for-profit 4Public 4-year Public 4-Year profit 4-yr Year Non-doctorate Doctorate Nondoctorate Doctorate Private For- Attended More profit 2 Years Than One or More Institution Total Total Enrollment 39.9 9.4 20.1 7.4 6.2 8.4 8.7 ! 100% $105,000 or more 20.2 9.3 37.8 13.6 9.4 1.8 7.9 ! 100% Prior degree: Undergraduate certificate or diploma No Yes 43.4 61.0 13.6 4.8 9.6 5.6 !! 3.5 2.1 !! 7.0 ! 4.5 10.6 13.4 12.3 ! 8.5 ! 100% 100% Undergraduate degree program Certificate Associate's degree Bachelor's degree Not in a degree program or others 80.4 80.7 0.8 67.3 0.5 ! 0.1 !! 17.9 7.6 !! 0.0 0.0 39.8 0.0 1.1 !! 0.0 !! 14.4 2.2 2.7 ! 0.8 11.4 0.5 !! 9.5 9.1 8.0 3.6 ! 5.8 ! 9.3 ! 7.8 18.9 ! 100% 100% 100% 100% Field of study: Undergraduate Undeclared Humanities Social/behavioral sciences Life sciences Physical sciences Math Computer/information science Engineering Education Business/management Health Vocational/technical Other technical/professional 23.0 50.3 3.1 8.4 4.8 !! 7.2 !! 37.2 33.9 12.5 33.6 62.0 84.3 38.3 6.7 4.6 13.4 13.3 12.3 ! 11.3 !! 6.5 13.1 25.5 14.5 4.6 2.1 ! 9.9 6.8 !! 23.2 55.3 50.1 18.2 !! 41.8 !! 4.5 !! 43.1 16.4 !! 9.1 ! 2.9 ! 5.6 !! 24.9 26.2 8.4 12.8 9.0 34.3 ! 9.6 !! 3.8 ! 0.8 !! 20.5 6.1 1.6 ! 0.0 4.6 14.5 ! 3.0 7.0 9.2 ! 21.9 !! 26.8 !! 5.7 !! 0.6 !! 11.0 ! 9.8 7.2 0.5 !! 3.5 11.7 2.3 0.5 !! 1.2 !! 0.0 0.0 31.0 5.0 1.4 !! 19.8 11.6 3.7 ! 11.5 11.1 ! 8.3 ! 7.9 ! 8.8 ! 8.5 !! 3.3 !! 11.4 !! 3.5 ! 12.7 ! 7.2 10.1 ! 3.9 ! 7.3 ! 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Highest level of education ever expected Certificate Associate's degree Bachelor's degree 85.7 77.0 45.6 0.0 0.3 10.1 0.0 0.0 14.3 0.0 0.1 !! 5.5 0.6 !! 0.8 !! 5.9 9.3 14.9 10.3 4.4 ! 6.8 ! 8.3 ! 100% 100% 100% New Pharos Consulting Fields with single or double exclamation points have wide confidence intervals 61 Appendix 3 Demographic, Social-Economic and Academic Variables for Minnesota Higher Education Sectors Percent of Total by Variable Public 2Year Private NotPrivate Not-for- for-profit 4Public 4-year Public 4-Year profit 4-yr Year Non-doctorate Doctorate Nondoctorate Doctorate Private For- Attended More profit 2 Years Than One or More Institution Total Total Enrollment 39.9 9.4 20.1 7.4 6.2 8.4 8.7 ! 100% Post-BA or post-master certificate Master's degree Doctoral degree First-professional degree 33.5 27.4 18.5 18.3 8.0 13.6 5.5 9.8 23.4 ! 24.9 46.3 34.4 11.8 10.1 12.2 ! 12.0 10.9 !! 8.5 5.9 7.7 4.0 ! 6.5 2.3 ! 5.5 ! 8.5 ! 9.0 ! 9.3 ! 12.3 100% 100% 100% 100% Work: Primarily student or employee Student working to meet expenses Employee who decided to enroll in school 35.0 58.1 9.9 5.7 24.8 4.4 ! 8.9 1.7 6.6 4.5 5.6 17.9 9.2 7.7 ! 100% 100% Work: Hours per week 1-15 hours 16-25 hours 26-39 hours 40 or more hours 23.2 34.8 54.3 53.4 9.6 10.4 8.2 7.1 30.0 29.0 12.1 ! 6.5 14.1 7.2 2.6 3.6 10.6 4.1 5.2 4.6 3.3 5.3 8.8 16.7 9.3 ! 9.1 8.7 ! 8.2 ! 100% 100% 100% 100% Job: earnings from work while enrolled (include work-study/assistantship) $1-2,399 29.2 11.2 $2,400-5,999 32.0 7.9 $6,000-12,999 45.9 10.7 $13,000 or more 51.6 6.8 19.6 31.4 19.9 9.6 14.9 9.8 2.9 3.1 10.9 6.1 4.1 4.5 4.5 4.5 7.4 15.8 9.7 8.2 ! 9.1 ! 8.5 ! 100% 100% 100% 100% Independent student and spouse income Less than $11,000 $11,000-25,999 $26,000-48,399 $48,400 or more 16.5 10.4 ! 5.4 ! 4.8 !! 1.4 ! 1.8 ! 2.4 2.9 ! 5.9 ! 2.7 3.6 6.0 11.7 17.4 19.3 12.4 6.5 8.8 ! 8.0 ! 9.0 ! 100% 100% 100% 100% 51.5 52.5 56.0 56.9 6.5 6.4 5.4 8.0 New Pharos Consulting Fields with single or double exclamation points have wide confidence intervals 62 Appendix 4 Sources of Information for Economic Impact Study 1. General Fund Budget History; Minnesota Department of Management and Budget 2. Presentations from Tom Stinson and Tom Gillaspy on Budget Trends, 2011 3. November 2011 Budget Forecast; Minnesota Department of Management and Budget 4. ISEEK web site; Minnesota Department of Employment and Economic Development 5. Integrated Postsecondary Education Data System (IPEDS); U.S. Department of Education 6. National Postsecondary Student Aid Study (NPSAS) 2008; U.S. Department of Education 7. Industry and Occupational Projections 2009- 2019; Minnesota Department of Employment and Economic Development 8. Baccalaureate and Beyond Longitudinal Study, 2008-09; U.S. Department of Education 9. Minnesota Office of Higher Education; Web site and personal communications 10. Regional Input-Output Modeling System (RIMS II); Bureau of Economic Analysis 11. Help Wanted: Projections of Jobs and Education Requirements Through 2018;June 15, 2010, Anthony P. Carnevale; Georgetown University Center on Education and the Workforce. 12. Occupational Projections; US Bureau of Labor Statistics 13. Tax Incidence Study, 2011; Minnesota Department of Revenue 14. Input/Output Tables and Gross Domestic Product Estimates; Bureau of Economic Analysis 15. American Community Survey, 2007-2009, Minnesota; US Bureau of the Census 16. Population Projections, Minnesota State Demographic Center