Labour Market Relevance and Outcomes of Higher Education in Four US States

Ohio, Texas, Virginia and Washington

Report

Higher Education

8 July 2020

6. Virginia

Abstract

This chapter provides an overview of the labour market and higher education system in the state of Virginia, an assessment of the labour market outcomes of graduates, and a discussion of state policies that contribute to aligning higher education and the labour market. The policy discussion focuses on four policy areas – strategic planning and co-ordination of higher education; educational offerings, student supports and pathways; funding; and information – and includes policy recommendations in each area.

6.1. The labour market and higher education in Virginia

The economy and labour market

Virginia’s economy relies heavily on federal spending and is bolstered by a growing professional services industry

With a Gross Domestic Product (GDP) of USD 533 billion and a population of 8.5 million, Virginia is an economy comparable in size to Sweden, with economic output that represents around 2.6% of total GDP in the United States (U.S. Bureau of Economic Analysis, 2019[1]). Virginia’s recovery from the recession of 2008-09 has been slow. However, between 2017 and 2019, the economy experienced faster growth in real GDP than in the previous decade, though still a lower rate of growth than for the country as a whole (U.S. Bureau of Economic Analysis, 2019[2]). The state’s economy relies heavily on federal government spending, accounting for about one-third of economic activity. In addition, recent economic gains have been concentrated primarily in the northern and central regions of the state. Consequently, Virginia’s current economic development policy is focused on diversifying and fostering growth in other sectors of the economy to ensure shared growth in all regions of the state (Northam Administration, 2018[3]).

Virginia has a reputation for business-friendliness and scores highly on a number of national rankings of best states to live in and do business (U.S. News & World Report, 2019[4]; CNBC, 2019[5]). Amazon’s decision in 2019 to establish its second headquarters in Virginia is a sign of the state’s economic attractiveness. The company’s decision rested largely on Virginia’s educated workforce and the state’s commitment to invest further in building advanced technology skills (VEDP, 2019[6]), as described in Box 6.1. Following a period of accelerated job growth in professional and business services, which includes management and professional, scientific and technical services, this sector now employs the most people in Virginia (U.S. Bureau of Labor Statistics, 2019[7]). As of 2018, the professional and business services sector accounts for about 19% of total employment, followed by the government sector (18%) and the trade, transportation and utilities sector (16%) (U.S. Bureau of Labor Statistics, 2019[8]). Professional, scientific and technical services contribute substantially to Virginia’s economy, representing approximately 15% of all establishments and more than 20% of total wages, which is driven largely by computer systems design and related services (VEC, 2019[9]). The education and healthcare services sector has also been growing, while the manufacturing sector has fluctuated between decline and stagnation in the last decade, demonstrating Virginia’s shift to a service-based economy (VEC, 2019[9]).

Like many states, however, Virginia is characterised by a distinct rural-urban divide. The state boasts a high-tech economy in the north, which includes professional services supporting the federal government in the area surrounding Washington, DC. Northern Virginia hosts many large technology companies, as well as the largest concentration of data centres in the world. Moreover, the arrival of the new Amazon headquarters in the region is expected to add 25 000 new high-skill jobs within the next 10-20 years, in addition to boosting employment in supporting sectors (VEDP, 2019[6]). The south-western region – formerly reliant on coal and tobacco – is now primarily dominated by agriculture and health services, and an emerging advanced manufacturing sector. The coastal area to the east includes the diverse Hampton Roads region, and is the third most populous region in the state and home to the world’s largest naval base. This region specialises in naval shipbuilding and relies heavily on federal defence spending. Despite the ongoing presence of large-scale military employment, parts of the region are heavily distressed, with low labour force participation and low levels of post-secondary educational attainment (Old Dominion University, 2018[10]).

Thus, the economic profile of Virginia’s more rural and coastal areas differs widely from the urban centres of Northern Virginia and the capital region. Median household income is about three times higher in Northern Virginia than in the south-western region, and the predominant share of economic growth since 2010 has been concentrated in the metropolitan areas around Richmond, Charlottesville, Blacksburg, and Northern Virginia (Old Dominion University, 2018[10]). While population growth in the south-central and south-western regions has been declining in the last decade, the population in urban areas of Virginia has continued to grow, with Northern Virginia now representing two-thirds of the total population in the state (UVA Weldon Cooper Center, 2019[11]). In the state overall, out-migration has been larger than in-migration in recent years, but this pattern may shift in coming years, particularly with the continued growth of the technology sector. The proportion of Virginians above the retirement age of 65 (15.4%) is offset by a larger youth population, which forms the basis of the future workforce. The dependency ratio in Virginia is lower than both the US and OECD average. Table 6.1 presents an overview of some key contextual indicators for Virginia.

	Virginia	United States	Source
Population
Population estimate	8 517 685	327 167 434	U.S. Census
Dependency ratio (% 65+ over population aged 15-64)	23.3%	24.5%	OECD regional statistics
Percentage of individuals under the age of 18	22.0%	22.4%	U.S. Census
Percentage of individuals aged 65 and over	15.4%	16.0%	U.S. Census
Percentage of Black or African American individuals	19.9%	13.4%	U.S. Census
Percentage of Hispanic or Latino individuals	9.6%	18.3%	U.S. Census
Percentage of Asian individuals	6.9%	5.9%	U.S. Census
Percentage of American Indian or Alaska Native individuals	0.5%	1.3%	U.S. Census
Percentage of White (non-Hispanic) individuals	61.5%	60.4%	U.S. Census
Economy and labour market
GDP per capita	USD 56 110	USD 57 052	U.S. Bureau of Economic Analysis
Labour force participation rate (out of civilian population aged 16+, November 2019)	66.5%	62.9%	U.S. Bureau of Labor Statistics
Unemployment rate (seasonally adjusted)	2.7%	3.9%	U.S. Bureau of Labor Statistics
Median annual earnings for working-age population aged 25-64	USD 55 000	USD 50 000	American Community Survey
Estimated annual wage needed to cover basic expenses for a full-time working adult	USD 29 474	USD 25 297	MIT Living Wage Calculator
Percentage of population aged 25-64 with an associate’s degree or higher	49.6%	42.5%	American Community Survey

Notes: All numbers are for 2018 unless otherwise noted. Racial and ethnic categories are mutually exclusive. MIT Living Wage annual calculations are based on full-time working hours (2 080 hours per year).

Statlink

Box 6.1. Virginia’s investment in advanced technology skills

In 2019, Amazon confirmed that it would establish its second headquarters (HQ2) in Arlington, Virginia. Virginia’s proposal to Amazon, spearheaded by the Virginia Economic Development Partnership (VEDP), was based on a package of corporate incentive grants, regional infrastructure expansion, and a commitment to building a highly skilled, technical workforce

The cornerstone of this investment is the Tech Talent Pipeline initiative, which includes state investments of up to USD 1.1 billion to increase the supply of graduates in computer science and closely related fields. For higher education institutions to be eligible for a grant from the state, each institution is required to enter into a memorandum of understanding that sets criteria for eligible degrees, degree production goals, and graduation rates. The initiative also includes a Tech Internship Program, which aims to increase internship opportunities for baccalaureate students, as well as the launch of a new Virginia Tech campus in Northern Virginia (VEDP, 2017[12]).

Today, there are approximately 60 higher education institutions in Virginia that offer computer science degrees. The Tech Talent Pipeline initiative aims to expand and strengthen the capacity of programmes through a performance-based tech talent investment fund, from which higher education institutions can apply for funding for faculty recruitment, state capital investment and enrolment support. While the largest proportion of funds (up to USD 710 million) will go towards supporting bachelor’s level education at higher education institutions across the state, funding will also be set aside for master’s level programmes at institutions in Northern Virginia. Virginia’s community college system has also committed to designing short degree and certificate programmes in technical fields to complement the bachelor’s and master’s programmes.

A final component of the Tech Talent Pipeline initiative is strengthening STEM (science, technology, engineering and mathematics) and computer science learning in the public primary and secondary schooling system, for which USD 25 million has been earmarked.

Sources: HQNOVA (2017[13]), VEDP (2017[12]).

Virginia has high levels of educational attainment and low unemployment, although regional variations are significant

The labour market in Virginia has tightened in recent years. In November 2019, the unemployment rate in Virginia was 2.7%, significantly lower than that for the United States overall (3.5%) (U.S. Bureau of Labor Statistics, 2019[14]). Furthermore, based on measures of labour underutilisation used by the U.S. Bureau of Labor Statistics, Virginia has significantly lower rates of underemployment in the working population compared to the United States as a whole. Employment growth, combined with negative net domestic migration, has likely contributed to the historically low unemployment level in Virginia, as the size of the labour force has grown at a slower pace than the rate of employment (Old Dominion University, 2018[10]). Rural areas, however, tend to face tougher labour market conditions, due in part to declining industries and fewer new job opportunities. In Wise County, located at the western end of the state, the unemployment rate in November 2019 was 4.2% (not seasonally adjusted), compared to 1.7% in Arlington County in the north and 2.2% in Shenandoah County in the valley region (U.S. Bureau of Labor Statistics, 2019[8]).

The total number of jobs in Virginia increased by a moderate 5.6% from 2013 to 2018, which was below the national growth rate of 7.4% during this period, but positive and stable (Emsi, 2018[15]). Long-term labour market projections suggest an employment growth of approximately 10% by 2026 (VEC, 2019[16]). Virginia is currently facing shortages of healthcare professionals, educators, and skilled tradespeople (e.g. welders, plumbers, electricians), reflecting a nation-wide trend (Virginia Career Works, 2019[17]). According to projections from the Virginia Employment Commission, a substantial share of job growth in the next five years is expected to occur in the healthcare, social assistance and professional services sectors. Moreover, technological change will affect the pattern of skill demand as jobs with more routine-based tasks gradually disappear (Autor, Levy and Murnane, 2003[18]; Frey and Osborne, 2017[19]). In Virginia, there is some evidence of a growing polarisation of jobs, with job growth concentrated in low-wage and high-wage occupations between 2008 and 2016 (UVA Weldon Cooper Center, 2017[20]). There is also evidence to suggest that the globalisation of labour markets has caused a decline in real earnings among those without a college degree (Autor, 2014[21]).

Educational attainment levels are higher in Virginia than in most other states, with about 50% of the working-age population (aged 25-64) having earned either an associate’s degree, a bachelor’s degree or a graduate or professional degree, as shown in Figure 6.1. When including post-secondary certificates, approximately 55% of the working-age population has some form of post-secondary credential, which is higher than the US average of 48% (Lumina Foundation, 2019[22]). Educational attainment is a strong predictor of employment, with average earnings generally rising with the level of attainment (OECD, 2018[23]). A relatively large proportion (almost 18%) of working-age adults in Virginia have a graduate or professional degree, contributing to a workforce that is highly educated. At the same time, attainment rates vary substantially by county and region. By region, attainment of an associate’s degree or higher among working-age adults ranges from 74% at the highest, to 25% at the lowest (Lumina Foundation, 2019[22]).

Figure 6.1. Levels of educational attainment for Virginia residents aged 25-64, 2018

The rural-urban divide is also evident in patterns of labour force participation. In the south-western region of the state – a population of about 600 000 – the labour force participation rate (LFP) is much lower than the state average, and in some places is less than 50% (VEC, 2019[9]). In Wise County and Lee County, the LFP for the working-age population declined by 22% and 19%, respectively, between 2011 and 2017 (Old Dominion University, 2018[10]). The state-wide labour force participation rate is 65%, just above the national average of 63% (U.S. Census Bureau, 2018[25]). Similar to the rest of the country, the LFP has been in decline since the recession of 2008-09. At the national level, declining labour force participation has largely been attributed to structural changes in the economy, including waning demand for routine-based manual labour (Abraham and Kearney, 2019[26]; U.S. Bureau of Labor Statistics, 2016[27]). Figure 6.2 shows Virginia’s labour force participation rate, wage growth, employment and unemployment in a ten-year perspective.

Figure 6.2. Trends in key labour market indicators in Virginia, 2009-19

A recent analysis of Virginia’s out-of-work population, conducted by the University of Virginia’s Weldon Cooper Center for Public Service, defines the population of adults aged 25-64 who are “out of work” as both the unemployed (seeking work) and those outside of the labour force (not seeking work), excluding students, stay-at-home parents, retirees or those with disabilities. In the south-western region of Virginia, the out-of-work proportion of the population is estimated to be 15%, while this proportion is estimated to be as high as 19% in the southern region. In the state as a whole, that proportion is closer to 10% (UVA Weldon Cooper Center, 2018[29]). The Weldon Cooper Center estimates that the majority of those out of work in Virginia are under the age of 34. In fact, 25-34 year-olds are over-represented in the out-of-work population compared to their proportion of the overall population. Females and Black/African Americans are more likely to be out of work than other minority groups. Most of these individuals do not have more than a high school diploma, but a substantial share are defined as having some college but less than a bachelor’s degree (UVA Weldon Cooper Center, 2018[29]).

The higher education system

The State Council of Higher Education for Virginia takes a proactive role in achieving state-wide goals for higher education

The State Council of Higher Education for Virginia (SCHEV) is Virginia’s co-ordinating body for higher education. Established in 1956, the 13-member State Council makes higher education policy recommendations to the Governor and the General Assembly (Legislature) of Virginia. 12 members of SCHEV are appointed directly by the Governor, with the 13th member as the sitting president of the Virginia Economic Development Partnership. The State Council is supported by a secretariat, headed by a director appointed by Council members.

SCHEV manages state-wide co-ordination and strategic planning for public higher education in Virginia, which includes administering state financial aid programmes, approving new degree programmes, maintaining a comprehensive data system, and providing budget recommendations for higher education. Like many states with a single co-ordinating board for higher education, SCHEV serves both a co-ordinating function for the public system and an administrative function that benefits students and graduates of all higher education institutions in Virginia.

The governance of public higher education institutions in Virginia is a shared responsibility between the General Assembly, Governor, SCHEV and the institutions. At public four-year institutions (including research and comprehensive universities), the members of each institution’s governing board—the Board of Visitors—are appointed by the Governor. The Code of Virginia prescribes the composition of the board as well as the board’s powers and duties (Virginia General Assembly, 2019[30]). Each board appoints the president or chief executive for its institution.

Public four-year institutions in Virginia enjoy considerable academic, financial and managerial autonomy. The Restructured Higher Education Financial and Administrative Operations Act of 2005 (the Restructuring Act) granted public institutions greater operational and administrative autonomy, reaffirming institutional authority to set their own tuition fees, in exchange for a commitment by institutional boards to meeting state-wide performance goals. SCHEV is responsible for monitoring the progress of such accountability measures, as well as approving new degree programmes, major changes to existing programmes or their discontinuation, and changes to institutional mission statements. Quality assurance more broadly falls under the purview of the regional accrediting body for all public and private degree-granting institutions in the southern states, the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC).

Twenty-three of the state’s twenty-four public two-year institutions comprise the Virginia Community College System (VCCS), which was established in 1966. Initially, the system comprised two community colleges and five technical schools, but has grown to a total of 23 community colleges across 40 campuses in the state. As opposed to having a local governing board for each college, all 23 community colleges are governed by the Virginia State Board for Community Colleges and, as such, form part of a unified system with a centralised administration.

There are 15 members of the Virginia State Board for Community Colleges, appointed by the Governor. The State Board selects a system chancellor, who hires the presidents of each college. The State Board approves new degree programmes in applied fields related to career and technical education. New degree programmes in disciplines that are transfer-oriented – for students who intend to transfer to a four-year institution – must be approved by SCHEV.

Higher education policy in Virginia involves multiple agencies and stakeholders, and recent efforts have been made to improve links to workforce development

With the Higher Education Opportunity Act of 2011, also known as the Top Jobs Act, the alignment between higher education and the economic development and workforce objectives for the state became an explicit policy goal. Multiple agencies and stakeholders across both education and workforce policy environments are involved in developing policy for higher education in Virginia.

Appropriations for higher education are approved on a biennial basis by the state Legislature. Matters related to higher education are referred to the Senate Education and Health Committee, the House Committee on Education, and the subcommittees on education and higher education under the Senate Finance Committee and the House Appropriations Committee, respectively. Additionally, as part of the Opportunity Act, the Higher Education Advisory Committee was created to provide recommendations on financial and budgetary matters, including an assessment of whether or not the higher education system is meeting state-wide objectives. The Advisory Committee includes representatives from the executive and legislative branches of government, as well as the presidents of at least five public higher education institutions.

In addition to SCHEV and the Virginia Community College System (VCCS), the Council of Independent Colleges in Virginia (CICV) is an important stakeholder as the representative body for private, not-for-profit colleges. Multiple advisory committees have been set up by SCHEV to represent various stakeholder groups, including the Career College Advisory Board, which represents private, proprietary (for-profit) higher education institutions, and the Private College Advisory Board, which consists of the presidents of private, not-for-profit institutions.

Several members of the SCHEV Council, including the president of the Virginia Economic Development Partnership, represent or engage regularly with the business community in Virginia. Private entities such as the Virginia Chamber of Commerce and the Virginia Business Higher Education Council are frequently consulted in strategic planning processes for higher education and workforce development.

The Virginia Board of Workforce Development is a business-led advisory board that provides policy recommendations on the public workforce development system in Virginia. The Board also includes members of the Governor’s cabinet. Recently, the position of Chief Workforce Development Advisor was created to oversee all state, regional and local initiatives for workforce development in Virginia. The position was created at the cabinet level to advise the Governor, along with the Board of Workforce Development, the Secretary of Education, and the Secretary of Commerce and Trade, on matters related to skills development and the quality of the labour force.

In 2016, the Chief Workforce Development Advisor, in co-ordination with the Virginia Board of Workforce Development, established state-level performance metrics for career and technical education (CTE) and workforce training programmes, mainly provided at the post-secondary level by the state’s public two-year institutions. As described in Box 6.2, it is the responsibility of the Chief Workforce Development Advisor to develop an integrated workforce system based on better information about workforce supply and demand, in order to meet the needs of the labour market. This also requires understanding the quantity and quality of skills produced in the higher education system.

Workforce development policy at the state level is also the primary vehicle for implementing the federal Workforce Innovation and Opportunity Act of 2014 (WIOA). WIOA-funds are typically distributed through workforce boards at the regional and local levels within the state. In Virginia, there are 15 local workforce development regions, each served by a local workforce board. These workforce boards are intended to serve as the link between the labour market and educational providers, ensuring that regional needs for workforce development are met.

Box 6.2. Developing an integrated workforce system in Virginia

In March 2018, the General Assembly of Virginia passed a bill (HB 1006) to implement a system to better align education and workforce programmes to meet current and projected skills requirements of the labour force. One component of this system has been the development of a workforce dashboard tool to provide information on the supply and demand for workers. Other actions include:

provide policy advice to the Governor on workforce and workforce development issues in order to create a business-driven system that yields increasing rates of attainment of workforce credentials in demand by business and increasing rates of jobs creation and attainment;
provide policy direction to local workforce development boards;
identify current and emerging state-wide workforce needs of the business community;
advise and oversee the development of a strategic workforce dashboard and tools that will inform the Governor, policy makers, system stakeholders, and the public on issues such as state and regional labour market conditions, the relationship between the supply and demand for workers, workforce programme outcomes, and projected employment growth or decline;
determine and publish a list of jobs, trades, and professions for which high demand for qualified workers exists or is projected by the Virginia Employment Commission.

Source: Virginia General Assembly (2018[31]).

The post-secondary landscape in Virginia is diverse, but the majority of students are enrolled in public and private, not-for-profit institutions

The post-secondary education landscape in Virginia encompasses private and public higher education institutions, as well as private vocational schools. In 2017, an estimated 45% of 18-24 year-old Virginia residents were enrolled in post-secondary education, which is higher than the US average of 42% (Lumina Foundation, 2018[32]). The majority of students in Virginia are enrolled in public institutions, though this share decreased from 81% in 2003 to 69% in 2018. Out of total enrolments, the share of students enrolled in private (not-for-profit and for-profit) institutions increased during the recession of 2008-09, and has since remained at around the same level. Overall, 94% of students in Virginia are enrolled in public and private, not-for-profit institutions, of which approximately 80% are enrolled in undergraduate (baccalaureate and sub-baccalaureate) programmes. In terms of total headcount, the undergraduate population at Virginia’s public and private, not-for-profit institutions was 424 949 in 2018, with about 39% enrolled in public two-year institutions, 41% in public four-year institutions, and the remaining 20% in private, not-for-profit institutions.

Figure 6.3 shows enrolment trends for first-time, full-time students over a 15-year period across different institution types in Virginia. Because many students at public two-year institutions are part-time students, enrolment numbers for full-time equivalent (FTE) students are substantially lower than total headcount numbers. The trend lines show that enrolments at private, for-profit institutions increased rapidly during the recession of 2008-09 and saw a decline after 2012, while enrolments at public four-year and private, not-for-profit institutions have seen a steady increase over time. The figure also shows a sharp decline in enrolment at public two-year institutions since 2011-12. Because of their strong role in workforce training for the local and regional labour market, enrolment in community colleges tend to be counter-cyclical: when employment opportunities increase, enrolments and completion rates at community colleges typically decrease. Much of the growth, and subsequent decline, in enrolment in Virginia’s public higher education institutions overall, is attributed to enrolment fluctuation in Virginia’s community colleges (SCHEV, 2019[33]).

Figure 6.3. Fall higher education enrolment in Virginia, 2003-18

All private and out-of-state higher education institutions are required to obtain approval from SCHEV in order to operate in the state. Private institutions that have been operating for at least 20 years, and have been continuously approved, no longer need to seek approval from SCHEV. Table 6.2 provides an overview of accredited higher education institutions operating in the state.

	Public	Private
		Not-for-profit	For-profit
Four-year institutions (Baccalaureate colleges and universities)	15	31	9
Two-year institutions (Junior and community colleges)	24	0	0
Specialised institutions	1	0	0

Note: Does not include out-of-state higher education institutions operating in Virginia.

Source: SCHEV (2020[35]), Colleges and Universities, https://www.schev.edu/index/students-and-parents/explore/virginia-institutions.

Statlink

Public four-year institutions

Four-year institutions are those that offer at least a bachelor’s degree – International Standard Classification of Education (ISCED) Level 6. This category of higher education institutions includes research universities, comprehensive universities and colleges offering post-secondary education from ISCED Level 5 to ISCED Level 8 (associate’s degrees, bachelor’s degrees, master’s degrees and doctoral degrees). The majority of degrees awarded at public four-year institutions are bachelor’s degrees, accounting for 70% of all degrees awarded in 2018. Four-year institutions may also award certificates but do so infrequently.

There are 15 public four-year institutions in Virginia, many of which have a strong reputation nation-wide. Some of these institutions, such as the University of Virginia (UVA) and the College of William and Mary (CWM), are highly selective, whereas others, such as Virginia State (VSU), Norfolk State (NSU)1 and Old Dominion University (ODU) are close to open admissions, enrolling a large share of applicants. George Mason University (GMU) in the northern region of the state has the largest student population overall, with 37 677 students enrolled in 2018, followed by Virginia Tech in the southwest with 34 850 students, and Virginia Commonwealth University (VCU) in the state capital with 31 076 students.

Public two-year institutions

Public two-year institutions offer post-secondary education primarily at ISCED Levels 4 and 5. Two-year institutions award associate’s degrees and short- and long-term certificates. The associate’s degree normally requires two years of full-time college work and is designed either to prepare individuals for a career, as part of a technical career education programme, or for transfer to a four-year institution in order to pursue a bachelor’s degree. Thus, there are two types of associate’s degree programmes in Virginia: the technical associate’s degree and the transfer associate’s degree. The technical associate’s degree is offered in applied fields of study such as accounting, law enforcement administration, child care assistance and registered nursing. Transfer-oriented programmes are offered in a wide range of fields of study that aim to provide credits that will count towards earning a bachelor’s degree. On average, students enrolled in transfer programmes make up about two-thirds of enrolments at public two-year institutions in Virginia.

Workforce training programmes – also known as career and technical education (CTE) – encompass not only the technical associate’s degree but also short-term and long-term certificates. Workforce training programmes are often designed specifically to meet local industry needs. Short-term certificate programmes are typically of a duration of less than one year, and long-term certificate programmes are generally more than one year but less than two years. Some workforce training programmes are credit-bearing, even in fields such as welding, but most are non-credit-bearing. Industry-recognised certifications can also be built into credit-bearing certificates or associate’s degree programmes.

Of the 24 public two-year institutions in Virginia, there is one junior college (Richard Bland College) which is part of the College of William and Mary, and 23 community colleges that are part of the Virginia Community College System. The largest community college in terms of student population is Northern Virginia Community College (NOVA), with 50 929 students enrolled in the college in 2018, followed by Tidewater Community College with 20 941 students enrolled in 2018. Overall, enrolment in public two-year institutions has been declining since 2012.

Community colleges in Virginia are located in virtually all regions across the state and on multiple campuses, playing an important role in bringing educational opportunities to the regions of the Commonwealth.

Private four-year institutions

A diversity of not-for-profit and for-profit private higher education institutions operates in Virginia. The largest not-for-profit institution in the state is Liberty University, which, with an undergraduate enrolment of about 46 000 students, is also one of the largest higher education institutions in the United States. The largest for-profit institution is ECPI University, with about 12 000 undergraduate students. Both of these universities offer extensive online education programmes. Many of the 30 other private not-for-profit institutions are located in rural areas and enrol relatively small numbers of students. Excluding Liberty University, the average student population at not-for-profit private institutions was 1 875 in 2018 (2019[36]).

There is no comprehensive overview of private higher education institutions in Virginia, as only institutions that participate in federal or state student aid programmes are required to submit information to public authorities. Table 6.3 provides an overview of public and private, not-for-profit higher education institutions in Virginia that are eligible for federal (Title IV) funding and required to report to SCHEV.

	Public four-year institutions	Public two-year institutions¹	Private not-for-profit four-year institutions²
Total student population (headcount, fall enrolment 2018)	220 255	163 945	137 272
Undergraduate students as a percentage of total enrolment (fall enrolment, 2018)	79%	100%	63%
Percentage of undergraduate students who are Pell Grant recipients (2017/18)	27%	34%	45%
Average age of undergraduate students, at entry (first-time college students) in fall 2018	18	19	19
Average age of undergraduate transfer students, at entry (new transfer students) in fall 2018	22	25	25
Total number of post-secondary credentials awarded in 2018	54 317	32 746	32 993
Percentage of certificates awarded, out of total awarded credentials	0.2%	44%	1%
Percentage of associate’s degrees awarded, out of total awarded credentials	0.1%	56%	4%
Percentage of bachelor’s degrees awarded, out of total awarded credentials	70%	0%	52%
Percentage of professional or master’s degrees and above, out of total awarded credentials	27%	0%	40%
Percentage of post-graduate certificates,³ out of total awarded credentials	3%	0%	3%
First-year retention rate for first-time, full-time undergraduate students (fall 2018)	86%	63%	76%
150% completion rate (full-time, first-time college students)⁴	72%	19%	53%
Average tuition and mandatory fees for in-state undergraduate students (USD, 2019/20 academic year)	USD 13 699	USD 4 620	-
Percentage of undergraduate students receiving state financial assistance and average amount received per student (USD)	20% USD 4 311	21% USD 1 200	-
Percentage of bachelor’s degree students graduating with debt in 2017	59%	-	70%
Average student debt for bachelor’s degree graduates in 2017 (USD)	USD 28 859	-	USD 32 367

Notes: These figures are based on information from Virginia-based higher education institutions that are eligible for federal (Title IV) funding and that are required to report student-level data to the State Council of Higher Education for Virginia (SCHEV). Graduates of for-profit colleges and universities are not reported as they do not participate in the Tuition Assistance Grant or other forms of state-funded student assistance, and therefore, are not required to submit student-level data to SCHEV.¹ Includes Richard Bland College, which is a public two-year college that is not part of the Virginia Community College System.² Includes only institutions that are not required to obtain certification from SCHEV and that are fully accredited by an accrediting agency that is recognised by and has met the criteria for Title IV eligibility of the U.S. Department of Education.³ Post-graduate certificates normally require 24 credit hours beyond the master’s degree.⁴The 150% completion rate refers to the percentage of graduates who completed their degree within one and a half times the normal completion time; that is, 6 years for a four-year degree and 3 years for a two-year degree. The completion rate for four-year institutions is based on the 2012/13 graduate cohort and the completion rate for two-year institutions is based on the 2011/12 graduate cohort.

Source: SCHEV (2019[36]), Higher Ed Data Dashboard, http://research.schev.edu/fair/strategicplan3.asp.

Statlink

6.2. Assessment of labour market outcomes: The alignment between supply and demand of graduate skills in Virginia

Demand for advanced skills

New jobs increasingly require post-secondary education

There is a broad consensus that an increasing proportion of jobs in the United States – and in Virginia –require a post-secondary qualification. A majority of the new jobs created in the years following the recession of 2008-09 have required some form of post-secondary qualification (Carnevale, Smith and Strohl, 2013[37]), which may reflect a growing skill intensity within and between occupations (Autor, Levy and Murnane, 2003[18]; Altonji, Kahn and Speer, 2014[38]; Deming and Kahn, 2018[39]; OECD, 2019[40]). National employment projections for the ten-year period from 2018-28 indicate that 15 of the 20 fastest-growing occupations in the United States require some form of post-secondary education (U.S. Bureau of Labor Statistics, 2019[41]). 10 out of these 15 occupations require at least a bachelor’s degree or higher, including physician assistants, nurse practitioners, statisticians and information security analysts. Projections from Georgetown University’s Center on Education and the Workforce estimate that by 2020, 35% of all job openings will require at least a bachelor’s degree and a further 30% will require an associate’s degree or post-secondary certificate (Carnevale, Smith and Strohl, 2013[37]).

The Georgetown Center on Education and the Workforce has estimated further that 68% of all jobs in Virginia will require some post-secondary education or training in 2020 (Carnevale and Smith, 2012[42]). With a relatively high proportion of post-secondary-intensive occupations, Virginia’s economy depends on strengthening the supply of middle and advanced skills. Figure 6.4 shows long-term employment projections in Virginia for occupations that typically require some form of post-secondary education or training. As shown, employment of post-secondary graduates in business and financial operations, computer and mathematical occupations, and healthcare practitioner and technical occupations is high in Virginia and expected to continue growing. It should also be noted that these figures may not fully capture the growth in post-secondary-intensive jobs. Occupational projections of this type use an estimate of the minimum educational requirement per occupation and assume that this remains unchanged during the projection period.2 In addition, projections are based on current industry composition and therefore do not capture new occupations in emerging industries. Thus, projections of this type are likely to underestimate education requirements (Carnevale, Smith and Strohl, 2013[37]).

A skills gap analysis conducted by the National Skills Coalition (NSC) for the period 2014-24 estimated that approximately 45% of jobs in Virginia would require a post-secondary credential of less than four years, corresponding to a “middle skills” requirement (National Skills Coalition, 2017[43]). The NSC analysis maintains that the supply of middle-skill workers in Virginia has not been keeping pace with middle-skill job openings, in part because many middle-skill jobs that previously required a high school diploma now require some form of post-secondary education or training. Education and training for middle-skill jobs can vary from apprenticeships and short-term certificates to associate’s degree programmes typically offered by community colleges. Virginia’s workforce credential grant programme, which was launched in 2016, has been largely a response to the need to strengthen middle-skill educational opportunities and increase the number of credentialed graduates from programmes in high-demand fields. These fields include information technology, industrial maintenance and mechatronics, and welding (see Section 6.3).

Figure 6.4. Projected employment growth in occupations that typically require some form of post-secondary education, 2016-26

At the same time, the share of “good jobs” held by workers with less than a bachelor’s degree is lower in Virginia than in many other states, including Washington, Connecticut and Maryland (Carnevale, Strohl and Ridley, 2017[44]). The Georgetown Center on Education and the Workforce defines good jobs as those with minimum gross annual earnings of USD 35 000 for individuals under the age of 45 and USD 45 000 for those above 45. Based on estimates from 2015, about 60% of good jobs in Virginia are held by workers with at least a bachelor’s degree, suggesting a strong advantage for workers with advanced skills. Furthermore, as the technology sector expands, the demand for advanced skills in Virginia is expected to continue to grow. Nearly 150 000 new jobs in STEM-related fields are expected to be added to Virginia’s economy in the next five years (Northam Administration, 2019[45]). The arrival of Amazon’s new headquarters is expected to add at least 25 000 new jobs, most of which will require advanced skills in information and communication technologies (ICT). A large majority of jobs in other high-growth occupations such as community and social services, as well as healthcare practitioner and technical occupations, will require at least a bachelor’s degree.

SCHEV has estimated that there is a need for an additional 1.5 million post-secondary credentials at all levels by 2030 in order to meet the state’s post-secondary attainment goals (SCHEV, 2019[46]). In 2019, Virginia’s public and private not-for-profit institutions awarded a total of 104 188 degrees, a number that would need to increase annually in order to reach the state’s goals. As of 2018, 54% of the working-age population (aged 25-64) in Virginia have a post-secondary credential (degree or certificate) and 49% hold a post-secondary degree. At 51%, the degree attainment level of 25-34 year-olds in Virginia is not much higher than for the working-age population as a whole, creating the risk of inadequate skills supply in the future. Figure 6.5 shows post-secondary attainment levels among 25-34 year-olds in Virginia, including the share of young Virginians with some college but no degree. While the share of those with some college but no degree has been relatively large, it was lower in 2018 than in the previous ten years.

Figure 6.5. Post-secondary educational attainment levels of 25-34 year-olds in Virginia, 2003-18

Moreover, there are large attainment gaps by region and sub-population. Figure 6.6 shows average attainment rates across the fifteen local workforce regions in Virginia. In rural areas such as Southwestern Virginia, attainment of at least an associate’s degree among the adult population (aged 25-64) is as low as 24.7%, compared to 48.9% state-wide (Lumina Foundation, 2019[22]). Post-secondary enrolment and attainment numbers are also disproportionately lower for Black/African Americans and Hispanics, who constitute an untapped potential in the workforce (Jobs for the Future, 2019[47]; Lumina Foundation, 2019[22]). As the economy increasingly favours individuals with post-secondary education, a lack of qualifications negatively affects an individual’s ability to earn a sustainable wage and take part in the economic growth of the state.

Out-migration has been higher than in-migration in Virginia as a whole since 2012-13, particularly among the young adult population. As well as low-income rural areas, this trend has also affected large, high-income areas, such as Fairfax County in Northern Virginia, where many educated Virginians move from other counties for their first jobs, but then frequently move out of state for subsequent work opportunities. In general, out-migration from Fairfax County to other Virginia counties has slowed since the recession of 2008-09, with more Fairfax County residents moving out of state rather than to other Virginia counties. This has also led to slower population and school enrolment growth in counties that have traditionally attracted Virginians from Fairfax County (UVA Weldon Cooper Center, 2019[48]).

Figure 6.6. Higher education attainment in Virginia, 2013-17 average

The supply of skills fails to meet demand in certain fields

Available labour market information for Virginia shows there are gaps between supply and demand in several fields. Crucially, there are reported shortages of workers with advanced skills in information and communication technologies (ICT), primary and secondary school teachers, and nurse practitioners, as well as nursing aides and other critical healthcare support workers (Virginia Department of Education, n.d.[51]; VEC, 2019[9]). There are also reported shortages in the skilled trades, reflecting a nation-wide trend.

Virginia’s Demand Occupations Taskforce identifies in-demand occupations that require some form of post-secondary education, but less than a master’s degree. The taskforce, established by the Virginia Board of Workforce Development, releases a list of high-demand occupations on an annual basis. While the list does not indicate level or intensity of demand across or within occupational groups, it highlights specific job titles that are currently in high demand. Within computer and mathematical occupations for 2018-19, information security analysts, computer network architects, web developers, and statisticians are among the high-demand jobs listed. Within education, training and library occupations, twelve job types are listed as being in high demand, including teacher assistants, pre-school teachers and special education teachers (Virginia Career Works, 2018[52]).

Virginia’s Workforce Supply and Demand Dashboard was developed to show potential gaps in workforce supply and demand throughout the state and by region. Demand-side data, based on online job postings in Virginia, are matched with supply-side data on post-secondary graduate credentials obtained in Virginia from the Integrated Postsecondary Education Data System (IPEDS). While there are important limitations to these types of supply-demand models, a simple gap analysis can provide an indication of where misalignment exists between labour market demand and the supply of credentialed workers within the state (see further discussion in Section 6.3.4). Based on the existing data in Virginia’s supply and demand model, the dashboard indicates large supply gaps state-wide in healthcare, finance, computers and ICT, nursing, and human services and sales careers. At the middle skills level, large gaps have been identified in clerical and administrative work, healthcare support, and nursing (Virginia Career Works, 2019[17]).

At Virginia’s public and private not-for-profit institutions, the number of degrees in data science-related programmes, capturing fields of study such as computer and information sciences, applied mathematics, and management science, nearly doubled between 2008 and 2018. In 2018, almost 31% of bachelor’s degrees were awarded in science, technology, engineering, mathematics and health (STEM-H) fields, an increase from about 24% in 1998 (SCHEV, 2019[53]). These fields are important for healthcare support occupations as well, which are increasingly likely to require more technical skills, as advancements in technology permeate the health sector. While the majority of long-term certificates were awarded in STEM-H-related fields in the past, this proportion has diminished in the last ten years, as an increasing number of occupations, particularly in healthcare, now require a minimum of an associate’s degree.

Figure 6.7 shows the ten-year trend in credentials awarded in STEM-H fields by Virginia’s public and private, not-for-profit institutions. In terms of total numbers, STEM-H credentials are mostly awarded at the associate’s and bachelor’s degree levels. The greatest number of degrees are awarded at the bachelor’s degree level, with degree awards rising steadily over the last ten years. The number of master’s degrees awarded in STEM-H fields has increased in recent years, but until recently remained below the number of associate’s degrees produced. With growing demand for advanced skills, particularly in ICT-related fields, there will be a need for more advanced degrees at the master’s and doctoral levels. The state’s new Tech Talent Pipeline initiative aims to add at least 25 000 undergraduate and graduate degrees in computer science and related fields by 2039 (Virginia General Assembly, 2019[54]) (see also Box 6.1).

Figure 6.7. Trends in the production of degrees in STEM-H fields, 2007/08 to 2018/19

To fill current supply gaps and meet future demand, it is critical that Virginia’s higher education system produces enough graduates at both middle and advanced skill levels, particularly given recent out-migration of skilled workers. The need for healthcare and technology workers requires specific attention. Virginia has the fifth highest concentration of technology workers in the United States, with over 206 000 workers in computer and mathematical occupations (U.S. Bureau of Labor Statistics, 2019[7]) and demand for advanced ICT skills has persisted as job growth in computer and mathematical occupations has outpaced workforce supply. Moreover, other industries and occupational sectors increasingly require technical skills in areas such as data science.

Greater demand for specific, ICT-related skills may also be contributing to intensifying interest in micro-credentials, both within and outside the post-secondary environment. Many higher education institutions are responding to this need by offering additional specialisation tracks or certificates in ICT-related fields for degree-seeking students, regardless of their chosen field of study. To date, these types of micro-credentials serve mainly to supplement other degrees or credentials and are valued by employers as such (Gallagher, 2018[55]). However, the continuously evolving demand for ICT skills – in line with rapid advancements in technology – poses a particular challenge for the higher education system in terms of its ability to respond quickly to changing skills needs. With the rise of micro-credentials such as badges and endorsements, measuring skill demand and supply in computer and ICT fields may also become increasingly difficult.

Supply of skilled graduates in the labour market

Retention and completion rates are consistently lower for ethnic minorities and other under-represented groups

Virginia uses a definition of under-represented populations that includes race/ethnicity, federal Pell Grant eligibility (as an indicator of low income), and whether the student is from a city or county in the bottom quintile of bachelor’s degree attainment. Using this definition, the proportion of students from under-represented populations in the undergraduate student population at all public institutions and private not-for-profit four-year institutions increased from 56% to 63% between 2007 and 2017. The proportion of students of colour (non-white US citizens or resident aliens) increased from 29% in 2007 to 36% in 2017 (2019[33]).

Students from under-represented populations in Virginia have consistently lower completion rates than students who are not from these populations. At public four-year institutions, 40% of students of colour complete bachelor’s degrees within four years, compared to 54% of majority students. At private not-for-profit institutions, only 20% of students of colour graduate within four years, increasing to 29% within five years. As shown in Figure 6.8, at public four-year institutions, 66% of bachelor’s degree students from under-represented populations graduate within four years, compared to 73% of graduates who are not from under-represented populations. Completion rates are substantially lower at public two-year institutions: only 24% of students from under-represented populations complete a credential within the normal timeframe allotted for the programme. Furthermore, first-year retention rates (likelihood of transfer to the second year) have been declining in the last six years, with a substantial decrease in retention of students from under-represented populations (SCHEV, 2019[33]).

Figure 6.8. Degree completion rates at public institutions, 2018

The likelihood of completion tends to be associated with a student’s income and family wealth, as students from higher income backgrounds are likely to have greater access to pre-college preparation. Table 6.4 shows completion rates for different cohorts of bachelor’s degree graduates at the public and private four-year institutions by income group. The difference in completion rates between low- and high-income students is substantial; for the 2009/10 cohort at public four-year institutions, 77% of high-income students graduated within six years (150% completion rate), compared to 57% of lower-income students. However, completion rates for students from all income groups are markedly lower at private not-for-profit four-year institutions than for students at public four-year institutions; less than 60% of middle- and high-income students, and only 37% of lower-income students, complete their bachelor’s degrees within six years.

	Public four-year institutions			Private not-for-profit four-year institutions
	Lower-income	Middle-income	High-income	Lower-income	Middle-income	High-income
2000/01	52%	62%	74%	40%	51%	51%
2001/02	50%	63%	76%	40%	53%	55%
2002/03	52%	63%	76%	40%	50%	55%
2003/04	53%	63%	76%	42%	53%	57%
2004/05	53%	65%	74%	41%	53%	60%
2005/06	54%	65%	75%	39%	49%	54%
2006/07	55%	67%	75%	40%	48%	54%
2007/08	56%	65%	75%	37%	48%	54%
2008/09	56%	66%	73%	35%	48%	55%
2009/10	57%	69%	77%	37%	50%	58%

Note: Includes only full-time students enrolling at the institutions for the first time in fall, spring, or summer of the designated year.

Source: SCHEV (2019[53]), Virginia Longitudinal Data System 2000-2010 (database), https://vlds.virginia.gov/.

Statlink

Incomplete qualifications tend to lead to less rewarding labour market outcomes

As seen in Figure 6.8, average completion rates at public two-year institutions are substantially lower than at four-year institutions. Traditionally, community colleges in the United States are responsible for local workforce training and meeting the needs of employers in the region, and enrol students of all backgrounds and levels of academic preparation. Community college students are also more likely to be part-time, older, and from low-income backgrounds, with parents who do not have post-secondary education (JLARC, 2017[56]). As a result, completion rates at community colleges are typically lower than at four-year colleges and universities nation-wide (Levesque, 2018[57]).

Completion rates at Virginia’s community colleges have been declining in recent years as a result of a strong economy and a tight labour market that have discouraged students from enrolling in post-secondary education. Indeed, enrolments and completions at public two-year institutions are positively correlated with unemployment rates (SCHEV, 2019[33]). Nonetheless, during the 2008-09 recession and at the peak of student enrolment, the completion rate was 42% for students at public two-year institutions, compared to 70% at public four-year institutions.

Students who earn some college credits but do not obtain a credential (non-completers) risk a lower return on their educational investment because they do not gain the earnings premium associated with holding a completed post-secondary credential, even if they end up earning more with some college credits than they would have with only a high school diploma. Recent studies have shown that non-completers have higher earnings potential compared to those with a high school diploma or less, but that returns for non-completers also vary by field of study (Belfield and Bailey, 2017[58]). Completing credits in some career and technical education (CTE) fields, linked to less credential-intensive sectors, can yield positive wage returns (Bahr, 2019[59]).

While non-completers may do better in the labour market with some college credits as opposed to having just a high school diploma, data from different US states shows their earnings potential in the long term is greatly reduced. Credits from certain fields or programmes may not be valued in the labour market and may thus yield a negative return on investment for an individual who has spent time and money on post-secondary education. For example, the most popular programme-types at Virginia’s public two-year institutions are in the broad fields of “general studies” and “liberal arts or liberal studies”, which have little immediate relevance to the labour market. Non-completers from these types of programmes are likely to experience greater difficulty in finding well-paid employment.

According to data by the National Student Clearinghouse, 908 882 Virginians have earned some college credit but do not have a degree or certificate (National Student Clearinghouse, 2019[60]). 73% of the “some college, no degree” population were last enrolled at a public two-year institution and 12% were enrolled at a public four-year institution, with the remaining proportion last enrolled at a private institution. The State Council of Higher Education for Virginia estimates that 20% of individuals with some college credit have earned more than 30 credits at the post-secondary level (SCHEV, 2019[46]). The share of those with some college, but no degree, is slightly larger among young adults (22%) than among older cohorts (19%) in Virginia. However, among young adults, the share of those with some college, but no degree, appears to be declining. Findings from the National Student Clearinghouse indicate that potential completers – those with some college and no degree who are likely to return to post-secondary education – tend to remain in the state and return to the same institution type, though not necessarily the same institution. This is an important population to reach in order to improve credential attainment.

Figure 6.9 compares the inflation-adjusted earnings of Virginia residents with either an associate’s degree, bachelor’s degree, or some college but no degree, which includes individuals who hold a post-secondary certificate. It confirms that having no degree carries a disadvantage in terms of earnings in comparison to associate’s degree and bachelor’s degree holders, with bachelor’s degree holders at a far greater earnings advantage. The earnings of those with some college but no degree have risen slightly between 2013 and 2018 after a long decline, narrowing the gap between those with associate’s degrees and those with only some college. Nevertheless, the labour market value of incomplete or non-degree qualifications remains low. The median earnings for these individuals are only slightly above the estimated annual wage needed to cover basic expenses for a full-time working adult in Virginia (MIT, 2019[61]).

Figure 6.9. Median annual earnings for individuals aged 25-64 by higher education level, 2000-18

Variations in labour market outcomes

On average, post-secondary graduates in Virginia enjoy favourable earnings and employment prospects

Most students who pursue post-secondary education do so in search of a meaningful job and a sustainable wage. In 2015, approximately 70% of Americans agreed that it will be more important in the future to have a post-secondary degree or professional certificate in order to obtain a good job (Gallup, 2016[62]). While several factors such as individual choice and local labour market conditions influence the outcomes of graduates in the labour market, graduate earnings and employment outcomes provide an important indication of how graduates are valued in the labour market through the skills they bring to the workplace.

Data from the American Community Survey (ACS) show that individuals aged 25-64 in Virginia with post-secondary education enjoy higher employment rates than those without a post-secondary degree. On average, the likelihood of being employed increases with the level of educational attainment, which in turn improves an individual’s earnings potential. Similarly, labour force participation rates generally increase with educational attainment. In Virginia, the labour force participation rate for 25-64 year-olds with post-secondary education is in line with the US average. The rate of labour force participation for individuals aged 25-64 with a bachelor’s degree is approximately 87%, compared to the US average of 86%. For individuals with master’s and professional degrees, the labour force participation rate in Virginia is 89% and 91% respectively.

There is little variation in labour force participation by ethnic or racial group for post-secondary graduates in Virginia. However, male graduates participate in the labour force at a higher rate than females, with a difference of ten percentage points. Figure 6.10 shows employment rates by gender and educational attainment level for young adults, aged 25-34, in Virginia. While the gender gap is larger for those without post-secondary education, there is also a gap in employment rates between males and females at higher attainment levels, even among young adults. The lower rate of employment for females is often due to barriers to labour market participation such as childcare provision. Overall, however, employment rates rise with the level of educational attainment for both genders.

Figure 6.10. Employment rate of 25-34 year-olds in Virginia, by gender and level of educational attainment, 2018

Employment rates for post-secondary graduates vary somewhat by field of study, with higher employment rates associated with higher earnings. Figure 6.11 shows the employment rates of 25-64 year-olds with bachelor’s degrees by field of study in Virginia compared to the US average. With employment rates close to 90%, graduates with degrees in STEM-related fields such as architecture, engineering, computers, statistics and mathematics enjoy the highest employment rates compared to other fields. Several fields enjoy higher rates of employment in Virginia than in the United States on average. In addition to STEM fields, these include the humanities and the arts. However, employment prospects for individuals with a bachelor’s degree in education or psychology and social work are markedly lower than in other fields of study and slightly below the US average. Given that educators are in high demand, it is a concern that graduates with degrees in education have trouble obtaining employment in Virginia.

Figure 6.11. Employment and earnings of bachelor’s graduates aged 25-64, by key field of study, 2018

There is limited evidence of over-qualification or underemployment of post-secondary graduates in Virginia. Obtaining accurate and systematic data on the employment outcomes of graduates, however, is challenging (TICAS, 2018[63]). At the national level, graduate employment outcomes are surveyed through the National Association of Colleges and Employers (NACE) First-Destination survey, which provides information on employment outcomes of graduates six months after graduation. However, this survey is primarily focused on whether or not graduates find full-time employment, are seeking continuing education or are still looking for work. It does not attempt to measure whether or not a graduate is employed in an occupation that matches his or her field of study and level of qualification. State-level post-secondary data systems that collect data on earnings through Unemployment Insurance (UI) wage records typically cannot obtain information on the specific occupation of graduates. While UI records often indicate the industry in which an individual is employed, this does not provide enough information to assess qualification or field mismatch.

In order to obtain information on in-field job placements, higher education institutions typically use alumni surveys. However, these data can be unreliable due to low response rates and poorly designed surveys. To inform public policy, a systematic, state-wide review of graduate outcomes would be necessary in order to assess the extent to which graduate skills (both quality and quantity) are meeting employer needs. The General Assembly of Virginia recently granted funding for the development of a graduate outcomes survey which, among other things, aims to collect information on whether or not graduates have secured employment related to their degree. The survey will be developed by the State Council for Higher Education of Virginia, in collaboration with the Virginia Economic Development Partnership.

Earnings data suggest that post-secondary graduates in Virginia are, on average, rewarded for higher skills levels. Figure 6.12 shows that the earnings advantage for individuals with post-secondary education in Virginia is around the same level as, or higher than, the US average at every level of attainment. On average, the earnings advantage for individuals with master’s degrees or professional degrees is substantially higher in Virginia than in the United States overall. While there is still an advantage to earning an associate’s degree compared to a high school diploma or some college, there is a much higher earnings advantage for graduates with a bachelor’s degree or above. Median annual earnings for bachelor’s degree holders in Virginia are about 45% higher than for associate’s degree holders. This reflects a nation-wide trend, with the earnings premium for bachelor’s degree holders rising substantially since the recession of 2008-09, partly due to stagnating wages for those with a high school diploma or less (Baum, 2014[64]; Carnevale, Smith and Strohl, 2013[37]).

However, the earnings advantage of an associate’s degree in Virginia is only marginally higher than for some college but no degree, representing a 7% increase, on average, in annual earnings. At Virginia’s community colleges, associate’s degrees are offered in either a technical track (applied associate’s degrees) or a transfer track (providing academic credit that count towards a bachelor’s degree). The majority of degrees awarded are transfer-oriented, of which a significant proportion are awarded in fields that may yield relatively low earnings premia compared to a high school diploma, such as general studies and liberal arts.

Figure 6.12. Earnings advantage of post-secondary education compared to upper secondary, by level of attainment, 2018

There are notable disparities in graduate earnings and debt levels by field and level of study

Through the Virginia Longitudinal Data System, analysts and policy makers have been able to track the wage outcomes of graduate cohorts from as far back as the 1990s (SCHEV, 2019[65]). Because data on post-secondary degrees and certificates are matched to Unemployment Insurance (UI) wage records from the Virginia Employment Commission, the data include only information on graduates who joined the workforce in Virginia after graduation and thus do not include graduates from Virginia’s higher education system who moved out of state. However, it is estimated that about 82% of Virginia bachelor’s degree graduates and 88% of associate’s degree graduates remain in the state after graduation (SCHEV, 2019[53]).

Recent research has demonstrated that a student’s chosen field of study is one of the strongest predictors of future earnings (Carnevale, Cheah and Strohl, 2012[66]; Carnevale et al., 2017[67]; Schneider, 2015[68]; Kim, Tamborini and Sakamoto, 2015[69]). Among Virginia graduates, there is substantial earnings dispersion both within and between fields of study. Figure 6.13 shows the long-term earnings trajectory of the 1992/93 cohort of Virginia graduates by field of study at the bachelor’s degree level. STEM field graduates had the highest median earnings five years post-completion (USD 38 346) and enjoyed the steepest earnings trajectory over time compared to other fields of study.

Figure 6.13. Earnings trajectory of Virginia bachelor’s degree graduates by field of study, 6-26 years after graduation

At the same time, data on Virginia graduates from the 1992/93 cohort also demonstrate wide earnings variation within fields of study. 24 years post-completion (i.e. in 2017), the difference between the highest STEM earners (75th percentile) and the lowest STEM earners (25th percentile) was USD 91 806. By comparison, the difference in median earnings between the highest earning field (STEM) and the lowest earning field (liberal arts) was USD 44 924 (SCHEV, 2019[53]).

Wide earnings dispersion within fields of study is also observed in the working-age population as a whole. Figure 6.14 shows wide earnings dispersion within fields of study at the bachelor’s degree level, with the greatest differences within communications and journalism fields, as well as in the social sciences and STEM fields. The smallest dispersion is observed in education and health fields, typically linked to regulated professions. Notwithstanding variation in local labour market conditions, within-field earnings disparities also arise from differences in industry, occupation, and individual skills sets.

Figure 6.14. Distribution of earnings of 25-64 year-old bachelor’s graduates, 2018

Longitudinal data from the 2005/06 cohort of Virginia graduates show clear wage differentials by degree level over a ten-year period. Figure 6.15 illustrates that bachelor’s degree graduates enjoy a relatively steep climb in earnings immediately after graduation that continues to rise over time. Graduates with associate-technical degrees start with comparatively high earnings that continue to rise, but at a slower pace than for bachelor’s degree graduates. The data also show a substantial earnings gap between associate-technical and associate-transfer graduates that narrows over time. While there is variation by field of study for associate’s degree holders, data from multiple cohorts over time have demonstrated that, on average, the earnings of associate-transfer graduates eventually catch up to associate-technical graduates (SCHEV, 2019[33]; Schneider, 2016[70]).

Figure 6.15. Annual earnings of graduates 1-12 years after graduation

In addition to earnings, an important aspect of assessing the returns on investment in post-secondary education is the impact of individual student debt. The cost of higher education in Virginia has been rising steadily since 2002, shifting the cost burden increasingly to students and their families (SCHEV, 2019[71]). According to national data collection of debt levels of bachelor’s degree graduates, Virginia ranks 17^th out of 50 states for highest average debt levels. In 2018, the average amount of student debt for Virginia graduates was USD 30 363, compared to USD 19 728 in Utah (with the lowest average debt nation-wide) and USD 38 669 in Connecticut (with the highest average debt nation-wide) (TICAS, 2019[72]).

For low-income students in Virginia, tuition and fees as a proportion of family income has been increasing over the past five years. At Virginia’s public two-year institutions, tuition and fees are the 8^th highest in the country. At public four-year institutions, net tuition revenue per full-time equivalent (FTE) student has increased by 53% since the 2008-09 recession, which in 2018 was USD 9 241, compared to the US average of USD 6 788. Net tuition revenue represented over 60% of total educational revenue in 2018, placing Virginia in the top quartile of states with the highest tuition revenue as a proportion of total educational revenue. In contrast, state educational appropriations per FTE were USD 5 420 in 2018, notably below the US average of USD 7 853 (SHEEO, 2019[73]).

While loan default rates for Virginia graduates are lower than the US average, the proportion of students with debt has been increasing along with the amount of debt each student carries upon graduation. In 2018, approximately 62% of bachelor’s degree students in Virginia graduated with debt, compared to 52% ten years earlier (SCHEV, 2019[74]). At the same time, median student debt for bachelor’s degree graduates increased by 43% over the ten-year period between 2008 and 2018 (SCHEV, 2019[75]). The increasing student debt burden lowers the return on investment in post-secondary education, and disproportionately affects those from lower-income backgrounds. While bachelor’s degree holders are less likely to default on their loans compared to associate’s degree and certificate holders, first-generation bachelor’s degree graduates are more than twice as likely to default on their loans than students whose parents have attained higher education (TICAS, 2019[72]).

Data from SCHEV on the debt and earnings profile of Virginia graduates from public higher education institutions, by sub-baccalaureate and at baccalaureate level, show that bachelor’s degree graduates have a comparatively higher debt burden on average, with a greater proportion of students graduating with debt and a higher debt-to-earnings ratio than associate’s degree graduates. However, bachelor’s degree graduates are more likely to earn a sustainable wage relatively soon after graduation and enjoy a substantially higher earnings trajectory over time compared to associate’s degree graduates (SCHEV, 2019[74]).

Not all post-secondary graduates achieve a sustainable wage three years post-completion

One of the goals of the Virginia Plan for Higher Education is to ensure that 75% of graduates earn a sustainable wage – defined as a wage at or above 200% of the federal poverty level – three years after graduation. Research has shown that the level of earnings reached within the first few years after graduation is a determining factor for earnings progression later in life (Carnevale, Rose and Cheah, 2013[76]). A detailed analysis of programme-level wage outcomes of Virginia graduates has shown that the wages of bachelor’s degree graduates who earn below the median wage for the state three years post-completion tend to remain below the state-wide median ten years post-completion (Schneider, 2016[70]).

As seen in Figure 6.16, 61% of graduates from transfer-oriented associate’s degree programmes achieve a sustainable wage three years post-completion, compared to 81% of graduates from technical programmes. There is also a substantial earnings gap between associate-technical and associate-transfer graduates. While the earnings of graduates with associate’s degrees can reach the level of some bachelor’s degree graduates over time, the lower end of the earnings distribution is substantially lower. Based on data from recent cohorts, the earnings of associate-transfer graduates at the lower end of the distribution can be less than USD 20 000 per year. While the objective of transfer-oriented programmes is to prepare students for transfer to four-year institutions, only approximately 12-15% of students enrolled in transfer-oriented programmes in any given year actually transfer to a four-year institution. Based on transfer outcomes data, these students are likely to complete a bachelor’s degree successfully. However, that still leaves a large proportion of students with relatively poor labour market prospects if they leave post-secondary education with an associate-transfer degree as their highest educational attainment. Given that transfer students make up roughly two-thirds of community college enrolments, this affects a considerable portion of college students.

Overall, despite substantial variation by type of degree and field of study, available data raise concerns about the value of an associate’s degree in the labour market. Figure 6.16 shows the proportion of all undergraduates earning a sustainable wage one, two and three years post-completion. It shows that 74% of undergraduates from the latest cohort (graduating in 2015) earned a sustainable wage three years post-completion, just short of the 75% goal in the Virginia Plan for Higher Education. This has been attributed to the relatively low wages of graduates from associate-transfer programmes (SCHEV, 2019[33]). The figure also shows that the proportion of graduates achieving a sustainable wage has been declining over the last fifteen years. This may be partially due to the slow recovery of Virginia’s economy after the 2008-09 recession.

Figure 6.16. Percentage of graduates earning a sustainable wage after graduation, 1998-2015

6.3. Policies to improve the alignment of the higher education system and the labour market in Virginia

The assessment of the labour market outcomes experienced by higher education graduates and graduate employers in Virginia in the Section 6.2 suggests that, to achieve a good alignment between the supply of middle and advanced skills, and demand for these skills, the Commonwealth needs to:

increase higher education attainment across the state to meet this overall increase in skills demand;
enhance, in particular, post-secondary entry and completion rates among under-represented student populations, whose talent will increasingly be needed to meet skills demand;
optimise alignment between skills supply and demand by encouraging students to obtain credentials in high-demand fields (and potentially – discouraging them from study in low-demand fields).
This section of the chapter provides an assessment of the current policy environment for higher education in Virginia. It identifies current strengths and provides recommendations on where improvements could be made to develop a system that is more likely to lead to a good alignment between skills supply and demand. The analysis has been structured using the policy analysis framework (see Chapter 1), which identifies the main policy levers that policy makers can use to influence the characteristics of higher education systems that affect labour market alignment.

Strategic planning and co-ordination

In the context of this review, strategic planning refers to the stage of policy making through which high-level priorities and goals concerning higher education-workforce alignment are established. These priorities and goals guide more detailed policy design and implementation in specific areas, such as regulation, funding or information provision. Co-ordination refers primarily to co-operation between relevant policy-making bodies in the field of education and workforce development in a given jurisdiction, with a view to establishing a “joined up” strategy and ensuring coherence between policies and programmes for implementation.

Experience from many OECD countries and multiple policy fields has shown the value of strategic planning processes in establishing a common understanding of the problems that policy needs to address and building a shared vision of how to tackle these problems. Where problems cut across the responsibilities of different policy departments and public agencies, inter-departmental and inter-agency co-ordination are necessary. Higher education-workforce alignment is a clear example of such an issue, affecting as it does, the activities and responsibilities of autonomous higher education providers, state authorities directly responsible for higher education policy, other state education agencies and bodies involved in workforce development. Furthermore, the broad nature of the topic means policies in this area affect and serve a wide range of stakeholders – students, graduates, employees or those seeking employment, and employers – whose priorities and views should ideally inform strategy and policy making.

Virginia’s strategic plan for higher education has a strong focus on workforce preparation and alignment

Virginia has a well-established, long-term strategy for higher education, which plays a clear role in steering higher education policy making and has a strong focus on the relationship between higher education, employment and economic development. Among SCHEV’s legal responsibilities is the task of developing, every six years, a strategic plan for higher education in the Commonwealth that identifies goals for the sector and sets out co-ordinated strategies for achieving these goals. The Virginia Plan for Higher Education, adopted by SCHEV in 2014 and subsequently endorsed by the General Assembly, establishes the overall goal for Virginia to respond to growing demand for skills by becoming “the best-educated state by 2030”. The Virginia Plan establishes explicitly that, by 2030, 60% of the working-age population (aged 25-64) should have obtained an associate’s degree or higher, and a further 10% some form of workforce credential,3 meaning a post-secondary attainment rate of 70% (SCHEV, 2019[33]). To put this in context, in 2017, the proportions of the working-age population in Virginia with degrees and workforce credentials were, respectively, 48.9% and 5%, resulting in an overall attainment rate of around 54%.

As summarised in Box 6.3, the Virginia Plan also establishes four overarching goals for higher education in Virginia related to affordable access; student success; effective investment and innovation; and the contribution of the sector to regional and economic development. For each of the four goals, the Virginia Plan identifies broad lines of action (“strategies”) to guide the more detailed design of state policy and funding initiatives. In addition, the Plan includes six more detailed quantitatively measurable targets to be achieved by 2030, relating to number of degrees awarded, completion rates and attainment among under-represented groups,4 affordability, tuition and fees, research activity and graduate earnings. The last target specifies that 75% of graduates should “earn sustainable wages three years after graduation”, where a “sustainable wage” is defined as earnings at or above 200% of the federal poverty level. In 2016, around 73% of associate’s and bachelor’s degree graduates from 2012/13 (three years earlier) attained this level of earnings (USD 25 100 per year).

Box 6.3. Goals and strategies of the Virginia Plan for Higher Education (2014-20)

“Virginia will be the best-educated state by 2030”

1. Provide affordable access for all:

a) Expand outreach to PK-12 and traditionally underserved populations; b) improve the college readiness of all students; c) cultivate affordable post-secondary education pathways for traditional, non-traditional and returning students; d) align state appropriations, financial aid, tuition and fees so that students have broader access to post-secondary education opportunities, regardless of their ability to pay.

2. Optimize student success for work and life

a) Strengthen curricular options to ensure that graduates are prepared with the competencies necessary for employment and civic engagement; b) provide effective academic and student services infrastructures focused on persistence and completion; c) increase on-time completion of certificates and degrees; d) engage adults and veterans in certificate and degree completion and lifelong learning.

3. Drive change and improvement through innovation and investment

a) Identify and implement public funding strategies to sustain long-term planning and responsiveness; b) cultivate innovations that enrich quality, promote collaboration and improve efficiency; c) foster faculty excellence, scholarship and diversity; d) enhance higher education leadership, governance and accountability.

4. Advance the economic and cultural prosperity of the Commonwealth and its regions

a) Build a competitive, future-ready workforce for all regions; b) become a catalyst for entrepreneurship and a model for business incubation; c) target funding, resources and partnerships to support research and development; d) expand participation and engagement in public service and institutional service to the community; e) demonstrate the impact of higher education on state and regional economic development.

Source: SCHEV (2014[77]).

SCHEV produces an annual report for the General Assembly examining progress in relation to these different goals and quantitative targets and adopts its own “priority initiatives” (SCHEV, 2019[78]) for achieving the goals and strategies of the Virginia Plan for each two-year legislative period. At the time of writing, the State Council and SCHEV staff are discussing possible revisions to the Virginia Plan for Higher Education and the refinement of priority initiatives, although it is understood that there are unlikely to be changes to the overall goals and headline strategies of the current plan.

At a strategic level, the current goals of the Virginia Plan address the three key labour market alignment challenges identified in Section 6.2, through seeking to expand the overall supply of graduates; better serve the needs of under-represented groups most affected by the “leaky pipeline”; and ensure alignment between the programmes students enrol in and the skills requirements of the Virginian economy. In providing such a clear focus and direction, the Plan creates a strong framework within which to design and implement specific policies and programmes to achieve the high-level goals.

The Commonwealth’s system of six-year plans for public higher education institutions ensures close articulation between state-wide goals and institutional strategies

As part of the 2005 Restructuring Act (SCHEV, 2019[79]), public higher education institutions in Virginia were granted greater operational and administrative autonomy and, in exchange, required to participate in formalised accountability mechanisms. The Restructuring Act and the subsequent (2011) Higher Education Opportunity Act require all public four-year institutions and the Virginia Community College System to prepare institutional plans with a six-year time horizon, setting out institutional strategies designed to contribute to state-wide higher education goals and, in parallel, to meet quantitative Institutional Performance Standards (IPS) relating to enrolment, progression, transfer and completion.

In their six-year plans, institutions are required by SCHEV to identify specific institutional initiatives that contribute to state-wide goals, prioritise these initiatives and indicate funding needs for each institutional initiative for the coming biennium (see example in Box 6.4). After initial checks by SCHEV staff, plans are formally submitted for review to the six-person Operating Advisory Committee (OpSix), composed of representatives of the executive branch, the General Assembly and SCHEV.5 OpSix provides (non-binding) feedback on the plans. After OpSix provides feedback on the initiatives, the Governor, the Senate and the House of Delegates independently determine which initiatives they wish to (and can) fund.

Box 6.4. Institutional six-year plans in Virginia: The example of George Mason University (GMU)

Adopted every two years, in each odd-numbered year, at the time of writing, institutions are preparing revised plans for the biennium 2020-22. Alongside information on planned tuition and fees, financial aid and capital investment, institutions submit detailed descriptions of strategies (“initiatives”) that contribute to state-wide goals, each costed, with an indication of institutional investment and incremental funding requirement from the state. The initiatives – and thus funding requests to the state – are prioritised then by the Operating Advisory Committee (OpSix).

As an illustration, in its current six-year plan (GMU, 2018[80]), George Mason University, a large doctoral university in Northern Virginia, which has expanded rapidly in recent years, identifies 12 institutional initiatives, of which the top 6 prioritised for 2018-20 were:

1. Provide affordable access for all students: Increase student financial aid for both undergraduate and graduate students.

2. Enrolment growth and degree awards for Virginia undergraduate and graduate residents.

3. Student success initiatives – student experience redesign: Focus on the integration of technologies used by students, faculty and advisors to improve student success.

4. New and enhanced programs: New vision for undergraduate education – Mason impact and enhance current programs.

5. Online Degrees: Provide some of GMU's leading programs online through the Online Virginia Network partnership.

6. Accessible Pathways: Partnering with Northern Virginia Community College (NOVA) to create a two-to-four year transfer model.

Source: Based on information from SCHEV and GMU (2018[80]).

The six-year institutional planning process appears to be an effective way to ensure the engagement of public higher education institutions with the goals of the Virginia Plan for Higher Education, as well as for Virginia’s public authorities to steer institutional strategy. The development of plans within institutions and subsequent feedback processes from SCHEV and OpSix create space for dialogue and development of a shared understanding of how the state’s goals – including those related to workforce alignment – might be achieved. Experience from other advanced higher education systems with institutional performance agreements, such as Denmark, the Netherlands, Ireland or Ontario (Canada), suggests that the planning process itself and related dialogue between institutions and authorities create considerable added value, even before any results of the planning process and implementation are achieved. The data submitted by institutions in their plans is also used by SCHEV to inform its budget planning. The ability for authorities to interact with and influence institutions is a valuable feature of the governance system for higher education in Virginia, where public institutions are considerably more autonomous than their counterparts in US states with centralised governing boards.

While the institutional planning process is positive and appears to be widely accepted by public institutions in Virginia, two main weakness emerge from discussions with stakeholders and reviews conducted within the state. First, limits on state funding mean many institutional initiatives proposed in the plans do not receive additional state funding in practice – thus limiting the scope and potential impact of proposed actions and the incentives provided to institutions to commit to state goals (see Section 6.3). Second, the OECD understands that there is no systematic follow-up of implementation of the initiatives in the state plans beyond self-reporting by institutions in the subsequent planning round. This lack of systematic follow-up is, at least in part, due to limited capacity and resources within SCHEV to evaluate the implementation of institutional initiatives or co-ordinate external evaluations.

Institutional Performance Standards are a way to focus institutional efforts on workforce issues, but current targets could be pushed further and linked funding is limited

The institutional six-year plans are complemented by Institutional Performance Standards (IPS), which include six general education-related performance measures, as well as financial and administrative standards that apply to all public higher education institutions. The education-related performance measures establish standard institutional targets for enrolment, total degree awards, degree awards in STEM-H, awards to under-represented groups and two-year to four-year transfers (Box 6.5). These targets relate directly to identified needs to expand graduate numbers, increase the supply of graduates in STEM-H fields and improve performance among under-represented groups. Institutional performance against the measures is assessed annually by SCHEV to certify institutional performance.

Institutions that meet the established performance standards (financial and administrative compliance and education targets) have been eligible to receive additional funding in some, but not all, budgetary periods. While no funding for performance-related funding of this kind was made available in the last biennium (2015-18), the General Assembly did approve a budget for the current biennium, albeit one limited to a total of USD 13 million for all public institutions in the Commonwealth.

While reports by SCHEV (SCHEV, 2018[81]) highlight that some institutions have failed to meet individual Institutional Performance Standards, whether in terms of administrative and financial compliance or educational performance, such cases appear to be rare. In the current biennium, all institutions were eligible for the award of financial benefits for the financial year 2018 and it is understood that all institutions have been certified as eligible for benefits for 2019 and 2020. If the purpose of the Institutional Performance Standards is indeed to ensure compliance with basic standards, then the current system may adequately fulfil this role. However, if the ambition is to use the system of performance standards to “push” institutions and incentivise even greater efforts to meeting Virginia’s labour market needs, then more ambitious targets, even more tailored to the contexts of individual institutions, and a greater allocation of public funding may be required.

Box 6.5. Educational Institutional Performance Standards for public higher education institutions in Virginia

Introduced in the 2005 Restructuring Act and assessed by SCHEV every two years, the institutional performance standards are:

1. Headcount: Institution meets at least 95% of its State Council-approved biennial projections for in-state undergraduate headcount enrolment.

2. Degree awards: Institution meets at least 95% of its State Council-approved biennial projections for the number of in-state associate and bachelor degree awards.

3. STEM-H degree awards: Institution meets at least 95% of its State Council-approved biennial projections for the number of in-state STEM-H (science, technology, engineering, mathematics and health professions) associate’s and bachelor’s degree awards.

4. Progression and retention: Institution meets at least 95% of its State Council-approved biennial projections for the number of in-state, upper level - sophomore level for two-year institutions and junior and senior level for four-year institutions - program-placed [enrolled], full-time equivalent students.

5. Degrees for under-represented students: Maintain or increase the number of in-state associate’s and bachelor’s degrees awarded to students from under-represented populations.

6. Two-year transfers: Maintain or increase the number of in-state two-year transfers to four-year institutions.

Source: SCHEV (2018[82]).

The goals and activities of the multiple state agencies involved in workforce development are not fully aligned

While SCHEV co-ordinates Virginia’s efforts to develop its higher education system, as in other jurisdictions, a range of other state agencies, often working in partnership with non-government actors, are involved in the broader policy field of workforce development in the Commonwealth. Key among these are the state’s Public Employment Service, the Virginia Employment Commission (VEC), which works with a network of regional Local Workforce Development Boards (LWDBs) and local job centres; the Virginia Economic Development Partnership (VEDP), which co-ordinates the state’s inward investment strategies; and the newly created state regional development board, operating under the banner of GO Virginia, which co-ordinates the activities of devolved councils in nine regions across Virginia (GO Virginia, 2019[83]).

Each of these bodies, understandably, has their own institutional strategy and perspectives on the question of the alignment between higher education and the labour market. Whereas the VEC and the broader workforce system in Virginia focus, to a large extent, on supporting individuals with comparatively low skills to access relevant training (some post-secondary) and employment, the VEDP has focused on marketing Virginia’s highly skilled talent pool in its work. The most striking example of this has been the recent successful bid, led by VEDP, to attract Amazon’s second headquarters (HQ2) to Northern Virginia. A core component of the state’s offer was the promise to boost Virginia’s “tech talent pipeline” with at least 25 000 additional bachelor’s and master’s graduates over the next 20 years (HQNOVA, 2018[84]). GO Virginia, meanwhile, with its state board and nine regional councils, is seeking to mobilise projects to “foster private-sector growth and job creation through state incentives for regional collaboration by business, education, and government” (GO Virginia, 2019[83]). Each GO Virginia regional council has identified priority industry clusters to support through collaborative initiatives, including in the area of skills and workforce development.

The complex landscape of state bodies with a mandate that encompasses workforce development has resulted from various state and federal policies that have added layers of activity over time. For example, Virginia’s public workforce system, although co-ordinated by the Virginia Board of Workforce Development (VBWD), in reality involves eight agencies6 and 25 funding programmes, many of which are mandated by federal law and governed by federal rules (Virginia Career Works, 2017, p. 4[85]). GO Virginia, with its nine regional councils, is a state initiative, but needs to find its place alongside the existing workforce system bodies, education and training agencies, including SCHEV, and the existing dense network of business associations, such as Chambers of Commerce. While the existence of different bodies with distinct but related missions is positive and, by no means, unique to Virginia, it does create risks related to fragmentation and incoherence in strategies and actions. This can reduce focus on key state targets and create unhelpful confusion about messages and responsibilities for employers, educational providers, students and citizens.

Three potential sources of tension stand out in particular in the strategies and focus of the different agencies responsible for workforce development and higher education in Virginia.

First, while Virginia’s overarching goals and the Virginia Plan for Higher Education stress the need to focus on developing middle and advanced skills, the public workforce system focuses its activities, to a large extent, on those with low skills. This is important work and understandable given the role of the public employment service in helping those sections of society most in need of support to access work. However, the other main goal of the workforce system is to ensure employers can find the skilled workers they need. While a large proportion of demand is for workers with post-secondary qualifications, the interaction between the workforce system and higher education in Virginia appears relatively limited. In the VBWD’s latest strategy document, the Virginia Community College System is mentioned as one of the core agencies in the public workforce system because of its role in certain specific programmes (Virginia Career Works, 2017[85]). SCHEV and the rest of the higher education system are not mentioned, despite the goals of the strategy to identify workforce needs and recommend strategies “to better prepare and match trained workers with available and emerging jobs” (Virginia Career Works, 2017, p. 9[85]). There appears to be scope to align the state’s workforce and higher education strategies more clearly.

Second, some stakeholders interviewed during the OECD visit to Virginia argued there is a risk that the state’s strategy becomes skewed towards promoting credentials in the field of computer science and related fields, at the expense of other areas of high demand, such as health-related occupations, teaching and skilled trades, or valuable non-technology-related degrees. It is true that the Tech Talent Pipeline Initiative that formed part of Virginia’s bid for Amazon’s HQ2, and related Tech Talent Investment Fund recently adopted by the General Assembly, have attracted a great deal of attention in the state in the last year. At the centre of this initiative are plans to nearly double the number of bachelor’s and master’s degrees awarded in computer science and closely related fields in Virginia in the period up to 2039, compared to current graduate trends. This will be supported with targeted funding for institutions across the state and for new master’s level campuses in Northern Virginia. It is also true that, even with the arrival of Amazon, computer science will only be one of several important high-demand skills fields in Virginia, and that many employed in tech companies do not require computer science qualifications. However, it would be wrong to claim Virginia is focusing exclusively on tech talent. Other policy initiatives are in place to increase the supply of health-related professionals, teachers and workers with workforce credentials in high-demand fields. It will be important, however, for the Tech Talent Pipeline Initiative to be embedded in a holistic view of the Commonwealth’s skills needs, as the Virginia Plan for Higher Education is revised.

A final potential source of tension in the strategic planning and co-ordination environment for higher education in Virginia is between the largely state-wide focus of SCHEV and the goals of the Virginia Plan for Higher Education and the local and regional focus of the public workforce system, GO Virginia and, to some extent, the VEDP. The Virginia Plan for Higher Education does include a focus on under-represented population groups and on regional development, but the measures and targets and annual reporting focus almost exclusively on state-wide averages in enrolment, completion and attainment, rather than providing a more differentiated picture of performance across Virginia’s diverse regions. A greater regional focus within the Virginia Plan could help strengthen coherence with regional workforce and economic development strategies.

Recommendations for strategic planning and co-ordination

1. Review the system of Institutional Performance Standards to: a) introduce more differentiated goals related to skills development in high-demand fields for individual institutions or groups of institutions; and b) consider level of ambition of targets established to ensure that goals “push” institutions, while remaining realistic. In order to create greater incentives for institutions to work towards the targets established, a high-level of performance funding would be beneficial (see also Section 6.3.4).

2. As part of the process to prepare the revised Virginia Plan for Higher Education (2020-26), review (potentially through a temporary joint taskforce co-ordinated by SCHEV) state strategies and work programmes with a direct impact on higher education-labour market alignment; identify potential incoherence and overlap; and agree actions to ensure coherence and complementarity in the future. If necessary, recommendations should be made to the General Assembly to modify legislation governing the mandates and activities of key state agencies. The core objective should be to ensure effectiveness, efficiency and readability for target audiences.

3. Explore ways to strengthen co-operation between SCHEV and the Virginia Board of Workforce Development (and other relevant components of the public workforce system), to ensure better alignment between workforce development policy and higher education policy, in terms of messaging, strategy and activities. Part of this should include co-operation to improve the quality and accessibility of data on labour market skills demand in the Commonwealth (see Section 6.3.5).

4. Policy makers (Legislature, executive, state agencies) should seek to ensure Commonwealth policies on skills and workforce development take a balanced and realistic view of the diversity of demand for post-secondary graduates in light of resources and competing skills needs. This includes paying adequate attention to requirements in fields such as education, healthcare and skilled trades, as well as the unquestionable demand in ICT-related occupations.

5. If resources within SCHEV allow, introduce regional measures linked to the goals of the Virginia Plan for Higher Education to help ensure actions address the distinct higher education and labour market challenges of different regions and create better links to state agencies whose activities have a strong territorial dimension.

Student supports and pathways

The educational programmes delivered to students in Virginia’s autonomous higher education institutions are primarily the responsibility of institutions and their teaching faculty. Interviews with institutional leaders demonstrated a strong commitment in universities and colleges across the Commonwealth to meeting skills demand and ensuring graduates have relevant skills. This is reflected in numerous institutional initiatives to enhance curriculum design, exploit online learning and offer guidance and co-curricular activities to students, many of which are highlighted in the six-year institutional plans discussed above.

However, as in other jurisdictions, public policies are in place in Virginia that influence the programmes offered by institutions and ensure certain quality standards; that create pathways for students between different types of programme and institution; and that seek to ensure students receive guidance and advice to help them move into and through the post-secondary education system. These policies influence the post-secondary educational offerings available, students’ ability to progress and transfer, and the level of support available to students and prospective students, and are all relevant in the Commonwealth’s efforts to strengthen alignment between higher education and the labour market.

In the absence of a distinct system of higher vocational education, Virginia’s higher education system encompasses a wide range of programme types, from workforce credentials lasting less than one year to doctoral degrees. The remainder of this section reviews the policies affecting the numbers, type and format of educational programme provided by higher education institutions in Virginia, as well as the pathways open to students and the guidance and counselling available to them.

SCHEV considers labour market alignment in its upfront approval process for new programmes, but not in ex-post programme productivity reviews

Although public higher education institutions in Virginia enjoy considerable administrative and operational autonomy, they are required to gain approval from SCHEV to create, discontinue or make substantial amendments to degree-level study programmes. SCHEV has responsibility for approving the creation of new programmes, programme closures and substantive changes to existing programmes in public higher education institutions at the associate’s degree level and above. Non-degree programmes, such as workforce certificates, do not fall under the system, but public institutions – in most cases the Virginia Community College System – are required to notify SCHEV of the creation of new programmes at the certificate level.

For entirely new degree programmes, the first professional degrees offered in institutions and all modifications to health-related programmes, SCHEV’s academic approval policy requires formal approval by the SCHEV Council. For discontinuance of programmes, mergers, spin-offs and modifications, approval can be given at operational level by SCHEV. A “facilitated approval” system exists for four-year institutions, whereby new programmes can be approved by SCHEV rather than having to go to Council, when the institution’s own Board of Visitors has approved the programme in question. The basic criteria and evidence requirements for approval under this theoretically “lighter-touch” process remain the same.

A key objective of the programme approval process is to ensure the efficient use of public resources in public higher education institutions by avoiding a potentially wasteful proliferation of publicly subsidised programmes with low enrolment and high staff costs. Institutions are required to demonstrate that there is adequate student demand for new programmes (to ensure sufficient tuition revenue) and that the programmes are not “unnecessarily duplicative” of other programmes in the state. In addition, SCHEV approval policy calls on institutions to provide evidence of the employment demand for graduates from proposed programmes, with explicit requirements to use the Virginia Employment Commission’s annual and ten-year labour market projections and examples of relevant job vacancies in the Commonwealth (Box 6.6).

Box 6.6. SCHEV academic approval: Criteria for justifying new programmes

SCHEV’s approval process for new academic programmes (associate’s degree and above) requires institutions to provide a detailed description of the proposed programme, an in-depth justification of the need for the new programme, an estimation of projected enrolment and detailed information on the cost implications of the programme (including potential calls on state funding). In their justification of the need for the proposed programmes, institutions must demonstrate:

how the programme responds to current needs in terms of broad developments in the discipline, societal challenges or economic trends;
evidence of employment demand, based on state and national labour market demand projections (Virginia Employment Commission and Bureau of Labor Statistics) showing demand for graduates from the programme in directly related or closely related occupations, a minimum of 20 job advertisements in related fields, and testimonials from employers;
evidence of student demand, based on application data, survey data, or other evidence such as correspondence with prospective students;
evidence that the programme is not “unnecessarily duplicative” of degree programmes at other institutions in Virginia.

Source: SCHEV (2016[86]).

In addition to its upfront approval process, SCHEV is also tasked with reviewing existing undergraduate and postgraduate programmes in public higher education institutions every five years to ensure they are meeting established standards in terms of “productivity”7. This is the other main lever the agency can use to regulate directly the programmes provided in public institutions. Regarding the programme productivity review, the Code of Virginia states that SCHEV is required to:

“Review and require the discontinuance of any undergraduate or graduate academic program that is (i) nonproductive in terms of the number of degrees granted, the number of students served by the program, the program's effectiveness, and budgetary considerations or (ii) supported by state funds and unnecessarily duplicative of academic programs offered at other public institutions of higher education.” (Code of Virginia, 2019[87])

In practice, for SCHEV, the process involves checking that programmes are meeting expected quantitative standards in terms of enrolment and degree awards according to an established methodology and, after obtaining the opinion of institutions with programmes that fail to meet the established standards, making recommendations to the SCHEV Council on the continued operation of programmes (SCHEV, 2013[88]). Evidence from past rounds of the productivity review process shows that institutions frequently opt to close programmes with low productivity on SCHEV measures, but in some cases provide justification for the continued operation of programmes on the grounds they are central to institutional missions, recently established or unique to the region where the institution is located. SCHEV reports suggest SCHEV staff usually follow institutional positions in their own recommendations to the SCHEV Council (SCHEV, 2014[89]).

Representatives of public higher education institutions in Virginia view SCHEV’s current system of upfront programme-level approval as bureaucratic and burdensome. They argue the procedures limit their flexibility to respond in a timely way to changing skills demand in the labour market, noting that it can take up to two years to gain approval for new programmes. Furthermore, several institutional representatives met by the OECD team argued that SCHEV’s previous requirement to provide evidence of real current job postings to demonstrate labour market demand was unrealistic in fields where institutions are seeking to anticipate future demand. In response to these critiques, SCHEV staff point to their statutory role in ensuring the relevance and efficiency of Virginia’s public higher education system and recent efforts to streamline the approval process. These efforts have included the “facilitated approval” process and updated policy that allows institutions to demonstrate labour market demand for new programmes through any reasonable means.

A balance needs to be found between SCHEV’s legal oversight function and legitimate concern to ensure relevant programmes are provided and state resources are well used, and the desire for institutions to adapt their programme offerings flexibly to changing demand from students and employers. SCHEV’s requirement for institutions to demonstrate in advance the relationship between planned programmes and official labour market projections and demonstrated economic trends is entirely sensible (as discussed below, there is a case for improving the quality and visibility of these labour market projections). However, institutions’ concerns about the need to show current demand in fast-evolving fields and about the time taken to approve new programmes also appear legitimate. These are factors that will need to be taken into account in refining policy in the short to medium term.

It is notable that the ex-post programme productivity reviews do not use the labour market outcomes experienced by programme graduates as a criterion to identify programmes that are performing poorly8. The language in the Code of Virginia relating to SCHEV’s duties in terms of productivity review, cited above, refers to “program effectiveness” as one of the criteria for discontinuing a programme. While the current SCHEV policy for productivity review focuses on enrolment and graduate data, this language in the legal basis would appear to leave scope for the inclusion of other effectiveness criteria – such as graduate labour market outcomes – in the periodic review. As suggested in the recommendations below, it may be possible to adjust the current balance between upfront and ex-post regulation of programme provision.

Institutional accreditation standards used in Virginia pay no attention to labour market issues, but are outside the direct control of Virginia’s law makers

Before turning to other policies that Virginia can and does use to steer the post-secondary educational offerings in the Commonwealth, it is worth noting a relevant policy area where state authorities have little or no real power. In contrast to a majority of national higher education systems in OECD countries, US states have no direct responsibility for the design or implementation of external quality assurance and accreditation systems in higher education. Instead, a general requirement for external accreditation has been mandated by the federal government for institutions receiving federal student aid and implementation of such external accreditation is delegated to regional or sectoral accreditation bodies. As such, the system is effectively a form of self-regulation. Public and private not-for-profit higher education institutions in Virginia are generally accredited by the regional accreditor for the southern states, the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC).

The institutional accreditation standards and processes used by SACSCOC verify a wide range of institutional characteristics and policies, and provide a basic guarantee of academic quality (SACSCOC, 2018[90]). However, the institutional standards make no explicit reference to institutional policies and practices to align skills development with labour market needs and do not examine the real-world labour market outcomes of graduates. Institutional reaffirmation of accreditation is infrequent (every ten years) and although institutions are required to seek approval for “substantive changes” to programmes, the relevant procedures do not take into account labour market relevance of the programmes in question.

Virginia has also used targeted funding to expand provision of non-degree workforce credentials and promote work-based learning in programmes

In addition to their role in programme approval and monitoring, public authorities in Virginia have used two other main policy levers to influence the scale and form of post-secondary educational provision in public institutions. First, as discussed in the previous section, the state-wide and institutional planning process has included efforts to increase degree awards in fields with high labour market demand, including in STEM-H fields. Second, the Commonwealth has used targeted funding programmes to promote certain types of provision designed to equip graduates with labour market-relevant skills. In this latter category, two initiatives stand out in particular: the New Economy Workforce Credential Grant Program and the Commonwealth Innovative Internship Program.

Box 6.7. The New Economy Workforce Credential Grant

In 2016, the General Assembly approved the establishment of the New Economy Workforce Grant Program, as a way to support more Virginians to complete workforce training that leads to a credential (typically a certificate of less than one year) in a high-demand field. The Program covers training provided in public two-year institutions (VCCS and Richard Bland College). In the VCCS, the Grant Program is marketed under the banner of “FastForward”. The grant system uses the following performance-based model:

1. Students (who must be Virginia residents) are required to pay one-third of the total cost of the programme upon enrolment, but may use third-party funds, such as non-credit financial aid, training vouchers or employer payment to cover this cost.

2. If the student completes the training, the state provides one-third of the cost of the programme, up to USD 1 500 to institution. If the student does not complete the programme, then the student is required to pay this portion of the total cost.

3. If the student satisfactorily completes the workforce credential after completing the training, the institution receives the remaining one-third of the cost of the programme up to USD 1 500. The combined maximum award to an institution is USD 3 000 for completion of training and a credential.

The General Assembly allocated USD 5 million in 2017, USD 7.5 million 2018 and USD 9.5 million annually for 2019 and 2020. Funds are awarded to institutions on a first-come, first-served basis.

“High-demand fields” are identified in the Virginia Employment Commission’s labour market projections and eligible credentials include those related to occupations in transport and material moving (including Commercial Driver’s License – CDL), healthcare support, production (including welding and mechatronics), skilled trades and ICT-related professions (such as CISCO networking technician and associate or information systems security project management professional).

SCHEV is responsible for administering the programme, conducting periodic assessments of its performance, collecting student data, and making final decisions on disputes between eligible institutions and grant recipients.

Sources: SCHEV (2019[91]), SCHEV (2019[92]), VCCS (2017[93]).

The Workforce Credential Grant, approved by the General Assembly in 2016, provides funding to public two-year institutions to allow them to offer low-cost opportunities for students to receive training in a high-demand field and obtain an industry-based certification. The state covers up to two-thirds of the cost of the programme, provided the student completes the training and successfully obtains the credential (see Box 6.7 above). As noted in the previous section, the Virginia Plan for Higher Education aims for 10% of the working-age population in the state to have a workforce credential by 2030 to complement the targeted 60% with a degree. Moreover, individuals undertaking workforce credential programmes – as opposed to degree programmes at associate’s level and above – are not eligible for federal Pell Grants, meaning immediate financial support for gaining these types of credential was limited. As such, the programme seeks both to contribute to the state attainment goal and fill a gap in existing student aid mechanisms.

In 2018, 3 700 individuals enrolled in training under the programme and 2 518 gained a credential, with the largest numbers of awards in the fields of commercial driver’s licenses, training related to highway construction, welding and medical care. The average age of students was 35. 92% of students completed their training and 73% obtained a credential. In 2018, SCHEV reports that the average cost to students participating in the program was USD 904, although this does not take account of third-party financial aid received. The average cost to the state of Virginia per credential attained was USD 2 004. Through analysis based on Virginia Longitudinal Data System data for the first student cohorts, SCHEV calculates that individuals who were earning less than USD 20 000 a year before enrolment earned 71% more on completion of the training and 138% more on completion of a credential (SCHEV, 2019[92]). As a result of this success, SCHEV recommended an annual budget increase to USD 13.5 million for 2020.

Alongside the focus on promoting workforce credentials, Virginian authorities have recently introduced a programme to tackle another acknowledged weakness of the current post-secondary education system: a lack of work-based learning opportunities (internships or other forms of practical experience) in degree programmes. Established in 2018, the Innovative Internship Fund and Program aim to expand paid or credit-bearing student internships and other work-based learning opportunities in collaboration with Virginia employers. The programme funds public higher education institutions through a competitive process to develop partnerships with business and the public sector to provide paid internship opportunities for students, and supports a state-wide initiative to improve the “readiness” of students, employers and higher education institutions to participate in internships and other work-based learning opportunities. Building on a successful pilot in 2019 involving Northern Virginia Community College (NOVA) and chipmaker Micron in Northern Virginia, the programme is currently in its second round of implementation.

In the second round of funding, SCHEV received 15 applications requesting slightly more than USD 1 million and awarded nearly USD 330 000 to five universities and colleges by a panel of workforce experts appointed by SCHEV (SCHEV, 2019, p. 245[94]).

Virginia is making efforts to improve pathways between levels of education, although challenges remain

Virginia, like other states, has promoted alternative educational pathways to give students – particularly adult learners and those from under-represented groups – a wider range of options to obtain a bachelor’s degree. Two long-established mechanisms, both of which are distinctive features of American higher education in comparison to other OECD systems – are dual enrolment programmes, involving co-operation between high schools and community colleges, and two-year transfer programmes in community colleges, which can allow students to move into the third year of a bachelor’s programme at four-year institutions with which transfer agreements exist. In addition to these established pathways, the proliferation of workforce credentials and industry certificates has increased focus on how skills gained in these short programmes can be bundled together and recognised as counting towards credit in high-level qualifications such as applied associate’s degrees.

Dual enrolment involves students taking high school courses and community college-level courses in parallel with the expectation that the credits gained will count towards an associate’s degree at a community college or a bachelor’s degree at a four-year college. A key objective for students is that they save time, and thus money, in gaining a post-secondary qualification. A 2017 report by Virginia’s Joint Legislative Audit and Review Commission (JLARC) found that although dual enrolment students who enrol in community college after high school take about one semester less, on average, to earn a post-secondary credential than non-dual enrolment students, most dual enrolment students who transitioned to four-year colleges did not save time (JLARC, 2017[56]). This is most often because credits gained through dual enrolment courses, which are primarily taught in high schools, were not considered of adequate quality to be accepted as transfer credits in four-year colleges. In response to the recommendations of the JLARC report, VCCS reformed the way it oversees dual enrolment programmes and improved information for students on the transferability of dual enrolment courses to community colleges and four-year institutions (JLARC, 2019[95]).

Students entering community college with the aim of reducing the cost of obtaining a bachelor’s degree through taking a transfer-oriented associate’s degree programme before transitioning into the third year of a bachelor’s programme in a four-year college have faced similar problems. Most transfer students who do go on to earn a bachelor’s degree – many do not – take longer and earn more (unnecessary) credits than their counterparts who start college in a four-year institution (JLARC, 2017[56]). In its analysis in 2017, JLARC highlighted the large number of transfer agreements between individual community colleges and four-year public institutions, which makes it hard for students to navigate the system and, in particular, to ensure they choose study options in community college that will be accepted for credit accumulation by four-year institutions.

State authorities, including SCHEV, have primarily sought to encourage institutions to facilitate transfer between two-year and four-year colleges through the six-year planning system and Institutional Performance Standards (with one of the targets focusing on transfer). Legislation passed by the General Assembly in 2018 also requires four-year institutions to develop “transfer maps” to improve the legibility of transfer pathways for students (JLARC, 2019[95]). The “Transfer Virginia” initiative, recently launched by the Virginia Community College System aims to simplify the transfer process with clearer pathways and more systematic guidance to students about the study options they should select in order to transition smoothly to specific majors in four-year institutions (VCCS, 2019[96]).

Ultimately, it is entirely legitimate that decisions on whether or not student credits gained in one programme can be accepted towards a credential in second programme rest with academic faculty responsible for ensuring the coherence and quality of the second programme. There is thus a tension between maintaining standards and facilitating student transfer. Recognition of credits and prior learning is a challenge in higher education systems in many OECD member countries. The measures underway in Virginia to create fewer, more coherent pathways, ensure adequate co-operation between staff in two- and four-year institutions, and improve advising and support to students in their study choices are appropriate ways to increase the likelihood of students being able to transfer smoothly.

It is harder to obtain a clear picture of the broader question of the “stackability” of non-credit programmes and credentials, owing to the vast number of industry certifications and other learning elements that could theoretically be combined in a single credential such as a certificate. Ensuring the quality of the different elements and that the combined credential constitutes a meaningful and coherent whole that can be understood and accepted by employers are key challenges. Nevertheless, representatives of community colleges in Virginia told the OECD team that industry credentials are increasingly being embedded in longer programmes (two-year certificates or applied associate’s degrees, for example) to provide students with highly relevant qualifications in a combined package.

Guidance and advising services for students in Virginia are often inadequate for the Commonwealth’s increasingly diverse student population

Guidance and counselling to help students choose the right study options for them and to cope with entering and completing higher education are an important component in effective and inclusive higher education systems. While the availability of advising services is important for all students, it is particularly relevant for supporting students who are from low-income backgrounds or the first in their family to attend higher education, as well as for older and part-time students, who may be returning to college after time in the workforce. Experience from Virginia and elsewhere shows these groups are most at risk of dropping out of post-secondary education before completing their training and acquiring a credential (JLARC, 2017[56]). If students receive appropriate advice when they are choosing what to study, it is likely to increase their chances of selecting study options that fit well with their capabilities and interests and which they are more likely to complete. Early intervention by career and study counsellors can also help students select study options that lead to good career options, thus benefitting the prospective student and the economy.

Different analyses in Virginia, as well as the OECD team’s discussions with stakeholders in Virginia, suggest the number of counsellors per student in both the secondary and post-secondary educational systems is low (SCHEV, 2019[46]). This limits the capacity of existing counsellors to provide high-quality services to help guide students into appropriate higher education options and provide ongoing support within higher education. The OECD does not have access to data on the number of counsellors in secondary education in Virginia (whether dedicated support staff or teaching staff with an advising role). However, at post-secondary level, JLARC reported in 2017 that within Virginia’s community colleges, the median number of students to faculty advisers was 55 and the number of students to non-faculty advisers (full-time equivalent) was 250. In three colleges, there were more than 500 students per non-faculty adviser (JLARC, 2017[56]). Given the student profile within community colleges, where there are many first-time, older and low-income students, this level of advising capacity is almost certainly inadequate.

In part as a response to the challenges highlighted by JLARC, VCCS has developed a system-wide policy to identify at-risk community college students who should receive proactive, customised advising services. New students who are identified as being at risk for not completing a credential or degree are now invited to attend orientation before enrolling in courses and to complete a student development course during their first semester. VCCS is also implementing a Navigate software tool – based on national system called iPASS – at each community college to guide students through the “on-boarding”, academic planning, and advising processes (JLARC, 2019[95]). Another VCCS initiative – the Great Expectations programme – provides dedicated coaching to students who have experienced foster care as they enter and progress through the community college system (VCCS, n.d.[97]).

Despite these initiatives, the fundamental resourcing challenge for advising services has only partially been tackled. In 2019, JLARC noted that although the General Assembly appropriated USD 5.5 million in the 2019 session to VCCS for general operating support, it did not earmark any funding specifically to expand academic advising capacity (JLARC, 2019[95]).

Recommendations on programmes, pathways and guidance

1. Take steps to simplify and accelerate the initial (ex-ante) programme approval processes used by SCHEV, while introducing stronger monitoring of student and graduate outcomes from approved programmes. One option would be to shift to a system based on “proof of concept”, whereby new or substantially reformed programmes are subject to a lighter-touch ex-ante approval process checking alignment with institutional mission and Commonwealth goals, and must then provide evidence of graduation patterns and labour market outcomes after three or five years, depending on programme duration.

The follow-up of programmes could be integrated into the existing processes for reviewing programme “productivity”. This could be done either by interpreting the current language in the Code of Virginia concerning productivity review more broadly, with the concept of “program effectiveness” more explicitly defined to include the labour market performance of graduates; or by revision of the relevant statutes. Programmes with poor outcomes could be discontinued as is currently possible for programmes that do not meet expected productivity targets. Expected outcomes would need to be adapted to the specificities of different programme types, including low earnings in education; higher non-completion in community college programmes and so forth.

2. Through all possible channels, public authorities in Virginia should encourage SACSCOC to include a greater focus on labour market relevance in its accreditation standards and procedures.

3. Continue to use the six-year planning process and related funding (see recommendations on funding) to provide incentives to institutions to implement programmes that embed content and use pedagogical approaches that help students to acquire workforce-relevant skills.

4. Subject to continued positive evaluation of its results, maintain and expand the New Economy Workforce Credential Grant. In public-facing communication, ensure consistency in the branding and description of the programme to provide clear messaging to target populations. This could be through adopting VCCS’s FastForward branding for the grant scheme as a whole.

5. Closely evaluate the results of the Innovative Internship Fund and, provided it proves successful, allocate additional resources in future financial years. This initiative appears to respond to a real need to enhance work-based learning opportunities in the Commonwealth, to allow students to gain work-relevant skills.

6. Closely monitor the results achieved by the Transfer Virginia initiative and the lessons learned from other states through involvement of the Aspen Institute. The measures taken to date in Virginia to improve the quality of two-year transfer programmes, particularly by the Virginia Community College System, should lead to enhanced transfer rates and provide four-year institutions with better assurances of graduate quality. If transfer rates do not improve in the next two years, policy makers should remain open to legislative measures.

7. Introduce more explicit requirements for institutional student guidance and success strategies as part of the six-year planning process for institutions and allocate earmarked funding for advising and guidance functions – particularly for the Virginia Community College System.

Funding

In Virginia, as in jurisdictions across the OECD, public funding is arguably the most important tool that law makers and policy makers have at their disposal to help steer and shape the higher education systems under their responsibility. From a labour market alignment perspective – and in Virginia’s labour market environment in particular – public funding is necessary to achieve a balance between two main goals. First, the goal of ensuring higher education programmes are of high quality and able to equip students with relevant skills. Second – leaving aside social equity objectives – to ensure higher education is affordable for a sufficient number of citizens to permit an adequate supply of middle and advanced skills for the workforce. Adequate funding (whatever its source) is required to allow institutions to cover the costs of operating high-quality educational programmes, including remuneration of faculty at competitive rates. However, if students are required to cover the full education-related costs of programmes, higher education becomes unaffordable for all but the wealthiest in society.

In Virginia, as in other parts of the United States, public authorities seek to balance this equation by providing operating subsidies to public higher education institutions to allow them to charge lower tuition and fees for in-state students and providing student aid to low and middle-income Virginia residents to further reduce the cost of studying. Student aid provided by the state for Virginian students is in addition to Pell Grants provided by the federal government, which are awarded nationally, using the same criteria for students across the United States. Students from outside Virginia are required to pay the full cost of study in the Commonwealth’s public colleges and universities, although they are also eligible for federal student aid. In addition to institutional operating subsidies and student aid, Virginia’s law makers have periodically sought to moderate the amount students must pay by placing restrictions on the expenditure of higher education institutions (and thus the costs that must be covered) and on the amount by which institutions can increase the tuition and fees for in-state students (JLARC, 2014[98]).

Achieving the appropriate balance between allowing institutions to obtain the resources they need (as well as determining the level of resources they need) and ensuring affordable higher education for Virginia residents remains one of the key policy challenges in higher education in Virginia.

Basic state operating funding per student has declined, contributing to increased fees and declining affordability in public institutions in Virginia

In a pattern seen in many OECD jurisdictions in recent years, state operating subsidies per student for public higher education institutions in Virginia have declined in real terms in recent years. This trend has been driven by increasing student enrolment, combined with fiscal consolidation in the wake of the 2008-09 financial crisis, which has limited the capacity of Virginia’s law makers to maintain spending per student at previous levels.

Figure 6.17. Average funding per FTE student at four-year institutions for education and general programmes, 1992/93 to 2019/20

As illustrated in Figure 6.17, SCHEV calculates that the average state subsidy (General Fund appropriation) per full-time equivalent (FTE) student in public four-year institutions for the 2020 financial year is 41% below its peak in 2001 in constant dollars, despite recent increases in the General Fund subsidies for institutional operating budgets.9 Data from the national association of State Higher Education Executive Officers (SHEEO) for 2018 place Virginia just 39^th of the 50 states in the nation in terms of total state appropriations (of all types, including student aid) to public two-year and four-year higher education institutions. The average appropriation per student in Virginia is USD 5 701, compared to the US average of USD 7 853 (SHEEO, 2019[73]).

In parallel, revenue from tuition and mandatory fees (non-General Fund revenue) has more than doubled in real terms in the last 20 years, with an average annual increase of over 4% in the period between 1993 and 2020. As a result, total funding per FTE student for educational and general activities in four-year institutions has increased by 50% in constant US dollars since the early 1990s.

Box 6.8. State funding for public higher education institutions in Virginia

Different tools, guidelines and processes inform decisions about the level of state subsidies provided to public higher education institutions in Virginia. In practice, the actual level of funding awarded to institutions is always a political decision by the Governor and General Assembly.

SCHEV calculates the assumed cost of operating different types of educational programmes in public higher education institutions using a “base adequacy model”. This model uses national data on spending levels and pre-determined student-faculty ratios by academic disciplines and level of instruction to provide an estimate of the “education and general” (E&G) operating costs required by each institution. E&G operating costs include faculty and staff compensation, instructional materials and equipment, a basic allocation for research, student and institutional support services, and operations and maintenance of facilities. The model is used purely to calculate resource needs, without consideration of where these resources should come from. The model is widely accepted as providing a transparent and objective means to estimate and benchmark operating costs, although it has been criticised for using outdated salary data and assumptions (JLARC, 2014[98]).
Since 2004, Virginia has had a cost-share goal, according to which the state should aim to meet two-thirds of the cost of education for in-state students, with students responsible for covering the remaining one-third, theoretically from the federally mandated Expected Family Contribution (which may be zero) and federal and state student aid. Cost of education includes core educational services, but excludes mandatory fees for non-educational services and room and board, which represent additional costs of attendance for students. The actual level of funding provided is routinely benchmarked against this target (which has not been met in recent years).
Taking into account the results of the base adequacy model and the cost-share goal, as well as the availability of public funds, SCHEV makes budget and policy recommendations to the Governor and General Assembly each financial year. These recommendations propose budget changes for institutional operating costs (education and general programmes), state student aid and targeted initiatives (SCHEV, 2018[99]). The state’s Governor and Legislature are free to accept, reject or amend SCHEV’s recommendations. No policy or mechanism current exists to increase institutional operating funds consistently in line with inflation and student enrolment.

Sources: JLARC (2014[100]), SCHEV (2018[99]).

One of the key reasons for the decline in state operating funding is that resources are allocated by the General Assembly through the annual budget process primarily on an incremental basis, rather than through a funding formula that takes into account enrolment or increases in the general cost of living. As highlighted in Box 6.8, Virginia does have in place a sophisticated model for estimating the operating costs of education programmes in its public institutions in light of enrolment numbers. Combined with an aspirational goal that the state should fund two-thirds of the cost of education for in-state students, this model provides a basis for calculating the “desirable” level of state subsidy for each public institution. In practice, however, this level of subsidy has only rarely been achieved since the early 1990s. In the academic year 2019/20, state operating subsidies covered 48% of the cost of education for in-state students – 19 percentage points below the legal target.

As Box 6.8 shows, the corollary of declining state operating subsidies has been increasing student tuition and mandatory education and general fees. Average annual tuition and mandatory education and general fees for in-state students in public four-year institutions in Virginia in 2019/20 were USD 9 274 (on top of which came average mandatory non-E&G fees of USD 4 425 and average room and board of USD 11 000, making a total of USD 24 699). Total average annual fees in public two-year colleges in 2019/20 were USD 4 620. SCHEV estimates that Virginia’s doctoral universities (William & Mary, George Mason University, Old Dominion University, University of Virginia, Virginia Commonwealth University and Virginia Tech) had the 7^th highest in-state fees in public institutions of the 50 states. Comprehensive universities (such as Norfolk State University) were the fourth most expensive and public two-year institutions were eighth most expensive among their peers across the nation (SCHEV, 2019[71]).

As a rule, Virginia’s autonomous higher education institutions have considerable freedom to increase fees as they please, despite their public commitment to promoting affordability (see Section 6.3.1). Virginia is one of only eight states whose co-ordinating board has no direct budget authority. SCHEV reviews and makes recommendations about each institution’s budget, but is not authorised to modify institutional budget requests (JLARC, 2014, p. 13[98]). Nevertheless, over the years, Virginia has introduced legislation to encourage public institutions to limit their fee increases. After a number of years of large fee increases, the 2019 General Assembly provided an additional USD 52.5 million in state support to establish the “tuition moderation fund” for public higher education institutions where affordability is a concern. In exchange, public institutions were required to maintain their 2019/20 tuition for in-state undergraduate students at their 2018/19 level. All institutions complied with the requirement (SCHEV, 2019[71]). Virginia’s General Assembly had already introduced an annual cap on increases in non-educational mandatory fees (a large proportion of which contribute to inter-collegiate athletics), which have seen very high rates of growth over the last decade. Annual increases in these fees in public higher education institutions are limited to 3% by the state budget legislation (Virginia General Assembly, 2019[101]).

Virginia invests a comparatively high level of resources in financial aid for in-state students, although not always in a targeted way

Given the increases in tuition and fees in recent years, as well as the limited scope for reducing these, financial support to students is an important tool with which Virginia seeks to increase the affordability of higher education and support more of its citizens in obtaining credentials relevant to workforce needs. State policies for student aid aim to increase social equity and support the Commonwealth’s goals in terms of skills supply.

Virginia has a number of financial aid programmes, of which the Virginia Student Financial Assistance Program (VSFAP) is by far the largest. The VSFAP provides funding to public higher education institutions, which then allocate resources to students, primarily based on financial need and following state-wide guidelines. These guidelines use a specific definition of “financial need”10 (the cost of tuition and mandatory fees minus the federally regulated Expected Family Contribution11) and a “partnership model” under which all students are expected to contribute a proportion of the cost of tuition and mandatory fees (JLARC, 2014[98]). Awards to students vary considerably between institutions, firstly, because of differences in the cost of fees – and thus the “financial need” calculated for students with the same family income – and, secondly, because institutions have flexibility in the way they award aid, provided they stick to the broad state-wide guidelines.

In 2019/20, Virginia allocated almost USD 250 million of public funds to its student financial aid programmes for students in public institutions. A nation-wide comparison of student aid in 2016/17 showed almost 45 % of all enrolled students in the state received some form of state-sponsored financial aid – the 8^th highest rate among all 50 states (SCHEV, 2019, p. 82[102]).

The Virginia Plan for Higher Education (see Section 6.3) establishes a target on affordability for 2030, whereby students from low- or middle-income families12 should receive half of the cost of attendance from a combination of the Expected Family Contribution (EFC) and federal and state grants. The expectation in Virginia is that the remaining half of the cost of attendance should come from institutional aid, loans, and income from work and other sources (SCHEV, 2015[103]). However, SCHEV’s most recent report on progress towards state goals shows that the state is some way from meeting this target. As show in Figure 6.18, in 2016/17, federal and state aid, in combination with the EFC, met only one-third of the cost of attendance for low-income students at public four-year institutions in Virginia, and under 40% of the cost of attendance at two-year institutions. The level of coverage of the cost of attendance for middle-income students was slightly higher owing, primarily, to the higher expected family contribution.

Figure 6.18. Student aid and expected family contribution vs. cost of attendance, 2016/17

Alongside concerns about the inadequacy of state financial aid budgets in light of growing levels of unmet financial need, current resources are not always targeted effectively to students in public institutions with the greatest financial need. Firstly, the model used to allocate VSFAP resources to individual public higher education institutions uses past enrolment data to estimate the proportion of students from different income groups, rather than data on the current cohort. As enrolment among low-income students has expanded, some institutions have found themselves with growing levels of unmet financial need that are not covered by the allocation model. While VSFAP allocations meet 77% of financial need recognised at the University of Virginia (UVA), they meet only 40% or less at six institutions that have a greater percentage of low- and middle-income students: VCU, Norfolk State, ODU, Virginia State, George Mason, and Mary Washington (SCHEV, 2019[104]).

Secondly, institutions often award aid on a first-come, first-served basis, irrespective of income level, meaning some students from relatively high-income backgrounds receive financial aid because they have “financial need” and applied for aid early. In 2017/18, 24% of VSFAP grants awarded in public four-year institutions, totalling almost USD 12 million were awarded to students with family incomes over 400% of the federal poverty level (SCHEV, 2019, p. 183[94]).

In addition to the VSFAP and other smaller appropriations for financial aid to public higher education institutions, Virginia also allocates public resources to support Commonwealth residents studying in the state’s private, not-for-profit institutions. The Virginia Tuition Assistance Grant program (TAG) received USD 71 million of public funding in 2019 (SCHEV, 2019, p. 70[102]), accounting for more than one-fifth of total state spending on student aid. However, unlike the VSFAP and the smaller targeted financial aid funds highlighted below, there is no requirement for TAG funds to be allocated based on financial need or criteria related to workforce skills needs. The programme is thus an untargeted subsidy to private higher education providers.

Virginia has also used student financial aid to incentivise study in certain fields to meet labour market demand

In addition to the main Virginia Student Financial Assistance Program, which is a purely needs-based programme, Virginia has also used financial aid as a tool to encourage students to pursue studies in high-demand fields. Under the Two-year College Transfer Grant, for example, which provides a top-up grant of USD 1 000 per year to eligible students transferring from a two-year to a four-year public institution, an additional USD 1 000 per year bonus award is provided to students pursuing undergraduate degrees in engineering, mathematics, nursing, teaching or science. The smaller “Grow Your Own Teacher” pilot programme provides scholarships of up to USD 7 500 per year to low-income high school graduates who obtain a teaching qualification in a public four-year college, provided they commit to subsequently teach in high-need public schools in the school divisions in which they graduated from high school (Virginia General Assembly, 2019[105]).

Other examples include the New Economy Workforce Credential Grant Program (FastForward programme), discussed earlier, that covers up to two-thirds of the cost of acquiring workforce credentials in high-demand fields. Although this programme is strongly focused on meeting workforce needs in high-demand and shortage fields and is not needs-based, in practice, a majority of students benefitting from the support are from under-represented population groups. Finally, the Cybersecurity Public Service Grant provides state-matched funding towards grants of up to USD 20 000 per year for bachelor students in cybersecurity, without need-related conditions, if matched funding is provided by an employer.

Elements of performance-related funding have been introduced into the state’s overall higher education funding model, but the level of resources attached is limited

Core institutional funding to contribute to basic operating costs and grant aid to reduce costs for individual students are the two main pillars of Virginia’s higher education funding model. Although some workforce-related targeting has occurred in the student aid budget (as discussed above), most of the funds invested support the more general workforce-relevant objectives of maintaining quality education and increasing access and attainment. However, the Commonwealth has also experimented with performance-related institutional funding, which seeks to reward institutions for particular initiatives or for attaining state goals, including in terms of workforce development.

Across the two and four-year sector, two main performance-related funding mechanisms have been used, both related to the state and institutional planning processes discussed in Section 6.3.1. First, in their six-year plans, public institutions propose institutional initiatives that contribute to state-wide goals and typically request additional state funding each biennium to implement these strategies. Recommendations on which initiatives to fund are made by the state’s Operating Advisory Committee (OpSix), taking into account a prioritisation among initiatives made by institutions themselves. Many initiatives, particularly those related to enrolment, completion, enhancement of learning approaches and student advising and counselling services, contribute to the Commonwealth’s high-level skills needs. Overall, however, the level of funding awarded under this upfront targeting mechanism is limited: in 2014, it amounted to a total of USD 16.2 million, equating to less than 2% of state operational support to public institutions (JLARC, 2014[98]). As noted in Section 6.3.1, the lack of systematic evaluation of institutional initiatives means the real “performance” of institutions is not verified and the system relies on the stated intentions of institutions.

Virginia has also made a modest level of appropriations conditional on institutions’ achievement of the Institutional Performance Standards (IPS), also discussed in Section 6.3.1. The IPS focus on access and completion, but include a specific target to increase the supply of graduates in STEM-H, a key area of labour market demand. However, here again, the level of funding linked to achievement of the targets is modest (USD 7.2 million in total for 2019 and 2020).

In its 2014 analysis of Virginia’s higher education funding system, JLARC argued that the limited level of funding attached to institutional initiatives and Institutional Performance Standards reduced the incentives created by these mechanisms, particularly given the significant shortfalls in state core operational funding (JLARC, 2014[98]). However, evidence from elsewhere in the world suggests relatively modest amounts of additional funding can motivate institutions in their behaviours. In the Netherlands, for example, a relatively small amount of institutional funding was made conditional on development of institutional strategies and achievement of national goals including completion rates and time to degree. Here, while use of standard quantitative targets has been contested, university leaders and public officials believe the marginal funding did incentivise change at institutional level (Evaluatiecommissie Prestatiebekostiging Hoger Onderwijs, 2017[106]). In the United States, including Virginia, where public institutions receive a greater proportion of funding from third parties than in largely public systems like the Netherlands, the level of state funding required to create incentive may be larger.

Within the public two-year college sector in Virginia, the Virginia Community College Board has introduced its own system of performance-related funding to allocate 20% of the educational appropriations it receives from the state budget among the 23 colleges in the Virginia Community College System. The system uses measures of course completion, student retention from one year to the next, and credential acquisition to incentivise colleges to support students in progressing efficiently, while recognising the specific mission of community colleges to educate a wide range of student types, including those who are not seeking credentials (VCCS, 2017[107]).

Although institutions are responsible for setting staff compensation, state funding decisions have a major impact on faculty conditions

A last issue related to higher education funding that was frequently raised during the OECD’s visit to Virginia is faculty salaries. In order to maintain and expand high-quality educational offerings in certain high-demand fields – and notably those related to computing – Virginia’s public higher education institutions need to be able to attract good teaching faculty. In an increasingly tight labour market, this means being able to offer competitive salaries. However, institutions report difficulties in attracting staff because they cannot meet salary expectations and are competing with other institutions across the nation. SCHEV has identified an inability to attract and retain high-calibre faculty as a major risk for the quality of higher education in Virginia and the achievement of skills pipeline ambitions in fields such as ICT (SCHEV, 2018[99]).

The Governor and General Assembly authorised a 3% increase in general fund appropriations for college and university faculty recruitment and retention for 2019, recognising this concern. However, this increase has been applied across all faculty and staff, rather than being targeted specifically to fields where competition for staff is greatest and salaries are highest. With this in mind, and given the constraints on core institutional funding from limited increases in state appropriates and tuition moderation, SCHEV has invited law makers to consider a targeted salaries fund (SCHEV, 2018[99]).

Recommendations on funding higher education

1. Take steps to ensure the level of Commonwealth funding appropriations for higher education are commensurate with the stated political ambitions to increase higher education attainment and moderate tuition costs to meet labour market demand.

2. Use the current review of higher education funding to rethink Virginia’s approach to institutional funding. As part of this, adapt the current cost-share policy to a level of state funding for in-state students that is financially more feasible than the current 67% (perhaps 60%); then work progressively to raise institutional core funding to reach this level. Update the existing base adequacy model with recent data on faculty and staff salaries and use this as part of a more transparent institutional funding model to provide increased and more predictable state resources to institutions.

3. Establish a transparent mechanism for estimating projected need for student financial assistance among students (in workforce credentials, two-year and four-year programmes) in each public higher education institution and use this as a basis for allocating general fund resources for student financial aid to institutions. Financial need should be the primary allocation criterion (to help boost overall attainment), with additional awards for low- and middle-income students in high-demand fields (the model used in the Two-year College Transfer Grant). Clear allocation criteria will also be needed to deal with the (very likely) eventuality that available resources are insufficient to meet projected financial need. Policy makers should consider restricting financial aid eligibility to low- and middle-income students to ensure a better targeting of scarce resources.

4. To support Virginia’s overall post-secondary attainment targets and goals for specific skills areas, the General Assembly would be wise to allocate additional general fund resources to student financial aid, to be awarded through the revised methodology suggested above.

5. Consider making a proportion of all financial aid allocations to institutions dependent on students successfully gaining a credential following the basic principles used for the New Economy Workforce Credential Grant.

6. Either: a) revise the objectives of the TAG program to define its purpose as an institutional subsidy to private providers; or b) require funds to be awarded on the basis of students’ financial need or enrolment in high-demand fields.

7. Complement the core institutional funding with increased initiative-based funding linked to the institutional six-year plans, and revised and differentiated Institutional Performance Standards (see Section 6.3.2). This should be backed up with enhanced resources for SCHEV to allow more systematic monitoring of the implementation of funded initiatives.

8. Establish a targeted fund to support recruitment of faculty for programmes in high-demand fields, in line with SCHEV’s budget and policy recommendations for 2020.

Information

Information about the skills requirements of the labour market, now and in the future, helps educational providers plan and adapt their educational offerings and allows policy makers to ensure they have well-targeted policies in place. At the same time, information about the labour market outcomes of past higher education graduates can provide an indication of the labour market demand for graduates from specific programmes or fields. Such information can help students make informed choices about what to study (although does not guarantee that they will make rational choices) and provides an indication to educators, institutions and policy makers of how well programmes prepare graduates for the workforce. As graduates’ labour market outcomes also depend on personal choices, economic and labour market conditions, as well as wage levels in specific sectors, care is always needed in interpreting such data.

Virginia has a comprehensive and well-established longitudinal data system for tracking graduate outcomes

Launched in 2012, Virginia’s post-secondary longitudinal data system - Virginia Longitudinal Data System (VLDS) - is recognised as one of the most comprehensive in the United States (SCHEV, 2019[108]; SCHEV, 2019[65]). It centralises data on higher education programmes, students and graduates collected from public higher education institutions and private not-for-profit institutions that receive state funding, notably through the Virginia Tuition Assistance Grant program. The VLDS links this information to data on individual earnings collected by the Virginia Employment Commission to produce information on the earnings over time of graduates from Virginia’s universities and colleges who go on to work in the state. This comprehensive set of administrative data provides a valuable resource for policy makers seeking to understand the performance of the higher education system as a whole, as well as the outcomes of under-represented populations and other groups of interest. The breadth of SCHEV’s data collection, particularly its financial aid data, is greater than that of similar exercises in most other states. A recent analysis of SCHEV’s data system found that while other states, such as Colorado and Minnesota, also report graduate wage information, Virginia’s data systems were more comprehensive (SCHEV, 2019[108]).

To complement existing data, SCHEV is supporting a survey of graduates from Virginia higher education institutions, co-ordinated by a team at Virginia Commonwealth University. This will obtain more detailed information on graduates’ post-graduation employment trajectories and their engagement in “civic life”. This survey, the results of which will be available in 2020, is currently designed to be a one-off activity and to contribute to policy making and potential future communication activities (SCHEV, 2019[94]).

Virginia’s data on labour market outcomes could be better exploited to provide easy-to-access information for citizens and students

The Commonwealth has a strong post-secondary data system that merges data across several state agencies. However, from a user perspective, the data collected and stored are not exploited to their full potential. One of the stated objectives of the Virginia Longitudinal Data System is to “provide one-stop access to education and workforce data by policy makers, educators, the public, program directors, researchers, etc.” (SCHEV, 2019[65]). However, in a recent survey of policy makers in Virginia, two-thirds of respondents said the “user experience” on SCHEV’s own data webpages needed to be improved, with the clarity of tables and graphics receiving the lowest ratings among users (SCHEV, 2019[108]).

Moreover, Virginia does not yet have a clearly established information strategy for prospective and current higher education students and their families seeking information about the likely employment implications for choosing different study options. Ordinary users are currently left to find their way between various national and institutional data sources, which use different measures of quality and workforce success, and sometimes provide contradictory information. Moreover, students in Virginia currently lack easily accessible information on the rates of return on investment in different study programmes. Although there are limits to the impact of high-quality information on labour market outcomes, consolidated information on likely future employment prospects should be part of Virginia’s future communication and awareness-raising activities.

Students would benefit from access to reliable data about tuition and fees, average debt levels, earnings and employment outcomes in order to increase their awareness of the expected rates of return on post-secondary education (TICAS, 2019[72]). Although SCHEV has begun to generate information about debt-to-earnings ratios and loan default rates at programme level, these data are not currently available to the public. Increased transparency about the expected return on investment for students before entering post-secondary education is critical in order to inform student choice and ensure that a larger proportion of post-secondary graduates have the opportunity to achieve a sustainable wage.

Co-ordination at the state level could be enhanced to facilitate and improve the use of labour market information for strategic planning in higher education

In addition to collecting information on current employment and earnings, the Virginia Employment Commission (VEC) publishes short and long-term labour market projections following the methodology of national projections produced by the U.S. Bureau of Labor Statistics (BLS). These provide current and projected employment numbers by industry and occupation for Virginia and each of the 15 workforce development areas in the state. The long-term projections estimate changes in employment over a ten-year period, including educational and training requirements for entry into each occupation. SCHEV requires higher education institutions to refer to these labour market projections in their justification of new programme proposals (see Section 6.3).

The occupational projections for Virginia are published on the VEC website (VEC, 2019[109]), but are not presented in a user-friendly form for a wider public of educators or policy makers. Moreover, projections of this kind generally make a conservative assessment of the level of educational attainment needed for each occupation, potentially underestimating current and future demand for post-secondary graduates (see also Section 6.2.1). In addition, information about projected employment demand does not appear to be linked to information about projected supply, for example through national or state post-secondary data systems. A relatively simple gap analysis can be done by matching post-secondary records of credential production by field of study to occupational projections in order to indicate future gaps in supply and demand, provided there is agreement on a suitable way to match field of study to occupation (Wilson, 2014[110]).13 This can provide a point of departure for further analysis, supplemented with both quantitative and qualitative information from employers and other stakeholders, for example by region or within a particular field.

In some states, gap analyses are conducted systematically at the state level, using diverse methodologies. In Washington, for example, a workforce supply and demand analysis is conducted every two years as a joint agency initiative, using both national and state-level data (WSAC, SBCTC and WTECB, 2018[111]; Hershbein and Hollenbeck, 2015[112]). The Virginia Employment Commission is currently developing a methodology for linking the production of credentialed graduates from Virginia’s higher education institutions to projected employment demand by occupation, as well as injecting known in-migration patterns by occupation. Supply and demand analyses may also incorporate real-time data from online job advertisements as a basis for assessing demand for skills and qualifications, allowing for more granular information about skills needs. Using real-time labour market information may therefore become increasingly useful for strategic planning and curriculum development in higher education, thereby improving labour market outcomes over time (Dorrer, 2016[113]). In Virginia, a Workforce Supply and Demand Dashboard was launched within the last three years, showing demand for different occupations based on real-time data from job advertisements (using commercial services from Burning Glass Technologies) and supply based on current graduation patterns (Virginia Career Works, 2019[17]). The dashboard tool shows occupations where there is estimated to be undersupply and oversupply in Virginia based on the available data, along with average salary levels in different occupational clusters. The precise methodology underpinning the dashboard is not entirely transparent; in particular, it is not clear to the average user whether it is a dynamic tool with the data constantly updated or an analysis based on a snapshot at a particular point in time. Moreover, it is unclear if the intended users of the dashboard are primarily workforce development boards or if the tool is intended for a broader group of users within both the higher education and workforce development communities.

While many higher education institutions engage directly with employers and conduct their own labour market analyses to understand skills needs and inform programme and curriculum design, they also rely on public workforce data and labour market information. This underlines the importance of ensuring transparent, consistent and easily accessible information about the labour market and state-wide workforce needs. Virginia’s workforce data are presented in multiple places with information that is not entirely consistent across sites. Moreover, it is unclear how the Virginia Career Works Supply and Demand Dashboard relates to information provided by the Virginia Employment Commission and the Virginia Workforce Connection, all of which appear to use different sources of data to measure labour supply and demand. Improving the alignment between higher education and the labour market thus requires co-ordination between agencies – and across traditional policy silos – in the interest of ensuring that Virginia’s future skills needs are met. A recent SCHEV Council initiative to identify data needs related to workforce supply and demand could be an important step in the right direction in improving the quality and use of labour market information in higher education.

Recommendations on information

1. Build on existing co-operation between state agencies to design and implement a single online information portal connecting higher education and labour market information for policy makers and educational institutions. This enterprise could be overseen by a joint taskforce involving SCHEV, VEC, the Virginia Board of Workforce Development and other relevant stakeholders. It is important that the information be presented in an accessible and logical way and that data be downloadable in an easy-to-use format for further analysis. Existing web resources targeting policy makers and educational institutions should be rationalised or removed once the new portal goes live online. The portal should include a state-wide analysis of skill supply and demand. The recommendations of SCHEV’s recent report on using data to inform policy (SCHEV, 2019[108]), and recent Council initiatives on higher education and workforce alignment, may provide additional pointers.

2. Develop a user-friendly web-based information tool for students and citizens in Virginia to allow them to learn more about skills demand in the labour market and the employment prospects for graduates from different programmes and fields. A tool provided by the state could be marketed as an objective reference point for impartial information, in contrast to many existing information sources. The public-facing information tool should use a limited number of the most important variables. Key variables to date include: a) high-demand occupations; b) earnings prospects by programme of study; and c) observed rates of return (based on costs of attendance and average earnings and debt levels). International examples that could serve as a source of ideas for different elements of a new informational tool include New Zealand’s “Occupation Outlook” (MBIE, 2019[114]), Finland’s Occupational Barometer (TEM, 2019[115])and Denmark’s comprehensive one-stop portal “The Education Guide” (UVM, 2019[116]).

References

[26] Abraham, K. and M. Kearney (2019), “Explaining the Decline in the U.S. Employment-to-Population Ratio: a Review of the Evidence”, NBER Working Paper No. 24333, The National Bureau of Economic Research, https://www.nber.org/papers/w24333.

[38] Altonji, J., L. Kahn and J. Speer (2014), “Trends in Earnings Differentials across College Majors and the Changing Task Composition of Jobs”, American Economic Review, Vol. 104/5, pp. 387-393, http://dx.doi.org/10.1257/aer.104.5.387.

[21] Autor, D. (2014), “Skills, Education, and the Rise of Earnings Inequality among the Other 99 Percent”, Science, Vol. 344/6186, pp. 843-851, http://dx.doi.org/10.1126/science.1251868.

[18] Autor, D., F. Levy and R. Murnane (2003), “The Skill Content of Technical Change”, Quarterly Journal of Economics, https://economics.mit.edu/files/11574.

[59] Bahr, P. (2019), “The Labor Market Returns to a Community College Education for Noncompleting Students”, The Journal of Higher Education, Vol. 90/2, pp. 210-243, http://dx.doi.org/10.1080/00221546.2018.1486656.

[64] Baum, S. (2014), Higher Education Earnings Premium: Value, Variation, and Trends, Urban Institute, https://files.eric.ed.gov/fulltext/ED544781.pdf (accessed on 20 May 2019).

[58] Belfield, C. and T. Bailey (2017), “The Labor Market Returns to Sub-Baccalaureate College: A Review”, CAPSEE Working Paper, Center for analysis of postsecondary education and employment, https://ccrc.tc.columbia.edu/media/k2/attachments/labor-market-returns-sub-baccalaureate-college-review.pdf.

[66] Carnevale, A., B. Cheah and J. Strohl (2012), Hard Times: College Majors, Unemployment and Earnings: Not All College Degrees Are Created Equal, Georgetown University, https://repository.library.georgetown.edu/bitstream/handle/10822/559308/Unemployment.Final.update1.pdf?sequence=1&isAllowed=y (accessed on 20 May 2019).

[67] Carnevale, A. et al. (2017), Major Matters Most. The Economic Value of Bachelor’s Degrees from The University of Texas System, Georgetown University, https://vtechworks.lib.vt.edu/bitstream/handle/10919/86951/UTSystemMajorsMatter.pdf?sequence=1&isAllowed=y (accessed on 20 May 2019).

[76] Carnevale, A., S. Rose and B. Cheah (2013), The College Payoff: Education, Occupation, Lifetime Earnings, Georgetown University, https://1gyhoq479ufd3yna29x7ubjn-wpengine.netdna-ssl.com/wp-content/uploads/collegepayoff-completed.pdf (accessed on 20 May 2019).

[42] Carnevale, A. and N. Smith (2012), A Decade Behind: Breaking out of the Low-Skill Trap in the Southern Economy, https://1gyhoq479ufd3yna29x7ubjn-wpengine.netdna-ssl.com/wp-content/uploads/2014/11/DecadeBehind.FullReport.073112.pdf.

[37] Carnevale, A., N. Smith and J. Strohl (2013), Recovery: Job Growth and Education Requirements through 2020, Georgetown University Center on Education and the Workforce, Washington, DC, https://1gyhoq479ufd3yna29x7ubjn-wpengine.netdna-ssl.com/wp-content/uploads/2014/11/Recovery2020.FR_.Web_.pdf (accessed on 10 May 2019).

[44] Carnevale, A., J. Strohl and N. Ridley (2017), Good Jobs That Pay Without a BA: A State-by-State Analysis, https://1gyhoq479ufd3yna29x7ubjn-wpengine.netdna-ssl.com/wp-content/uploads/Good-Jobs-States.pdf.

[5] CNBC (2019), America’s Top States for Business in 2019, https://www.cnbc.com/2019/07/10/americas-top-states-for-business-2019.html (accessed on 2 August 2019).

[87] Code of Virginia (2019), § 23.1-203. Duties of Council, Code of Virginia, https://law.lis.virginia.gov/vacode/title23.1/chapter2/section23.1-203/ (accessed on 21 September 2019).

[39] Deming, D. and L. Kahn (2018), Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals, University of Chicago, https://www.journals.uchicago.edu/doi/pdfplus/10.1086/694106 (accessed on 16 May 2019).

[113] Dorrer, J. (2016), “Using real-time labor market information to achieve better labor market outcomes”, Lumina Issue Papers, Lumina Foundation, https://www.luminafoundation.org/files/resources/using-real-time-labor-market-information-full.pdf.

[15] Emsi (2018), Virginia: Economy Overview, http://www.economicmodeling.com.

[106] Evaluatiecommissie Prestatiebekostiging Hoger Onderwijs (2017), Van afvinken naar aanvonken - Rapport over de evaluatie van de prestatiebekostiging hoger onderwijs (Report on the evaluation of the higher education performance-based funding), Evaluatiecommissie Prestatiebekostiging Hoger Onderwijs (Evaluation committee on higher education performance funding), https://www.rijksoverheid.nl/documenten/rapporten/2017/03/20/van-afvinken-naar-aanvonken (accessed on 13 March 2020).

[19] Frey, C. and M. Osborne (2017), “The future of employment: How susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114, pp. 254-280, http://dx.doi.org/10.1016/J.TECHFORE.2016.08.019.

[55] Gallagher, S. (2018), Educational Credentials Come of Age: A Survey on the Use and Value of Educational Credentials in Hiring, Northeastern University, Center for the Future of Higher Education and Talent Strategy, https://www.northeastern.edu/cfhets/wp-content/uploads/2018/12/Educational_Credentials_Come_of_Age_2018.pdf (accessed on 20 July 2019).

[62] Gallup (2016), The 2015 Gallup-Lumina Foundation Study of the American Public’s Opinion on Higher Education, https://www.gallup.com/services/190583/americans-value-postsecondary-education-report.aspx.

[80] GMU (2018), George Mason University 2018 Six-year Plan, George Mason University, Fairfax, https://www.schev.edu/docs/default-source/institution-section/6-year-plans/gmu/2018/gmu-2018-six-year-plan-part-ii.pdf (accessed on 19 September 2019).

[83] GO Virginia (2019), GO Virginia, Virginia Initiative for Growth and Opportunity, https://govirginia.org/ (accessed on 20 September 2019).

[117] Goldman, C. et al. (2015), Using Workforce Information for Degree Program Planning in Texas, RAND Corporation, https://doi.org/10.7249/RR1011.

[112] Hershbein, B. and K. Hollenbeck (2015), Refining Workforce Education Supply and Demand Analysis: Final Report, W.E. Upjohn Institute for Employment Research, https://research.upjohn.org/cgi/viewcontent.cgi?article=1034&context=up_technicalreports.

[84] HQNOVA (2018), Tech-Talent Pipeline Initiative, Virginia Economic Development Partnership, Richmond, https://hqnova.com/assets/pdfs/NOVA_Higher-Ed.pdf (accessed on 16 September 2019).

[13] HQNOVA (2017), Virginia’s Amazon Proposal, https://hqnova.com/info.html#va-proposal (accessed on 14 September 2019).

[95] JLARC (2019), JLARC Impacts - Actions taken on report recommendations 2015-2018, Joint Legislative Audit and Review Commission, Richmond, http://jlarc.virginia.gov/pdfs/reports/Rpt517-3.pdf (accessed on 21 September 2019).

[56] JLARC (2017), Operations and Performance of the Virginia Community College System, Joint Legislative Audit and Review Commission, Richmond, http://jlarc.virginia.gov (accessed on 2 July 2019).

[98] JLARC (2014), Addressing the Cost of Public Higher Education in Virginia, Joint Legislative Audit Review Commission, Richmond, http://jlarc.virginia.gov (accessed on 4 September 2019).

[100] JLARC (2014), Addressing the Cost of Public Higher Education in Virginia, Joint Legislative Audit Review Commission, Richmond, http://jlarc.virginia.gov (accessed on 4 September 2019).

[47] Jobs for the Future (2019), Making Higher Education Policy Work for the Untapped Workforce, Jobs for the Future, https://www.jff.org/resources/policy-work-untapped-workforce-brief/.

[69] Kim, C., C. Tamborini and A. Sakamoto (2015), “Field of Study in College and Lifetime Earnings in the United States”, Sociology of Education, Vol. 88/4, pp. 320-339, http://dx.doi.org/10.1177/0038040715602132.

[57] Levesque, E. (2018), Improving community college completion rates by addressing structural and motivational barriers, Brown Center on Education Policy, Washington DC, https://www.brookings.edu/research/community-college-completion-rates-structural-and-motivational-barriers/ (accessed on 11 February 2019).

[22] Lumina Foundation (2019), A Stronger Nation: Virginia Report 2019, http://blob:http://strongernation.luminafoundation.org/3d08f981-8b2b-4cff-857a-d5fb67b04ec0.

[32] Lumina Foundation (2018), A Stronger Nation, https://www.luminafoundation.org/resources/a-stronger-nation-report (accessed on 19 June 2019).

[114] MBIE (2019), Occupation Outlook, Ministry of Business, Innovation and Employment, https://occupationoutlook.mbie.govt.nz/ (accessed on 25 September 2019).

[61] MIT (2019), MIT Living Wage Calculator, http://livingwage.mit.edu/states/51 (accessed on 10 August 2019).

[43] National Skills Coalition (2017), Middle Skill Jobs State by State: Virginia’s Forgotten Middle, https://www.nationalskillscoalition.org/resources/publications/2017-middle-skills-fact-sheets/file/Virginia-MiddleSkills.pdf.

[60] National Student Clearinghouse (2019), Some College, No Degree: A 2019 Snapshot for the Nation and 50 States, https://nscresearchcenter.org/some-college-no-degree-2019/.

[34] NCES (2019), Integrated Postsecondary Education Data System (database), National Center for Education Statistics, https://nces.ed.gov/ipeds/use-the-data (accessed on 18 January 2020).

[45] Northam Administration (2019), Establishment of the Virginia STEM Education Commission, https://www.governor.virginia.gov/media/governorvirginiagov/executive-actions/EO-36-Establishment-of-the-Virginia-Stem-Education-Commission.pdf (accessed on 26 August 2019).

[3] Northam Administration (2018), A Comprehensive Economic Development Policy for the Commonwealth, https://www.vus.virginia.gov/media/governorvirginiagov/governor-of-virginia/pdf/A-Comprehensive-Economic-Development-Policy-for-the-Commonwealth.pdf.

[40] OECD (2019), OECD Skills Outlook 2019 : Thriving in a Digital World, OECD Publishing, Paris, https://dx.doi.org/10.1787/df80bc12-en.

[23] OECD (2018), Education at a Glance 2018: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/eag-2018-en.

[10] Old Dominion University (2018), 2018 State of the Commonwealth Report, https://www.ceapodu.com/wp-content/uploads/2018/12/2018SOC-Report.pdf.

[90] SACSCOC (2018), Resource Manual for The Principles of Accreditation: Foundations for Quality Enhancement, Southern Association of Colleges and Schools Commission on Colleges, Atlanta, http://www.sacscoc.org (accessed on 1 July 2019).

[35] SCHEV (2020), Colleges and Universities, https://www.schev.edu/index/students-and-parents/explore/virginia-institutions.

[71] SCHEV (2019), 2019-20 Tuition and Fees: Tuition and Fees at Virginia State-Supported Colleges and Universities, State Council of Higher Education for Virginia, https://schev.edu/docs/default-source/Reports-and-Studies/Tuition-and-Fees/201920tuitionandfeereportacc.pdf.

[102] SCHEV (2019), Agenda Book - July 16, 2019, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/about-section/council-files/2019-council-meetings/july-council-files/agenda-book-(for-posting-on-sitefinity).pdf (accessed on 14 September 2019).

[94] SCHEV (2019), Agenda Book - September 16-17, 2019, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/about-section/council-files/2019-council-meetings/september-2019/rev-agenda-book-pdf-(w-page-numbers-for-posting).pdf (accessed on 21 September 2019).

[36] SCHEV (2019), Higher Ed Data Dashboard, https://research.schev.edu/fair/strategicplan3.asp (accessed on 8 April 2020).

[65] SCHEV (2019), History of the Virginia Longitudinal Data System, http://research.schev.edu//info/Articles.The-Virginia-Longitudinal-Data-System.ashx (accessed on 20 August 2019).

[75] SCHEV (2019), Median Graduate Debt, 10 Year Trends, https://research.schev.edu//studentdebt/DebtProfile_SL021.asp.

[91] SCHEV (2019), New Economy Workforce Credential Grant, https://www.schev.edu/index/institutional/grants/workforce-credential-grant (accessed on 21 September 2019).

[74] SCHEV (2019), Outcomes of Virginia Graduates: An Emerging Picture, https://www.schev.edu/docs/default-source/about-section/council-files/2019-council-meetings/may-2019/undergrad-outcomes-of-virginia-public-institutions_final_may-19.pdf (accessed on 26 August 2019).

[79] SCHEV (2019), Restructuring Act of 2005, https://www.schev.edu/index/institutional/planning-and-performance/restructuring (accessed on 19 September 2019).

[104] SCHEV (2019), Review of Financial Aid Funding Formulas and Awarding Practices State Council of Higher Education for Virginia, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/Reports-and-Studies/2019/financialaidreport102019.pdf (accessed on 10 November 2019).

[78] SCHEV (2019), Strategic Plan - Initiatives, https://www.schev.edu/index/statewide-strategic-plan/initiatives (accessed on 19 September 2019).

[46] SCHEV (2019), The Cost of Doing Nothing: An Urgent Call to Increase Educational Attainment in the Commonwealth, State Council of Higher Education for Virginia, Richmond, http://www.schev.edu (accessed on 27 June 2019).

[92] SCHEV (2019), The New Economy Workforce Credential Grant - Annual Report for 2018, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/Reports-and-Studies/2019/workforcereport2018.pdf (accessed on 21 September 2019).

[33] SCHEV (2019), The Virginia Plan for Higher Education - Annual Report 2018, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/virginia-plan/Reports-and-Updates/the-virginia-plan-annual-report-2018.pdf (accessed on 27 June 2019).

[108] SCHEV (2019), Using Data to Inform Policy - Report of Council’s Ad Hoc Committee on Data and Policy, State Council of Higher Education for Virginia, Richmond, https://schev.edu/docs/default-source/Reports-and-Studies/2019/reportadhoccommitteeondataandpolicy.pdf (accessed on 15 September 2019).

[53] SCHEV (2019), Virginia Longitudinal Data System, https://vlds.virginia.gov/ (accessed on 1 July 2019).

[81] SCHEV (2018), Agenda Book - September 17-18, 2018, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/about-section/council-files/2018-council-meetings/september/agenda-book-w-pg-links-(for-posting).pdf (accessed on 19 September 2019).

[99] SCHEV (2018), FY 2020 Budget and Policy Recommendations for Higher Education in Virginia, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/Reports-and-Studies/2018-reports/fy2020budget-recommendations11918.pdf (accessed on 29 June 2019).

[82] SCHEV (2018), Resources and Planning Committee #C3-Action on Measures of Institutional Performance - Meeting of September 17, 2018, State Council of Higher Education for Virginia, Richmond.

[86] SCHEV (2016), State-Level Requirements for Approval of Various Academic Program Actions at Public Institutions, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/institution-section/GuidancePolicy/academic-approval-policies/2016-academic-approval-policy.pdf (accessed on 1 July 2019).

[103] SCHEV (2015), The Virginia Plan for Higher Education - Measures and Targets - Additional Information, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/virginia-plan/measuresandtargetsadditionalinformation07212015.pdf (accessed on 23 September 2019).

[89] SCHEV (2014), Agenda Book - July 21-22, 2014, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/about-section/2014-council-meetings/july2014/072114agendabook.pdf (accessed on 21 September 2019).

[77] SCHEV (2014), Framework of the Statewide Strategic Plan for Higher Education (As Adopted by the State Council of Higher Education for Virginia on September 16 and October 28), https://www.schev.edu/docs/default-source/about-section/council-files/2014-council-meetings/nov2014/framework-statewidestrategicplanforhighered-2014-10-28.pdf (accessed on 30 June 2019).

[88] SCHEV (2013), Virginia Public Higher Education Policy on Program Productivity, State Council of Higher Education for Virginia, Richmond, https://www.schev.edu/docs/default-source/institution-section/GuidancePolicy/policies-and-guidelines/programproductivitypolicyrevisedoct2013.pdf (accessed on 21 September 2019).

[70] Schneider, M. (2016), Degrees of Value: Differences in the Wages of Graduates From Virginia’s Colleges and Universities, American Institutes for Research, Washington, D.C., https://www.air.org/system/files/downloads/report/Differences-in-Wages-Graduates-Virginia-Colleges-Universities-September-2016.pdf (accessed on 1 July 2019).

[68] Schneider, M. (2015), Higher Education Pays: But a Lot More for Some Graduates Than for Others, College Measures, https://www.air.org/sites/default/files/Higher_Education_Pays_Sep_13.pdf (accessed on 20 May 2019).

[73] SHEEO (2019), SHEF: FY 2018 State Higher Education Finance, State Higher Education Executive Officers Association, http://www.sheeo.org/shef (accessed on 30 July 2019).

[115] TEM (2019), Ammattibarometri - Occupational Barometer, Ministry of Economic Affairs and Employment, https://www.ammattibarometri.fi/?kieli=en (accessed on 25 September 2019).

[72] TICAS (2019), Student Debt and the Class of 2018, The Institute for College Access and Success, https://ticas.org/interactive-map/. (accessed on 23 October 2019).

[63] TICAS (2018), Of metrics and markets: Measuring post-college employment success, The Institute for College Access and Success, https://ticas.org/accountability/metrics-and-markets/.

[1] U.S. Bureau of Economic Analysis (2019), Gross Domestic Product by State: Fourth Quarter and Annual 2018, https://www.bea.gov/news/2019/gross-domestic-product-state-fourth-quarter-and-annual-2018 (accessed on 31 May 2019).

[2] U.S. Bureau of Economic Analysis (2019), Regional BEARFacts: Virginia, https://apps.bea.gov/regional/bearfacts/pdf.cfm?fips=51000&areatype=51000&geotype=3 (accessed on 11 March 2020).

[41] U.S. Bureau of Labor Statistics (2019), Fastest growing occupations: 20 occupations with the highest percent change of employment between 2016-26, https://www.bls.gov/ooh/fastest-growing.htm (accessed on 23 August 2019).

[28] U.S. Bureau of Labor Statistics (2019), Labor force statistics from the current population survey, https://www.bls.gov/cps/ (accessed on 12 March 2020).

[14] U.S. Bureau of Labor Statistics (2019), Mid-Atlantic Information Office; Virginia, https://www.bls.gov/regions/mid-atlantic/virginia.htm (accessed on 4 August 2019).

[7] U.S. Bureau of Labor Statistics (2019), Occupation Employment Statistics, https://www.bls.gov/oes/home.htm (accessed on 26 August 2019).

[8] U.S. Bureau of Labor Statistics (2019), State and Metro Area Employment, Hours and Earnings, https://www.bls.gov/sae/data/home.htm (accessed on 8 August 2019).

[27] U.S. Bureau of Labor Statistics (2016), Labor force participation: what has happened since the peak?, Monthly Labor Review, https://www.bls.gov/opub/mlr/2016/article/pdf/labor-force-participation-what-has-happened-since-the-peak.pdf.

[24] U.S. Census Bureau (2019), American Community Survey 2018 (database), https://www.census.gov/programs-surveys/acs/data.html (accessed on 18 January 2020).

[50] U.S. Census Bureau (2018), American Community Survey 2013-2017 5-year Data Release.

[25] U.S. Census Bureau (2018), QuickFacts: Virginia, https://www.census.gov/quickfacts/fact/table/VA,US/PST045218 (accessed on 4 August 2019).

[4] U.S. News & World Report (2019), Best States Rankings, https://www.usnews.com/news/best-states/rankings (accessed on 4 August 2019).

[11] UVA Weldon Cooper Center (2019), Observed and total population for the U.S. and the States, https://demographics.coopercenter.org/national-population-projections (accessed on 6 August 2019).

[48] UVA Weldon Cooper Center (2019), Out-migration from Virginia continues for the fifth consecutive year, pushing population growth below national levels, http://statchatva.org/2019/01/28/out-migration-from-virginia-continues-for-the-fifth-consecutive-year-pushing-population-growth-below-national-levels/ (accessed on 4 December 2019).

[29] UVA Weldon Cooper Center (2018), Who is out of work in Virginia?, Demographics Research Group, https://demographics.coopercenter.org/sites/demographics/files/FullReport_VBWD_OWW_UVACooperCtr.pdf.

[20] UVA Weldon Cooper Center (2017), Job polarization in Virginia means fewer middle-wage jobs, http://statchatva.org/2017/06/06/job-polarization-in-virginia-means-fewer-middle-wage-jobs/ (accessed on 16 August 2019).

[116] UVM (2019), About UddannelsesGuiden, Børne- og Undervisningsministeriet, https://www.ug.dk/programmes/aboutugdk (accessed on 25 September 2019).

[96] VCCS (2019), Transfer Virginia launches, http://www.vccs.edu/vccsblog_post/transfer-virginia-launches-goal-is-to-ensure-that-students-are-able-to-make-the-jump-from-community-college-to-the-states-four-year-universities-more-efficiently/ (accessed on 21 September 2019).

[93] VCCS (2017), FastForward - Credentials Available, Virginia Community College System, https://www.fastforwardva.org/credentials-available/ (accessed on 21 September 2019).

[107] VCCS (2017), VCCS E&G Outcomes-Based Funding Model, Virginia Community College System, Richmond, https://www.ccleague.org/sites/default/files/images/overview_vccs_outcomes_based_funding_model.pdf (accessed on 19 February 2020).

[97] VCCS (n.d.), Great Expectations | Promising futures for Virginia foster youths - Virginia Community College System, http://greatexpectations.vccs.edu/ (accessed on 6 May 2020).

[16] VEC (2019), 2016-2026 Industry and Occupational Projections, Virginia Employment Commission, https://virginiaworks.com/occupational-projections/page79862/1/size79862/12/page80257/1/size80257/12?page79862=2&size79862=12&page80257=1&size80257=12&page81630=1&size81630=12 (accessed on 9 August 2019).

[9] VEC (2019), Statewide Economic Analysis Report, Virginia Employment Commission, https://virginiaworks.com/Portals/200/Publications/LMI%20Publications/Statewide%20Economic%20Analysis/PDF/SEA%202019.pdf (accessed on 3 January 2020).

[109] VEC (2019), Virginia Labor Market Information Long and Short-Term Occupational Projections, Virginia Employment Commission, https://virginiaworks.com/occupational-projections?page79862=1&size79862=12&page80257=1&size80257=12&page81630=1&size81630=12#Long-Term-Occupational-Virginia-2016-2026-Projections-2536 (accessed on 25 September 2019).

[49] VEC (n.d.), Local Workforce Development Areas (LWDAs), Virginia Employment Commission, https://virginiaworks.com/Portals/200/Publications/LWDAs/Maps/LWDA%20Regions.pdf (accessed on 22 January 2020).

[6] VEDP (2019), Virginia Economic Review (First Quarter), Virginia Economic Development Partnership, https://issuu.com/vedpvirginia/docs/vedp_q2_v11spreads?fr=sZWRlZDEyMDc5Mg (accessed on 2 August 2019).

[12] VEDP (2017), Amazon HQ2 Submission: Partnership Proposal, Virginia Economic Development Partnership, https://issuu.com/teamsubjectmatter/docs/nova_r1_proposal_full_doc_single_pa?e=35488011/65733964.

[17] Virginia Career Works (2019), Workforce Supply and Demand Dashboard, Virginia Board of Workforce Development, https://virginiacareerworks.com/dashboards/ (accessed on 2 July 2019).

[52] Virginia Career Works (2018), 2018-2019 Virginia Demand Occupations List, https://virginiacareerworks.com/wp-content/uploads/2018-2019-Virginia-Demand-Occupations-List-FINAL.pdf (accessed on 8 July 2019).

[85] Virginia Career Works (2017), Virginia Board of Workforce Development - Strategic Plan 2017-2019, Virginia Board of Workforce Development, Richmond, https://virginiacareerworks.com/wp-content/uploads/VBWD-Strategic-Plan-2017-2019.pdf (accessed on 15 September 2019).

[51] Virginia Department of Education (n.d.), Education workforce data and reports, http://www.doe.virginia.gov/teaching/workforce_data/index.shtml (accessed on 14 September 2019).

[101] Virginia General Assembly (2019), Budget Bill - HB1700 (Chapter 854) Item 4-2.01 b Nongeneral Fund Revenues., https://budget.lis.virginia.gov/item/2019/1/HB1700/Chapter/4/4-2.01/ (accessed on 12 November 2019).

[105] Virginia General Assembly (2019), Budget Bill - HB1700 (Chapter 854) Item 141 (SCHEV) Higher Education Student Financial Assistance., https://budget.lis.virginia.gov/item/2019/1/HB1700/Chapter/1/141/ (accessed on 24 September 2019).

[54] Virginia General Assembly (2019), HB 2490: Tech Talent Investment Program and Fund, https://lis.virginia.gov/cgi-bin/legp604.exe?191+sum+HB2490 (accessed on 4 December 2019).

[30] Virginia General Assembly (2019), Title 23.1. Institutions of Higher Education; Other Educational and Cultural Institutions, https://law.lis.virginia.gov/vacode/title23.1/ (accessed on 12 August 2019).

[31] Virginia General Assembly (2018), § 2.2-2472. Powers and duties of the Board; Virginia Workforce System created., https://law.lis.virginia.gov/vacode/title2.2/chapter24/section2.2-2472/ (accessed on 12 August 2019).

[110] Wilson, B. (2014), How many more skilled workers do we need? Using supply and demand reports for state workforce planning, National Skills Coalition, State workforce and education alignment project, https://www.nationalskillscoalition.org/resources/publications/file/how-many-more-skilled-workers.pdf.

[111] WSAC, SBCTC and WTECB (2018), A Skilled and Educated Workforce 2017 Update: An analysis of postsecondary education, workforce preparation, and employer demand in Washington, Washington Student Achievement Council/State Board for Community and Technical Colleges/ Workforce Training and Education Coordinating Board / Workforce Training and Education Coordinating Board, Olympia, https://wsac.wa.gov/sites/default/files/2017.ASkilledAndEducatedWorkforce.pdf (accessed on 31 May 2019).

Notes

← 1. Virginia State and Norfolk State are also Historically Black Universities.

← 2. The minimum educational requirement for entry into an occupation is based on the U.S. Bureau of Labor Statistics (BLS) education and training classification system, which also underlies the Occupational Information Network (O*NET). Occupational employment projections provide a general overview of estimated replacement needs and employment growth per occupation. However, there are limitations to using the educational and training requirements for projections of future education or skills needs. Occupational employment projections are based on current industry composition and thus do not adequately capture new occupations in emerging industries. They also assume that education requirements per occupation remain unchanged during the projection period. As employer demand for post-secondary qualifications and skills has been rising at a rapid pace in the last decade, estimates of projected educational requirements based on occupational outlooks are likely to be conservative. See (Carnevale, Smith and Strohl, 2013[37]) for a more detailed discussion of the limitations of BLS estimates of education requirements.

← 3. A post-secondary certificate, industry certification, state licensure or apprenticeship.

← 4. Under-represented groups are defined as non-White populations; Pell Grant recipients; those aged 25 or older and those from a Virginia locality with low undergraduate attainment rates (most of which are rural).

← 5. Operating Advisory Committee (OpSix) comprises the Secretaries of Finance and Education, the Director of SCHEV, a representative of the Department of Planning and Budget, and the Chairs of the House Appropriations and Senate Finance Committees.

← 6. Department for the Blind and Vision Impaired, Department for Aging and Rehabilitation Services, Department of Education, Department of Labor and Industry, Department of Social Services, Virginia Community College System, Virginia Economic Development Partnership, Virginia Employment Commission.

← 7. For associate’s degrees in the Virginia Community College System, this task is delegated to the VCCS Board.

← 8. The employment outcomes of graduates are considered when determining whether programmes identified as performing poorly in terms of student numbers and graduation rates should nevertheless be allowed to continue.

← 9. General fund appropriations for educational and general Programs (the primary operating funds for colleges and universities) increased 11.2% between FY 2018 and FY 2020.

← 10. Financial need is more generally calculated in the United States by subtracting the Expected Family Contribution from the total cost of attendance, including room and board, as well as books and equipment.

← 11. The EPC is calculated based on family income by the federal government for each student applying for federal aid and, for low-income families, may be zero.

← 12. Low-, middle- and high-income levels are defined in relation to the federally defined poverty level. Low-income students have household incomes less than 200% of the federal poverty level (below around USD 50 000 per year for a family of four); middle-income students have household incomes of 200-400% of the federal poverty level (between USD 50 000 and USD 100 000); and high-income students have household incomes exceeding 400% of the federal poverty level (over USD 100 000).

← 13. There are important limitations to supply-demand analyses linking post-secondary credential production with occupational demand. There is not always a one-to-one relationship between fields of study and occupations or jobs, particularly in fields such as the social sciences, humanities, and liberal arts. Furthermore, a graduate with a credential in a particular field of study may have the skills and competencies required for multiple occupations. This kind of flexibility in the labour market is generally desirable. In addition, degree completions may capture workers who upgrade skills for their current jobs but are not available to fill new openings, and not all degree completers will enter the labour market. Still, supply-demand models and gap analyses can provide an indication of where there is likely to be considerable misalignment between labour market demand and the supply of credentialed graduates. To inform policy, however, these models should be supplemented with other qualitative and quantitative information, including estimates of net migration of skilled workers in the labour market. See for example (Goldman et al., 2015[117]) for a discussion of various supply-demand models and data sources, and additional guidance on using workforce information for programme planning in higher education.

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment

Governance

Health

Industry, business and entrepreneurship

Regional, rural and urban development

Science, technology and innovation

Society

Taxation

Trade

Energy

Nuclear energy

Transport

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment

Governance

Health

Industry, business and entrepreneurship

Regional, rural and urban development

Science, technology and innovation

Society

Taxation

Trade

Energy

Nuclear energy

Transport

Countries A - C

Countries D - I

Countries J - M

Countries N - R

Countries S - T

Countries U - Z

Regional and global engagement

Countries

Countries A - C

Countries D - I

Countries J - M

Countries N - R

Countries S - T

Countries U - Z

Regional and global engagement

Publications

Publications

Featured publications

Data

Data

Featured data

News & Events

News & Events

Featured Events

About OECD

About

Engage with us

Work with us

Featured topics

Agriculture and fisheries

Climate change

Development

Digital

Economy

Education and skills

Employment

Environment

Finance and investment