The contribution of higher education to economic growth

The contribution of higher education to economic growth Craig Holmes Higher Education II seminar 25th October 2012

Seminar outline • Aims: • understand what economic theory says drives national income and growth • look at how people have tried to measure the contribution of (higher) education to growth • discuss the limitations of these measurements • think about the implications for policy

Seminar outline • From last week: • More education  higher individual earnings • Questions for this week: • Does more (higher) education  higher national incomes? • Does more (higher) education  faster growth? • For next week: • How have answers to these questions lead to the “high skills vision”?

Key terms and definitions • National income = national output = national expenditure • Gross domestic product (GDP): • Private consumption • Investment • Government expenditure • Trade balance • GDP per capita = income per person • Real vs. nominal

National inc ome Source: World Bank

Economic growth Source: ONS

Economic growth Source: World Bank

Output and productivity • Simple production function: • Mankiw, Romer and Weil (1992): • Diminishing returns to each factor of production – α, β and γ < 1 • Constant returns to scale – α+β+γ = 1 • A = productivity, for a given set of production factors. Captures what can’t be measured, including technological progress, resource shocks and the health of institutions

Output and productivity • What is H? • “Stock” of human capital – depends on skill levels and number of workers  H = hL • Average skill level relates to schooling: • Exponential function so reflects micro evidence on link between schooling and wages (see last week) • Expressed in linear form:

Output and productivity Source: World Bank, Barro-Lee (2000)

Output and productivity Source: World Bank (2000)

Output and productivity • Variations around these trends reflect differences in: • Other aspects of human capital (e.g. quality, types of skills) • Capital stock (k) • Total factor productivity (A) • Production technologies (α, βandγ) • Hall and Jones (1999): • Differences in A – which is unobservable – are more important than differences in H and k

Education and growth • National income and education offers a snapshot at a point in time • Economists are interested in why national output changes over time, and what drives long-run growth • There are numerous economic models which have been developed to analyse this • Human capital plays a key role in most of these, however, • They often equate education and human capital but the way HE affects productivity may not be quite the same as how the role human capital is envisioned in these models.

Education and growth • Key literature: • Neoclassical growth model (Solow, 1956; Mankiw, Romer and Weil, 1992) • Endogenous growth models • Spillovers to physical capital investment (Romer, 1986) • Spillovers to human capital investment (Lucas, 1988) • R&D-driven models (Romer 1990, Jones 1995)

Neoclassical growth model • Solow (1956): • Same production function as before: • No human capital: α + β = 1; γ = 0 • Assume A = 1 • Output per capita (y) depends on capital stock per capita (k) • Investment per worker = saving per worker = sy= skα • Replacement capital demand = (n + d)k • As economy grows, more investment is needed for replacement rather than additional capacity

Neoclassical growth model • Economy reaches a steady state (k*): Replacement capital = k *(population growth + depreciation rate) Saving and investment Investment in k = saving rate * output new investment Required replacement capital k0 + new investment k* Capital stock per worker, k k0

Neoclassical growth model • Short run growth is driven by accumulation • Output growth = population growth + capital growth + technical progress • Output per capita growth = capital growth per capita + technical progress • Higher saving rate creates short-term increase in output • Long run growth at the steady state • Capital stock increases until investment = depreciation • Long run growth = technical progress

Neoclassical growth model • Mankiw, Romer and Weil (1992): • Same idea, except both human and physical capital is accumulated • Human capital: • requires investment (share of national output diverted into education and training) • depreciates (skills lost if underused, or become obsolete following technical progress, or people retire) • Increased spending on education has short-term effects only • There is a steady state level of both K and H. Once reached, long run growth depends on A.

Neoclassical growth model • Problems: • Technical progress is exogenous – models offer not explanation of what drives A • Model predicts convergence • Eventually, all countries will grow at same rate (the rate of technical progress) • Countries should grow faster if they are behind • Not much evidence of convergence – Long run growth trend of around 2% in rich countries. Poor countries have not caught up.

Human capital and growth • Lucas (1988): • Human capital produces spillovers that raise overall productivity: • Suppose that β=δ: • Constant returns to K and h – a broad measure of capital. • No steady state:

Human capital and growth Investment in k = saving rate * output Saving and investment Replacement capital = k *(population growth + depreciation rate) Broad capital stock per worker, k

Human capital and growth • How does human capital accumulate? • In Lucas, it builds upon existing human capital (spillovers again): • Increase in h = (1 – time working) x h • Therefore, it can increase without bound, like physical capital. • If (1 – time working) is the proportion of working life given over to going to university, is this a satisfactory assumption for investment in HE?

Human capital and growth • Human capital in Lucas’ model is non-rival: • Can be used by everyone, including later generations • A lot of education and training produces rival human capital • Can only be used by the person who possess it • Knowledge and technical know-how fits into the former category • Skills fit into the latter category

Human capital and growth • Romer (1986): • Spillovers from investing in physical capital • New investments embody new technology which generates spillovers to workforce (for example, through new skills needed to operate new technology). • These skills can be shared amongst all workers, including those at firms who haven’t made the investment in new physical capital • Some higher education skills may facilitate learning new skills once investment in technology have been made.

Human capital and growth • Romer (1990): • Two sectors – goods and R&D • Endogenous growth – ideas generates new ideas (“If I have seen further, it was by standing on the shoulders of Giants”) • Technological progress proportional to human capital employed in R&D sector • More human capital facilitates innovation and its diffusion • Large countries grow faster? Growth rates keep increasing if population grows? (See Jones, 1999) • How does HE fit into this model?

Measuring the effect of education on growth • Benhabiband Speigel (1994): • Relationship between growth and initial level of education • No relationship (or ‘wrong’ relationship) between increase in education and growth • Supportive of Romermodel • Krueger and Lindahl (2001) find numerous problems with macro growth studies: • Errors in education data • Controlling for capital • Assumptions about returns being constant across time • Causality • Other omitted variables

Measuring the effect of education on growth • Krueger and Lindahl (2001) attempt to control for some of these problems: • Find a relationship between growth and changes in education (Lucas or MRW) • Returns to more schooling high (compared to micro estimates) • Social returns to schooling (spillover effects) • Omitted variables (increased schooling happened at same time as some other, unmeasured factor)

Measuring the effect of education on growth Source: World Bank

Measuring the effect of education on growth • Linear regressions: • Evidence of convergence • Years of schooling matters and change in years of schooling are correlated with growth, but effect disappears when stage of development is taken into account

Measuring the effect of education on growth • Hanuschek and Woessmann (2007): • Quantity of schooling effects disappear when quality data is used • PISA scores in maths and science at age 16 correlate with growth rates, with years of schooling then having no effect • What does this imply for HE?

Exercise • Read handout on Alison Wolf’s (2002) book ‘Does Education Matter?’ • How would you reconcile her view with evidence linking greater educational attainment to economic growth? • Omitted variables • Causality: growth  spend more on education? • Difference between primary, secondary and tertiary education • Signalling • What are the policy implications?

The contribution of higher education to economic growth