LABOUR ECONOMICS:
WEEK 1 – THEORY OF HUMAN CAPITAL:
-Workers make different labour market related (human capital) investments; to be educated, to
undergo training, to search for new jobs etc.
-Education can only be a source of future earnings if wages reflect differences in productivity. Wages
may differ for other reasons though (eg – job by job); this means that the wage differential is partly
due to the difference in the stock of human capital.
-There are a number of different views of human capital. The main definition is as the set of skills or
characteristics that increase a worker’s productivity.
-Becker’s view thinks of human capital as represented by the stock of knowledge or skills
that is directly useful in the production process.
-Gardner’s view views human capital in terms of mental vs physical abilities.
-Schultz/Nelson view thinks of it as the capacity to adapt in dealing with the equilibrium.
-Bowles-Gintis view reflects the idea of human capital as the capacity to work in
organisations, eg – obey orders.
-Spence view is the signalling model view, with human capital thought of as a signal of
ability, as opposed to characteristics useful in the production process.
-We focus mainly on Becker’s view and Spence’s view.
Sources of Human Capital:
-Innate ability – this is important because it is a source of heterogeneity even when individuals have
access to the same investment opportunities and economic constraints. Empirical applications also
important – we use this in literature as it is likely to be correlated with other variables of interest, eg
– genetics.
-Schooling – most easily observable component of human capital investment.
-School quality also included in the above.
-On the job training – joint decision of workers and employers to involve employees in more
advanced training that may increase their productivity levels.
-Pre labour market influences – eg – individuals encouraging each other to invest in themselves, go
to classes etc. that could effect their productivity.
Becker’s Theory suggests that education can only be a source of future earnings if wages reflect
differences in productivity, which is itself influenced by investments into education/training.
-To acquire the competencies that the labour market will reward, we compare the cost of
investment vs the increase in future earnings.
Relation between earnings and human capital has long been studied in literature – it is not at all
self-evident that improved productivity on the part of the wage earner leads to a systematic increase
in their wage, even in a perfectly competitive labour market.
,-In reality, a worker who improved productivity can only make it pay off if they are able to play two
or more employees against each other (competition in the market).
-Thus, Becker specifies between:
-General training – enhances productivity of an individual for all types of job and thus
employers have no incentive to finance this type of training.
-Specific training – enhances productivity for a specific job and thus the employer has an
incentive to invest in this type of training.
-This means that in a perfectly competitive economy, individual choices regarding training
are socially efficient. In reality however, markets are not perfectly competitive.
-Wages and productivity differ and educational choices are no longer efficient in reality.
-If wages are lower than productivity (because firms dispose of monopsony power), then investment
in human capital is less than socially optimal. This is hardly the case however, as monopsony is
unlikely
-Having said that, there are externalities (mostly positive), associated with education – better
educated individuals share their knowledge which boosts productivity of those around them. Hence,
the collective return to education is greater than the individual return.
-Individuals do not take the positive externality of education into account though, which implies
education effort is generally insufficient in the absence of public authorities – we require a third
agent in the dynamic to influence the socially optimal decision of how much human capital to
generate.
Empirical Difficulties:
-Mincer (1974) devises the Mincerian human capital earnings function:
-y measures individual
earnings.
-S represents years of
education completed. X represents experience, where X= A – S – 6 (A=Age), where 6 represents
years not in schooling in early childhood.
-There have been many positive results confirming better educated individuals earn more and
experience less unemployment.
-BUT is our estimate of b consistent? Is it constant across individual characteristics? Is S an increase
in human capital or productivity or a signal of ability?
WEEK 1 – MODEL OF EDUCATION CHOICE:
-The basic human capital model implies that people finish their formal schooling prior to entering
the labour market – this means we can separate the effect of schooling from that of experience.
-We assume an individual wishes to maximise their utility function, ,
where y is average annual earnings and is an increasing convex function that reflects cost.
,-We say earnings (opportunities) are given by - a concave return function of schooling.
-To find the equilibrium level of schooling, we differentiate w.r.t earnings the equation
and we get .
-The marginal rate of return = marginal cost at this level.
-We therefore determine the optimal number of schooling years but this can vary by individual
discount rates and differences in ability.
-As the rate of interest falls, years of
schooling increases. We can use the
same logic for discount rate.
-Person A has greater discount factor
than person B and thus they invest in
fewer years of schooling, despite both
having the same MRR.
-We end up with one earning curve
but respondent to different wages for
two different years of schooling;
higher schooling = higher wages.
-Individuals with higher ability
tend to invest more in schooling,
ie – they have a higher MRR. Note
r is fixed (equivalent to same
discount rate).
-On the wage-schooling dynamic,
we find different earnings curve –
person B has a higher earning
curve with higher education. We
link the two to find the earning-
school curve that connects
different level of wages with
different schooling.
-We note heterogeneity in
optimal schooling choice comes down to marginal
return to schooling (1) and cost of schooling (2).
-Assuming two linear functions for both:
-Marginal return to schooling given by ability, which
is diminishing (by constant k1) as schooling increases.
-Marginal cost of schooling given by access to funds
add is increasing in schooling at a rate k2.
, -Optimal level of schooling given by setting the
two above equations equal to each other.
-Substitute this into individual MRR to school.
-This allows us to calculate the marginal return
across the population to schooling, unless
ability is equal among everyone, or
opportunity is equal among everyone (access
to funds).
-Average marginal return to education is given by:
-This is the expectation of the marginal return to schooling. We call this the average treatment effect
– expected increase in average log earnings if a random sample of the population acquired an
additional year of education.
-Not particularly relevant, but very useful as a benchmark against which we can compare the
probability limit of different estimators of the return of schooling.
Labour Market Trends:
-OECD (2021) notes distinctions between public and private spending on tertiary education. We note
education is typically financed by public spending. Very few countries (UK, USA, Japan) have more
contribution to education spending from the private sector than the public sector.
-Looking at educational attainment at a glance (OECD, 2020), we note about 60% invest in a higher
education degree.
-We note financial returns for men for education is greater than that for women. Average returns to
education in OECD countries for women is $227,000 and for men over $250,000.
-We also note private net financial returns for attaining tertiary education are greater than the public
net financial returns, but both are still considerably positive. Again, returns for women are lower,
but also positive.
-OECD average percentage of unemployment for 18-24 year olds (not in education) is around 6%.
Among 25-29 year olds, we note clearly that higher educational attainment reduces chance of being
NEET (neither employed or in education or in training).
WEEK 2 – IDENTIFYING THE CAUSAL RELATIONSHIP BETWEEN EDUCATION
AND INCOME:
Education as a Signalling Device:
