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Applied Economics Lecture Notes 22/23
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Applied Economics Lecture Notes 22/23
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Applied Economics (Autumn)Topic 1 Lecture 1: Returns to education (week 2)
Returns to education
Around the 1960s, Economists started trying to understand why some individuals pursue post-
compulsory education and others not. Traditionally, we studied how/why agents invest in capital.
Education is the earliest investment an individual will make.
There is overwhelming evidence of a positive correlation between earnings and education:
For every extra year of schooling, what is the gain to human capital?
Which economic theories predict a return to education?
- Human capital theory
,Education is useful – you will learn productive skills which employers value and therefore you will
earn a higher wage. (Becker, 1964)
- Signalling theory
Qualifications serve only as a signal and solve the problem of adverse selection
Asymmetric information
The productivity of an individual is hidden – private information. High productivity can be signalled
through high levels of schooling – this signal must be more costly for low productivity otherwise
everyone would get that signal.
The Mincer earnings regression
The Human Capital Earnings Function (HCEF) or ‘Miner regression’ (Mincer, 1974):
𝑙𝑜𝑔𝑦 = 𝛼 + 𝛽𝑆 + 𝛾𝑋 + 𝛿𝑋 2 + 𝑢 (1)
Parameters to be estimated in regression analysis:
- 𝛼 - constant, logy if S=0 and X=0
- 𝛽 - correlation between 𝑆 and 𝑦. When 𝑆 increases by 1, 𝑙𝑜𝑔𝑦 increases by 𝛽
- 𝛾
- 𝛿
Data (observations):
- 𝑙𝑜𝑔𝑦 – log of earnings
- 𝑆 – level of schooling
- 𝑋 – years of experience
- 𝑢 - error term
There are some econometric issues with (1):
- It assumes a quadratic form in X
- It assumes linear returns to schooling
- The measurement of S and y
,Wages and age (proxies for experience)
𝑙𝑜𝑔𝑦 = 𝛼 + 𝛽𝑆 + 𝛾𝑋 + 𝛿𝑋 2 + 𝑢
X and 𝑋 2 are included to represent the data:
𝛾 > 0: when years of experience (X) increases, earnings (y) increase
𝛿 < 0: rate of increase slows across X
As we in increase X, what is the change in log y? First order condition wrt X:
𝑑𝑙𝑜𝑔𝑦
= 𝛾 + 2𝛿𝑋
𝑑𝑋
From your first year of work, you increase X from 0 and so log y increases by 𝛾. After 10 years (X =
10), when you increase X by 1, log y increases by 𝛾 + 2𝛿 (10) where 𝛾 > 0 and 𝛿 < 0 so earnings
increase but at a decreasing rate.
The shape of the graph produced by Card (1999) roughly justifies the quadratic functional form
proposed by Mincer (1974) (with 𝛾 > 0 and 𝛿 < 0).
Measurement of education
S typically measured as number of years of schooling, but are all years the same? The productivity of
a year of primary education is not the same as the productivity of a year of tertiary education.
, Nonlinearities is when the marginal effect of your final year (if successful) is much greater than the
marginal effect of your first year.
Relationship between schooling and later earnings (other measures could be poverty, crime,
happiness). 𝛽 is the causal (policy) effect which will tell us, if we increase schooling, by how much
the outcome (earnings) will increase.
𝑢 is the error term is everything you haven’t controlled for which can cause omitted variable bias if
unobserved traits 𝑢 are correlated with S and earnings. Some error terms are easy to measure such
as gender or region, whereas others are harder to observe such as ability (ability bias may occur).
Essay advice
- Read the papers on the reading list and find other papers to read – engage
- Not about views but evidence – always evidence your arguments
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