# The Case Against Education

The Case Against Education: Why the Education System Is a Waste of Time and Money[1] is a book written by libertarianeconomistBryan Caplan and published in 2018 by Princeton University Press. Drawing on the economic concept of job market signaling and research in educational psychology, the book argues that much of higher education is very inefficient and has only a small effect in improving human capital, contrary to the conventional consensus in labor economics.

Caplan argues that the primary function of education is not to enhance students’ skills but to certify their intelligence, conscientiousness, and conformity—attributes that are valued by employers. He ultimately estimates that approximately 80% of individuals’ return to education is the result of signaling, with the remainder due to human capital accumulation.

## . . . The Case Against Education . . .

The foundation of the drive to increase educational attainment across the board is the human capital model of education, which began with the research of Gary Becker.[2] The model suggests that increasing educational attainment causes increased prosperity by endowing students with increased skills. As a consequence, subsidies to education are seen as a positive investment that increases economic growth and creates spillover effects by improving civic engagement, happiness, health, etc.

The simple human capital model tends to assume that knowledge is retained indefinitely, while a ubiquitous theme in educational interventions is that “fadeout” (i.e., forgetting) reliably occurs.[3] To take a simple example, we may compute the present value of a marginal fact

${displaystyle F}$

that increases a person’s productivity by

${displaystyle V}$

as:

${displaystyle PV(F)=int _{0}^{infty }e^{-rt}Vdt={V over {r}}}$

where

${displaystyle r}$

is the discount rate used to compute the present value. If

${displaystyle V}$

is \$100 and

${displaystyle r}$

is 5%, then the present value of learning

${displaystyle F}$

is \$2,000. But this is at odds with the concept of fadeout. To correct for this, assume that the probability density function for retaining

${displaystyle F}$

follows an exponential distribution—with the corresponding survival function

${displaystyle S(t)=e^{-lambda t}}$

. Then the present value of learning

${displaystyle F}$

, accounting for fadeout, is given by:

${displaystyle PV(F)=int _{0}^{infty }e^{-rt}S(t)Vdt=int _{0}^{infty }e^{-(r+lambda )t}Vdt={V over {r+lambda }}}$

Since the expected value of an exponential distribution is

${displaystyle lambda ^{-1}}$

, we may tune this parameter based on assumptions about how long

${displaystyle F}$

is retained. Below is a table showing what the present value is based on and the expected retention time of the fact:

Present Value of Learning

${displaystyle F}$

3 Months 6 Months 1 Year 2 Years 3 Years 5 Years 10 Years
\$24.69 \$48.78 \$95.24 \$181.82 \$260.87 \$400.00 \$666.67

Regardless of the retention time assumption, the present value of learning

${displaystyle F}$

is significantly reduced.