The Big Five personality traits and earnings

Human capital is the education, training, experience, intelligence, motivation, and other human factors that workers use to be successful in their job. In effect, it is the capital that is bound up within the workers as a person, and inseparable from them. When economists measure human capital, we usually use education as a proxy. Sometimes we use job experience, or measures of intelligence or skill. Sometimes we use a combination of two or more of those measures.

It is less common for economists to use more intrinsic measures of human capital, such as personality. However, it seems likely that personality traits should affect earnings. So, I was interested to see what this 2023 meta-analysis article by Melchior Vella (University of Essex), published in the journal Bulletin of Economic Research (open access), would turn up. A meta-analysis is a way of quantitatively combining the results of many studies into a single overall estimate of the relationship. If done well, it can also account for publication bias (where studies that fail to show statistically significant effects are less likely to be published).

Vella looks at studies that estimate the relationship between earnings and the Big Five personality traits, which are:

...openness to experience (ability to be creative, curious, intellectually engaged, honest/humble, and inquisitive), conscientiousness (self-discipline, punctuality, and organized and general competence), extraversion (how talkative, friendly, energetic, and outgoing the person is), agreeableness (the tendency to be kind, charitable, warm, and generous), and neuroticism (fear, worry, paranoia, and stress)...

While the Big Five has copped a lot of criticism (for example, see here), the taxonomy is still widely used, which means that there are a lot of studies that Vella can draw upon in the meta-analysis. Altogether, after a thorough search and screening process, they include 52 studies, with 1307 estimates of the relationship between the Big Five personality traits and earnings (most studies have many estimates of the relationship, with various different combinations of control variables, different specifications, or robustness checks). Applying a random effects model (which is quite common in meta-analysis, since it assumes that the true effect sizes vary across studies, not just due to random sampling error, but also due to real differences between the studies), Vella finds that:

For openness to experience, the true effect size is 0.019, indicating that a one standard deviation increase in openness to experience corresponds to a 1.92% increase in earnings. Similarly, conscientiousness (θ = 0.016, 1.61%) and extraversion (θ = 0.003, 0.30%) are positively correlated with earnings, whereas agreeableness (θ=−0.017, −1.69%) and neuroticism (θ=−0.018, −1.78%) show negative correlations.

Vella checks for publication bias in the results, and finds that there is statistically significant publication bias in estimates for conscientiousness, agreeableness, and neuroticism. After adjusting for publication bias, the relationships become much smaller, and are only statistically significant for openness to experience (positively correlated with earnings) and agreeableness (negatively correlated with earnings). The other traits are not statistically significantly related to earnings, once publication bias is controlled for.

Vella then looks at heterogeneity across studies, identifying what factors are associated with the size of the reported estimates. This analysis identifies that studies that fail to account for demographic characteristics (age), family background (parental education and/or income), socioeconomic status, education, occupation, or cognitive ability, are likely to have biased estimates of the relationship between the Big Five personality traits and earnings. Since most studies won't control for all of those factors, that explains part of the reason why there is publication bias, since studies that have biased estimates may be more likely to show statistically significant effects, if the true effect is small or zero.

Overall, this meta-analysis finds evidence that people who have higher openness to experience (ability to be creative, curious, intellectually engaged, honest/humble, and inquisitive) earn more, and those who have higher agreeableness (the tendency to be kind, charitable, warm, and generous) earn less. Other personality traits don't appear to matter (or the effects are too small to be measured). Vella stops short of trying to explain why it is that openness to experience and agreeableness matter, which is just as well. These studies, and therefore the meta-analysis as well, only show the correlation between the personality traits and earnings. They do not show that the personality traits cause differences in earnings. Nevertheless, it probably wouldn't hurt for people to be a little more open to experience (although I'm less keen on people being more disagreeable!).

A qualitative assessment of the implications of AI for New Zealand

Back in July, Treasury released an analytical note written by Harry Nicholls and Udayan Mukherjee on the implications of artificial intelligence for New Zealand. I read it last month, but didn't have time to write up my comments until now due to travelling. The analytical note is not a quantitative assessment on the economic or labour market or other impacts, but really just testing the waters and providing some broad overview of some of the issues. Fortunately (or unfortunately, depending on your perspective), this hasn't been superseded by subsequent quantitative analysis of the impacts, so it still provides a starting point for some discussion.

Nicholls and Mukherjee see three main issues: (1) the impacts of AI on productivity and investment; (2) the impacts on employment and the labour market; and (3) the development of regulatory approaches for AI. It may be my bias showing, but I think they miss the impact of AI on education and human capital development as a fourth main issue, but perhaps they see it as being subsumed under their first main issue (although they don't mention education at all, and mention human capital only once in the note).

The note discusses each of the three main issues in turn, but first it lays out frameworks for thinking through each issue. There are two (competing) approaches. The first is based on macroeconomic growth accounting framework, where:

AI can be thought of as a form of capital deepening, improving the quantity and quality of capital inputs. It can also be thought of as labour-augmenting technology that improves the quality of labour inputs. And it might be a form of technology that more effectively combines capital and labour, improving multi-factor productivity.

The second is a microeconomic approach, which sees:

generative AI as a form of workplace automation...

There are three types of effects [Acemoglu and Restrepo] identify in this framework:

• Displacement Effect: Automation makes it more efficient to produce some tasks by capital that were previously done by labour, so it reduces the share of labour in production.

• Reinstatement Effect: Automation can allow a more flexible allocation of tasks in production, and so can create a range of new tasks in which labour has a comparative advantage.

• Productivity Effect: Automation allows some of the tasks previously performed by labour to now be performed more cheaply by capital, and so increases the value-added in production and so potentially the overall demand for labour.

The benefit of this framework is that it helps to think through how the net impact of new automation technologies can be thought about as the result of the combination of the strength of these effects.

For example, the total effect of AI on labour demand is a combination of the Productivity Effect increasing the demand for labour and the Displacement Effect replacing labour from tasks it previously performed. Similarly, the impact of AI on wages could be thought of as the net change on marginal productivity from the Reinstatement Effect creating new tasks for labour demand and the Displacement Effect create new labour supply from replaced tasks.

I found the frameworks quite useful, but it is difficult to see how they can be reconciled (which, to be fair, is a common problem comparing macroeconomic and microeconomic frameworks). Nicholls and Mukherjee then turn to thinking through the three issues, but the frameworks seem to be a bit lost in those sections. However, they conclude by offering some potential areas for future research, which are:

• A deeper exploration of the policy levers that might accelerate the diffusion of AI, reducing the lag that characterises New Zealand’s diffusion of new technology.

• Investigating the likely impacts of AI on the productivity and competitiveness of particular sectors, or small-to-medium sized enterprises (SMEs), given their significance to the New Zealand economy.

• Taking a closer look at the implications of AI for employment and labour markets, with a particular focus on how these impacts should inform our policy settings around skills, immigration, and labour markets.

• Examining how AI intersects with our economic security, particularly if AI development is concentrated in a small number of large multinational overseas-based technology companies.

• Considering how AI could lift the productivity of New Zealand’s public sector, in order to maintain or enhance service levels in the face of pressures like an aging population.

Again, I would add the impacts on education and human capital development here, as well as research on the distributional consequences of AI. Those questions are important to understanding the future labour market impacts, as well as the need for changes to policy settings on taxes and transfers. Anyway, this is a good starting point as a think piece, and it is good to see that Treasury are actively thinking in this space, and hopefully there is more careful analytical work to come from them.

[HT: Inside Government, back in July]

Signalling theory suggests that we may need two categories of university degree

In The Conversation last week, Ananish Chaudhuri (University of Auckland) argued the case for having two categories of university degree:

And while cognitive abilities such as reading, writing and maths matter, so too do social skills such as empathy, resilience and an ability to work in diverse groups and with diverse views...

Universities play a crucial role in developing these skills. But the emerging two groups of students – on campus and off – are not getting the same education. The increasing emphasis on online instruction and exams is devaluing degrees...

This suggests we may need to distinguish between online and on-campus students in each of our courses. The course content will be the same, but the assessment methods will be different.

Online students can take tests, quizzes and exams remotely. Some of this may also be available to on-campus students. But on-campus students will be expected to come to lectures regularly, ask questions, write, speak and engage in interactive tasks, including group work.

Would students sign up for on-campus courses but simply not attend? This could be prevented by making sure each student completes tasks that earn participation marks that count toward on-campus credits. If they fail to do so, they will automatically become online students.

Is this unfair to online students? Not necessarily. Many with jobs may prefer it. In any event, they will have to consider whether the benefits of coming to campus are worth it in terms of job prospects or earning potential.

It is worth reviewing the purposes of education from the perspective of the student. On the one hand, education provides useful cognitive and non-cognitive skills that have value in the workforce. In theory, skills development need not necessarily be different between in-person students and online students. However, some 'transferable skills' like teamwork and interpersonal skills are more difficult to develop in an online environment (as Chaudhuri notes).

On the other hand, education provides a signal to employers about the quality of the job applicant. Signalling is necessary because there is an adverse selection problem in the labour market. Job applicants know whether they are high quality or not, but employers do not know. The 'quality' of a job applicant is private information. High-quality (intelligent, hard-working, etc.) job applicants want to reveal to employers that they are hard-working. To do this, they need a signal - a way of credibly revealing their quality to prospective employers.

In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality job applicants, would provide the signal), and it must be costly in a way that makes it unattractive for the lower quality job applicants to attempt (such as being more costly for them to engage in).

Qualifications (degrees, diplomas, etc.) provide an effective signal (they are costly, and more costly for lower quality applicants who may have to attempt papers multiple times in order to pass, or work much harder in order to pass). So by engaging in university-level study, students are providing a signal of their quality to future employers. The qualification signals to the employer that the student is high quality, since a low-quality applicant wouldn't have put in the hard work required to get the qualification. Qualifications confer what we call a sheepskin effect - they have value to the graduate over and above the explicit learning and the skills that the student has developed during their study.

Now we can see where there is a key difference between in-person and online education. In-person education is more costly than online education, even if the tuition fees are the same, because it requires effort for a student to get themselves onto campus and into class each day. Higher-quality students (who will be higher-quality job applicants) are more likely to be conscientious and make this effort than lower-quality students. So, having an in-person education should provide an additional signal of quality to employers, over and above the signal provided by the degree itself.

However, if there is no way for the in-person students to distinguish themselves from the online students, then the value of the signal provided by in-person education is lost. Chaudhuri argues that the solution is to create a two-tiered qualification system. One (in-person) qualification would convey a signal of high quality to employers, and the other (online) qualification would convey a signal of lower quality to employers.

I'd argue that this already happens to some extent. It's the reason why Massive Open Online Courses (MOOCs) haven't displaced traditional education, despite being free or low cost. Employers can tell the signalling value of a degree when compared with studying online using a MOOC. The problem that Chaudhuri notes is that students' university transcripts probably don't clearly identify students who have done their study in-person from those who have done their study online.

Of course, savvy employers don't only rely on job applicants providing signals. Employers engage in screening, in order to reveal the private information for themselves. They do job interviews and subject job applicants to various pre-employment tests, which helps the employer to tell the high-quality and low-quality job applicants apart. It would not surprise me at all to learn that, as part of the standard job interview script, employers now ask how much of their degree a job applicant completed online. However, job applicants can lie in job interviews, so screening is unlikely to be as effective as signalling in solving the adverse selection problem. Perhaps, two categories of university degree is the best solution after all.

Regardless, the takeaway for students is, as I noted in this 2017 post, that they should be acutely aware of the signals that they are sending to future employers. Education is a signal, but the type of education matters as well.

Read more:

• Why all students need to understand adverse selection and signalling

The value of exams as a signal

Exams have been in the news this week for all the wrong reasons. However, last week Michael Lee (University of Auckland) wrote an article in the New Zealand Herald on the real value of exams as a teaching tool rather than just an assessment:

We use exams as an encouragement tool to compel greater engagement with the material. That is actually where the real value of an exam is. When students feel the stakes are high and are unsure of what exactly will be asked, they are incentivised to take a look at everything seriously.

That's why teachers should never tell students exactly what will be examined, because 99 per cent of students will then focus only on that material, which defeats the true purpose of the exam.

In exams and in the real world, the first step to topic mastery is a general overview of key concepts and facts with as much detail as one can remember. Clearly, exams reward students that can do these things in a relevant way to answer a specific question.

A more advanced stage of mastery is the ability to creatively apply, integrate, and challenge the knowledge you have been taught. But it is difficult to get to that level if you haven't got enough base material to work with.

I have a slightly different take on the value of tests and exams. I agree with Lee that they are useful as learning exercises, especially if organised well. I disagree that we shouldn't tell students what will be examined (although I will admit that when asked what will be examined my usual answer is "everything we have covered", which is true!). However, I see tests and exams as having another important function for students, as an important signal that students can give to future teachers and employers. This relates to solving the employers' adverse selection problem that I have written about before:

Students are engaging in a sophisticated array of signals, on multiple levels. It's not possible to avoid signalling in this case, since trying not to provide a signal is itself a signal. The problem that this signalling is trying to avoid stems from private information about the quality of the student - students know whether they are high quality (intelligent, hard working, etc.), but employers don't. Employers want to hire high-quality applicants, but they can't easily tell them apart from the low-quality applicants. This presents a problem for the high-quality applicants too, since they want to distinguish themselves from the low-quality applicants, to ensure that they get the job. In theory, this could lead the market to fail, but in reality the market has developed ways for this private information to be revealed.

One way this problem has been overcome is through job applicants credibly revealing their quality to prospective employers - that is, by job applicants providing a signal of their quality. In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality applicants, would provide the signal), and it must be costly in a way that makes it unattractive for the lower quality applicants to do so (such as being more costly for them to engage in).

Qualifications are an effective signal (they are costly, and they are more costly for lower quality students, who face having to expend more time and effort to complete the qualification). Exams are also an effective signal for exactly the same reason (though not at the same level as the whole qualification). Because exam performance is an effective signal, high quality students can use their performance in exams to separate themselves from lower quality students, because it is very difficult for lower quality students to pass themselves off as higher quality students in the exam format. The quality of the signal is much lower for other types of assessment such as take-home tests, assignments, or group projects, where lower quality students can easily get additional help (often from the high quality students!) to boost their grades.

To me, that is one of the key reasons why we shouldn't eliminate high-stakes tests and exams from student assessment. Take-home or open-book tests, online tests, group projects, and the myriad of other assessment types that are used all have their place, and can all be valuable as learning exercises if used well. But they'll never be able to provide the same quality of signal of student quality as a test or exam.

Read more:

• Why all students need to understand adverse selection and signalling

[HT: David, one of my ECON100 tutors]

Gary Becker on human capital

In ECON110 this week, we've been covering the economics of education. In this topic we theorise that, from the perspective of the individual, education is an investment in human capital. This 'human capital theory' comes from the works of Jacob Mincer and from 1992 Nobel Prize winner Gary Becker, who sadly passed away in 2014 (although it was Arthur Pigou who much earlier coined the term human capital). So it is timely that the Economist has had two excellent articles on Gary Becker and human capital over the last couple of months. It was the second one, from The Economist Explains, that caught my attention this week, but I think the earlier article from August is better so I'll quote from that one:

...human capital refers to the abilities and qualities of people that make them productive. Knowledge is the most important of these, but other factors, from a sense of punctuality to the state of someone’s health, also matter. Investment in human capital thus mainly refers to education but it also includes other things—the inculcation of values by parents, say, or a healthy diet. Just as investing in physical capital—whether building a new factory or upgrading computers—can pay off for a company, so investments in human capital also pay off for people. The earnings of well-educated individuals are generally higher than those of the wider population...

Becker observed that people do acquire general human capital, but they often do so at their own expense, rather than that of employers. This is true of university, when students take on debts to pay for education before entering the workforce. It is also true of workers in almost all industries: interns, trainees and junior employees share in the cost of getting them up to speed by being paid less.

Becker made the assumption that people would be hard-headed in calculating how much to invest in their own human capital. They would compare expected future earnings from different career choices and consider the cost of acquiring the education to pursue these careers, including time spent in the classroom. He knew that reality was far messier, with decisions plagued by uncertainty and complicated motivations, but he described his model as an “economic way of looking at life”. His simplified assumptions about people being purposeful and rational in their decisions laid the groundwork for an elegant theory of human capital, which he expounded in several seminal articles and a book in the early 1960s.

His theory helped explain why younger generations spent more time in schooling than older ones: longer life expectancies raised the profitability of acquiring knowledge. It also helped explain the spread of education: advances in technology made it more profitable to have skills, which in turn raised the demand for education. It showed that under-investment in human capital was a constant risk: young people can be short-sighted given the long payback period for education; and lenders are wary of supporting them because of their lack of collateral (attributes such as knowledge always stay with the borrower, whereas a borrower’s physical assets can be seized).

So many of the things we covered in class this week are found there, including the decision about private investment in education, the credit constraints that low income students face in borrowing towards their education costs (which is part of the rationale for a system of student loans), and one of the rationales for government involvement (that students would under-invest in their own education). Even though, as the article notes, behavioural economics has been used to attack the foundations of Becker's theories, on that last point I think behavioural economics actually makes the case stronger. One of the biases that behavioural economics has identified is present bias - quasi-rational decision-makers heavily discount the future (much more so that a standard time-value-of-money treatment would). Since the benefits of education happen in the future, those benefits are discounted greatly compared with the costs of education that occur in the present. So, quasi-rational people would tend to under-invest in education because they under-weight the value of the future benefits relative to the current costs.

The whole article (or both articles) is a good introduction to Becker's work on human capital. For a broader perspective on Becker's work, from the man himself, I highly recommend his 1992 Nobel lecture.

Why all students need to understand adverse selection and signalling

Of the economic concepts we cover in ECON100 and ECON110, adverse selection is one of the most deceptively difficult problems to explain. It is easy to understand that some people simply know some things that others don't (economists call that private information, and when there is private information we also say that there is information asymmetry). However, in order for there to be an adverse selection problem, the private information needs to lead to market failure in some way, and explaining the market failure is more difficult than explaining the private information. Not all private information leads to market failure, and the market failure is the reason why we need to have ways of dealing with the adverse selection problem. Since the problem stems from private information, solving an adverse selection problem involves revealing the private information to the uninformed party. When the informed party (the one that knows the private information) tries to credibly reveal that information, economists call that signalling.

Students are engaging in a sophisticated array of signals, on multiple levels. It's not possible to avoid signalling in this case, since trying not to provide a signal is itself a signal. The problem that this signalling is trying to avoid stems from private information about the quality of the student - students know whether they are high quality (intelligent, hard working, etc.), but employers don't. Employers want to hire high-quality applicants, but they can't easily tell them apart from the low-quality applicants. This presents a problem for the high-quality applicants too, since they want to distinguish themselves from the low-quality applicants, to ensure that they get the job. In theory, this could lead the market to fail, but in reality the market has developed ways for this private information to be revealed.

One way this problem has been overcome is through job applicants credibly revealing their quality to prospective employers - that is, by job applicants providing a signal of their quality. In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality applicants, would provide the signal), and it must be costly in a way that makes it unattractive for the lower quality applicants to do so (such as being more costly for them to engage in).

Qualifications (degrees, diplomas, etc.) provide an effective signal (they are costly, and more costly for lower quality applicants who may have to attempt papers multiple times in order to pass, or work much harder in order to pass). So by engaging in university-level study, students are providing a signal of their quality to future employers. The qualification signals to the employer that the student is high quality, since a low-quality applicant wouldn't have put in the hard work required to get the qualification. Qualifications confer what we call a sheepskin effect - they have value to the graduate over and above the explicit learning and the skills that the student has developed during their study.

However, there are actually multiple levels of signalling associated with university study. Employers are faced with many applicants that have similar qualifications, and it is difficult to distinguish who, among those with the qualification, is the better applicant. So, the choice of major provides an additional signal to employers. Some majors are clearly more difficult than others - students who can complete a degree while majoring in more difficult majors are signalling to employers that they higher-quality employees than students who complete a degree with easier majors. I leave it up to you to determine which majors might be easier, and which might be more difficult.

Within majors there is a further signal, which is the student's choice of papers. Taking economics as an example, econometrics is likely to be the most difficult paper that students will take. So, students who complete an economics major without completing an econometrics paper are signalling to employers that (among economics graduates) they are the lower-quality economics graduates. [*] I'm sure there are certain papers within other majors that are perceived as difficult and provide a similar signal for students taking those majors.

Within papers there is a yet another signal, which is the grade the student receives. It is harder to get an A than to get a C, so the grade a student receives in any paper also provides a signal of student quality to employers. The saying goes that "C's get degrees", which may be true, but students with C's don't get their first choice of jobs (or at least, they have a lesser chance of getting the good jobs).

But there is still one more signal that students engage in, which isn't a signal to employers but a signal to their lecturers. Just like employers, lecturers don't know who the high quality students are (remember this is private information). So, how students engage in class, and how they perform in assessments, is a way for the students to signal their quality to lecturers. Students who don't complete some assessment items, or who don't attend lectures or tutorials, or who don't complete online tests, and so on, are providing a signal to their lecturers and it's not a signal of their high quality. Even avoiding a small piece of assessment, or an optional task in class, is a signal to the lecturer. And that signal may make the difference between an A and B grade, or between a pass and fail. Which in turn becomes a signal to employers, as noted above.

So, students are generating many signals (by completing a qualification, by their choice of major and individual papers to include in their qualification, by their grades in those papers, and by how they engage and perform within each paper). All of which means that every student needs to understand adverse selection and signalling. Otherwise, they might just end up providing the wrong signals.

*****

[*] Which is why I recommend to economics majors that they include econometrics in their programme of study, even though it is not compulsory.

Why study economics? Sheepskin effects edition

Over at Offsetting Behaviour, Eric Crampton highlighted that economists have higher lifetime earnings than other graduates. The source of this claim is from this Jordan Weissmann article. I've previously written on the reasons why students should study economics (see here and here), and for a lot of prospective students deciding on their majors it really does come down to how much they can earn having done different majors. On this metric, economics does pretty well for the median graduate. However, the key addition from the Weissmann article is that, at the top end of the income distribution for each major, economists earn more than all other majors. Here's why:

So why are econ grads so good at making it rain? Part of it is that the finance and consulting industries like recruiting them, not necessarily for their specific skills, but because they consider the major a basic intelligence test. Granted, we're probably not seeing the effect of Goldman Sachs or Private Equity salaries in these charts, since they only stop at the 95th percentile of earners—but banking is a big industry, and it pays well.

Eric adds a good point which I want to expand on:

Grade-seeking students of lesser abilities drop economics for other majors; those who are left earn their grades.

One of the key characteristics of a degree or diploma is the signal that it provides to prospective employers about the quality of the applicant for positions they have available. Employers don't know up front whether any particular applicant is good (intelligent, hard working, etc.) or not - there is asymmetric information, since each applicant knows their own quality. One way to overcome this problem is for the applicant to credibly reveal their quality to the prospective employer - that is, to provide a signal of their quality. In order for a signal to be effective, it must be costly (otherwise everyone, even those who are lower quality applicants, would provide the signal), and it must be more costly for the lower quality applicants. Qualifications (degrees, diplomas, etc.) provide an effective signal (costly, and more costly for lower quality applicants who may have to sit papers multiple times in order to pass, or work much harder in order to pass). Qualifications confer what we call a sheepskin effect - they have value to the graduate over and above the explicit learning and the skills that the student has developed during their study.

Now, economics may provide a stronger signal of quality (a more valuable sheepskin effect) than other majors. Why would that be? Economics is by no means an easy major for most students. It involves learning calculus and statistics, and developing important critical thinking and logical reasoning skills. All of these things are hard (but ultimately worthwhile, given the returns to an economics major in terms of higher earnings) so, as Eric argues, lower-ability students tend to select themselves into majors other than economics.

When it comes to graduates, employers can't easily tell the difference in quality between economics and knitting [*] graduates in terms of their quality. However, if economics is known (by employers) to be more difficult (i.e. more costly in terms of time and effort) for students, then an economics major would provide an additional signal (over and above that of the degree itself) of the higher-than-average quality of the student.

We could make a similar argument for econometrics (regarded by most students as the most difficult part of an economics major). Top grades in econometrics (or even slightly-above-average grades in econometrics) may provide an additional signal of quality, over and above the signal provided by the economics major (and the degree). Which is why I always strongly recommend to our economics students that they include econometrics in their programme of study. That allows them to take advantage of multiple layers of sheepskin effects.

So, sheepskin effects provide another reason why studying economics is a great idea. If you want to convince employers that you are a top-quality student, it's hard to beat receiving top grades in a more difficult major.

*****

[*] OK, I made that up - we don't have knitting graduates. I don't doubt there are some potential students who would be keen on a Bachelor of Knitting though.

Why study economics? NZ graduate earnings edition

I have posted before on why studying economics is a good idea.

Recently though, I was pointed to the Careers NZ website, which has a useful tool ("Compare Study Options") that compares the earnings of graduates of various disciplines. It uses data from the excellent Integrated Data Infrastructure from Statistics New Zealand, which is a relatively new and exciting database combining datasets from many sources.

I had a bit of a play with the Compare Study Options tool, and this is what I found when comparing Economics and Econometrics graduates with graduates from the Management and Commerce disciplines:

Now, I have no idea what the standard errors are like in these median salaries (and the full 2013 report on the Education Counts website from which these median salaries are taken doesn't say either), but I imagine that there isn't much to choose between many of the majors. But there are some clear things to see here:

• Tourism is clearly on the bottom in terms of expected salary for graduates. Slightly above Tourism are Sales and Marketing and Business and Management. The other majors are clearly above those three.
• All majors increase salaries with more study with the exception of Banking, Finance and Related Fields, where Honours graduates earn more than Masters graduates. Maybe that's a small sample size issue?
• Economics and Econometrics improves in ranking and relative earnings with more study, compared with the other majors.

The latter bullet point is the important one. Studying economics is associated with higher graduate salaries, particularly at higher levels of study. Note that we can't say for sure that there is a causal mechanism here. Economics and Econometrics might lead to higher earnings, but there are a number of reasons why Economics and Econometrics graduates may earn more than graduates in Management and Commerce disciplines. Jonah Sinick recently outlined the reasons in a pretty comprehensive way (see here for the general considerations, and here for further exploration of the data). In short, he suggested three reasons (drawn from Bryan Caplan here):

• Human capital acquisition: Education develops students’ employable skills. 

• Ability bias: Obtaining an educational credential reflects greater or lesser pre-existing ability (that exists independently of what’s learned in school), which is later reflected in earnings. 

• Signaling: An educational credential signals pre-existing ability (which as before, can be independent of what’s learned in school) which makes employers more likely to hire one

Under the first reason, if Economics and Econometrics creates greater human capital than other majors, then that higher human capital is rewarded in the labour market with higher salaries. Under the second reason, Economics and Econometrics graduates may have higher ability than graduates of other majors, and that higher ability is rewarded in the labour market with higher salaries. Under the third reason, a degree with an Economics and Econometrics major signals to employers that graduates are high quality, perhaps because Economics and Econometrics is a more difficult major than other majors. So Economics and Econometrics majors find it easier to get higher-paying jobs because of the value of the signal to employers.

Like Jonah, I believe the publicly available data (such as on the Careers NZ website or in the related report) on their own don't give us enough to be able to disaggregate these effects. As a teacher of economics, I would hope it is a human capital thing, but having taught both first-year and graduate economics for a number of years I strongly suspect that ability bias is significantly more at play than human capital. And signalling is ever-present in markets with incomplete information. At this point, I don't see any reason to disagree with Bryan Caplan, who suggests 10% Human Capital, 50% Ability Bias, and 40% Signalling.

[HT: Dan Marsh; also Guido Stark of Statistics New Zealand]