Sunday, 31 May 2020

If robots are coming for our jobs, someone forgot to tell the labour market data

It seems that every other week there is a new article written about how robots are coming to take all our jobs - the coronavirus pandemic is just the latest reason to be worried about this (for example, see here). However, this 2017 article by Jeff Borland and Michael Coelli (both University of Melbourne), published in the journal Australian Economic Review (sorry, I don't see an ungated version online), provides a reasonable and evidence-based contrast to the paranoia.

Borland and Coelli essentially argue that, if robots are taking our jobs, we should be able to observe this in the data. And we don't. This is analogous to Robert Solow's famous 1987 quip that "You can see the computer age everywhere but in the productivity statistics".

Borland and Coelli note that:
Evidence for the claimed effects of computer-based technologies on the labour market, however, is remarkably thin. Sometimes it consists simply of descriptions of the new technologies, perhaps with an assertion that these technologies are more transformative than what has come before. Sometimes it consists of forecasts of the proportion of jobs that will be destroyed by the new technologies. Sometimes measures are presented that, it is argued, establish that the new technologies are causing workers to lose their jobs or be forced to shift between jobs more frequently than in the past. Sometimes the evidence is an argument that categories of workers not previously displaced by technological change are now being affected.
They then present data from Australia that basically shows that the adoption of computer technology ('robots', broadly defined) is having little effect on the total number of jobs in the Australian labour market. That is:
From our analysis of employment outcomes in the Australian labour market we arrive at two main findings. First, there is no evidence that adoption of computer-based technologies has decreased the total amount of work available (adjusting for population size). Second, there is no evidence of an accelerating effect of technological change on the labour market following the introduction of computer-based technologies.
Interestingly, the common claim that the cohort entering the workforce now will work many more jobs over their working life that the cohorts before them comes in for a particularly rough time:
Not only is there no evidence that more workers are being forced to work in short duration jobs, but what is apparent is that the opposite has happened. The proportion of workers in very long duration jobs has increased from 19.3 per cent in 1982 to 26.7 per cent in 2016, and there has been a corresponding decrease in the proportion of workers in their jobs for less than a year.
Of course, none of this means that computer-based technology is having no effect on jobs. Like most pervasive technological changes, the rise of computer-based technology changes the type of jobs that are available, and the distribution of those jobs between groups of workers and between regions (or between urban and rural areas). However, based on the data we have so far, it is not correct to be making the apocalyptic claims that some pundits are making.

So, why is everyone so afraid of the robots? Maybe those making the claims are not really afraid. Maybe it's just incentives at work, leading them to make those claims. Borland and Coelli note that:
You are likely to sell a lot more books writing about the future of work if your title is ‘The end of work’ rather than ‘Everything is the same’. If you are a not-for-profit organisation wanting to attract funds to support programs for the unemployed, it helps to be able to argue that the problems you are facing are on a different scale to what has been experienced before. Or if you are a consulting firm, suggesting that there are new problems that businesses need to address, might be seen as a way to attract extra clients. For politicians as well, it makes good sense to inflate the difficulty of the task faced in policy making; or to be able to say that there are new problems that only you have identified and can solve.
That makes a lot of sense, and on the surface so do the claims of the robot apocalypse. At least, until you start to look at the data, which make those claims shaky at best.

Read more:


Tuesday, 26 May 2020

Oliver Williamson, 1932-2020; and Alberto Alesina, 1957-2020

We had a double dose of sad news over the last few days, first with the passing of 2009 Nobel prize winner Oliver Williamson, and then with the passing of Harvard University political economist Alberto Alesina. Williamson was 87, while Alesina was just 63 years young.

Williamson is best known for his work on transaction cost economics, industrial organisation, and the theory of the firm. My ECONS102 students get the very briefest of introductions to his work on transaction costs, which is important to understanding societal and political organisation, as well as the structure of firms.

Alesina is best known for his work on political economy and economic systems, and I really enjoyed his 1997 article with Enrico Spolaore on the optimal number and size of countries, published in the Quarterly Journal of Economics (ungated version here). I've previously blogged about some of his research (see here and here).

You might not think that these two great economists shared much in common, but A Fine Theorem has an excellent article that links both of them:
While one is most famous for the microeconomics of the firm, and the other for political economy, there is in fact a tight link between their research agendas. They have attempted to open “black boxes” in economic modeling – about why firms organize the way they do, and the nature of political constraints on economic activity – to clarify otherwise strange differences in how firms and governments behave.
That article is excellent in explaining the importance of the research of each of them. Haas News has more detail on the life and work of Williamson, while the Washington Post has an excellent obituary for Alesina. They will both be missed.

Monday, 25 May 2020

A few papers on grade inflation at universities

University lecturers who have been around for a while often lament the incentives that universities have to inflate students' grades, and many claim that grade inflation has been an ongoing phenomenon for decades. As I noted in this 2017 post, grade inflation has negative implications for students, because it makes it more difficult for the truly excellent students to set themselves apart from students who are merely very good. However, it isn't just universities that have incentives for grade inflation - the student evaluation system creates incentives for staff to inflate grades too.

However, there are reasons to doubt whether grade inflation is real. High school teaching has improved over time, so perhaps students are coming to university better prepared for university study, and higher grades reflect that better preparation. On the other hand, university teaching has also improved over time, so perhaps students are learning more during their university classes and higher grades genuinely reflect better performance as a result. Finally, as I have noted in relation to economics, over time cohorts of students have increasingly selected out of 'more difficult' courses and into 'easier' courses. So, improving grades may simply reflect changes towards courses that are more generous in offering higher grades. Untangling these various effects, and whether any grade inflation remains after you control for them, is an empirical research task.

I've just finished reading a few articles on the topic of grade inflation, so I thought I would share them here. In the first article, by Rey Hernández-Julián (Metropolitan State University of Denver) and Adam Looney (Brookings Institution), published in the journal Economics of Education Review in 2016 (ungated earlier version here), the authors use data from Clemson University of:
...over 2.4 million individual grades earned by more than 86,000 students over the course of 40 academic semesters starting in the fall of 1982 and ending in the summer of 2002.
They note that:
Over the sample period, average grades increased 0.32 grade points (from 2.67 to 2.99), similar to increases recorded at other national universities... At the same time, average SAT scores increased by about 34 points (or roughly 9 percentile points on math and 5 percentile points on verbal sections)... 
So, while university grades improved over time, so did the SAT scores of the incoming cohorts of students. Moreover, they note that there has been a shift over time in course selection, so that students have increasingly selected into courses that have higher average grades (arguably, those courses that 'grade easier'). Once they decompose the change in grades into its various components, they find that:
...more than half of the increase in average grades from 1982 to 2001 at Clemson University arises because of changes in course choices and improvements in the quality of the student body. The shift to historically easier classes increased average grades by almost 0.1 grade point. Increases in SAT scores and changes in other student characteristics boosted grades by almost another 0.1 grade point. Nevertheless, almost half of the increase in grades is left unexplained by observable characteristics of students and enrollment — a figure that suggests the assignment of higher grades plays a large role in the increase.
In other words, even after controlling for the quality of incoming students and their course choices, grades increased over time, providing some evidence of 'residual' grade inflation. However, this article is silent as to why they observe this grade inflation, and of course it relates to the experience of just one university in the US.

The second article I read recently, by Sergey Popov (National Research University, Moscow) and Dan Bernhardt (University of Illinois, Urbana-Champaign), published in the journal Economic Inquiry in 2013 (ungated earlier version here), develops a theoretical argument for why we might observe grade inflation over time, and for why grade inflation would be greater at 'higher quality' universities. Their theoretical argument rests on the following:
Firms learn some aspects of a student’s non-academic skills via job interviews, and forecast academic abilities using the information contained in the ability distribution at a student’s university, the university’s grading standard, and the student’s grade...
Universities understand how firms determine job placement and wages, and set grading standards to maximize the total expected wages of their graduates.
The incentives this creates leads to a situation where:
...top universities set softer grading standards: the marginal “A” student at a top university is less able than the marginal “A” student at a lesser university. The intuition for this result devolves from the basic observation that a marginal student at a top school can free ride on the better upper tail of students because firms cannot distinguish “good A” students from “bad A” students. In contrast, lesser schools must compete for better job assignments by raising the average ability of students who receive “A” grades, setting excessively high grading standards.
So, top universities can benefit their students by giving more of them higher grades. It turns out this situation is exacerbated when the number of 'good jobs' is increasing over time. However, Popov and Bernhardt's paper is purely theoretical, and therefore they don't provide any strong empirical analysis to support their theory.

The third article I read recently, by Geraint Johnes and Kwok Tong Soo (both Lancaster University), published in the journal The Manchester School (ungated earlier version here) actually provides evidence against Popov and Bernhardt's theoretical model. Johnes and Soo look at aggregate data from all UK universities over the period from 2003/04 to 2011/12, and specifically look at the proportion of 'good degrees' (first or upper second class honours degrees) awarded. They use a stochastic frontier model in order to control for the inefficiency of some universities in producing top graduates - the most efficient universities form the frontier in this analysis. They find little evidence of grade inflation:
The evidence to support the existence of grade inflation is, at best, patchy. The quality of the student intake to universities has typically been rising over this period, and there have been changes in other factors that might reasonably be supposed to affect degree performance too.
In relation to the Popov and Bernhardt theory, they find that:
...although better universities award more good degrees, we find little evidence that different groups of universities exhibit different degrees of grade inflation over time.
However, there is a real limitation of this study relative to Hernández-Julián and Looney study I mentioned first, that identified grade inflation at Clemson University. The first paper controlled for student quality using SAT scores, while the Johnes and Soo paper controlled for student quality using student results in A levels. So, rather than finding no evidence of grade inflation, it would be more correct to say that Johnes and Soo found no evidence of grade inflation at university to a greater degree than the extent of grade inflation at high school. Because Hernández-Julián and Looney use the results of a standardised test, their analysis isn't subject to the same limitation. So, grade inflation may be real, but in Johnes and Soo's results it is no worse at university than it is at high school.

Overall, these three articles present contrasting views on grade inflation. There is definitely more research required on the extent to which grade inflation is a real phenomenon, and how much of it can be explained by changes in student quality, teaching quality, or course selection by students.

Read more:


Saturday, 23 May 2020

What happens to pawnbrokers when you ban payday loans?

Many people believe that payday loans are exploitative. They come with high fees and high effective interest rates (some can exceed 500% on an annualised basis). They tend to be targeted at low income people, who are excluded from traditional lending due to being perceived as high risk and/or lacking the collateral and credit history or rating necessary to obtain a loan from a more traditional lender.

A common policy solution to the perceived exploitation of low income borrowers is to restrict lending practices, such as setting a maximum effective interest rate (annual percentage rate, or APR). If the maximum rate is set too low though, it makes it uneconomic for any payday lender to lend to low income borrowers, effectively closing the market for payday loans, and further excluding low income borrowers from credit markets. However, that doesn't mean that these low income borrowers aren't going to look elsewhere for short-term loans.

If the government bans payday loans, or makes them uneconomic to offer, these borrowers might turn to other sources of loans, such as informal and unregulated loan sharks, or pawnshops. This increases the demand for those services, and makes them more profitable. If the barriers to entry into the loan shark or pawnshop market are low, then new firms may enter these markets to take advantage of the new profit opportunities.

That is the hypothesis that was tested in this article by Stefanie Ramirez (University of Idaho), published in the journal Empirical Economics (sorry, I don't see an ungated version). Ramirez looked at how the number of financial institutions (of various types) changed when Ohio set a maximum APR of 28% on payday loans in 2008, effectively making the industry uneconomic. Using monthly data at the county level from 2006 to 2010, she found that:
...the payday lending industry was demonstrably populated and active within the state prior to the ban with an average of 123.85 county-level operating branches per million. The effects of the ban can most definitely be seen as the average number of operating branches decreases to 10.14 branches per million in periods with the ban enacted.
Ok, so the ban was effective. Turning to other financial institutions, she finds that:
Pawnbrokers and precious-metals dealers are similarly concentrated to one another pre-ban, with an average of 16.65 branches per million and 18.51 branches per million, respectively. However, while there was an increase in concentration in both industries after the ban, growth in the pawnbroker industry was more pronounced than with previous-metal dealers, with the pawnbroker industry nearly doubling in size...
Small-loan lenders are the least populated industry but also show slight growth between pre- and post-ban periods. The average number of operating branches per million increased by approximately 21% between regulatory periods...
Finally, the average operating second-mortgage licensees per million shows no growth, however shows no decline between pre- and post-ban periods.
After controlling for other variables (like the price of gold, and the real estate index, population and other demographic variables) in a regression analysis, she found that pawnbrokers, small-loan lenders and second-mortgage licensees all showed statistically significant increases in numbers after the law change came into effect.

Low income people want access to credit. If policymakers ban one source of credit, they simply shift those borrowers into other markets, and those markets become more active. As Ramirez concludes:
In an effort to eliminate payday lending and protect consumers, policymakers may have simply shifted operating firms from one industry to another, having no real effect on market conduct.
The sad thing is that these other markets (e.g. pawnbrokers) may be even more difficult to regulate than the payday loan industry was.

[HT: Marginal Revolution, last year]