The Science of Economics? What Works, and How Much…

We do seem to be talking more about economics – what it should do and look like. But there is still a whiff of revolution about calls for the discipline to be more evidence-based and, well, scientific. This article, by Philip Aldrick in the Times yesterday, argues for more careful scientific approaches, and this is worth noting. Of course, in the natural sciences, this would be taken as read. Drugs need to be extensively trialled before they are sold and used to treat disease in humans. But for some reason, in the social sciences, theory and ideology have the ability to shape policy just as much as evidence.

Aldrick’s piece cites two studies launched by Nesta, a UK Innovation think tank, roughly seven years ago. The first was a retrospective review of the effectiveness of business clusters; do small businesses do better when they are closely located and can share location and labour advantages? The second was a randomised controlled trial on whether tax relief for small creative companies worked. The results of the studies were not their most important findings however.

For the sake of finishing a story, the first study proved relatively inconclusive, and could not find any clear correlation between clusters and growth. The second study found that tax and financial incentives were helpful in the short term for small creative businesses, but after 12 months any advantage had faded.

So, what was the main impact? The reason these two studies are remarkable are for their illustration of research methods. While retrospective reviews – generally the majority of most empirical work in the social sciences – can only look for correlation, randomised controlled trials (RCTs) can go deeper, further, and can identify causative factors. In other words, we can target specific factors and identify why things happen. This is important because it means we can be more scientific about what works, how it works, and why. And this means we can begin to base policy on evidence rather than theory. RCTs also offer a way of measuring the extent of policy impacts. By having a test group and a control group, we can gauge the extent to which a policy really makes a difference. And that means we can evaluate whether a policy is financially and economically viable. So, RCTs offer a way of seeing not only what works, but how much.

Why is this news? Similar to other recent posts on here, there is increasing discussion of economics and how the discipline can be improved in the mainstream media. Aldrick’s argument is that economics – both the research and the formulation of policy – can and should be more scientific in its approach. And to this end he calls for more RCTs and longer term studies testing causation before policy is enacted. The government has launched the Business Basics Fund with Nesta to carry out trials investigating, among other things, productivity. UK productivity lags behind that of other countries, attributed generally to poor management practices. But how can management practices be altered to improve productivity?

Questions like this lend themselves readily to RCTs where different techniques can be trialled in comparison with a control group. Nevertheless, there are questions of macroeconomics that are not suitable for trials. We cannot test interest rates or tariffs, for example, against control groups. And this remains a problem for the larger questions tackled in macroeconomics, where theory remains a significant influencer of policy.

Calls for greater use of careful empirical data in shaping economic, legal and social science policy is not new though. Economic sociology, economic sociology of law, and sociolegal approaches have long stressed the need for analysis and understanding to be based firmly in the real world, on real data, and about real people. Increasing access to big data and AI could enhance this. As Aldrick states, “Economics is a social science. Why not make it more scientific?”

Between the Gigs and the Reels: novel approaches to understanding the gig economy

The rise and rise of companies like Uber, Lyft, TaskRabbit, UpWork, JustEat and Deliveroo has been termed the “gig economy”. Even the term itself can be wince-inducing to labour lawyers who are uncomfortable with the implications of the term “gig”. But there are broader issues, and a lot of ink has already been devoted to the economic and regulatory issues that arise as a result of this new form of working. At its heart, the issues tend to arise initially from the introduction of technology into the labour market that enables informal work. This has allowed the regulatory and statutory protections that workers have campaigned and fought for over the past two centuries to be summarily side-stepped. Sure, there are benefits, and it can present opportunities for people to get out of the house and supplement their income. But the problem is when this form of working begins to challenge the main, or more formal, economy.

The gig economy really took off in the wake of the 2008 financial crisis and the unemployment wave that followed, and is rooted in the informal sector which lacks government oversight, creating dilemmas about regulation and worker protections. Not that this is a completely new issue. The matter of informal sector work with a lack of recognition and worker protections and rights is a problem that women have been facing since labour rights came onto the scene. Women’s work – work generally done in the home – has yet to receive the same recognition as work done in the formal sector. Informal sector work includes not only a lack of pay, but also rights, holidays, sickness, insurance and so on. This analogy tends to be underplayed in the public debate about gig economy pros and cons. As Catherine Powell notes, the gig economy is business as usual for women. What’s new here is that the advent of technology and the self-employment or contractor-status of (usually) men has been sold to us as the epitome of the free market “in which app-driven services are seen as an example of unfettered market activity that is free of the intrusive, cumbersome hand of government regulation”.

At the same time, drivers working for Uber are contractors rather than employees, and therefore do not have access to the regulatory protections that employees do, like holiday pay, sick pay, pensions, national insurance contributions, and so on, although there are legal and regulatory challenges underway. In her recent book, “Hustle and Gig”, Alexandrea Ravenelle also argues that society is the poorer for this labour market model, as reduced tax incomes and reduced provision of social security nets by employers as well as the state have wider detriments throughout society.

In a different approach to most of the literature on the gig economy, economist and Stanford Professor Paul Oyer signed up with Uber and worked as a driver for them to understand the gig economy from the inside. This article links to an interview with him about his research and findings.

If you’ve read any of my previous posts, you’ll be expecting a comment here about economic methodology and how exciting it is to have an economist taking a sociological, quasi-ethnographic approach to understanding how a market works. Oyer notes that he took care not to tell his Uber customers that he was an undercover economist, and as he was employed by Stanford, his Uber wages were donated to charity. Nevertheless, his observations are valuable for their insight into the inner workings of companies leading and shaping the gig economy, like Uber. One observation that stands out here is the lack of social interaction among gig economy workers. If you work in an office, you see the same people every day, and a sense of community can develop. But working as a contractor in a car or on a bike can mean that you are isolated and solitary, and the loneliness of this was highlighted by Oyer’s undercover work; a sociological commentary on an economic phenomenon.

There are few insider accounts in general, and little literature on the importance of algorithms that form the backbone of the company like Uber. These are being developed by economists brought in by the firms, and have unpleasant, although perhaps unsurprising, side effects. The algorithms tend to channel higher paying work to men who are prepared to work at short notice, during “surges” in demand where prices are higher, and at periods of scarcity. Thus, male Uber drivers earn on average 7% more than female drivers. Male drivers are also more likely to “game” the system, learning how to be strategic in their pick ups and how to cancel less profitable journeys without incurring a penalty, provoking angry discussions like this online. Women are less likely to engage is this less-than-honest behaviour, putting their wages further behind.