By Professor Ruth Towse (Professor of Economics of Creative Industries, Bournemouth University).
First of all, I’d like to say that I am now at the age where I’m playing the old experienced hand in all this. What I’m doing here is to distil my 25 years’ experience of working in this area in a quick 20 minute talk. There has been work on this topic in cultural economics going back to the early 1980s. My first work in artists’ labour markets, which is one of these subjects that’s been researched by various disciplines and where there has always been overlap with areas such as sociology, was in 1988.
The first question in researching artists’ labour markets is to ask what the research is for? Cultural economics research on artists’ labour markets has tended to focus on earnings, supply decisions of professional artists, economic influences (whether they are motivated by money, grants, what role do grants play and that kind of thing); and that related to a wider issue that everybody knows about from cultural economics, i.e. Baumol’s cost disease, which was one of the big questions which was first asked in cultural economics: what is the role of labour costs in the arts (which were in the first place thought of mostly in terms of performing arts and high art, big cultural organisations, etc)?
A second topic has been the implications for higher education, training, human capital formation; the other thing I’m not talking a great deal about is ‘methodology’. I have very strict views about the distinction between methodology and methods (which I’m not going to say a word more about right now). But I think it’s worth considering that what really has been done in cultural economics has been a combination of fact finding and testing theories. The theories being tested relate back to the wider field of labour economics that is a major topic in economics. Labour economics is a very big specialisation in the field that overlaps with economics of education. So the fact finding in cultural economics has been related to these theories.
These theories use micro-economic analysis. Now people talk about micro and macro in all fields. In economics, micro and macro have rather specific differentiation, I would say. Macro-economics is looking at the big picture, dealing with GDP, employment, inflation, those big things, whereas micro-economics is very specifically concerned with motivation, not self-consciously as in other disciplines, but with responsiveness to economic variables. Of course, the price mechanism is the most fundamental one – how do people respond to prices? So microeconomics not just looking at small units, like the firm or the artist or the worker. It’s about how they respond to specific things like income, prices and so on. I think that’s an important point to put over because I’m not sure that every disciplines uses micro and macro in that same context.
There is a lot of sociological research on artists’ labour markets that has a very similar approach to that of economists, though looking at careers, professional-ization and so on. I’m not going to talk about that because it’s not what I’m expert in. One topic is the whole question whether artists’ labour markets are a sort of template for the modern economy in which people have a portfolio of careers and so on and so forth – they are not employed for life in one firm, they’re ducking about changing jobs or working for themselves. The other discipline that has been use made, particularly by Bruno Frey, in trying to understand the motivation of artists and creativity in artists’ labour markets is social psychology.
So, to recap the first question, what’s the research for? Why are you doing this? What are you trying to find out? Whatever you’re trying to do will influence how you set about it and what the rules of the game are. So which labour market? Well this, of course, is where we come on the big distinction between the macro and the micro. Macro employment studies, for example, how many people are employed in the music industry; that uses sectoral employment figures. But for some purposes you might want to narrow this down. For example, you might want to talk about all performers or you might want to talk about all orchestral musicians or you might be doing a study of tuba players or something, so there are different labour markets. Each one is a labour market but at a different level of definition.
The second thing is that there are two aspects to looking at employment. One is looking at people we call ‘artists’. By the way, in the cultural economics literature, that’s a very broad group of people, including craftspeople – it doesn’t just mean visual artists. It includes primary creators and performers, people who are actually trying to do creative work in the arts and the cultural industries, as opposed to the non-artist employment in the industries. There’s been a great deal of confusion in the statistics about this and you’ll see figures that might include, for instance in the fashion industry, the people who are doing the making up, who could go and work, another industry. Or people working in box offices in theatres tend to get included in these things. So there’s a distinction that we want to make I think, particularly in this context, between people who are trying to work as artists and general employment in the sector. The latter group I’m going to say absolutely nothing about, because I totally disapprove of all the work people have done on this!
I refer to this business of ‘creatives’ – I don’t like the word anyway – but ‘creatives’ has been used, there’s a been a whole lot of research commissioned by some policy bodies which is about these people they call ‘creatives’ who are trained artists in the sense that they’ve been to art college or something like it and they work outside the creative industries; this is part of this whole big, spill over discussion – nobody does anything that doesn’t spill over on to everybody else. Anyway we could talk about all of that. I don’t want to deflect from my carefully thought out talk by going on about that!
Well, what about sources of data? Official statistics, such as contribution of creative industries to the GDP, have improved considerably over the time in which I’ve been working in the field. I think just lately they’ve improved even more with official statistics on employment in the creative industry done by two organisations: Eurostat and UNESCO [also read David Throsby for more on this]. What we look for are standard classifications where standardisation serves two purposes. One is so that there isn’t double counting, so that you don’t have people in one group or another that overlap, so that you actually get a unique picture of the size of employment or whatever. Two, is that you can do inter-country comparisons, and particularly within the EU: because of this standardization, we can compare the UK’s performance in the creative industries to that of, for example, Latvia (which does better, by the way!). I will not go into the whole thing of the GDP statistics because I’m talking mainly about occupation and employment here.
So, there is the ISIC (International Standard of Industrial Classification) for industries and its occupational equivalent ISOC). So if you think of these as being two circles, what you’re actually looking for in a lot of this research is the overlap between them, so you’re looking for people who have an occupation that’s classified as a cultural occupation and you’re looking for the industry that they’re working in, if that’s a cultural industry. That’s how you look for the figures on employment in the creative or cultural industries. NACE is the EU standard classification of economic activities, which include occupations and industries. And in the USA they have an equivalent called NAICS.
These classification statistics exist at different levels: so you might get service industries at Level 1 and then Level 2 would be say, banking and other service industries, or banking retail and other, Level 3 might be all entertainment industries, which would include sports (at one time they used to include window dressers in shops) and that sort of thing. What you really want for the sort of research that we’re talking about here is level 4, which you can identify somebody who’s a composer, somebody who’s a musician, somebody who’s a fashion designer and so on and so forth. Unfortunately, every country doesn’t have very good level 4 digit figures, so they’re often not available, which means of course, that although there are official data that can be referred to, effectively we’re going to be in the hands of surveys for the sort of work that we’re talking about here.
The other point is that the data of occupations, the ISOCS, are usually based on labour force surveys and so you need to know what the labour force survey is and does. The problem has been that for a lot of artists that there is confusion over self-employment, which is quite common because often they’re looking for people who are employed versus self-employed. (If you’re self-employed you can’t get unemployment benefit, etc. etc). Also there’s a lot of multiple job holding, so it depends what somebody was doing in the week of the survey…. that gives you an idea of problems with these NACE categories from which you may be going to look up your data.
The next point is about surveys as a source of data. Cultural economists have had to use surveys for a long time or have chosen to use surveys because they were the only source from which they could find out the kind of information that they wanted. Surveys are very expensive and they’re difficult to do well as it’s difficult to get high response rates from them and very often you see reported work from surveys where you’ve got about a 15% response rate, which is simply not high enough for statistical significance. Secondly, you’re getting an unrepresentative sample and that might be a problem in all kinds of ways – are you getting the successful people who want to tell their story or are you getting the unsuccessful people who’ve got plenty of time to answer your questions? One of the things you want to do to get a representative sample is to draw it from a population and this is where you need probably to go back to the official statistics. You need to know not only what the population is, but you need to know how to contact the sample, which is another problem. Also you need to understand that if you are, say, using representative organisations, you need to know whether there is any bias that comes in through, say, their rules for membership. So for example take Equity, the performers’ union: you have to have worked for so many weeks at Equity minimum rate to be a member of Equity – whereas for the Musicians Union, anybody can join by paying a membership fee. So there’s a different type of population that you’ll get from each. Another thing is that you may need some criterion for what constitutes professional work. The usual way that it’s done in cultural economics has been to look for the percentage of time spent on arts work, what percentage of income comes from it, and so on.
Another thing to talk about is defining creators in terms of higher education diplomas. There have been some studies of following up people in art colleges using first destination studies and so on. Here you want to know is how strict are the entry requirements to the course. I have to say that the way that HE has gone in the UK, they have got less and less strict over time. If somebody goes to a music college you can be sure that they are pretty well trained originally as musicians. If they go to the University of … I don’t know, somewhere, you don’t necessarily know. In addition, we know from artists’ labour market studies that diplomas are not respected, generally speaking, by employers. They use their own screening processes such as auditions, portfolios and so on. It’s no good going along to your opera audition and saying I got a first class degree in singing. They say, oh yes dear, let’s hear what you can do.
Now, let me run through very quickly the findings that we have from research cultural economics on artists’ labour markets. 1) Excess supply of artists and low pay; probably the excess supply causes low pay. 2) Artists and creators have higher average education but lower than average incomes. 3) Many trained artists drop out but the benefits of their training are not lost, hence ‘creatives’ all over the place. 4) About one third of artists in England, when I did a study some years ago, had no artistic training. They had learned, they taught themselves, they picked it up, they’d been apprenticed (by the way, apprenticeships are a thing I think we might talk about). 5) Artists’ earnings reflect the winner takes all, ‘superstar’ domination we call it in cultural economics, so therefore when you’re dealing with earnings statistics you do not want to deal with the average, you want the median. Interestingly, the average income of artists has risen but more slowly than the national rate. 6) Finally there’s no standard career; in many artistic occupations there’s no identifiable career structure or earnings curve.
As we are in Glasgow University, which is the home of course of Adam Smith, Adam Smith talked about the over-wheaning conceit of the young in trying their hand at any sort of profession. He actually referred to the ‘lottery of the law’ because lawyers were coming out of the university at a rate of knots, obviously, even in 1776 and couldn’t get jobs, so it’s an old problem! I have applied his ideas in a paper on the motivation to go into these low paid, creative occupations – would-be artists over-estimate their chances of success. The other thing we should mention of course is that any start up, in all businesses, start-ups, whether they’re of individuals or little businesses, the failure rate is one third within five years (I think, is what business schools always come up with): failure is endemic in the capitalist system as a matter of fact, that’s how it works.
Hence, finally we come to the point that artists’ earnings and success cannot really be easily understood from conventional economic models. That’s probably fairly obvious at one level but at another, what you find is that there is always an unexplained element in any statistical work that’s done on this, that is, talent is the unexplained thing and that leaves us sort of not knowing; we also know that luck is an important thing but it’s very hard to quantify luck. So there’s a high degree of uncertainty and unpredictability about this. So the way forward from here in research on artists’ labour market is just up!