Are you in the 1%?
Oliver Denk, OECD Economics Department
The 1% are back in the news following last week’s Oxfam report claiming that the world’s 62 richest billionaires own as much wealth as the poorest 3.6 billion people on the planet combined. But what about labour income rather than wealth: Who are the 1% when earnings are counted, and not shares, property, and so on? We have a good idea of how much they earn thanks to the administrative records studied by researchers like Thomas Piketty. But these studies don’t actually tell us much about the personal characteristics of the top earners, such as their education, occupation, or the industry they work in.
That’s where my new research comes in, which for the first time puts hard numbers on who the top earners are across 18 European countries. The data source I use is the Eurostat Structure of Earnings Survey for 2010. It is the largest harmonised dataset on employees’ earnings across Europe, with a total of 10 million observations.
Thanks to these vast data, I was able to compare the top 1% earners with the bottom 99%, focusing on the employee’s age, gender, and highest attained level of education, in addition to the number of years the employee has been with the firm, industry, and occupation. You can find the details of the sample, analysis and results in OECD Economics Department Working Paper N°1274.
So, what do the data show? I’m sure you’ll be as unsurprised as me to learn that, whatever the country, if you’re a middle-aged man working as a financier, doctor, or engineer you’ve a better chance than most of being among the top 1% of earners. The typical person in the top 1% is male, in his 40s or 50s, has a tertiary education degree, works in finance or manufacturing, and is a chief executive, manager or professional.
Digging deeper shows that the top 1% have an average age of 47, hence are five years older than the average worker in the bottom 99%. Around 80-85% in the top 1% are men versus 50-55% in the bottom 99%, and the share of men among the top 1% is actually above 90% in Germany and Luxembourg. Likewise, 80-85% among the top 1% completed tertiary education, compared with 30-35% among the bottom 99%.
There are however important differences between countries and regions. And these appear to be connected to political and economic institutions. Thus, some of the policies your governments are choosing may matter for whether you are in the 1%. I’ll highlight a few of these differences.
Top earners are disproportionately younger, often in their 30s, in Eastern Europe (the Czech Republic, Estonia, Hungary, Poland and the Slovak Republic). At first glance, you might think this is because the workforce is younger in these countries than elsewhere, but the analysis doesn’t support this explanation. The much younger age of top earners in Eastern Europe is probably related to the economic transformation of these countries after the fall of the Iron Curtain. Workers already in the labour market during the 1980s, the last years of communism in Eastern Europe, have less chance than in Western Europe of having moved up to the top 25 years later.
In countries where overall female employment is higher, more of the top 1% are women. The paper does not attempt to establish that it is higher female employment, rather than a related factor, that “causes” more women to be at the top. Nevertheless, one way to interpret this finding is that public policies to broaden female participation in the labour market might also have the benefit of facilitating high-paying careers for women.
Length of career with a particular employer shows contrasting results. One in five top 1% earners has worked for the same employer for more than 20 years. On the other hand, almost one-third of the 1% are new recruits. This pattern is quite different though in Southern Europe (Greece, Italy, Portugal and Spain) where top earners tend to have stayed much longer with their current firm than other workers. The difference could be a sign of stronger family ties or lower labour market flexibility at the top in these economies.
Health professionals are a large group of top earners in several countries, and how much they are paid appears to be linked with life expectancy for the population as a whole. The data suggest: wealthy doctors=healthy people, or that life expectancy is higher the larger the share of the top 1% who are health professionals. Spain and Italy, for example, have both the best-paid doctors, relative to other occupations, and the highest life expectancy, though the analysis does not preclude that better weather, nutrition or other factors might be at work.
Finally, industry structure can affect how concentrated labour income is. Comparing countries with one another shows that the more of the 1% who work in finance, the higher is the share in total earnings that goes to the top 1%, and the smaller is the share that goes to the bottom 99%. That’s one indication that more finance may increase inequality. In earlier work with Boris Cournède and Peter Hoeller, I showed that financial expansion more generally, of bank credit or stock markets, is connected with a widening of the income distribution. Now we’ve come full circle, as the Oxfam report actually draws on our results.
What next? The analysis suggests several questions to be explored in future work, for example trying to establish causality for some of the correlations. This kind of data could also serve as the basis of a study of what’s known as the “rent extraction view”, according to which sectors that are more strongly regulated relative to other sectors and other countries attract more top 1% incomes. And of course it would be interesting to extend the geographical scope if suitable data became available for other countries.
Income Inequality: The Gap between Rich and Poor, Brian Keeley, OECD Insights, 2015