The recent statement by one of the country’s leading bankers that SA’s unemployment rate is grossly overstated as a function of our poorly measured informal sector deserves closer reflection, if only because it goes to the heart of one of SA’s domestic economic policy challenges.
Let me start with a seemingly obtuse observation: both statements — a very high and very low level of informality — can be independently true. Why? Simply because the sample being referred to is important here.
I suspect the Capitec CEO was indirectly inferring from his internal sample, where a large number of bank accounts are held by individuals who seem to derive an income without any ostensible formal registration attributes such as a VAT or personal income tax number. He is probably correct as this is the daily data he lives and breathes.
The same is true for an NGO working in a local township, where the apparent frenetic informal sector activity belies the national estimates. Yet a critical and common mistake made when analysing such “local statistics” is to assume that what is true at the local level must translate to a 1:1 relationship at national level.
Hence, a township with 40,000 informal firms from a population of 100,000 must mean the national informal sector rate is 40%. Alas, that is fatally incorrect. Part of statistics is to avoid this “aggregation bias” error — that what we see locally must be true for the economy as a whole.
As many of my colleagues in academia have noted over the past few days, the Stats SA data on unemployment, informality and the self-employed is correct. It is not perfect on technical matters around weighting, for example, but it is definitely not incorrectly measuring the unemployment rate to the extent that one can claim an SA unemployment rate of 10% rather than the standard rate in excess of 30%.
Academic economists have spent the better part of three decades analysing and engaging with the nationally representative statistics provided by the quarterly labour force survey, general household survey and other similar national surveys. Often not appreciated by nonacademic audiences is that these economists use the underlying, unit record data when analysing indicators such as self-employment, informality and unemployment. It is a serious, statistically careful business and cannot and should not be lightly disregarded.
So, what does the data show? In broad terms we consistently find that SA simultaneously has one of the highest unemployment rates in the world, at 33.6%, but also one of the lowest rates of informality at about 16.3%. Work we are doing at the UCT Development Policy Research Unit also indicates that while the average emerging market’s labour market ratio of wage employed to informally employed to unemployed is 45:45:10, for SA this ratio is 50:16:34.
Most emerging markets reduce unemployment not by having far higher levels of wage employment compared with us, but rather by allowing large swathes of individuals to engage unhindered in the informal economy. This observation is part of a larger, ongoing research we are undertaking, but I am now empirically satisfied that the most important reasons for SA’s inordinately high unemployment rate lies not in poor employment elasticities, restrictive labour regulations and so on, but in an economy that is designed to stymie the flourishing of the informal sector.
But I digress: for now, it is important to recognise that while one’s specific engagements with clients, suppliers and consumers at the local level may suggest a higher rate of informality, the nationally representative, stratified data of Stats SA will respectfully — and correctly — disagree.
• Bhorat is professor of economics at the University of Cape Town' School of Economics and director of the university's DPRU.
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