(OPride) — In a recent report entitled Africa at a Tipping Point, the Mo Ibrahim Foundation presented Ethiopia as the brightest spot – an African success story in maintaining low and stable youth unemployment rate. The report was then rehashed by Quartz Africa reporter, Lynsey Chutel, as “youth unemployment is a problem all over Africa, except for one country” – Ethiopia. The Mo Ibrahim Foundation (MIF) maintained that:
While the continent’s general unemployment and youth unemployment rates were 8 percent and 13 percent in 2016, these figures were significantly lower in the case of Ethiopia (5.7 percent and 8.1 percent, respectively).
The rosy assessment in the MIF report has raised eyebrows of many observers who are familiar with the dire state of youth unemployment in Ethiopia. The report does not only contradict the official storyline but it is also factually flawed. In this piece, I will present a comprehensive and consistent set of data on Ethiopia’s unemployment and then indicate the extent to which the MIF report has misrepresented and misinterpreted data on Ethiopia’s youth unemployment.
Ethiopian authorities must be the ones most baffled by the MIF report. High youth unemployment has been a topical storyline of the Ethiopian government in recent months, particularly in the context of addressing the ongoing upheaval in the country. Ethiopia has been rocked by popular protests over the past two years. Protests in the populous Oromia region, which began around April 2014, have also expanded to the Amhara region. Whereas the protests are rooted in a wide ranging political and economic discontents, authorities in Addis Ababa have squarely put the blame on the intolerably high youth unemployment. Therefore, MIF’s findings are at odds with the Ethiopian government’s own reports and assessments over the last 12 months.
One does not need to be a seasoned statistician or researcher to know Ethiopia has been suffering from a high youth unemployment. It is enough to look at the large exodus of its youth, who are prepared to take unimaginable risks– walking across the Sahara desert and confronting high seas in the Mediterranean or the Red Sea to reach Europe or the Middle East. The migration of Ethiopia’s youth to Yemen continues unabated years after that country was plunged into civil war. The European Union has an ongoing agreement to fund local job creation across Africa to stem the flow of refugees to Europe. Ethiopia is one of the countries that EU has identified for this purpose.
Besides, the MIF report flies in the face of everything we know about unemployment in Ethiopia. It does not only contradict the official story line, but it also utterly lacks common sense.
Far from being exceptional, Ethiopia actually has unemployment rate that is worse than Sub-Saharan Africa average.
The MIF researchers claim that Ethiopia is an exception in that the country’s unemployment rate — both for the youth as well as general — are much lower than most other African countries. But it is not clear from where all these assertions emerge. On page 18 of the report, the International Monetary Fund (IMF) and International Labor Organization (ILO) are cited as data sources for generating a chart that was presented, indicating “smooth and stable” unemployment rate, between 5 and 6 for general unemployment, and between 7 and 8 per cents for youth unemployment. Importantly, this is presented as an “exception” by African standard. It is useful to say a few words about the data sources here. First, IMF does not publish much data on African unemployment, one can find data for only six African countries, and Ethiopia is not one of them. In that case, the MIF report must have replied only on data obtained from ILO database.
I will return to youth unemployment but for a moment let’s focus on overall unemployment rate. I constructed Figure 1 (above) from the same ILO database. Ethiopia’s overall unemployment was 5.7 percent in 2016 as reported by MIF. But MIF’s assertion that Ethiopia is an exception is totally off mark. The African average is 8 percent, which means Ethiopia is very close to the regional average. Importantly, Ethiopia’s average unemployment rate is slightly higher than the Sub-Saharan African average of 5.5 percent, but only slightly lower than the East African average. In fact, if we exclude Kenya, Ethiopia’s unemployment is much higher than the Eastern Africa average. Therefore, the MIF report on Ethiopia is factually wrong. In other words, the right figure is used but it is wrongly interpreted and presented.
The rural-urban divide
It is useful to deal with preliminary conceptual issues before we can discuss youth unemployment. In a developing economy, it does not make much sense to discuss open unemployment in rural areas. In fact, in development economics there is a different category of unemployment for it – disguised unemployment, or underemployment. Disguised unemployment simply means that people in rural areas, who are in working age group, mostly have something to do but a significant proportion do not have activities that fully engage them. In other words, they are employed only some of the times. So they are unemployed in some ways. However, that kind of unemployment is often hidden, since people do not have job centers to which they go and register themselves and declare that they are looking for jobs.
Unfortunately, it appears that novice researchers and policy practitioners applied the same concept of unemployment designed for economies such as UK, EU, and US to developing economies and then built it into the ILO labor force surveys. In Europe or the US, regardless of whether one is living in rural or urban areas, any working age person actively seeking jobs would register to seek some kind of job seekers allowances. Such systems are non-existent in economies like Ethiopia. In such circumstances, any discussion of unemployment should focus on urban unemployment, where job seeking can at least be more easily observed, even if not registered. Now, let us look at the details of unemployment in Ethiopia, classifying this joblessness by age, rural, and urban categories. In order to facilitate comparison with Ethiopia, unemployment data for selected African countries is presented in Table 1. Except for Ethiopia 2013, data presented in the table are all compiled from the same source – ILO. Except for Ethiopia, although labor force data are not consistently available for all years, recent year data is available for latest years for most countries.
Ethiopia is the odd one out in that its data for rural and urban unemployment is available only for 2005. Hence, it is important to present data for another (latest) year for Ethiopia. This is done by relying on Ethiopia’s Central Statistical Agency, (CSA 2014, and Analytical Report on the 2013 National Labor Force Survey, Summary Table 6.1 Unemployment Rate by Age group, Place of Residence and Sex, Country – Total 2013). Since Ethiopia’s data is presented for two years, which are 8 years apart, it is reasonable to compare them with data from the other countries.
In Table 1, data for both the all age and the youth unemployment groups are sorted by urban unemployment in descending order. This reveals some interesting facts. Starting with the all age group, Ethiopia is the worst in terms of urban unemployment: it ranks first with 21 percent in 2005 and third with 17 percent in 2013. In youth unemployment, a central point in the MIF report, Ethiopia’s data for 2005 as well as 2013, rank first and second. In fact, Ethiopia’s urban youth unemployment has gotten worse, increasing from 19 percent in 2005 to 23 percent in 2013.
Again, it does not make much sense to dwell on comparing rural unemployment, and by extension national unemployment, since unreliability of rural unemployment would logically make national unemployment figures unreliable. The latter is an average of rural and urban unemployment rates. Even if we consider rural data, Ethiopia still cannot be an exceptional case as MIF claims, since rural unemployment figures for Zambia, Tanzania, and Togo are about the same with that of Ethiopia.
The gender imbalance
A closer look at Ethiopia’s unemployment data is even more revealing. Figure 2 displays the gender dimension, in addition to the urban-rural divide displayed in Table 1. It is important to pay attention to the urban unemployment with its gender dimension. It is by far the most reasonable barometer of unemployment in Ethiopia, and any other developing economy for that matter. As shown in Figure 1, in 2013, total urban unemployment for all ages and the youth were 17 percent and 23 percent respectively. Now let’s look at the disaggregation of these rates across gender dimension. Urban male unemployment rates were 11 percent and 19 percent, while the corresponding figures for the young female were 23 percent and 26 percent. This means that one out of four working age young girls in urban Ethiopia looked for jobs but found none. The figures reported on rural unemployment rate can by no means be taken seriously. CSA reports 2 percent unemployment (all working ages, 15+) and 3 percent (for the youth). Even female youth unemployment in Ethiopia’s rural areas is reported to be 5 percent. These are not only fancy numbers but also irresponsible way of presenting facts.
Projected, not actual
It is established that the conditions of Ethiopia’s unemployment is not as rosy as depicted in the MIF report. That Ethiopia’s general unemployment rate in 2016 was reported as 5.7 percent in the ILO database is accurate but the MIF report misrepresented and misinterpreted this figure by stating that Ethiopia was an exceptional, success story. The country is not an exception, in fact it performs slightly worse than the Sub-Saharan African average. Facts about youth unemployment rate are completely distorted in the MIF report.
As discussed earlier, one struggles to get a satisfactory and consistent labor force data on Ethiopia in the ILO database. In fact, Ethiopia’s labor force data is among the least frequently updated. It has been more than 12 years since Ethiopia submitted its labor force data to ILO. This raises the question: If Ethiopia’s data is that much outdated, where did the figures in ILO database come from? This includes the 5.7 percent rate presented in in the MIF report.
When countries do not update their database, ILO uses a model to project and fill the gaps. The bulk of labor force statistics we read in ILO database are not actual but only projected figures. It is an unsound and strange projection for lots of years, relying on very few data points.
Ethiopia’s socio-economic data is perhaps the most thoroughly manipulated in the world. In order to substantiate double digit growth all variables have to be systematically adjusted. Labor force data is among those that receive the most “severe treatment.” After all, the best way to inflate GDP growth is to assume that the largest proportion of working age population is gainfully employed (that is to say unemployment is low), and hence everyone is engaged in creating material wealth.
In other words, GDP inflation amounts to declaring as if unemployed persons are actually receiving monthly wages. By accepting and endorsing Ethiopia’s inflated GDP, donors and other members of the international community, like MIF, have taken a morally and ethically corrupted position, effectively being complicit in a cruel act of saying to the millions of unemployed Ethiopians: “go hungry, we won’t recognize or validate your unemployment.”
Ethiopian authorities appear adamant to hide the facts presented in figure 2, particularly, the unrealistically low rural unemployment. Urban unemployment itself is likely to be underestimated, but the official rural unemployment is rather laughable. But, for Ethiopian leaders, if rural unemployment is grossly underestimated, it pulls down the overall unemployment. The more “rural” a country is the larger influence the rural figures would have on the national average. Ethiopia is among the least urbanized countries in the world. With only 17 percent of the population living in urban areas, Ethiopia’s ranks 196th out of 223 countries. Yet using the dodgy 2013 official statistics (Figure 2), although urban youth unemployment was relatively high (23 percent), national (average) youth unemployment became only 7 percent simply because rural youth unemployment was reported as 3 percent. It is this fact that the authorities are uncomfortable with to frequently report the country’s latest labor statistics. Instead, they found it convenient to report only the national average, hiding the embarrassingly low rural unemployment.
Saying the right thing for wrong reason
The Ethiopian authorities have become adept at churning out unrealistically low socioeconomic statistics. Unemployment figures are no exceptions. At the same time, they have the audacity to come out and declare unemployment in general and youth unemployment in particular as the most severe challenges and threats to Ethiopia’s stability and security. But how can we explain this inconsistent position of the government in Ethiopia?
Authorities in Addis Ababa have perfected the art of deception over the years. They know very well that there is enough pool of gullible analysts around, like the MIF researchers, who do not take enough time to bring together different arguments and establish consistency. They run away with some headlines and arrive at hasty generalizations. And the regime in Addis Ababa has found this rather rewarding. After all, Ethiopia’s current image as Africa’s fastest growing non-oil economy was gained through statistical lies that got propagated, recycled in various global databases and became the basis for existing “knowledge” on Ethiopia.
The regime in Addis Ababa appears to have recognized that they do not even need to be consistent with their statistical lies. As such, when a few opportunities arose around unemployment, authorities could not resist the temptation to indulge in some inconsistencies.
The first opportunity was the EU pledge to fund youth employment schemes in African countries in order to stop the exodus across the Sahara and the catastrophic incidences of death in the Mediterranean Sea. Sure enough Ethiopia became a beneficiary. EU never bothered to check the official statistics, perhaps because they knew Ethiopia is a high unemployment country.
The second motivation for the authorities to depart from the previously held storyline and shift to high youth unemployment rhetoric was to distort the cause of the ongoing popular uprising in Ethiopia, which erupted in central Oromia and then spread to all corners of the country. The root causes of the Oromo protests and Amhara resistance are severe political repressions, multifaceted economic injustices and rampant corruption. In a desperate attempt to hang onto power indefinitely, the Ethiopian regime has framed the debate surrounding popular protest as an economic policy problem, narrowing this even further to an exogenous demographic phenomenon, the youth bulge, which in turn resulted in high youth unemployment.
“Youth bulge” is a convenient way to refuse taking responsibility even for the economic malaise. So the Ethiopian government is effectively externalizing the real causes of the popular uprising. If the officials in Addis Ababa framed the issue this way, then that is understandable, it is a matter of survival for them.
However, it is perplexing to observe the international community following suit and discussing Ethiopia’s troubles in a manner the authorities framed it for them, effectively avoiding the real sources of the crisis. It is even more baffling when the Mo Ibrahim Foundation gets entangled in this quagmire. The organization has a reputation of working to promote democracy in Africa. In a bizarre twist, this deeply flawed assessment puts the foundation in a rather contradictory position, being complicit with a dictatorial regime that stayed on power for 26 years ruling with an iron-fist, and now doing everything it can to indefinitely clutch on power.