Somali-American activists recently scored a victory against Amazon and against colorism, which is prejudice based on preference for people with lighter skin tones. Members of the non-profit The Beautywell Project teamed up with the Sierra Club to convince the online retail giant to stop selling skin lightening products that contain mercury.
After more than a year of protests, this coalition of anti-racism, health, and environmental activists persuaded Amazon to remove some 15 products containing toxic levels of mercury from its website. This puts a small but noteworthy dent in the global trade in skin lighteners, estimated to reach US$31.2 billion by 2024.
What are the roots of this sizeable trade? And how might its most toxic elements be curtailed?
The online sale of skin lighteners is relatively new, but the in-person traffic is very old. My book Beneath the Surface: A Transnational History of Skin Lighteners explores this layered history from the vantage point of South Africa.
As in other parts of the world colonized by European powers, the politics of skin color in South Africa have been significantly shaped by the history of white supremacy and institutions of racial slavery, colonialism, and segregation. My book examines that history.
Yet, racism alone cannot explain skin lightening practices. My book also attends to intersecting dynamics of class and gender, changing beauty ideals and the expansion of consumer capitalism.
A deep history of skin whitening and skin lightening
For centuries and even millennia, elites in some parts of the world used paints and powders to create smoother, paler appearances, unblemished by illness and the sun’s darkening and roughening effects.
Cosmetic users in ancient Mesopotamia, Egypt, Greece, and Rome created dramatic appearances by pairing skin whiteners containing lead or chalk with black eye makeup and red lip colorants. In China and Japan too, elite women and some men used white lead preparations and rice powder to achieve complexions resembling white jade or fresh lychee.
Melanin is the biochemical compound that makes skin colorful. It serves as the body’s natural sunscreen. Skin lighteners generate a less painted look than skin whiteners by removing rather than concealing blemished or melanin-rich skin.
Active ingredients in skin lighteners have ranged from acidic compounds like lemon juice and milk to harsher chemicals like sulfur, arsenic, and mercury. In parts of precolonial Southern Africa, some people used mineral and botanical preparations to brighten—rather than whiten or lighten—their hair and skin.
During the era of the trans-Atlantic slave trade, skin color and associated physical differences were used to distinguish enslaved people from the free, and to justify the former’s oppression. Colonizers paired pale skin color with beauty, intelligence, and power while casting melanin-rich hues as the embodiment of ugliness and inferiority. Within this racist political order, where small differences carried great significance, some people sought to whiten and lighten their complexions.
By the twentieth century, mass-produced skin lightening creams ranked among the world’s most popular cosmetics. Consumers of commercial skin lighteners included white, black, and brown women.
In the 1920s and 1930s, many white consumers swapped skin lighteners for tanning lotions as time spent sunbathing and playing outdoors became a sign of a healthy and leisured lifestyle. Seasonal tanning embodied new forms of white privilege.
Skin lighteners became cosmetics primarily associated with people of color. For black and brown consumers, living in places like the United States and South Africa where racism and colorism have flourished, even slight differences in skin color could have substantial social and political consequences.
The mercury effect
Skin lighteners can be physically harmful. Mercury, one of the most common active ingredients, lightens skin in two ways. It inhibits the formation of melanin by rendering inactive the enzyme tyrosinase; and it exfoliates the tanned, outer layers of the skin through the production of hydrochloric acid.
By the early twentieth century, pharmaceutical and medical textbooks recommended mercury—usually in the form of ammoniated mercury—for treating skin infections and dark spots while often warning of its harmful effects. Cosmetic manufacturers marketed creams containing ammoniated mercury as “freckle removers” or “skin bleaches.”
When the US Congress passed the Food, Drug and Cosmetics Act in 1938, such creams were among the first to be regulated.
After World War II, the negative environmental and health consequences of mercury became more apparent. The devastating case of mercury poisoning caused by industrial wastewater in Minamata, Japan prompted the Food and Drug Administration to take a closer look at mercury’s toxicity, including in cosmetics. Here was a visceral instance of what environmentalist Rachel Carson meant about small, domestic choices making the world uninhabitable.
In 1973, the FDA banned all but trace amounts of mercury from cosmetics. Other countries followed suit. South Africa banned mercurial cosmetics in 1975, the European Economic Union in 1976, and Nigeria in 1982. The trade in skin lighteners, nonetheless, continued as other active ingredients—most notably hydroquinone—replaced ammoniated mercury.
Meanwhile in South Africa
In apartheid South Africa, the trade was especially robust. Skin lighteners ranked among the most commonly used personal products in black urban households. During the 1980s, activists inspired by Black Consciousness and the “Black is Beautiful” sentiment teamed up to make opposition to skin lighteners a part of the anti-apartheid movement.
In the early 1990s, activists convinced the government to ban all cosmetic skin lighteners containing known depigmenting agents—and to prohibit cosmetic advertisements from making any claims to “bleach,” “lighten” or “whiten” the skin. This prohibition was the first of its kind and the regulations immediately shuttered the in-country manufacture of skin lighteners.
South Africa’s regulations testify to the broader anti-racist political movement from which they emerged. Thirty years on, South Africa again possesses a robust—if now illicit—trade in skin lighteners. An especially disturbing element of the trade is the resurgence of mercurial products.
South African researchers have found that over 40 percent of skin lighteners sold in Durban and Cape Town contain mercury. Mercurial skin lighteners tend to surface in places where regulations are lax and consumers are poor.
The activists’ recent victory against Amazon suggests one way forward. They took out a full-page ad in a local newspaper denouncing Amazon’s sale of mercurial skin lighteners as “dangerous, racist, and illegal.” A petition with 23,000 signatures was hand-delivered to the company’s Minnesota office.
By combining anti-racist, health, and environmentalist arguments, activists held one of the world’s most powerful companies accountable. They also brought the toxic presence of mercurial skin lighteners to public awareness and made them more difficult to purchase.
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Elite Power-Sharing: How Presidential Candidates Buy-off Disgruntled Leaders and Maintain Their Coalitions
There is clearly a pressing need to improve the nomination procedures to EALA so that they become transparent, such that those who are nominated understand the key issues facing the region and are accountable to its citizens.
As the August 9 general elections draw ever closer in Kenya, the month of April saw political party “nominations”, when the respective parties and coalitions select their candidates for a range of different positions including Member of Parliament and Governor. The political parties had a deadline of 2 April to conclude the process and submit the names of successful candidates to the Independent Electoral and Boundaries Commission (IEBC) for approval. Although the 2022 nominations appears to have been a small improvement on the 2017 experience, with less violence and chaos, they did little to strengthen Kenyan democracy.
In most cases the parties behind former opposition leader turned establishment candidate Raila Odinga – the Jubilee Party and the Orange Democratic Movement – did not hold elected primaries at all, and instead used “direct nominations”, much to the chagrin of those who were overlooked. The parties behind Odinga’s main rival, Deputy President William Ruto, held more primaries, but here too there were many controversies and numerous disgruntled “losers”. As a result, both Ruto and Odinga faced a major challenge: how to prevent losing candidates from defecting and running as independents.
This is a particularly dangerous prospect in the context of a close elections, because a popular independent can divide the vote, enabling a rival party to secure an unexpected victory. In response, coalition leaders spend the post-nominations period frantically calling those who feel they have been cheated to offer then a range of rewards to stay loyal, including the promise of future tenders; appointment to cabinet positions; appointment to various state commissions; nomination to legislative bodies; appointment to ambassadorial roles oversees; and, good old fashioned cash.
It is common knowledge that the nomination of candidates for some state jobs is heavily influenced by coalition realities rather than the ability and qualifications of the individuals concerned. It is disturbing that this trend is also extending to regional institutions, however. Party leaders are now using the promise of slots as representatives in the East African Legislative Assembly (EALA) to buy-off disgruntled leaders. A case in point is Ruto’s promise to give Charles Kanyi (Jaguar) a slot in the East African Legislative Assembly (EALA) if he quit the Member of Parliament race for Starehe Constituency in favour of Simon Mbugua. It is a powerful demonstration of how this particular political cycle works that Mbugua had himself been nominated to the EALA in 2017, after he was persuaded by the Jubilee Party leadership to step down in favour of Yusuf Hassan in the Kamukunji primaries.
Such bargains are the lifeblood of Kenyan electoral politics, but undermine accountability and embed deal making – and hence potentially corrupt practices – at the heart of the not only the Kenyan state, but also regional governance.
How EALA was turned into an “employment bureau”
The East African Legislative Assembly is an independent arm of the Community that is supposed to advance the interests of the East African Community bloc as well as provide oversight. The membership of EALA currently stands at 62, with nine elected members from each of the six East Africa Community (EAC) partners states: Kenya, Uganda, Rwanda, South Sudan and Burundi and eight ex-officio members. However, political parties have been blamed for nominating politicians who lose in nominations, or their own relatives, to the regional body. For Instance, Kennedy Musyoka, who was nominated to EALA in 2017 is the son of Wiper party leader Kalonzo Musyoka.
This is not only happening in Kenya. A similar scenario is playing out in Uganda where political parties are also rewarding rejects to EALA. In 2017, the candidates presented for nomination to EALA were mostly those that had lost after vying to be a candidate for the ruling National Resistance Movement (NRM) in the parliamentary elections. Revelations about this practice triggered criticism in Uganda, which in turn prompted President Yoweri Museveni to declare that EALA was not an “employment bureau” for political job seekers. Despite being president – and hence the person with the greatest power to decide how his party operates and who is sent to the EALA – Museveni sought to displace blame, stating that. “This election is just an election; elections are not employment bureaus that you are here to give jobs to jobless people but people who will support the integration process”.
The problem facing Museveni is that even if he wants to end the practice of using such nominations as patronage, he may not be able to. Facing a growing challenge from Bobi Wine, and recognizing the threat that the NRM may start to fragment during the succession struggle to replace him – should he ever stand down – Museveni knows full well that buying off disappointed candidates is critical to his regime’s survival.
More broadly, the practice of playing politics with nominations to legislative organs such as EALA is problematic because it sacrifices regional interests at the expense of the personal desires of politicians, many of whom were overlooked for good reason. As one of Africa’s fastest growing regions in desperate need of a more unified and effective approach to issues such as infrastructure and food security, this is a major shortcoming that needs to be urgently addressed.
The road back to effective leadership and administration
There is clearly a pressing need to improve the nomination procedures to EALA so that they become transparent, such those who are nominated understand the key issues facing the region and are accountable to its citizens. The process of nomination is supposed to include tough legislative vetting, with hearings to interview potential candidates, and a strong set of minimum requirements established. Some of this is already in place in Uganda, where nominees are required to appear before parliament to campaign for their bids, but could be further strengthened to enable a broader range of individuals to be considered. In countries such as Kenya, these procedures have been considerably watered down, undermining the process, and the credibility of the representatives it produces.
The changes required do not only relate to selection. Once they are in position, it will then be essential to ensure continuous performance assessment during their five-year term to assess their commitment and contribution and weed out incompetent representatives. But even these changes may not be enough if political leaders continue to see positions as an entitlement rather than something to be earned. It is the practice of paying off losing candidates that the ultimate driver of the nominations game – and the quality of domestic administration and regional leadership is unlikely to improve until this is brought under control.
A Just Energy Transition
As coal is dying we must be prepared to absorb the transferable infrastructure of this industry and re-tool it for use in the emerging economy.
The alternative energy economy is moving in the right direction. But there’s a devil in the detail: the extraction of rare-earth minerals (REM) needed to develop the technology and infrastructure for renewables are as devastating to the environment as the fossil fuels they seek to replace. Transitioning to a clean energy economy is imperative, and to do so justly, we must ensure job security, reparations, and environmental remediation to the communities where mining has both historically and currently exist.
In moving forward, we must ensure a just transition towards an alternative energy economy for our planet and for those who live on it. A just transition moves our economy off of fossil fuels, and toward clean energy, while providing just pathways for workers to transition to high-quality work with integrity. A just transition leaves no worker behind. This transition focuses on environmental and economic sustainability through decent work, social inclusion and poverty eradication.
Lithium, cobalt, arsenic, gallium, indium, tellurium, and platinum, just to name a few, are needed to develop a strong alternative energy economy and shift away from fossil fuels. Unfortunately, they are being mined with the same harmful practices and disregard as the very fossil fuels they’re meant to replace. The affected communities are often not those with purchasing power, but the Indigenous communities living in proximity to the mines.
In 2019, Evo Morales, Bolivia’s first Indigenous president was ousted in a coup, partly due to his push to nationalize the country’s lithium mines. It is believed that Elon Musk, the South African tech mogul had a hand in this process. Lithium is a mineral used in batteries that power various clean energy technologies, including electric cars. According to The International Energy Agency, the demand for lithium is slated to outpace the growth of fossil fuels two-fold in lieu of policies that will be in place to support its growth.
The low-carbon energy economy is at a cusp where the cross section of public policy and social consciousness has the potential to merge and propel our society into the future. In this transition to a clean low-carbon economy there are many jobs to be created, not only manual labor that poses high risks to human health but also jobs in electrical engineering, plant management, and urban design that are ultimately more skilled and higher paying. This transition will also require the implementation of educational infrastructure to help communities adapt.
It is imperative that we work to improve the ability of the people most affected by mineral extraction to have a say over that of the companies in what happens in their communities. We must ensure that the communities impacted have a pathway into the new economy and that nobody is left behind.
The process of mineral extraction is exactly that, extractive. Not only does it take from the earth, it also diminishes the value of the land over time and more often than not leaves a toxic mess that destroys entire ecosystems to which a monetary value can not be assessed or assigned. This process also requires immense human capital; the labor needed to get the job done. The pool of labor is often relegated to those within proximity to the mine, who exist at the cross section of poverty and opportunity. Even if the ultimate cost of this opportunity is the future of the local environment. When it comes between feeding one’s family where few other options are available, it’s a choice that must be made. It is the responsibility of the government and corporate interests to come together to ensure a just transition from the fossil fuel industry to an alternative energy economy.
As coal is dying we must be prepared to absorb the transferable infrastructure of this industry and re-tool it for use in the emerging economy. Factory buildings can be rehabilitated into solar power plants. Railways and transportation waterways can be retrofitted [with minimal effort] to assist in not only the transportation of goods and raw materials but for public transportation as well. The framework of industrial boom towns can lay the groundwork for community housing and commerce. Ensuring job training and security to the new energy economy is a vital form of equity for the communities impacted by mining. Alongside the reabsorption of the existing built infrastructure, we need to establish comprehensive and culturally comprehensive education apparatus to support the growth and development of a varied, skilled workforce required for such a transition.
South Africa, while one of the most mineral rich countries on the planet, cannot sustain its historical extractive economy. Research completed by the Alternative Information Development Center (AIDC) in Cape Town, shows that the legacy of post colonial-extractivist economics in South Africa has resulted in widespread resource depletion across the range of the country’s key resource sectors. It indicates that coal reserves have been mined downwards of 20% of original estimates. As of 20 years ago, 98% of South Africa’s total water resources had already been allocated, and the degradation of soil fertility is confirmed on 41% of cultivated land.
The extraction of resources and human capital is the keystone of colonialism and post-colonial neoliberal economics. This long standing tradition of the powerful taking something and leaving the poor with little or even something far worse continues unabated in our globalized world. Consequently, the framework of global energy infrastructure is a living relic of colonialism, harmful neoliberal economics, and ecological devastation. A debt left to the Indigenous communities where those, now even more, precious. minerals exist.
In order to transition towards a more just energy economy we must come together to:
- Nationalize all existing and future energy infrastructure and each citizen is given a stake in the economic and functional well being of the grid and energy infrastructure. This also requires that industry is purchased or seized from foreign interest to retain as much of the economy in-house and circulate the wealth within the community.
- Ensure all residents have fair and equitable as well as free or affordable access to all forms of energy.
- Retrofit existing mining town infrastructure, factories, warehouses, storage facilities, waste sites are assessed with the appropriate environmental impact assessments and made available to the new economy.
- Establish education and training programs to allow the existing and emerging workforce to be trained with job skills for the new economy. Some jobs will be lost indefinitely, so an economic safety net must be available along with a plan for those who may not be able to transition to the new economy so quickly.
- Ensure that minerals such as titanium and lithium are mined with the full consent of the local community. They must not be extracted in the current, harmful ways.
- Give priority to local peoples throughout all levels of employment in the mines and adequately training and developing the capacity of local leadership.
While these are just a few major considerations, it is important to involve the community to holistically define not only what a “just transition” can look like, but what a just transition will be for them—from the way rare earth minerals are mined, to the way we ensure job security and access to the new economy for those whose backs it has rested on for so long. Otherwise, the energy economy from fossils to alternatives remains a colonialist institution of quid pro quo for Indigenous, Black and Brown people.
Why Opinion Polls May Not Always Predict Election Outcomes in Kenya
This is the second in a series of articles that will review and comment on surveys related to the August 2022 general election, providing analytical tools to enable the reader to assess their credibility and potential impact.
This is the second in a series of pieces I have been engaged by The Elephant to write on the place of surveys in the forthcoming election. As I noted in my first piece, I do so as objectively as I can, although for the sake of transparency I again declare that I am currently engaged as a research analyst for one of the firms whose results I review here (Trends and Insights for Africa, or TIFA).
A handful of recent polls
Since my previous piece was published at the beginning of April, the results of three credible surveys were published through the middle of May: those released by the Radio Africa Group in The Star on 18 April, by Trends and Insights for Africa (TIFA) on 5 May, and by Infotrak (sponsored by the Nation Media Group) on 12-13 May. While the first two indicate that Deputy President William Ruto was leading former Prime Minister Raila Odinga in the presidential race by 5 and 7 per cent respectively, they differ somewhat in important details, even beyond their respective headlines. They are also at odds with the most recent of these three surveys: those of Infotrak which have the two in a dead heat at 42 per cent each. I will begin with a visual comparison of the figures from the first two polls.
|Survey Firm||Sample Size/No. of Counties||Data Collection Dates||Ruto||Odinga||Others||Undecided/NR|
|Radio Africa/The Star||4,057 / 47||1-5 April 2022||46%||41%||1%||12%|
|TIFA Research||2,033 / 47||22-26 April 2022||39%||32%||1%||28%|
The first point to make is that although the TIFA results gave Ruto a larger lead margin (7 per cent vs. 5 per cent), the difference may not be significant, given that it falls almost entirely within their respective margins of error of around +/-2 per cent. The main difference is a whopping 16 per cent variance in the proportion who failed/declined to answer the presidential choice question. Two obvious questions arising from this are: (1) what might explain this gap, and (2) can any “deeper” analysis of the data suggest the “leanings” of these “silent” respondents?
Regarding the first question, no clear answer is apparent. However, it may be relevant that the Radio Africa poll was conducted by sending text messages to the sample derived from their data-base of phone numbers, whereas TIFA’s was conducted through the more standard Computer-Assisted-Telephonic-Interview (CATI) method (i.e., through live phone calls), based on the (comparable) TIFA data-base. Still, why this would make respondents in the latter survey less willing to reveal their presidential preferences is unclear.
Another puzzling issue has to do with Radio Africa’s stated margin of error, which is given as +/ 4.5 per cent. Yet with a reported sample size of 4,497, based on some 22 million registered voters (a figure against which the data were reportedly weighted), the margin of error is actually only +/-1.5 per cent. Why would the “Star Team” who produced the story want to make their findings look less precise than they are? (I made two phone calls to journalists at The Star about this but neither was able to provide a definitive explanation.)
Further, while the article by The Star made reference to findings from two previous similar Radio Africa surveys – showing Ruto leading Odinga by a huge 29 per cent last July (i.e., 43-14 per cent), and Odinga taking the lead for the first time in March, by 4 per cent (i.e., 47-43 per cent)—no suggestion was offered as to what could account for Ruto’s regaining the lead he previously enjoyed. Indeed, even when reporting the results in March, all The Star could offer was that “Poll ratings can go up and down so Radio Africa will continue to conduct its monthly opinion polls up to the August 9 elections.” In any case, it remains to be seen whether the just concluded party nominations and running mate selections will further reshape the race.
Why would the “Star Team” who produced the story want to make their findings look less precise than they are?
In addition, while the text of the article reported some results by region (i.e., North Rift, South Rift, Central, Upper Eastern, Lower Eastern, Nyanza, Western and Coast), the kind of quite useful table of results that had been included in the mid-March story was absent, as were any explicit comparisons of results at this sub-national level, leaving such calculations up to its readers to make—assuming they had kept the March results for such a purpose: e.g., an increase in support for Ruto in North Rift (from 63 per cent in March to 69 per cent now) but basically no change in Central (57 per cent in both March and April). While such changes may help to explain the overall result—the re-establishment of Ruto’s lead – it would be useful if the respective margins of error for each of the regions was also noted so that such sub-national changes from one poll to another could be put in a clear statistical perspective. But the earlier point remains: could the “Star Team” have provided any explanation as to why Odinga’s rating in Nyanza fell by 11 per cent in a little over one month?
Yet another methodological point is important to make here. If Nyanza constitutes about 13 per cent of the total sample, that generates a margin of error of +/-6 per cent, which means Odinga’s actual proportion in March (shown in The Star’s table as 78 per cent) could have been anywhere in the range of 71-84 per cent, and in April (shown as 67 per cent) in the range of 61-73 per cent. Note here that at the low end of the March figure and the high end of the April figure there is an overlap of 2 per cent, meaning that it is possible—though unlikely—that his “true” Nyanza figures did not change at all over this period.
Could the “Star Team” have provided any explanation as to why Odinga’s rating in Nyanza fell by 11 per cent in a little over one month?
Another point: according to the Market Survey Research Association (MSRA) Guidelines, its members should interview at least 1,500 respondents in any survey reporting national (presidential) voting intention results, and while there is no such minimum for any sub-national polls (county, parliamentary constituency, ward), the margin of error should be made clear. There is, of course, no such requirement for the Radio Africa survey since the regions for which results are reported are not electoral units, even were this news organization an MSRA member (which it is not).
Stop press—another poll is out!
As I was writing this piece, the results of another national survey were released, this one by Infotrak, commissioned by the Nation Media Group. Its headline findings may be compared with TIFA’s in the same way as the latter are compared with Radio Africa’s above:
|Survey Firm||Sample Size/No. of Counties||Data Collection Dates||Ruto||Odinga||Others||Undecided/NR|
|TIFA Research||2,033 / 47||22-26 April 2022||39%||32%||1%||28%|
|Infotrak||2,400 / 47||8-9 May 2022||42%||42%||1%||15%|
Before addressing the main contrasts in these results, several points regarding methodology should be made. As Macharia Gaitho, writing an accompanying piece for the Daily Nation, pointed out, “The dates the data was [sic] collected will always have a bearing on the outcome in a fluid political situation, but unless there were very major shifts and realignments in the intervening period, even a fortnight between two polls cannot account for such variations.” This assertion makes sense given the absence of any dramatic events relevant to the fortunes of the two main contenders during the period between the two polls—the departure of Governors Alfred Mutua and Amason Kingi from Azimio to Kenya Kwanza coming just after data collection for the Nation/Infotrak poll. And he went on to add: “Other factors which influence the outcome of an opinion poll are methodology and sampling.” Note that Gaitho had raised some of these same important issues in a Daily Nation piece he authored just before the August 2017 election which I responded to and published in the Nation a few days later, identifying issues that I felt he had not sufficiently addressed.
Leaving aside the semantic error that sampling and methodology are distinct (since sampling is an inherent aspect of methodology), the factors he cites are certainly relevant: sample size and distribution across the country.
Sample size in this case is not an issue, since despite Infotrak’s being slightly larger, the respective margins of error are nearly identical: +/-2 per cent vs. +/-2.17 per cent. Further, even though Infotrak’s question wording differs slightly from TIFA’s (“…who would you vote for…?” vs. “…who do you want Kenya’s next president to be?”), it can be assumed that much of the contrast between the two surveys is a consequence of sampling. Simply put: how each sample was distributed across the country, and what the achieved distribution (whether through the “pot luck” willingness of those selected to participate, or through post-data collection weighting—or some combination of both) is for each demographic variable that might be relevant to the issue at hand: presidential candidate preference.
Unfortunately, and in contrast to TIFA’s release, the Nation gives us no basis for comparing these two samples in demographic terms. That is, while TIFA included figures for gender, age groups, and education level (the latter required by the relevant law enacted in 2012)—as well as the proportion allocated to each of the nine “zones” for which results were reported—we have no such data from the Nation (whatever Infotrak may have provided).
Of course, for most political surveys in Kenya the most salient variable in assessing the representative accuracy of a sample is its ethnic distribution. Most survey firms collect data on the ethnic make-up of its sample but, as far as I know, no survey firm has ever published it nor does the relevant Act require it. Presumably, this is for reasons of “sensitivity”—a form of “self-censorship” that appears universally accepted. This is not to suggest that ethnicity explains “everything” about the achieved results, but it is critical when gauging whether samples are truly representative. For example, in TIFA’s survey, well over half—but nowhere near all—of Ruto’s and Odinga’s “home” ethnic groups (i.e., the Kalenjin and Luo) expressed support for their respective presidential candidates. Given this reality, it is impossible to judge the comparability of the two samples involved here without knowing what proportion of the total sample in each survey is comprised of the country’s main ethnic groups. Specifically, was there a “sufficient” number of Kalenjin in the Infotrak sample, and a “sufficient” number of Luo in TIFA’s (and Radio Africa’s)? Of course, such figures should reflect “correct” random sampling based on the geographical distribution of registered voters according to the IEBC, rather than any “search” for these ethnic proportions.
Of course, for most political surveys in Kenya the most salient variable in assessing the representative accuracy of a sample is its ethnic distribution.
It should also be recognized that whether using the eight pre-2010 Constitution provinces, or TIFA’s nine “zones”, none of them is mono-ethnic, with some of the more homogeneous—Central, Nyanza and Western—having less than 90 per cent of their dominant ethnic groups (Kikuyu, Luo and Luhya, respectively). TIFA’s data indicates Lower Eastern is the most ethnically homogenous “zone” although even here, Kalonzo Musyoka, the leading Kamba candidate, only polled at 15 per cent support.
Another problem arises when using survey results to predict election outcomes. While the presidential contest results of both surveys were generally presented by the media as reflecting actual 9 August ballot choices, in neither case was it reported whether all respondents were registered voters, and among those who claimed to be (assuming they were asked), how “certain” they were that they would actually vote on Election Day. Despite this, the main Nation newspaper article reporting the results refers to the survey’s respondents as “voters”. This raises the possibility that actual voter turnout (that is known to vary across the country in every election) will deviate from the samples of these three surveys, even if they all claim to have used the distribution of registered voters as their sampling “universe”.
Since this cannot be precisely known in advance even much closer to the election, it is misleading to use such survey results to suggest, let alone predict, actual outcomes, as the Daily Nation did in its front-page caption by stating that neither Ruto nor Odinga “has enough backing to cross the 50%+1 threshold to win”. (It may be assumed that those involved in producing these stories are also aware of the additional requirement of obtaining at least 25 per cent in at least 24 of the 47 counties.)
But there is an even more blatant flaw in this “run-off contest” statement: an actual voter who wishes his/her vote to count cannot be “undecided” or “refuse to answer” as one can in a survey interview, since voters must choose from actual ballot choices. In response to one of the several 2013 presidential election petitions, the Supreme Court ruled that spoiled ballots are removed from the count of “total votes cast”, so that the denominator of the calculation for each candidate is based on total valid votes cast, not the total number of people who walked into a polling station.
TIFA’s data indicates Lower Eastern is the most ethnically homogenous “zone” although even here, Kalonzo Musyoka, the leading Kamba candidate, only polled at 15 per cent support.
Based on this reality, for example, the Infotrak results imply that each of the main candidates “would get” 49 per cent, since only about 1,008 of the reported total sample of 2,400 respondents mentioned Odinga and Ruto, and this figure should be divided by roughly 2,040, which is the figure we are left with after subtracting those who said they were undecided or who refused to answer the question—about 360 respondents.
In other words, even if these top two candidates are nearly tied on 9 August, it would seem that a run-off contest would be unnecessary unless the combined figure of all the other presidential candidates exceeds 2 or 3 per cent—a much more likely prospect should Kalonzo Musyoka insist on “going it alone”.
Another contrast between the Infotrak and TIFA polls is important to point out, as it, too, could help to explain their contrasting results.
In terms of the distribution their samples, TIFA uses nine ethno-political “zones” while Infotrak (like Radio Africa) continues to use the former eight provinces. As such, the only sub-national results that can be compared (since they are used by both firms) are: Nairobi, Coast, Western and Nyanza. The table below shows the figures for these four units (comparing the TIFA figure on the left with Infotrak’s on the right, and the difference in parentheses on the far right):
TIFA / Infotrak
TIFA / Infotrak
TIFA / Infotrak
TIFA / Infotrak
|Ruto||25% / 33% (+8%)||26% / 29% (+3%)||21% / 18% (-3%)||37% / 33% (-4%)|
|Odinga||40% / 51% (+11%)||36% / 55% (+19%)||56% / 72% (+16%)||29% / 48% (+19%)|
Even setting aside the higher error margins for each of these regions, which range between about +/-6 and 7 per cent—and thus equal to 12 per cent and 14 per cent spreads, based on their respective sub-national sample sizes—these contrasts are remarkable, especially the larger figures for Odinga in the Infotrak survey. (Note that given the lower Infotrak figures for “undecided” and “no response”—10 per cent and 5 per cent, respectively, as compared to TIFA’s 16 per cent and 12 per cent—Infotrak’s overall figures for both candidates are higher, as is also the case in the comparison of Radio Africa’s figures with TIFA’s.)
While the contrasting figures for Nairobi are minimal, since the 8 per cent difference between Infotrak’s and TIFA’s figure for Ruto is only 3 per cent lower than the difference in Odinga’s numbers for the other three sub-national units (Coast, Nyanza and Western), Odinga’s “Infotrak advantage” is much higher: 16 per cent, 19 per cent, and 23 per cent, respectively. As noted, while TIFA provides the proportions allocated in its sample to each of the nine zones for which it presents results, Infotrak—or at least the Daily Nation—does not. But assuming they were roughly similar—and, as indicated, even taking the higher error margins for each into account—Gaitho’s summary point stands: that such disparate results cannot be accounted for by a minor discrepancy in the dates of the two surveys, given that no major events occurred that might have caused any significant shifts in attitudes towards either of the two main presidential candidates.
In connection with such contrasts between Infotrak and other credible survey firms in Kenya, a little history may be useful. For example, in their last surveys before the contested 2007 election, The Steadman Group gave Odinga a 2 per cent advantage over Kibaki, while Infotrak gave him a 10 per cent lead. Just before the 2013 election, Infotrak “predicted” an outright win for Odinga, whereas Ipsos’ results indicated that neither candidate would achieve this in the first round. A little earlier, an Infotrak survey conducted at the end of December 2012 and into the first few days of 2013 gave Odinga a 12 per cent advantage over Uhuru Kenyatta (51 per cent to 39 per cent), whereas an Ipsos poll conducted only about two weeks later gave the former only a 6 per cent advantage (46 per cent to 40 per cent). Similarly, in their final surveys before the 2017 election late July, Infotrak had Odinga and Kenyatta in a virtual tie (47 per cent vs. 46 per cent) —on which basis it suggested that no one would win on the first round—whereas Ipsos gave Kenyatta a clear outright win: 52 per cent vs. 48 per cent, excluding those who claimed to be undecided or who declined to reveal their preference (which when included, generated a 47 per cent vs. 43 per cent advantage for Kenyatta), although leaving room for some minor deviation from these figures based on differential voter turnout across the country. In fact, Ipsos offered four possible voter turnout scenarios, none of which put Odinga closer than 4 per cent behind Kenyatta.
Of course, given the disputed nature of the official results in all three of these elections, it is impossible to know which survey firm’s results were closer to “the truth”. But they do reveal a clear pattern: that Infotrak has consistently given more positive results to Odinga than any of the other reputable survey firms in the country. I should stress, however, that this “track record” should not be the sole basis for dismissing Infotrak’s current figures, but it does underscore Gaitho’s point that whenever there are differences in survey firms’ results that go beyond the stated margins of error, additional scrutiny is warranted—preferably to a degree that goes beyond what the Nation Media Group offered its readers/viewers on this occasion. Of course, given the fact that they sponsored the survey, such rigorous scrutiny might have been considered “inappropriate” at best.
On the other hand, it should also be recalled that in the polls conducted just before the August, 2010 constitutional referendum, Infotrak produced results that were slightly more accurate than those of Synovate—and there were no claims of any “rigging” in response to the official results.
In any event, the promise of continued polling by at least these three firms—Infotrak, TIFA and Radio Africa—should not only provide Kenyans with an evolving picture of the possible electoral fortunes of particular candidates and political parties/coalitions (as well as identifying the issues motivating voters at both the national and sub-national levels), but also invite them to more thoroughly scrutinize the performance of these firms and of any others that may appear and attain any serious media coverage. This is so even if the announcement of deputy presidential running-mates will make the next set of polls non-comparable with those examined here.
Of course, given the disputed nature of the official results in all three of these elections, it is impossible to know which survey firm’s results were closer to “the truth”.
The most useful data, however, would be the actual results announced by IEBC about which no credibility doubts are raised, so that only differential voter turnout would have to be taken into account in assessing the performance of the pollsters in their final survey rounds.
A concluding comparison: in the recent, second round run-off French presidential election, the final national poll had President Emmanuel Macron defeating Marine Le Pen by 56 per cent to 44 per cent, and the official (uncontested) results gave him a 58.5 per cent to 41.5 per cent victory (just within the poll’s margin of error). Given that, so far at least, there is no evidence that Kenyans lie any more than French people do when answering survey questions, let us hope that Kenyan survey firms can both individually and collectively achieve such accuracy.
Just after completing this piece, on May 18 TIFA released the results of another CATI survey it had conducted the day before, comprised of 1,719 respondents. This came in the immediate wake of two days of drama: first, on 15 May, the announcement by Kenya Kwanza Alliance’s presidential candidate, DP William Ruto, of his coalition’s deputy president running mate, Mathira Member of Parliament Rigathi Gachagua, and on the following day, the announcement by Azimio la Umoja’s Raila Odinga of former member of parliament and cabinet minister Martha Karua as his running mate.
The most useful data, however, would be the actual results announced by IEBC about which no credibility doubts are raised.
The two main questions it sought to answer were: (1) How many Kenyans were aware of each of these running mates? (2) What were their presidential voting intentions?
For whatever reasons, it emerged than far more people were aware of Karua’s selection than of Gachagua’s (85 per cent vs. 59 per cent), a pattern which was replicated among those who expressed the intention to vote for Odinga or Ruto, respectively (90 per cent vs. 69 per cent). By contrast, awareness of Kalonzo Musyoka’s choice of running mate, Andrew Sunkuli, was far lower among both the general public and among those (few) who reported an intention to vote for Musyoka (21 per cent and 38 per cent, respectively).
But as expected, it was the results of the second issue that attracted most attention, which gave Odinga a modest but measurable lead over Ruto: 39 per cent to 35 per cent. The central question these figures raised was whether Odinga’s jump into the lead, beyond the tie that Infotrak had reported just one week earlier, was to any degree a consequence of the identification of these two running mates, a question that will be addressed in the next piece in this series, by which time it is hoped at least one additional poll would have been conducted so as to confirm whether TIFA’s most recent figures do indeed represent a major shift in the presidential electoral terrain.
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