Patrick! I have nothing to add. You are my hero on this issue. Thank you for the excellent “initial thoughts”. Best Regards, Jimmy Gitonga Web Software Design and Development LinkedIn: Jimmy Gitonga | Twitter: @Afrowave ______________________________________ Web: afroshok.com <http://afroshok.com/>
On 18 May 2019, at 9:41 PM, kictanet-request@lists.kictanet.or.ke wrote:
Message: 2 Date: Sat, 18 May 2019 18:38:43 +0000 (UTC) From: "Patrick A. M. Maina" <pmaina2000@yahoo.com <mailto:pmaina2000@yahoo.com>> To: KICTAnet ICT Policy Discussions <kictanet@lists.kictanet.or.ke <mailto:kictanet@lists.kictanet.or.ke>>, Barrack Otieno <otieno.barrack@gmail.com <mailto:otieno.barrack@gmail.com>> Subject: Re: [kictanet] [preliminary analysis] After Access report launch Message-ID: <43006288.2640308.1558204723308@mail.yahoo.com <mailto:43006288.2640308.1558204723308@mail.yahoo.com>> Content-Type: text/plain; charset="utf-8"
Hi Barrack, I'm still studying it but can share my initial thoughts... I had a quick look at GSMA's mobile economy report 2019 (link #5 below) and it pegs Kenya's internet penetration number at ~24% up from 16% based on lagging indicators (2014-2017). It also says 3G coverage was 85% as at 2017 with 4G reaching more than a third of the population (~16.7 Million people assuming ~50M total).
The GSMA report is alluding to a high supply - low demand situation (high network coverage and low internet uptake) which is congruent with Research ICT Africa's findings.
The data (and economic theory - see link #1 below) is telling us not to focus on supply side but to develop the demand side - because supply is abundant - but there is something that is impeding demand.
This means looking at whether internet pricing (a factor of value perception) or internet quality (or both) are impeding penetration. It's hard to argue that QoS is a significant factor with 85% 3G coverage - because 3G internet is pretty good (even for compressed video 240-480p which is adequate for budget phones).
The more credible explanation for low demand is affordability - and this has been alluded in several studies - including Research ICT Africa's study. Majority of Kenyans are poor - yet mobile service pricing is inequitably skewed in favor of the urban middle-class or higher who comprise < 12% of our 26.8Million (~57%) labor force, say ~3.2 million people filing tax returns as a rough indicator (see link #3 for Kenya's demographic profile).
So ~88% of the work-capable population gets to pay a significantly higher premium per MB, as well as for voice and SMS.
The poorer you are the heavier you are penalized. A 6MB for 3/- offer bundle with 24hr validity works out to 2MB per shilling. But if you can afford to pay 50/- for an hour you get ~40MB per shilling. The poor person pays 20 times more for the same commodity than the wealthier person. This is penalizing poverty - and quite ruthless, considering the essential nature of the services.
Economic theory says that when supply is high and the pricing is uncompetitive in a market characterized by low disposable incomes, it can be expected that the demand would be low (unless some coercive factors exist e.g. for utilities like Kenya Power). The data supports this.
The data is also challenging the procurement driven narrative that internet coverage is driven by technical gaps (coverage and capacity), rather than underlying economic issues (lack of quality jobs and stifled opportunities). So what should be examined instead is the relationship between disposable income, bundle pricing and internet uptake.
Although one service provider in Kenya is making super profits, and has been growing in double digits, the broader economy does not reflect their vibrancy (the disparity is so wide, it is like the company exists in another dimension or on another planet) - which is a compelling indicator of serious market dysfunction e.g. unfair competition, excessive market power by one player, economically risky dominance and/or predatory practices (see link #4).
When market dysfunction persists for long it can be an indication of regulatory capture or excessively high economic dependency, such that regulator is afraid to interfere - which severely degrades economic resilience via single point of failure scenario, and allows the problem to baloon to catastrophic levels over time (see link #2).
More telling is the fact that we don't have a "number two" in terms of profits. So the demand/supply data is supporting the oft posited assertion that there is an unfair competitive landscape and monopolistic practices in the Telecomms sector (e.g. customer lock-in via network effects thus high switching costs / stifled innovations when the leader wants to get involved in everything - from taxi services to selling vegetables online etc).?? What the data is telling us is that initiatives like the National Broadband Strategy should focus more on addressing demand side factors (e.g. deploying USF towards local content production, seed funding for startups and so on). Increasing supply at this time will be inefficient and wasteful (only a few will benefit at the expense of majority).
Good evening.
Links / References: 1. Market gap analysishttp://www.consumerpsychologist.com/dist_Gap_Analysis.html <analysishttp://www.consumerpsychologist.com/dist_Gap_Analysis.html> 2. Regulatory Capture: A reviewhttps://www.jstor.org/stable/23606888?seq=1#metadata_info_tab_contents <reviewhttps://www.jstor.org/stable/23606888?seq=1#metadata_info_tab_contents> 3. Kenya Demographic Profile 2018 https://www.indexmundi.com/kenya/demographics_profile.html <https://www.indexmundi.com/kenya/demographics_profile.html> 4. Market Failure https://en.wikipedia.org/wiki/Market_failure <https://en.wikipedia.org/wiki/Market_failure> 5. GSMA Mobile economy 2019 https://www.gsma.com/r/mobileeconomy/ <https://www.gsma.com/r/mobileeconomy/> Brgds,Patrick. [Cross-domain Innovator | Independent Public Policy Analyst - Indigenous Innovations]