Kenya marked her 51st Jamhuri day last week. Traditionally, the sitting president has used such occasions to speak about the challenges facing the nation and outline the government’s road map in terms of addressing those challenges.
President Kenyatta’s second Jamhuri day as president came in the wake of the recent terror attacks and the continued implementation of devolution. So looking at this year’s speech, there is no surprise at the prominence of security and devolution but where is health, education, corruption, climate change etc?
The World Aids Day was marked earlier this week.
According to Getting to Zero: HIV in Eastern and Southern Africa. UNAIDS, 2013. , Eastern and Southern Africa is home to half the world’s population living with HIV. This despite the region constituting only 5 per cent of the world’s population.
Eastern and Southern Africa has 48 per cent of the world’s new HIV infections among adults, 55 per cent among children, and 48 per cent of AIDS-related deaths.
The map below confirms the region as the official epicentre of the HIV/AIDS epidemic globally.
A chart comparing the growth in the number of mobile cellular subscriptions in Kenya, Nigeria, Brazil, Montenegro, Uganda, China, Iraq and Somalia over a number of years.
Source: World Bank.
The global agitation for the shift from use of fossil fuels like crude oil to the use of renewable alternatives like wind and solar energy has been there for a number of decades now.
The reason for that is clear: fossil fuels draw on finite resources that will eventually dwindle, becoming too expensive or too environmentally damaging to retrieve while, in contrast, the many types of renewable energy resources – such as wind and solar energy – are constantly replenished and will never run out.
It is therefore shocking that many governments and organizations are making minimal efforts to instigate the move to the use of renewable energy.
In Kenya, for example, the continued dependence on firewood, charcoal and paraffin as the main cooking fuel – although in various proportions when broken down to use in urban and rural areas – is worrying.
The use of solar energy, for instance, is a paltry 0.1% nationally. Even in traditionally hot areas such as northern Kenya and the former North Eastern province, where the wind and solar energy would make sense, the insistence on firewood is still rampant and the use of solar energy negligible.
I know the examples outlined here are more about home use than industrial use but they give a good idea of the general trend and the extent to which measures on energy use and environmental conservation are being applied.
The recent strides being made on geothermal energy are commendable but it is hard not to feel that more could be done in that regard.
The environmental and economic gains to be made fully justify a radical change in energy policy and the feeling is that we should have it sooner rather than later.
Immigrants run 40% of America’s Fortune 500 companies. Yes, Apple, Google and Yahoo are on that list.
Every year, students from all over the world flock to American Universities, the prestigious Ivy League schools in search of education, world-class education that would improve their lives and their chances in the job market. Competitive advantage.
The thing that is most depressing for the home countries is, they send possibly their smartest brains to foreign nations with no guarantee of their return to apply what they have learnt abroad to improve their home countries.
Rates of urbanization in countries or continents have long been celebrated as signs of development and economic growth. Every year, thousands of people move to urban areas and cities in search for education and jobs. Various villages and rural areas send their smartest brains mostly between the age of 20 and 30 to go study in the cities, where the more advanced learning institutions are, with no guarantee of return.
In Kenya, for example, the population of the capital city is seen to swell up within the age group of 20 – 30, an indication of a growth in students flocking the universities & colleges and in turn a great benefit to the Nairobi County in skilled labor after graduation.
Most of these students rarely return to their home counties and this is also partly because of the larger job market and improved infrastructure within the city.
The obvious benefit of this can be seen in Nairobi’s economic growth since at any given time, Nairobi county has a great resource of labor; not just any labor but the top of the rural county’s brain, during their most productive and innovative years.
Kiambu County is a big beneficiary by its proximity to Nairobi County. A lot of the skilled young labor would prefer to live in Kiambu due to its affordability while at the same time closeness that affords them to work in Nairobi. The Thika Super highway is an enhancing factor to this. This is also seen in the national data that shows that after Nairobi, Kiambu has a lower poverty rate compared to other counties. The establishment of shopping malls in Kiambu, a good indicator of the middle class populations, is also another good pointer.
Basically, the people make their money in Nairobi and spend it in Kiambu.
Again, looking at Kiambu’s 20–30 year olds age group, this is evident.
On the flip side, taking Kakamega County that has a good collection of national schools and top performers, the exodus is evident. The labor force and brains of the county make a major migration to the cities in search for education and jobs.
Areas like these leave a population back home that is mostly only capable of unskilled labor and whose demand for development is low as demonstrated in their poverty rates that despite having the best schools, suffer the consequence of a search for better higher education.
In my opinion, counties need to start a drive of maintaining or attracting back their top students as these will be the well trained and skilled labor that will bring forth development, innovation and demand for better and improved services & infrastructure at the grassroots.
Source for all data: Kenya Open Data Initiative – www.opendata.go.ke
“God could not be everywhere, and therefore he made mothers.”
These words by late Victorian poet Rudyard Kipling succinctly capture the role of mothers in human development and are further backed by evidence which shows that infants whose mothers die are more likely to die before reaching their second birthday than infants whose mothers survive.
This is why the Millennium Development Goals include a call for a 75 per cent reduction in maternal mortality between 1990 and 2015.
Maternal mortality Ratio (MMR) is basically the number of maternal deaths per 100,000 live births within a given time period.
In Kenya, over the last one year, 21 per cent of deaths among women of reproductive age were as a result of pregnancy-related causes.
Of those, 26 per cent occurred during pregnancy, 48 per cent during delivery and 26 per cent were within 2 months after delivery.
Kenya’s MMR in that period is 488 deaths per 100,000, an abnormally high figure.
Looking at individual counties, first by number of maternal deaths and then by the Maternal Mortality Ratio, Mandera leads the way in both rankings curiously contributing 2,136 out of 6,623 maternal deaths, that is thirty three percent of the national figure!
No woman in this day and age should have to die to give life and as such, more should be done to help avoid unplanned pregnancies and improve access to skilled pre-natal and post-natal care.
In the recent past, a lot of reports have been released defining the African middle class. From the McKinsey report to the African Development Bank and most recently, Standard Bank. After reading through all of these, I still cannot get a clear picture of who the middle class really is.
Taking the Standard Bank definition that used the Living Standard Measure, (LSM) – a South African formula for calculating the middle class – two things stand out: 1) South Africa was not in the list of the 11 countries assessed; and that, 2) Some of the indicators used should not be a blanket one-size-fits-all measure for all countries.
This is comparing apples to oranges; both might look round but none is eaten like the other. A washing machine and dryer in South Africa might be a common phenomenon given the winter periods and white population while it means extreme wealth in Kenya. And 3 mobile phones in a Kenyan household might mean access to cheaper communication networks and mobile money while in Nigeria it means a third member of the household, most likely a child has a mobile phone. A lesson in context.
The report picks apart East African countries for not doing so well in the measure of becoming middle class economies but I believe that there are some things that have been ignored and especially culture and mineral wealth in these calculations.
So, who really is in the middle class?
A lot of reports looking at the middle class tend to look more at consumption than they do disposable income in relation to dependants. It is surprising that most of the people I spoke to, asking this question actually thought that the middle class is calculated depending on how much you earn not how much you spend.
Scenario. Imagine a married man with a stay at home wife with five kids, earning KES 140,000. Now imagine a single man earning KES 70,000 and living by himself. Who among the two has a higher spending power?
According to a lot of studies, it is the man that earns 140,000 simply because at the end of each day, he has to buy three packets of milk, a loaf of bread and a packet of flour. He pays school fees, rent and generally spends more per day than the person that earns half his salary but lives by himself and has no dependant and therefore less daily expenditure.
In the logical sense, if you were selling an expensive watch, the man that earns 70,000 is in a better position to buy it compared to the man that earns 140,000. Simple.
In my opinion, the economic class should be calculated bearing in mind how much people earn and how many people depend on that earning. The highest ranked individual should be the one with the highest likelihood to spend beyond basic and household needs given the number of dependents on their income. The KES 140,000 has 7 dependents (roughly 20,000 each assuming everyone spends the same amount per month) while the KES 70,000 has one dependent.
But in this measure, again, what happens to those people whose income is not documented and who do not need to have a car to get to work every morning because their work is in a farm within which they live and who do not need washing machines because they have adequate paid labor to do their laundry? Or the farmers, who do not need to spend a dollar a day on food because they grow their food and milk their cows and farm their maize. Would these be considered as middle class people or are they simply a segment of the population with a disregarded spending power that, if incorporated, would drastically redefine economic classes as we know them?
The economists would argue that the best sale of the middle class agenda is for manufacturers and service providers to know what they can sell to you and at what price.
In light of this, since one of the measures of a middle class population is their frequency in the supermarkets, what if Nakumatt were to set up shop right in the middle of Kericho County with all its tea estates and farmers, would these people then abandon their kiosks for the shopping mall experience? A lot of people in Kericho might not necessarily buy a lot of harpic since they primarily use pit latrines as opposed to flush toilets but could they potentially buy more gum boots since they are huge on tea farming?
According to Dr. Ndemo’s recent article in the daily, “probably the most successful people in Kenya or East Africa come from Rwathia, a small village in central Kenya that arguably controls almost 20% of Kenya’s GDP and 40% of the stock market…” From a Google map image of this village, the roads look paved and the roofs look tiled. What if someone built a TRM in Rwathia?
In my opinion, a lot of the studies that have been conducted are measuring urban middle class while ignoring the rural population that as much as is unbanked and undocumented, might potentially have a more promising spending future.
A friend once told me that if your father was a teacher in the 80s, you were in the middle class. Does having grown up in the village, and the fact that my father was a teacher change anything in these dynamics? We had a television set since about 1988 and I grew up with electricity. So did some of our neighbors in the village but does the fact that my village still heavily relies on kiosks for basic goods indicate that the middle class is yet to be born there?
Is it time we started focusing on some of the unbanked populations that might trust their mattresses more than banks but who have the potential to create a consumer class that is currently unmapped? I think so.
Free Primary Education was launched in 2003 to increase school participation among the Kenyan population. This was to be achieved by abolishing any form of payments by parents with the government taking over expenses such as books and stationery that were the burden of parents.
Comparing the net enrollment before and after FPE shows virtually no increase in enrollment in public schools but a doubling of enrollment in private schools.
This could potentially indicate there was an increase in the number of pupils in primary schools and then to the lowering of the quality of education offered in public schools, able parents may have transferred their children to private schools to access better quality education. This would in turn have stagnated the numbers in public schools but lead to an increase in numbers for private schools.
The number of teachers is an important resource in education which should have been invested in to maintain if not improve the quality of education but that has not been the case.
The pupils per teacher ratio, which shows how many pupils a teacher manages on average, significantly increased between years before and just after FPE indicated by the step-up in the line graph.
This is just one resource, others include classes and learning equipment. More investment in these areas in line with the increased pupils numbers in schools would have gone a long way in achieving the intended goal.
Many reports have been written about The Kenya Open Data Initiative and how it has died.
Many have questioned how government projects run and if Open Data in Kenya is facing the same fate.
What a lot of people have not realized is that beyond the portal www.opendata.go.ke open data is a concept. Open Data Initiatives all around the world are about governments making their data freely and publicly accessible. The beauty of open data portals is they consolidate the data and put it within a single URL so the user does not have to remember multiple addresses but open data as a concept….
Definition: Open data is data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike. – Source: Open Data Handbook
In no place, in this definition and many other definitions is there a mention or a requirement that Open Data must exist within one portal.
Various ministries within government have made their data publicly available through their own web portals and while we are working on legislation and policy to change that, there is nothing that requires them to make their data available through the open data portal except for goodwill.
To name a few examples:
- The ministry of agriculture and fisheries makes available all market data on the sale of commodities in all major markets on a daily basis.
- The Kenya national bureau of statistics has made a ton of data available including the statistical abstracts and economic surveys all the way from the 60s
- The ministry of health makes available all data about the health facilities in the country, what services they offer, how stocked they are, capacity: beds, doctors, nurses
- The Kenya open data initiative (KODI) has made important information like CDF expenditures, demographics, poverty rates, education etc available
These are just but a few examples and these particular examples actually make the data available in open formats. We are always working to ensure that all this data can exist within one portal.
The KODI portal has had over 4 million views since its inception in 2011 and I can almost say this is the most viewed government website as shown below for this year:
That said, Kenya, like many other countries with an open data initiative hosts a big load of data within one portal (the one that has everyone confused about what open data is) but this portal so far has had tremendous results of access and as much as the data available is not breaking news, has brought to light a lot of data that would normally not be accessible to the public without written consent from the host government organization.
The Open Data Initiative has not come without challenges and to understand these challenges, read my post where I talked about why Kenya Open Data is taking so damn long!
The reason I decided to write this post is to bring out some of the benefits of the ideas of open data within government and other organizations that I am sure would have taken longer to be realized if the initiative had not been launched in 2011.
The Access to information and Data Protection bills were crafted in 2006 and since then, they had been in negotiation and basically were shelved for a while. After the inception of Open Data in 2011, the debate on the two bills was rekindled in 2012 and in september 2014, the cabinet, chaired by the president approved the two bills that are now waiting for the final parliament approval to become law.
Before the open data ideas, there was no policy guiding how data should be shared or made available within government. I am happy to report that since open data, the ICT Authority is working on a policy for data sharing, the KNBS has a policy in place and many other ministries are looking at these conversations.
Have you stopped to ask why government is heavily digitizing its records? Access. Transparency. Openness. Many government agencies including the civil registry departments on births and deaths, ministry of education, Ministry of health are now heavily digitizing to not only save on storage space but also make the data publicly and easily accessible.
A lot of government institutions collect data every so often. There are departments within departments and the way it happened, everyone would collect their own data in their own format using their own codes and fields. Since the inception of open data for instance, the World Bank gave the Ministry of Education $1 million to create an integrated education database that would implement a standardized way of data collection and naming. This data was to be made available through the open data portal, the process is ongoing.
Through the Open Data movement, a lot of companies have been formed in Kenya to utilize open data and citizen awareness. Organizations like Open Institute, Code4Kenya, iHub Research, Mzalendo, DataScience utilize open data in a lot of their activities both nationally and at the county level.
The media has not been left behind. Data journalism like the fantastic work Internews is doing in Kenya, a lot of it relies on open data from the government. NTV has done a few features that have been based on data from KODI and other government sources to name but a few.
We come from a history of no sharing. The traditional secrets act has been in place since the 70s and still going. Most recently (2012), Kenya joined the Open Government Partnership with her key mandate being using open data to be transparent and more engaging with citizens. I can confidently say that over the past 4 years we have seen great information flows from government than ever before. We do not yet have everything, but something sure is happening.
Most of these government systems have been in existence for decades and have created a government culture that will take a while to dilute but the milestones that the idea of an open government have achieved cannot be underplayed.
So, my question to those saying open data is dead, what exactly do you mean? What is open data in your definition and what signifies its death?
Open data is not an end in its self, it is a small cog in a big big machine and maybe i am privileged because i experience this progress everyday but i would say, look a little more critically before you throw stones.
So with a recalculation of its GDP, Kenya effectively became a lower-middle-income economy.
According to the World Bank, a lower-middle-income economy is one where the GNI per capita falls between $1,045 and $4,125. After the 25% upward revision of Kenya’s GDP, Kenya’s GNI per capita is put at $1,160, just above the $1,045 cut-off point.
But does that paint the true picture of the economy and the well being of the population?
Comparing Kenya’s to other economies within the same economic class from Asia (India), Europe (Ukraine) and Africa (Egypt) in areas such as poverty rate, unemployment, Debt to GDP ratio, Urbanization and contribution of manufacturing sector to the overall GDP, Kenya still lags behind.
A large proportion of Kenyans remain unemployed at 40%, well higher compared to the the rest; the economy is still dependent on subsistence agriculture; while a high poverty rate despite an average middle-income indicates high income disparities.