For now the answer is probably no.

The legacy of Tanzania’s language policy has been that English language instruction only begins to be done seriously in high school. Obviously, four years are simply not enough to master a language, let alone sit a major national examination in that language. The result has been an astonishingly high failure-rate in the national end of high school exams in Tanzania. Earlier this year 60% of high school (Form Four) students failed, prompting jokes like “I’m a rocket scientist in Tz” on this side of the border. In reality even fewer made the cutoff to get a place in institutions of higher learning. In addition, a recent survey done by Twaweza, an education Think Tank, found that 72% of sampled primary school kids and 66% of high school students could not do second grade maths. English reading and comprehension was equally bad.

It goes without mention that the state of Tanzania’s education system has serious implications for human resource development in the country. The impending commodities boom in many parts of the country will certainly not benefit locals if workers have to be imported. Oddly enough, despite its obvious shortage of human capital, Tanzania is the most restrictive state with regard to labor mobility in the East Africa Community (EAC). Dodoma is especially hostile to Kenyan workers that it sees as a threat to local workers (Kenyans and their alleged aggressiveness rudeness have jokes about Tanzanians’ work ethic….. I should add though that Tanzanians tend to be stereotyped unfairly, both at Mang’u and in New Haven I went to school with some very smart and hardworking Tanzanians).

In the final analysis, although Kenya’s post-independence education and language policy left us with a ‘tribe eat tribe’ legacy, it allowed the country’s education system to focus on English language instruction from early on, and a chance to develop a relatively more globally competitive human resource base. Nation building may have taken a hit in the process but I would argue that internal economic ties – the result of man eat man competition – have now made it such that the Kenyan nation-state will only get stronger. The challenge for Tanzania is to ensure that nation building does not limit the development of a globally competitive human resource capacity.

Since the announcement of the high school exam results earlier this year the country (Tanzania) has been debating possible avenues of reform. Better teacher training, more books and equipment and more teachers have been cited as possible remedies. Strategic review of the country’s language policy should also be put on the table.

In my opinion the EAC should adopt a language policy in which our history, social and religious studies and civics are taught in Swahili while everything else is taught in English. This would not be a selling out to a foreign language (with due respect to Ngugi) but an investment in global competitiveness. Many decades down the road, once we have universal literacy in both English and Kiswahili, we can have a full switch to universal Kiswahili language instruction in all subjects.

Many papers use regression discontinuity (RD) designs that exploit “close” election outcomes in order to identify the effects of election results on various political and economic outcomes of interest. Several recent papers critique the use of RD designs based on close elections because of the potential for imbalance near the thresholg that distinguishes winners from losers. In particular, for U.S. House elections during the post-war period, lagged variables such as incumbency status and previous vote share are significantly correlated with victory even in very close elections. This type of sorting naturally raises doubts about the key RD assumption that the assignment of treatment around the threshold is quasi-random. In this paper, we examine whether similar sorting occurs in other electoral settings, including the U.S. House in other time periods, statewide, state legislative, and mayoral races in the U.S., and national and/or local elections in a variety of other countries, including the U.K., Canada, Germany, France, Australia, India, and Brazil. No other case exhibits sorting. Evidently, the U.S. House during the post-war period is an anomaly.

The paper is here (PDF).

H/T Monkey Cage Blog.

The 58 nations included in the report “produce 85 percent of the world’s petroleum, 90 percent of diamonds and 80 percent of copper.” Ghana, where we are doing some evaluation  work on extractive sector transparency initiatives, is the best performing African country on the list. 

More here. 

And in related news, The Africa Progress Report was released last week. The report details the massive loss of revenue by African governments through mismanagement – either by commission and/or omission – of extractive resources. For instance:

The report details five deals between 2010 and 2012, which cost the Democratic Republic of the Congo over US$1.3 billion in revenues through the undervaluation of assets and sale to foreign investors. This sum represents twice the annual health and education budgets of a country with one of the worst child mortality rates in the world and seven million pupils out of school.

The DRC alone is estimated to have 24 trillion dollars worth of untapped mineral resources.

The most bizarre case of resource management in Africa is Equatorial Guinea, a coutnry that is ranked 43rd on the global per capital GNI index but ranks 136th on the Human Development Index (2011).

Below is a map showing flows related to Africa’s vast resources:

This study investigates circumstances that affect individuals’ decisions of whether or not to flee their homes during civilian conflicts. Building on the “choice-centered” approach to studying forced migration, I test the argument that people make a decision to flee or stay even under highly dangerous circumstances. Using primary data collected through a public opinion survey in Nepal, I test a number of hypotheses regarding the impact of factors such as violence, economic opportunity, physical infrastructure or geographical terrain, and social networks on forced migration, providing an individual-level test of the choice-centered approach to studying forced migration. The empirical results are consistent with the major hypotheses developed in aggregate-level studies and provide better insightsinto the factors that affect individual-level behavior. Beyond conflict, there are a number of significant economic, social, physical, and political factors that affect individuals’ choice to flee.

As expected, the threat of violence and actual experience of violence increases the likelihood that one would flee – by about 8 and 32 percentage points respectively. Economic opportunity, on the other hand, reduces the likelihood of fleeing by 19%. High income individuals are also less likely to flee by about 1-2%. In addition, social ties decrease the likelihood of fleeing, although the effect is not statistically significant in Adhikari’s model.

The paper primarily looks at conflict, but may also apply to cases of overall economic collapse and political turmoil as has plagued Zimbabwe in the last one and a half decades. It is estimated that almost a quarter of Zimbabweans have fled the country. But three quarters remained, through the hyper-inflation and acute restriction of political space and personal freedoms.

In particular, the Zimbabwe case provides a good case for knowing why elites (who presumably can leave if they want) choose to stay in states like Zimbabwe – and thereby continue to provide the means of survival for the regime.

For those who have chosen to stay in Zim it might be that they have a lot to lose by fleeing (they have jobs, own some property, have strong extensive social ties, are too patriotic to leave …..?) or are actually plugged into the patronage system of Robert Mugabe, supported by illicit trade in diamonds, foreign aid, and control of the economy.

H/T Joshua Keating over at War of Ideas

Starting in fall 2014, the Master of Science in Foreign Service (MSFS) at Georgetown University is offering a full- tuition scholarship for a talented graduate student from sub-Saharan Africa.

MSFS is a two-year, full-time graduate degree program in international affairs. Students will take courses in international relations, international trade, international finance, statistics and analytical tools and history. In addition, students choose an area of concentration such as International Relations and Security, International Development or International Business.

I have been spending a lot of time in government offices and libraries in Nairobi lately. One thing that struck me this morning is that China Daily now publishes an Africa Weekly magazine and that government offices in Nairobi actually subscribe to it. They do not subscribe to the IHT.

Blattman’s latest post decries Bill Gates’ (and much of the development community’s) focus on data gathering, and may add, strengthening of statistics departments. He writes:

I would like to see better GDP numbers–who wouldn’t?–but it’s hard for me to see the constraint on development this revelation would relieve, and why it’s anywhere close to the top ten constraints poor countries face.

The problem with those of us in the development complex, be we academics or Presidents or foundations or NGOs, is we want the world nicely ordered with levers to pull and a dashboard to monitor. And so we put a lot of energies into levers and dashboards and monitors.

I think of poverty and political powerlessness in terms of constraints and frictions–the limitless host of things, little and big, that made it more difficult to run a business profitably or turn a profit or invent a new product or get your kid educated or select the leader who serves your interests. States and institutions and norms and technology and organizations reduce these frictions and relieve these constraints. That is the fundamental driver of development. This is the basic logic behind almost every theory of development in your textbooks, from growth models to poverty traps to everything in between.

Blattman is right that improving the capacity of statistics departments will not do much to alleviate poverty now (although as I write this in the basement of a government library in Nairobi I can’t stop thinking that stats departments need to do more). At the same time however, I would be wary of an outright dismissal of the need for better data gathering by governments, for two reasons.

Firstly, at the core of state capacity is the ability to make legible (depite Scott’s observations) the terrain over which the state claims to have dominion. Strong states are those that know your home address, the number of children you have and how much money you made last year. When governments have the capacity to get better GDP data, they will also know how many kids died or were not immunized last year, etc etc. And perhaps more importantly, they will be able to know how much you made last year and how they can get a bigger share of it. As Besley and Persson have argued, there is a strong case to be made for the centrality of public finance to development. Poor countries have small tax bases yes, but tax evasion in these countries still denies national treasuries lots of cash. And it is not just a question political will. Low capacity plays a role. Imagine trying to implement an income tax in a country of about 20 million adults but where under 4 million are in formal employment and can have their taxes withheld.

Secondly, Blattman seems to be making an argument for the private sector as a key part of greasing frictions that stifle development (which is true). But the private sector initiatives he cites can only flourish when there is strong state monitoring (with reliable data) in the background. Credit bureaus need a strong and enforceable regulatory framework. Otherwise no one will believe their credit reports. Freedom of (government) information laws are cool, but such information must first exist, and in reliable format. In other words stats departments must do their job well.

Lastly, good data also make for more informed politics. Kenya, for instance, could do with more disaggregated GDP data – by counties or lower – as it attempts to implement a devolved system of government and revenue allocation.

All this to say that when states have a handle on how much is produced, they will know how and where to get their share. And the more they demand a bigger share, the more the people will demand some of it to be returned as public goods (and these can also include reliable information that would be accessed via freedom of information laws). Yes, GDP data was invented post-WWII when some countries were already winning against poverty for decades. But even before that the more successful states were the ones that were better at information gathering. Flying blind is simply not an option for states.

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The video below has been making the rounds in the Kenyan online community. The Daily Nation even reported on the claims by UCSD Professor Clark Gibson and James Long, Asst. Prof. and University of Washington, that President Uhuru Kenyatta may not have crossed the 50% threshold in the March 4th election. The duo conducted an exit poll (N = 6000) on election day that showed both candidates in a statistical tie at 40.9% for Odinga and 40.6% for Kenyatta. In the presentation Clark and James make the case that exit polling is superior to PVT because it is immune to things like ballot stuffing and tallying fraud. NDI sponsored ELOG conducted a PVT that confirmed the results announced by the Kenyan EMB, the IEBC.

I do not really know what to make of this poll finding by James and Clark at the moment. I am waiting for the actual MP and Governor elections results to be published by the IEBC so I can try and see if the results in these local races were in line with the presidential results.

My conversations with some of the CSOs working on various kinds of transparency initiatives revealed to me that the problem of government opaqueness in reference to resource sectors is not just because of lack of political will. It is also about governments’ lack of capacity to supply accountability. Take the example of oil drilling. In order to provide transparency to its citizens the government has to have a proper revenue management system (complete with accurate models of predicted production, prices, etc); well trained technical staff that can hold their own against the savvy experts (engineers, geologists, etc) of the oil companies; and the technical means of delivering information in a digestible form to the masses. As it is in most governments, Ghana included, ministries in charge of resources are often staffed by loyal political appointees, some whom lack the technical expertise to effectively carry out their jobs.

This is not to downplay the political economy aspects of resource sector accountability. Just to say that there is a difference between Obiang’ and Mahama. For the former, technical fixes may do little to increase transparency in Equatorial Guinea’s oil sector. But for the latter, the political situation necessitates the provision of set minimum levels of accountability. So to the extent that there is a failure to do so in the case of Ghana we should not be quick to scream politics but instead also consider ways of improving the state’s capacity to supply transparency and accountability.

But on the other other hand grumblings emerged on the lack of regional (read ethnic) balance in the appointments. Kenya is an ethnically fragmented country, with 11 (out of 42) ethnic groups with populations over 1 million (Kikuyu/GEMA, Luhya, Kalenjin, Luo, Kamba, Somali, Kisii, Meru (part of GEMA), Mijikenda, Turkana, Maasai). For most of the country’s history ethnicity has been a key organizing principle of politics, with people largely voting along ethnic lines for various instrumental reasons.

So what is the ethnic breakdown of Kenyatta’s cabinet so far? Here is my guestimation based on their last names: Kikuyu (3), Kalenjin (4), Somali (3), Luo (1), Meru (1), Kisii (1), Kamba (1), Luhya (1), Arab (1). Two slots remain unoccupied.

Only two groups (Kalenjins and Somalis) are clearly overrepresented in the cabinet appointments in proportion to their relative ethnic group size in the country (Kalenjin 25% vs 13.2%; Somalis 18.75 vs 6.3%). Those groups in the top ten that are underrepresented missed their “objective proportion” of the cabinet by about one slot, on average.

On an aside, historically African presidents have actually been pretty good at ethnic balancing in the appointment of cabinet ministers – as Francois, Rainer and Trebbi show in this paper. They claim to “show that African ruling coalitions are large and that political power is allocated proportionally to population shares across ethnic groups. This holds true even restricting the analysis to the subsample of the most powerful ministerial posts. We argue that the likelihood of revolutions from outsiders and the threat of coups from insiders are major forces explaining such allocations.“

If the ethnic composition of the cabinet is anything to go by, it shows the extent to which deputy president William Ruto is more of an equal than deputy to President Kenyatta. His part of the Jubilee coalition dominates the list of cabinet nominees. Or it might just be a case of Mr. Kenyatta, being president, having opted to have his half of the cabinet “represent the face of Kenya” (Kenyatta and Ruto had a 50-50 pre-election appointments sharing agreement at the formation of the Jubilee coalition).

Despite Kenyans’ relief at the end of Odinga and Kibaki’s coalition government, the era of nusu mkate might still be among us.

In related news, president Kenyatta broke one of his campaign promises by not appointing an ethnic Turkana as secretary in charge of Energy and Petroleum. Kenya’s oil discoveries have been mostly in Turkana County. The Standard reports:

Uhuru [President Kenyatta] repeated he will appoint a Turkana to head the Ministry of Energy portfolio should he take over the next Government.

Kenyatta said his Government would give first priority to locals to manage the oil resources that were discovered in the area.

“Our mandate is to ensure that every Kenyan gets equal share of national cake. But locals where such resources are found should benefit more as a right stipulated in the Constitution,” he said.

Deputy President William Ruto also broke a promise he made yesterday. He said at a presser that the cabinet will not have any politicians, yet Charity Ngilu and Najib Balala have been nominated.

Kenyatta’s Cabinet Nominees:

1. Fred Matiang’i (Information, Communication and Technology) – Kisii
2. Henry K. Rotich (The National Treasury) – Kalenjin
3. James Wainaina Macharia (Health) – Kikuyu
4. Amb Amina Mohamed (Foreign Affairs) – Somali
5. Adan Mohammed (Industrialisation) – Somali
6. Ann Waiguru (Devolution and Planning) – Kikuyu
7. Davis Chirchir (Energy and Petroleum) – Kalenjin
8. Amb Raychelle Omamo (Defence) – Luo
9. Eng Michael Kamau (Transport and Infrastructure) – Kikuyu
10. Phyllis Chepkosgey (East African affairs, Commerce and Tourism) – Kalenjin
11. Prof Jacob Kaimenyi (Education) – Meru
12. Felix Kosgey (Agriculture, Livestock and Fisheries) – Kalenjin
13. Prof Judy Wakhungu (Environment Water and Natural Resources) – Luhya
14. Dr Hassan Wario (Sports, Culture and Arts) – Somali
15. Najib Balala (Mining) – Arab
16. Charity Ngilu (Lands, Housing and Urban Development) – Kamba
17. Ministry of Labour, Social Security and Services (Vacant)
18. Ministry of Interior and Coordination of National Government (Vacant)