Ronald Wesonga http://www.wesonga.com Stochastic Computing For Development Wed, 07 Jul 2010 03:07:25 +0000 http://wordpress.org/?v=2.2.2 en Towards the bigger vision for Africa http://www.wesonga.com/my-docs/towards-the-bigger-vision-for-africa.htm http://www.wesonga.com/my-docs/towards-the-bigger-vision-for-africa.htm#comments Tue, 06 Jul 2010 11:54:40 +0000 wesonga http://www.wesonga.com/my-docs/towards-the-bigger-vision-for-africa.htm

Profs. Mohiddin and RutabajukaThe stochastic nature of political and social processes determines economic development of the world’s poorest continent, Africa! History plays a fundamental role in all these interactions! I was either unfortunate or fortunate not to have studied history, not because it was a very difficult subject for me since I was the best in my senior one when I last offered it! I will, however, always be appreciative to Professor Simba Kayunga, who taught me political education, then, at my senior three and four in which I excelled.

There are some key elements that form the core requirements for a bigger vision for Africa of which any country whose governance system applies them is graded as more developed. One of them is learning from history, and preserving the good history to building on. Professor Ahmed Mohiddin, who studied and lectured at Makerere University, is a moving encyclopedia of the African leaders who have made Africa proud as far as sustaining good history is concerned and also otherwise in pursuit for a bigger vision for Africa. The other key elements are i) avoiding the blame game ii) amicably  managing diversity iii) change process management iv) commitment and transparency v) accountability to the citizens vi) practicing self-last theory in governance vii) protecting constitutionalism viii) rule of law ix) professionalism (as a governance practice, human development and consultations) and x) democracy and human rights protection.

Africa Governance Report is a project by UNECA and UNDP to assess and monitor the progress towards good governance in Africa by identifying capacity gaps in governance institutions and subsequently propose policy interventions to promote good governance. So far, two publications have been made; AGR I that was published in the year 2005 applied three methods of statistical data collection; the expert panel survey, the household survey and the desk research to develop seven chapters of the report. AGR II was published in the year 2009 and used the same methodology as AGR I, with a target of a representative sample of 100 experts. On the other hand, AGR III intends to use expert panel, focus group discussions and desk research to collect data focusing on the otherwise tricky theme of elections and the management of diversity. The fascinating aspect of AGR is the fact that it is locally created by Africans and for Africans, hence a greater possibility of ownership. The other is that it’s collaborative nature between UNECA, UNDP regional and country offices, the national research institutions and the experts at country level.

Over to you…

]]> http://www.wesonga.com/my-docs/towards-the-bigger-vision-for-africa.htm/feed Data in measuring corruption and governance at country level http://www.wesonga.com/my-docs/data-in-measuring-corruption-and-governance-at-country-level.htm http://www.wesonga.com/my-docs/data-in-measuring-corruption-and-governance-at-country-level.htm#comments Thu, 17 Jun 2010 17:43:31 +0000 wesonga http://www.wesonga.com/my-docs/data-in-measuring-corruption-and-governance-at-country-level.htm

Thomas Wollnik

Delving into this area is often a nightmare because little has been done even by the national statistical offices in Africa. It is a bear truth that national statistical offices have the duty of collecting and safeguarding all the data in a given country, but even a simple survey shows that none of them currently manages corruption and governance data in Africa. You and I know the significance of these attributes of corruption and governance towards national development, so there is no way we can reason that they are missing in national data archives because of their little or no importance towards national development!

Well aware of the challenges of developing indicators for measuring corruption and governance, national statistical offices need to take the lead in developing necessary indicators. Any survey is guided by its objectives. The common specific direct objectives for this kind of survey can be categorized under three main themes namely; measurement, reduction and preventive. To respond to these objectives, the following models of data source considerations are suggested;

Survey data collection approach: perception surveys have been used over time in developing global indices and should be maintained and customized even at country level. These surveys tend to estimate the probability or the likelihood of occurrence, but not the occurrence of corruption itself. They are more qualitative in nature, but easily adaptable quantitatively, hence the derivation of indices such as the CPI (corruption perception index) and WGI (world governance index). This type of data attempts to respond to the measurement objective mentioned. But should the fight against corruption only be measured? What then is the relevance of these indices in the fight against corruption and bad governance?

Administrative data collection and institutionalization approach: these are majorly secondary data sources including reports. They are indicative of the actual values and incidences of corruption and either bad or good governance practices. They are based on the precinct of administrative data which is known to be flawed in the most developing countries, but if strengthened is best method of establishing the actuality of corruption and also its magnitude. However, this approach like the one mentioned before, just helps us to measure the extent and subsequently suggest measures to reduce the tendency, but will not prevent the corruption and bad acts of governance. In the medical statistical terms this is an autopsy!

Surveillance data collection approach: these are primarily instantaneous and can be preventive. Corruption and bad governance tend to follow a known procedure which takes time to complete. Therefore, if intervened along their way, it is possible to prevent the occurrence of these societal evil practices! In order to achieve this strong objective of prevention, there is need to strengthen information and communication in the following; anti-corruption organizations, watchdogs and also empowering national systems.

You are welcome to comment…

]]> http://www.wesonga.com/my-docs/data-in-measuring-corruption-and-governance-at-country-level.htm/feed Climate governance and the question of equity http://www.wesonga.com/my-docs/climate-governance-and-the-question-of-equity.htm http://www.wesonga.com/my-docs/climate-governance-and-the-question-of-equity.htm#comments Tue, 15 Jun 2010 23:43:41 +0000 wesonga http://www.wesonga.com/my-docs/climate-governance-and-the-question-of-equity.htm

Wesonga at the climate governance conferenceThe just concluded international conference on climate governance in Berlin, Germany, held on the 15th June, 2010, organized by Inwent and Transparent International attempted to discuss the key issues of knowledge & information access, equity and the economy in addressing climate change. However, the common denominator seemingly emerged as failure to define equity! Does equity in the context of climate governance mean being fair or vice versa?

One school of thought defined equity as that which should focus on the sources of finance, the financial obligation and its distribution amongst countries. Regardless of their levels of development, countries should show their responsibility and capacity to mitigate climate change. A quick quantification was made pointing at the proportion of financial commitment of two different blocks of countries, the developed and the developing countries in the ratio of 3:1 respectively. A few questions arise and remain unanswered as to who determines these proportions? And what is the basis on which they are developed? Are the equity issues addressed in deriving these proportions?

The other school of thought pointed at the historical facts of climate change and the trends of the mitigation efforts based on the IPCC (Intergovernmental Panel for Climate Change) and subsequently the UNFCCC (United Nations Framework Convention on Climate Change). It was argued that developed countries pledged to take lead in combating climate change and its adverse effects, but they are not, is this equity? It was also noted that, climate change system failed due to the historical industrial revolution that saw developed countries catapulting into industrialised countries at the expense of the developing countries. So, how equitable should the national adaption programmes of action (NAPA) be between the developed and developing countries in the stabilization of the greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system? Therefore, shouldn’t legal action be taken against the developed countries for causing climate change!

My school of thought is coined on two basic assumptions (i) development strategies including NDPs (National Development Programmes) inversely affect climate change. Implying that whenever, a development project, say, construction of sugar production plant, a natural forest is cleared for sugar cane growing to serve as raw materials for the project. (ii) Difficulty of maintaining a difference between development fund and climate change adaptation finance, both of which are financial assistance towards the developing countries. Based on the two assumptions, I define equity as vetoing the developed countries from further development, unless such a development has no or negative foreseeable impact on climate change within a period of three hundred years. On the other hand, allow developing countries a maximum of 2 degrees of climate change for a period of one hundred years, thereafter the definition for developed countries should apply.

What is view?

]]> http://www.wesonga.com/my-docs/climate-governance-and-the-question-of-equity.htm/feed Measuring socio-economic impacts of HIV/AIDS & malaria http://www.wesonga.com/workshops/measuring-socio-economic-impacts-of-hivaids-malaria.htm http://www.wesonga.com/workshops/measuring-socio-economic-impacts-of-hivaids-malaria.htm#comments Sat, 05 Jun 2010 21:20:53 +0000 wesonga http://www.wesonga.com/workshops/measuring-socio-economic-impacts-of-hivaids-malaria.htm

Wesonga presenting at Accra conferenceIt seems straightforward, but a second-long pause reveals serious hitches especially when you intend to carry out this measurement in a developing country. Inwent (read as Invent), a Germany based, non-profit organisation with a major focus on Capacity Building with worldwide operations dedicated to human resource development, advanced training and dialogue delved into this important, but often ignored subject in Africa. Dr. Bernd Gutterer facilitated the three months online workshop and later the on-site discussion in Accra, Ghana with participation from representatives from different African countries.

Impact measurement studies for health related indicators normally make the following assumptions. (i) That measurement of socio-economic impacts in the strict sense should be subjected to a relatively longer time period otherwise such studies are measuring effects. This implies, therefore that if a study is about impact measurement of Malaria, unlike HIV/AIDS, for example, the ultimate sample would be composed of mortality due to malaria. Thus, such studies are difficult because they would be focusing on  forensic autopsy. Otherwise, malaria disease is known to have a cure with known average treatment period, but also known to recur and kill. (ii) That the sample should focus on identical populations based on age, sex, location, household income, accessibility to a health unit and where possible, other genetically-related considerations such as blood groups. (iii) That the basic sampling unit be a person and not a household because different people although live in the same household present different scenarios.

Developing countries present a very difficult case in measuring HIV/AIDS & Malaria and their socio-economic impacts. Among the challenges presented are deriving the actual indirect costs of a disease.  Most studies that attempt to measure the socio-economic impacts establish that the indirect costs are actually greater than the direct cost of treatment! This is unrealistically unacceptable, but practically true for most African countries. The second challenge is estimation of the real economic productivity of someone who has either been unemployed or partially employed for a very long time! And the third challenge is both quantification and estimation of social benefits accumulated by the patient whose social behavior is stochastic.

What is your take on this?

]]> http://www.wesonga.com/workshops/measuring-socio-economic-impacts-of-hivaids-malaria.htm/feed Data discrepancy: a key challenge to monitoring MDGs in Africa http://www.wesonga.com/workshops/data-discrepancy-a-key-challenge-to-monitoring-of-mdgs-in-africa.htm http://www.wesonga.com/workshops/data-discrepancy-a-key-challenge-to-monitoring-of-mdgs-in-africa.htm#comments Tue, 16 Feb 2010 15:11:01 +0000 wesonga http://www.wesonga.com/uncategorized/data-discrepancy-a-key-challenge-to-monitoring-of-mdgs-in-africa.htm

Data discrepancy is a situation whereby different data collection methods about the same indicator for the same period of time produce statistically different data points. By statistically different, it is meant that although the sampling errors are indicated for the different data points, the data still remains different. It worth noting that differences in the data points are expected for as long as different methodologies are employed in data planning and collection, even for the same indicator. However, it becomes a challenge if the differences are significantly different at the same level of significance. One may require to be availed with all the metadata of the data in question such as the survey design, sample size, population size and number of indicators so as to analyse and come up solutions for the discrepancies in order to inform the indicator monitoring process. Failure to have a metadata analysis system at country level poses a bigger challenge in combating data discrepancies in Africa at country levels.

On the contrary, having multiple sources of data can be healthy for the measurement and monitoring of MDGs at country level. Validation is a basic tool for ensuring data quality and yet there is no better way of data validation than either a repeated survey or use of a similar or multiple data sources. A repeated survey seems a more costly venture than having a compromise amongst multiple data sources measuring the same indicator. Basically, sample surveys are designed to meet specific objectives, but it is also true that certain sample surveys may have the same specific objectives; hence the likelihood to yield more reliable data when used for validation purposes to improve data quality for the timely monitoring of MDG indicators.

Data discrepancy therefore poses a more fundamental challenge to researchers in the field of statistics to develop more reliable methodologies for detection of data discrepancies and align them accordingly. Data discrepancy management systems (DDMS) can then be proposed and developed to handle this eminent challenge to monitoring of MDGs in Africa as we tend towards the year 2015.

Kindly give your opinion below…

]]> http://www.wesonga.com/workshops/data-discrepancy-a-key-challenge-to-monitoring-of-mdgs-in-africa.htm/feed Are model-based data estimates deterrent to monitoring MDGs in Africa? http://www.wesonga.com/workshops/are-model-based-data-estimates-deterrent-to-monitoring-of-mdgs-in-africa.htm http://www.wesonga.com/workshops/are-model-based-data-estimates-deterrent-to-monitoring-of-mdgs-in-africa.htm#comments Tue, 16 Feb 2010 14:29:56 +0000 wesonga http://www.wesonga.com/workshops/are-model-based-data-estimates-deterrent-to-monitoring-of-mdgs-in-africa.htm A Model is a representation of the actual observable phenomenon. In the absence of an observable phenomenon, a framework is developed to represent it in order to proceed with the desirable inferences. In aeronautics, for example, costs are substantially reduced when one uses a model to design and test an aircraft or its spare parts before the actual mass manufacturing process is initiated. Thus, we can classify three basic approaches of generating statistical data. They are: design based approach where sampling techniques are dully employed to collect reliable data for decision making or even to monitor MDGs. The second approach is the Model-assisted approach where data gaps are filled using tested and approved statistical methodologies for missing data so as to confidently derive population estimates. The third approach is model-based where all the data for a given indicator are solely generated using a given model that has been tested using historic data over time. This is normally done through aspects commonly referred to as data simulation and modeling for extremely hard to get obtain data. The major aim of application of this kind of approach is to maintain sustainability in planning, decision making, monitoring and inferences about a particular development indicator.

During the presentation at the workshop on the development and management of MDGs databases at the country level held in Nairobi, Kenya for 15th to 19th February, a presenter in his analysis gave model-based estimates as one of the causes of discrepancies between national and international data. Whereas it might hold some drops of water, there is no sufficient evidence to believe that the national estimates are better given the fact that they suffer from very high sampling errors of the magnitude higher than 11 percent with low study coverage, bias error and poor indicator specifications. Many developing countries are faced with scarcity of sufficient and consistent data to monitor MDGs. Besides, between the year of MDG declaration, 2000 and 2005, no data, hence no report was published showing the status of MDGs in any African country. For example, in Uganda, the first ever substantive MDG report was released in the year 2007 and as I write, the second MDG country status report is yet to be released. Therefore, under such circumstances, shouldn’t model based data be used to facilitate monitoring of MDGs at country level? What about situations whereby resources both financial and human resource are insufficient for carrying out reliable surveys and yet the country or the international community need to know its MDG status? On the positive note, what if the country has good statisticians that can develop data based models whose sensitivity analysis show very high correlations between expected data and model-based data?

In conclusion, model based data are increasingly becoming an integral part in the monitoring of MDGs in the both developed and developing countries, but more so in the developing countries majorly because of their unsustainable ability to regularly collect quality data over time.

What is your opinion?? Kindly give your opinion below…

]]> http://www.wesonga.com/workshops/are-model-based-data-estimates-deterrent-to-monitoring-of-mdgs-in-africa.htm/feed What then are statistics? http://www.wesonga.com/my-docs/what-then-are-statistics.htm http://www.wesonga.com/my-docs/what-then-are-statistics.htm#comments Tue, 06 Oct 2009 07:57:36 +0000 wesonga http://www.wesonga.com/my-docs/what-then-are-statistics.htm


In the previous posting, we made an attempt to understand what statistics is all about. Thanks for the compliments from all readers especially about the article ‘statistics aren’t just figures’, may God bless you to read even more!

Let’s make an attempt to define statistics without reservations. Statistics is all encompassing and therefore, it would be egotistical to restrict its definition to only quantitative aspects. In the beginning when the author of this article was growing up as a young statistician in a remote village, his teachers in primary school by then sarcastically didn’t know the existence of statistics. They simply knew mathematics, and other subjects like science and social studies! Someone has just told me here that even up to now, mention of statistics begins somewhere after primary education level. Corollary, the beginning of statistics in Uganda comes after a child is a teenager. Possibly, the curriculum developers think this is the right age for the child to appreciate the otherwise all inclusive subject of statistics.

Harris and Taylor (2009) in a forward of their book Medical Statistics Made Easy wrote “A love of statistics is, oddly, not what attracts most young people to a career in medicine and I suspect that many clinicians, like me, have at best a sketchy and incomplete understanding of this difficult subject.”

According to the American Statistical Association, Statistics is the scientific application of mathematical principles to the collection, analysis, and presentation of numerical data. Statisticians apply their knowledge of statistical methods to a variety of subject areas, such as biology, economics, engineering, medicine, public health, psychology, marketing, education, and sports.

A more open definition of statistics is given by the department of statistics of the University of Nebraska, Lincoln whose motto is turning data into knowledge to solve real world problems. They define statistics as the development and application of methods to collect, analyze and interpret data. Modern statistical methods involve the design and analysis of experiments and surveys, the quantification of biological, social and scientific phenomenon and the application of statistical principles to understand more about the world around us.

An interesting definition is that statistics is the study of data. This implies that a study involving either quantitative or qualitative data is basically statistics.

Many issues arise from these definitions, but I will just conclude without delving into the discussion by pointing out few generic questions. Does statistics imply strictly quantitative data? Hence, is statistics restricted to the study involving strictly numeric data? Is quantification of qualitative data a function of a statistics? Thus, is qualitative study independent of statistics? 

]]> http://www.wesonga.com/my-docs/what-then-are-statistics.htm/feed On measuring democracy, governance and human rights http://www.wesonga.com/workshops/on-measuring-democracy-governance-and-human-rights.htm http://www.wesonga.com/workshops/on-measuring-democracy-governance-and-human-rights.htm#comments Sat, 05 Sep 2009 12:17:15 +0000 wesonga http://www.wesonga.com/workshops/on-measuring-democracy-governance-and-human-rights.htm


The magnitude of statistics should not be limited by only what is taught in class, rather there is a great need to attempt looking outside the statistical black box. Participating in the workshop at Windhoek, Namibia from 3rd – 16th October 2007 that was organized and funded by the Inwent Centre for Economic, Environmental and Social Statistics was an eye opener of how wide and broad statistics can be. The theme was ‘On Measuring Democracy, Governance and Human Rights’ and managed by Dr. Uwe Singer. At first, I wanted to turn down the offer to attend reasoning that I have nothing to do with political issues since at no time have I ever dreamt of being even a local council one chairman and worse still, although it is my right, I have never voted! Agnes, Abraham and I thought we were going to learn more about Democracy, Governance and Human rights aspects, but instead we went through the application of statistics.

 

It was not long after introductions that Dr Ken Mease of the University of Florida and University of the West Indies, Trinidad introduced the Statistics Camp and this took the lion’s share of the time as he led us through surveys, data quality and measurements. Some of the survey types he introduced that of course seemed unique from what you learn from the sampling class included.

  • Country reports
  • Cross-comparative surveys
  • Expert assessments
  • Expert panel
  • Household survey
  • Mass opinion survey
  • Media Indicators
  • Business surveys

I guess you may be wondering the theoretical linkage of this kind of survey type classification, but the truth is that these types are generated from the practice of statistics. It does not matter whether it is you, the Statistician or otherwise practicing it, but comes as a result of the need to see statistics at work.  

 

The workshop advanced and stochastically need arose of practicing what we had learned. As participants put it, ‘talking statistics is one thing, practicing it and bringing it to live, is another – at times quite exhausting, but equally exciting issue.’. After ten days of theoretical and group work, the trainees were asked to split up into groups, put on their walking shoes and do a comparative household survey in three different parts of the Namibian capital, Windhoek: Klein Windhoek, a residential area; Windhoek West, with a lot of middle-class citizens and Katutura, an area consisting of poorer neighborhoods. ”Are you happy with the delivery of public services like water, sanitation, electricity and garbage collection?” was the overall question to ask the citizens of the different parts of the city.  “How do you consider the performance, fairness, responsiveness, participation and accountability in the provision of these services?”

 

What seemed to be an easy task at first glance, turned out to be challenging – for various and sometimes unforeseen reasons. For example, I was in the group that walked out to Windhoek West. We were very early and we were confronted with a late waking up in that area. We had to deal with vicious barking dogs of all shapes and sizes. We spent most of the first hour ringing several gate bells in vain and hoping that the fences were strong enough to restrain the barking dogs. Some of the residents, mainly of European descent, did not even realize that this was a household survey, they thought, Africans knocking on doors during a weekend are simply seeking for servants jobs whereas others were reluctant to answer the questions.

 

We learnt that every household survey should be announced and advocated properly in advance, in order to be most successful and effective. Despite these difficulties, the comparative data collection exercise was an enriching experience, according to the participants. They also appreciated that they were greatly involved in the data entry, analysis and interpretation. Donald, a lawyer and one of the Ugandan participants put it as “This was truly statistics in motion!” The interesting part however, was that I took over as a facilitator towards the end of the workshop as I handled the nasty parts of data management and analysis of the aspects of Measuring Democracy, Governance and Human Rights.

 

What is your view?

]]> http://www.wesonga.com/workshops/on-measuring-democracy-governance-and-human-rights.htm/feed Statistics aren’t just figures http://www.wesonga.com/workshops/statistics-arent-just-figures.htm http://www.wesonga.com/workshops/statistics-arent-just-figures.htm#comments Fri, 04 Sep 2009 09:19:38 +0000 wesonga http://www.wesonga.com/workshops/statistics-arent-just-figures.htm


Defining statistics these days has become a statistic itself. The institute of statistics and applied economics, ISAE, the only and only recognized statistical training institute for the Anglophone speaking countries in the region and Africa as a whole is associated with the saying ‘using figures to make informed decisions’. So would you like to believe like others who read ISAE fliers and its website  ( http://www.statistics.mak.ac.ug ) that figures are synonymous to statistics? Maybe that could be one definition, but remember there should be only one correct definition else it is incomplete thus incorrect!

Alright at the recently concluded 57th ISI conference, the first ever ISI conference (refer to http://www.wesonga.com ), that took place in Durban from 16th to 23rd August 2009 there was a display of papers on statistics. One motivating finding was that there were basically two types of papers presented, one was statistics and the other was application of statistics. Oh no, do not ask me yet what statistics is, but the conference reality was that in some sessions, a paper would be presented introducing a new theorem, its lemma and proofs! In the end one may or may not conclude with just an example, not case study findings – no figures at all!  So is theorem tantamount to statistics?

If we took an example of a statistic, the most popular one which even politicians know, known as the mean, a measure of central tendency and we translated it into something like the one below.

  1. for (i=0; i <= N; i++)
  2. {
  3.             sum += X[i];
  4. }
  5. mean=Sum/N;

We should note that the given five statements above are referred to as code, but not figures. So is code equivalent to statistics?

Hopefully, we now know what statistics is else if not yet known then watch this space, but  you may also contribute for a more detailed discussion and definition of statistics.

Over to you!!

]]> http://www.wesonga.com/workshops/statistics-arent-just-figures.htm/feed ISI Durban 2009 - first ever http://www.wesonga.com/workshops/isi-durban-2009-first-ever.htm http://www.wesonga.com/workshops/isi-durban-2009-first-ever.htm#comments Sat, 01 Aug 2009 10:28:59 +0000 wesonga http://www.wesonga.com/workshops/isi-durban-2009-first-ever.htm Many changes we deserve are happening in the world, not by mistake but by the efforts of our predecessors, both living and resting. In the year 2007, the first ever international young statisticians conference took place in  Pretoria, the capital city of the Republic of South Africa (ref. to my earlier article). At the conference, Uganda was second most populous in representation with over thirty participants only next to the hosts, the republic of South African (nothing to do with its known high fertility rates)!

Plans were hatched and thanks to the Almighty, a declaration was made that Africa will host another first ever International Statistics Institute conference on the African continent at one of the most beautiful city of Durban still in the Republic of South Africa. This 57th conference is slated to commence on the 16th till 22nd August, I mean this month, remember today is the 1st August, 2009. Never has this ever happened in Africa, note none of all the 56 conferences held before! Actually the president of ISI (www.isi.cbs.nl) in her closing remarks of the International Young Statisticians conference wondered the level at which Young African Statisticians really displayed statistics during the presentations of their papers, remember stochastic model of statistical displays!! Actually, none of them thought there was going to be any significant methodological and applicable statistics at the conference, but alas, they got convinced and decided that we now host the ISI-2009 at Durban city port! For conference details, go to www.statssa.gov.za/isi2009.  So let’s fold our sleeves, unbutton our shirts and fold our trousers to get ready not only to attend, but to participate fully at the first ever International Conference in South Africa. Whether, it means walking, we will because yes, yes we can, go anywhere on our continent!

Other first ever cases include: first ever black US president, first ever woman ISI president, first ever woman ISAE Director, first ever Math Makerere University VC,…. to mention, but a few!!

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