When a new technology is invented, the number of people who are using it is often small for a while, then increases quickly, then slows down. Some earlier posts showed the emergence pattern in different countries of ICT technologies (for example, here).
There are competing explanations how the characteristic S-curve arises. One is that people start using the technology as they are exposed to it by other users. When there are few people using the technology, not too many people are exposed to it and it spreads slowly. When more people are using it, exposure is greater and spread is faster until almost everyone is using it and so spread slows again. Other explanations are based on the speed of the technology's acceptance or to its skill requirements and the distribution of people's skills.
The explanations give the same predicted curves, but the implications for technology policy are different. If technology spread is limited by exposure, then a policymaker may best promote it by publicity or other measures to increase knowledge of it. If on the other hand, spread is limited by skills, then training is the best way of promoting it.
For readers interested in the mathematical models, Wikipedia has a page on them here. The derivations corresponding to each of the above explanations would be a little different, but the outcomes are similar. The models are also used in modelling disease spread, so are very useful for comparatively little work.
Tuesday, 30 June 2009
Free good-quality maths software
Maxima (available at no charge here) does maths analysis, and lots of it: integration, differential equations, simplifications, graphs, roots, and more. Thanks to the public spirited providers. There is science and other software on the host site, SourceForge, but I have only looked at a fraction of it to date.
Contender for charity of the year - Toilet Twinning
A strong contender for charity of the year is Toilet Twinning (here). You pay £60 and your toilet is twinned with one built with the money in Burundi. You get a picture of the unique latrine. That's it.
The project is run by a major UK charity, so presumably its value-for-money is comparable to other aid projects. But even if the money was being burnt; even if it was being used for palaces; look at the logo...
I have no affiliation with Toilet Twinning, and they had no connection with this post.
The project is run by a major UK charity, so presumably its value-for-money is comparable to other aid projects. But even if the money was being burnt; even if it was being used for palaces; look at the logo...
I have no affiliation with Toilet Twinning, and they had no connection with this post.
Friday, 26 June 2009
Pressing the economic spring in Eastern DR Congo
I wrote in a previous post that Western DR Congo could and should develop economically as rapidly as possible despite the fighting in Eastern Congo, even if no adequate solution can be found for the East. The advice leaves open the question of what to do there.
I have been thinking about the description in my recent post on output (here) of how some economies were like coiled springs when the government enforced conditions that varied from growth maximisation. The economies had high levels of technological knowledge or education, but restrictions on accumulation. When the economies transitioned to an open capitalism, they experienced rapid growth.
There is a parallel between these countries during their pre-maximisation stages and Eastern Congo, in that private accumulation of fixed physical assets is very difficult there because of the conflict. So preparatory human capital and technological skill accumulation might be a way of preparing the economy to bound forward after the end of conflict.
My idea is not advanced at the moment, and I am trying things out. I think that it might turn on flexible education, creating the conditions to promote such education, and exposure to international technology.
I have been thinking about the description in my recent post on output (here) of how some economies were like coiled springs when the government enforced conditions that varied from growth maximisation. The economies had high levels of technological knowledge or education, but restrictions on accumulation. When the economies transitioned to an open capitalism, they experienced rapid growth.
There is a parallel between these countries during their pre-maximisation stages and Eastern Congo, in that private accumulation of fixed physical assets is very difficult there because of the conflict. So preparatory human capital and technological skill accumulation might be a way of preparing the economy to bound forward after the end of conflict.
My idea is not advanced at the moment, and I am trying things out. I think that it might turn on flexible education, creating the conditions to promote such education, and exposure to international technology.
Is female parliamentary representation or professional representation more associated with greater gender equality?
My previous post (here) looked at the UN's gender equality measure (GEM) and its components. Across countries, there was not a strong relationship between parliamentary representation of women and their representation in professional and technical jobs. I decided to look at the relation in more detail.
The first graphics show the ratio of women's share in parliamentary seats to their percentage representation in professional and technical jobs. For ease of representation, I have split the data into two graphs, showing high and low ratios. At the top of the list comes highly gender equal countries in Northern Europe, as measured by the GEM, highly unequal countries in South Asia, and a scattering of other countries including the two East African countries of Tanzania and Ethiopia. At the base of the list is low gender equality former socialist countries and Middle Eastern countries.
The third graph shows the ratio plotted against the GEM measure. As the ratio of parliamentary representation to professional representation increases, gender equality increases but more slowly than the ratio.
The relationship between the ratio and GEM appears to be non-linear, so I took logs before regressing GEM on the ratio. Including the three South Asian countries of Pakistan, Nepal, and Bangladesh weakened the relation considerably and were dropped. The outcome was a good-fitting relation
ln(GEM) = -0.12 [0.06] + 0.40 [0.06] * ln(parl/prof) + robust error
(R^2=0.61; n=78; s.e.s in brackets)
or GEM = 0.89*(parl/prof)^0.4, with some error.
The regression and graphs raises some questions:
1. if there is a causal link for parliamentary representation increasing gender equality more than professional representation does, or
2. if it is the other way round, so gender equality brings increased parliamentary representation above increased professional representation,
3. why East Africa (and into the Great Lakes regions too) have high levels of female parliamentary representation,
4. why in South Asia parliamentary representation is associated with less gender equality than elsewehere, and
5. why former communist countries have far more professional equality than political equality.
[A technical clarification on spurious regressions:
I thought a little about the estimation ln(GEM) = -0.12 [0.06] + 0.40 [0.06] * ln(parl/prof) + robust error. There is a technical issue that many readers may wish to ignore, but I should really clarify to avoid misleading impressions.
GEM has a form (approximately) like parl+prof+another term. ln(GEM) may be roughly approximated as parl+prof+other term. ln(parl/prof) may be roughly approximated as parl-prof. Thus we have a regression that looks a bit like parl+prof = a + b*(parl-prof). Now if parl and prof are independent, we would have a regression estimate for b (assuming parl and prof are zero mean to simplify the algebra) of sum((parl-prof)*(parl+prof))/sum((parl+prof)^2). Taking expectations and using limiting theorems we have b=sum(E(parl^2)-E(prof^2))/(positive number). If E(parl^2) does not equal E(prof^2) then we obtain a positive coefficient for b. It has arisen solely by virtue of the algebraic manipulations used; any two variables parl and prof would serve equally well.
If there is a further relationship between parl+prof and parl-prof not produced solely by manipulations, then by the reverse argument we would have parl and prof not independent (perhaps this statement could be made more precise). Now the observation made in the earlier post was that there is not a strong relationship between the two; in fact correlation is very low. But looking at their graph, there seems to be a relationship over much of the variables' domains. So the variables do not seem to be independent, although having a low correlation. Thus, the relationship between ln(GEM) and ln(parl/prof) does not seem to be spurious.]
The first graphics show the ratio of women's share in parliamentary seats to their percentage representation in professional and technical jobs. For ease of representation, I have split the data into two graphs, showing high and low ratios. At the top of the list comes highly gender equal countries in Northern Europe, as measured by the GEM, highly unequal countries in South Asia, and a scattering of other countries including the two East African countries of Tanzania and Ethiopia. At the base of the list is low gender equality former socialist countries and Middle Eastern countries.
The third graph shows the ratio plotted against the GEM measure. As the ratio of parliamentary representation to professional representation increases, gender equality increases but more slowly than the ratio.
The relationship between the ratio and GEM appears to be non-linear, so I took logs before regressing GEM on the ratio. Including the three South Asian countries of Pakistan, Nepal, and Bangladesh weakened the relation considerably and were dropped. The outcome was a good-fitting relation
ln(GEM) = -0.12 [0.06] + 0.40 [0.06] * ln(parl/prof) + robust error
(R^2=0.61; n=78; s.e.s in brackets)
or GEM = 0.89*(parl/prof)^0.4, with some error.
The regression and graphs raises some questions:
1. if there is a causal link for parliamentary representation increasing gender equality more than professional representation does, or
2. if it is the other way round, so gender equality brings increased parliamentary representation above increased professional representation,
3. why East Africa (and into the Great Lakes regions too) have high levels of female parliamentary representation,
4. why in South Asia parliamentary representation is associated with less gender equality than elsewehere, and
5. why former communist countries have far more professional equality than political equality.
[A technical clarification on spurious regressions:
I thought a little about the estimation ln(GEM) = -0.12 [0.06] + 0.40 [0.06] * ln(parl/prof) + robust error. There is a technical issue that many readers may wish to ignore, but I should really clarify to avoid misleading impressions.
GEM has a form (approximately) like parl+prof+another term. ln(GEM) may be roughly approximated as parl+prof+other term. ln(parl/prof) may be roughly approximated as parl-prof. Thus we have a regression that looks a bit like parl+prof = a + b*(parl-prof). Now if parl and prof are independent, we would have a regression estimate for b (assuming parl and prof are zero mean to simplify the algebra) of sum((parl-prof)*(parl+prof))/sum((parl+prof)^2). Taking expectations and using limiting theorems we have b=sum(E(parl^2)-E(prof^2))/(positive number). If E(parl^2) does not equal E(prof^2) then we obtain a positive coefficient for b. It has arisen solely by virtue of the algebraic manipulations used; any two variables parl and prof would serve equally well.
If there is a further relationship between parl+prof and parl-prof not produced solely by manipulations, then by the reverse argument we would have parl and prof not independent (perhaps this statement could be made more precise). Now the observation made in the earlier post was that there is not a strong relationship between the two; in fact correlation is very low. But looking at their graph, there seems to be a relationship over much of the variables' domains. So the variables do not seem to be independent, although having a low correlation. Thus, the relationship between ln(GEM) and ln(parl/prof) does not seem to be spurious.]
freerice.com expands to foreign languages, maths, science, and art
When last featured on this site, freerice.com was giving charitable donations of food if visitors to its site performed well at a quiz. Back then, one had to say what English words mean. Now the site has been expanded to quizzes about other languages, maths, chemistry, and art. I like, j'aime, yo gusto.
Monday, 22 June 2009
Changing food production techniques
For the food production techniques in a country to change, its food producers must know about the possibility of changing, have access to the alternative techniques, and decide that the benefits of changing are greater than the costs. The costs of changing are often discussed in the literature on technology transfer. Features that are relevant to costs include:
- whether the new techniques are suitable for the company's available productive resources (for example access to water),
- how much finance and capital is required (techniques requiring very large-scale production may be unsuitable for instance),
- how much knowledge is required and its learning costs,
- how geographically distant the company is from expertise about the technique,
- how much support and advice it can get,
- whether the techniques are associated with the use of commercialised or free products (for example in using genetically modified seeds),
- and whether the techniques are socially suited to the area (for example meat rearing in a certain way may have social meaning).
For a private company, the benefits can be measured as income, which depends on end product market demand. When technology transfer research examines the product market, it is often by assuming a simple form of substitution between the new technology's product and the old technology's product. For particular forms of food, more sophisticated modelling of the end market is possible. The demand for the end product is influenced by factors including:
- price,
- the exact type of the product (for example the type of coffee),
- preparation given to the product (for example by cutting into chunks before selling).
- nutritional content,
- safety (some foods being associated with more health risks than others),
- public information about the product's benefits or risks,
- appearance,
- perceptions of naturalness and wholesomeness,
- advertising and branding,
- place of consumption (for example in a restaurant or at home),
- social context (some foods may be considered suitable in certain circumstances, for example holidays),
- familiarity,
- habit,
- and population demographics.
The chances of a technique being successful in a country depends on the cost and benefit factors being favourable for profit. Other things being equal, shifting a single factor in favour of the technique will make it more likely that it is successful in the country. So a technique where the knowledge requirements are not excessive in the country is more likely to be successful than one where great knowledge advances are required. A technique which does not create health concerns in the public is more likely to be successful than one that does. Countries have different circumstances, and no single technique can be successful in every country. It matters to choose techniques well.
- whether the new techniques are suitable for the company's available productive resources (for example access to water),
- how much finance and capital is required (techniques requiring very large-scale production may be unsuitable for instance),
- how much knowledge is required and its learning costs,
- how geographically distant the company is from expertise about the technique,
- how much support and advice it can get,
- whether the techniques are associated with the use of commercialised or free products (for example in using genetically modified seeds),
- and whether the techniques are socially suited to the area (for example meat rearing in a certain way may have social meaning).
For a private company, the benefits can be measured as income, which depends on end product market demand. When technology transfer research examines the product market, it is often by assuming a simple form of substitution between the new technology's product and the old technology's product. For particular forms of food, more sophisticated modelling of the end market is possible. The demand for the end product is influenced by factors including:
- price,
- the exact type of the product (for example the type of coffee),
- preparation given to the product (for example by cutting into chunks before selling).
- nutritional content,
- safety (some foods being associated with more health risks than others),
- public information about the product's benefits or risks,
- appearance,
- perceptions of naturalness and wholesomeness,
- advertising and branding,
- place of consumption (for example in a restaurant or at home),
- social context (some foods may be considered suitable in certain circumstances, for example holidays),
- familiarity,
- habit,
- and population demographics.
The chances of a technique being successful in a country depends on the cost and benefit factors being favourable for profit. Other things being equal, shifting a single factor in favour of the technique will make it more likely that it is successful in the country. So a technique where the knowledge requirements are not excessive in the country is more likely to be successful than one where great knowledge advances are required. A technique which does not create health concerns in the public is more likely to be successful than one that does. Countries have different circumstances, and no single technique can be successful in every country. It matters to choose techniques well.
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