Musing About the Digital Divide

Rich countries have per capita GDP’s that are orders of magnitude greater than those of the poorest countries. There is a tendency for rich countries to spend a larger portion of their GDP on ICT. Thus, rich countries spend orders of magnitude more money per person on ICT than do poor countries. So too, the rich in any country tend to appropriate more of the benefits of the Information Revolution than do the poor.

The term “Digital Divide” was coined to dramatize these disparities. The term seems often to hide rather than clarify the equity issues created by the Information Revolution.

Let me discuss for a moment the “food divide”. Rich people spend more money on food than do poor people (although, in this case, they tend to spend a smaller portion of their income on food than do the poor). In most of Asia, for example, poor people eat a lot of rice. Rich people in the same countries do not simply eat several times as much rice per capita as do the poor; rather they diversify their diet, eating meat and other grains, vegetables, and indeed even going out to restaurants to eat professionally prepared and served food.

So too for ICT, the rich do not simply buy hundreds of times as many transistor radios and cell phones as do the poor. Indeed, in rich countries governments and industries invest in kinds of ICT that are scarcely known in poor countries.

Hardware is not the Issue

When I was starting out in the ICT business in the early 1960’s, computers were expensive, and software was often free from users groups. In those ancient days, we grew accustomed to thinking of the hardware as defining ICT capacity. In subsequent decades, in part due to the operation of Moore’s law, organizations learned that hardware was a small part of ICT, and investments in software, staff and organization dwarfed the cost of hardware.

But today, many of the discussions of the digital divide focus on access, and especially access to the Internet, computers, and telephone connections. Of course, access indicators are easy to measure, and indeed important in themselves.

I would suggest that we should be more interested in the distribution of benefits from the Information Revolution, than in distribution of access to hardware! Of course, it is hard if not impossible to identify and quantify the benefits people receive that are attributable to ICTs, other than the most trivial. How does one quantify the increase in income poor people see as a result of a country’s investment in industrial ICTs? The improvement in health? In education? Still the difficulty in quantifying such benefits does not make them less important.

E-readiness indicators may also be seen as related to a Digital Divide, in that the gap between the e-ready and the e-unready might be so described. The e-readiness indicators too, often include a hardware-focused connectivity index, as well as policy indicators. But the e-readiness indicators I know of don’t describe the readiness of societies to utilize ICTs to meet the basic human needs of their entire populations.

Equity and the Digital Divide

The recently published World Development Report, is titled Equity and Development. Equity, is defined there as “primarily as equality of opportunities among people”. There has been some discussion of equitable access to the Internet and ICT as a criterion for the Digital Divide. But what is equitable access to ICT, and is equity enough?

I would say it is more equitable that villagers in South Asia have access to public telephones than that they do not, even realizing that middle class urban residents have phones in the home and workplace as well as personal phones. Some access would be a start if not equity.

Consider, however, life expectancy. Is it equitable that life expectancy in some countries in Africa is less than 40 years, while we know from Japan that human life expectancy in a society can be more than 80 years? It seems to me that there are basic human rights that trump “equality of opportunity”. It is simply not fair that so many should die so young, when society is capable of so much more. So too, I would suggest it is not equitable when one person dies for lack of access to ICT (say to call for transport to a hospital or for lack of an x-ray machine in that hospital) where a richer neighbor with the same condition but better ICT would have lived.

Consider further the equity of a society the rations health services, denying the poor as seems always to be the case, but does not use ICT to maximize the amount of health service that can be delivered with the existing budget. Nor uses ICT to target the existing health services most equitably. Or consider the society in which some people go hungry, that does not use ICT to maximize the availability of food, nor to do everything possible to see that the hungry have access to that food. The “digital divide” in these societies seems to me to be measured in disease and hunger, death and disability.

Examples: What ICT do Rich Countries Buy that Poor Countries Don’t

The quick defeat of Iraq’s large, experienced military, twice, by the ICT intensive military of the United States illustrates that rich countries buy more ICT intensive, and better weapons than do developing nations. The last Economist Technology Quarterly, however, describes software used by the U.S. military that accurately predicts the outcome of such conflicts, and indeed seems designed and likely to predict the outcome of a large variety of military conflict. The Tactical Numerical Deterministic Model TNDM) was used in 1990, in conflict with the conventional wisdom of the time, to accurately predict that the war would be quick, the ground war very short, and U.S. casualties would be quite low.

“To model a specific conflict,” according to the Economist, “analysts enter a vast number of combat factors, including data on such disparate variables as foliage, muzzle velocities, dimensions of fordable and unfordable rivers, armour resistance, length and vulnerabilities of supply lines, tank positions, reliability of weapons and density of targets. These initial conditions are then fed into the mathematical model, and the result is a three-page report containing predictions of personnel and equipment losses, prisoner-of-war capture rates, and gains and losses of terrain.” What gives TNDM its edge is “the mountain of data on which it draws, thought to be the largest historical combat database in the world. The Dupuy Institute's researchers comb military archives worldwide, painstakingly assembling statistics which reveal cause-and-effect relationships, such as the influence of rainfall on the rate of rifle breakdowns during the Battle of the Ardennes, or the percentage of Iraqi soldiers killed in a unit before the survivors in that unit surrendered during the Gulf war.

“Analysts then take a real battle or campaign and write equations linking causes (say, appropriateness of uniform camouflage) to effects (sniper kill ratios). These equations are then tested against the historical figures in the database, making it possible to identify relationships between the circumstances of an engagement and its outcome.”

In this case, the U.S. Institute has not only computerized an extensive mathematical model, but it has spent the money to compile a knowledge base covering actual experience in many battles in many countries.

Perhaps a better example is product life-cycle management (PLM) software. PLM suites of software evolved from computer aided design (CAD). Another article in the Economist’s Technology Quarterly says PLM suites “have now been extended so that they are not just for designers and engineers, but also include tools for use by senior managers and marketing people. According to AMR, CAD still accounts for 53% of spending on PLM, but non-CAD spending is growing twice as fast, or 13% a year. The latest PLM systems may include “requirements management” and “portfolio management” tools, so that managers can, for example, look across the range of products in development to make sure they match the demands of the market in question. If they do not, then specifications can be tweaked or products killed off.



“PLM systems also allow packaging to be mocked up quickly and then displayed to focus groups via the web, so that the most effective packaging can be identified. In theory, all of this means that the needs of the market can be anticipated and communicated back along the chain to the research and development department. PLM has, then, evolved from humble design tools into elaborate systems that help companies develop and manufacture products that their customers actually want to buy.”

In this case, it is rich corporations that are buying PLM suites that corporations with shallower pockets can not afford, and of course the rich corporations are predominantly headquartered in rich countries. PLM gives the companies that utilize it a significant competitive advantage in many fields, from automotive and aircraft manufacture to research-intensive pharmaceuticals.

The Economist goes on to note that “for companies that rely on outsourcing or have multiple design and manufacturing centers around the world, PLM can simplify things by allowing people in different countries to communicate and collaborate within a single, secure environment. Rolls Royce, for example, used PLM to facilitate around-the-clock development by engineering teams in Britain, India and America of the Trent 900 engine for the Airbus A380.”

This raises another point: enterprises in developing countries may well have limited access to PLM, and their work coordinated by PLM, while seeing the control of the work going to larger corporations in developed countries who bought the systems.


The Counterexample Or Is It So Different

The United States spends a lot of money on weather prediction, disaster preparedness, and disaster relief. A huge network of sensors gathers data on the Atlantic and Caribbean, including a network of satellites devoted to remote sensing. Specially equipped aircraft fly through tropical storms and hurricanes. Their data is analyzed and trajectories accurately predicted by supercomputers. Computer programs, many very good, predict the impact of different storm conditions on a vast number of vulnerable sites. Wireless radio systems are pre-positioned to be available if storms knock out regular communications. The rich network of electronic media practice storm warnings. Elaborate call center systems are available to handle emergency communications, and command centers with extensive command and control software have been funded. Now global positioning systems (GPS) are being installed in the fleet of trucks that handle relief shipments to allow micromanagement of relief supplies from a command center.

The experience with Hurricanes Katrina and Rita has shown that the technology does little good when placed under the control of inexperienced managers of understaffed agencies! It should also be noted that those who suffered most from the hurricanes were not those who understood the warnings, drove their cars inland, and stayed in good hotels or with friends or relatives; it was those already deprived by poverty and incapacity who suffered most greviously, frequently those also suffering from social or racial discrimination — people on the wrong side of the real “digital divide”.

So what?

The rich countries are spending more on ICT, and the investments are buying knowledge and information processing capacities that potentially increase productivity and improve targeting of efforts. The increase in productivity, in relatively egalitarian rich societies, benefits large numbers of people, helping them further away from all aspects of poverty. This ICT investment provides a competitive advantage for the rich countries versus the poor. The real digital divide is not measured in personal computers and cell phones, but in the power that people and societies have to appropriate ICT for their benefit.