Essays on Topics in Science & Innovation

Energy Return on Energy Invested: An Historic and Economic Treatise

2010-09-18T17:28:00.000-07:00

Introduction

Energy return on energy invested, or EROI, is a tool of net energy analysis, a methodology that attempts to compare the amount of energy delivered to society by a technology to the total energy required to find, extract, process, deliver, and otherwise render that energy to a usable form.

Net energy analysis was developed in response to the emergence of energy as an important economic, technological, and geo-political tool following the energy price increases in the mid-1970s and early-1980s. Interest in the field has been rekindled in response to yet another round of energy price increases and growing concern about energy’s role in climate change and the debate surrounding the remaining stocks of conventional fossil fuels. With many different sources being dubbed “the fuel of the future” it is important to determine which of these should receive the greatest share of society’s resources and thus be proliferated.

Energy is defined as the physical ability to do work: The exertion or effort directed to produce or accomplish something. Work itself is measured in heat units, e.g., British thermal units (Btus), Watts, or calories. EROI is the ratio of the work that can be done by a certain energy source to the amount of work required to convert that energy source into a usable form. However, there is not a universally-accepted formula for calculating EROI, so all EROI figures are approximations. Despite the definitional ambiguities it is, if nothing more, conceptually useful because it alters the way in which we view the so called “fuels of the future.”

In this paper, we will explore: The concept of EROI as well as some of the underlying economic principles; the difficulties with reducing it into a simple algorithm; and how it can be used to make more objective decisions about resource allocation in the context of proliferating non-conventional fuel sources.

EROI: A Conceptual Overview

No living organism, whether man, beast, plant, or economy can survive if, on the net, it expends more energy than it consumes. In order to sustain itself, this must be a one-to-one relationship: One calorie consumed for one burned, one dollar earned for one spent, etc. And to grow, this relationship must be even more lopsided. If a business wants to grow and bury its competition, its output must grow by a factor greater than its increased level of inputs. In microeconomics, this is known as increasing economies of scale. This seems to be logical enough, yet it often goes overlooked in the context of energy itself. We understand that if we want to lose weight we have to eat less and spend more time at the gym or that a lioness must be successful hunt at least one in five times in order to sustain her pride. Each of these is an example of an energy system. The purpose of energy systems is the fulfillment of demand for energy services. As a tool, EROI helps us gain a better understanding the energy involved in fulfilling this demand.

An energy system has two elements: An energy supply sector and an energy end-use. The energy supply sector consists of a sequence of processes for extracting, converting, and delivering the useful, i.e., heat, energy contained within primary energy sources to the end-users. In the example of the lioness: She is the supply sector, the primary energy is the flesh of her prey, and the end-users are the other members of her pride. The energy demands of her pride cannot be met until the prey is found, pursued, captured, and delivered to the pride. In order for this energy system to perpetuate itself, she must exert less energy performing the tasks necessary to meet the demands of end-users than she consumes herself. Or, the energy returned must be greater than the energy invested.

Calculating EROI

EROI is the ratio of the input of energy to the output of energy. It captures the fact that it requires energy to find, acquire, transport, store, and use energy. After all of these tasks and processes have been completed, how much actual energy is left?

One of the fundamental areas of ambiguity is a question of boundary:[1] How many elements should be included in the energy supply sector? Should the energy required to feed employees at an oilrig be included? How about the energy required to run the farming equipment that produces the food that feeds them? Or the energy required to build the vehicles that transport them to work? Or the energy required to mitigate the adverse environmental impacts associated with a particular supply chain sector?

As you can see, the energy inputs can be regressed almost infinitely. So in order to conduct any analysis at all, one must artificially draw a boundary for what is counted and what is not. This may be problematic because putting any sort of bounds on the system would produce inflated EROI estimates. On the other hand, trying to incorporate and properly weight every variable leads undoubtedly to “paralysis by analysis,” which may be as unbeneficial as inaccuracy.

Other pundits posit that a price-estimated EROI should be used. This school of thought is predicated on the notion that energy price serves as a good proxy for all of the energy involved in the production process. The idea is that the more energy involved, the higher the price. And naturally, over time, the market will steer the course of society from expensive, and energy-intensive fuel sources towards cheaper, more abundant fuels that can more easily be to use. Supporters of this methodology contend that the market implicitly agglomerates all of the data necessary to perform a “bounded” EROI analysis thereby making it a more attractive tool of net energy analysis. There are, however, weaknesses to this approach.

For instance, tax credits, subsidies, and other policy instruments that provide incentives to proliferate a certain energy source distort the prices by artificially augmenting the quantity demanded. A production subsidy, for example, makes a commodity such as corn-based ethanol cheaper for the consumer, while increasing the price paid to the supplier. So, because the fuel is cheaper, it must have a higher EROI than say gasoline. This is universally regarded to be false by “bounded” EROI proponents. Most estimates indicate that corn-based ethanol’s EROI is between 0.8 and 1.6:1, whereas gasoline’s is estimated to be between 11 and 18:1.[2] In addition to distorting prices, subsidies also distort the distribution of social welfare, a byproduct that even proponents of price-estimated EROI freely admit is difficult to measure. So, even if price-estimated EROI calculations are capable of capturing all of the energy inputs necessary, they cannot accurately measure the adverse economic impacts caused by policy instruments, which could make them just as misleading as the “bounded” estimates with which it competes.

Due to the importance of energy quality on EROI estimates, particularly in the frequent disparity between the quality of energy inputs and outputs, a third alternative is proposed by some.

The Divisia Index, which accounts for energy quality of both inputs and outputs is one such method. The Divisia corrects for the quality of the net energy of oil and gas extraction. As explained by the creators of the Divisia, “[t]he Divisia EROI is consistently much lower than the thermal equivalent EROI. The principal reason for this is the difference in the fuel mix, and hence fuel quality, between the numerator [output] and denominator [input] of the EROI. The outputs are crude, unprocessed forms of oil and natural gas. The inputs are electricity and refined fuels such as gasoline…The latter are higher quality than the former and have higher prices. Refined fuels and electricity are, therefore, weighted more heavily in the Divisia formuation.”[3] Still, this methodology is subject to the same question of boundaries as the traditional EROI approach.

Economies of Scale and the Industrial Revolution

Prior to the Industrial Revolution the principal fuel sources was wood and biomass. Initially, wood was plentiful, seemingly inexhaustible, widely distributed, easily harvested and therefore cheap. Also, is does not need to be refined into a more useful form. It was the fuel for cooking and for crafting iron tools. With their beasts of burden outfitted with iron ploughs, humans were able to till more land with less effort and reap larger harvests, ergo, realize increasing returns to scale. However, by the 17^th century, timber stocks in Europe were falling and prices were climbing precipitously (see graph)[4] so out of necessity was born the need for a cheaper, more plentiful, and therefore more easily harvested fuel source. Enter coal.

Not only was coal ubiquitously distributed, but it was found relatively close to the Earth’s surface, which made it easy to mine. Distribution and ease of extraction are important to EROI because they both equate to less time and work involved in the process. In other words, it takes less than the energy produced from a tonne of coal to mine and render a tonne of coal into a useful form.

Coal also has another property that makes it an attractive fuel source. In some forms, it burns at a higher temperature and a higher temperature equates to greater work potential. One ton of anthracite, the type of coal with the highest energy content, contains approximately 25 million Btus. Compare this to one ton of high energy hard wood, such as cedar, which contains roughly 20 million Btus. However, because most energy sources are not uniform in their quality, these sorts of comparisons do not always hold and therefore are not always useful; we must also consider the quality, ergo, the heat content of each type of fuel.

For instance, coal is ranked according to the degree of metamorphosis or “coalification” undergone by the carbon content of organic matter. For example, anthracite is hard, black, contains very little water, burns at a high temperature, and is a rather clean burning fuel. Compare this to low-grade coal such as sub-bituminous lignite, which is more brown in appearance, softer, dirtier, and has a relatively high water content, thus making it an inferior source of energy in terms of heat content compared to even many types of woods and other biomass. So to say that coal replaced wood entirely because it is a better source of energy would be inaccurate. Society makes the transition from one source of energy to another because as one fuel source becomes increasingly rare, it must exert more and more effort just to maintain the same level of energy production. This is the minimum necessary to maintain the status quo. And if we want to grow, to experience increasing returns to scale, we must exert much less energy, i.e., input less relative to output.

Shadow Costs and Substitutability

When society began to make the transition from wood and biomass to coal as its primary fuel source, fuel was still only used for a few simple applications: Home heating and cooking and a few industrial applications. Although we have known about the existence of coal since the Roman Empire, it did not really begin to use it as a source of energy until the start of the 17^th century. This was primarily the case in Europe, which had decimated its forests but America’s timber stocks had yet to be fully tapped. The real, global transition from wood to coal did not really begin until the early 1800s and it wasn’t until a century later that coal had replaced wood as the world’s primary fuel source. Part of the reason why coal so easily replaced wood was because the two are close substitutes.

When a consumer is indifferent between two commodities, each of these commodities is said to provide her with the same level of utility. This is of course subject to an income constraint. The consumer will choose the basket (combination) of the two goods that maximizes her utility relative to her monetary constraints. This depends heavily on the individual. Plausibly, some consumers could have chosen to consumer equal amounts of coal and wood while others could have just as easily chosen to heat their homes will one or the other. After all, some ranks of coal and some grades of wood are capable of producing the same levels of heat. However, as the supply of wood fell and its price rose, slowly more and more people opted for coal to meet their cooking and heating needs. What begins at the micro-level snowballs overtime and turns into a macro-level shift. So, while coal and wood provide the same service, people did not begin to replace one with the other until the felt their wallets shrinking. In economics, this is known as shadow cost.

The shadow cost is the value of relaxing a certain constraint, e.g., a household’s monthly budget or a firm’s capital installations. For a household, more money means being able to buy more of some good. For a firm, this equates to replacing older machinery with newer, more efficient machinery. In the case of the latter, better machinery leads to higher productivity which in turn leads to higher revenues. The shadow cost is the change in revenue associated with a capital upgrade. And the higher the shadow cost, the more it behooves the firm to make the change. Why is this important in the context of EROI?

Today, the shadow cost felt by coal-based generation facilities is not high enough for them to switch to wind or solar-powered technology; too much energy is required to produce and install wind turbines relative to continuing to mine, ship, process, and install pollution abatement technologies at their plants. For instance, in the U.S. and China, two of the most coal-dependent economies, coal remains plentiful and easily extracted at home so mining and transportation costs are cheap relative to other fuels. France on the other hand, has coal but it is found much deeper in the Earth’s crust. When coal was being proliferated in the 19^th century by neighboring Germany and the nearby U.K., France had to import expensive technologies. And when fusion technology was available, the switch from coal-based electricity production to nuclear was logical. Now, France is the world leader in nuclear power; approximately 75% of its electricity is generated by nuclear plants.[1] So until the EROI of other forms of fuel can compete with coal and the shadow cost of employing coal-based technologies become too high, coal will continue to be the most heavily-used fuel source on the planet. According to the Energy Information Administration, the statistical office of the United States Department of Energy, electricity demand is projected to grow at 1% per year through 2029. Over this same period, coal is projected to account for 47% of U.S. electricity generation, declining only 2 percentage points from its 2009 level.[2]

Sure, wind and solar energy are capable generating the electricity necessary to heat and power our homes. With time, they will replace coal; perhaps even more quickly than is currently foreseen. They are not, however, capable of replacing petroleum. None of the proposed gasoline replacements make much sense when one scrutinizes them under the EROI lens.

The Crude Oil Conundrum

Bioethanol, whether corn- or sugarcane-based, fuel cells, and electricity are three of the most heavily supported replacements for gasoline. As we saw earlier, corn subsidies in the U.S. have led to myriad economic problems including reduction consumer and producer surpluses, diverting social welfare towards the government, and a rising grain prices, which was not discussed.

In a nutshell, because subsidies raise the price received by suppliers above that paid by consumers, with the difference being paid for by the government, many farmers were drawn away from corn for food production towards corn for ethanol production. This led to a price increase in food costs. Corn is used to feed livestock, make flour, make syrups, etc. Moreover, the fertilizers used by many farmers are petroleum-based.

Rising food costs often equates to greater inflation.[3] And higher inflation means that purchasing power is weakened. Though inflation and unemployment are often inversely correlated,[4] people have to work more hours just to be able to afford the same basket of goods as before. This means machinery runs for longer, which requires more energy and can lead to more work-related accidents which strains hospitals and leads to, yup, you guessed it – additional energy use. Though there is a paucity of data to support this is, it is not farfetched to imagine that the increase in energy use far exceeds the energy created by the ethanol. In short, ethanol may indeed be an energy sink.[5]

Electric-powered cars have been around since the advent of the automobile in the late 19^th century. Initially, approximately 30% of the vehicle fleet was powered by electricity.[6] However, these cars, much like today’s, could not travel as far as their gas-powered counterparts and recharging stations were few and far between. Soon, the stock of electric-powered cars petered out.

A simple search of Google suggests that there are between 600 and 800 million cars in use worldwide. Also, assume for a second that recharging your car’s battery is just as easily as filling its gas tank. Retrofitting all of these cars to run on electricity is out of the question. It doesn’t take a mathematician to see that the EROI here is miniscule. Suppose instead that the goal is to slowly supplant the existing fleet of gas-powered cars with electric-powered. This is more realistic, but will take some years.

The near term EROI might be higher than initially expected. The auto industry is a highly mechanized one and once batteries are affordable, they will be as easily installed as standard combustion engines. So the issue lies in the arithmetic of market for automobiles. The energy required developing the batteries aside, it may take a few generations to weed out every gas-powered car and replace it with one that runs on electricity. In 2009, total production of automobiles worldwide was slightly over 41 million. So even if the industry could produce just as many electric cars it would take between 14 and 20 years before everyone on Earth was driving an electric car. Even factoring in an average lifespan for cars of 10 years yields the same results. The next logical question is: Where is the additional electricity going to come from? As suggested by the Annual Energy Outlook report cited earlier, the energy portfolio for the U.S. is not likely to change drastically over the next 20 years, which brings us back to square one: How to determine the fuels of the future?

Crude oil will run out eventually. No one really knows for sure exactly when. If crude oil cannot be replaced as quickly as thought by even the most optimistic of people, we have no choice other than to continue to search for additional reserves no matter how obscure they may be. Deep oil wells in the Gulf of Mexico are seriously being considered and oil trapped in pre-salt formations off the coast of Brazil are thought by some to be untapped black-gold mines. Last year in a speech before the Association for the Study of Peak Oil & Gas – USA. Dr. Marcio R. Mello, president of HRT Petroleum and an expert in petroleum exploration and petroleum geochemistry, stated that over 500 billion barrels of oil could be found in deep in the bowels of the Earth off of the coast of Brazil.[7] This is equal to roughly the amount of oil produced over 2 decades.

Final Remarks

If this discourse has at all been useful, we know by now that a figure such as the one stated above, is misleading because it will require billions of dollars, thousands of man-hours, and countless Btus, or joules, or kilocalories of heat energy to convert the crude oil into usable energy. Though the science of EROI may be inexact it is at least a useful tool to determine the true energy requirements necessary for society to perpetuate itself. It is not simply enough to say that we should move forward and proliferate only those fuels that have high EROIs now. EROIs as we know are largely dependent upon the technology used to in the energy supply sector and more efficient technologies will lead to higher EROIs, regardless of the measure. Also, it is an important reminder that attractive energy alternatives of today, will be onerous dinosaurs in the future. With that said, a balance must be struck between building the most effective infrastructure possible today while not allowing their shadows to loom over investment decisions in the future. Furthermore, I acknowledge the absence of any discussion on revamping the energy infrastructure which will be necessary in many places, particularly those where the current capital installations are burdened by high shadow costs. But the argument is the same: No matter the energy mix, the ease and energy efficiency with which it can be delivered to our homes, offices, and schools must be taken into account.

[1] Nuclear Power in France in France. World Nuclear Association London. January 2010

[2]Figures are based on the 2009 Annual Energy Outlook’s “Reference Case” scenario.

[3] Doroodian, K. et al. “The Impact of Removing Corn Subsidies in Mexico: A General Equilibrium Assessment.” Atlantic Economic Journal June (1999): 150 – 169

[4] In economics, this is known as the Phillips Curve.

[5] See for example: Lancaster, Don. “Some Energy Fundamentals.” The Blatant Opportunist October (2002): p. 71.2

[6] Surowiecki, James. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. New York: Doubleday, 2004.

nMello, Marci R. “System Reset: Global Energy and the New Economy.” 2009 ASPO-USA. Sheraton Hotel, Denver, Colorado. 11 October 2009.

[1] See for example: Kavanagh, Etta. “Looking at Biofuels and Bioenergy.” Science Magazine June (2006): 1743 – 1748.

[2] For a complete list of EROI estimates, please see the Appendix.

[3] Cleveland, Cutler J. (1988) Net Energy from the Extraction of Oil and Gas in the United States.” Paper for the Center for Energy and Environmental Studies and the Department of Geography, Boston University, p.12.

[4] Allen, Robert C. (2006) The British Industrial Revolution in Global Perspective: How Commerce Created The Industrial Revolution and Modern Economic Growth, Oxford University, p. 12.

The IPCC: Flawed Methods, Sound Message

2010-08-12T14:32:00.000-07:00

Since its creation in 1988, the Intergovernmental Panel on Climate Change (IPCC, or The Panel) has been the leading scientific body on climate change. It was created to find just cause for proliferating nuclear energy in the United Kingdom to reduce dependency on oil from the Middle East and coal from fickle coal workers. To do this, it would need to determine the extent that human activity is responsible for global warming.

This goal is pursued by periodically preparing comprehensive Assessment Reports about climate. The IPCC’s reports, of which there have been four, are the most cited and most influential documents in climatology. Its recommendations to legislators and public officials are behind many policies (e.g., cap-and-trade) and the global climate change political movement.

Many such policies acknowledge that there continues to be significant uncertainty about the exact scope and timing of climate change-related impacts. Despite these uncertainties, they propose moving forward with goals and response actions justified on a set of assumptions taken from IPCC reports. However, such policies unduly take these assumptions to be the scientific consensus.

Members of the scientific community, some of whom contributed to the Panel’s Assessment Reports, continue to refute some of its findings and each of the IPCC’s reports has been met with controversy. Controversies have ranged from scientists claiming that the reports have been tainted by political motivation, to abuse of the peer-review system, and finally, to the use of specious data. All of these things debase the models used by the IPCC and render its recommendations questionable. .

Rather than making overt affronts on the IPCC, other scientists have simply and astutely pointed out that if the climate’s fundamental dynamics are not completely understood then the models will therefore also be incomplete, possibly inaccurate.

The Panel’s reports should not be taken as gospel, but rather as evolving works in progress. As such, the findings should be viewed as inconclusive and incomplete.

Well-informed policies are effective policies and the policies implemented today should not be based on science that will be understood tomorrow. There remain principal gaps and limitations in what is understood and what can be modeled by climatologists. And until these gaps can be filled we should not be precipitous in our actions. For the costs of ill-advised or ill-directed actions may very well outweigh those of inaction.

Three of the principal gaps in climatology today are: (1) Scientific understanding of climate science; (2) Modeling systems are most reliable on large geographic scales and unreliable on smaller scales; and (3) Modeling capabilities are constrained by computing capability.

Gaps in scientific understanding of climate science

There are many interrelated elements that contribute to the Earth’s climate which makes understanding it difficult and complex. Some of these elements include: The occurrence of sun spots, the number of trees, ocean temperature, desertification, cloud cover, the list goes on. The IPCC model attempts to quantify and properly weight the importance of these variables to make its predictions. To illustrate the complexity of the climate system and the difficulty of reducing it to an algorithm consider the following. A “+” signifies a temperature increase and “-” signifies a decrease.

- An increase in the concentration of CO₂ in the Earth’s atmosphere traps solar radiation that would otherwise be reflected back into outer space. ( + )

- As the temperature increases, the rate of evaporation increases which leads to the formation of more clouds. More clouds means more solar radiation get reflected back into space. ( - )

- However, with more cloud cover comes more rain and more trees. Since trees cannot use all of the water they absorb, more water is returned to the skies and leads to the creation of clouds. ( - ) Also, more trees equates to more CO₂ being absorbed. ( - )

- But trees have a relatively high Albedo Effect, i.e., they absorb heat. ( + ).

This example, though crude, illustrates that reliance on one variable is insufficient to make even general statements about the climate’s behavior.

There are also low-frequency variables purported to be indicative of climate change such as hurricanes and El Niño that are even less understood. This further limits the confidence that can be placed on the ability of climate models to realistically simulate the climate.

Insufficient research has also been conducted to allow climatologists to delineate clearly between the anthropogenic climate-changing variables and those that occur naturally. This brings into question the fundamental assumption of the IPCC: climate is being driven by human activities.

The IPCC has relied heavily upon the graph to the right.[1] The graph illustrates that since circa 1850, the global average temperature has risen to its highest level in a millennium and, that the timing of this increase coincides with industrial expansion, ergo, more GHG emissions.

This graph, known pejoratively by skeptics as the “hockey stick” graph has been highly scrutinized.

For example, as the graph indicates, the increase in temperatures sharply increased between 1850 and 1940. Though many processes were industrialized, the world economy was not in full swing. In fact, there was a lengthy global recession in the 1930s. But, when widespread economic activity began post-World War II, the temperature dipped for several years before increasing again circa 1975. This is inconsistent with the theory that increased carbon concentration warms the planet.

This has led many in the scientific community to question whether GHGs are in fact drivers of the Earth’s climate. And with many scientists studying possible alternative explanations for the recent temperature increases,[1] it is clear that the IPCC Assessment Reports are far from representing a scientific consensus.

One of the most promising alternatives to the GHG explanation is solar activity. Briefly: solar activity (e.g., solar winds) prevents subatomic particles from entering the Earth’s atmosphere. These particles react with water vapor to form clouds, which shield the Earth from the sun. Fewer particles and therefore fewer clouds equates to more solar radiation being absorbed by the oceans and the Earth’s surface, which could explain the observed temperature increases.

The point of all this is that climatology is a relatively new science. As such, there are many gaps in what is understood about the climate in broad terms. This does not mean that the IPCC should suppress alternative theories or should not incorporate them in to its models so that its general findings remain consistent.[2]

In any event, if the climate’s general dynamics are uncertain then its peculiarities are more unclear.

Unreliability of modeling at sub-continental scales

Existing climate models used for the IPCC process are comprehensive global models designed for mitigation, on large space and time scales. The IPCC concentrates on global averages and freely admits that the smallest region for which the models are useful is the continental scale, about 3,000 miles.[3] On smaller scales the models are not useful.

Members of the scientific community contend that a less coarse spatial resolution, e.g., 2 km or even 100 km is integral to modeling changes for municipal leaders, decision makers, and planners who use climate information as it pertains to water supply, infrastructure, inland transportation, etc.

But the IPCC is not entirely to blame for modeling problems. Even if the reports are based on insufficient and spurious data, they cannot account for technical limitations.

Limitations of computing capability to support modeling

Running a climate simulation model can take days, weeks, even months depending on the number of variables under consideration and the degree of spatial resolution. Further, preparation of the model, i.e., data collection and model construction adds considerably to the time required. But regardless of the credibility of the data and the model’s structure, there appear to be limitations in what computers can generate.

Dr. Alexander MacDonald of National Oceanic and Atmospheric Administration stated in an expert witness testimony before the United States Senate that “[m]odels are constrained by available computing.”[4] Dr. James J. Hack of the National Center for Computational Sciences further bolstered this position as well as others held in this essay in his testimony, noting that “[m]eeting future challenges in climate change science will require qualitatively different levels of scientific understanding, modeling capabilities, and computational infrastructure that are currently available to the climate science community.”[5]

With processing capabilities doubling every 18 months or so it may be several years until computational capacity will meet the needs of the climate modelers today. And as the level of complexity tends to rise with the level of understanding, the computing power may continue to lag behind the science.

Moving Forward

With all of these things said it does not mean that policy makers should not move forward with certain actions. For instance, allocation of public funds towards additional research, model development, and education can be easily implemented and do not carry high shadow costs.

Secondly, while the science may be uncertain and subject to continued debate, efforts undertaken to reduce energy consumption and therefore make our planet cleaner simply make sense and can be easily achieved through education and demand-management, the sciences of which are not as polarizing as the veracity of climate change.

Thirdly, the long-term effects of elevated CO₂ concentrations are also unknown. Currently, the concentration is approximately 380 ppm, 30 ppm higher than the historical equilibrium level. So even if GHGs do not contribute to global warming there may be other undesirable effects from higher concentrations that we have yet to recognize.

Finally, whether you agree with the IPCC or not, it seems that we all can agree on the following: Reducing energy consumption saves money and puts less of a strain on scarce energy resources that will only become more expensive. For this reason, non-renewable energy sources must be pursued and proliferated. Not because they will save the planet, but because they may very well save us and ensure the continued existence humanity.

We may be able to point out the flaws in the IPCC’s methods and lambast it for being the root of sensationalist propaganda but by focusing too much attention on the Panel’s shortcomings we may not see the forest for the trees. The Panel may very well put us on a path consistent with that sought when it was originally created – to reduce dependence of fossil fuels and to spur the development of more diverse, more ecologically-friendly energy portfolios – something we may one day thank it for.

[1] See for example: Usoskin, I. G., M. Schuessler, S. K. Solanki, and K. Mursula (2005), Solar activity, cosmic rays, and Earth’s temperature: A millennium-scale comparison, J. Geophys. Res., 110, A10102, doi:10.1029/2004JA010946.

3 Durkin, Martin. The Great Global Warming Swindle, The Channel Four Television Corporation, 2007.

[3] IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, et al]. Cambridge University Press, Cambridge, United Kingdom, pg. 183

[4]Hearing before the United States Senate, Committee on Commerce, Science, and Transportation “Improving the Capacity of U.S. Climate Modeling for Decisionmakers and End-users.” (May 8, 2008). Available electronically at commerce.senate.gov.

[5] Id

[1] IPCC, 2001: Climate Change 2001: The Physical Science Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, et al]. Cambridge University Press, Cambridge, United Kingdom.

Understanding the Digital Divide

2010-08-11T16:30:00.000-07:00

Source: eBiquity Research Group, 2007

The Digital Divide presents a rather puzzling phenomenon. When examined in micro terms, it is nondiscriminatory; it afflicts people regardless of age, gender, ethnicity, or socioeconomic status. However, as the lens is broadened, a very clear duality – the ever-present North-South division – appears. Most argue that the aforementioned traits are contributors to the Digital Divide, but when the issue is distilled to its bare core, it presents itself as just another way in which the ever-present problem of the “haves” and “have nots” manifests itself. It is a problem that runs deeper than race or wealth. Therefore, to understand it we must understand the roots of inequality. After all, many of the afflicted peoples did not just miss the Digital Revolution; they also missed the Industrial and Agricultural Revolutions.

Getting to the Root of the Digital Divide

Jared Diamond, author of the hit book, Guns, Germs, and Steel, argued that the roots of inequality are a function of path dependence. In other words, geography is to blame. The Earth’s natural barriers determined migratory flows, how and with whom information was shared, and how this information snowballed to become knowledge and tool building capability. Along these same lines, we may posit that nowadays, the barriers to growth and the emergence of a more equal human populace are artificial, human-made constructs. Other scholars agree.

In a paper being considered for publication in Econometrica,[1] a Ph.D. candidate at the Massachusetts Institute of Technology (henceforth, “MIT”) found that institutions of yore can and appear to have an impact on how well-off people are today. Using regression discontinuity, i.e., designs that evaluate causal effects of interventions, the author found that mita, a practice whereby local Incas were forced by Conquistadors to leave their homes to work in gold and silver mines may explain why people that inhabit this area today are poorer than their neighbors despite being equal by other measures, e.g., natural resource endowment.

The lesson from Diamond and the MIT graduate student is clear: The Digital Divide, like other manifestations of the problem of “haves” and “have nots” is the product of a long chain of choices, activities, interventions, and policies. These things facilitated the formation of the requisite institutions in some places and not others upon which digital technology is based. Nevertheless, as globalization is now in full-swing we can all benefit from narrowing the Digital Divide. This problem is of importance in the context of this Masters program: Innovation will only continue to occur in the “informationally-blessed” parts of the world. For a concept that is predicated on the idea that more people equates to more ideas, it is important for us help those who are lagging to catch up so that they too may contribute their ideas. But until they have access to basic infrastructure they will only develop that which we already have and the vicious cycle will continue.

This paper will emphasize that the reasons for redress are more economic and less equality-based. Additionally, we will examine the impacts of policies designed to narrow the gap.

What is the Digital Divide?

Typically, the term Digital Divide (henceforth, “the Divide”) describes the disparity of access to digital and information technology. This definition, perhaps only tacitly, suggests that the problem is one of wealth and ethnicity. But this is not entirely true. For those who have access to these technologies, a disparity exists in how much benefit they are able to leverage from it. For instance, mobile phones and the internet are supposed to make our lives easier and to be gateways to an endless stream of information. But for many people, computers remain typewriters with a jack for their headphones and a screen on which to watch movies and television.

To paint a more complete picture of how the Divide manifests, allow us to make a few, further subdivisions.

Source: U.S. Bureau of Labor Statistics, 2008

Part I: The Economic Divide

In the simplest of terms, digital and information technology (henceforth, “DIT”) are inaccessible to many due to cost. True the cost of a personal computer has fallen significantly over the last few decades (see graph) but with a considerable number of people living on less than one dollar per day, the cost remains prohibitive.[1]

As the global economy is increasingly dependent on DIT it is of increasing importance that more people have the opportunity to integrate said technologies into their lives. This is one of the fundamental precepts of a market economy; the greater the number of participants, the greater the social welfare. This division weighs heavily on the afflicted. And anytime people can benefit from having burdens of this ilk lifted, the door is opened for a discussion of shadow cost.

Shadow cost is the value associated with easing a particular constraint. For the world’s destitute, this is immeasurable. The transition to the digital economy was easy because of the gain in utility attainable. Moreover, the sheer amount of time savings associated make life much easier for people. The cost of communication has decreased significantly. And cheaper, easier modes of communication allow people to take on new economic opportunities that otherwise would have been unavailable. And the more expensive information/communication is, the more scarce it is. This is precisely why the relative lack of information is a key element of poverty.

For roughly one decade, India has become the poster child of the “emerging” economies. And the rapid proliferation of cellular phones, i.e., the cheapening of information, among its people is a big reason why. For fishermen in the country, for instance, they can begin negotiating with local mongers long before they return to port. Whereas in the past, said mongers could take advantage of informational asymmetries, today the fishermen can shop, so to speak, for the best price with a few clicks of a button. [2] And in addition to economic benefits, reducing the cost of information can also be beneficial for public health.

The use of mobile technology among doctors in the developed world will only provide marginal benefits to patients. We are talking about seconds and minutes saved. But In poorer economies, the benefits will be substantial as they will not only save time, but will help doctors deliver basic health services to the populace. Cell phone use has risen over 500% in Africa since 2004.^{^[3]} Today, approximately 350 million Africans have subscriptions with perhaps millions more using rechargeable credit services.^{^[4]} On March 21^st, Rwanda’s President, Paul Kagame, delivered on his promise to provide community health workers with 2,500 mobile phones.^{^[5]} Subsequently, the speed with which doctors communicate with patients, other doctors, and medical service providers, e.g., EMTs can decrease considerably. As a practical example, the sooner a doctor knows that that an ambulance is on the way, the sooner they can move on to other patients. This means that each patient is less burdensome to the next and to the doctor.

For the Rwandan doctors, less time spent caring for a particular patient means that they can see more patients during the day, i.e., alleviation of the shadow cost. Due to the time saving associated with mobile telephony, doctors can increase their coverage areas. More patients tended to means greater overall health. And healthy people are more productive. As you can see the benefits of mHealth to Third World countries are immense.

Nicholas Negroponte, founder of the Media Lab at the Massachusetts Institute of Technology, designed a cheap laptop to be distributed to children in the developing world. The computer was sold at a cost of $100 (roughly one-third what I paid for my low-end netbook) through the One Laptop per Child program.[6] Sounds just dandy doesn’t it? The problem with this project, according to usability guru, Jakob Nielsen[7] was that Negroponte did not adequately study the needs of the target group; there was no user testing.

But having access does not necessarily equate to efficacious use. If everyone could afford a computer, would they leverage the maximum benefit possible?

Part II: The Usability Divide

In the summer of 1999, the United States witnessed the household ownership rate of personal computers climb above 50%.[8] This figure says nothing about purpose, whether it is exclusively or primarily for game playing, “chatting” with friends, or more serious activities. Nor does it speak about time and quality of use. Because of this we don’t tend to think of the “rich” as being afflicted by the Divide.

A 2002 study by the Nielsen Norman Group, a company that helps corporations to extend products via the web by improving website usability, found that the internet was twice as hard for seniors to use as compared to their younger counterparts.[9] For a group that did not grow up using a computer in schools or households, dedicating more time to deciphering the web is quite cumbersome and not a realistic option. A 2005 report published by the Kaiser Family foundation corroborates this. The report relied upon a random, digitized survey with 1,450 respondents aged fifty and older. According to the survey, less than one-third of Americans ages sixty-five and older have ever used the internet.[10]

People use DIT for myriad purposes: Communication, work, entertainment, shopping, and etcetera. The possibilities are virtually endless. Yet very few of us actually optimize the way in which we use these technologies. From where I’m sitting in the library, I can see the computer screens of over a dozen fellow students and nearly all of them are on Facebook. If they are not on Facebook, they are on YouTube. Which begs the question: Even if everyone had access to easy-to-use technologies, would we make the most out of this access?

Part III: The Empowerment Divide

In researching how people use search engines for the Nielsen Norman group has found that many users don't know how to use search to truly master the Web. People don't understand advanced search features; they rarely employ query reformulation; and many uncritically select the first search results. Also, many users don't understand how search engines prioritize their listings, and some users don't even know that the euphemistic label "sponsored links" refers to paid advertisements.[11]

Because they lack the initiative and skill to take matters into their own hands, some users remain at the mercy of other people's decisions. For example, people sometimes accept the default home page chosen by their computer vendor or ISP rather than select one that's better suited to their needs. Again, this means that the user's attention can be sold off like a sheep to slaughter, as indicated by deals where search engines pay computer vendors millions of dollars to be the default setting on shipping PCs.[12]

Similarly, some users limit themselves to "free" Web applications that display ads. What such users don't realize is that better, i.e., more appropriate, powerful, and liberating applications are available at a cost that's far less than the value of the time they waste trying not to look at the ads.[13]

The story here is very much the same as in the preceding section: The acquisition of information is empowering and has economic benefits. Indeed, narrowing the knowledge gap is very much at the heart of this issue. Unfortunately, there are quite a few practical, logistical matters that make this quite a difficult task.

Increased Bandwidth and other Infrastructure Requirements

In computer networking and computer science, bandwidth is a measure of consumed data communication resources expressed in bits. It goes then that the more users, the greater the bandwidth requirements. On the internet-superhighway, information travels as light through fiber-optic cables. Each fiber carries with it a stream of information “coded” as a color. The more information that needs to be transmitted, the more colors are required. And the more users, means more cables. Therefore, in order to properly combat the Divide, the necessary infrastructure must be in place.

With the large-scale outsourcing that has occurred, groups such as India’s Tata Group has launched a multi-terabit cable connecting eastern India to Singapore where it can connect to other cables that traverse the Pacific Ocean to the United States. Other cables are being planned along the same route as well as towards the Middle East and Europe.[14]

In practical terms, a typical multi-terabit, transoceanic submarine cable system – such as the Unity Cable Google is building along with 5 other members of the consortium, the likes of which have become the industry norm – costs several hundred million dollars to construct.[15] Projects of this ilk aren’t usually preëmptive; they usually represent a supply shift in response to increasing demand. For instance, trans-Pacific internet traffic rose 41 percent between mid-2006 and mid-2007.[16] The industry has learned to be responsive to avoid supply gluts that led to the bursting of the “Broadband Bubble” in 2000. It is unclear whether current bandwidth installations would be able to handle a hypothetical situation, such as a rate of computer ownership in Africa of 50 percent.

Source: International Telecommunications Union, 2008

Increased connectivity has implications in other sectors as well; notably, telecommunications where the construction of new towers in sparsely-populated areas and perhaps pico grids in more densely-populated urban zones could appear. Already, the ceiling is being approached on cell phone use in the developed world and the rate of penetration is rising quickly in developing countries (see graph). Furthermore, as the internet and cellular phones are increasingly used for data-intensive media applications, bandwidth, tower construction, and cell shrinking will become of greater importance to keep information flowing and cheap.

Liberalization of the Telecommunication Industry

In general, liberalization is the act of relaxing certain government restrictions, usually state-held monopolies. By design, liberalization increases the number of competitors in the market, which drives prices and profits down and consumer surplus up. Lower costs, increased teledensity, innovation, greater availability of services, and foreign investments are among the benefits associated with open and competitive telecommunications markets.

The World Trade Organization (henceforth, “WTO”) has held member states to a higher standard when it comes to telecomm liberalization. It seems to be working – fixed line penetration is markëdly higher (almost four times) among, for example, low-income countries in Southeast Asia that have implemented WTO liberalization standards versus those that have not.[1] More specifically, the rate of penetration in India rose by a factor of eight between 1996 and 2004 after liberalizing its telecommunications sector.[2] Over that same period, India’s gross domestic product, in terms of purchasing power parity, rose from $1.41 to $3.56 trillion.[3]

Among economists, when liberalization is being discussed, the conversation often involves the occurrence of externalities, or “spillovers.” The concept is simple: Markets operate under the precept that transactions will only occur if both parties, i.e., buyer and seller, can benefit from said transactions. However, these exchanges can positively or negatively affect third parties. These effects can either diminish or contribute to social welfare. In the case of positive externalities, third parties benefit at no additional explicit cost. Because this demand cannot be monetized, too little of the good will be produced and vice versa. In the realm of telecommunications the occurrence of “network” externalities is well-documented.[4]

Consumers joining a network obtain benefit from making and receiving calls, emails, text messages, and etcetera.

Effect on equilibrium condition associated
with a positive spillover.
Available under GNU Free Documentation

They therefore have a private valuation of being part of the communications network. This value derives from being able to communicate with other people. It therefore increases with the number of people connected to the network: the bigger the network, the more valuable it is to both existing and potential members. This effect is known as a “network externality.” Thus is manifested the paradox that exists in telecommunications: Liberalization (read here as, deregulation) of telecom markets is necessary to reduce the price of information but some form of regulation, in the form of price floors or production subsidies are needed so that the full-economic benefit derived from access is captured in the price.

In the context of combating the Digital Divide, it is important that more people gain access to information pathways. Similarly, it is important that purveyors of access to information networks are able charge the appropriate, socially-optimal price. Yet they must also keep in mind that the more people in the service area, the cheaper it becomes due to economies of scale. Indeed the relationship is mutually beneficial.

Concluding Remarks

In the context of this Masters program, closing the information and knowledge gaps are an integral part of maintaining the innovative process. We have seen some present initiatives that are designed to close these gaps. Philanthropic efforts such as One Laptop per Child as well as WTO-backed liberalization initiatives have been positive contributors to the cause.

To emphasize the need for a pragmatic rather than an idealistic approach, we discussed some of the infrastructure requirements and strayed from the typical analysis of the various, often homogeneous program. These are rather basic in the developing world, e.g., electricity and cellular towers, and likely to change in rapidly developing, urban areas.

Regrettably, there wasn’t much room for a discussion on the issue of Standardization. It was not due to lack of importance but rather it was an unfortunate omission that was necessary in order to incorporate the various economic themes. After all this is a school of economics. We will say this, on the matter, as information technologies become more pervasive in the developing world, it is important that cultural identities are not forced by the wayside. This is by no means an easy task, as many languages, particularly in sub-Saharan Africa only exist in oral forms.

Finally, in acknowledgement that many other initiatives designed to combat the Divide went unmentioned, we will provide a list of further readings to show that we were aware that these initiatives existed. Once again, the economic issues presented in this paper are very much at the heart of these initiatives.

[1] “Making the Most Out of the Doha Opportunity: Benefits from Services Liberalization.” Coalition of Service Industries, 2006. Available electronically at:

[2] Ibid

[3] “The World Factbook.” The United States Central Intelligence Agency, 1996 & 2010. Available electronically at:

[4] For instance see: “Network Externalities in Telecommunications: Theory and Application.” Frontier Economics, 29 June 2005. Available electronically at:

[1] I could not find a reliable source to support this. Nevertheless, conceptually is rather well-known.

[2] Sullivan, Kevin. “For India’s Traditional Fishermen, Cell Phones Deliver a Sea of Change.” The Washington Post 15 October 2006. Available electronically at:

[3] Smith, David. “Africa calling: mobile phone usage sees record rise after huge investment.” The Guardian 22 October 2009. Available electronically at: < http://www.guardian.co.uk/technology/2009/oct/22/africa-mobile-phones-usage-rise>

[4] Ibid

[5] Nambi, Irene V. “Community Health Workers Get Cell Phones.” The New Times 22 March 2010. Available electronically at:

[6] Ledell, Joshua. “The $100 Laptop and the Usability Divide.” Compete on Usability. Joshualedell.typepad.com, 20 March 2007. Web. 28 April 2010

[7] Jakob Nielsen holds a Ph.D. in Computer-human Interaction from the Technical University of Denmark in Copenhagen.

[8] Bowie, Nolan A. “The Digital Divide: Making Knowledge Available in a Global Context.” Learning to Bridge the Digital Divide. Paris: The Organisation for Economic Co-operation and Development, 2000. 37-40

[9] Nielsen, J. and Pernice K. “Web Usability for Senior Citizens: 46 Design Guidelines Based on Usability Studies with People Age 65 and Older.” San Carlos, California: The Nielsen Norman Group, 2002. Available electronically at: <http://www.nngroup.com/reports/seniors/>

[10]“ e-Health and the Elderly: How Seniors Use the Internet for Health – Survey.” The Kaiser Family Foundation January 2005. Available electronically at: <http://www.kff.org/entmedia/upload/e-Health-and-the-Elderly-How-Seniors-Use-the-Internet-for-Health-Information-Key-Findings-From-a-National-Survey-of-Older-Americans-Survey-Report.pdf>

[11] Marable, Leslie. “False Oracles: Consumer Reaction to Learning the Truth About How Search Engines Work: Results of an Ethnographic Study.” Context-Based Research Group 30 June 2006. Available electronically at:< http://www.consumerwebwatch.org/pdfs/false-oracles.pdf>

[12] Ibid

[13] Ibid

[14] Lynch, Grahame. “The New Bandwidth Bubble? Foreign Barons Corner the Market, Much to the U.S. Government’s Dismay.” America’s Network. FindArticles.com, 15 November 2004. Web. 29 April 2010

[15] Gardiner, Bryan. “Google’s Submarine Cable Plans Get Official.” Wired 25 February 2008

[16] Ibid

[1] Dell, Melissa. “The Persistent Effects of Peru’s Mining Mita.” Department of Economics, Massachusetts Institute of Technology (2010). Available electronically at:

An Examination of the Economics of eHealth

2010-08-09T12:19:00.000-07:00

Introduction

The European Commission describes eHealth as “a set of tools based on information technologies and communication tasks used in prevention, diagnosis, treatment, monitoring and management in health and lifestyle.”[1] The term can encompass a range of services that are at the edge of medicine/healthcare and information technology such as:

- Electronic health records: Enable easy communication of patient data between different healthcare professionals, e.g., general practitioners, specialists, care teams, and pharmacies

- Telemedicine: includes all types of physical and psychological measurements that do not require a patient to travel to a specialist. When this service works, patients need to travel less to a specialist or conversely the specialist has a larger catchment area.

- Consumer health informatics: both healthy individuals and patients want to be informed on medical topics (think: WebMD).

- Healthcare information systems: software solutions for appointment scheduling, patient data management, work schedule management and other administrative tasks surrounding health.

- Virtual healthcare teams: consist of healthcare professionals who collaborate and share information on patients through digital equipment, e.g., for transmural care.

- mHealth: includes the use of mobile devices in collecting aggregate and patient level health data, providing healthcare information to practitioners, researchers, and patients, real-time monitoring of patient vitals, and direct provision of care.

Implementing eHealth as a large-scale tool of public health policy could lead to a range of costs, benefits, and levels of effectiveness depending on any number of extant conditions in a given economy such as the extent that a particular country suffers from “digital divide,” mobile telephony usage rates, population size and growth rates, the number of health care practitioners and professionals, etcetera.

eHealth’s role in the realm of health policy is certain to grow in coming years but it remains a largely under-studied issue. Literature on the subject is rather sparse so including the sort of quantitative analysis to which economists are accustomed to seeing is not something upon which this author will touch. Having said that, there are certain economic underpinnings – namely those of health economics – that can be addressed in this paper. These can lend themselves to make reasonable inferences as to the way that eHealth could redress several public health concerns. This paper will examine eHealth’s potential impact on aging populations, the increase in demand for healthcare service, the increasing expectations of consumers in developed economies, the change in expectations in emerging economies, and the provision of basic healthcare in undeveloped economies. But before we delve into these issues, a bit of background.

Primer on Health Economics

In a 1963 article, American economist Kenneth Arrow became the first eminent academic to examine health-care through an economist’s lens.[2] In this paper, Arrow posited that the apparent existence of stochastic elements, namely, disease incidence and the efficacy of treatment made health-care related issues worthy of formal economic study.[3] Subsequent research led to the appearance of other characteristics[4] – i.e., externalities, informational asymmetry, and the lack of a competitive equilibrium – made health-care a formal branch of economics.

Perhaps what makes health-care most interesting of all is the fact that individuals must allocate resources in order to consume and produce health. This dichotomy is perhaps more clear when you think of health as both an investment and consumption good.[5] Health is a consumption good in the sense that it provides direct utility to us; we enjoy being healthy. It is an investment good very much in the same way as other forms of capital: It enables production and the more you invest, the more productive you are. However, health capital investments are expensive. Going to the gym, consulting a nutritionist, and regularly seeing your physician and dentist are all examples of health capital investments and all have accounting and economic costs; they all require time, money, and effort that require us to miss work or forego other activities.

Once we reach a certain age our health stock begins to deteriorate more rapidly, hence the phrase, “In my prime.” To abate this deterioration, we must investment at an increasing rate, thus our healthcare costs rise explicitly. They also rise implicitly: Older Employees generally earn more than their younger colleagues, so each day of work missed due to illness or to visits to the doctor is more expensive.

In the context of health economics, as the investment rate and the depreciation rate of our bodies increase with age so too does the demand for healthcare[6]. This will be a recurring theme throughout this paper.

What we can the following away from this discourse: Time represents a major element of healthcare costs. The more time that we dedicate to maintaining our health, the less time we have to write our blogs, travel the world, or watch football with our friends. So any measure that reduces the amount of time we spend getting healthy will boost productivity in other areas of our lives.

Attribute of eHealth: Time Saving

Google Health, a free, electronic health service launched in 2008 is designed to reduce the amount of time spent by consumers as well as health practitioners on healthcare. Our health records can quickly be imported and stored which means no more sifting through paper-based copies. These can then quickly be shared with our doctors. This may not represent a high added value for those of us who have long-standing relationships with family doctors, but in the event that you need to find a new doctor either due to the retirement of your family’s primary physician or because you need a one-time service such as wisdom tooth extraction, the electronic service may be quite helpful. Last summer I had did in fact have my wisdom teeth extracted. My dentist had recommended an oral surgeon who was well-renowned in the D.C. metropolitan area but who unfortunately did not accept my insurance plan. Instead I had to find another surgeon with whom my coverage was compatible. I spent a few hours engaged in correspondence between my dentist and the new surgeon who did not have a preëxisting relationship requesting record sharing before and after the surgery. Needless to say, an internet-based record sharing service would have made my life easier.

This past week, Barcelona hosted the 2010 eHealth week, where several new technologies were unveiled: a SMS-based medication deliver system, remote surgery and others that represent time-saving tools as well as new advancements in medical technology. The use of mobile telephony in healthcare, such as SMS reminders when your prescription is running low, is on the rise and not just in the developed world.

Tool of eHealth: mHealth

The use of mobile technology among doctors in the developed world will only provide marginal benefits to patients. We are talking about seconds and minutes saved. But in poorer economies, the benefits will be substantial as they will not only save time, but will help doctors deliver basic health services to the populace. Cell phone use has risen over 500% in Africa since 2004.[7] Today, approximately 350 million Africans have subscriptions with perhaps millions more using rechargeable credit services.[8] On March 21^st, Rwanda’s President, Paul Kagame, delivered on his promise to provide community health workers with 2,500 mobile phones.[9] Subsequently, the speed with which doctors communicate with patients, other doctors, and medical service providers, e.g., EMTs can decrease considerably. As a practical example, the sooner a doctor knows that that an ambulance is on the way, the sooner they can move on to other patients. This means that each patient is less burdensome to the next and to the doctor.

For the Rwandan doctors, less time spent caring for a particular patient means that they can see more patients during the day. There is an underlying economic principle here: Shadow cost. The shadow cost measures the strength of a constraint. In the preceding example, time is the constraint. Due to the time saving associated with mobile telephony, doctors can increase their coverage areas. More patients tended to means greater overall health. And healthy people are more productive. As you can see the benefits of mHealth to Third World countries are immense.

The mobility of doctors has another economic implication: infrastructure, rather the lack thereof.

Tool of eHealth: Telemedicine

Time Magazine recently published an article about the rising use of Skype among psychiatrists in the United States.[10] Telepsychiatry, as it is known, allows patients and their mental health professionals to communicate in real time and offers the same face-to-face interaction without the need to commute. This translates to time and cash savings for both parties. Whether telepsychiatry is as effective traditional psychiatry remains unknown. In fact, the National Institute of Mental Health has offered a generous grant to Children’s Hospital in Seattle, to study the effectiveness of telepsychiatry.[11]

One of the potential implications for eHealth generally and telemedicine specifically is infrastructure requirements will lessen. A therapist in the United Kingdom may not need to rent office space when she can practice from home. Moreover, her catchment area will increase as web-based communication is not bounded by distance.

Changing Expectation in the Developing World

The provision of mental healthcare seems to be a luxury item. That is, the demand for it increases only when a certain wealth threshold has been passed. This is perhaps evidenced by the recent rise in demand for mental health services in India, which represents one of the fastest growing economies in the world. The incidence of depression, anxiety, and other mental health concerns has risen sharply in India.[12] Yet, the number of psychiatrists and therapists cannot meet the demand. A simple Google search yields several recent news items that point out the inadequacy of mental health services in India.[13] To date there has not been a formal analysis that examines the correlation between rising wealth and demand for mental health services. Therefore, we will examine a case at the other end of the wealth spectrum.

My fiancée who is an audiology resident at New York Presbyterian Hospital told me one evening in a conversation on Skype that the demand for cochlear implant surgery was virtually nonexistent in the Dominican Republic (many of her patients have been Dominican émigrés) but quite high among her more affluent patients. The reason in economic terms seems simple enough: poorer patients can afford to forego the surgery (which by the way is covered by Medicaid, a Federal program designed to help the poor obtain the medical services they demand). Much like poorer patients can afford not to seek mental healthcare. In fact the same Google search we performed earlier but this time for the Dominican Republic does not yield any hits that relate to shortages of mental health practitioners in the Dominican Republic. While this analysis is sophomoric the lesson is clear: Healthcare expectations change with wealth much in the same way that demand for environmental health rises with income – something that is universally-accepted among economists.

So how do changing expectations fall within the context of this paper? For starters, public health officials can better craft health policy to meet foreseeable needs. If telemedicine booms, for example, hospitals can amend the budgets accordingly. Less spatial requirements in mental health wing will free up space and money to be used to develop other branches. Governments may allocate public funds that would have be used in the upkeep of these hospitals to purchase additional bandwidth or additional cell phone towers so that information continues to move swiftly.

As the number of doctors increases so too will the number of patients and governments must be properly equipped. mHealth in the developing world for instance will increase the coverage areas of doctors and in order to meet increasing demand additional infrastructure will be required. Roads to travel from town-to-town and to transport patients for instance will become an increasingly pressing issue.

Difficulty with Implementing eHealth: The Digital Divide

The general ageing of the world’s population is one of public health’s primary concerns. As we saw earlier, healthcare costs increase with age. So on a global scale, the demand for healthcare is also increasing. There is a major underlying problem, however, much of the elderly population is subject to a condition known as the Digital Divide.

In a nutshell, the Digital Divide “refers to the gap between those who benefit from digital technology and those who do not.”[14] The problem encompasses inaccessibility to computers and the internet as well as mobile telephony and applies to the affluent and impoverished alike. As visible in the graphic to the right, the disparity in internet use is quite high between the developed (blue) and the undeveloped worlds (red).[15] Fortunately, the picture is not so grim for mobile telephony.[16] So while web-based eHealth solutions may not be realistic in the undeveloped world, mobile phone-based solutions are.

Still, the Digital Divide remains somewhat of an issue even for richer countries.

A 2002 study by the Nielsen Norman Group, a company that helps corporations extend products via the web by improving website usability, found that the internet was twice as hard for seniors to use as compared to their younger counterparts.[17] For a group that already has to invest more time to maintain their health maintenance, dedicating more time to deciphering the web is quite cumbersome and not a realistic option. A 2005 report published by the Kaiser Family foundation corroborates this. The report relied upon a random, digitized survey with 1,450 respondents aged fifty and older. According to the survey, less than one-third of Americans ages sixty-five and older have ever used the internet.[18] What is more, only one-fifth have searched for health information on the internet.[19]

This presents an interesting problem: In the developed world, seniors have the most to gain from taking advantage of eHealth tools yet they do not. Redressing this problem will require a lot of resources such as making websites more usable and educating the millions of senior citizens on the benefits to be had from using web-based health tools and resources. It is just as important for governments to help increase internet usage rates in order to cope with the ever-increasing demand for healthcare.

Concluding Remarks and the Path Forward

In this paper we have examined the economic theory behind the eHealth movement. Namely, how digitized and electronically-based medicine can lead to productivity gains and reduce investment requirements for people as we attempt to maintain our health stocks. We also looked at some specific tools of eHealth and how they can benefit patients and providers alike regardless of socioeconomic status. eHealth is going to change people’s expectations and governments must be ready to help deliver these services to people.

With improved healthcare comes another economic issue that we did not touch on in this paper: Population growth. Improved healthcare is largely behind the population boom seen in the 20^th century as infectious diseases were nearly eradicated. This places an ever-increasing burden on public health professionals because an unmanageable ailment could lead to catastrophe above and beyond anything Thomas Malthus could have foreseen. So the use of eHealth to better serve and protect people is just as important form them as it is for physicians.

[1] “What is eHealth”, European Commission March 2010. Available electronically at:

[2] See: Arrow, K. (1963), "Uncertainty and the Welfare Economics of Medical Care", American Economic Review 53 (5): 941–973

[3] Ibid

[4] Phelps, Charles E. (2003), Health Economics (3rd ed.), Boston: Addison Wesley

[5] Grossman, Michael (1972), "On the Concept of Health Capital and the Demand for Health", Journal of Political Economy 80 (2): 223–255

[6] Ibid

[7] “Africa calling: mobile phone usage sees record rise after huge investment.” The Guardian 22 October 2009. Available electronically at: < http://www.guardian.co.uk/technology/2009/oct/22/africa-mobile-phones-usage-rise>

[8] Ibid

[9] “Community Health Workers Get Cell Phones.” The New Times 22 March 2010. Available electronically at: <http://allafrica.com/stories/201003220001.html>

[10] “Telemental Health: Videoconferencing as Psychiatry Aid.” Time Magazine 21 March 2010. Available electronically at:< http://www.time.com/time/health/article/0,8599,1974196,00.html>

[11] Ibid

[12] “Inequality and Poverty Cause Mental Health.” Info Change India 9 April 2010. Available electronically at: < http://infochangeindia.org/2001010980/Health/Features/Inequality-and-poverty-cause-mental-illness.html>

[13] Web-based search via Google.com: “number of psychiatrists in India.”

[14] “Digital Divide: What it is and Why it Matters.” Digital Divide.org Available electronically at:

[15] “Global ICT Developments.” International Telecommunications Union. Available electronically at:

[16] Ibid

[17] Nielsen, J. and Pernice K. (2002), “Web Usability for Senior Citizens: 46 Design Guidelines Based on Usability Studies with People Age 65 and Older.” Nielsen Norman Group. Available electronically at: <http://www.nngroup.com/reports/seniors/>

[18]“ e-Health and the Elderly: How Seniors Use the Internet for Health – Survey.” The Kaiser Family Foundation January 2005. Available electronically at: <http://www.kff.org/entmedia/upload/e-Health-and-the-Elderly-How-Seniors-Use-the-Internet-for-Health-Information-Key-Findings-From-a-National-Survey-of-Older-Americans-Survey-Report.pdf>

[19] Ibid