“You can see the computer age everywhere but in the productivity statistics.” Robert Solow (1987 Nobel Prize in Economics) summed up with this harsh remark his celebrated “productivity paradox”, which itself started a research frenzy to find counterexamples to refute it. It took more than a decade, because there is a strong inter-relationship between information technologies and the human capital that they are at the same time complementing and substituting for, but at the end these affirmations could be discredited. Furthermore, another profound change with much more evidences against the paradox occurred parallel to the wide expansion of computer technology, which was also easier to measure and prove: the global spread of the digital mobile phone. To get a better understanding of its true economic impact, nothing better than to sum up the relevant literature regarding this topic.

From a purely microeconomic perspective, Jensen was able to prove that the introduction of mobile phones incremented the profits of North Kerala’ fishers to a whopping 8%, reducing at the same time the final consumer price by 4%: better communications enabled the access to wider markets, expanding the dealing possibilities of those offered by the previous local fish market, enhancing overall market efficiency via an stronger the law of one price. From a macroeconomic point of view, Waverman used statistical and econometric techniques to isolate cause from effect, to find that an increase of 10 devices per 100 in a developing country did add 0.6 points to GDP growth per capita and 0.5 to GDP growth: these results bring out the transformative power of technology to the the global economic activity.

And to gain a better understanding of how technological innovations are transmitted into the economy, I’ve put together a stylized model in an Excel workbook offering a mechanistic explanation of how a successful general purpose technology is able to impact economic growth in such a significant way: in the first sheet, a general Bass model is used to quantify the transition to digital mobile technology from 1996 to 2011 (taking care of network effects in a gross manner, better modelled using Becktrom’s law); in the second sheet, and by using the previously calculated penetration level of the digital mobile phone technology as one of the inputs, a neoclassical economic growth model (Solow-Swan) is utilised to explain its economic impact: note this particular model was the first used to introduce technological progress as a fundamental variable to explain economic growth, making it look like a component that increments the productivity of the labour factor and that also complements capital accumulation at the same time, itself divided in different periods of decreasing value to account for the technological depreciation process. The only negative aspect of this model is that technological progress is supposed to be constant over the full period of analysis, leaving aside the possibilities of a growing innovation rate, or a much more realistic decreasing innovation rate. In addition, other variables that are taken into account by the model are: capital depreciation, savings rate, population growth and the relationship between capital and labour in the resulting economic production. Besides, other technological changes could be analysed with the same Excel workbook, because they feature similar diffusion processes and economic impacts: the adoption of the car, substituting for horses; the diffusion of electricity; or the diffusion of computer, replacing the typewriter.

Later economic models supplement the previous one introducing the accumulation of human capital next to technological change, giving birth to endogenous economic growth theories that better explain the relationship between computer technology and economic growth: even if information technologies are mostly deployed for the purpose of substituting the labour factor, their true nature is incredibly complementary to human capital, but this is more difficult to prove econometrically. Last but not least, the entertainment potential of computer technology makes it to negatively redound in productivity growth statistics: for example, the 5 million hours that Angry Birds is played every day should also be counterbalanced in other ways.

The biggest paradox of the Internet is that, being the epitomeness of freedom and openness, its actual implementation is even more closed than the old mainframes. And despite the fact that the whole thing has always been properly documented in RFC memorandums, the oddness and peculiarities of the concrete implementations have always lied hidden within router images, even for the most important inter-domain routing protocols, the biggest concern during interoperability tests.

So it turns out that the real definition of openness is a very nuanced one: in the software world, licensing and governance are paramount, meanwhile standards and interoperability are crucial and strategic in the networking world.

Fortunately, OpenFlow is unlocking a new window of openness in this closed world:  its approach to Software-Defined Networking enables reprogrammable traffic management techniques in the Layer 2 much like MPLS does in Layer 3, but in much more heterogeneous environments. Its first version is very featureless, missing IPv6, QoS, traffic shaping and high-availability, and lacking a killer app, its general adoption will take time, if ever. Even so, its ability to complement recent virtualization technologies in the data center, and being the only practical way for researchers to try out new experimental protocols, makes it a key technology to watch for in the next years.