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Innovation and the Economic Frontier

May 30, 2018

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    Javier Cabrerizo- Insead EMBA 2008-Implementation Essay- Macroeconomics

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    Table of Contents

    INTRODUCTION .......................................................................................................................................3

    TECHNOLOGY IS FROM MARS, ECONOMY IS FROM VENUS...............................................................4

    IT AND PRODUCTIVITY: ROBUST AND MIXED RELATION.......................................................................7Why IT productivity improvements do not propagate equally across countries? .........................8Why IT productivity improvements do not propagate equally across sectors? .............................9Why are some firms more successful than others when applying IT innovation? ........................10

    CONCLUSSION: DIFFUSION AND ADOPTION MATTER MOST.............................................................11

    SOURCES................................................................................................................................................12

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    Javier Cabrerizo- Insead EMBA 2008-Implementation Essay- Macroeconomics

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    INTRODUCTION

    Economic growth in the last 100 years has shown a surprisingly consistent behavior: an almost

    constant rate of long run growth of GDP of 2,5-3% per year. This is traditionally known as the

    technology frontier, implying that for a developed economy to continue growing it mustinnovate, but that this innovation is only translated into economic growth via increased

    productivity, at the mentioned rate of 3% per year.

    In contrast with this view of the growth rate of developed economies, we have the speed of

    technological innovation that can be seen across multiple industries like semiconductors,

    biotechnology, nanotechnology, genomics, IT, etc. In these and multiple other areas, the

    innovation rate is much faster than a 3% per year. In fact, in many cases we can see

    exponential growth rates with constant or even accelerating growth factors that multiply, not

    merely add, to the previous year situation. For example we can see how the Moore Law predicts

    double capacity of integrated circuits every 18 months (and this law has been happening for

    the last 20 years).

    So why does it happen that the very fast technological innovation that we see across multiple

    industries is only translated into economic growth at a 3% per year?

    We will try to analyze this phenomenon and answer the following critical questions:

    1- Why does the economy grow only at a predictable 3% per year, when some

    fundamental technologies show growth rates of x2 (100%)or more per year?

    2- How do other processes, like the diffusion, adoption and leverage of innovation affect

    the ultimate impact on the economy growth of the technological innovation growth?

    3- How do external factors like regulation, demand and competition can accelerate

    adoption and hence faster translate the technological innovation into economic

    growth?

    We will focus in one industry, IT. We will analyze its growth rates and how it is impacting

    productivity and growth in the economy as a whole, but trying to understand which sectors

    have benefited from higher productivity provided by their investment in IT and which sectors

    have not seen this benefit. We will also explore differences across countries and identify how

    external factors affect the impact on productivity.

    IT is an excellent area to analyze because it has been extensively studied on the back of the

    very special period that constituted the New Economy era, that is the 90s till 2001 with the

    burst of the technology bubble. During that period, the US economy significantly improved its

    productivity growth, moving from 1,4% during 1973-1995, to 2,4% from 1995-2000. How much ofthat growth was due to the impact of IT, and how much growth has IT brought to the US

    economy after the burst of the bubble, are two crucial elements to understand the impact of IT

    innovation in the economy.

    In the end, technological innovation is at the center of economic growth. However, there seem

    to be significant differences between the speed at which different firms, sectors and countries

    adopt the same technologies. It seems that, for technological innovation to be translated into

    real economic growth, it needs to be co-developed with other innovations in business processes

    that can extract all the benefits from the technological innovation.

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    Javier Cabrerizo- Insead EMBA 2008-Implementation Essay- Macroeconomics

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    TECHNOLOGY IS FROM MARS, ECONOMY IS FROM VENUS

    An analysis of the history of technology shows that technological change is exponential,

    contrary to the common-sense "intuitive linear" view. So we won't experience 100 years of

    progress in the 21st century -- it will be more like 20,000 years of progress (at today's rate). The"returns," such as chip speed and cost-effectiveness, also increase exponentially. There's even

    exponential growth in the rate of exponential growth.

    The paragraph above can be read in a famous article published in 2001 by Ray Kurzweil, one of

    the best-known researchers of the evolution of technology. In his works Kurzweil concludes that

    we are doubling the rate of progress every decade; in other words, we will see a century of

    progress-at todays rate- in only 25 years. In different ways, on different timescales, and for a

    wide variety of technologies ranging from electronic to biological, the acceleration of progress

    and growth applies.

    He enunciates the Law of Accelerating Returns in which he states that the rate of progress of

    an evolutionary process increases exponentially over time the "returns" of an evolutionary

    process (e.g., the speed, cost-effectiveness, or overall "power" of a process) increaseexponentially over time... as a particular evolutionary process (e.g., computation) becomes

    more effective (e.g., cost effective), greater resources are deployed toward the further progress

    of that process. This results in a second level of exponential growth (i.e., the rate of exponential

    growth itself grows exponentially).

    Kurzweils analysis of technological evolution also introduces the idea of paradigm shift,

    explaining that a specific paradigm (a method or approach to solving a problem, e.g.,

    shrinking transistors on an integrated circuit as an approach to making more powerful

    computers) provides exponential growth until the method exhausts its potential. When this

    happens, a paradigm shift (i.e., a fundamental change in the approach) occurs, which enables

    exponential growth to continue.

    The paradigm shift rate (i.e., the overall rate of technical progress) is currently doubling

    (approximately) every decade; that is, paradigm shift times are halving every decade (and the

    rate of acceleration is itself growing exponentially). So, the technological progress in the twenty-

    first century will be equivalent to what would require (in the linear view) on the order of 200centuries. In contrast, the twentieth century saw only about 25 years of progress (again at

    An example of the Law of

    Acceleration Returns: "Moore's

    Law."

    Gordon Moore, then Chairman ofIntel, noted in the mid 1970s that

    we could squeeze twice as many

    transistors on an integrated circuit

    every 24 months. Given that the

    electrons have less distance to

    travel, the circuits also run twice

    as fast, providing an overall

    quadrupling of computational

    power

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    Javier Cabrerizo- Insead EMBA 2008-Implementation Essay- Macroeconomics

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    today's rate of progress) since we have been speeding up to current rates. So the twenty-first

    century will see almost a thousand times greater technological change than its predecessor.

    In these terms, technology is one manifestation (among many) of the exponential growth of the

    evolutionary process. The exponential growth of computing is a marvelous quantitative example

    of the exponentially growing returns from an evolutionary process. We can also express the

    exponential growth of computing in terms of an accelerating pace: it took ninety years toachieve the first MIPS (million instructions per second) per thousand dollars, now we add one

    MIPS per thousand dollars every day.

    It is also important to note that in the evolution of technology we need to distinguish between

    the "S" curve (an "S" stretched to the right, comprising very slow, virtually unnoticeable growth--

    followed by very rapid growth--followed by a flattening out as the process approaches an

    asymptote) that is characteristic of any specific technological paradigm and the continuing

    exponential growth that is characteristic of the ongoing evolutionary process of technology.

    Specific paradigms, such as Moore's Law, do ultimately reach levels at which exponential

    growth is no longer feasible. Thus M

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