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Welcome to the succession of the Globalization of
Business Enterprise course.
Today we're going to focus on the E in the cage framework.
We're going to be talking about Economic Distance, and
how it affects cross-border interactions.
More specifically, the outline for today is that we're going to start off
by looking at some of the globaloney that surrounded economic differences.
Then we'll turn to looking at how economic differences and
distances actually affect international interactions.
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And then, we're going to drill down, and start looking in some detail at
emerging economies at the big shift, in the world center of
economic gravity that's occurring from advanced economies to emerging economies.
And that will set up our next session as well, where we'll drill
deeper into the implications of the whole cage framework for strategy.
So that's the, that's the plan for
the session to get started with the first segment of this video.
You saw the little video of me in Geneva, pointing out that the density of
millionaires in Geneva, is much higher than the density of millionaires in China.
That was perhaps not so surprising.
So let's take a look at a broader set of data looking at
average GDP per capita across a range of countries.
And let's look at those data over time to gain a sense of not just how
large economic differences continue to be, but also how they've evolved overtime.
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So, this is a slide on which I've calculated Real GDP Per Capita
on a purchasing power parity basis since 1950 through to 2010.
And what we basically see from this chart and some of that data the lied behind it,
is that over time industrialized countries have tended to
converge roughly speaking, in terms of per capita and GDP.
But the only once recently developing countries that have
managed to break away from the developing world and
really catch up with the advanced economies are the four Asian tigers,
Hong Kong, Singapore, Taiwan, and South Korea.
More sophisticated tests confirm that, you know, what we're seeing in looking at
per capita income, is not convergence per capita income around the world.
But rather we're seeing something called conditional convergence,
where the steady state levels towards which different countries per
capita incomes are increasing depend on things such as investment,
education, population growth et cetera.
And, so it would be an exaggeration to say that, you know,
the data from post-war period demonstrate conversion across all countries.
It's conversions only to a limited extent with large disparities still persisting,
and it's interesting in particular when you look at this slide to realize how big
the different still is on a per capita basis between, say China or
India on the one hand, and the industrialized countries on the other.
So, this chart is a little bit of a reminder.
If a reminder was needed about how limited cross border
integration actually is, because we know from some early work by Nobel Prize
winner Paul Samuelson on the so called Factor Price Equalization Theorem.
That if, for instance, trade flows pass borders without any
significant resistance what so ever, what we would expect to see is a,
is an equalization of prices of capital, prices of labor.
In other words, equalization of wage rates around the world,
even if there was no mobility of capital or no mobility of people across borders.
So, the consequences of perfectly integrated product markets
are convergence in factor market prices as well, and since we are seeing these
big differences in wage slash income levels around the world, that provides
another bit of evidence on how limited the forces for integration actually are.
And if you want to think of a less abstract example of why this logic breaks
down, think back to the software industry that we started off by talking about,
at the beginning of the course.
Greatly celebrated by Tom Freedman,
because it was allegedly competing in a flat world.
But the point is, more than eight years after Freedman's book was published,
wage rates for Indian software programmers are still less than a fifth of
the wage rates for U.S. software programmers.
And so, it's this continuing difference that
really powers the industry's strategies going forward.
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Pankaj GhemawatProfessor of Strategic Management Anselmo Rubiralta Chair of Strategy and Globalization
