Book Review: Globalization Paradox

I enjoyed this book although I admit that global trade and world economy are way out of my depth. Rodrik presents economic globalization as a trilemma. As the saying goes, "winners write the history." The post-war world economy system was penciled by policymakers and economists, laying a path for their own (in their self-interest) and the underrepresented nations. Rodrik presents economic globalization as a trilemma. That is, we cannot pursue democracy, national sovereignty, and globalization all at once. Pick two. There is no one-size-fits-all solution. A "thin" layer of international rules that leaves room for nations is better globalization.

Summary

Different societies have different needs and preferences in shaping their institutions, democratic pressures are likely to lead to a variety of different institutions across different territories. This diversity inhibits the global integration of markets by raising transaction costs across jurisdictions. As a consequence, a world which is fully responsive to democratic preferences will be unable to achieve full globalization. A "thin" layer of international rules that leaves rooms for nations is a better globalization.  

Two takeaways from the book:

  • Market and government are complement to each other, they do not substitute each other.  As contrary to the popular belief to free market, market are most efficient and developed when they are blended with governmental institutions. Long distance market exchange does not exist without rules. In today's globalization world, government provides legal framework to conduct international trade. Therefore, governmental bodies are largest in those economies most exposed to international markets.
  • Capitalism does not come with a unique model. Economic stability and prosperity can be achieved through combinations of institutional arrangements in labor markets, finance, corporate governance, social welfare, and other areas.

Who should read the book:

Those interested in globalization and free market.

* * *

Unlike the last two books, this book is very much thought-provoking. Of particular interest to me, is how Rodrik describes the phenomenal rises of Pacific and Southeast Asian economies, including South Korea, Taiwan, Hong Kong, Singapore, Malaysia, Indonesia, and Thailand, since the early 1960s. Citing the examples from South Korea and Taiwan, the two of the "East Asian Tigers" adopted strategies to reduce frictions between government interventions and private investment and grant tax holidays to foreign investors, fueling the private sector. Corruptions, poor infrastructure, and high inflation are greatly emphasized. The goal was to create new, modern industries by diversifying the domestic market and less reliance on natural resources. When these "infant industries" became capable in the world market, these governments lowered the trade barriers, inviting international competition. China did something different. They pulled off a miracle with a series of experiments tailored to their needs and societal preferences like the Special Economic Zones and Township and Village Enterprises. By the time China joined the WTO, it had already created a strong industrial base. For this reason, globalization --- exporting --- plays a crucial role in these countries.

On the contrary, the East Asian countries' counterparts in Latin America, the Middle East, and Africa adopted a completely different strategy, called "import-substituting industrialization." These nations shut off their borders and gradually replaced import goods with domestic productions, by heightening governmental interventions. Most adopters did well; others, not quite. Brazil, Mexico, Turkey, and others saw the highest economic growth in their histories. Globalization does not work well in these nations. This diversity inhibits the global integration of markets by raising transaction costs across jurisdictions. Consequently, a world that is fully responsive to democratic preferences will be unable to achieve full globalization. As a side note, Rodrik pointed out the underlying force for ambitious economic growth comes from determined leaders.

Another interesting point is the labor market, the only market that is still highly protected. Rodrik proposes rich countries to loosen their immigration quota with a temporary visa program. These visa holders come in for 5-year, then return to their homes; replaced by new waves of foreign workers. The trained returnees would spark positive economic and social dynamics in their home countries. This does not only fill the void of labor shortage in rich nations but also produces a substantial gain in these developing countries, arguably the poor countries. That is a smart proposal. But I argue that it only works for early-career workers, who are ironically relatively inexperienced. Who in their mid- or late-career would venture out of their norms? I may be missing a point. Someone in the room, please advise.

PD Model: Curve fitting with Python

With a set of data, we will see how we can fit the data the phenomenological model by using \(A\) and \(b\) parameters with Python. A sample PD data can be downloaded on Github. The curve fitting process in Python is fairly easy. It uses a non-linear least square method to fit a function. We will need to import the Scipy libraries to perform the fitting. The code snippet is provided as below:


Lines #1-4: Read the .csv file. We see that there's a total of 251 lines of data with each line contains permanent strain in percent, number of load cycle, deviatoric stress, and shear stress ratio. Obviously, in this exercise, we are only interested in the accumulation of permanent strain due to load cycles. Therefore, we are going to ignore the effects of stress state for now.

P_Strain_%Load_Cycle_NdS_psiSSR
0            0.17          1     16.030.25
10.32  4116.190.25
20.358116.110.25
30.37 12116.270.25
40.3816116.110.25
...............
2460.52984116.190.25
2470.52988116.110.25
2480.52992116.190.25
2490.52996116.190.25
2500.52999616.030.25

Line #7: explores the correlation between each feature. This is a quick way to see any correlation between each feature or variable. For example, we see that the strongest relationship exists between P_Strain_% and Load_Cycle_N, i.e. 0.792972. Another strong positive relationship also exists between dS_psi and SSR. This is expected because deviatoric stress (dS_psi) is directly computed from shear stress ratio derived from the Mohr-Coulomb envelope. Again, we will come back to the effects of stress state later.  

P_Strain_%Load_Cycle_NdS_psiSSR
P_Strain_%            1.0000000.792972-0.096899-0.036616
Load_Cycle_N0.7929721.000000-0.079329-0.040335
dS_psi-0.096899-0.0793291.0000000.764572
SSR-0.036616-0.0403350.7645721.000000

Lines #9-29: This is where we define the phenomenological model and run the curve fitting method. Note that we call "curve_fit" from the scipy.optimize library, then fit the "phe_model" with the xdata and ydata, which respectively, are number of load cycle and permanent strain. Note that "curve_fit" includes several other useful parameters, including uncertainty (sigma), initial guess (p0), and bounds etc. You do not need to input these parameters if you do not know any. The breakdown of the next few lines are as below:  
  • Line #22 returns an array of the model parameters \(A=0.25561268\) and \(b=0.0786587\). The \(b\) value is reasonable as discussed in the previous post
  • Lines #23 &24 provides a covariance matrix with respect to \(A\) and \(b\); all of which are near zero. 
  • Lines #27-29 is a further step to find out the \(R^2\) value of the fitting. In this case, \(R^2 = 0.965\). This high \(R^2\) value should not be a surprise for a power relationship.

Now, let's plug back \(A\) and \(b\) to the model and plot the graph. An easy way is to create an empty array, p_strain, using Numpy and parse in \(A\) and \(b\) to the "phe_model" function. The computed permanent strain are stored in the p_strain array. Following that, we plot both original data and model results against the number of load cycle.



Read more about scipy.optimize.curve_fit
Download sample file from Github

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