Among Emerging Countries, the Weakness of Latin America

With the digital revolution, the unit price of computing power per second has fallen exponentially since 1950, and even more so since the aughts. Computer and digital technologies, integrated with other technologies, improve performance. In short, these technologies are paving the way to particularly powerful network effects that enable production to increase.⁴ As the Conseil d’Orientation pour L’Emploi ^{(COE) (2017, p. 15)} pointed out: "By fostering the appearance of new models, digital technologies are no longer merely a new component to go further in terms of automating and optimizing production processes: they are radically disrupting the way in which certain sectors and fields operate and are organized, and lead to the rise of other new ones, which entails regulatory challenges.” We are therefore witnessing a profound transformation.

To a great extent, innovation performance reflects the efforts invested in new technologies. In a recent study, ^{Scott (2017)} featured a series of compound indices built to measure innovation; they consider research and development as a percentage of gross domestic product (GDP), the number of students enrolled in the sciences, the number of scientific publications, high-tech product exports, number and even quality of patents, and institutions, etc. The chosen sub-indices, their numbers, and their weightings vary depending on the compound indices calculated. The National Innovation Index, designed by the Chinese Ministry of Science and Technology, looks at 40 countries; China is currently ranked 18th, a slight improvement over 2011. The Bloomberg index includes 69 countries; China has been climbing the ladder since 2014, reaching spot 21 by 2016. The World Economic Forum index gathers together 135 countries; China was ranked 29th in 2009 and 2016, with a bump between 2010 and 2015. The Cornell University index, called INSEAD, considers 128 countries, and is constructed around 103 indices; China was ranked 25th in 2016 with a particularly high score on the sub-indices of technology and knowledge production (ranked sixth), and particularly low on quality of institutions (ranked 79th). Finally, in 2016, the United States earned the highest score, followed by Germany and South Korea, Japan, China, and much further down the list, India and Brazil (see Figure 1).

Source:^{Scott op. cit., p. 14}.

Figure 1 Comparison and Evolution of Innovation Indices by Country 

When the comparison is limited to research and development, China went from a little over 1% of GDP in 2000, to a little over 2.1% in 2016, but remained far behind South Korea (4.3%), Japan (3.4%), and the developed countries (Germany and the United States: 2.9%), although it has advanced more than Brazil (1.2% in 2014) or Argentina (0.6%), according to the ^{World Bank (2018)} and Unesco (see Table 1). Measuring the same statistic in absolute terms, the gap between China and Brazil is even wider, at a ratio of 12 to 1, to the extent that when GDP (in PPP) per capita is equivalent, the population in China is seven times larger. The understanding is that "small" countries or countries that are less technologically advanced need to dedicate more resources to research and development as a percentage of GDP than big countries (and need more targeted policies). Otherwise, they will be losing out on the ongoing Industrial Revolution. Such is the case of South Korea and Israel, each of which allocate 4.3% of their GDPs.

Table 1 Latin America, Research and Development as a Percentage of GDP 

Source: ^{OECD-ECLAC (2012)}

The rise in digital technologies and their applications is, therefore, profoundly unequal across countries. Several advanced economies are on the cutting edge of producing new technologies, like Japan, Germany, the United States, and Switzerland, not to mention another country extremely specialized in producing a limited number of technologies: Israel. Other emerging countries are starting to catch up with the advanced nations: China and South Korea; yet others are being left out or threatened,⁵ like Brazil, Argentina, Mexico, contrary to the optimistic forecasts from the CAF-Latin America Development Bank (2017).⁶ In spite of that, several flagships in these latter countries use new imported technologies, like Embraer in Brazil or Softtek in Mexico. But, generally speaking, according to The Future of Work, Regional Perspectives (^{ADB, ERBD, IADB, 2018, p. 90}):

Information and communication technologies (ICT) development and penetration are lower in the region than in the developed world. Latin America and the Caribbean takes advantage of only about 6 percent of its digital potential, compared to an average of 12 percent in Europe and 18 percent in the United States...In terms of personal, business, and government adoption of ICTs, Latin America and the Caribbean scores considerably lower than the subgroup of advanced economies, and lags the emerging Asian, European, Middle Eastern, and North African countries, and Pakistan.

Two cases are worth highlighting here: the countries that produce new technologies spread them throughout their industrial fabric, services, and other consumers; and, the non-producers, which only manage to introduce them to their industrial sectors, services, and consumers. The former group produces these new technologies and therefore enters the international division of labor in a positive manner. Their exports acquire demand elasticity with respect to high incomes and competitiveness at no cost. Limiting our view to Latin American countries, it emerges that they are at a disadvantage, and it may get worse if they fail to capitalize on the spread of new technologies to upgrade their industrial sectors, not to mention engage in producing new technologies, because the region’s role in the international division of labor becomes regressive, due to its relative inability to export complex products (^{Xiaodan and Metodij, 2017}).⁷

Access to imported goods that include new technologies, like capital goods, is increasingly reliant on the price of the merchandise that each country sells abroad. This new risk of dependency can limit a country’s investment capacity when it becomes more expensive to import capital goods. And that is by no means the only risk. It has frequently been said that industrial revolutions have not led to a rise in unemployment in the medium term precisely because so many jobs have been created to manufacture new machines. If these machines are not produced, however, then the risk of being unable to avoid rising unemployment becomes real.

The Mass Arrival of Several Asian Countries to the Ranks of the Emerging Economies

South Korea seems to have managed to come onto the scene in certain sectors, like robotics (widespread production and use in the automotive sector), chip production, and microprocessors. So, is that true of China, too? Responding to this question is no easy feat, because changes are happening rapidly in the country. The case of the integrated circuits can help elucidate it, though. In 2010, only several advanced countries and South Korea were able to produce digital circuits. Manufacturing them required tremendous amounts of investment with a very high research quotient. From 2011 to 2016, Chinese production of integrated circuits tripled. By 2017, its growth had far overcome the rates seen in advanced countries. Nevertheless, the processes and the memory are imported or made in China by non-Chinese players, like Samsung or Intel.

The example of the cloud is also interesting. China is making considerable efforts to develop the cloud and catch up with the top companies in the United States, like Amazon Web Service, Microsoft, etc., especially with Alibaba ‘Cloud (Aliyun). The cloud is evolving thanks to lower production costs, the advent of artificial intelligence, and the introduction, in some countries, of policies to protect information in the cloud, as it is considered to be strategic.⁸ The top American companies are losing out, in relative terms, on their ability to monopolize the cloud, and in response, they are now aiming to “colonize,” which is to say, to control the borders beyond their control.

Therefore, it might be speculated that this would make a favorable climate for major Chinese companies. That is not necessarily the case. In fact, the history of the top companies mentioned above with respect to the cloud is quite different than it is for the case of the Chinese companies, as in the former, the cloud was planned for new clients and large companies, while in the latter, cloud services are tied to consumption. Moreover, as ^{The Economist (2017)} pointed out, Chinese companies are struggling to design complex applications.⁹

However the case may be, China is becoming a major player in certain sectors, which is not the case of the emerging countries in Latin America, as has been seen. According to ^{Woetzel et al. (2017 a} and ^b), Chinese growth sometimes moves at "lightning speed," in certain sectors like e-commerce and other transaction-type services, like mobile phone payments.

China was ranked first in 2016 in e-commerce, despite the sector barely existing at all back in 2005. Mobile phone payment volumes were 11 times higher in China than in the United States in 2016 (see Table 2). E-commerce continues to be marginal in Latin America, as shown in Figure 2.

Table 2 Retail E-Commerce as a Percentage of Global E-Commerce. 

Source: ^{Woetzel et al. (2017 a, p. 18)}

Source:^{Suominen (2017, p. 21)}.

Figure 2 E-Commerce in Billions of Dollars by Principal Region, 2012-2017. 

While China makes massive use of robots¹⁰ in the automotive industry, up until now, it has produced relatively few so as to make the progression significant in any way. According to ^{UNCTAD (2017, p. 46)}, China produced 5.3% of the world's robots in 2013, and 8% in 2015, the same as Germany (7.8%), but much less than Japan (54.4%) and South Korea (12.6%), with the rest produced principally by the United States and Switzerland. In 2013, robots accounted for one-fourth of the Chinese market, and by 2016, one third. Progress in using robots in the automotive industry has been tremendous since 2012. Among a group of six countries (China, United States, Germany, South Korea, Japan, and Mexico), China experienced the most growth since 2010 (between 20% and 25% a year), with half of the robots going to the automotive industry (^{UNCTAD, 2017, p. 48}). Nevertheless, if we look at the number of robots for every 10,000 employees in 2014, China was still far off from South Korea (around 370 robots), Japan (320 robots), Germany and Sweden (180 robots, respectively), the United States (140 robots), Taiwan, France (100 robots), Mexico (20 robots), and Brazil (5 robots), pursuant to data compiled by ^{UNCTAD (2017, p. 49)}.¹¹

Progress in the realm of new technologies in China is uneven. Digitalization is shifting rapidly, but in an unbalanced fashion: venture capital investments in 2016 were concentrated in financial technology (7.158 billion dollars versus 5.437 billion in the United States and 1.793 in the United Kingdom; virtual reality (1.312 billion versus 1.437 billion in the United States and 166 million in Japan, 20 million in France); self-driving cars (357 versus 582 million in the United States); mobile (992 million dollars against 1.724 billion in the United States); technology education (681 million against 1.282 million in the United States); robots and drones (227 million against 728 million in the United States); 3D printers (221 million versus 602 million in the United States). In all of these sectors, China was ranked second, except in financial technology, and in others, it was ranked third or even fourth. Such is the case of big data, where the United States is far ahead (6.085 billion) and so is the United Kingdom (1.673 billion), as the venture capital investments amounted to 942 million in China. In artificial intelligence, China is investing four times less than the United States (^{Woetzel et al., 2017 a, p. 19}).

New Employment Models

^{Benhamou (2017)} outlined four types of employment models coexisting at companies with ten employees or more in advanced countries. These models are defined around 15 variables. The first two of these variables aim to measure job autonomy. The next two, how the quality of the job is determined. Three variables evaluate the cognitive dimensions of the job, another four examine the limitations of the job, and, finally, the three speeds. These latter variables refer to the degree of novelty in the tasks to be carried out. Two employment models are considered to be the simple and classic Taylorist models (low autonomy, low self-control, repetitive work, etc.), and the other two are modern “learning” and “adjusted production” or “Toyotism.” The so-called “learning” models are characterized by organizational participation practices (decentralization, autonomy, teamwork). They are different from the Toyota method, which is more oriented to market limitations (quality, just-on-time, etc.).

With the digital revolution, employment models are shifting. Work is becoming more flexible and precarious, and in the future, the so-called “apprentice” form of a job will develop more or less quickly depending on the sectors affected by the digital boom, in forms parallel to the so-called conventional model, principally where unskilled labor is concerned, as it is threatened by the robotics of the future. Nevertheless, robotics does not, and will not, have the same impact on every sector of employment. Some sectors will end up with the learning model of employment (for example, the automotive sector), and others much less so (for example, distribution, like Amazon).

These disruptions to the employment model do not and will not have consequences for jobs and wages. Nevertheless, the overall trend will be one that moves toward differentiated labor polarization, a trend accentuated by the fact that it will make jobs more or less precarious.¹²

In general, ^{Berger et al. (2017)} showed that the percentage of employees who have the opportunity to leave their jobs in old industries to find jobs in the new industries plummeted in the United States between the nineteen-nineties and the aughts, going from 4.4% to a little less than 0.5%. The opportunity for unskilled workers to find jobs in the new industries is, therefore, much lower, except where particularly active training policies are in place. Mobility does not necessarily mean firing, even when changes are more or less offset by the creation of new job positions.¹³ This desired mobility can be managed via sustained training policies. It is known that job stability is frequently a precondition to achieving greater productivity. On the contrary, precarious conditions do not encourage employees to engage with the company employing them. If precarious conditions are destined to become structural, due to the burgeoning use of new technologies, then the perverse effects of it will become evident and the gap between the expected increase in productivity and the real productivity level attained will remain, or even widen.

With the use of new technologies, employment is becoming more fragmented. Jobs tend to become increasingly flexible, both in terms of remuneration (as is the case of competition from low-wage Asian workers, and the impact of insecurity on wages) and the versatility of tasks, with no possibility for social mobility to be sufficiently satisfied in case of a dismissal,¹⁴ especially for employees at small- and medium-sized enterprises. In the extreme case, workers become their own employers, and not only lose their liberty, but also their sense of belonging to a group. Workers are facing the restrictions over which they do not have control: the cloud and big data. The worker is “Uber.”

The Bipolarization of Jobs and a Potential Increase in Unemployment and/or Part-Time Work

Myriad studies have been conducted in various countries, primarily the advanced ones (^{Acemoglu and Restrepo, 2017}; ^{Autor, 2015}; ^{COE, 2017}; ^{Hualde, 2018}). Some studies assert that the rise in automation ought to lead to a rise in the unemployment rate, while others show that this is not necessarily the case, as the lost jobs are replaced by other jobs created either directly or indirectly by automation. In certain sectors (like distribution, but also finance, medicine, and justice), the software using big data will likely do away with many jobs, including the experts managing inventory, making diagnoses for prescription medicine, or analyzing legal situations). However, these jobs destroyed by automation could be the source of other indirect jobs.

The job structure changes depending on the speed at which new digital technologies spread throughout different sectors. In industry, medium-skilled jobs will suffer from automation, because they are generally more routine and easier to replace by robots or articulated arms, unless the cost of the equipment is not a definite incentive.¹⁵

Automation in the industry and in several services, like distribution, ought to exacerbate job bipolarization: more low-qualified and highly-qualified jobs at the expense of unqualified jobs. What appears, more or less, is this trend in companies engaging in automation; worldwide, it is less evident (see Table 3). To be exact, the degree of bipolarization falls as digitization rises, which is a paradox, as demonstrated by ^{Autor (2015)}, who compared the rise (fall) of salaried jobs and the degree of skill measured by wage level, and found that the more wage jobs go up, the more important qualification becomes to the global economy. In the time periods 1970-1989 and 1989-1999, which is to say, a bit before the digital revolution, 5% and 12% of lower-skilled jobs increased with respect to total employment and 52% and 47% of the more-skilled jobs increased strongly in relative terms, while the two extremes (unskilled and over-skilled) did the same, but to a lesser extent. With the digital revolution, the situation changed starting in the year 2000: the portion of low-skilled jobs rose considerably in relative terms, and the number of more-skilled jobs fell considerably, especially between 1999 and 2007. Paradoxically, bipolarization fell and disappeared in the latter period to give rise to just polarization. With the advent of the digital revolution, the only observation really necessary to make is that the number of skilled jobs fell with respect to earlier time periods. These shifts are summarized in Table 3.

Table 3 During the Digital Revolution, Less Bipolarization than Before. 

Source: Created by the author based on ^{Autor (2014)}.

Are income inequalities the result of the technology revolution?