Coming Soon (Hopefully)
Geopolitics and the English Language
Mackinder and Keynes in 1919
Founders and Friends (1700s World Leaders)
The Cedar-Sinai Region
In 1492, Other Stuff Happened Too
The Future of Shortcuts
Geopolitics 101: A Suggested Reading List
With a slowing economy, a rival Congress Party forming alliances with regional parties in states like Tamil Nadu, and a separate alliance being formed between two former Chief Ministers of India’s most populous state, Uttar Pradesh, many had expected Narendra Modi to risk losing his majority government, and perhaps even his position as prime minister, following the elections held in India this month. Instead, Modi’s Bharatiya Janata Party (BJP) increased the size of its majority, winning 56 percent of seats—or 65 percent, when combined with smaller BJP-allied parties—in the lower house of India’s parliament. BJP’s rival the Congress Party, which had held the office of prime minister in 55 out of India’s 67 years prior to Modi’s being elected, won just 10 percent of seats. Congress’ alliance won 17 percent of seats, mainly thanks to voters in Tamil Nadu.
As in the previous election in 2014, the BJP and its alliance dominated the north and west parts of India, leaving Congress’ alliance, along with several parties unaffiliated with either Congress or the BJP, to split the smaller south and east[*]. The unaffiliated BSP-SP alliance in India’s north, meanwhile, which was formed in response to the BJP sweeping Uttar Pradesh in 2014 and winning state-level elections in Uttar Pradesh in 2017 (for the first time since 1996), won just three percent of seats[**].
[*]More so than in 2014, however, the BJP now made inroads into the south and east, notably in West Bengal, Odisha, Telangana, and Karnataka
[**]Despite receiving a sizeable chunk of India’s popular vote, because of how populous Uttar Pradesh is. This occurred to an even greater extent in the previous election: in 2014 the BSP received more than 20 million votes, the third most of any party in India, yet did not get even a single seat in parliament
Modi’s BJP was thus able to be re-elected with a majority government for the first time in its history. The only politicians who had ever previously been re-elected with a majority were India’s founding prime minister, Jawaharlal Nehru, and Nehru’s daughter Indira Gandhi. By defeating Indira’s grandson Rahul in the past two elections, Modi has now joined this illustrious list.
Modi has many skills that have contributed to this political success. He is notoriously hard-working and uncorrupt, for example. Yet Modi has also been in possession of an even more important attribute thus far during his political career, the most important a politician can have: luck.
A Quick Analysis of Modi’s Career
Modi’s political career, first as Chief Minister of Gujarat from 2001 to 2014 and then as Prime Minister of India since 2014, has been based on two pillars:
- Economic Ability
- Gujarat was often the most dynamic economy in India while Modi was leading it
- India, despite slowing along with much of the world economy, has maintained a decent economic performance since 2014, and recently overtook China’s growth rate
- Hindu Nationalism
Arguably, some of the most extreme examples of this include:
- Modi’s reaction to (or even deliberate failure to prevent) the 2002 Gujarat riots
- Modi’s selection of Yogi Adityanath to be the Chief Minister of Uttar Pradesh in 2017
These two aspects of Modi’s appeal have contributed to his political success in northern India in particular, where Hindi(-Urdu) is spoken relatively widely and where, especially in inland states like Uttar Pradesh and Bihar, poorer populations live who may be more susceptible to BJP-style nationalism or promises of economic growth (or at least, of reduced corruption). Modi himself represents the constituency of Varanasi, in Uttar Pradesh. 20 percent of BJP seats are from that state.
This has led to an obvious, arguably misleading debate in Western media, over whether Modi’s economic pros justify his political cons. This might or might not be a legitimate debate, but it also overlooks one of the key realities of Modi’s career: the fact that much, maybe most, of his economic success has been due to factors beyond his control. Modi has been extremely lucky in relation to factors such as global economic growth, oil and gas prices, and the utterly different economic characteristics of Gujarat (the state where Modi rose to fame) compared to India as a whole.
Gujarat, 2001 to 2014
Modi was Chief Minister of the state Gujarat from October 2001 until May 2014, when he became India’s prime minister. Two facts must be recognized to put Modi’s time in Gujarat into context: the exceptional status of Gujarat, and the exceptional nature of the period from 2001-2014.
The period from 2001 to 2014 was the 2000s commodity boom, the period that followed the early 2000s recession when, apart from a sharp dip during the 2008-2009 recession, energy and other commodity prices were high and global economic growth was significant, particularly in China and other developing markets but also in North America and (before the 2010s) Europe. Brent crude oil, for example, rose from all-time lows of $9 in 1998 to $144 in 2008 and $128 in 2012. Modi came into office in Gujarat when oil prices were $20, exited office with oil at $110, then watched from his new office in New Delhi as oil prices fell to $46 in the subsequent seven months.
The characteristics of Gujarat’s economy are similarly exceptional. Together with its next-door neighbor the city of Mumbai, the state of Gujarat is India’s leading commercial hub. This is largely a result of Gujarat’s uniquely long and naturally sheltered coastline, which has allowed it to account for an estimated 69 percent of all cargo volume handled at India’s private ports, as well as being home to India’s busiest public port, a remarkable feat considering that Gujarat’s 60 million people are only 5 percent of India’s population.
Just as remarkable is the Gujarati diaspora, which leads in commercial activity throughout much of the Indian Ocean, particularly in eastern Africa. (The most famous Gujarati abroad was, of course, Mohandas Gandhi, who lived in South Africa for more than two decades). The diaspora thrives as far away as the US, where 20 percent or so of US-Indians are Gujaratis, and are one of America’s most successful groups.
The Gujarati diaspora has historically also been prominent in the nearby Gulf region of the Middle East. It remains active in the Gulf today, particularly in Oman and the coast of Pakistan. Gujarat itself, moreover, holds the most prominent position in India’s oil and gas industries, in terms of oil production, oil refining, oil pipelines, gas pipelines, LNG regasification, and petrochemicals.
India, 2014 to 2019
India’s economy is the opposite of Gujarat’s. It is relatively insular rather than dependent on global economic activity, the major exception to this being the large amount of oil it imports, more than any country apart from the US or China. Global economic conditions since Modi became prime minister are unlike those which existed prior to 2015, however. Oil prices have fallen to a range of $30-$70, benefiting India. Global and developing markets have slowed, which has hurt India but not nearly as much as it has hurt most other economies, in particular commodity exporters like Brazil or Russia. Indeed, India has become the fastest-growing “BRIC” economy.
There is even a possibility that India’s slowing economy has helped Modi. It may be that the slowing was not severe enough to undercut Modi’s reputation as a great economic steward, yet was significant enough for people to want a great economic steward – Modi – to remain in charge in order to deal with it. In other words, the lucky timing that helped Modi to build up his economic reputation in Gujarat, combined with the luck which has prevented India’s recent economic slowdown from being severe like many other countries’, may have helped lead to Modi’s huge victory.
This is not a unique situation. Politicians, no matter how praiseworthy or skilled, often do not control their own fortunes. Modi remains in luck now. More troublingly, perhaps, so does Yogi Adityanath.
2008 was as significant a year from a demographic perspective as it was from a financial one. In 2008 the world’s age dependency ratio — the number of people who are either younger than 15 or older than 65, relative to the number of people aged 15-65 — reached its lowest point. From a peak of approximately 77 in 1967, the ratio fell to a floor of 54 in 2008, a level it has remained at every year in the decade since. This low is not likely to be surpassed. The UN predicts that the ratio will rise again during the generation ahead, albeit at a far more gradual pace.
The age dependency ratio is a useful, though obviously imperfect, measure of economic potential. The larger a country’s dependency ratio, the heavier the economic burden (to put it crudely) its working-age population may need to bear. The country with the highest such ratio in the world, Niger, with a ratio of 112, has a burden 1.12 times as heavy as those who bear it. The country with the lowest dependency, South Korea, with a ratio of 38, has a burden that is only about a third as heavy as those who carry it. The Gulf Arab kingdoms have even lower ratios than that (the UAE’s is just 18!), but only because they have so many temporary foreign workers.
It is not surprising that a lower dependency ratio tends to correlate somewhat with economic success. Not only is a country with fewer dependents more able to invest its time and money in increasing its productivity, but productive countries also tend to have low fertility rates, which keep dependency levels low in the short-term (though not in the long term, when low fertility rates lead to small working-age populations). As such, a low dependency ratio can be both a cause and an effect of economic growth. Even the oldest country in the world, Japan, only has a dependency ratio of 66.5, much lower than those of the young countries within Sub-Saharan Africa.
In recent history, the correlation between economic growth and age dependency can be seen most clearly in East Asia. China’s rapid economic growth has tracked its dependency ratio’s steep fall, while Japan’s stalled economic growth has tracked its own dependency ratio’s rise. China’s dependency ratio, which is today the lowest in the world apart from South Korea (not counting city-states or the Gulf Arab monarchies), was almost twice as high a generation ago, and only fell below the US’s in 1990. That same year, Japan’s ratio fell below Germany’s to become the world’s lowest other than Singapore or Hong Kong. A rapidly aging population has since made Japan’s become by far the highest in the developed world, however. Japan’s ratio has also risen higher than those of many developing nations in recent years, even than some of the world’s poorest nations, such as Haiti.
Outside Japan, East Asia now has the lowest dependency ratios of any region, by far. Not only China and South Korea but also Thailand, Taiwan, Singapore, Hong Kong, Vietnam, Malaysia, and even North Korea all have ratios between 38-44, the lowest in the world anywhere outside of the Persian Gulf. Indonesia’s too, at 48.5, is now lower than those of most countries in the world, while the Philippines, the major outlier in the region with a dependency ratio of 57.5, no longer has a high ratio by global standards either. This trend, however, is finally beginning to change. China’s ratio has begun to rise since 2010, prompting many to worry that the country “will become old before it becomes rich”. The dependency ratios of Vietnam, Thailand, and South Korea have also begun rising during the past several years. And Japan’s already high ratio will continue to rise quickly unless it finally decides to raise its extremely low immigration rate.
The years 2008-2010, in addition to being when the global dependency ratio and the Chinese dependency ratio both reached their lowest levels, was also when the EU’s dependency ratio rose higher than that of the US, for the first time since 1984. The EU’s dependency burden has continued to rise relative to the US in the decade since, a fact that has perhaps contributed, at least to a minor extent, to the US’s stronger economic performance during this period. Indeed, at the risk of attributing far more significance to the age dependency ratio than is justified, I will also point out the fact that countries in Central Europe have enjoyed a much lower ratio and a much stronger economic performance than has the EU as a whole. Similarly, Canada has had the lowest dependency ratio and one of the strongest economies among rich Western nations in recent years. Ratios in Canada and Central Europe were particularly low during the financial crisis:
Another intriguing case is Italy, which has a ratio that has been rising at fast pace since 2010, reaching the highest level in its modern history in 2017, at the same time as its economy has become perhaps the primary point of concern in European politics. A similar trend has existed throughout Southern Europe, with the ratios of Greece, Spain, and France reaching high levels in the years after 2010. Although it is actually France which has the highest dependency ratio of these countries, a result of its having a relatively large population of children, it is Italy which has their highest old age dependency ratio (population older than 65, relative to population 15-65):
If we look at Europe as a whole, including countries in its surrounding region, we can see there is a divergence occurring between northern and southern countries. Northern countries such as Germany, Russia, and Poland, which have had some of the lowest dependency burdens in the world in recent decades, will see sharp increases in the years ahead because their largest population cohorts are approaching 65 years old and they have few teenagers approaching 15 years old. (An exception to this is Ireland, which has had a fairly high ratio because of relatively high birth rates, but is not likely to have this increase much going forward, as it has few people approaching 65). Mediterranean countries, in contrast, will have their dependency ratios rise more slowly, because they have more children or because (particularly in Spain) their largest age cohorts are now only in their forties rather than their fifties. Within the EU this is especially true of France, but it is even more true of non-EU Mediterranean countries such as Turkey and Tunisia. These countries used to have far higher ratios than the EU or Russia, but no longer do today.
This fall in dependency in places like Turkey and North Africa is part of a greater trend, in which countries in the “global south”, particularly those outside of Sub-Saharan Africa, have recently seen their ratios fall much more quickly than countries in Europe, North America, or Northeast Asia. India’s dependency ratio, for example, fell below both the US’s and Germany’s in 2016. So did Bangladesh’s. (Pakistan’s ratio is falling too, but still remains high, around the level of Japan’s). Latin America’s is even lower; it recently became the lowest of any region, excepting East Asia. The major country that has had the most significant fall in dependency, however, is Iran:
Of course, age dependency ratios are simplistic. They treat all people above the age of 65 and below the age of 15 as if they were the same, and all people between 15 and 65 years old as if they were the same. Yet if (for example) we were to change the upper limit of working age from 65 to 70, Japan’s dependency ratio would fall substantially as a result, because Japan’s largest age cohort today is 65-70 years old. If, on the other hand, we were to change the lowerlimit of working age from 15 to 20, many middle-income countries’ ratios would rise substantially. To address these obvious shortcomings, alternative measures of dependency have been created. Examples of these include the economic dependency ratio, health care cost age dependency ratio, pension cost dependency ratio, and prospective old age dependency ratio. For each of these measures, Canada is forecast to have the biggest increase in the decade ahead among significant OECD countries, while Italy and Britain are expected to have among the smallest increases.
A primary lesson that can be learned from the analysis of age dependency ratios is that the common “young population good, old population bad” view of countries’ economic prospects is a misleading one. In reality countries with young populations tend to remain poor, in part because the youngest countries in the world (in Sub-Saharan Africa) are much younger than the oldest countries in the world are old. It will still be a number of decades before aging populations lead Europe or North America to have a higher age dependency ratio than Sub-Saharan Africa. And even that assumes that no unexpected shifts in migration or fertility will occur.
What age dependency ratios do show is two big trends, both of which have to do with middle-income economies. The first trend is the emergence of what we might call a goldilocks belt, located between the aging populations of North America, Europe, and Northeast Asia and the youthful populations of Sub-Saharan Africa. South Asia, North Africa, and Latin America all now appear to be in the process of supplanting high-income countries in terms of having the demographic trends that are arguably most conducive to (or at least, indicative of) economic growth.
The second trend is that Northeast Asia’s dependency ratio, which has been the lowest in the world for a generation and probably played a significant role in helping the region emerge from a low-income to middle-income level, bottomed out almost a decade ago and is continuing to rise.
Taken together, these trends suggest opportunities for middle-income countries, particularly those countries located in or near to the Mediterranean and Caribbean regions, to increase their exports to developed economies, given the aging labour forces of developed economies and traditional exporters in East Asia. In contrast, these trends also suggest that there should perhaps be a greater level of caution regarding the younger, high-growth economies in East Africa, such as Ethiopia or Kenya, which have recently been among the favorites of some emerging market investors.
By far the biggest advantage that a large truck has over a small truck is that a large truck has lower labour costs, per unit of cargo transported. Self-driving technologies that reduce or eliminate labour costs may therefore lead to an increased use of smaller trucks.
This could be particularly likely to occur in areas that have rugged terrain, where labour costs tend to be especially high as a result of slower driving speeds and higher insurance costs.
Small vehicles are also better at handling rugged terrain than large vehicles are. They can make sharper turns, have better control on narrow lanes, can pass through narrower tunnels or overhangs, and can manage steeper inclines.
Indeed, the biggest beneficiaries of automation might be the smallest roads of all: mountain paths that can today only be used by very small vehicles or pack animals. Very small autonomous vehicles could revolutionize transport on such paths not only by eliminating the need to pay drivers’ wages and insurance, but also by gaining more space to carry cargo as a result of no longer needing space for the driver, the steering wheel, and spare tires. These vehicles could be used to facilitate shortcut routes that pass through rugged terrain, or to open up rugged terrain to increased economic activity.
The use of small autonomous vehicles in rugged terrain might also allow for the introduction of another new technology : roll-on, roll-off ropeways. These would be ropeways that small autonomous vehicles would drive on and off of, or clip on and off of, in order to be carried above natural barriers such as steep inclines, rivers, flash-flooded roads, or snowed-in high-altitude mountain paths.
They could be especially efficient at handling inclines, not only by allowing direct as-the-crow-flies routes to replace winding, hairpin roads, but also because ropeways operate as a pulley system wherein the weight of descending vehicles does much of the work — and often does all of the work — of lifting the weight of the ascending vehicles.
Here you can see a very primitive RoRo-Ropeway at work. Here, you can see a somewhat less primitive, though still limited, version built in a Volkswagon factory in Slovakia. If automation leads to a proliferation of small autonomous cars, working ant-like to transport goods in rugged terrain, then perhaps we will see systems like these increase and improve. Economically, it may be as close as we get to flying cars anytime soon.
Roads and railways are great, but they are not portable, scalable, or particularly well suited to ideally handling rugged terrain. You cannot easily disassemble a road or railway in order to move it from location to another. You cannot easily drive trucks or trains up and down steep hills, or across icy or snowy or flooded-out landscapes. And, you cannot widen a road or railway very much without facing sharp increases in expense. The wider you build them, the more likely they are to find a natural or man-made barrier in their way. The widest stretches of highway in the world rarely exceed 150 metres.
Ropeways, in contrast to roads or railways, are portable, are scalable, and are ideally capable of handling rugged terrain. You can fairly easily disassemble and transport them. You can scale them horizontally or vertically. Ropeway corridors could be made extremely wide without being blocked by natural barriers. So long as they are portable and temporary, wide ropeway corridors might also be able to avoid unduly bothering the owners of the private farmland they would inevitably need to cross above at times.
Their potential combination of portability and scale could make ropeway corridors useful for transporting bulk, time-dependent goods: for transporting crops at harvest time, or transporting cargo during the rainy season when roads are flooded out, or in snowy areas during the winter, or to help construct and access mines or dams.
There are a number of factors that could make ropeway corridors become common in the future:
- cheaper intermodal transportation as a result of autonomous cargo transferring. Thus far, the costs associated with transferring cargo from one mode of transport to another have led trucks to account for an estimated 70% of all US cargo transport, despite trucks being generally much less efficient than railways or waterways. If loading, unloading, and handling cargo becomes automated, we might expect that railways, waterways, and perhaps even ropeways will become used more widely as a result
- automating cargo-handling also means that ropeways would be able to add many more entrance and exit points than they have had in the past. Loading cargo on and off ropeways is especially labour-intensive, because the cargo arrives and departs at a slow trickle, each vehicle on the ropeway carrying much less than a truck or train. This has meant that ropeways have tended to move goods only from point A to point B, without many or any intermediate stations. Autonomous cargo-handling could change this, dramatically increasing the usefulness of the ropeway system
- cargo tracking systems — ropeways are slow, which in the past created uncertainties for those waiting for the goods they are bringing. With today’s cheap GPS tracking systems and software that can estimate arrival times (and adjust those estimations when there are unexpected delays of one sort or another), these uncertainties are reduced
- partially-autonomous maintenance: some of the technologies now being deployed or developed for maintaining vast electric grid systems should be applicable for ropeways as well. These include, for example, sensors and computer systems that monitor the entire length of the system in real-time, and drones that can be used as cameras to make inspections
- in some cases passenger cable cars might be able to share the same ropeway as cargo systems – perhaps with passengers being transported during the daytime and cargo transported overnight. Ropeways might also benefit, therefore, from technologies that facilitate intermodal passenger transportation: for example car-sharing, ride-sharing,autonomous valet parking, and other technologies might make it easy for a passenger to drive to a cable-car’s entrance and then get in a different car at the cable-car’s exit.
Let’s discuss two sets of three: the land-labour-capital trinity of conventional economics, and the human-computer-telecommuter set that may soon become the three main categories of labour.
To state the obvious, the key relationship during the past generation has been the “capital” of North Atlantic economies (whether that capital be military power, technological innovation, or consumer demand), chiefly that of the United States, and the labour and “land” (most notably, the fossil fuels in that land) of Asia, chiefly that of the Chinese.
Even in recent years, this relationship between North Atlantic capital and Asian land and labour has arguably continued to intensify. Specifically, if we characterize “land” as being the type of energy production that has the greatest impact on local environments — if, for example, we define it as coal production, coal consumption, and the building of massive hydroelectric dams — then we can see that in recent years the employment of Asian “land” has continued to grow at a rapid pace relative to that of the North Atlantic economies.
This has been the result of a number of different significant trends: the growing “green economy” of Europe, the coal-to-gas electricity switchover in the United States that has been the result of shale gas production, the growth of coal and gas consumption in Japan as a result of Fukushima, the growth of hydroelectric power in China (though China’s coal industry growth has been flattening), and the growth of coal industries in southern Asia.
We know that poorer Asian populations in countries like China and India hold the weaker positions in this trade relationship. They supply the labour and “land” chiefly because the wealthier economies of the world mostly do not want to allow large-scale immigration or domestic environmental despoliation, yet are not able or charitable enough to furnish poor countries with capital wealth without demanding labour and natural resource wealth in return.
We also know that this global trade relationship might soon decrease to some extent, whether because of automation or protectionism in capital-rich countries, aging labour forces in Northeast Asia, or an attempt to reduce pollution in China.
The view of world trade decreasing because of automation and protectionism has become especially popular during the past year, because of political developments in both the US and China. Upon closer investigation, however, a reduction in trade may not actually be likely. The hitch here is the limitation of automation in wealthy economies. While computers and computer-run machines may now be excellent at doing tasks that humans are bad at — like being a grandmaster at chess or driving a truck for days without taking a pit stop — they are still terrible at a task that even human children find easy: manipulating objects.
The result of the limitation of automation may be the second set of three mentioned above: a human-computer-telecommuter division and cooperation of labour. Imagine, for example, an industrial or commercial site in the US that employs not only human labour, and not only machine labour, but instead a combination of a small number of on-site labourers, a large number of autonomous machines, and a large number of machines controlled by lower-wage labourers working remotely from poor locations in foreign countries.
In one sense, every party involved would gain in this relationship: rich countries would gain access to cheap labour without needing to outsource, poor countries would receive wages, and both would be allowed to harness the productive power of machines without having to wait until robotic technology is good enough to allow machines to replace labour altogether. Or without having to deal with the economic and social consequences of that day finally coming.
On the other hand, “telecommuters” might further income inequality within wealthy countries, by forcing labourers in those countries into even closer competition with labourers in poor countries. Moreover, it might make it more difficult to ignore the unfairness that exists as a result of real wages in rich countries far exceeding those of poor ones.
The effect of telecommuting — which includes, but is not limited to, a worker being able to control a machine that is located thousands of kilometres away — may be to make labour much more easily tradable across long distances. Since “capital” is easily tradable too, this may leave “land” as the odd man out. Land considerations, for example the location of cheap and/or clean electricity, or of ports capable of importing natural resources from abroad, may therefore become more important, at least relative to labour considerations, when choosing where to locate a new industrial or commercial site.
A place like Iceland, for example, which has abundant and clean power, difficulty in exporting that power directly because of its island location, ports proximate to North America and Europe, and yet no real labour force to speak of, could use a combination of autonomous and remotely-controlled machines to become a major industrial or commercial production site. A similar thing may be true of economies like Quebec, Norway, Manitoba, or British Columbia.
Remote-controlled machines do not get very much press — even if you Google it, you will probably not find much, with the exception of medical tele-surgeries — when compared to discussions of a far future in which widespread, wholly autonomous machines run the labour force. What is so scary, or exciting, about the possibility of remote-controlled machines, and of telecommuting labour forces in general, is that we may not have to wait until the far future for them to become widespread.