India

Modi’s in Luck Now: The importance of good luck, and Uttar Pradesh, in the political success of Narendra Modi

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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.

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Figure 1– Note: the All India Trinamool Congress, the leading political party in India’s most populous coastal state, West Bengal, is incorrectly included in the Mahagathbandhan (“Grand Alliance”) in the right-hand map above. That alliance, which was formed recently between the Bahajun Samaj Party (BSP) and the Samajwadi Party (SP), is led by two former political rivals of one another — Akhilesh Yadav and Mayawati – who had consecutively been Chief Minister of Uttar Pradesh from 2002 until 2017

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:

  1. 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
  2. Hindu Nationalism
            Arguably, some of the most extreme examples of this include:

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.

Uttar

With more than 200 million people, Uttar Pradesh is by far the most populous state not just in India, but anywhere in the world. Its neighbours too are populous: Uttar Pradesh directly borders India’s third and fifth most populous states, Bihar and Madhya Pradesh. It also directly borders India’s capital city-state, Delhi. Modi, in addition to being MP for the holy city Varanasi in Uttar Pradesh, has had his political career intertwine with this state in other significant ways. Notably, by way of the 2002 Gujarat Riots’ direct connection to the Ayodhya Mosque/Temple Dispute (a long, complex story that is analogous to, though in many ways different from and even more tragic than, the Middle East’s dispute over Jerusalem’s holy sites), and the continuing involvement in that dispute by BJP leaders such as Modi’s designated Chief Minister of Uttar Pradesh, Yogi Adityanath. The BJP’s rival, the Congress Party, also has roots in Uttar Pradesh. Its city of Allahabad (now renamed Prayagraj by Yogi Adityanath’s government) was the home of the Nehru-Gandhi dynasty (unrelated to the Gandhi), a family that has supplied four generations of Congress’ leaders. The current scion of the family, Rahul Gandhi, is however an MP for a constituency in Kerala, in India’s far south. Rahul also contested, but lost, his family’s historic seat in Amethi, Uttar Pradesh (located just outside Lucknow, the state’s capital).

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.

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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.

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As such, the commodity boom and global economic growth helped Gujarat remain the fastest-growing Indian economy in the 2000s, apart from the small Himalayan states Uttarakhand and Sikkim.


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.

Modi

 

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East Asia, Europe, Middle East, North America, South Asia

Adventures in the Age Dependency Ratio: A Demographic Overview of the World

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.

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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.

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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.

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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.

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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.

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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:

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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):

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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.
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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:

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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.

 

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North America, South America

RoRoRo Your Car

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.

 

 

 

 

 

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North America, South America

Superhighway in a Box

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.

 

 

 

 

 

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Uncategorized

Humans, Computers, and Telecommuters

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.

 

 

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North America

North Side, South Side: Real Estate in the Greater Golden Horseshoe 

Toronto-Hamilton-Buffalo Populations

The horseshoe-shaped region that includes Toronto and Buffalo is one of North America’s most populous, with more than 10 million inhabitants.The Horseshoe’s northern half extends roughly 100 km from Oshawa in the east to Burlington in the west, and 50 km from downtown Toronto north to Newmarket. The Horseshoe’s southern half is also close to 100 km in length, from Hamilton in the west to Lockport in the east. It is 50 km from the St Catharines-Niagara area south to Buffalo.

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In order for us to analyze real estate in this region, we first need to discuss three basic differences between the Horsehoe’s northern and southern halves: political, geographical, and historical differences.

Political 

The political distinction is the most obvious of these. Whereas the northern half is entirely within Canada, the southern half is split between a Canadian side and an American side. The Canadian side of the southern half is home to roughly 1 million people, of whom 550,000 live in Hamilton. The American side is home to 1.2 million people, most of whom live in the suburbs of Buffalo. The international border runs directly through the Niagara-Buffalo urban area, making it by far the most populous urban area shared by the two countries with the exception of Detroit-Windsor:

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US-Canada Border Cities

 

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Geographical

There is also a geographic difference between the Horseshoe’s northern and southern halves. Namely, it is that the Horseshoe’s southern cities are characterized by their relationship to water and to wind:

  1. Hamilton’s significance comes historically from the city’s harbour, which is by far the largest in the western half of Lake Ontario. The harbour facilitated shipments of bulk goods, helping Hamilton to become Canada’s Steeltown. It continues to host Canada’s largest Great Lakes port.

    Hamilton port

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  2.  The St Catharines-Niagara urban region, which is the 12th most populous in Canada, derives its significance from two water features. One is Niagara Falls, which draws both tourists and hydropower. The other is the Welland Canal, which connects Lake Ontario to the other Great Lakes via a series of locks, bypassing the Falls. Niagara Falls was the site of the world’s first major hydroelectric station, built in 1895. It continues to generate more power than any single dam in the United States. The Welland Canal was first built in the 1820’s, and is a key link in the St Lawrence Seaway shipping route that was opened in the mid-twentieth century.

    Welland Canal

  3. Upstate New York was shaped by a canal too: the Erie Canal. The canal is the main reason why Buffalo, Rochester, and Syracuse were able to grow as cities despite the heavy snowfall they receive (they are, by some estimates,  the three snowiest major cities in the world, outside of cities in Quebec, Newfoundland, or Japan). In the present day the canal is used primarily (but not entirely) by pleasure-craft. However during its heydey in the nineteenth century it was one of the most economically significant waterways in North America.

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Average Snowfall; Source: Current Results

Snow in upstate New York comes mainly from winter winds blowing atop the relatively warm water of the Great Lakes. Because of these wind patterns, Buffalo actually receives twice as much snow per year on average than does Toronto. Indeed Buffalo gets more snow than any of Canada’s 18 most populous cities (a lot more snow, in most cases), with the exception of Quebec City.

Buffalo and Rochester are located in the middle of a “snowbelt”, which extends from Cleveland’s eastern suburbs all the way to the Adirondack Mountains east of Lake Ontario. The only other snowbelt cities with more than 100,000 inhabitants are Sudbury, Barrie, Syracuse, and Grand Rapids.

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While Hamilton lies outside of any snowbelts (it gets the same amount of snow as Toronto, on average), it too is impacted by wind, being hit by among the most windstorms of any Canadian city:

windiest cities

Historical 

Today, the Greater Toronto Area has an estimated 6.4 million inhabitants. The southern side of the Horseshoe (Hamilton + the Niagara Region + the Greater Buffalo Area) has just half that, 3.2 million.

A little over a century ago these positions were reversed. Back in the late nineteenth century Buffalo’s population was more than twice as large as Toronto’s. In 1900 Buffalo was the eighth largest city in the US, and the fourth largest without an ocean port. Even Hamilton was not much smaller than Toronto in those days:

Toronto-Hamilton-Buffalo Populations.png

Relative population sizes; Toronto = 100

There are a number of reasons for this historic reversal, but they all have to do with the price of energy:

  1. Oil
  2. Automobiles
  3. Air Conditioning

oil prices historical.png

Cheap oil in the twentieth and late nineteenth centuries, and the technological advances of automobiles and air conditioning that cheap energy helped to make feasible, resulted in the decline of Buffalo and Hamilton relative to Toronto.

-Home air conditioning began to become widespread in the middle of the twentieth century. Not surprisingly, it led many Americans to move from cities like Buffalo to the Sunbelt. An estimated 28 percent of Americans lived in the Sunbelt in 1950; 40 percent did in 2000.

-For Sunbelt cities in the arid American Southwest, cheap energy was also necessary to ensure freshwater supplies, given the energy-water nexus. And for cities in the western half of the United States in general, cheap energy was needed to facilitate long-distance intercity transportation.

-Cheap oil also allowed land transportation — trains and automobiles — to supplant water transportation. Water transportation is far more energy-efficient than any other type of transportation, but it is also slow and inconvenient. With land transportation becoming dominant during the twentieth century, the importance of cities which were based around water transportation declined. Buffalo and Hamilton were two such cities.

-Buffalo and Hamilton were also not ideally suited to land transportation. For the Niagara peninsula, Lake Ontario and Lake Erie serve as transportation barriers for cars, trucks, and trains; so too does the Niagara Escarpment, which divides the peninsula (and Hamilton) into upper and lower segments. For Buffalo, lake-effect snow also frequently serves as a severe transportation barrier.

Toronto, in contrast, has been able to use automobiles and low energy prices to expand  approximately 50 km deep into its GTA suburbs to the east, west, and north. Because it is a Canadian city, Toronto has also not had to worry as much about people moving south to the Sunbelt, as Buffalo has.

 

Speculating About The Future

Since we do not know what future energy prices will be, prudence suggests that we should prepare for the worst: high prices. Indeed, it seems far from implausible that high prices will become a reality, whether because of carbon pricing or because of a diminishing supply of “conventional” oil. Even in spite of the current shale oil boom in the US, few people have predicted a repeat of the low prices of the 1990s or the 1880-1970 era.

If energy prices do become high, the Golden Horseshoe may look more like it did in the late nineteenth century. Just like how cheap energy allowed the Greater Toronto Area to grow relative to Buffalo and Hamilton, so might expensive energy allow Buffalo and Hamilton to grow relative to the GTA. Similarly, what growth the GTA does experience in an energy-expensive world would be likelier to occur mainly within the City of Toronto, rather than in the GTA’s sprawling suburbs as has occured in recent decades.

At the same time, we can also expect technology to have an effect on the region. In the last century new technologies like automobiles and air conditioners had the largest impact. But how will today’s new technologies – digital technologies – impact the Golden Horseshoe?

One impact of digital technology is likely to be that computers and machines will allow more work to be outsourced or automated. As such, people’s leisure time will increase faster than will their disposable income. From a transportation perspective, this will probably benefit water transportation, which is the cheapest but also the slowest form of transportation. Only someone with a limited budget and a lot of free time would find travelling by water useful; especially if they are trying to avoid carbon emissions.

In particular, water-based shortcuts could become popular. It is just 47 km from St Catharines to downtown Toronto by water, but 113 km by road. Given that ferries are already more energy-efficient than automobiles or even trains on a km-by-km basis, having such a significant shortcut could be highly useful. Buffalo is in a somewhat similar position: it is 93 km from Buffalo to downtown Toronto as the crow flies, but 161 km by road.

Greater Golden Horseshoe

Technology could also make intermodal transportation more convenient. For example, one lesson of the failed Toronto-Rochester ferry was the importance of the “first-mile/last-mile” challenge. Because downtown Rochester is over a dozen kilometres inland from its ferry port, and because downtown Toronto did not have good transit ties to its own ferry port in the Portlands, the ferry was not very useful. The ferry had to reserve most of its space for cars rather than for passengers, so that passengers could drive to and from its ports. The cars also accounted for most of the weight on the ferry, reducing the ferry’s energy efficiency.

With new technologies, however, such as car-sharing services or even self-driving cars, the challenge of getting to and from the ferry port could be eliminated. The ferry would no longer need to be a car-ferry.

More leisure time could also help cities like St Catharines, Welland, Niagara Falls, and Buffalo. It is difficult for cars to cross the Welland Canal because, given the large ships that use the canal on a frequent basis, the only bridges allowed over the canal are lift-bridges. Traffic backups frequently ensue when the lift-bridges are raised. This is why urban development in St Catharines, Welland, and Port Colborne has been mostly limited to only the western side of the canal.
Welland Canal

If people have more free time, however, they may not mind waiting as long — particularly if their car is driving autonomously while they are waiting. A similar thing is true for waiting in a long line of vehicles to cross the US-Canada border.

Autonomous vehicles could be useful in other ways as well.  In areas where human drivers face difficulty or delay, such robots could be highly useful. For example in upstate New York’s snowbelt, cars and trucks with high-tech safety features could be a game-changer for transportation during the winter.

So too could autonomous snowplows. Snowplow drivers are expensive to employ, given that it takes a long time to plow snow and given that they are often hired to work in the wee hours of the night. Autonomous snow cleaners could also help a lot in hard-to-reach places where snow can be very damaging: on rooftops.

Autonomous trucks could also help Buffalo and the Niagara Region by making it cheaper to cross the US-Canada border, where currently it is often expensive to pay truck drivers to wait in long, slow border lines.

Autonomous cargo ships could benefit this region too. They could allow for smaller vessels to be used on the Great Lakes at times when they would otherwise not be employed, such as at night during the winter. They could help save on labour costs for ships traversing the Welland Canal, which because of its locks takes around 10 hours to cross despite being just 43 km in length. They could also save on labour costs on the Erie Canal, which takes over a week from Buffalo to New York City and cannot be used by very large ships.

Finally, cargo shipping on the Great Lakes and their canal systems could be used more because of autonomous machines loading and unloading containers, thereby saving on labour costs and so perhaps allowing intermodal transportation to become competitive even for relatively short-distance water shipping.

Horseshoe

Conclusion

If a world of high energy prices and even higher technology does come into being, it might have three major effects on the Golden Horseshoe. First, it would be likely to cause the Horseshoe’s southern half to grow more quickly than its northern half. Second, it would be likely to cause the City of Toronto to grow more quickly than its surrounding suburbs. And third, it would be likely to cause Toronto to become more connected to the Niagara-Buffalo region, via Lake Ontario’s shortcuts.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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East Asia

Geopolitics within China

The year 2017 has short, medium, and long-term significance in China.

Its short-term significance comes from the Communist Party’s quinquennial leadership transition, which is being held a week from today.

Its medium-term significance comes from being the twentieth anniversary of the most recent notable geopolitical transition in China; namely, of Hong Kong leaving the British to join (in effect) China’s largest province Guangdong, and of Chongqing leaving China’s formerly-largest province Sichuan, in 1997*.

Its long-term significance comes from being the 100th anniversary of the Russian Revolution; of which, with the Soviet Union now long gone, the Chinese Communist Party is the only major remnant. The Party’s centennial is itself arriving in 2021, the first deadline in Xi Jinping’s “Chinese Dream”.

It is interesting to think on how these factors may overlap. The Russian Revolution of course brings to mind the Soviet collapse. That collapse occured 69 years after the Soviet Union’s formation; next year will be 69 years since the People’s Republic of China’s formation. These memories may be reenforcing the desire of China’s leadership to avoid the mistakes they perceive Gorbachev to have made. In a small way, this might be contributing to the Party’s granting more power to Xi Jinping. The promotions Xi makes this week are being watched closely, worldwide, as a yardstick of his clout.

Geopolitics within China 

The twentieth anniversary of the political changes to the Hong Kong-Guangdong and Sichuan-Chongqing regions are, arguably, deeply relevant to this issue.

First, the two men Xi is expected to highlight as long-term successors of himself and of Premier Li Keqiang currently lead those regions. Chen Min’er is the party chief of Chongqing, Hu Chunhua is the party chief of Guangdong. Both will have an incentive to keep their regions pliant, in order to realize this rise to the top.

Second, the strongest moves in Xi’s anti-corruption campaign have been taken against top leaders in the Sichuan-Chongqing region: against Sun Zhengcai, party chief of Chongqing, a few months ago, and against Zhou Yongkang, a former chief of Sichuan, in 2015. Sun will be the first Politburo member kicked out under Xi. He will be just the third incumbent Politburo member to fall in the past 20 years, and yet the second party chief of Chongqing (the other being Bo Xilai, in 2012) to do so.

Third, Guangdong and Sichuan are by far the largest of China’s “peripheral” provinces (see graph); provinces outside of the part of China that, roughly speaking, lies between or near Beijing and Shanghai. Few recent Chinese leaders have been born in peripheral provinces; the new Standing Committee that Xi is expected to pick will not have anyone born in a peripheral province. Neither was anyone on the current Standing Committee* born in a peripheral province. Indeed, nobody born in Guangdong or Sichuan holds any of the 43 positions within the Communist Party’s Politburo, Secretariat, or Central Military Commission.

China's Peripheral Provinces

Read the full article here: Geopolitics within China

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