Open borders and the economic frontier, part 2

In the first post in this three post series, I gleaned a theory of the economic frontier from some of BK’s comments and offered a few of my own responses. In this post, I’ll expound my own theory.

Two general points. First, how the economic frontier advances is both enormously important for human welfare and quite mysterious. It is important because long-run economic growth will determine how well we can mitigate world poverty and deliver ever-improving lives to future generations. A tiny increase in the rate of advance of the economic frontier, say from 3% to 5%, would make our descendants a century hence almost an order of magnitude wealthier. Second, open borders would likely affect the rate of advance of the economic frontier. Before reading BK’s comments, I had pretty much taken it for granted that open borders would boost growth, at least in the short run, as people move from low-productivity countries to high-productivity countries. Based on Clemens (2011)  and Kennan (2012), the modal result of formal studies so far seems to be that open borders would double world GDP, and the assumptions on which this result is based are actually conservative in some ways, e.g., they don’t assume that everyone would migrate to where their marginal product is highest. Negative institutional/productivity side-effects of open borders on frontier countries would have to be very large to offset this, but such effects are not beyond the range of plausibility.

My theory, which I’ll call the “Endogenous Division of Labor” or “EDOL” model, was the topic of the second chapter of my dissertation, Complexity, Competition and Growth (but don’t pay $103, it’s available here for free). More recently, and I think more accessibly, I published a new version as an SSRN working paper here, under the title “Development as Division of Labor: Adam Smith Meets Agent-Based Simulation.” All the data is drawn from a simulation I wrote, which is introduced in this video, and I’ll be happy to send the simulation (as a runnable JAR file) if you’re interested in exploring its properties on your own. It’s not that user-friendly, but I’ll even be glad to give you a tutorial via Skype. I’ll also be glad to present it at academic conferences or seminars or whatever. I think it would lend itself to public presentation quite well, though I haven’t got the chance to transfer. I’m trying to publish it. So far, the Journal of Political Economy rejected it, with some harsh but useful feedback. I plan to submit a completely rewritten version to the Journal of Economic Growth. Any feedback is welcome.

So, down to business.

The first thing to mention about my model is that there are no institutions in it, and no government. It is purely about how markets work. You can say, if you like, that government is implicitly present, enforcing property rights and contracts. Second, as is typical in economic models, agents are homogeneous. There is no high IQ vs. low IQ distinction here: all inter-personal differences are assumed away.

On a more methodological note, the model is implemented using an agent-based simulation. I use the simulation as a substitute for the solving equations. In solving equations, an economic theorist impersonates a Walrasian auctioneer, equilibrating markets by finding a price vector that causes quantities demanded and supplied of every good to be equal. This method of economic theory has been objected to on the grounds of its lack of realism, but I object to it on the ground that it lacks generality. It cannot deal with nonconvexities. The simplest case that flummoxes a Walrasian auctioneer is the Triangle Economy, in which three agents have one x and one y and a utility function u=x^2+y^2. A price more or less than one would cause them all to want to buy the same good; a price of one would split them at random 3/0 or 2/1 in favor of one good or the other; but in any case, the market will not clear. The Triangle Economy is one of a huge range of cases where Walrasian equilibrium fails to exist, including the case with individual-level fixed costs of production in which I’m interested. For more technical details, see the paper.

The model starts by specifying the technology (Figure 1). Each good is a point in L0/alpha space, where L0 represents “how difficult to make” and alpha “how well-liked” a good is.

Figure 1: Specifying the technology

As a professor, I’ve become accustomed to showing students charts whose meaning seems obvious to me, but of which students find all sorts of ingenious ways of misunderstanding. Of course, the reason is that after years of looking at and manipulating and arguing from and through charts generally, and certain standard charts in particular, using them becomes “second nature” in a way that students can’t easily grasp. This chart, however, will not be very easy to understand even for trained economists. But let me try to explain.

L0 may be called, more technically, “labor fixed cost.” Its role lies in the production function, which, for each good i, is:

(1) M_i = L_i – L0_i

On the horizontal axis is “alpha.” This enters into the following utility function:

(2) $latex U = \left(M_T^\sigma + \displaystyle \sum_{i \in V} \alpha_iX_i^\sigma\right)^{1/\sigma}$

What equation (2) says is that there’s a taste for variety, so agents want to consume a wider range of goods in small quantities rather than a narrow range in small quantities; however, on the other hand, some goods are better liked than others, i.e., enter the utility function with larger coefficients.

In the Goods Space chart, each point in the chart represents a different good. Its vertical position represents how hard the good is to make, while its horizontal position represents how well liked it is. I have added extra information to the Goods Space chart by replacing neutral round dots with three types of marks representing different series, thus classifying the goods as:

(a) those for which active industries emerged in all four of a series of simulations with N=50, N=100, N=200, and N=500;

(b) those for which active industries emerged in either the N=50 or the N=100 economy; and

(c) those for which active industries emerged only in the N=200 or N=500 economy.

We see here the phenomenon of technological “low-hanging fruit,” goods which are well-liked and easy to make, being picked first. Later, the easiest technologies are already in use, so further progress depends on “high-hanging fruit” and is somewhat harder. This sounds like a pessimistic result, but as we will see, the EDOL model can yield optimistic results as easily as pessimistic ones. Note, in any case, that when the society runs out of low-hanging fruit, it doesn’t get poorer, it merely can’t grow as fast. The title of Tyler Cowen’s The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better uses the phrase “low-hanging fruit” in the same way, meaning not that we are destined to become poorer, but only that further growth may become harder.

How economies emerge in the simulator is too technical for a blog post, but here’s an overview. The model is inhabited by agents. Agents have a few skills, which get swapped in and out randomly when unused. They can self-supply some goods. They also have money and may have opportunities to trade. Because agents can’t make everything themselves, and because they value vareity, they may want to buy goods on the market, using their cash reserves but also earning more reserves by selling some of the goods they make at home. Because there are fixed costs to producing each good, it is inefficient for agents to make multiple goods themselves, and they usually specialize in a single good and buy everything else from the market. Trade is mediated by a retail sector. Retailers are founded by agents. Each retailer trades in only one good. It does not explicitly maximize profits, but instead adjusts prices in order to balance supply and demand over time while charging a markup to compensate its owner-operator. When the Simulator launches, there will be only agents, no retailers. Autarky. But soon retailers begin to be founded, and a division of labor emerges. A larger population makes it possible to overcome the fixed costs and transactions cost (i.e., retailer costs) of introducing a new good. Consequently, the variety of goods available increases, and productivity and living standards rise.

The EDOL Simulator is able to map a goods space into an aggregate production function linking output to capital and population. Let me unpack that. Start with a goods space, a level of “capital,” which augments labor according to the formula L=1+K^a, a<1, and a population. Set the model in motion. Let the retail sector emerge and the model equilibrate, i.e., the moving average of utility settles into some roughly stable level. Now vary the level of capital, and/or the population, and run to equilibrium again. Gradually data accumulates. What results is that from a goods space like that shown in Figure 2…

Figure 2

… you can derive a production function like that shown in Figure 3:

Figure 3

Some things to note here. First, the larger the population, the greater the payoff to capital. Capital allows more goods to be introduced, but this is less useful if the population is not large enough to supply labor and distribution for a wide variety of industries. Second, returns to capital at the aggregate level are much higher, especially in the N=500 case, than returns to capital in any given production process. In this case, the exponent on capital was 0.5, yet in the N=500 case, for k>3, a regression of the log of GDP against the log of capital yields an estimate of the aggregate production function as y=0.7*k^1.08. By allowing for the introduction of new goods, by inducing a more intensive division of labor, capital raises productivity so much that it output rises more than proportionately with capital. Third, the big lesson of the EDOL model is much the same as the big lesson of the first three chapters ofThe Wealth of Nations. It might be called a formalization of Adam Smith.

We can stop here and start listing some stylized facts the model can (help to) explain.

1. Why are there cities? Cities arise because they facilitate an intensive division of labor, raising productivity enough to offset the artificial scarcity of certain resources, especially land, which they induce. Of course, this is also why urbanization and development exhibit such a strong correlation.

2. Why is free trade so beneficial? Empirical studies such as Sachs and Warner (1994) and Dollar and Kraay (2001) find gains that seem larger than the neoclassical theory of comparative advantage, with its “Harberger triangles” of welfare increase, can explain. This is because countries that open themselves to free trade can plug into a global division of labor, finding new narrower niches of specialization while getting access to a wider variety of consumer, intermediate and capital goods.

3. How does the economic frontier advance? Doubtless, new ideas are important, but growth theory since Romer (1991) may have overstressed the role of R&D and invention. We can agree with Romer in characterizing the state of technology as a set of blueprints, but just because a good has been invented– just because the blueprint is available– doesn’t mean it’s economically viable. A lot of technological progress may consist of occupying, or exploiting, a goods space that technology had already mapped out. What makes a good economically viable is primarily the size of the market (population) and the availability of capital. The best way to promote technology may be, not to fund science or even to protect patents, but to liberalize trade, encourage savings and investment (down with Social Security!)… and open the borders.

4. Why “and open the borders?” Because it grows the market, which can make latent technologies economically viable. For one thing.

5. If science is so important to progress as economists claim, why do you sometimes meet these engineers and inventors who are beating their heads against the wall because they have brilliant ideas and can’t get the capital to fund them? Are these guys just deluded about the value of their ideas? Not necessarily. Engineers and inventors add blueprints to society’s reserve of technological knowledge. In other words, they populate the goods space. But society probably isn’t fully exploiting the goods space. There are probably a lot of latent technologies, things that technologists know how to do, but which can’t yet be run on a paying basis. Some frustrated may be quite right that they’ve invented a technology that could change the world. But the economy needs to develop further before that technology can be activated.

6. Why did Western Europe dominate the world so much in the 19th century? And why did East Asia experience such a spectacular rise in the late 20th century? There are a lot of candidate explanations for these ones, and several may be valid, but an important factor is access to the sea. Europe is a very strange continent in one respect: its ratio of coastline to surface area is extraordinarily high. It is full of peninsulas: Scandinavia, Denmark, Iberia, Italy, the Balkans, the Peloponnese. Plus the British Isles. In the age of sail, when water transport was far cheaper than the alternatives and no slower, easy access to the sea made it easier for Europeans to move goods and people around within Europe and to other parts of the world, greatly enlarging the market. East Asia, too, is notable for its peninsulas (e.g., Malaysia, Korea) and islands (Japan, Taiwan), and when containerized shipping lowered the cost of moving goods by sea in the late 20th century, East Asia was poised for an export-led manufacturing boom.

7. Why do US cities seem to specialize in certain industries, e.g., the Bay Area in high technology, Boston/Cambridge in higher education, New York in finance, Washington in government/lobbying/journalism, Houston in energy, Detroit in cars, Seattle in airplanes, LA in movies, Nashville in music? Economies of scale. These don’t just operate at the level of the firm. They can operate at a higher level. Alfred Marshall coined the term “external economies of scale” for this, but his explanation bundled a lot of things together. It may be true, it may even be important, that many benefits from being “where the action is,” so to speak, in a particular industry, are pure, disinterested spillovers. You might learn a lot about politics in DC just by hanging out at certain parties, from people who are talking just for the fun of it or to be polite, about what they know. But in other cases, the market size is what matters. You don’t want to run a full-service think tank in a town where only one person is informed about an important issue, say health policy, or foreign affairs. That person might die, or move away, or clash with the culture of your organization. They might also perceive that they’re a monopolist vis-a-vis your think tank– without them, you’ll have nothing to say on a key issue– and “hold you up,” demanding better terms. You want a deep labor market, so that everyone in your organization is easily replaceable. They, too, want a deep market, so that if your think tank treats them unreasonably they can move to another one. And so everyone assembles in DC. Similarly, screenwriters and actors and producer all want to live in LA. All this regional specialization makes the US economy more productive. And of course, open borders would allow that to happen at the global level. (More than it does already.)

8. What’s the deal with jobs? Why do they seem to be a peculiarly modern concern? There has always been work, but people haven’t always had jobs, exactly. In some parts of the world even today, the bad situation to be in is not so much to be jobless as to be landless, because subsistence agriculture is still the norm. If you have land, you have work: you work the land. Modern capitalist economies are different because they have much more complex and productive networks of specialization. Autarky is still an option in principle– an unemployed homeowner could dedicate himself to growing peas and beans in his backyard– but since its productivity is so negligible compared to occupying even the least favorable specialized niches in the economy– say, selling popcorn in a movie theater– that we don’t even take the autarky option into account. Farmers in advanced modern economies, of course, are no more autarkic than anyone else: they produce the vast majority of their food for others, relying on the larger economy for equipment, fuel, finance, and nowadays even the best GMO seeds to do so, and they enjoy a far higher living standard than subsistence farmers would.

I could extend this list further, but let me focus on one issue that will tie it back to open borders. I said in Part 1 that I had a fifth reason for being skeptical of BK’s ideas about why high IQ people are less productive in many regions where they’re in a minority. I’ll call it the automotive revolution, opaquely for the moment.

Transportation technology facilitates the division of labor. Improvements in transportation technology permit a more extensive division of labor. Today, there are five major modes of transport: air, water, rail, motor (vehicle), and pipeline (only for fluids). Air is by the far the fastest but also by far the most expensive. Water is by far the cheapest but also by far the slowest. Rail is much cheaper than motor, though more expensive than water, but it doesn’t have “last mile” capability. Goods shipped by rail usually have to go intermodally with trucks. For this reason, rail is likely to be slower than motor, even though the fastest trains go as fast or faster than the fastest trucks. Trucks dominate, carrying 70% of US freight by value.

But it was not always so. In the 18th century, shipping goods from London to Edinburgh by sea and by land took about the same time but was a couple orders of magnitude cheaper by sea. Since then, shipping has improved. Steamships are faster and more reliable than sailing ships. But land transport has improved much more, first with the advent of the railroad, second, even more importantly, with the automotive revolution. Consequently, it has become much less relatively important whether two places are connected by sea, and much more important whether they are connected by a road. Economic space has been drastically transformed. On land, economic distance is usually roughly proportional to physical distance now, obviously excepting off-road destinations. Meanwhile, for most purposes– since the speed of trucks is usually more important than the cheapness of ships– all economic distances over water are larger than equivalent physical distances over land would be.

The automotive revolution transformed the economy. It hollowed out many city centers, as people could live in quiet green suburbs while still benefiting from an intricate urban division of labor. The 1920s and 1930s probably saw the fastest productivity growth of the 20th century, in an important degree thanks to the automobile. Now, while probably almost every region of the world could make some use of the automobile, some places were better positioned to benefit from it than others. A big continental land empire like the United States was perfect. Once the highways were built, the common man could zoom from Chicago to New York in a (long) day. The division of labor could elaborate itself on a continental scale. Continental Western Europe, once it got the world wars out of its system, was well positioned too, as cars and trucks could drive from the toe of Italy to Granada or Normandy or Berlin or the northern tip of Denmark. The British Empire, by contrast, suddenly made much less sense, as its sea links lost economic importance. And Britain itself, as an island, was perhaps not as well served by the advent of cars as were France, Germany, and the Benelux countries. It saw three decades of relative decline after World War II, and the automotive revolution may have been partly to blame.

Now compare the situation of the white post-colonial diasporas in Rhodesia/Zimbabwe, South Africa, the Caribbean, and South America. The relative economic distance to Europe has suddenly increased. They are not well positioned to plug into Europe’s new automotively-enhanced economy. What matters now is the volume of economic activity in a given land mass, and this was everywhere inferior in the colonies to what it was in western Europe or even Great Britain, let alone the United States. So why were the European diasporas to some extent left behind economically, becoming less productive than their genetic kin in Europe? It might not be because they were in a minority, and a poorer/low IQ/less enlightened/whatever minority was making the rules. It might be because the automotive revolution favored big continental empires like western Europe and the United States, and to a lesser extent Japan, but certainly not Caribbean islands, or remote landlocked Rhodesia/Zimbabwe, or faraway Argentina and Chile, or Brazil with its population concentrated on the seacoast, or Spanish America where the elite had often lived in coastal cities and felt closer to Europe than to their own mountainous hinterlands.

In the next post, I’ll try to appraise IQ-based theories of the wealth and poverty of nations in the light of my own EDOL model, and vice versa. I don’t know where I’ll come down, because I need to read more Garett Jones and more of BK’s links.

Nathan Smith

Nathan Smith is an assistant professor of economics at Fresno Pacific University. He did his Ph.D. in economics from George Mason University and has also worked for the World Bank. Smith proposed Don’t Restrict Immigration, Tax It, one of the more comprehensive keyhole solution proposals to address concerns surrounding open borders.

See also:

Page about Nathan Smith on Open Borders
All blog posts by Nathan Smith

14 thoughts on “Open borders and the economic frontier, part 2”

  1. Interesting post. I won’t say much about the model until I’ve had a chance to read through the papers, but here are a few things that jump out from the post.

    ” Note, in any case, that when the society runs out of low-hanging fruit, it doesn’t get poorer, it merely can’t grow as fast. The title of Tyler Cowen’s The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better uses the phrase “low-hanging fruit” in the same way, meaning not that we are destined to become poorer, but only that further growth may become harder.”

    Are you talking about total GDP or per capita GDP? Per capita GDP should be expected to fall if the rate of technological improvement falls much towards the levels that have prevailed until the last couple centuries.

    Depletion of fossil fuels, minerals, topsoil, and other environmental factors such as climate change lower standards of living unless offset by technological and economic improvements.

    Also, population growth reduces the supply of land and other natural resources per person, pushing wages to subsistence levels through Malthusian pressures in the absence of technological improvement.

    “I’ll call it the automotive revolution, opaquely for the moment.”

    This is worth highlighting in the context you raise:

    ” This is because countries that open themselves to free trade can plug into a global division of labor, finding new narrower niches of specialization while getting access to a wider variety of consumer, intermediate and capital goods.”

    Progress doesn’t have to come only from serving new micro-niches, straight economies of scale are good too.

    “Now compare the situation of the white post-colonial diasporas in Rhodesia/Zimbabwe, South Africa, the Caribbean, and South America. The relative economic distance to Europe has suddenly increased.”

    OK. Some questions for you:

    1. Would you predict that Iceland would be poor due to its separation from the land mass of Europe?

    2. What about New Zealand and Australia, which do rather well, despite being even further from Europe? Perhaps you would say they have access to SE Asia by ship, but you could say similar things about the Caribbean and much of Latin America relative to the US.

  2. Quickly:

    Economies of scale and specialization are closely related. Structures can have inherent economies of scale, of which a shorthand summary is the physical fact that volume is proportional to the cube of the dimension, surface area to the square. A cottage requires more walls relative to the usable space it contains than a mansion. But some economies of scale that happen within firms might be primarily because you can give people narrower tasks in which they excel more. By the same token, economies of scale that are “external” to firms might ultimately depend on physical economies of scale. For example, suppose a city out-competes smaller towns for attracting high-tech professionals because of the excellent selection of cuisine it offers. Niche restaurants are able to operate cost-effectively in the city because large markets enable them to buy large equipment which lowers unit costs. Is this a story about “straight economies of scale” or “specialized niches?” I’m not contradicting you, of course, just stressing how subtle the story really is.

    Your point about Malthusian pressures is well-taken. It’s omitted from my model, and I don’t think Cowen has it much in mind either, but of course some of the constraints on economic growth are just given by nature, and can’t be expanded, or not without raising unit costs. I don’t think this is TOO important today, since (a) pure natural-resource value-added isn’t a big part of people’s budgets (even indirectly), (b) population growth is slowing worldwide anyway, and (c) I think more people tend to create more positive externalities by stimulating idea-creation and permitting greater specialization than negative externalities by competing for natural resources.

    Natural resources can be very important for particular countries, however, which is why I don’t find the high prosperity of comparatively remote and sparsely-populated Norway troubling for the model. Would my automotive revolution sub-thesis predict problems for Australia? Hard to say. On the one hand, Australia doesn’t have huge economic mass in itself, so there should be less scope for new economies of scale via specialization to be exploited. But Australia does have a big continental land empire, and I suppose cars probably helped them to exploit their natural resources.

    Iceland’s economy ( seems to depend a lot on its natural resource endowments, too. Hydroelectric power makes it a good place for energy-intensive manufacturing, and fishing is a huge industry. Also, it has a big financial industry, which is distinctive in that its products are largely intangible and therefore immune to the ordinary disadvantages of economic distance.

    My crude first guess is that New Zealand is at an economic disadvantage for the reasons elucidated in the post, and that this largely explains why it lags behind western European countries. It is about 15-20% poorer than big western European countries like France, Germany, and the UK, and its below the EU average, in spite of having the important advantage of an English legal and constitutional tradition, and perhaps of being free from EU law.

    That said, this kind of back-of-the-envelope empirics is no substitute for a thorough and systematic econometric investigation, which it’s making me realize I should probably undertake.

    1. I think distance from centers of economic activity has an effect, but I would guess you are overestimating it if you don’t use demographic controls. For example, the comparison of Australia and New Zealand is complicated by a major demographic difference:
      In Australia, the indigenous population is a very small portion of the population:

      “After adjustments for undercount, the indigenous population as of end June 2006 was estimated to be 517,200, representing about 2.5% of the population”
      In New Zealand self-identified Maori and non-Maori Pacific islanders make up just under a quarter of the population. And the population is admixed, with self-identified Maori and PIs having European ancestry, and Europeans having Maori/PI ancestry.

      There are significant income, educational, and other gaps between European and Maori/PI New Zealanders, and differences in political behavior:
      “n 2006 the mean (average) income of Māori was 73.2% of that of non-Māori. But in terms of median incomes (the middle) the disparity was less (85.7% of the median income of all residents). In 2006 Pacific people’s median income was slightly below the Māori figure, at 84% of the total median income.”

      Compare to U.S. economic disparities by ethnicity:

    2. Also of interest, New Zealand has a very unusually high fraction of its native-born skilled workers in Australia (see the table in the link below), which is tightly integrated economically with New Zealand. This boosts the Australian figure while lowering the New Zealand GDP figures (incidentally, this should affect our estimates for other ‘brain-drained’ countries, which may cut in different directions elsewhere).
      “Table 1 shows that there were something in excess of 460,000 New Zealand-born living outside New Zealand in 2001. Of these, almost 360,000 were living in Australia.”

      The table has “naturals abroad” data for other British colonies, European countries, and East Asian countries.

      This is also suggestive of gains from the economic integration of New Zealand with Australia, at least for a country of only a few million, but also is a measurement problem.

        1. ““The 1973 Trans-Tasman Travel Arrangement has allowed Australian and New Zealand citizens to enter each other’s country to visit, live and work, without the need to apply for authority to enter the other country before travelling.””

          Yes, it looks great. There are also restrictions on welfare claims and separation of work and citizenship:

          “Unemployment issues
          Originally, New Zealand citizens arriving in Australia were entitled to unemployment benefits immediately on arrival in Australia. Similarly, Australian citizens were entitled to social security benefits in New Zealand. During the 1980s and 1990s, this became a hotly debated political issue. Starting in 1986, New Zealand citizens were required to be resident in Australia for six months before receiving benefits, and in 2000, New Zealand citizens were required to reside in Australia for two years before they can receive payments. This is also the case for Australian citizens residing in New Zealand.[3] However, this has been restricted further in 26 February 2001 and a New Zealand citizen must apply for and be granted a formal Australian permanent visa to obtain certain social security benefits not covered by the bilateral Social Security Agreement in 2001.”

          “While this still allowed the freedom to live and work indefinitely in Australia, it restricted access to certain privileges of holding a formal Australian permanent visa, such as access to certain social security payments and Australian citizenship.”

          Also, Australia has one of the world’s biggest immigrant fractions, and a policy for non-New Zealand immigration that is strongly biased towards bringing in high skill rather than low skill workers:

          Nearly one in four of Australia’s 21 million people was born overseas. The number of settlers arriving in Australia from more than 200 countries between July 2008 and June 2009 totalled 158 021. Most were born in New Zealand (16.2 per cent), the United Kingdom (13.6 per cent), India (10.9 per cent), China (10.0 per cent) and South Africa (4.6 per cent)

          “Ross Gittins, an economics columnist at Fairfax Media, backs up the Treasury study, claiming that the Liberal Party’s focus on skilled migration has reduced the average age of migrants. “More than half are aged 15 to 34, compared with 28 per cent of our population. Only 2 per cent of permanent immigrants are 65 or older, compared with 13 per cent of our population.”[41] Because of these statistics, Gittens claims that immigration is slowing the ageing of the Australian population. He also claims that the emphasis on skilled migration also means that the “net benefit to the economy is a lot more clear-cut.””

          Looks like a pretty good policy mix, as countries go today. However, it would be vastly improved if they just eliminated the (high by world standards, but lower than demand) caps for high-skill immigration. The situation is fairly similar to Canada’s.

          1. The welfare restrictions started soon after New Zealand suffered a severe terms-of-trade shock that cut its income relative to the OECD average by a third.

  3. Nathan,

    In the past, my reasoning for thinking that these spatial effects were not very large stemmed from two stylized facts.

    First, trade is significantly hampered across international borders, even in the presence of nominally low barriers to trade, within the WTO and even free trade areas such as NAFTA and the EU:
    “A large body of empirical research finds that a pair of regions within a country tends to trade 10 to 20 times as much as an otherwise identical pair of regions across countries. This result has been called the “border” effect.””

    Second, my understanding is that small countries are not systematically poorer than large countries in regressions. But perhaps I’m wrong about this, am I?

    So my thinking was that if these changes in costs of trade were not very significant, then neither would the spatial considerations you are discussing.

    But perhaps you would say that small country status and distance from economic centers of gravity have cumulative effects. And further, just small size isn’t enough to cause effects because small countries compensate by taking greater efforts to be open economies, but you might argue that such compensation gets “used up” so that increasing barriers to trade have increasing marginal impacts. Or you may have some other response. I would be curious to hear it.

  4. Your argument seems to imply that international trade is not very important. But that can’t be true. It seems to have played a big role in the rise of East Asia, no? Yes, I’m aware of the strangely large effect of international borders on patterns of trade. But it’s also true that small countries are more open, in the sense of having higher ratios of exports and imports to GDP. How these factors offset each other is hard to say.

    What I’d really like to do to answer this question is to build an explicitly spatial simulation. This simulation is designed to show “exploration of the goods space” and aggregate increasing returns through individual-level specialization. When I talk about the effects of the automotive revolution, I’m reaching intuitively beyond what my results clearly show; I’m trying to apply it, without doing the work of extending the simulation itself to cover the phenomena under consideration.

    The model certainly lends itself to a spatial extension. Indeed, I’m confident that the model is capable of all sorts of further development. A spatial extension might introduce markets in land, making it a factor in both production and utility functions, but scarce, with transport costs for traveling across it. When I started working on early versions of this model in 2007, there was an explicit spatial aspect, but I dropped that after my first attempt was a failure. I had a lot more learning to do, not just about techniques but also, through tackling these technical challenges and being forced to rethink the nature of exchange, about the logic of the economy itself. I simplified as much as possible and there is a sense in which the model is now rather simple and minimalist… yet the agent and retailer behaviors are still complex enough that I almost despair of finding readers who can understand the model. I could envision extensions to capital markets too. I could develop “factories” in addition to retailers, who “trade with nature” by converting one good into another by a specified process, and see the emergence of ROUNDABOUTNESS, which would be a less arbitrary way of modeling capital than what is used in the current version of the paper. I could be implementing Austrian capital theory. I would also like to introduce FIRMS, and shed some light on institutional questions. For that matter, I might like to alter retailer behavior so that they resemble the suppliers in the first chapter of my dissertation, who use regressions to predict demand and then engage in a kind of profit maximization (instead of just trying to “balance” supply and demand as these retailers do).

    Equilibrium economics has, I think, been almost tapped out. There are diminishing returns, at this point, to equilibrium theorizing: the best insights that can be got that way have been got already. By contrast, the methodology I develop here can do all sorts of things, deal with all sorts of questions that equilibrium economics is poorly suited to answer. There is a lot of mileage left in it. Unfortunately, few people have the skills to do it, or for that matter to understand it. I have the skills, but right now, not the time. It is, still more unfortunately, a very laborious process. I think the labor you have to invest in a simulation is a lot more than in a neoclassical model consisting of equations. Probably it’s not the right of looking at it, but I’m afraid I have a bit of a frustrated-pioneer-of-a-scientific-revolution-that-deserves-better-than-I-can-do-for-it complex. Glimpse into my life.

  5. One more thing though: in my mind, the EDOL story is wedded to a particular kind of institutional story which I haven’t modeled yet, but which would make institutions more about facilitating a complex division of labor than about getting incentives right or allocating resources efficiently. The corporation as a form of business organization is part of that. A well-developed financial system is part of that. Enforcement of complex contracts is part of that. Norms and trust are probably part of it, too. Poor countries suffer from inefficiently small markets and small scales of organization not just because their total economic mass is small but because institutions don’t facilitate self-organizing cooperation on a large scale. Markets are often much smaller than national, not because the good is inherently local or because localism is efficient, but because social infrastructure doesn’t support efficient large-scale enterprise. It’s interesting how much trade in poor countries seems to go on in little bazaars and marketplaces instead of in the context of any formal firm.

    Of course, this could be an argument against open borders if migration would degrade the infrastructure so it couldn’t support efficient large-scale enterprise. I don’t see evidence or likelihood that it would tend to do that, and it is interesting to note that western European countries which are much more redistributive, and likely for that reason poorer due to the weakening of incentives, nonetheless seem to be able to support efficient large-scale enterprise, and for that reason are almost as rich as the United States. Things like protection of minority shareholders that undergird the effectiveness of the corporate form of business governance are technocratic issues that hardly come up in elections, and the technocrats learn their trade before they get on the job and then have a lot of respect for precedent. They seem well-suited to maintain their integrity amidst an influx of immigration. Hmm… I’ve bitten off more than I can chew here.

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