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Cuadernos de economía

versión On-line ISSN 0717-6821

Cuad. econ. v.40 n.121 Santiago dic. 2003 

Cuadernos de Economía, Año 40, Nº 121, pp. 423-433 (diciembre 2003)




This paper presents an alternative view of why geography is a key determinant of the process of wealth creation and distribution of the countries. A new set of supporting evidence is also provided. The core ideas explored in the paper are: a) the exporting sector offers a picture (an x-ray) of a country's underlying process of wealth creation and distribution. Efficient producers and therefore exporters of manufactures, for example, have high incomes and low levels of inequality while exporters of crops and raw materials have low incomes and unequal income distributions; b) the export mix of a country is largely determined by three fundamentals: resources, remoteness, and climate. Manufacturing, for example, likes cool climates, educated workforces, and locations close to high-wage marketplaces.

Two are the suggested mechanisms linking geography to growth and inequality that are not present in the existing literature. First, because of high fixed costs, manufacturing requires operating the equipment at high pace for long hours, creating a distinct disadvantage for the tropics. Second, because the exchange of complex uncodifiable messages can only be done on a face-to-face basis, with the participants within a handshake of each other, the production of ideas and new products is firmly rooted where it has always been, in the economic centers of the globe. As a result, toys, apparel, and footwear are footloose. Machinery and pharmaceuticals are not.

Links between physical geography and economic development have been proposed at least since Machiavelli (1519). More recently Gallup, Sachs, and Mellinger (1998) indicate four major areas where it has been suggested that physical geography may have a direct impact on economic productivity: transport cost, human health, agricultural productivity, and proximity and ownership of natural resources. In that paper, as well as in Sachs (2001), empirical evidence is provided supporting that geography indeed has direct, as well as indirect, effects on economic development. Hall and Jones (1999) and Engerman and Sokoloff (1997) argue that physical geography may affect economic development by shaping the countries' institutions. Acemoglu, Johnson, and Robinson (2001), Rodrik, Subramanian, and Trebbi (2002) and Easterly and Levine (2002) go one step further suggesting that once controlling for the effects of institutions, geography has no direct impact on economic development. However, McArthur and Sachs (2001) show that the results in Acemoglu et al. (2001) are not robust to increases in the sample of countries used in the analyses.

The main conclusion of the paper is very disturbing to far away -resource abundant- tropical countries. Climate, natural resources and remoteness can together explain a great deal of the variability of incomes and inequality across countries. This suggests that governments may have a much smaller role on economic development than what is usually supposed by economists. After controlling for climate, natural resource abundance, and location, Latin America, for example, is not unusual in its trade dependence, export composition, GDP per capita or income inequality.


Wealth is primarily generated by exports. By definition this is not true for the globe overall, but it is indisputably true for individuals in advanced developed countries, who "export" almost 100% of what they produce. Although large countries might experience internally-driven growth, most countries are more like individuals. Their production structures are specialized and their growth comes from expanding efficiently the activities they are good at, and exporting the surplus to the rest of the world.

The export mix also determines the way income is distributed among production factors. Factor returns vary greatly depending on the set of products in which a country has comparative advantage. Wherever the output mix is such that raw labor or human capital (specially the type created by training) are intensively used, inequality tends to be low because those inputs are, by their own nature, widely owned. That is the case of manufacturing. Where human capital is poorly rewarded compared with other capital assets, like physical capital or land, ownership of the most important productive inputs tends to be concentrated on the few and inequality is high. That is the case of countries that export primary products and crops and ideas.

Besides being associated to equality, manufacturing has also been the main source of wealth in the industrial age. That does not mean that other activities have been technologically stagnant. On the contrary, both agriculture and resource extraction have experienced an increase in mechanization that closely parallels the observed progress in manufacturing. Indeed, mechanization of agriculture and raw material extraction is similar to mechanization of manufacturing activities. In the sense that it puts into the hands of workers expensive equipment that needs to be operated for long hours at high pace to cover the capital costs. This creates high-effort high-wage opportunities for workers with some formal education. It is also similar in the sense that it lowers the labor to output ratio. The difference is that agriculture and resource extraction have a fixed input: land. Mechanization lowers the worker to land ratio and thus reduces the number of jobs in agriculture.

In manufacturing the number of jobs can be maintained or even increased in the face of increased mechanization provided that manufacturing can attract the needed amount of capital. In addition to that, and maybe more important, throughout the 20th century it was product innovation that allowed manufacturing to keep its employment level.


The ability of a country to attract manufacturing is determined by three features: resources, location and climate. Manufacturing prefers cold climates where equipment can be operated without breakdowns at high pace for long hours during the day. Manufacturing seeks an educated workforce what is usually not offered by natural resource rich countries. And many manufacturing activities prefer to cluster next to like activities and close to the high-wage markets of North America and Europe and Japan.

a. Resources: Industrialization is harder for natural resource rich countries

Natural resources may have been helpful in the pre-industrial age but make it harder for a country to develop manufacturing activities. Natural resource rich communities invest their scarce savings mostly in land improvements, in permanent crops, in extractive equipment, and very little in human capital, which has a very low return on a coffee plantation or the equivalent. This creates a barrier to development since once the resource is fully developed and further wealth accumulation could come only from growing manufacturing, the educational system may not be ready to prepare the workforce for jobs on the factory floor. Equipment may then seek workers in other communities that have the literacy skills needed in the command-and-control hierarchical organizations that lead the global competition in manufacturing.

There are some notable exceptions to the hindering effects of natural resources in the northern regions of Europe (Finland and Sweden) and North America (Canada). The comparison between Latin America and these northern softwood producers may not be completely meaningful. Softwood logs are different from coffee, since wood processing can extend from sawing to the much more human and physical-capital intensive operations in pulp and paper. Food processing is more limited in scope and may not support extensive investment in human capital. Secondly, as we will argue below, manufacturing likes cold weather, which is in abundant supply in Canada, Finland and Sweden, but very scarce in Latin America. Also, these softwood producers may be different from Latin American countries with regard to human capital formation, since these northern countries may have made a heavy commitment to broad human capital accumulation for non-economic reasons prior to the period when the private rate of return to human capital exceeded the private rate of return to physical capital. Furthermore, these softwood producers sit right on top of the attractive markets in Europe and North America, while Latin America is far away. For that reason, and others, educational investments may not have a sure payoff. Indeed, Argentina, a formerly wealthy natural resources exporter still had substantial measures of human capital accumulation, but nonetheless did not manage to make the transition to an industrial economy.

b. Climate: Equipment doesn't like hot humid areas

The need to spread the large fixed cost of capital over the labor input makes industrial equipments and factories seek climates in which the equipment can be operated for long hours at high speeds (see Leamer 1999). The problems confronting manufacturing in the tropics are many. Human effort and attentiveness are hard to maintain for extended periods of time in hot and humid climates, and machines break down more frequently. It is only with the advent of air-conditioning that manufacturers started moving "south" in search of low wages, but in these hot and humid climates workers must, in effect, rent the equipment, and pay the added capital costs for the air-conditioning, and the marginal operating costs as well. This keeps a permanent gap between wages in the "North" and wages in the "South."

c. Location: Communication of complex ideas requires face-to-face meetings

Both the industrial age and the post-industrial age require workers to master complex new tasks that the new equipment and new products demand. Leamer and Storper (2001) argue that this human capital is created only by close human interactions (watching the master), a communication technology which dictates the geographic concentration of innovative manufacturing. While great improvements in transportation and communication technologies have made it much cheaper to transport goods and codifiable messages, these technologies help very little in the transshipment of uncodifiable knowledge. Only when products mature and become standardized can the knowledge of how to produce them be codified in words and blueprints and sent to remote locations where the products can successfully be made. The productive activities at these remote locations tend toward the mundane and the repetitive, and thus require much less human capital than the innovative activities done at the great centers of both the industrial and post-industrial ages.

d. Location: Enforcement of contracts is best done in close proximity

In addition to allowing the transfer of complex messages, closeness can be important for the maintenance of guarantees. "Search" goods whose value is transparent from a single inspection can be exchanged through long-distance and faceless transactions. But "experience" goods have value that is revealed only through years of use, and it is essential for the buyer to be able to find the seller in the event that the product does not live up to its explicit or implicit guarantees.


This section shows some new evidence supporting the views discussed above. Specifically I seek support for the notion that cross-country differences in export composition explains a sizeable amount of the cross-country variation in income and inequality levels, and that export mix is ultimately determined by remoteness, resources, and climate. Given the limited space available only a selected set of new evidence is shown, most of it regarding the remoteness effect on output and export mix. A larger bulk of evidence as well as a detailed discussion of the dataset used can be found in Blum and Leamer (2003).

4.1. Data correlations

Table 1 displays simple correlations among trade patterns, GINIs and per capita GDPs for 71 countries in 1987. Land shares in different climates and a measure of remoteness were also added to the table. Countries' exports are grouped in 10 aggregates1.

The top of the table shows the variables that are highly correlated with inequality and per capita incomes while the bottom displays the variables that are mildly or not correlated with income or inequality. Inequality and per capita income are heavily correlated with exports composition, location, and climate zones. Countries closer to markets, net exporters of machinery and chemicals, and countries located in temperate and snow humid or ice tundra climates tend to have higher incomes and more equal income distributions. Farther away countries, net exporters of tropical agricultural products, and countries located in tropical and subtropical climate zones tend to have lower per capita income and more inequality.

In Table 2 countries are clustered according to their main export products in 1987. The average per capita GDP and Gini coefficients by group are reported at the bottom of the table. The second column, for example, shows the countries whose export mix is concentrated on tropical agriculture products. The columns of the table are sorted according to the group's per capita income starting from the lowest one. Notice that the categories are a little different from the product aggregates defined above. One group, for example, is defined as forest products and machinery exporters. The reason for that is that exporters of forest products are also exporters of machinery.

The results are clear: exporting tropical agriculture, cereals, raw materials, and petroleum is associated to low incomes and high inequality. Exporting forest products, machinery, capital intensive manufactures, and chemicals is associated to high incomes and low inequality. Exporting labor-intensive manufactures helps a little, but not much.

4.2. The remoteness effect

Who is remotely located and who is not? In order to answer this question I use Leamer 's measure of distance from global GDP2. Such a metric is suggested by the traditional gravity model that explains trade between pairs of countries as a function of their distance and economic masses.

Table 3 shows the countries' distance to the world GDP in 1997 together with the number of positions gained/lost since 1982. A couple of important facts are apparent from Table 3. European and North American countries are, by and large, close to world's GDP. Asian, Northern African, and Central American countries are intermediately located while South American, Oceania, and Central and Southern African countries are very far away. Moreover, Table 3 indicates that the relative positioning of the countries in the world is quite stable over time, hardly varying in the last 15 years. The main change happened as a consequence of the sparkling economic growth experienced by Asia and the bad performance of Latin America. The economic success of the Asia NICs (newly industrialized countries) pulled the whole region closer to the world markets. The moving up of the Asian economies came mostly at the expenses of the countries in the Americas.

Table 4 shows the effects of distance on countries' output patterns. Using the distance measure discussed above and output data I compute the share of the world production originating at countries that are closer than 6,000 Km. to the world's GDP and the share produced in countries that are farther away than that. The choice of 6,000 Km. is arbitrary and largely motivated by the fact that the South American countries are all farther away than that. As a basis for comparison the second row of the table shows the share of GDP originating in these two groups of countries. The data is sorted starting with the products that production is more concentrated around the GDP center of the world and ending with the products which production is typical of remote areas.

Once again supporting the view presented in the previous sections, Table 4 shows that physical and human capital intensive manufacturing, most typically the sectors where performing complex tasks are required -like instruments and machinery- are the ones heavily concentrated around the GDP centers of the world. Although not shown here, even after controlling for the endowments' distribution in the world, the central economies have a disproportionately large share of the world's production in sectors like Chemicals, Machinery, and Instruments.3

4.3. The exogenous determinants of wealth and inequality

Even though all sorts of correlations have lent support to the ideas pushed in this paper, I am fully aware of the gap between those and causal relations. I argue, however, that of the variables expected to be linked to the countries' income and inequality, share of area under a given climate zone, remoteness, land endowments, and energy reserves are truly exogenous to the process of wealth creation and distribution. Table 5 adapted from Blum and Leamer (2003) shows how these variables affect income and inequality in 1987. These are weighted regressions with variance of the residual assumed to be equal to the labor force, thus producing regression estimates analogous to a mean with weights equal to the labor force. There is no constant in the equation because the climate shares add to one. Furthermore, for computing the t-statistics, the mean has been subtracted from the dependent variable, and the t-values on the climate proportions test if that climate zone is unusual compared with all the others, not a test if the effect is zero. Finally, the resource variables are standardized to have mean zero and variance one, thus allowing the coefficients on the climate variables to refer to the effect of climate on a country with average endowments and to allow the coefficients on the resource variables to measure the effect of a one-standard error increase. The climate variables in the table have been sorted by the climate effect on GDP per capita and the resource variables by the resource effect. The final columns of the table indicate climate shares of several countries to help make clear what these climate variables represent.

For GDP per capita, the best climate zones are the cold and cool ones (snow humid and temperate humid), climates the US and Sweden "enjoy", but Brazil does not. These climates are associated with per capita GDPs of $19,000 and $12,000. These climates support GDP per capita that are statistically higher than average. In the other direction, the climates that are statistically inferior are tropical dry winter and arid desert. Brazil has the former and Argentina and Chile (28%) the latter. These climates support GDP per capita of only $2,926 and $1,506 respectively. Chile is also abundant in highland climate (35% of its land enjoys such a climate) which supports GDP per capita of only $2,968. Abundance of cropland contributes to GDP per capita. A one standard deviation increase in cropland increases GDP per capita by $2,898. Remoteness is not a good thing. A one standard deviation increase in remoteness reduces GDP per capita by $1,828.

The Gini regression confirms what we suspected: the climate with tropical dry winter which yields a weak GDP per capita, also yields a high Gini coefficient. A highland climate (think Chile) is also associated with unequal incomes while ice tundra (think Canada and Norway) comes with equal incomes. While remoteness lowers per capita incomes, it does not increase inequality. Forestland is estimated to raise inequality. That variable does not distinguish hardwood from softwood forests and may be reflecting mostly the cutting of tropical hardwoods. Indeed, as can be seen in the data it is Brazil not Sweden that has abundant forestland.


This paper presented the very disturbing empirical regularity that climate, natural resources and geographic location, can together explain a great deal of the variability of trade, incomes and inequality across countries. It also suggested a couple of new mechanisms explaining why that might the case. The findings discussed above imply that governments may have a much smaller role on economic development than what is usually supposed by economists. After controlling for climate, natural resource abundance, and location, Latin America is not unusual in its trade dependence, export composition, GDP per capita or income inequality.

A ray of hope may come from the examples of Canada, Sweden and Finland, which are countries that are rich in natural resources but nonetheless have managed to attract high-wage complex manufacturing tasks. The successes of these countries surely depend partly on education and closeness. This suggests that economies like the Latin American's could possibly escape the curse of geography through improvements in education and through reductions of the economic, legal and social distances from the high-income markets in North America and Europe.


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* Rotman School of Management, University of Toronto.
Email: The ideas presented in this paper were developed in detail in Blum and Leamer (2003). The empirical evidence shown here is new and complement the evidence provided in that paper.

2 See Leamer (1997) for details about this measure.

3 See Blum and Leamer (2003).

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