Abstract
This paper addresses the timing of a location’s historical transition from rural to urban activity. We test whether urbanization occurs sooner in places with higher agricultural potential and comparatively lower transport costs, using worldwide data that divide the earth’s surface at half-degree intervals into 62,290 cells. From an independent estimate of each cell’s rural and urban population history over the last 2,000 years, we identify the date at which each cell achieves various thresholds of urbanization. Controlling for unobserved heterogeneity across countries through fixed effects and using a variety of spatial econometric techniques, we find a robust association between earlier urbanization and agro-climatic suitability for cultivation, having seasonal frosts, better access to the ocean or navigable rivers, and lower elevation. These geographic correlations become smaller in magnitude as urbanization proceeds, and there is some variation in the effects across continents. Aggregating cells into countries, we show that an earlier urbanization date is associated with higher per capita income today.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The papers discussed here all focus on how physical geography might have influenced historical events and institutions. A related but different literature looks at the contemporary effects of geography, including for example the role of access to navigable waterways, frost prevalence, and malarial ecology (Gallup et al. 1999; Masters and McMillan 2001; Sachs 2003). Some authors, such as Easterly and Levine (2003), find that time-invariant geographic variables influence modern incomes only through their historical effect on institutions, but climatic conditions could play an ongoing role through a variety of other mechanisms (Dell et al. 2014).
Urbanization has been associated with higher productivity and more desirable living standards ever since antiquity. Long before Adam Smith observed his pin factory, the ancient Greeks observed shoe-making. Xenophon’s Cyropedia (1914) contains this remarkable passage about the value of agglomeration for specialization and productivity, written in the early fourth century BC:
It is impossible that a single man working at a dozen crafts can do them all well; but in the great cities, owing to the wide demand for each particular thing, a single craft will suffice for a means of livelihood, and often enough even a single department of that; there are shoe-makers who will only make sandals for men and others only for women. Or one artisan will get his living merely by stitching shoes, another by cutting them out, a third by shaping the upper leathers, and a fourth will do nothing but fit the parts together. Necessarily the man who spends all his time and trouble on the smallest task will do that task the best.
For example, if economically successful societies tend to seek control over locations with attractive geographical features (e.g., access to navigable waterways), their institutions would become correlated with geography even if their initial wealth accumulation was caused by something else.
The regions are grouped following Maddison (2001), with the exception that we combine east and west Asia into one category from which we exclude Japan.
We use the History Database of the Global Environment (HYDE 3.1) developed by Klein Goldewijk et al. (2010, 2011) to define the timing of urbanization. With this data set the selection of other outcome measures, such as the size of the urban, rural or total population or their densities, would have been feasible as well. The analysis of these measures is, however, outside the realm of the current paper.
We use the year 2000 as the base year, since that is the last year for which data are available.
This smallest of all urbanized grid cells lies above the Arctic Circle, along the northern coast of Norway. A threshold of 5,000 urban people divided by its area of 881 km\(^2\) yields the 5.67 urban people per square kilometer.
The 95-percent confidence intervals around each line correspond to fitting a zero-degree polynomial with bandwidths of 0.12 for the year 1800, 0.1 for the year 1900, and 0.09 for the remaining years. Bandwidths are selected by rule of thumb using Stata’s lpolyci command.
An additional complication that arises with the interpretation of transportation cost as reflecting access to markets, such as in Bosker et al. (2008), is that this approach takes the existence of cities as given, whereas our goal here is to predict the actual formation of urbanization. One of the reviewers rightfully highlighted the importance of more complex human geographies playing a role; for instance, the availability of a river may be correlated with access to drinking or irrigation water. Our simple specification primarily attempts to compare the exogenous effect of a trade-relevant feature of geography on the timing of urbanization, but we admit that access to rivers may be partly confounded with availability of water for consumption and/or irrigation.
In applying the logarithmic transformation to the urbanization variables, ln(0) is defined as zero. The logarithm of distances shorter than 1 km is also fixed at zero, and negative elevations are set to the smallest positive elevation in the data set. One of the reviewers suggested that the cultivation suitability index can also be interpreted as a probability, and a logarithmic transformation may therefore not be necessary. We have experimented with this, and it does not affect the qualitative conclusions of our empirical analyses in a meaningful way.
The borders of the super-grids are defined as the 5-percentile points of the entire longitude and latitude span of the globe, which leads to 19 \(\times \) 19 (=361) super-grids. The spatial distribution of inhabited grid cells is such that they are located in 169 of these super-grids.
The large probability mass at zero makes the distribution of the urbanization threshold variables very right-skewed and leptokurtic (peaked). For instance, the 5.67 inhabitants per square kilometer urbanization variable has a skewness of 8.4 and a kurtosis of 108. The logarithmic transformation results in a distribution that is more similar to the normal distribution, with skewness (1.6) and kurtosis (4.1) indicators being much closer to zero.
A spatial lag specification, comprising the spatially lagged dependent variable, is not well suited to these economic data since it would assume that spillovers are global in nature. We also experimented with the addition of spatial cross regressive variables, which measure the interaction with immediate neighbors and hence imply local spillovers, but these estimated effects are difficult to interpret. Detailed results are not reported here for reasons of space, but they are available from the authors upon request; see also the appendix in Motamed et al. (2014).
The estimated coefficients for cultivation suitability, distance to the coast, and elevation are elasticities, and their marginal effect at the sample mean, in years, therefore equals \(\partial y /\partial x = \beta \bar{y}/\bar{x}\). The estimated coefficient for order of streams is a rate of change, with \(\partial y /\partial x =\beta \bar{y}\) as the corresponding marginal effect. Frost is a dummy variable and we define its “marginal” effect as \(\partial y /\partial x = (\text {exp}^\beta - 1)\bar{y}_0\), where \(\bar{y}_0\) is the sample mean of \(y\) in grid cells without frost (see Halvorsen and Palmquist 1980). To provide intuition about the magnitude of the effects we report “marginal effects” as the number of years earlier a cell reaches the 10 % urbanization threshold, based on a 0.01-change in cultivation suitability, a 100 km increase in coastal distance, a 100 m increase in elevation, an increase in the Strahler index by 1 unit, and a unity switch for the frost variable. In judging these “marginal effects” one should keep in mind that for large parts of the world, urbanization is a relatively recent phenomenon, and comparing the effects against the entire development span of 2000 years would not be appropriate.
Although not a formal statistical test, it is easy to see that 99-percent confidence intervals for the estimated coefficients across the models with different urbanization thresholds are frequently non-overlapping.
An important question for future research is to investigate the interaction effects among geographical endowments in more detail. Our regressions containing interaction terms generally show that access to transportation is a complement rather than a substitute to agricultural suitability. We also experimented with an additional quadratic term for cultivation suitability (without the logarithm used in the base model specification), which reveals a statistically significant diminishing effect of cultivation suitability on the timing of urbanization. However, interaction and higher-order polynomial effects are easier to interpret for variables without logarithmic transformations, and an interaction model should preferably also include threshold effects and non-linearities in addition to coefficients that are allowed to vary over space.
The predictions are calculated using the OLS coefficients in column (3) of the second panel of Table 2, and the associated unreported coefficients for fixed effects. Since the prediction is in years rather than in the logarithm of years, two subtleties are important. First, assuming independence of the errors and the explanatory variables, it can be shown that (Cameron and Trivedi 2010):
$$\begin{aligned} \text {E}(T|X) = \text {E}(\text {exp}(\varepsilon )) \text {E}(\text {exp}(\text {ln}T)). \end{aligned}$$We can therefore calculate the predicted effect on the timing of urbanization in years as:
$$\begin{aligned} \widehat{T} = \alpha _0 \text {exp}(\widehat{\text {ln}T_G} +\widehat{\text {ln}T_C}), \end{aligned}$$where the subscripts refer to the geography and the country fixed effects parts of the specification, and \(\alpha _0\) is the expected value of \(\text {exp}(\varepsilon )\). A consistent estimate of \(\alpha _0\) can be obtained from the auxiliary regression:
$$\begin{aligned} T=\alpha _0 \text {exp}(\widehat{\text {ln}T}) + \mu . \end{aligned}$$In our case, the OLS estimate for \(\alpha _0\) equals 1.45. Second, in the maps we show \(\widehat{T_G}\) and \(\widehat{T_C}\) at the mean sample value for \(\widehat{\text {ln}T_C}\) and \(\widehat{\text {ln}T_G}\), respectively. Our results should therefore be interpreted as showing the global distribution of the number of years since a grid cell belonging to an “average” country reaches the 10-percent urbanization threshold due to its geography. Similarly, we show the global distribution of the number of years since a country with “average” geographical endowments reaches the 10-percent urbanization threshold due to its country characteristics.
We use the country’s first cell to urbanize, rather than the average date of a country’s cells, to capture the start of a country’s transition wherever it may occur.
The Penn World Table Version 6.2 has 188 countries. In order to facilitate the spatial analysis and correct for missing data we excluded small island economies (Antigua, Bahrain, Barbados, Bermuda, Dominica, Grenada, Kiribati, Macao, Maldives, Malta, Micronesia, Netherlands Antilles, Palau, Seychelles, St. Kitts & Nevis, St. Lucia, St. Vincent & Grenadines, Tonga) as well as Serbia and Montenegro, leading to a sample of 169 countries.
The latter regression basically only takes into account the left hand side of the scatterplot in Fig. 8. In the case of subsampling we used the OLS rather than the spatial HAC estimator, because subsampling creates artificial spatial “holes” in the data set.
References
Acemoglu, D., Johnson, S., & Robinson, J. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5), 1369–1401.
Acemoglu, D., Johnson, S., & Robinson, J. (2002). Reversal of fortune: Geography and institutions in the making of the modern world income distribution. Quarterly Journal of Economics, 117(4), 1231–1294.
Acemoglu, D., & Robinson, J. (2012). Why nations fail: The origins of power, prosperity, and poverty. New York: Crown Business.
Allen, R. C. (2008). The nitrogen hypothesis and the English agricultural revolution: A biological analysis. The Journal of Economic History, 68(1), 182–210.
Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer.
Anselin, L. (2000). Spatial econometrics. In T. Mills & K. Patterson (Eds.), The Palgrave Handbook of Econometrics, Volume 1 Econometric Theory, Chapter 5 (pp. 83–98). New York: Oxford University Press.
Anselin, L., Bera, A. K., Florax, R., & Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77–104.
Ashraf, Q., & Galor, O. (2011). Dynamics and stagnation in the Malthusian epoch. American Economic Review, 101(5), 2003–2041.
Ashraf, Q., & Galor, O. (2013). The ’Out of Africa’ hypothesis, human genetic diversity, and comparative economic development. American Economic Review, 103(1), 1–46.
Bairoch, P. (1998). Cities and economic development: From the dawn of history to the present. Chicago: The University of Chicago Press.
Bosker, E., Buringh, E., & van Zanden, J. (2013). From Baghdad to London: Unraveling urban development in Europe, the Middle East, and North Africa, 800–1800. The Review of Economics and Statistics, 95(4), 1418–1437.
Bosker, M., Brakman, S., Garretsen, H., De Jong, H., & Schramm, M. (2008). Ports, plagues and politics: Explaining Italian city growth 1300–1861. European Review of Economic History, 12(1), 97–131.
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata. College Station: Stata Press.
Chorley, G. P. H. (1981). The agricultural revolution in northern Europe, 1750–1880: Nitrogen, legumes, and crop productivity. The Economic History Review, 34(1), 71–93.
Clark, G. (1992). The economics of exhaustion, the Postan thesis, and the agricultural revolution. The Journal of Economic History, 52(1), 61–84.
Clark, G., & Clark, A. (2001). Common rights to land in England, 1475–1839. The Journal of Economic History, 61(4), 1009–1036.
Comin, D., Easterly, W., & Gong, E. (2010). Was the wealth of nations determined in 1000 B.C.? American Economic Journal: Macroeconomics, 2(3), 65–97.
Dell, M., Jones, B., & Olken, B. (2014). What do we learn from the weather? The new climate-economy literature. Journal of Economic Literature Literature (forthcoming).
Diamond, J. M. (1997). Guns, germs and steel: The fates of human societies. New York: W.W. Norton & Company.
Duranton, G. (1999). Distance, land, and proximity: Economic analysis and the evolution of cities. Environment and Planning A, 31(12), 2169–2188.
Easterly, W., & Levine, R. (2003). Tropics, germs, and crops: How endowments influence economic development. Journal of Monetary Economics, 50(1), 3–39.
Fujita, M., & Krugman, P. (1995). When is the economy monocentric? von Thünen and Chamberlin unified. Regional Science and Urban Economics, 25(4), 505–528.
Fujita, M., Krugman, P., & Venables, A. J. (2001). The spatial economy: Cities, regions, and international trade. Cambridge: The MIT Press.
Funke, M., Zuo, J. (2003). Annual hard frosts, scale effects and economic development: A case not closed. Quantitative Macroeconomics Working Papers 20308, Hamburg University, Department of Economics.
Gallup, J. L., Sachs, J. D., & Mellinger, A. D. (1999). Geography and economic development. International Regional Science Review, 22(2), 179–232.
Galor, O. (2011). Unified growth theory. Princeton: Princeton University Press.
Galor, O., & Weil, D. N. (2000). Population, technology, and growth: From Malthusian stagnation to the demographic transition and beyond. The American Economic Review, 90(4), 806–828.
Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agriculture in development. The American Economic Review, 92(2), 160–164.
Halvorsen, R., & Palmquist, R. (1980). The interpretation of dummy variables in semilogarithmic equations. The American Economic Review, 70(3), 474–475.
Hansen, G. D., & Prescott, E. C. (2002). Malthus to Solow. The American Economic Review, 92(4), 1205–1217.
Heston, A., Summers, R., Aten, B. (2006). Penn World Table Version 6.2. Technical report, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978.
IPCC (2002). The IPCC Data Distribution Centre: Downloading Scenarios and Climate Data from the DDC.
Jedwab, R., Kerby, E., Moradi A. (2014). History, path dependence and development: Evidence from colonial railroads, settlers and cities in Kenya. CSAE Working Paper WPS/2014-04, Center for the Study of African Economies.
Johnston, B. F., & Mellor, J. W. (1961). The role of agriculture in economic development. The American Economic Review, 51(4), 566–593.
Kelejian, H. H., & Prucha, I. R. (2007). HAC estimation in a spatial framework. Journal of Econometrics, 140(1), 131–154.
Kiszewski, A., Mellinger, A., Spielman, A., Malaney, P., Sachs, S., & Sachs, J. (2004). A global index representing the stability of malaria transmission. The American Journal of Tropical Medicine and Hygiene, 70(5), 486–498.
Klein Goldewijk, K., Beusen, A., & Janssen, P. (2010). Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1. The Holocene, 20(4), 565–573.
Klein Goldewijk, K., Beusen, A., Van Drecht, G., & De Vos, M. (2011). The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Global Ecology and Biogeography, 20(1), 73–86.
Kögel, T., & Prskawetz, A. (2001). Agricultural productivity growth and escape from the Malthusian trap. Journal of Economic Growth, 6(4), 337–357.
Krugman, P. (1991). Increasing returns and economic geography. The Journal of Political Economy, 99(3), 483–499.
Kuznets, S. (1973). Modern economic growth: Findings and reflections. The American Economic Review, 63(3), 247–258.
Lucas, R. E. (2000). Some macroeconomics for the 21st century. The Journal of Economic Perspectives, 14(1), 159–168.
Maddison, A. (2001). The world economy: A millennial perspective. Ottawa: Development Centre of the Organisation for Economic Co-operation and Development.
Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.
Masters, W. A., & McMillan, M. S. (2001). Climate and scale in economic growth. Journal of Economic Growth, 6(3), 167–186.
Matsuyama, K. (1992). Agricultural productivity, comparative advantage, and economic growth. Journal of Economic Theory, 58(2), 317–334.
McCloskey, D. N. (1972). The enclosure of open fields: Preface to a study of its impact on the efficiency of English agriculture in the eighteenth century. The Journal of Economic History, 32(1), 15–35.
McMillen, D. (2003). Spatial autocorrelation or model misspecification? International Regional Science Review, 26(2), 208–217.
Michaels, G., Rauch, F., & Redding, S. J. (2012). Urbanization and structural transformation. The Quarterly Journal of Economics, 127(2), 535–586.
Michalopoulos, S. (2008). The origins of ethnolinguistic diversity: Theory and evidence. American Economic Review, 102(4), 1508–1539.
Motamed, M. J., Florax, R. J. G. M., Masters, W. A. (2014). Agriculture, transportation and the timing of urbanization: Global analysis at the grid cell level. Tinbergen Institute Discussion Papers TI 2014–002/VIII, Tinbergen Institute.
Murata, Y. (2008). Engel’s law, Petty’s law, and agglomeration. Journal of Development Economics, 87(1), 161–177.
Ngai, L. R. (2004). Barriers and the transition to modern growth. Journal of Monetary Economics, 51(7), 1353–1383.
Nordhaus, W. D. (2006). Geography and macroeconomics: New data and new findings. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3510–3517.
North, D. C. (1990). Institutions, institutional change, and economic performance. Cambridge: Cambridge University Press.
Nunn, N. (2008). The long-term effects of Africa’s slave trades. Quarterly Journal of Economics, 123(1), 139–176.
Nunn, N. (2014). Historical development. In S. Durlauf & P. Aghion (Eds.), Handbook of Economic Growth, Vol. 2a, Chapter 7. Amsterdam: Elsevier.
Nunn, N., & Qian, N. (2011). The potato’s contribution to population and urbanization: Evidence from a historical experiment. The Quarterly Journal of Economics, 126(2), 593–650.
Olsson, O., & Hibbs, D. A. (2005). Biogeography and long-run economic development. European Economic Review, 49(4), 909–938.
Puga, D. (1999). The rise and fall of regional inequalities. European Economic Review, 43(2), 303–334.
Putterman, L. (2008). Agriculture, diffusion and development: Ripple effects of the neolithic revolution. Economica, 75(300), 729–748.
Putterman, L., & Weil, D. (2010). Post-1500 population flows and the long-run determinants of economic growth and inequality. The Quarterly Journal of Economics, 125(4), 1627–1682.
Ramankutty, N., Foley, J. A., Norman, J., & McSweeney, K. (2002). The global distribution of cultivable lands: Current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392.
Sachs, J. D. (2003). Institutions don’t rule: Direct effects of geography on per capita income. Working Paper 9490, National Bureau of Economic Research.
Sokoloff, K. L., & Engerman, S. L. (2000). History lessons: Institutions, factors endowments, and paths of development in the new world. The Journal of Economic Perspectives, 14(3), 217–232.
Solow, R. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94.
Spolaore, E., & Wacziarg, R. (2009). The diffusion of development. Quarterly Journal of Economics, 124(2), 469–529.
Spolaore, E., & Wacziarg, R. (2013). How deep are the roots of economic development? Journal of Economic Literature, 51(2), 1–45.
Strulik, H., & Weisdorf, J. (2008). Population, food, and knowledge: A simple unified growth theory. Journal of Economic Growth, 13(3), 195–216.
Vörösmatry, C. J., Fekete, B. M., Meybeck, M., & Lammers, R. (2000). Global system of rivers: Its role in organizing continental land mass and defining land-to-ocean linkages. Global Biogeochemical Cycles, 14(2), 599–622.
Williamson, J. G. (1988). Migration and urbanization. In H. Chenery & T. Srinivasan (Eds.), Handbook of Development Economics, Vol. 1, Chapter 11 (pp. 425–465). Amsterdam: Elsevier.
Xenophon (1914). Cyropaedia: The education of cyrus. (J. M. Dent and Sons. Translated by H.G. Dakyns, revised by F. M. Stawell).
Author information
Authors and Affiliations
Corresponding author
Additional information
We have profited substantially from comments and suggestions of five anonymous reviewers as well as the editor of this journal. Nathan Nunn, Remi Jedwab, Justin Sandefur, Xiaofei Li and other colleagues at various seminars and conferences have contributed through discussions of previous versions of this paper. The first author notes that the views expressed are those of the author and should not be attributed to the Economic Research Service (ERS) or the United States Department of Agriculture (USDA).
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Motamed, M.J., Florax, R.J.G.M. & Masters, W.A. Agriculture, transportation and the timing of urbanization: Global analysis at the grid cell level. J Econ Growth 19, 339–368 (2014). https://doi.org/10.1007/s10887-014-9104-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10887-014-9104-x