There is a debate on whether spatially-neutral or spatially-targeted policy is a better strategy for the future development of economic geography and the spatial pattern of urbanization at the national scale. Economists emphasize the spatially-neutral policy and advocate for the free flow of production factors to the developed regions in southeast China, while economic geographers support the latter and the development of central and western China. We argue that the radiation radius of an economic hub is not limitless because of geography, more precisely, it is distance that plays an important role in the distribution of production activities and urbanization. We also believe that not only should economic benefits be taken into account, but also other factors such as politics, ethnic minority and national security. The core point of this paper is that the scale of a nation is the key determinant of the spatial layout of economic geography and urbanization, and larger countries should follow a relatively balanced development path. Based on both cross-sectional and fixed-effect panel models, we explore the relationship between the scale of a nation and city size distribution, the proxy for the spatial pattern of national economic geography. The results show that, (1) all else being equal, the scale of a nation, represented by either population or land area, is positively associated with a balanced city size distribution. That is, the economic geography in large countries is inclined to a spatially balanced layout; (2) a nonlinear relationship is identified between the spatial pattern of national economic geography and per capita GDP. That is, national spatial pattern is unbalanced at lower levels of economic development and evens out at higher levels of development; (3) urbanization, industrialization, and a stable political environment also help balance the national economic geography layout. This study's policy implication is that large countries such as China should implement the strategy of a relatively balanced development of economic geography and urbanization. Considering the objectives of national security, social stability, and the fact that China has a large population and a vast territory, it is reasonable for China to promote the development of the central and western regions with spatially-targeted policies.
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This paper assesses the empirical validity of Zipf's Law for cities, using new data on 73 countries and two estimation methods鈥擮LS and the Hill estimator. With either estimator, we reject Zipf's Law far more often than we would expect based on random chance; for 53 out of 73 countries using OLS, and for 30 out of 73 countries using the Hill estimator. The OLS estimates of the Pareto exponent are roughly normally distributed, but those of the Hill estimator are bimodal. Variations in the value of the Pareto exponent are better explained by political economy variables than by economic geography variables.
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Using two comprehensive datasets on population of cities (1800-2000) and metropolitan areas (1960-2000) for a large set of countries, I present three new empirical facts about the evolution of city growth. First, the distribution of cities growth rates is skewed to the right in most countries and decades. Second, within a country, the average rank of each decade's fastest growing cities tends to increase over time. Finally, this rank grows faster in periods of rapid growth in urban population. These facts can be interpreted as evidence in favor of the idea that urban agglomerations have historically grown following a sequential growth pattern: within a country, the initially largest city is the first one to grow rapidly for some years. At some point, the growth rate of this city slows down and the second largest city is then the fastest-growing one. Eventually, the third largest city starts growing fast as the two largest cities slow down, and so on.
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This paper examines the Pareto and primacy measures of the size distribution of cities. The mean Pareto exponent for a sample of 44 countries is 1.136, somewhat greater than the exponent of one implied by the rank-size rule. We find that value of the Pareto exponent is quite sensitive to the definition of the city and the choice of city sample size. The significance of non-linear terms in variants of the Pareto distribution also indicate that the rank-size rule is only a first approximation to a complete characterization of the size distribution of cities within a country. The relatively low correlation between primacy and Pareto measures confirms the need for a variety of measures of city size distributions. This paper also suggests that large cities are growing faster than small cities in most of the countries in our sample. This is indicated by the positive coefficient on the first non-linear term introduced into the Pareto equation. Finally, variations in the Pareto exponent and measures of primacy are partly explained by economic, demographic, and geographic factors.
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Many of the world's largest cities are now in developing countries. We develop a simple theoretical model, inspired by the case of Mexico, that explains the existence of such giant cities as a consequence of the strong forward and backward linkages that arise when manufacturing tries to serve a small domestic market. The model implies that these linkages are much weaker when the economy is open to international trade -- in other words, the giant Third World metropolis is an unintended by-product of import-substitution policies, and will tend to shrink as developing countries liberalize.
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In this paper, based on the recent advances in the new economic geography (e.g., Fujita etal. ), we analyze impacts of transport costs on the spatial patterns of economic agglomeration. We first identify prototypes from the existing models, and explain the mechanism of how transport costs influence the balance between economic forces of agglomeration and dispersion. We then investigate the transformation of the agglomeration/dispersion patterns given gradually decreasing transport costs for different goods.
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Using theory, case studies, and cross-country evidence, we investigate the factors behind the concentration of a nation's urban population in a single city. High tariffs, high costs of internal trade, and low levels of international trade increase the degree of concentration. Even more clearly, politics (such as the degree of instability) determines urban primacy. Dictatorships have central cities that are, on average, 50 percent larger than their democratic counterparts. Using information about the timing of city growth, and a series of instruments, we conclude that the predominant causality is from political factors to urban concentration, not from concentration to political change.
Henderson JV, Wang HG.Urbanization and city growth: The role of institutions. , 2007, 37(3): 283-313.http://www.sciencedirect.com/science/article/pii/S0166046206001049
This paper examines how urbanization is accommodated by increases in numbers and in sizes of cities. Political institutions play a key role. Estimation uses a worldwide data set on all metro areas over 100,000 from 1960 to 2000. The degree of democratization and technological advances strongly affect growth in both city numbers and individual city sizes. Effects on city sizes are heterogeneous. Technology improvements help bigger cities relative to smaller ones. Increasing democratization levels the playing field across the urban hierarchy, helping smaller cities. Given these opposing effects, the overall relative size distribution of cities worldwide is unchanged over the time period.
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Despite the availability of more sophisticated methods, a popular way to estimate a Pareto exponent is still to run an OLS regression: log(Rank) = 61 65log(Size), and take as an estimate of the Pareto exponent. The reason for this popularity is arguably the simplicity and robustness of this method. Unfortunately, this procedure is strongly biased in small samples. We provide a simple practical remedy for this bias, and propose that, if one wants to use an OLS regression, one should use the Rank 61165/652, and run log(Rank 61 165/652) = 61 65log(Size). The shift of 165/652 is optimal, and reduces the bias to a leading order. The standard error on the Pareto exponent is not the OLS standard error, but is asymptotically (265/65). Numerical results demonstrate the advantage of the proposed approach over the standard OLS estimation procedures and indicate that it performs well under dependent heavy-tailed processes exhibiting deviations from power laws. The estimation procedures considered are illustrated using an empirical application to Zipf’s law for the United States city size distribution.
CheshireP.Trends in sizes and structures of urban areas., 1999(3): 1339-1373.http://www.sciencedirect.com/science/article/pii/S1574008099800042
This chapter reviews the literature dealing with systems of cities and the patterns of development within such systems. It starts with the longstanding question of the distribution of city sizes, both in relation to how this distribution can be described and, given the form that it takes, how that form can be explained. Such explanations frequently invoke various sorts of agglomeration economies and so some of the literature relating to these is included here. The chapter then surveys the literature that examines patterns of development within urban systems, and then work at a more disaggregated level on suburbanisation. The chapter concludes with a summary of research into recent patterns of urbanisation, including relative recentralisation.
BlackD, HendersonV.Urban evolution in the USA. , 2003, 3(4): 343-372.http://joeg.oxfordjournals.org/content/3/4/343.short
On a sustained basis, cities are of non-uniform relative sizes. This paper addresses three basic issues which arise from this simple observation by examining the size distribution of US cities over the period 1900--1990. First, we explore the reasons why there is a wide distribution of city sizes. Second, we characterize the evolution of the size distribution of cities, documenting growth in sizes and numbers of cities. We ask whether the relative size distribution of cities has remained stable over time, or if it has displayed, instead, a tendency to collapse, flatten, or otherwise change its shape. We also examine evidence on whether the size distribution obeys Zipf's Law. Third, we examine the degree and determinants of mobility of individual cities within this distribution, asking to what extent cities are moving up and down in the distribution and how this movement is influenced by cities' geographic characteristics. We use a newly constructed data with consistent metropolitan area definitions over this century, discussing the issues and linking our results to the relevant literature. Copyright 2003, Oxford University Press.